INVASIVE SPECIES IN AQUATIC SYSTEMS: POPULATION, COMMUNITY, FOOD WEB AND

LANDSCAPE PERSPECTIVES.

by

NORMAN MERCADO – SILVA

A dissertation submitted in partial fulfillment of the

requirements for the degree of

Doctor of Philosophy

(Zoology)

At the

UNIVERSITY OF WISCONSIN - MADISON

2005

i

Table of Contents

Abstract…………………………………………………………………………………………ii

Acknowledgements…………………………………………………………………………….v

Chapter 1……………………………………………………………………………………….1

THESIS INTRODUCTION

Chapter II……………………………………………………………………………………....10

WALLEYE RECRUITMENT DECLINE AS A CONSEQUENCE OF RAINBOW SMELT

INVASIONS IN WISCONSIN LAKES

Chapter III………………………………………………………………………………………47

LONG-TERM CHANGES IN THE FISH ASSEMBLAGE OF THE LAJA RIVER,

GUANAJUATO, CENTRAL MEXICO.

Chapter IV………………………………………………………………………………………77

FOOD WEB STRUCTURE OF AN IMPACTED SEMI-DESERTIC FRESHWATER SYSTEM

IN MEXICO’S CENTRAL PLATEAU

Chapter V………………………………………………………………………………………119

FORECASTING THE SPREAD OF INVASIVE RAINBOW SMELT (OSMERUS

MORDAX) IN THE LAURENTIAN GREAT LAKES REGION OF NORTH AMERICA

ii

ABSTRACT

INVASIVE SPECIES IN AQUATIC SYSTEMS: POPULATION, COMMUNITY, FOOD WEB AND LANDSCAPE

PERSPECTIVES.

NORMAN MERCADO-SILVA

Under the supervision of Professor Jake Vander Zanden

At the University of Wisconsin - Madison

The introduction and establishment of invasive species is one of the major causes of changes in

the composition, structure, function and viability of freshwater ecosystems. Invasive species

have impacts at different levels of organization ranging from genetic (e.g., hybridization and

introgression) to ecosystem (e.g., nutrient flux alterations and disturbance regime alteration). At

each level, the consequences of the invader-native interaction pose important scientific and

management questions. I conducted four separate studies addressing exotic species in two

geographic areas, the Laja River in Central Mexico, and temperate lakes in the Great Lakes

region. Through these studies, I explored the interactions of exotics with native species from the

perspectives of populations, communities, food web interactions and their expansion across the

landscape.

At the population level, I studied how rainbow smelt (Osmerus mordax) invasion can

affect walleye (Sander vitreus) populations by reducing the recruitment of young-of- the-year

fishes to the adult population in invaded lakes in Wisconsin. This process has had significant

repercussions on invaded lakes, and could have serious consequences for walleye, one of the

iii

most important fisheries in the state, if smelt continue to expand. The potential of individual

lakes for smelt colonization was also investigated, resulting in the identification of a subset of

188 lakes where management and invasion prevention efforts should be prioritized.

From a community perspective, through a study of the long term changes of the fish

assemblage of the Rio Laja, I identified the changes in fish communities resulting from a mix of

human impacts. Significant declines in the number of benthivore, carnivore, and sensitive

species, and important increases in the number of exotic and tolerant species have occurred in

this system since the 1960’s, resulting in a present-day community of fishes where exotics are

common and few sensitive native species remain.

A stable isotope-based food web analysis of the fish communities in the Laja was carried

out to understand the historical and present-day food web interactions at a series of sites in the

watershed. This study revealed how reservoirs alter the basal resources that fish communities

depend on, and how invasive species now overlap with native species in terms of resource use.

Changes observed in the Laja from the perspective of communities and food webs have

important implications for understanding how fish communities in other rivers in central Mexico,

for which long-term data do not exist, have changed or could change if environmental

deterioration continues.

From a landscape perspective, using morphological, physical-chemical and biological

information from Maine (US) lakes where rainbow smelt are native, I developed a model to

predict which individual lakes in the Great Lakes region would be suitable for smelt invasion.

The model was successful in predicting smelt presence/absence in Maine and was used to make

broad scale predictions of smelt distribution for over 8000 lakes in Ontario and Wisconsin. The

analysis identified 4447 and 553 lakes in Ontario and Wisconsin respectively, suitable for smelt

iv

invasion. These suitable lakes are where preventive steps should be taken against the impact of

this invasive species.

Approved

--------------------------

Jake Vander Zanden

v

ACKNOWLEDGEMENTS

This thesis is the product of efforts and contributions of many people who have supported me

throughout my life. I will start by thanking my two major professors, Jake Vander Zanden and

John Lyons, for all the work they put into expanding and reshaping my ideas. I thank Jake for

the friendly attitude he always had when I showed up upon his door for advice, for his infinite

patience, and for understanding and expanding my limits of thought. I believe academic

advising is important in shaping a life - Jake’s good at it. I am grateful for his constant push for

improvement and for making me aim high with my work. It has been great to have such a great

teacher, collaborator, friend, and text editor (if it were not for Jake this thesis would be twice as

long – thanks for the Spanish → English word reduction).

John Lyons has been a most important person in shaping me into the biologist I am today.

I thank John for initiating and supporting my temporary importation to the US, and all my work

in Mexican systems. John is 1.5 academic fathers to me (½ for my BS, ½ for my MS and now,

I’d say ½ for my PhD). It has been a great honor to have him as an advisor and colleague. I

have benefited enormously from his expertise and knowledge, and to a certain degree from the

vast ‘John Lyon’s Fund for Graduate Students from the Third World’. Thank you Juan for all

these years of collaboration and guidance. A good part of my learning about my country I owe

to you.

Jim Kitchell, Emily Stanley, Timothy Moermond and Erick Nordheim always offered

good advice for the completion of my degree. I have learned much from their example. In

addition to their excellent academic advice, their performances as teachers, advisors, and as

graduate student and research institute managers are impressive examples to follow. Thank you

vi

all committee members for the guidance. Many other faculty members at UW and elsewhere,

like Steve Carpenter and Tom Hrabik were key in the completion of my studies at the CFL, and I

thank their advice and support in this process.

I am thankful for the opportunity to learn from and collaborate with graduate students and

post-docs (past and present) at the Center for Limnology and the Department of Zoology. I have

learned much from colleagues and friends like Julian Olden, Brian Weigel, and Kyle Piller. It is

good to know collaborations are ongoing. Cailin Orr and Greg Sass were two with whom I

shared a good portion of my time at the CFL. They went first in this PhD process, were always a

model to me, and above all, were great friends: Thanks! Olaf Jensen, Amy Kamarainen, Katrina

Butkas and Theo Willis always made coming to 126 CFL enjoyable. I thank their

encouragement and advice. Katie Hein, Matt Diebel, Chris Solomon, Jeff Maxted, David Gilroy,

Stephanie Schmidt, Katrina Butkas, and Zeb Hogan sailed with me in the Vander Zanden boat –

it was a fun and enriching ride. Matt Helmus was an important colleague in exploring Mexican

waters and I am glad he got interested in continuing work there. Many other friends and

colleagues helped in various stages of these projects, they include - but are not limited to: Isaac

Kaplan, Pieter Johnson, Brian Roth, Jeff Jorgensen, Caitlin Gille, Hem Nalini Morzaria, Chad

Harvey, Elena Bennet, Matt Van de Bogert, Paul Hanson, Kristy Rogers, Stacy Lishka, Oscar

Pérez, Genkai Kato, and Ken Forshay. I also want to thank Willaim Fetzer, Gretchen Anderson,

Ellen Feingold, and Dan Cobian for the good times procesing samples or catching fishes for

LTER.

I am thankful for all the efforts made by the staff at the CFL, the Dept. of Zoology and

the Trout Lake station. Pam Montz, Dave Balsiger, Barbara Benson, Ted Cummings, Pam

Fashingbauer, Dave Harring, Denise Karns, Tim Kratz, Marilyn Larsen, Tim Meinke, Anne

vii

Murphy-Lom, John Verhs, Georgia Wagner, Carol Schraufnagel, Mike Pecore, Jim Rusak,

Michelle Woodford and Scott Van Egeren were important in keeping this career going. I am

sure I am missing someone in all these acknowledgements right now. You will come up in my

memories I am sure, and I thank you as well.

I want to thank the Consejo Nacional de Ciencia y Tecnología (CONACyT) in Mexico,

the North Temperate Lakes Long Term Ecological Research Project and the Department of

Zoology for their support at different stages of my graduate studies.

The binational nature of this thesis requires my thanking many colleagues and friends

back home. Please change brain settings. Quiero agradecer el apoyo durante todos estos años,

de Guillermo Salgado Maldonado, mi director de tesis en mi alma mater, la UNAM. En especial

quiero agradecerle el haberme puesto en ruta – ya llegué, y acabo de comenzar. Edmundo Díaz-

Pardo ha sido un profesor y colega ejemplar, muchas gracias por el apoyo. Eduardo Soto-Galera,

Martina Medina, Omar Domiguez, Luis Zambrano, y Salvador Contreras-Balderas han sido

maestros y colegas importantes en mi trabajo en Mexico. A todos mis ‘docs’ en el Laboratorio

de Helmintologia, la Universidad Autónoma de Querétaro, y la Escuela Nacional de Ciencias

Biológicas les quiero agradecer la constante ayuda durante este proceso. En especial quiero

agradecer a Guillermina Cabañas-Carranza, Andrés Martínez-Aquino, Rogelio Aguilar –Aguilar,

Mirza P. Ortega-Olivares, Carlos Mendoza Palmero, Guadalupe Lara Figueroa, Jesus Guillermo

Jiménez Cortes, Miriam Erandi Reyna Fabián, Altargacia Gutiérrez Hernández, Jair Carcaño,

Carlos Pedraza Lara y Claudia Patricia Ornelas. Hay algunos mas que, quizas sin saberlo, han

sido siempre soporte y compañía: Miguel Rivas Bejarano y Javier Díaz de Sandi, un

agradecimiento enorme por su amistad y apoyo.

viii

Los cimientos para poder llegar a la culminación de este grado se encuentran en mi

familia. A mis padres y hermanos, mi agradecimiento eterno por los apoyos y por permanecer

siempre como guía y ejemplo.

Spanish and English together do not add up enough words to express my gratitude to my

wife Maryalyse Adams. She was a constant source of encouragement, support and happiness.

Our road ahead will be interesting, and it will be a good one wherever it leads. Six years ago I

came up to Madison looking for a little paper with a signature – today I am about to leave with

three. One of them has your signature on it – it is the most valuable of them all.

1

CHAPTER I

THESIS INTRODUCTION

The introduction of exotic species is recognized as a central agent of human-caused

global change (Mack et al. 2000) with important biological and economic consequences

(Pimentel et al. 2000, Leung et al. 2002). The invasion and establishment of non-indigenous

species is one of the major causes of change in the composition, structure, function and viability

of freshwater ecosystems (Richter et al. 1997, Benson 1999, Claudi and Leach 1999, Sandlund et

al. 1999). A large and growing volume of scientific literature has documented the impacts of

exotics on native fishes (Moyle 1987, Evans and Waring 1987, Contreras-Balderas and

Escalante-Cavazos 1993, Hrabik et al. 2001), nutrient cycles (Swanson et al. 2003), and food

webs (Vander Zanden et al. 1999).

Although the study of invasion biology has existed for over 60 years (Elton 1958, Baker

and Stebbins 1965), since the mid 1980’s this topic has resurged, because exotics are

increasingly identified as major agents for biological change and the number of invasions has

accelerated (Lodge 1993, Ricciardi 2001). Since the 1980’s, the study of invasive species has

been addressed from a variety of perspectives (Parker and Reichard 1998, Puth and Post 2005)

ranging from the identification of the biological traits that characterize good invaders (Lodge

1993, Williamson and Fitter 1996, Kolar and Lodge 2001) to the economic implications of

invasions (Pimentel et al. 2000), and the creation of predictive models to assist in the

management of native species threatened by exotics (e.g., Vander Zanden et al. 2004). The

biogeographic distribution of invaders, the study of impacts at different organizational levels, the

mechanisms for invasion and impact, and the implementation of restoration and containment

2

strategies, among others, are active areas of research in the field of invasion biology today

(Parker et al. 1999).

Exotic species have impacts at different levels of biological organization ranging from

genetic (e.g., hybridization and introgression) to ecosystem (e.g., nutrient flux alterations, and

disturbance regime alteration) (Parker et al. 1999) and their effects on native communities can be

studied from a variety of perspectives. The adverse impacts of invasive fish species on native

fish populations and communities have also been studied in a variety of freshwater ecosystems

(e.g., Hrabik et al. 1998. Madenjian et al. 2000, Velázquez-Velázquez and Schmitter-Soto 2004,

Krueger and Hrabik 2005).

An extensive body of literature addresses the changes that biological communities in

freshwater ecosystems undergo as a consequence of invasions (e.g., Colby et al. 1987, Ogutu-

Ohwayo 1990, Townsend 2003, Hewitt 2002, Contreras-Balderas et al. 2002, Beisner et al.

2003). Beyond the mere addition of species to communities, this abundant literature has

identified changes that occur in non-fish communities in these ecosystems, the change in

functional roles of members of the communities, and interactions among species. Closely related

to studies at the community level, the effect that invasive species have on energy and nutrient

allocations among species has also received increasing attention. Invasive species can bring very

significant alterations to aquatic food webs (Mills et al. 1995, Vander Zanden et al. 1999,

Beisner et al. 2003), and can cause the decline of important components of communities via

predatory or competitive interactions (Johnson and Goettl 1999, Kitchell et al. 2000). Through

food web alterations, exotic species can induce whole ecosystem changes and even help in the

biological magnification of contaminants that could potentially affect top carnivores (Vander

Zanden et al. 1996, Swanson et al. 2003).

3

From a landscape perspective, much attention has been directed to the development of

models that can predict the future occurrence of exotic species, and the identification of

freshwater ecosystems where they can become a threat. Some of these studies have focused on

identifying vectors of invasive species spread (Jarnagin et al. 2000, Johnson et al. 2001) while

others have focused on identifying those ecosystems that are most prone to invasions, and

subsequent impact on native biota (Koutnik and Padilla 1994, MacIsaac et al. 2004, Vander

Zanden et al. 2004,). These efforts are usually geared towards aiding fishery and environmental

resource managers in the identification of systems that are most prone to suffer impacts from

exotic species, so that management resources can be allocated to those systems most in need.

In the following four chapters, this thesis presents independent studies that address exotic

species from four different perspectives: 1) population, 2) community, 3) food web, and 4)

landscape. Rainbow smelt (Osmerus mordax) are incipient invaders in inland lakes throughout

North America, where they have had relatively well documented consequences on native fish

communities. This species is the focus of chapters II and V, in studies that encompass over 8000

lakes in Wisconsin and Maine (USA) and Ontario (Canada) and investigate the effect of this

exotic on walleye recruitment and the dispersal of the species through the landscape. Chapters

III and IV adopt community and food web perspectives to examine exotic fish impacts on the

fish assemblages of the Laja river in the state of Guanajuato, Central Mexico, one of the better

studied river systems in the country, and one facing multiple anthropogenic stresses.

Chapter II documents the impacts that rainbow smelt can have on the recruitment of

walleye (Sander vitreus), which comprise an important fishery in Wisconsin. Smelt are

suspected to impact young-of-year walleye, in a similar way that they have affected yellow perch

(Perca flavescens) and cisco (Coregonus artedi) (Hrabik et al. 1998, 2001). In this chapter I use

4

long term walleye recruitment information from more than 400 lakes in Wisconsin to detect the

impact of rainbow smelt on age-0 walleye, and then identify uninvaded lakes most prone to

colonization by rainbow smelt. Chapter II will be submitted to the Canadian Journal of Fisheries

and Aquatic Sciences with Steve Gilbert, Gregory G. Sass, Brian M. Roth and Jake Vander

Zanden as coauthors.

From a community perspective, Chapter III follows the fish assemblage of the Laja river

over 50 years, portraying how fish communities of this system have changed as a consequence of

anthropogenic impacts, including the intentional and accidental introduction of exotic fishes.

This chapter describes not only the species composition of the fish community but analyses the

functional role of each species in the assemblage and how these have changed since the 1960’s

when the first surveys were carried out in the system. The significance of this study transcends

the Laja and can be expanded to other basins in Central Mexico, for which historical information

is lacking. Chapter III has been accepted for publication in Aquatic Conservation: Marine and

Freshwater Ecosystems with John Lyons, Edmundo Díaz-Pardo, Altagracia Gutiérrez-

Hernández, Claudia Patricia Ornelas, Carlos Pedraza-Lara and Jake Vander Zanden as coauthors

(Mercado-Silva et al. 2005).

In Chapter IV I study the present-day food web of the Laja river using 13C and 15N stable

isotope analyses. As a consequence of human activities, the fish assemblage has experienced

several native fish extirpations and the introduction of exotic species, both of which have altered

the structure of food webs in the system. This chapter examines the effects of reservoirs on food

web structure, as well as the food web role of invaders at a series of 11 sites in the watershed.

Chapter IV will be submitted to River Research and Applications with Jake Vander Zanden,

Matthew R. Helmus, Edmundo Díaz-Pardo and John Lyons as coauthors.

5

In Chapter V, I return to rainbow smelt, which have successfully invaded many inland

lakes in North America (e.g., southern Ontario, areas of Maine, etc.), but continue to expand

their range as a result of illegal introductions and accidental transfers (Evans and Loftus, 1987,

Hrabik and Magnuson 1999). Based on their distribution in their native range, I build a

classification tree model that identifies morphological, physical-chemical and biological

variables that determine the presence or absence of rainbow smelt in Maine lakes. I then test this

model and use it to predict lakes that are suitable for smelt in Ontario, Canada, and Wisconsin,

USA. Chapter V was submitted to Conservation Biology with Julian D. Olden, Thomas R.

Hrabik, Jeffrey T. Maxted, and Jake Vander Zanden as coauthors.

6

REFERENCES

Baker, H.G. & G.L. Stebbins. 1965. The genetics of colonizing species. Academic Press, Burlington, MA.

Beisner, B.E., A.R. Ives & S.R. Carpenter. 2003. The effects of an exotic fish invasion on the

prey communities in two lakes. Journal of Animal Ecology 72: 331-342. Benson, A.J. 1999. Documenting over a century of aquatic introductions in the United States. pp.

1-32. In: R. Claudi & J.H. Leach (ed.) Nonindigenous freshwater organisms, Lewis, New York.

Claudi, R. & J.H. Leach. 1999. Nonindigenous freshwater organisms. Lewis Publishers, Boca

Raton FL. 464 pp. Colby, P.J., P.A. Ryan, D.H. Schupp & S.L. Serns. 1987. Interactions in north-temperate lake

fish communities. Canadian Journal of Fisheries and Aquatic Sciences 44: 104-128. Contreras-Balderas, S., R.J. Edwards, M.d.L. Lozano -Vilano & M.E. García-Ramírez. 2002.

Fish biodiversity changes in the Lower Rio Grande/ Rio Bravo, 1953-1996. Reviews in Fish Biology and Fisheries 12: 219-240.

Contreras-Balderas, S. & M.A. Escalante-Cavazos. 1993. Distribution and known impacts of

exotic fishes in Mexico. pp. 102-130. In: W.R. Courtenay Jr. & J.R. Stauffer Jr (ed.) Distribution, Biology and Management of Exotic Fishes, Johns Hopkins University Press, Baltimore.

Elton, C.S. 1958. The ecology of invasions by animals and plants. Methuen and Co LTD,

London. 181 pp. Evans, D.O. & D.H. Loftus. 1987. Colonization of inland lakes in the Great Lakes Region by

Rainbow Smelt, Osmerus mordax: Their freshwater niche and effects on indigenous fishes. Canadian Journal of Fisheries and Aquatic Sciences 44: 249-266.

Evans, D.O. & P. Waring. 1987. Changes in multispecies, winter angling fishery of Lake

Simcoe, Ontario, 1961-83: Invasion by Rainbow Smelt, Osmerus mordax, and the roles of intra- and interspecific interactions. Canadian Journal of Fisheries and Aquatic Sciences 44: 182-197.

Hewitt, C.L. & G.R. Huxel. 2002. Invasion success and community resistance in single and

multiple species invasion models: Do the models support the conclusions? Biological Invasions 4: 263-271.

Hrabik, T.R. & J. Magnuson. 1999. Simulated dispersal of exotic rainbow smelt (Osmerus

mordax) in a northern Wisconsin lake district and implications for management. Canadian Journal of Fisheries and Aquatic Sciences 56: 35-42.

7

Hrabik, T.R., J. Magnuson & A.S. McLain. 1998. Predicting the effects of rainbow smelt on

native fishes in small lakes: evidence from long term research in two lakes. Canadian Journal of Fisheries and Aquatic Sciences 55: 1364-1371.

Hrabik, T.R., M.P. Carey & M.S. Webster. 2001. Interactions between young-of-the-year exotic

Rainbow Smelt and native Yellow Perch in a northern temperate lake. Transactions of the American Fisheries Society 130: 568-582.

Jarnagin, S.T., B.K. Swan & W.C. Kerfoot. 2000. Fish as vectors in the dispersal of

Bythotrephes cederstroemi: diapausing eggs survive passage through the gut. Freshwater Biology 43: 579-589.

Johnson, B.M. & J.P. Goettl. 1999. Food web changes over fourteen years following introduction

of Rainbow Smelt intop a Colorado Reservoir. North American Journal of Fisheries Management 19: 629-642.

Johnson, L.E., A. Ricciardi & J.T. Carlton. 2001. Overland dispersal of aquatic invasive species:

A risk assessment of transient recreational boating. Ecological Applications 11: 1789-1799.

Kitchell, J.F., S.P. Cox, C.J. Harvey, T.B. Johnson, D.M. Mason, K.K. Schoen, K. Aydin, C.

Bronte, M.P. Ebener & M.J. Hansen. 2000. Sustainability of Lake Superior fish community: interactions in a food web context. Ecosystems 3: 545-560.

Kolar, C.S. & D.M. Lodge. 2001. Progress in invasion biology:predicting invaders. Trends in

Ecology and Evolution 16: 199-204. Koutnik, M.A. & D.K. Padilla. 1994. Predicting the spatial distribution of Dreissena polymorpha

(Zebra Mussel) among inland lakes of Wisconsin: Modeling with a GIS. Canadian Journal of Fisheries and Aquatic Sciences 51: 1189-1196.

Krueger, D.M. & T.R. Hrabik. 2005. Food web alterations that promote native species: the

recovery of native cisco (Coregonus artedi) populations through management of native piscivores. Canadian Journal of Fisheries and Aquatic Sciences 62: 2177-2188.

Leung, B., D.M. Lodge, D. Finnoff, J.F. Shogren, M.A. Lewis, and G. Lamberti. 2002. An ounce

of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proceedings of the Royal Society of London, Series B – Biological Sciences 269: 2407-2413.

Lodge, D.M. 1993. Biological invasions: lessons for ecology. Trends in Ecology and Evolution

8: 133-137. MacIsaac, H.J., J.V.M. Borbely, J.R. Muirhead & P.A. Graniero. 2004. Backcasting and

forecasting biological invasions of inland lakes. Ecological Applications 14: 773-783.

8

Mack, R.N., D. Simberloff, W.M. Lonsdale, H. Evans, M. Clout & F.A. Bazzaz. 2000. Biotic

Invasions: Causes, Epidemiology, Global Consequences, and Control. Ecological Applications 10: 689-710.

Madenjian, C.P., R.L. Knight, M.T. Bur & J.L. Forney. 2000. Reduction in recruitment of White

Bass in Lake Erie after invasion of White Perch. Transactions of the American Fisheries Society 129: 1340-1353.

Mercado-Silva, N., J. Lyons, E. Díaz-Pardo, A. Gutiérrez-Hernández, C.P. Ornelas-García, C.

Pedraza-Lara & M.J. Vander Zanden. In Press. Long -term changes in the fish assemblage of the Laja River, Guanajuato, central Mexico. Aquatic conservation: Marine and Freshwater Ecosystems.

Mills, E.L., R. O'Gorman, E.F. Roseman, C. Adams & R.W. Owens. 1995. Planktivory by

alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax) on microcrustacean zooplankton and dreissenid (Bivaliva:Dreissenidae) veligers in southern Lake Ontario. Canadian Journal of Fisheries and Aquatic Sciences 52: 925-935.

Moyle, P.B., H.W. Li & B.A. Barton. 1987. The Frankenstein effect: Impact of introduced fishes

on native fishes of North America. pp. 415-426. In: R.H. Stroud (ed.) The role of fish culture in fisheries management, American Fisheries Society, Bethesda, MD.

Ogutu-Ohwayo, R. 1993. The decline of native fishes of Lakes Victoria and Kyoga (East Africa)

and the impact of introduced species, especially the Nile perch (Lates niloticus) and the Nile tilapia (Oreochromis niloticus). Environmental Biology of Fishes 27: 81-96.

Parker, I.M. & S.H. Reichard. 1998. Critical issues in invasion biology for conservation science.

pp. 283-305. In: F.a. Karewa (ed.) Conservation Biology, Chapman & Hall. Parker, I.M., D. Simberloff, W.M. Lonsdale, K. Goodell, M. Wonham, P.K. Kareiva, W. M.H.,

B.V. Von Holle, P.B. Moyle, J.E. Byers & L. Goldwasser. 1999. Impact: Toward a framework for understanding the effects of invaders. Biological Invasions 1: 3-19.

Pimentel, D., L. Lach, R. Zuniga & D. Morrison. 2000. Environmental and economic costs of

nonindigenous species in the United States. BioScience 50: 53-65. Puth L.M. and D. M. Post. 2005. Studying invasion: have we missed the boat? Ecology Letters

8: 715–721 Ricciardi, A. 2001. Facilitative interactions among aquatic invaders: is an "invasional meltdown

ocurring in the Great Lakes? Canadian Journal of Fisheries and Aquatic Sciences 58: 2513-2525.

Richter, B.D., D.P. Braun, M.A. Mendelson & L.L. Master. 1997. Threats to imperiled

freshwater fauna. Conservation Biology 11: 1081-1093.

9

Sandlund, O.T., P.J. Schei & A. Viken. 1999. Invasive species and biodiversity management.

Kluwer Academic Publishers, Dordrecht, The Netherlands. 431 pp. Swanson, H.K., T.A. Johnston, W.C. Leggett, R.A. Bodaly, R.R. Doucett & R.A. Cunjak. 2003.

Trophic positions and mercury bioaccumulation in Rainbow Smelt (Osmerus mordax) and native forage fishes in Northwestern Ontario lakes. Ecosystems 6: 289-299.

Townsend, C.R. 2003. Individual, population, community, and ecosystem consequences of a fish

invader in New Zealand streams. Conservation Biology 17: 38-47. Vander Zanden, M.J., J.D. Olden, J.H. Thorne & N.E. Mandrak. 2004. Predicting occurrences

and impacts of smallmouth bass introductions in north temperate lakes. Ecological Applications 14: 132-148.

Vander Zanden, M.J., J.M. Casselman & J.B. Rasmussen. 1999. Stable isotopes evidence for the

food web consequences of species invasions in lakes. Nature 401: 464-467. Velázquez-Velázquez, E. & J.J. Schmitter-Soto. 2004. Conservation status of the San Cristobal

pupfish Profundulus hildebrandi (Teleostei: Profundulidae) in the face of urban growth in Chiapas, Mexico. Aquatic Conservation: Marine and Freshwater Ecosystems 14: 201-209.

Williamson, M.H. & A. Fitter. 1996. The charateristics of successful invaders. Biological

Conservation 78: 163-170.

10

CHAPTER II

WALLEYE (SANDER VITREUS) RECRUITMENT DECLINE AS A CONSEQUENCE OF RAINBOW SMELT

(OSMERUS MORDAX) INVASIONS IN WISCONSIN LAKES

ABSTRACT

Rainbow smelt (Osmerus mordax) are incipient invaders in North American inland lakes and

have affected native fish populations, ecosystem processes and important fisheries. Walleye

(Sander vitreus) comprise one of the most important recreational fisheries in Wisconsin, and

could be threatened by rainbow smelt invasions. Here we present evidence for the decline of

age-0 walleye (age-0-W) abundance in Wisconsin inland lakes that have been invaded by smelt,

present a list of the 26 smelt-invaded lakes in Wisconsin, and identify a subset of walleye

populations in lakes susceptible to future smelt invasion. Before-after-control-impact (BACI)

analysis identified significant declines in age-0-W in lakes with pre- and post-smelt invasion

age-0-W survey data. For lakes where pre-invasion age-0-W data was not available, we detected

lower age-0-W abundances than in non-invaded walleye populations. An average 23% decline

in abundance of age-0-W was calculated for smelt-invaded lakes. We identified 188 Wisconsin

lakes that are accessible to known rainbow smelt vectors (anglers), have walleye recruitment

estimations, and have the morphological, chemical, and physical attributes appropriate for

sustaining smelt populations. Our results indicate that rainbow smelt pose an incipient threat to

walleye populations in the state and also provide specific identification of those lakes where

management efforts need to be focused to prevent impacts from this invasive species.

11

INTRODUCTION

Invasive species are a major threat to freshwater ecosystems (Soulé 1990; Sala et al.

2000; Vander Zanden 2005) and can have significant impacts on valuable fisheries via habitat

alterations, and predatory and competitive interactions (Moyle et al. 1987, Madenjian et al. 2002,

Sullivan et al. 2003). Exotic fishes alone cause over $1 U.S. billion annually in economic losses,

even when many species are intentionally introduced to enhance fisheries (Pimentel et al. 2000,

2001). Identifying invasive species impacts and forecasting the spread of invaders are currently

areas of intense research (Lodge 1993; Lodge et al 1998; Vander Zanden et al. 2004). Impacts of

invasive species sometimes involve easily observable responses in the recipient ecosystems,

though more often these impacts are difficult to separate from natural variation because they take

place in ecosystems that are affected by a variety of factors.

Rainbow smelt (Osmerus mordax) are incipient invaders in inland lakes of the Great

Lakes region (Evans and Loftus 1987, Hrabik and Magnuson 1999; Mercado-Silva et al.

submitted) and their introduction has caused the extirpation of native fishes (i.e. yellow perch

[Perca flavescens], cisco [Coregonus artedi]) via competitive or predatory interactions (Evans

and Loftus 1987, McLain 1991, Hrabik et al. 1998, Hrabik et al. 2001). Smelt have also been

suspected of affecting walleye (Sander vitreus) (Schneider and Leach 1977; Krueger and Hrabik

2005), though direct evidence is lacking. Impacts on walleye would be of serious concern

because walleye are one of the most important tribal spearing and recreational angling fisheries

in the Great Lakes region (Schneider and Leach 1977, Becker 1983, BIA 2003).

Walleye recruitment is highly variable among Wisconsin lakes (Nate et al. 2000, Beard et

al. 2003, Sass et al. 2004) and age-0 walleye may be vulnerable to rainbow smelt invasions via

predation or competition (Schneider and Leach 1977, Jones et al. 1994, Johnson and Goettl

12

1999). However, rainbow smelt impacts could be hard to detect given this high variability, the

relatively low number of invaded lakes, and the likely variable intensity of impact. Regardless

of these limitations on detecting impacts, it is imperative to document evidence of impact

especially if it is accompanied by forecasts for other systems that could be affected in a similar

way. Smelt colonization potential (i.e. the probability of colonists to reach a new ecosystem

[Vander Zanden 2004]) has been little studied (but see Hrabik and Magnuson 1999).

Identification of systems where smelt could have access would increase the effectiveness of

efforts to prevent the spread of the invader.

Here we examine empirical evidence for the impact of smelt invasions on walleye

recruitment, and identify systems where smelt could invade and cause impacts on walleye

recruitment. Future management of walleye fisheries depend on the ability to detect ecological

impacts and identify systems where walleye populations could be potentially affected and where

efforts should be concentrated to prevent rainbow smelt invasions.

METHODS

Study Systems

In Wisconsin (USA), 859 inland lakes are known to contain naturally reproducing and

stock-supplemented walleye populations (Nate et al. 2000, Sass et al 2004). Strong variations in

recruitment of young-of-the-year (age-0) walleye to the adult population are affected by parental

stock size, predation, competition, water temperatures, changes in spawning habitat, or interyear

climate variability (Hansen et al. 1998, Nate et al. 2000, Beard et al. 2003), but other human-

derived factors could also play an important role.

Fishery pressure and habitat degradation have been identified as factors that threaten the

viability of Wisconsin walleye populations (BIA 2003) and could be affecting walleye

13

recruitment. Walleye recruitment declines have recently been linked to smelt introductions in

several systems in the United States via direct predation by smelt or through food web

disruptions (Schneider and Leach 1977, Jones et al. 1994, Johnson and Goettl 1999). Walleye

declines have been attributed to egg and larvae predation by smelt or food competition.

Walleye are distributed in all lake types in Wisconsin and are usually found in the quiet

waters of lakes, backwaters and sloughs where shelter, turbidity or color shields their eyes from

light (Colby et al. 1987; Becker 1983). They also prefer waters that stratify and have deeper

maximum depths (Bennet and McArthur 1990). Walleye fry move to open waters soon after

hatching and remain there until they attain a length of approximately 30mm and then return to

inshore areas of lakes. As young of the year, walleye consume plankton and eventually shift to

piscivory after reaching approximately 75mm (Becker 1983).

Rainbow smelt established in Lake Michigan and Lake Superior in the 1920’s and 1930’s

(Evans and Loftus 1987). By 1968, Wisconsin inland lakes had established smelt populations

(Becker 1983). Smelt thrive in deep, cold water, oligotrophic lakes. After hatching and as

postlarvae, smelt feed on zooplankton. Upon reaching adulthood, smelt are known to feed on

fish eggs and fish larvae (including cannibalism), although most of their diets consist of

zooplankton (Nellbring 1989, Vander Zanden and Rasmussen 1996). In situations where smelt

become abundant, walleye could be impacted via competitive or predatory interactions with

smelt.

Approach

We carried out three separate analyses to investigate evidence for smelt impact on

walleye populations. Recruitment declines were investigated in lakes with pre- and post-smelt

invasion walleye recruitment data, as well as in lakes with post-invasion data only. The impact

14

of smelt on walleye was also quantified for invaded lakes with reference to non-invaded lakes.

In addition, to aid in the future management of walleye populations, smelt introduction potential

was assessed based on boat-launch access, building on efforts to characterize invader

vulnerability of Wisconsin lakes (Hrabik and Magnuson 1999, Mercado-Silva et al., submitted).

Data

All available age-0 walleye (age-0-W) density estimates were obtained from Wisconsin

Department of Natural Resources (WDNR) and Great Lakes Indian Fish and Wildlife

Commission (GLIFWC) databases. For density estimation, the entire shoreline was sampled

with 230-V AC electrofishing boats from mid-September to mid-October of each year on lakes

selected under a randomized survey design. If the entire shoreline could not be sampled,

randomly selected sections were sub-sampled and the distance sampled recorded. The number of

lakes surveyed varied among years and most lakes were not sampled repeatedly in consecutive

years. Age of each walleye collected was estimated by examining the length frequency

distribution for modal lengths or through scale annuli analysis (Beard et al. 2003). The number

of age-0-W captured was summed and divided by the shoreline length (km) sampled to estimate

density. Systematic surveys started in 1990 and have continued to date, but were preceded by

monitoring efforts starting as early as 1958 in a limited number of lakes. For this analysis, we

used data from 432 populations surveyed between 1971 and 2004. Most information was

available for 1985-2004. Lakes used in this analysis ranged from 8 to 6200 hectares in surface

area. Lakes in which no age-0-W were collected were not used.

Rainbow smelt distribution in Wisconsin lakes was summarized from published literature

(Becker 1983; Colby et al. 1987; Hrabik and Magnuson 1999; Lyons et al. 2000; Mclain 2001;

Krueger and Hrabik 2005), interviews with W-DNR regional fish managers (see

15

acknowledgement section), datasets in the Wisconsin Aquatic Gap Mapping Application (W-

DNR) (http://web2.er.usgs.gov/wdnrfish/), and our own unpublished observations. Currently, 26

inland Wisconsin lakes are known to support rainbow smelt (Table 1), but we recognize that

other invaded lakes may exist that have not been reported. Of the 26 invaded lakes, 12 have

been surveyed for age-0-W and only 3 have pre-and post-invasion age-0-W data. Adult rainbow

smelt abundance data were available for a single invaded lake (Sparkling Lake) from long-term

sampling carried out through the North Temperate Lakes – Long Term Ecological Research

(NTL-LTER) program (http://limnosun.limnology.wisc.edu/) and the WDNR. Rainbow smelt

abundance was calculated as the number of smelt per fyke net from field samplings carried out

yearly in early spring from 1981- 2004.

To establish the potential for smelt colonization based on angler bait-bucket

introductions, one of their principal vectors of transfer (Evans and Loftus 1987), we used public

access data from an extensive 4600-lake dataset from the WDNR (Kathy E. Webster pers.

comm. [WDNR]).

Analysis

We calculated the mean age-0-W density for non-invaded Wisconsin lakes with natural

(n= 133) and stocked (n= 287) walleye populations. Mean age-0-W density was also calculated

for smelt-invaded lakes (n=12). Since all invaded lakes are classified as stocked, comparison of

invaded and uninvaded lakes is based on age-0-W estimates from uninvaded stocked lakes. Use

of stocked lakes as the benchmark for our analysis gives a conservative estimate of smelt impacts

since these lakes have lower age-0-W densities than naturally reproducing populations (Fig. 1.)

Pre-invasion/ Post-invasion Analysis

16

Tests for changes in age-0-W density between pre- and post-smelt invasion periods were

based on analysis of data from Sparkling, Long, and Keyes lakes. All were natural-recruitment

lakes until the mid-1980s, mid 1990s, and early 2000s, respectively, when stocking of fingerling

and extended growth walleye was started by the WDNR. We used a before-after-control-impact

(BACI) statistical test to test for changes between periods (Stewart-Oaten et al. 1986, Osenberg

et al. 1996). Mean age-0-W densities for all stocked, non-invaded lakes sampled in a given year

were used as a control group. The mean age-0-W density from invaded lakes was used as the

impacted group. We calculated:

pactedlakelakesAllstockedeinvadedlak ageage Im00 +−=∆

for each invaded lake to compare age-0-W density estimates with those of uninvaded

lakes. For the invaded lakes mentioned above, age-0-W data were collected from as early as

1971, but age-0-W estimates do not exist for the rest of Wisconsin lakes, with the exception of

Lake Escanaba, a long term WDNR monitoring lake (sampled consistently since 1958). To

estimate age0Allstockedlakes in the 1971 – 1985 period we used age-0-W data from Lake Escanaba,

which is a good predictor of age-0-W estimates in the rest of Wisconsin lakes used in this

analysis (r2 = 0.97) (Figure 2). We used this relationship to create estimates for the period 1971-

1984 and to obtain ∆Invaded lake.

To test for impact of the invasion, we calculated the difference between ∆Before Invasion and

∆After Invasion, and carried out t-tests in R (Daalgard 2002) between ∆s for both groups in invaded

lakes. We then calculated the percent change in delta pre- and post-invasion. Age-0-W density

estimations made up to five years after the known invasion date of each lake were considered as

“before” impact samplings. All samplings made after this period were considered “after”

impact. We thus account for a lag between the first smelt observation and biotic impacts.

17

Post-Invasion analysis

To assess whether invaded lakes for which pre-invasion data were lacking had

recruitment rates lower than uninvaded stocked lakes, we used a Mann-Whitney test between

invaded and uninvaded age-0-W data from all lakes in these two groups (R-program, Dalgaard

2002). We only included pre-smelt invasion data from lakes for which pre-invasion data were

available. We also estimated the difference in recruitment between all invaded and uninvaded

lakes (∆ = age-0-W/kmInvaded lakes – age-0-W/kmNot invaded lakes) for each year in the period 1986-

2003. Each ∆ was divided by the standard deviation of uninvaded lakes (∆/SDnot-invaded lakes),

producing a t-value that was evaluated for significance at α=0.05. Statistical significance of t-

tests indicated if invaded lakes had different recruitment than stocked uninvaded lakes for a

given year.

Impact Calculations

To measure the impact of rainbow smelt on walleye recruitment in invaded lakes we first

calculated the mean age-0-W for each year for uninvaded lakes in the period 1985 - 2004. Then

we estimated the deviation of the age-0-W estimate in each invaded lake from the above mean

for each year. The average of these deviations was calculated for each lake and was expressed as

a percent change (decline or increase) in age-0-W density. We tested whether the intensity of

these changes could be related to morphological and physiochemical variables by regressing

them on lake-specific area, maximum depth, mean Secchi depth, and pH.

Walleye – Smelt Impact Predictions

A biological invasion can be seen as comprised of three steps: 1) colonization, 2)

establishment, and 3) impact (Kolar and Lodge 2001, Vander Zanden et al. 2004). Mercado-

Silva et al. (submitted) identified 232 Wisconsin lakes with walleye populations that are suitable

18

for the establishment of rainbow smelt based on lake specific morphological, physio-chemical,

and biological attributes. However, there is also a need to identify which lakes are most prone to

smelt colonization based on known dispersal vectors. Aside from intentional introductions, bait

bucket transfers and boat live wells have been proposed as the main vectors for the dispersal of

rainbow smelt (Evans and Loftus 1987). To identify Wisconsin lakes most prone to

colonization, we used a geographic information system to identify lakes with the greatest access

to colonists by the two vectors mentioned above. We identified lakes with angler access among

the 232 lakes identified by Mercado-Silva et al. (submitted) and those with walleye populations

where recruitment has been estimated. These would be lakes where impacts would likely be

detected if they were invaded by smelt.

RESULTS

Estimates of age-0-W were variable among years but showed no overall directional trend

between 1985 and 2004 (Figure 1). Mean age-0-W for uninvaded smelt lakes was 17.5 age-0-

W/km. Lakes with natural recruitment had a mean 25.7 age-0-W/km (range: 0.07 to 74.93; max.

age-0-W in a lake: 263.06) and stocked lakes a mean 13.2 age-0-W/km (range 0.026 to 119.3;

max. density in a lake 283). Age-0-W were significantly lower in stocked lakes compared to

natural recruitment lakes (Mann-Whitney Z = 29.05, p < 0.001). Rainbow smelt-invaded lakes

had an average 8.27 age-0-W/km (range: 0.1 to 46.6; max. age-0-W in a lake: 87).

Walleye recruitment declined from pre-invasion to post-invasion periods in our three

invaded lakes: Sparkling (t = 3.68, p = 0.007), Long (t = 4.42, p = 0.02), and Keyes (t = 2.13, p =

0.04) lakes. Density of age-0-W decreased by an average 70% in these three lakes from pre-

invasion to post-invasion periods (Sparkling 90%, Long 70%, and Keyes 50%) (Figure 3). In

19

Sparkling Lake, rainbow smelt abundance increased dramatically since their invasion in 1980,

but their abundance has varied widely among years (Figure 3).

For lakes lacking pre-invasion age-0-W density data, Mann-Whitney tests showed higher

age-0-W abundance in uninvaded lakes than in invaded lakes (Z = 7.27, p< 0.001) when all data

were used for comparison. In 13 of 18 (72%) years with available data, walleye recruitment was

lower in invaded lakes than in stocked lakes (Figure 4 a and b). In two years (1988 and 1997),

walleye recruitment was higher in invaded than uninvaded lakes, and in three (1986, 1992, and

2002), differences were not significant.

For 10 invaded lakes with more than 2 age-0-W estimations between 1985 and 2004, 9

had lower recruitment than the mean age-0-W for uninvaded lakes. The average difference was -

23% (+10% to -70%) (Fig. 5.). Lake specific deviations did not vary depending on any of the

lake attributes analyzed (R = 0.00 for all lake-specific variables; F <1, p > 0.05).

The number of uninvaded lakes that could be colonized by rainbow smelt comprise 40%

of all 420 lakes used in this analysis. One hundred and eighty-eight lakes (62 non-stocked and

126 stocked) lakes of the 232 identified as vulnerable for rainbow smelt invasion by Mercado-

Silva et al. (submitted) had at least one walleye recruitment estimation. Of these, 176 (94%)

have public access or can be navigated into, while only 10 (5.3%) have no access or are located

in wilderness areas (Figure 6) (Appendix 2).

DISCUSSION

Species invasions have been responsible for the decline of important fisheries in the

Great Lakes region and elsewhere (Taylor et al. 1984, Franzin et al. 1994, Madenjian et al.

2000). Evidence for these declines, however, has often not become available until it is too late to

protect or recover remaining populations. When invasions are incipient, case studies are few and

20

definitive trends of impacts are not easily detected. Regardless, it is necessary to base future

management actions on scarce data, predictions derived from systems where the invading species

has already had impacts, and the biological and ecological information available for species

involved in the interactions.

Our results suggest an important link between walleye recruitment decline and rainbow

smelt invasions in inland lakes, and provide a reference for natural resource managers to identify

other systems where walleye populations could be at risk if they were invaded by rainbow smelt.

In lakes for which pre- and post-invasion data were available, declines in age-0-W were

significant, reducing the density of age-0-W by more than half compared to pre-invasion

estimates. For lakes without pre-invasion age-0-W data, we were able to detect significant

differences in recruitment between invaded and uninvaded lakes in most years between 1983-

2003. However, recruitment was not lower in invaded versus univaded lakes in all years, and the

number of invaded versus non invaded lakes was low. Notwithstanding, post-invasion age-0-W

densities remained below the average derived from all 287 uninvaded stocked lakes in 72% of

the years analyzed, and all invaded lakes are today considered lakes with stocked recruitment

(when some had natural recruitment in the past). In addition to causing the observed declines, it

seems likely that smelt reduce the variability of age-0-W density. The strong year classes

observed cyclically in most Wisconsin walleye lakes (Beard et al. 2003) do not seem to occur

after rainbow smelt invade and become abundant in a system. The magnitude of rainbow smelt

impact on age-0-W density varied among lakes, and we were unable to find a relationship

between lake-specific attributes and the extent of the decline. Although this may be a product of

the small number of lakes available for analysis, we suggest that factors such as lake-specific

walleye stocking histories could aid in future efforts to aid in explaining this variability. Further,

21

studying the stocking history of invaded and uninvaded lakes could provide better understanding

of walleye population management implications of smelt invasion, and help explain some of the

variability observed in recruitment between both sets of lakes.

Rainbow smelt effects on walleye populations described here have been suggested in

other systems. Schneider and Leach (1977) linked a decline in walleye stocks throughout the

Great Lakes in 1900-40 to an increase in rainbow smelt populations. Similarly, Jones et al.

(1994) and Goettl and Johnson (1996) reported declines in young of year walleye in Horsetooth

Reservoir (CO, USA) after increased abundance of rainbow smelt. Precise mechanisms for age-

0-W walleye reduction in abundance were not identified, but a reduction in zooplankton

abundance and direct smelt predation of age-0-W were implicated.

We have based our estimates of smelt impact on walleye on one of the most important

and variable processes in population biology: recruitment. The high variability of recruitment

among walleye populations and the variety of factors that influence this process (Hansen et al.

1998, Nate et al. 2000, Beard et al. 2003) made the detection of impact challenging. However,

the strong declines observed in Wisconsin lakes, supported by evidence from other areas where

smelt –walleye interactions have occurred, indicate that smelt can be a major driver of walleye

population dynamics. In addition, our study is based on a small number of invaded lakes and we

recognize the relatively low statistical power that is achieved when so few experimental systems

(invaded lakes) are compared to a large number of control systems (non-invaded lakes).

However, rather than a limitation, we have considered this lack of invaded systems as an

opportunity to help in the identification of systems where walleye could be affected. Resource

managers need to make decisions for the allocation of resources for conservation and invasion

22

prevention usually based on little data and models that help identify systems that need to be

prioritized (Peterson and Viegalis 2001).

Several studies have modeled the dispersal of exotic species, identifying areas or specific

systems where invaders could establish (Koutnik and Padilla 1994, MacIsaac et al. 2004, Vander

Zanden et al. 2004) based on the physio-chemical or biological attributes of the potential

recipient systems. However, few of these studies have addressed the links that could disperse

these invasive species into new systems (Vander Zanden et al. 2004). Here we have used public

access data (presence of boat landings, trails, or their connectivity with other lakes) for lakes

with walleye recruitment to identify those that could be most likely to be colonized by smelt.

This approach has identified a subset of lakes where walleye recruitment declines could be

observed if they were invaded by rainbow smelt. Eighty per cent of the 223 lakes that were

identified as vulnerable by Mercado-Silva et al. (submitted), have the potential to be among

those that are first colonized by rainbow smelt. Our use of accessibility of lakes as proxy for

colonization potential stems from known vectors of rainbow smelt dispersal. However,

awareness of the impact of invasive species through informational pamphlets and signs posted on

boat landings may reduce the potential for smelt dispersal. We note that this study is restricted to

the colonization of lakes with walleye populations, but other lakes where walleye are not present

or where their recruitment has not been assessed can also be prone to smelt colonization. Further

analysis on the accessibility of walleye lakes could include the connectedness of invaded lakes to

non-invaded lakes via roads, interconnecting streams, or the frequency with which anglers that

visit an invaded lake travel to nearby non-invaded lakes. Such an approach could help prioritize

efforts to those lakes with the highest visitation rates of the 188 lakes presented here.

23

We have focused on smelt impact on age-0-W because it is the most likely stage at which

walleye populations could be affected (Schneider and Leach 1977, Jones et al. 1994). Adult

walleye are known to benefit from the presence of smelt, and in some instances, they have been

used to aid in the recovery of other native species that have been affected by rainbow smelt

(Krueger and Hrabik 2005). However, in their early life stages, both species undergo spawning

migrations after ice-out when water temperatures are between 5.5 – 10°C and deposit their eggs

in suitable substrates (Becker 1983; Nellbring 1989) from which walleye eggs and early larvae

can soon be preyed upon by smelt (MacCrimmon and Pugsley 1979). Albeit likely, this

mechanism will prove hard to confirm, as fish eggs are known to be a relatively minor

component of smelt’s diets and are quickly digested (Crowder 1980; Nellbring 1989). In

addition, an impact at this level would only be detected in the presence of large smelt

populations (Nellbring 1989, Lodge and Shrader-Frechette 2003). Competition for similar

zooplanktonic resources could be an additional cause for an effect on age-0-W (Nellbring 1989;

Becker 1983), especially in cases when smelt become abundant, alter the zooplankton

community (Beisner et al. 2003) and eliminate the relatively larger zooplankton required by

walleye for adequate growth (Graham and Sprules 1992). In addition, under these circumstances

of depleted prey communities, cannibalism among larval and age-0-W could strengthen

(Loadman et al. 1986), adding one more stress to the walleye population. One more source of

impact to walleye recruitment from smelt could be related to thiamine (vitamin B1) deficiency

induced early mortaily syndrome (EMS) in age-0-W (Honeyfield et al. 2005, Tillit et al. 2005),

and further research should address this potential problem.

Throughout this paper, we have treated recruitment declines as a consequence of rainbow

smelt invasion. We must however address the possibility that smelt invasion could be associated

24

to lakes with already low walleye recruitment. In lakes with low top predator (walleye)

densities, smelt could have increased possibilities of establishment, and not necessariy have

caused the lowered recruitment. In addition, systems could exist where walleye and smelt

coexist without having negative effects upon each other. In shield lakes of southwestern Ontario,

Canada, both species coexist in large lakes. This coexistence could result from the existence of

enough habitats to be partitioned between both species (thus reducing interactions), or a low

abundance of smelt, such that effects on walleye recruitment are undetectable.

Further expansion of rainbow smelt in Wisconsin lakes will most likely be via intentional

introductions, accidental bait bucket transfers, and active dispersal between interconnected lakes

(Hrabik and Magnuson 1999). Smelt dispersal via any of these vectors, would be most likely

during smelt spawning runs in early spring, when smelt are most abundant in shoreline areas.

Increased human populations and traffic among lakes, is expected to facilitate the expansion of

smelt among lakes (Evans and Loftus 1987, Franzin et al. 1994). In Northern Wisconsin the

human population has increased by approximately 60% from1970 and 2000 (BIA 2003) and

continues to grow, bringing increased angling pressure to walleye populations that could also be

affected by rainbow smelt impacts. The depletion of strong year classes as a consequence of

these joint pressures should be taken into account when establishing future fishery quotas for

these lakes. Walleye recruitment declines associated with rainbow smelt invasion could increase

current walleye stocking costs for fisheries managers. Fingerling stocking has been adopted as a

strategy to support walleye populations in some smelt-invaded lakes. However, this practice is

expensive compared to stocking fry because of the labor costs associated with extensive pond

culture (Loadman et al. 1986). In view of the results from this research, we suggest that

exposure of walleye populations to invasive species could be added to the 30 key issues that have

25

been identified by the Wisconisn DNR Walleye Management Planning Committee (DNR 2003)

as those that need to be addressed for the future management of walleye populations in the state.

Here we have analyzed the significance of smelt impacts on walleye populations and

identified a number of systems where walleye populations could be at risk. This analysis can be

used to aid natural resource management decision making and invasive species monitoring

throughout Wisconsin and North America, as efforts are made to achieve the long term

sustainability of important recreational and economical resources, such as walleye fisheries.

ACKNOWLEDGEMENTS

We thank S.R. Carpenter, J. Wendel, H. Benike, A. Neibur, R. Young, S. Toschner, F.

Pratt, T. R. Hrabik, J. Jorgensen, K. Lord J. Hennessy, N. A. Nate, P. J. Schmalz, A. Fayram and

S. Hewitt , for providing information on walleye recruitment, rainbow smelt abundance and

distribution, and other assistance in the development of this manuscript. J. T. Maxted and K.L.

Dosch assisted with Fig. 6. We are thankful to all field crews that helped assemble the datasets

utilized in this project and the Great lakes Indian Fish and Wildlife Commission for providing

some of the data used in this manuscript. Funding for this project and other support was

provided by U.S. National Science Foundation grant DEB-0217533 to the Center for Limnology

University of Wisconsin - Madison for Long Term Ecological Research on North Temperate

Lakes, and financial support to JVZ from the WDNR. Other funding to NMS provided by

Consejo Nacional de Ciencia y Tecnología, Mexico City, Mexico.

26

REFERENCES

Beard Jr., T. D., M. J. Hansen, and S. R. Carpenter. 2003. Development of a regional stock-recruitment model for understanding factors affecting walleye recruitment in Northern Wisconsin lakes. Transactions of the American Fisheries Society 132:382-391.

Bennett, D. H., and T. J. McArthur. 1990. Predicting success of walleye stocking programs in the

United States and Canada. Fisheries 15:19-23. Beisner, B. E., A. R. Ives, and S. R. Carpenter. 2003. The effects of an exotic fish invasion on

the prey communities in two lakes. Journal of Animal Ecology 72:331-342. Becker, G. C. 1983. Fishes of Wisconsin. University of Wisconsin Press, Madison, WI. (BIA) Bureau of Indian Affairs. 2003. Fishery status update in the Wisconsin treaty ceded

waters. Casting light upon the waters ~ a joint fishery assessment of the Wisconsin Ceded Territory. US Department of the Interior, Bureau of indian Affairs, Minneapolis, MN.

Colby, P. J., P. A. Ryan, D. H. Schupp, and S. L. Serns. 1987. Interactions in north-temperate

lake fish communities. Canadian Journal of Fisheries and Aquatic Sciences 44:104-128. Crowder, L. B. 1980. Alewife, rainbow smelt and native fishes in Lake Michigan: competition or

predation? Environmental Biology of Fishes 5:225-233. Dalgaard, P. 2002. Introductory statistics with R. Springer-Verlag, New York. DNR, (Wisconsin Department of Natural Resources), 2003,

http://www.dnr.state.wi.us/org/water/fhp/fish/walleye/intro.htm. Drake, J.M. & J.M. Bossendroek. 2004. The potential distribution of zebra mussels in the United

States. BioScience 54: 931-941. Evans, D. O., and D. H. Loftus. 1987. Colonization of inland lakes in the Great Lakes Region by

Rainbow Smelt, Osmerus mordax: Their freshwater niche and effects on indigenous fishes. Canadian Journal of Fisheries and Aquatic Sciences 44:249-266.

Evans, D.O. & P. Waring. 1987. Changes in multispecies, winter angling fishery of Lake

Simcoe, Ontario, 1961-83: Invasion by Rainbow Smelt, Osmerus mordax, and the roles of intra- and interspecific interactions. Canadian Journal of Fisheries and Aquatic Sciences 44: 182-197.

Franzin, W. G., B. A. Barton, R. A. Remnant, D. B. Wain, and S. J. Pagel. 1994. Range

extension, present and potential distribution, and possible effects of Rainbow Smelt in Hudson Bay Drainage waters of Northwestern Ontario, Manitoba, and Minnesota. North American Journal of Fisheries Management 14:65-76.

27

Goettl, J. P., and B. M. Johnson. 1996. Fish Forage Studies, Federal Aid Project #F-240-R-3.

Report Fish Research Section, Colorado Division of Wildlife., Fort Collins, CO. Graham, D.M. & W.G. Sprules. 1992. Size and species selection of zooplankton by larval and

juvenile walleye (Stizosteidon vitreum vitreum) in Oneida Lake, New York. Canadian Journal of Zoology 70: 2059-2067.

Hansen, M.J., M.J. Bozek, J.R. Newby, S.P. Newman & M.D. Staggs. 1998. Factors affecting

recruitment of walleyes in Escanaba Lake, Wisconsin 1958-1995. North American Journal of Fisheries Management 18: 764-774.

Honeyfield, D.C., J.P. Hinterkopf, J.D. Fitzsimons, D.E. Tillitt, J.L. Zajicek & S.B. Brown.

2005. Development of thiamine deficiencies and early mortality syndrome in lake trout by feeding experimental and feral fish diets containing thiaminase. Journal of aquatic animal health 17: 4-12.

Hrabik, T. R., and J. Magnuson. 1999. Simulated dispersal of exotic rainbow smelt (Osmerus

mordax) in a northern Wisconsin lake district and implications for management. Canadian Journal of Fisheries and Aquatic Sciences 56:35-42.

Hrabik, T. R., J. Magnuson, and A. S. McLain. 1998. Predicting the effects of rainbow smelt on

native fishes in small lakes: evidence from long term research in two lakes. Canadian Journal of Fisheries and Aquatic Sciences 55:1364-1371.

Hrabik, T. R., M. P. Carey, and M. S. Webster. 2001. Interactions between young-of-the-year

exotic Rainbow Smelt and native Yellow Perch in a northern temperate lake. Transactions of the American Fisheries Society 130:568-582.

Johnson, B. M., and J. P. Goettl. 1999. Food web changes over fourteen years following

introduction of Rainbow Smelt into a Colorado Reservoir. North American Journal of Fisheries Management 19:629-642.

Jones, M. S., J. P. Goettl, and S. A. Flickinger. 1994. Changes in Walleye food habits and growth

following a Rainbow Smelt introduction. North American Journal of Fisheries Management 14:409-414.

Kolar, C.S. & D.M. Lodge. 2001. Progress in invasion biology:predicting invaders. Trends in

Ecology and Evolution 16: 199-204. Koutnik, M.A. & D.K. Padilla. 1994. Predicting the spatial distribution of Dreissena polymorpha

(Zebra Mussel) among inland lakes of Wisconsin: Modeling with a GIS. Canadian Journal of Fisheries and Aquatic Sciences 51: 1189-1196.

28

Krueger, D.M. & T.R. Hrabik. 2005. Food web alterations that promote native species: the recovery of native cisco (Coregonus artedi) populations through management of native piscivores. Canadian Journal of Fisheries and Aquatic Sciences 62: 2177-2188.

Loadman, N.L., G.E.E. Moodie & J.A. Mathias. 1986. Significance of cannibalism in larval

walleye (Stizosteidon vitreum). Canadian Journal of Fisheries and Aquatic Sciences 43: 613-618.

Lodge, D. M. 1993. Biological invasions: Lessons for ecology. Trends in Ecology and Evolution

8:133-137. Lodge, D. M., R. A. Stein, K. M. Brown, A. P. Covich, C. Brõnmark, J. E. Garvey, and S. P.

Klosiewsky. 1998. Predicting impact of freshwater exotic species on native biodiversity: Challenges in spatial scaling. Australian Journal of Ecology 23:53-67.

Lodge, D. M., and K. Shrader-Frechette. 2003. Nonindigenous species: Ecological explanation,

environmental ethics, and public policy. Conservation Biology 17:31-37. Lyons, J., P. A. Cochran, and D. Fago. 2000. Wisconsin Fishes: Status and Distribution. UW Sea

Grant, Madison, WI. MacIsaac, H.J., J.V.M. Borbely, J.R. Muirhead & P.A. Graniero. 2004. Backcasting and

forecasting bioogical invasions of inland lakes. Ecological Applications 14: 773-783. Madenjian, C. P., R. L. Knight, M. T. Bur, and J. L. Forney. 2000. Reduction in recruitment of

White Bass in Lake Erie after invasion of White Perch. Transactions of the American Fisheries Society 129:1340-1353.

Madenjian, C. P., G. L. Fahnenstiel, T. H. Johengen, T. F. Nalepa, H. A. Vanderploeg, G. W.

Fleischer, P. J. Schneeberger, D. M. Benjamin, E. B. Smith, J. R. Bence, E. S. Rutherford, D. S. Lavis, D. M. Robertson, D. J. Jude, and M. P. Ebener. 2002. Dynamics of the Lake Michigan food web, 1970-2000. Canadian Journal of Fisheries and Aquatic Sciences 59:736-753.

MacCrimmon, H. R., and R. W. Pugsley. 1979. Food and feeding of the rainbow smelt (Osmerus

mordax) in Lake Simcoe, Ontario. Canadian Field Naturalist 93:266-271. McLain, A. S. 1991. Conceptual and Empirical Analyses of Biological Invasion: Non-Native

Fish Invasion into North Temperate Lakes. PhD. Thesis. University of Wisconsin - Madison, Madison.

Mercado-Silva N., J.D. Olden, J.T. Maxted, T.R. Hrabik, and M.J. Vander Zanden (Submitted).

Suitability of inland lakes for rainbow smelt (Osmerus mordax) invasion in the Great Lakes Region. Submitted to Conservation Biology.

29

Moyle, P. B., H. W. Li, and B. A. Barton. 1987. The Frankenstein effect: Impact of introduced fishes on native fishes of North America. Pages 415-426 in R. H. Stroud, editor. The role of fish culture in fisheries management. American Fisheries Society, Bethesda, MD.

Nate, N. A., M. J. Bozek, M. J. Hansen, and S. W. Hewett. 2000. Variations in Walleye

abudance with lake size and recruitment source. North American Journal of Fisheries Management 20:119-126.

Nellbring, S. 1989. The ecology of smelts (Genus Osmerus): A literature review. Nordic Journal

of Freshwater Research 65:116-145. Osenberg, C. W., R. J. Schmitt, S. J. Holbrook, K. Abu-Saba, and A. R. Flegal. 1996. Detection

of environmental impacts. Pages 83-108 in R. J. Schmitt and C. W. Osenberg, editors. Detecting ecological impacts, concepts and applications in coastal habitats. Academic Press, San Diego.

Peterson, A. T., and D. A. Viegalis. 2001. Predicting species invasions using ecological niche

modeling: new approaches from bioinformatics attack a pressing problem. BioScience 51:363-371.

Pimentel, D., L. Lach, R. Zuniga, and D. Morrison. 2000. Environmental and economic costs of

nonindigenous species in the United States. BioScience 50:53-65. Pimentel, D., S. McNair, J. Janecka, J. Wightman, C. Simmonds, C. O'Connell, E. Wong, L.

Russel, J. Zern, T. Aquino, and T. Tsomondo. 2001. Economic and environmental threats of alien plant, animal and microbe invasions. Agriculture, Ecosystems and Environment 84:1-20.

Sala, O.E., F.S. Chapin III, J.J. Armesto, E.L. Berlow, J. Bloomfield, R. Dirzo, E. Huber-

Sanwald, L.F. Huenneke, R.B. Jackson, A. Kinzig, R. Leemans, D.M. Lodge, H.A. Mooney, M. Oesterheld, N.L. Poff, M.T. Sykes, B.H. Walker, M. Walker & D.H. Wall. 2000. Global biodiversity scenarios for the year 2100. Science 287: 1770-1774.

Sass, G.G., S.W. Hewett, T.D. Beard Jr., A.H. Fayram & J.F. Kitchell. 2004. The role of density-

dependence in growth patterns of ceded territory walleye populations of northern Wisconsin: effects of changing management regimes. North American Journal of Fisheries Management 24: 1262-1278.

Schneider, J. C., and J. H. Leach. 1977. Walleye (Stizosteidon vitreum vitreum) fluctuations in

the Great Lakes and possible causes, 1800 - 1975. Journal of the Fisheries Research Board of Canada 34:1878-1889.

Soulé, M. E. 1990. The onslaught of alien species and other challenges in the coming decades.

Conservation Biology 4:233-239.

30

Stewart-Oaten, A., W.W. Murdoch & K.K. Parker. 1986. Environmental impact assessment: 'pseudoreplication" in time? Ecology 67: 929-940.

Sullivan, W. P., G. C. Christie, F. C. Cornelius, M. F. Fodale, D. A. Johnson, J. F. Koonces, G.

L. Larson, R. B. McDonald, K. M. Mullett, C. K. Murray, and P. A. Ryan. 2003. The sea lamprey in Lake Erie: a case history. Journal of Great Lakes Research 29:615-636.

Taylor, J., W. R. Courtenay, and J. A. McCann. 1984. Known impacts of exotic fishes in the

continental United States. Pages 322-373 in W. R. Courtenay and J. R. Stauffer, editors. Distribution, biology and management of of exotic fishes. The John Hopkins Univeristy Press, Baltimore.

Tillitt, D.E., J.L. Zajicek, S.B. Brown, L.R. Brown, J.D. Fitzsimons, D.C. Honeyfield, M.E.

Holey & G.M. Wright. 2005. Thiamine and thiaminase status in forage fish of salmonines from Lake Michigan. Journal of aquatic animal health 17: 13-25.

Vander Zanden, M.J. & J.B. Rasmussen. 1996. A trophic position model of pelagic food webs:

Impact on contaminant bioaccumulation in lake trout. Ecological Monographs 66: 451-477.

Vander Zanden, M.J., J.D. Olden, J.H. Thorne & N.E. Mandrak. 2004. Predicting occurrences

and impacts of smallmouth bass introductions in north temperate lakes. Ecological Applications 14: 132-148.

Vander Zanden, M. J. 2005. The success of animal invaders. Proceedings of the National

Academy of Sciences USA 102:7055-7056.

31

Table 1. Rainbow smelt (Osmerus mordax)-invaded lakes in Wisconsin. Lake No.: Lake numbering for the purposes of this study.

Invasion year for rainbow smelt in each individual lake is indicated. Age-0-walleye data for analysis was available, pre invasion or

post-invasion, for some of these lakes. ‘Years sampled’ refers to the number of years that density of age-0-W was estimated for a

given inland lake. The presence of smelt in Emily Lake (Florence County) has been suggested, but has not been confirmed.

Georeference for each lake is included in Appendix 1.

Lake Name Lake No. County Invasion Max. Surface Pre-Invasion Post-Invasion Years year Depth Area age-0-W age-0-W Sampled (m) (ha) data data ------------------------------------------------------------------------------------------------------------------------------------------------------------------ Anderson 1 7 1980’s 19 13 0 Beaver Dam 2 1 1980 32 450 X 7 Big Cedar 3 8 1985 32 377 0 Cisco 4 2 1983 32 38 X 2 Crawling Stone 5 7 1975 26 593 0 Crystal 6 7 1980’s 20 35 0 Dead Pike 7 7 1990 24 120 X 5 Diamond 8 2 NA 25 138 X 9 Fence 9 7 1968 26 1438 X 1 Flambeau 10 7 NA 26 476 0 Keyes 11 4 1990 23 82 X X 13 Little Crawling Stone 12 7 NA 13 43 0 Little Trout 13 7 NA 29 395 0 Little Bass 14 7 1967 6.5 14 0 Long Interlaken 15 7 NA 20 149 0 Long 16 7 1985 29 352 X X 12 Lucerne 17 5 1965 22 415 X 20

32

Moss 18 7 NA 8 79 0 Placid Twin (N) 19 7 NA 7 13 0 Pokegama 20 7 NA 19 425 X 1 Sand Bar 21 2 1962 15 47 0 Sparkling 22 7 1981 20 51 X X 20 To-To-Tom 23 7 NA 0 Tomahawk 24 2 NA 13 54 0 Whitefish 25 6 1977 32 318 X 9 Whitefish (Bardon) 26 3 NA 31 336 X 13 ------------------------------------------------------------------------------------------------------------------------------------------------------- County Code: Barron 1, Bayfield 2, Douglas 3, Florence 4, Forest 5, Sawyer 6, Vilas 7, Washington 8.

33

FIGURE CAPTIONS

Figure 1. Trends in age-0-Walleye (Sander vitreus) density estimates in Wisconsin lakes with

natural and stocked assisted recruitment. Mean age-0-W for uninvaded lakes with natural

recruitment (A), stocked recruitment (B), both (C), and rainbow smelt (Osmerus mordax)

invaded lakes (D).

Figure 2. Relationship between the annual estimate of log10 age-0-Walleye (Sander vitreus)

abundance in Escanaba Lake (WI) and the average annual log10 age-0-W estimate for all

walleye-stocked lakes in the period 1985 - 2003.

Figure 3. Normalized age-0-walleye (Sander vitreus) density in lakes invaded by rainbow smelt

(Osmerus mordax) in Wisconsin (). Values are normalized with respect to the mean annual

average density of age-0 walleye in all walleye stocked lakes. Arrows indicate year when smelt

were first detected in each lake. Sparkling Lake also shows estimates of smelt abundance (grey

curve). Note difference in X-axis.

Figure 4. a) Trends in age-0-walleye (Sander vitreus) density estimates in invaded and non-

invaded (walleye-stocked) Wisconsin lakes in 1986 – 2003 (S.E. indicated for each estimate).

“N” of lakes for each year and lake grouping: Year (No. of uninvaded lakes per year, No.

invaded lakes per year); ’86 (35,3), ’87 (53,3), ’88 (39,3), ‘89 (59,3), ’90 (79,4), ’91 (129,6), ’92

(118,5), ’93 (116,3), ’94 (134,4), ’95 (103,3), ’96 (109,2), ’97 (125,2), ’98 (131,4), ’99 (138,2),

’00 (128,3), ’01 (162,4), ’02 (183,6), ’03 (106,4). 4b) values for tests between age-0-W

estimates in invaded and uninvaded lakes for a given year between 1986 - 2003. Open dots are

significant at α = 0.05. Grey dots non-significant.

34

Figure 5. Percentile rainbow smelt (Osmerus mordax) invaded lake-specific age-0-W (Sander

vitreus) deviations from the mean age-0-walleye in uninvaded lakes (± S.E.). Each bar labeled

by the lake ID corresponding to lake number in Table 1.

Figure 6. a) Map of Wisconsin lakes with walleye (Sander vitreus) presence vulnerable to

invasion and impact by rainbow smelt (Osmerus mordax) (estimated from Mercado-Silva et al.

submitted). Dark points indicate vulnerable lakes. Light gray points are the rest of Wisconsin

lakes. b) Map of Wisconsin lakes with walleye recruitment estimations, vulnerable to smelt

invasion, and with access to anglers. Dark points are non-accessible lakes, grey points are

accessible (See Appendix 2).

35

0

10

20

30

40

50

60

1984 1989 1994 1999 2004Year

Age

-0-W

alle

ye/k

mNaturalStocked

A = 25.7

B = 13.2

C = 17.5

D = 8.3

Figure 1

36

R2 = 0.97

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0.5 1 1.5 2 2.5 3Log10 age-0-W (Escanaba Lake)

Log 1

0 age

-0-W

(All

Lake

s)

Figure 2

37

-40

-20

0

20

40

60

80

100

1970 1975 1980 1985 1990 1995 2000 2005Year

∆ a

ge-0

-Wal

leye

/Km

0.0

50.0

100.0

150.0

200.0

250.0

Smel

t Cap

ture

d

-40

-20

0

20

40

60

1985 1990 1995 2000 2005Year

∆ a

ge-0

-Wal

leye

/Km

-40

-20

0

20

40

60

1970 1975 1980 1985 1990 1995 2000 2005Year

∆ a

ge-0

-Wal

leye

/Km

a) Sparkling

c) Keyes

b) Long

Figure 3

38

0

20

40

60

80

100

120

1985 1990 1995 2000 2005Year

age-

0-W

/km

Not InvadedInvaded

a)

b)

-8

-6

-4

-2

0

2

4

6

8

1985 1990 1995 2000 2005

Year

t-val

ue

Figure 4

39

-80

-60

-40

-20

0

20

40

17 222161126

257 8 4

% D

evia

tion

Figure 5

40

Figure 6

a)

b)

41

Appendix 1. Latitude and longitude of lakes invaded by rainbow smelt (Osmerus mordax) in

Wisconsin.

--------------------------------------------------------------------------------------------------------- Lake WBIC Latitude Longitude County --------------------------------------------------------------------------------------------------------- Anderson 968500 46.17 -89.34 Vilas Beaver Dam 2081200 45.53 -92.02 Barron Big Cedar 25300 43.38 -88.25 Washington Cisco 2899200 46.37 -91.25 Bayfield Crawling Stone 2322800 45.94 -89.89 Vilas Crystal 1842400 46.10 -89.87 Vilas Diamond 2897100 46.26 -91.14 Bayfield Fence 2323000 45.96 -89.85 Vilas Flambeau 2320500 45.96 -89.92 Vilas Keyes 672900 45.90 -88.30 Florence Little Bass 998300 45.91 -89.70 Vilas Little Crawling Stone 2324000 45.93 -89.90 Vilas Little Trout 2321600 46.08 -89.86 Vilas Long 1602300 46.06 -89.17 Vilas Long Interlaken 2322300 46.07 -89.02 Vilas Lucerne 396500 45.52 -88.85 Forest Moss 2322500 45.96 -89.89 Vilas Placid Twin (N) 2323800 45.92 -89.83 Vilas Pokegama 2320800 45.99 -89.88 Vilas Sand Bar 2494900 46.37 -91.53 Bayfield Sparkling 1881900 46.01 -89.70 Vilas Tomahawk 2501700 46.37 -91.52 Bayfield To-To-Tom 2322300 45.96 -89.91 Vilas Whitefish 2392000 45.86 -91.45 Sawyer Whitefish (Bardon) 2694000 46.20 -91.88 Douglas

42

Appendix 2. Wisconsin lakes vulnerable to rainbow smelt (Osmerus mordax) invasion with at

least one walleye (Sander vitreus) recruitment estimation and with access to anglers

(colonization vector, see text). WBIC = Wisconsin water body identification code.

Geographical coordinates are included for each lake.

WBIC County Lake Name Lat Long

1881100 Barron Silver Lake 45.58 -91.92 1882800 Barron Spring Lake 45.58 -92.04 2098000 Barron Poskin Lake 45.43 -91.97 2105100 Barron Bear Lake 45.63 -91.82 2109600 Barron Red Cedar Lake 45.60 -91.58 2661100 Barron Sand Lake 45.59 -92.11 2732600 Bayfield Namekagon Lake 46.22 -91.11 2734000 Bayfield Atkins Lake 46.28 -91.04 2742100 Bayfield Middle Eau Claire Lake 46.30 -91.52 2742500 Bayfield Bony Lake 46.32 -91.51 2742700 Bayfield Upper Eau Claire Lake 46.31 -91.48 2743700 Bayfield Sweet Lake 46.33 -91.45 2899400 Bayfield Drummond Lake 46.34 -91.26 2900200 Bayfield Lake Owen 46.27 -91.23 2902900 Bayfield Eagle Lake 46.50 -91.36 2903100 Bayfield Twin Bear Lake 46.51 -91.37 2903200 Bayfield Hart Lake 46.52 -91.36 2903700 Bayfield Lake Millicent 46.53 -91.37 2903800 Bayfield Buskey Bay 46.53 -91.37 2493100 Burnett Rooney Lake 45.96 -92.07 2495100 Burnett Sand Lake 45.92 -92.17 2651800 Burnett Dunham Lake 45.76 -92.47 2654500 Burnett Clam River Flowage 45.93 -92.52 2675200 Burnett Yellow Lake 45.92 -92.40 2676800 Burnett Big Sand Lake 45.82 -92.22 2706500 Burnett Middle McKenzie Lake 45.94 -92.04 2706800 Burnett McKenzie Lake 45.92 -92.04 2152800 Chippewa Lake Wissota 44.95 -91.32 2157000 Chippewa Otter Lake 45.07 -90.94 2174700 Chippewa Old Abe Lake 45.07 -91.25 2184900 Chippewa Unnamed 45.28 -91.11 2350500 Chippewa Chain Lake 45.29 -91.43 2351400 Chippewa Long Lake 45.25 -91.40 2492100 Douglas Red Lake 46.18 -91.76

43

2693700 Douglas Bond Lake 46.18 -91.87 2741600 Douglas Lower Eau Claire Lake 46.27 -91.55 2858100 Douglas Amnicon Lake 46.48 -92.06 2865000 Douglas Lake Nebagamon 46.50 -91.70 2866200 Douglas Lake Minnesuing 46.47 -91.74 2068000 Dunn Tainter Lake 44.98 -91.86 2149900 Eau Claire Dells Pond 44.83 -91.50 653700 Florence Patten Lake 45.85 -88.42 378400 Forest Roberts Lake 45.46 -88.79 394400 Forest Metonga Lake 45.54 -88.90 692400 Forest Butternut Lake 45.91 -88.99 692900 Forest Franklin Lake 45.94 -89.00 1614300 Forest Julia Lake 45.80 -89.04 2294900 Iron Turtle Flambeau Flowage 46.08 -90.18 2295200 Iron Trude Lake 46.11 -90.15 2303500 Iron Long Lake 46.26 -90.03 2306300 Iron Spider Lake 46.21 -90.03 2307300 Iron Fisher Lake 46.24 -89.97 2307600 Iron Owl Lake 46.28 -89.97 2949200 Iron Pine Lake 46.26 -90.14 1579700 Langlade Enterprise Lake 45.45 -89.24 1494600 Lincoln Wisconsin River 45.21 -89.76 1519600 Lincoln Deer Lake 45.55 -89.70 1555900 Lincoln Lake Alice 45.48 -89.64 1564400 Lincoln Squaw Lake 45.53 -89.48 1427400 Marathon Unnamed 44.75 -89.85 540600 Marinette High Falls Reservoir 45.30 -88.20 545400 Marinette Caldron Falls Reservoir 45.36 -88.26 417400 Oconto Archibald Lake 45.28 -88.59 439800 Oconto Wheeler Lake 45.32 -88.48 487500 Oconto Maiden Lake 45.27 -88.54 971600 Oneida Big Carr Lake 45.79 -89.63 977500 Oneida Clear Lake 45.87 -89.62 1001300 Oneida Long Lake 45.72 -89.60 1019500 Oneida Squash Lake 45.60 -89.55 1516500 Oneida Lake Nokomis 45.56 -89.73 1517200 Oneida Manson Lake 45.56 -89.63 1528300 Oneida Willow Reservoir 45.72 -89.88 1536300 Oneida Squirrel Lake 45.87 -89.90 1539600 Oneida Shishebogama Lake 45.90 -89.82 1542300 Oneida Kawaguesaga Lake 45.87 -89.74 1542400 Oneida Minocqua Lake 45.87 -89.70 1542700 Oneida Tomahawk Lake 45.82 -89.66 1543300 Oneida Katherine Lake 45.80 -89.71 1543900 Oneida Little Tomahawk Lake 45.80 -89.64 1564200 Oneida Crescent Lake 45.60 -89.52

44

1569900 Oneida Lake Thompson 45.63 -89.33 1579900 Oneida Pelican Lake 45.51 -89.20 1580200 Oneida Boom Lake 45.66 -89.42 1588200 Oneida Two Sisters Lake 45.77 -89.53 1589100 Oneida Hasbrook Lake 45.84 -89.58 1595300 Oneida Rainbow Flowage 45.86 -89.51 1595800 Oneida North Nokomis Lake 45.85 -89.45 1596900 Oneida Dam Lake 45.87 -89.40 1605800 Oneida Sevenmile Lake 45.88 -89.05 1609000 Oneida Long Lake 45.87 -89.14 1609100 Oneida Planting Ground Lake 45.83 -89.15 1610500 Oneida Island Lake 45.83 -89.13 1610600 Oneida Little Fork Lake 45.83 -89.12 1610700 Oneida Big Fork Lake 45.83 -89.10 1611700 Oneida Medicine Lake 45.81 -89.13 1612000 Oneida Spirit Lake 45.80 -89.13 1612200 Oneida Big Stone Lake 45.80 -89.10 1614100 Oneida Virgin Lake 45.79 -89.09 2490500 Polk Pipe Lake 45.52 -92.21 2615100 Polk Cedar Lake 45.21 -92.57 2618000 Polk Wapogasset Lake 45.33 -92.43 2620600 Polk Balsam Lake 45.47 -92.43 2621100 Polk Half Moon Lake 45.49 -92.42 2239300 Price Long Lake 45.69 -90.43 2279800 Price Bass Lake 45.94 -90.61 2283300 Price Butternut Lake 45.97 -90.52 2229200 Rusk Dairyland Reservoir 45.51 -91.01 2230100 Rusk Big Falls Flowage 45.57 -90.95 2350600 Rusk Clear Lake 45.30 -91.42 2353600 Rusk Sand Lake 45.30 -91.36 2355300 Rusk Potato Lake 45.32 -91.43 2046600 Sawyer Windigo Lake 45.93 -91.47 2113300 Sawyer Lake Chetac 45.71 -91.50 2275100 Sawyer Connors Lake 45.75 -90.74 2275300 Sawyer Lake of the Pines 45.78 -90.72 2390800 Sawyer Lac Courte Oreilles 45.90 -91.43 2391200 Sawyer Grindstone Lake 45.94 -91.42 2393200 Sawyer Sand Lake 45.85 -91.50 2393500 Sawyer Sissabagama Lake 45.79 -91.52 2395600 Sawyer Round Lake 46.01 -91.32 2399700 Sawyer East Fork Chippewa River 45.93 -91.20 2417000 Sawyer Teal Lake 46.09 -91.10 2435000 Sawyer Tiger Cat Flowage 46.05 -91.27 2435700 Sawyer Spider Lake 46.10 -91.22 2704200 Sawyer Nelson Lake 46.09 -91.47 2726100 Sawyer Smith Lake 45.92 -91.35

45

716800 Vilas Kentuck Lake 45.99 -89.00 968800 Vilas Anvil Lake 45.94 -89.06 995200 Vilas Lake Laura 46.06 -89.44 1013800 Vilas Razorback Lake 46.02 -89.52 1541300 Vilas Brandy Lake 45.91 -89.70 1541500 Vilas Arrowhead Lake 45.91 -89.69 1545300 Vilas Little Arbor Vitae Lake 45.91 -89.62 1545600 Vilas Big Arbor Vitae Lake 45.93 -89.65 1591100 Vilas Big Saint Germain Lake 45.93 -89.52 1592400 Vilas Plum Lake 46.00 -89.52 1593100 Vilas Star Lake 46.03 -89.48 1596300 Vilas Little Saint Germain Lake 45.92 -89.45 1600200 Vilas Eagle Lake 45.93 -89.21 1602600 Vilas Big Sand Lake 46.06 -88.98 1603700 Vilas Catfish Lake 45.90 -89.19 1621800 Vilas Upper Buckatabon Lake 46.02 -89.35 1623700 Vilas South Lakes Church 46.03 -89.17 1623800 Vilas North Twin Lake 46.05 -89.13 1629500 Vilas Big Portage Lake 46.13 -89.28 1630100 Vilas Black Oak Lake 46.16 -89.32 1631900 Vilas Lac Vieux Desert 46.14 -89.11 1835300 Vilas Big Muskellunge Lake 46.02 -89.61 2310400 Vilas North Turtle Lake 46.24 -89.88 2311100 Vilas Birch Lake 46.22 -89.84 2321100 Vilas White Sand Lake 46.01 -89.84 2327500 Vilas Rest Lake 46.14 -89.88 2328700 Vilas Papoose Lake 46.18 -89.80 2328800 Vilas Stone Lake 46.13 -89.83 2329000 Vilas Clear Lake 46.15 -89.81 2329300 Vilas Spider Lake 46.12 -89.82 2329400 Vilas Unnamed 46.11 -89.84 2329600 Vilas Alder Lake 46.09 -89.82 2330800 Vilas Upper Gresham Lake 46.07 -89.74 2331600 Vilas Trout Lake 46.04 -89.67 2334300 Vilas Little Star Lake 46.12 -89.86 2334400 Vilas Island Lake 46.12 -89.79 2334700 Vilas Big Lake 46.15 -89.77 2336100 Vilas Wolf Lake 46.16 -89.66 2336800 Vilas Wildcat Lake 46.17 -89.62 2338800 Vilas Big Crooked Lake 46.14 -89.67 2339100 Vilas White Sand Lake 46.09 -89.59 2343200 Vilas Fishtrap Lake 46.14 -89.58 2344000 Vilas High Lake 46.16 -89.55 2762200 Vilas Forest Lake 46.15 -89.38 2953500 Vilas Crab Lake 46.19 -89.72 2954500 Vilas Lynx Lake 46.20 -89.67

46

2954800 Vilas Oxbow Lake 46.24 -89.68 2956500 Vilas Presque Isle Lake 46.22 -89.78 2958500 Vilas Harris Lake 46.26 -89.83 2962400 Vilas Tenderfoot Lake 46.22 -89.53 2963800 Vilas Unnamed 46.20 -89.45 1884100 Washburn Stone Lake 45.84 -91.56 2106800 Washburn Long Lake 45.71 -91.69 2109300 Washburn Slim Lake 45.80 -91.56 2112800 Washburn Balsam Lake 45.65 -91.58 2113000 Washburn Birch Lake 45.67 -91.54 2470600 Washburn Island Lake 45.96 -91.94 2496300 Washburn Shell Lake 45.73 -91.90 2691500 Washburn Lake Nancy 46.09 -91.98 2695800 Washburn Gilmore Lake 46.12 -91.89

47

CHAPTER III

LONG-TERM CHANGES IN THE FISH ASSEMBLAGE OF THE LAJA RIVER, GUANAJUATO, CENTRAL

MEXICO

ABSTRACT

The structure and composition of fish communities in rivers of central Mexico have been

altered as a result of water overexploitation, habitat fragmentation, introduction of exotic species,

and pollution. However, the specific pattern and degree of change are poorly documented.

Long-term information from the Laja River (Guanajuato, Mexico) in the Lerma River basin was

used explore trends in fish species richness and community composition (species origin, trophic

niche, tolerance, and preferred habitat) from the 1960’s to the present in both river and reservoir

sites. Declines in native, sensitive, benthic native and carnivore species ranging from 11-30% per

decade, and increases in the number of tolerant and exotic species by 9-20% per decade, are

documented. Repeated measures ANOVA and sign tests revealed significant declines in the

number of benthic, native, carnivore, and sensitive species. Species richness, number of exotics,

tolerant, and omnivore species did not change statistically, though statistical power was low.

Some important changes occurred in these variables, such as the expansion and establishment of

exotics like Xiphophorus variatus and Micropterus salmoides, which pose a serious potential

threat to native species. The changes in fish community composition for the Laja portray how

the fish communities in other rivers in central Mexico, for which long-term data do not exist,

have changed or could change if environmental deterioration continues.

48

INTRODUCTION

Species loss in Mexican freshwater ecosystems is considered among the most dramatic

on the planet (Paxton and Eschmeyer, 2003). Freshwater fish extinctions and population

declines have been caused by habitat deterioration, overexploitation, and invasive species

(Contreras-Balderas and Lozano-Vilano, 1993; Contreras-Balderas and Escalante-Cavazos,

1993; Lyons et al., 1998). The high endemicity of the freshwater fish fauna of Mexico makes it

especially vulnerable to anthropogenic effects. Approximately 70% of the freshwater fishes in

the country are found nowhere else on earth and numerous species have very limited geographic

distributions and are located in highly sensitive regions where agriculture and industrial activities

are intense (Lyons et al., 1998; Soto-Galera et al.,1999a).

The Lerma-Santiago River drains most of central Mexico, including the region known as

the Bajío and is considered one of the most degraded river basins in the country (Soto-Galera et

al.,1999a; Lyons et al., 1998; de Anda et al., 2001; Rosales-Hoz et al., 2000). The fishes in this

basin have suffered significant declines (Díaz-Pardo et al., 1993). Lyons et al. (1998) identified

at least 3 extinct species as of the 1990’s and reported many sites that had been sampled prior to

1985 to be dry or too polluted to sustain fish. Only a few relatively isolated branches of this

large system maintain environmental conditions required for the survival of a relatively intact

fish fauna. The Laja River in the state of Guanajuato is one such tributary (Soto-Galera et al.,

1999a), but it is far from being considered pristine (Barbour, 2002) and has not escaped many of

the impacts that the Bajío, one of the most productive and densely inhabited regions in Mexico,

has imposed on its water resources. The Laja is unusual among Mexican rivers in that its fish

fauna has been documented during several time periods since the 1960s, allowing the study of

long-term changes in the composition and ecological structure of its fish community. Declines

49

(or increases) in species richness or populations can be indicators of human-derived

environmental change, but further information on the nature of the impact can be obtained from

the consideration of the ecological attributes of individual species (Karr, 1981, Lyons et

al.,1995). Trophic role, habitat requirements, tolerance to pollution, and origin (exotic/native)

are important factors to consider when analyzing community change, especially when there is a

need to understand biological impacts to the communities, such as those derived from the

invasion of exotic species, one of the leading threats to biodiversity (Soulé, 1990).

This study presents a long-term study of the changes in the composition and ecological

attributes of the fish community of the Laja River. In addition, it provides a broader perspective

on rivers of central Mexico, as the Laja is important for understanding general trends in the fish

communities in rivers of the region.

Study Area

The Laja watershed (21°33’– 20°58’N; 101°28’- 100°30’W) covers 3,476km2 of the

State of Guanajuato in Central Mexico (Figure 1). The watershed has numerous intermittent and

semi-intermittent tributary streams draining into the Laja River. This semi-arid area is

characterized by numerous mountain ranges and high plateaus (mean altitude >1800m) that have

a relatively short rainy season during the summer (Jul.-Oct.) with total annual precipitation of

400-800mm (CONABIO, 2000). Although much of the basin has been impacted by agriculture,

the typical natural vegetation types in the area include oak forests, chaparral, and xerophyte

vegetation. Riparian vegetation, where it occurs, consists of narrow strips of Salix sp. in small

stretches of the river with rushes along river banks. Substrates in the main river channel are

typically sand/silt-dominated with interspersed rocky segments. Practically all streams and

rivers in the watershed are truncated by reservoirs or other water extraction and storage

50

structures. Two major reservoirs and a water-diversion dam have been built in the Laja River,

but at least 12 more reservoirs are located on tributaries (CNA, 2002). The two major reservoirs,

Ignacio Allende and Jesús María, were built in 1968 and 1982, respectively, to provide water for

agriculture. The main stem of the Laja is 154 km in length, of which 124 km are located

upstream from the Ignacio Allende Dam (Hernández-Javalera, 2002) and ≈15km are located

above Jesús María. The water diversion dam was built in the 1970’s in the lower Laja to divert

water from the river into a series of irrigation canals in the agricultural valley of Celaya. Dry

land and irrigated agriculture dictate water use in the watershed; the number of deep wells in the

area has increased significantly from 210 in 1971 (145 Mm3/year water extracted), when the first

hydrological studies were carried out in the region, to 781 in 1999 (≈ 210 Mm3/year water

extracted) (CNA, 2002). Several other non-regulated water extraction operations are located

along the river, usually in the form of pumps that extract water directly from the Laja.

Numerous springs in the basin are used for recreation.

Except for the headwaters and a few canyons, the Laja has a generally low to moderate

gradient and seasonal- and dam-regulated flows. Water is stored in reservoirs during the rainy

season, thus altering natural flow regimes. Especially during the later part of the rainy season

when dams release water, the flow in the river quickly fluctuates from isolated pools separated

by dry river bed to fast runs exceeding 3m in depth. Even though a large portion of the Laja is

used for agriculture, its headwaters are considered as a priority area for watershed conservation

by CONABIO (2000) given their relatively high biodiversity.

METHODS

Historical Collections

51

During 2003, museums and research institutions were searched for samples and scientific

collection information from sites in the Laja River or any of its tributaries. Information was

obtained from museum records at the Laboratorio de Ictiología y Limnología, Escuela Nacional

de Ciencias Biológicas, Instituto Politécnico Nacional (ENCB-IPN), and the University of

Michigan Museum of Zoology (UMMZ), which hold most of the records from the Laja system.

Information from various independent fish collections in the Laja (Mercado - Silva et al., 2002;

Sistema de Información Miguel Hidalgo, Universidad Autónoma de Querétaro) and other

unpublished data were also used. Records were examined to ensure that the location of the

sampling sites was consistent among source institutions and individual collections. Sites with

geographical descriptions too broad or inaccurate were discarded from the analysis. From each

record, the number of species collected was obtained. For most historical accounts, no

information on sampling effort or gear type was available. Museum records often lacked counts

of individuals per species. Thus, only species presence/absence data were considered for

analysis. For all museum records, it was assumed that fishing effort was comparable and that all

species encountered were preserved or at least reported in field notes. It was also assumed that

all collection events adequately sampled the fish community.

To eliminate potential effect of seasonality on the results, t-tests were carried out between

collections made in dry versus wet seasons for each historical site to test for differences in the

number of species captured. The same was done for samples from 2003 -2004. In addition,

samples were examined to detect if a species presence was restricted to a given season.

Historical information was found for 6 sites (4 river and 2 reservoir) along the Laja River.

Table 1 presents the sources of data for each sampling included in the analyses. Two river sites

(Arroyo San Gabriel and Atotonilco) had been only sampled once in the past. All other sites had

52

been sampled three or more times in the period 1966 – 2003 (including authors’ samples). The

most historical information was available for the Ignacio Allende Reservoir and the Empalme

Escobedo sites (Table 1).

Recent sampling efforts

During 2003-2004, as part of a more general survey of the Laja system, four of the sites

that had been reported in museum records plus 13 additional sites in reservoirs, main-stem and

effluent streams in the Laja River watershed were intensively sampled (Figure 1). Samplings

occurred during January, June, August, November 2003 and January 2004 but not all sites were

sampled on every occasion. Seines, DC backpack electroshockers, and gillnets, as required,

were used to obtain representative samples of the fish community in all habitats at each sampling

site. Sampling efforts were continued until no changes were detected in the number of species

captured or their relative abundance. All fishes captured were identified and counted. Voucher

specimens for some of the collections were deposited in the Colección Nacional de Peces of the

Institute of Biology (Universidad Nacional Autónoma de México) in Mexico City. Most of the

fishes captured were released unharmed after processing.

Analysis

For each year represented in museum collections and other sampling efforts an analysis

was made of : 1) the number of species collected in each year, and 2) the ecological attributes of

each species. The ecological attributes origin (native vs. exotic), feeding habits (carnivore,

herbivore or omnivore), habitat use (pelagic vs. benthic) and tolerance to pollution or habitat

deterioration, were established based on the criteria of Lyons et al. (1995), Mercado-Silva et al.

(2002) and Medina-Nava (2003) (Table 2).

53

Annual information for each site was summarized to give an average value for each fish

attribute for each decade in the 1960-2000 period. Trends across decades for each fish attribute

were analyzed for river and reservoir sites separately and for the river as a whole. Repeated

measures analysis of variance (RMANOVA) (SAS V8 SAS Institute) with autoregressive type 1

model for covariance structure, was used to test for trends. Given the sparseness of the data in

time series and to increase confidence in the interpretation of the p-values generated from the

RMANOVA, a Sign Test (Zar, 1999) for the slopes (positive or negative) of the change of each

fish community variable through time was applied for each site independently.

To further assess changes in the fish community, the overall percentage change (per

decade) for each of the fish community variables was estimated. This was calculated as the mean

of the percentage change estimated from each decadal interval, excluding zero values to prevent

bias from extreme values. Standard errors were estimated based on the individual decadal

changes for each fish community variable.

RESULTS

Twenty-three fish species are known from the Laja River, based on historical accounts

and authors’ sampling efforts (Table 2). Historical records for the river accounted for 22 species.

The exotic Ctenopharyngodon idella (Cyprinidae) (one individual) was the only species reported

in 2003 that had not been previously reported. Fifteen species of fish from the 17 sites sampled

in the Laja River were collected in 2003 -2004. Chirostoma jordani (Atherinopsidae), Yuriria

alta (Cyprinidae), Xenotoca variata and Goodea atripinnis (Goodeidae), all native species, were

the most prevalent and abundant species in the basin (Table 3). Seven exotic species were

captured in the Laja in 2003-2004. The most widely distributed exotics were Oreochromis

aureus (11/17 sites) and Carassius auratus (10 sites). Of all sites sampled, only one (Rio San

54

Juan) lacked exotic species. Notropis calientis and Algansea tincella were the most restricted

natives and were found in 1 and 4 river sites, respectively. Six native and two exotic species that

had been historically collected in the Laja were not found in 2003: Scartomyzon austrinus

(Catostomidae), Ictalurus dugesi (Ictaluridae), Skiffia lermae and Alloophorus robustus

(Goodeidae), Notropis sallaei (Cyprinidae), Chirostoma humboldtianum (Atherinopsidae) and

the exotics Poecilia sphenops (Poecilidae) and Lepomis cyanellus (Centrarchidae). These are for

the most part categorized as carnivores, and as sensitive or medium tolerance species (Table 2).

Species tolerant to environmental degradation comprised most of the catch for the

samples collected during 2003. Notropis calientis was the only sensitive species captured in

2003 from a single river site. Algansea tincella, Chirostoma arge, and the exotics Lepomis

macrochirus, and Micropterus salmoides were the few species with medium tolerance captured

in 2003. Algansea tincella was captured at 2 sites located immediately below the Ignacio

Allende reservoir and in the headwaters of the Laja (Arroyo Cantera). Ch. arge was captured

from six river sites throughout the Laja, though it was present in low relative abundance at each

site.

No native benthic species were found in the 2003-2004 efforts. The only benthic species

collected were the exotics C. auratus and C. carpio. All other species were water column

species.

Four carnivore species were found in the Laja in 2003-2004, but only two were native:

Ch. jordani (found at most sites) and Ch. arge (6/17 sites). The exotic carnivore L. macrochirus

was well represented throughout the Laja, but the exotic M. salmoides was not widely

distributed. The rest of the species collected were omnivores.

Historical Comparisons

55

Seasonality of historical collections had no influence on the historical trends observed;

species richness in dry vs wet season collections were similar (p>0.05). When all sites (river and

reservoir) were analyzed together, the number of sensitive species and the number of native

benthic species showed significant declines over time (F=9.56, P>F = 0.005; F= 12.86, P>F =

0.0024; respectively) (Fig. 2) based on RMANOVA. Three other fish community variables,

number of exotic, medium tolerance, and carnivore species, showed marked, though not

statistically significant changes through time (Fig. 2).

When analyzed separately, neither river nor reservoir sites showed significant trends in

any of the community variables for the period 1960’s – 2000’s. The number of sensitive and

benthic species in river sites were the only variables that showed a near significant result based

on RMANOVA (F = 12.38; P>F= 0.076 and F = 11.32; P>F = 0.082, respectively). All other

variables had p-values ≥ 0.1. No variables were close to significance in reservoir sites (All p-

values ≥ 0.1) when analyzed independently, indicating that changes in the fish community are

stronger for river than reservoir sites.

The sign test analysis generally supported the results from the RMANOVA. The decline

in sensitive species through time at all 6 sites examined was significant (p= 0.031). However, the

decline in the number of benthic species was not significant in the sign test (p=0.125), even when

all four sites where this variable was applicable showed a decline. The scarcity of data for the

Laja limited the power of the statistical methods. Some fish community variables that did not

change significantly through time using RMANOVA showed significant trends using sign test

analysis. The decline in carnivore and native species at all six sites was statistically significant

(p=0.031 for both). The decline of medium tolerance fishes through time was not statistically

56

significant even when all five sites showed a decline through time (p=0.062). All other aspects of

the fish assemblage did not change significantly through time.

Fish community variables showed strong declines in the number of sensitive species

(30% per decade), species of medium tolerance (33%), benthic species (18%), carnivore species

(18%), and native species (13%). There was virtually no change in the total number of species

(2.5%) and the number of omnivore species (2.7%). There were overall increases in the number

of tolerant (9.7% per decade), exotic (22%) and exotic benthic species (20%) (Figure 3).

DISCUSSION

Rivers and lakes are considered the most threatened ecosystems because of human

alterations such as flow alteration, habitat fragmentation and degradation, introduced species and

land use change (Moyle, 1994; Miller et al., 1989). Fish communities of the Laja River have

changed significantly as a consequence of human impacts, resulting in a fish community

characterized by species that are tolerant and omnivorous, and in which exotics are very

common.

The disappearance of Scartomyzon austrinus and Ictalurus dugesi, two native benthivore

species requiring good water and habitat quality, parallels their loss in other rivers in Central

Mexico (Soto-Galera et al. 1999a, López-López and Paulo-Maya, 2001). Scartomyzon austrinus,

like most suckers, is a species commonly found in rocky areas of streams where water flow is

medium to low, albeit constant (Walsh et al., 1998). In the Laja, the main channel remains dry

for extensive periods as a consequence of closure of the various dams on the river. In addition,

unsustainable agriculture is widespread and promotes soil erosion that results in the siltation of

the river channel, thus deteriorating the rocky and gravel substrate required by the species.

Ictalurus dugesi is moderately sensitive to habitat degradation and was important to local

57

fisheries which may have caused their decline through overexploitation (Lyons et al., 1998).

Local fishermen report not having seen catfishes in recent years.

Notropis calientis is a sensitive, carnivorous cyprinid that was present at only one site in

2003. The species has declined throughout much of its range, which encompasses much of the

Bajío (Lyons et al., 1998, Soto-Galera et al., 1999a). Notropis calientis was found in a site

unaffected by reservoir management located in a tributary to the Laja, where immediate impacts

from pollution or agricultural activities were not evident. However, sites where the species had

historically been captured are currently polluted, dry or otherwise severely impacted and lack the

species. A similar situation arises with three sensitive species previously known from the Laja

basin: Skiffia lermae, Alloophorus robustus and Chirotsoma humboldtianum are no longer found

at the few sites where they were present historically (López-López and Díaz-Pardo, 1991).

These sites are located in the lowermost areas of the Laja, near the confluence of the Laja with

the Lerma River, which has suffered extensive alteration from agriculture and urban

development in the cities of Celaya and Apaseo el Grande (Guanjauato). In all, the number of

species that are sensitive or moderately tolerant to pollution or environmental degradation in the

Laja has been reduced by 66% (calculated for all species known for the Laja in these categories),

and today only three such species (out of 9) are still present in the river. The average 30% and

33% decadal declines we observed for sensitive and medium tolerance fishes highlights the

strong declines for fishes requiring pristine or relative pristine habitats.

The lack of a significant change in the number of exotic species in the Laja could be

explained by the relatively long history of the presence of exotic species in the system. The

introduction of exotic species into rivers in Central Mexico accompanied the construction of

reservoirs. The available data do not cover a sufficiently long period for a pre-reservoir – post-

58

reservoir (pre-exotic – post-exotic) comparison. Essentially all exotic species collected in 2003

were present in the 1960 – 1990 period, except for four species, Ctenopharyngodon idella,

Poecilia sphenops, Lepomis macrochirus and Xiphophorus variatus. These species had not been

reported in the Laja prior to the 1990s and this study. Exotics were deliberately introduced as

food resources in reservoirs or as accidental aquarium releases throughout the river and have

consistently increased in the river after the 1970s. The increase of exotics and the concurrent

loss of natives appear to be the trend throughout Mexico (Contreras-Balderas and Escalante-

Cavazos, 1993; Contreras-MacBeath, et al. 1998). The exotics L. macrochirus and X. variatus

are now widespread in the river. We stress that X. variatus can cause severe impacts in the Laja

and several other river systems in Central Mexico. Evidence of the impact of this poecilid on

other species is anecdotal, but observations show that its abundance is overwhelmingly higher

than that of other livebearers in several sites. The species is known to prey on larval fishes

(Borowsky, 1978) and can interbreed with native livebearers (Hems, 1977; Wischnath, 1993),

resulting in gene pool deterioration. One last exotic species, Micropterus salmoides, although

previously known in the Laja, is now widespread in the headwaters of the system, where it could

pose a serious threat to native fishes. This species is responsible for the extirpation of some

species of pupfish in the U.S. (Minckley et al., 1991) and has been reported as a grave threat to

natives in other aquatic ecosystems (Simberloff, 1999; Velázquez-Velázquez and Schmitter-

Soto, 2004). The biological significance of the introduction of a new species that can cause

broadscale changes for the rest of the community supercedes the lack of statistical significance

found for this variable.

It is not surprising that the community of fishes in the reservoirs remains unchanged

through time relative to lotic sites. ‘Exotic’ ecosystems, such as a reservoir, offer habitat for

59

only those river species that could adapt to them (Craig, 2000). If anything, an increase in the

number of exotic species stocked in these reservoirs would be expected. This increase however,

is not evident. However, dams and other forms of habitat degradation have isolated populations

of the native Algansea tincella. Once relatively widespread in the Laja, this species can only be

found in a relatively short reach of the river downstream from the Ignacio Allende reservoir and

at the uppermost sites sampled in 2003.

The lack of change in the species richness through time may be a function of the

combined loss of native species and gain of exotic species. However, the number of native

species showed significant decline that accelerates after the 1990’s (Fig. 2). Those species that

remain tend to be tolerant of extremes in temperature, habitat degradation and water quality (see

Table 2). López-López and Díaz-Pardo (1991) reported three additional native, sensitive species

for the Laja, Alloophorus robustus, Chirostoma humboldtianum and Skiffia lermae that were not

captured in 2003-04, thus increasing the significance of the decline of native species in the Laja

beyond what is shown in the present analysis. The sites where these species were found had only

been sampled once in the time frame presented here.

The number of omnivore species in the Laja has also remained relatively constant. Again,

the majority of the native species in the Laja are omnivores, and the introduction of exotic

species has not resulted in a significant change in this variable.

Native carnivores remain common in the Laja, although the number of carnivore species

has declined by an average 18% per decade. Today, instead of relatively large sized and benthic

carnivores such as S. austrinum and I. dugesi, or the sensitive N. calientis, carnivores are

relatively small sized atherinopsids (Ch. arge and Ch. jordani) which are generally considered

zooplanktivores. Chirostoma arge was mostly absent in reservoir sites, whereas Ch. jordani was

60

abundant throughout the basin where it plays an important role in food webs (Soto-Galera,

1993). This species was observed in great numbers below dams and in pools throughout the

river where it is subject to high mortality when dams are abruptly closed and downstream pools

dry out. There is no regulatory water management in the Laja that prescribes a minimum flow to

sustain biological communities or attempts to mimic the natural flow regime.

The fish attributes analyzed here allow examination of community changes from the

perspective of ecological function. These have been used as indicators of biological integrity

(Fausch et al. 1990, Lyons et al., 1995; Frenzel, 1996; Lyons et al., 2000; Vázquez and

Simberloff, 2002; Wang et al., 2003) and are valuable for understanding functional changes

resulting from environmental deterioration. In the Laja, they help portray the loss of ecological

specialists and their replacement by generalists that can survive in highly degraded adverse

conditions.

Datasets necessary for long term studies of biological communities in freshwater systems

in Mexico are rare. Most of the information is spread out in museum records, and published and

unpublished materials from taxonomic, biogeographic and/or phylogenetic studies. The Laja is

one of few rivers in Mexico with a relatively long series of sampling records. This study and

independent observations, along with Soto-Galera et al. (1999b) in the Río Grande de Morelia

(Michoacán, Mexico), and Contreras-MacBeath et al. (1998) in the Río Balsas (Central Mexico),

characterize fish community changes resulting from environmental degradation, and form the

basis for predicting how fish communities will change if regional environmental impacts

continue (Table 4). These predictions apply to a situation in which human impact is

intermediate, not inconsequential, but also not as bad as it could be. With severe degradation, as

observed in the Lerma River in areas near the cities of Celaya, La Piedad (Mestre, 1997), and

61

Toluca (Esteller and Díaz-Delgado 2002), fish decline would be dramatic and only a few of the

most tolerant species (probably exotics) would remain. In extreme cases no fish would survive.

Protecting the fish communities of the Laja will require significant changes at the

watershed scale. Definition of priority conservation and restoration areas using morphological

stream attributes (sensu Rosgen, 1994) (Hernández-Javalera, 2002) has initiated, and there are

similar efforts from local nongovernmental organizations (e.g., Salvemos al Laja A.C.) in

headwater areas. However, it remains unclear whether these efforts will have an effect

elsewhere in the river. Efforts to identify other priority areas, implementation of sewage

treatment facilities in larger cities (e.g., San Miguel de Allende and Dolores Hidalgo), erosion

control strategies, and restoration of natural flow regimes are incipient in the watershed and need

to be further expanded as part of a watershed-scale management plan. Other measures, such as

impeding further expansion of exotics throughout the watershed and restoring riparian vegetation

would also prevent further degradation of the Laja. Eradication of exotics does not seem likely

in this river. However, in secondary channels where morphological barriers (e.g., large step

pools) could help prevent exotics’ invasion to areas where sensitive natives still occur, deliberate

introductions should be prevented. These recommendations are made locally for the Laja

watershed, but are applicable to aquatic ecosystems throughout the country, since the changes

that have occurred in the Laja are emblematic of the declining state of aquatic biodiversity across

Mexico.

ACKNOWLEDGEMENTS

We thank, G. Salgado-Maldonado, M.G. Kato, M.R. Helmus, J. Carcaño, M. Adams,

D.C. Buth, G. Cabañas-Carranza, A. Martínez-Aquino, J. Barragán, R. Pineda-López, E. López-

López, R. Aguilar-Aguilar, D.W. Nelson (UMMZ), E. Soto-Galera (ENCB-IPN), C. Harvey,

62

E.V. Nordheim, and Clyde Barbour for logistical and field assistance, and manuscript

preparation. J. Maxted helped develop Figure 1. We thank the NGO “Salvemos al Laja” for

logistical help and their dedication to the conservation of the Laja River. Universidad Nacional

Autónoma de México and Universidad Autónoma de Querétaro provided field vehicles.

Funding: Latin American and Iberian Studies Program of the University of Wisconsin –

Madison, UCMEXUS Grant Number K3233, and Proyecto CONACyT-SIHGO2002020618 -

Diagnóstico y propuesta de conservación y manejo de la ictiofauna vivípara de las subcuencas

Medio Lerma y Alto Pánuco-.

63

REFERENCES

Barbour C. 2002. Chirostoma contrerasi (Atherinopsidae, Menidiinae), a new species from the lago de Chapala, Mexico. In Lozano-Vilano ML.(ed) Libro Jubilar en Honor al Dr. Salvador Contreras Balderas, Universidad Autónoma de Nuevo León, Monterrey, México; 23-33

Borowsky R. 1978. Social inhibition of maturation in natural populations of Xiphophorus

variatus (Pisces: Poeciliidae). Science 201:933-935. CNA. 2002 Determinación de la disponibilidad de agua en el acuífero Cuenca Alta del Río Laja.

Internal report. Comisión Nacional del Agua, Subdirección General Técnica, Gerencia de Aguas Subterráneas, Subgerencia de Evaluación y Modelación Hidrológica, Ciudad de México.

CONABIO. 2000. Regiones hidrológicas prioritarias para la conservación de la biodiversidad.

Consejo Nacional para la Conservación de la Biodiversidad. México D.F., México. Contreras-Balderas S, Escalante-Cavazos MA. 1993. Distribution and known impacts of exotic

fishes in Mexico. In Courtenay Jr. WR, Stauffer Jr. JR (eds). Distribution, Biology and Management of Exotic Fishes. Johns Hopkins University Press, Baltimore; 102-130

Contreras-Balderas S, Lozano –Vilano ML. 1993. Water, endangered fishes, and development

perspectives in Northeastern Mexico. Conservation Biology 8:379-387. Contreras-MacBeath T, Mejía-Mojica H & Carrillo-Wilson R. 1998. Negative impact on the

aquatic ecosystems of the state of Morelos, Mexico from introduced aquarium and other commercial fish. Aquarium Sciences and Conservation 2: 67-78

Craig JF. 2000. Large dams and freshwater fish biodiversity. In Berkamp G, McCartney M,

Dugan P, McNeely J, Acreman M (eds). Dams, ecosystem functions and environmental restoration, Thematic Review II1 prepared as an input to the World Commission on Dams, Cape Town.1-58. http://www.dams.org

de Anda J, Shear H, Maniak U, Riedel G. 2001. Phosphates in Lake Chapala, Mexico. Lakes and

Reserviors 6:313-321. Díaz-Pardo E, Godínez-Rodríguez MA, López-López E, Soto-Galera E. 1993. Ecología de los

peces de la cuenca del río Lerma, México. Anales de la Escuela Nacional de Ciencias Biológicas IPN 39:103-127

Esteller MV & Díaz-Delgado C. 2002. Environmental effects of aquifer overexploitation: A case

study in the highlands of Mexico. Environmental Management 29: 266-278 Fausch KD, Lyons J, Karr JR, Angermeier PL. 1990. Fish communities as indicators of

environmental degradation. American Fisheries Society Symposium 8:123-144.

64

Frenzel SA. 1996. An application of bioassessment metrics and multivariate techniques to

evaluate Central Nebraska Streams. 96-4152, U.S. Geological Survey, Lincoln, Nebraska.

Hems J. 1977. The variatus platy. The Aquarist XLI: 452-453. Hernández-Javalera II. 2002. Stream prioritization for natural resource recovery and

development in the Rio Laja watershed, Guanajuato, Mexico. PhD Thesis, Range Science and Geography, New Mexico State University – Las Cruces, New Mexico.

Karr JR. 1981. Assessment of biotic integrity using fish communities. Fisheries 6:21-27. López-López E, Díaz-Pardo E. 1991. Cambios distribucionales en los peces del río de La Laja

(Cuenca Río Lerma), por efecto de disturbios ecológicos. Anales de la Escuela Nacional de Ciencias Biológicas 35:91-116.

López-López E, Paulo-Maya J. 2001. Changes in the fish assemblages in the upper Rio Ameca,

Mexico. Journal of Freshwater Ecology 16:179-187. Lyons J, Navarro-Pérez S, Cochran PA, Santana-C E, Guzmán-Arroyo M. 1995. Index of biotic

integrity based on fish assemblages for the conservation of streams and rivers in West-Central Mexico. Conservation Biology 9: 569-584.

Lyons J, González-Hernández G, Soto-Galera E, Guzmán-Arroyo M. 1998. Decline of

freshwater fishes and fisheries in selected drainages of west central Mexico. Fisheries 23:10-18.

Lyons J, Gutiérrez-Hernández A, Díaz-Pardo E, Soto-Galera E, Medina-Nava M, and Pineda

López R. 2000. Development of a preliminary index of biotic integrity (IBI) based on fish assemblages to assess ecosystem condition in the lakes of Central Mexico. Hidrobiología 418: 57-72.

Medina-Nava M. 2003. Utilización del índice de integridad biótica (II) para determinar áreas de

conservación de peces en la cuenca Lerma-Chapala en Michoacán. Maestro Masters in Science Thesis. Conservación y Manejo de Recursos Naturales. Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Michoacán.

Mercado-Silva N, Lyons J, Salgado-Maldonado G, Medina-Nava M. 2002. Validation of a fish-

based index of biotic integrity for streams and rivers of central Mexico. Reviews in Fish Biology and Fisheries 12:179-191.

Mestre-R JE. 1997. Integrated approach to river basin management: Lerma-Chapala case study –

attributions and experiences in water management in Mexico. Water International 22:140-152.

65

Miller RR, Williams JD, Williams JE. 1989. Extinctions of North American fishes during the past century. Fisheries 14: 22-38.

Minckley WL, Meffe GK, Soltz DL. 1991. Conservation and management of short-lived fishes:

The cyprinodonts. In Minckley WL, Deacon JE (eds). Battle against extinction: Native fish management in the American West. University of Arizona Press, Tucson; 247-282.

Moyle PB. 1994. Biodiversity, biomonitoring, and the structure of stream fish communities. In

Loeb SL, Spacie A (eds). Biological monitoring of aquatic systems. Lewis Publishers, Boca Raton FL; 171-186.

Paxton JR, Eschmeyer W. 2003. Encyclopedia of Fishes: A comprehensive guide by

international experts, 2 edition. Fog City Press, San Francisco CA. Rosales-Hoz L, Carranza-Edwards A, López-Hernández M. 2000. Heavy metals in sediments of

a large, turbid tropical lake affected by anthropogenic discharges. Environmental Geology 39:378-383.

Rosgen D. 1994. A Classification of Natural Rivers. Catena 22:169-199. SAS (Statistical Analysis System) V8. 1992. SAS/SYSTAT Users Guide. Version 8. 4th Edition.

SAS Institute, Inc. Cary N.C. Simberloff D. 1999. Keystone species and community effects of biological introductions. In

Ginzburg LR. (ed). Ecological Risks of Biotechnology. Butterworth-Heinemann. Boston, MA; 1-19

Soto-Galera E. 1993. Depredación selectiva de Chirostoma jordani sobre el zooplancton en el

Embalse Ignacio Allende GTO. Master of Science Thesis. Instituto Poltécnico Nacional, México D.F. México

Soto-Galera E, Díaz-Pardo E, López –López E, Lyons J. 1999a. Fish as indicators of

environmental quality in the Rio Lerma basin, México. Aquatic Ecosystem Health and Management 1:267-276.

Soto-Galera E, Paulo-Maya J, López-López E. Serna-Hernández JA. Lyons J. 1999b. Change in

fish fauna as indication of aquatic ecosystem condition in Rio Grande de Morelia-Lago de Cuitzeo basin, Mexico. Environmental Management 24: 133-140.

Soulé ME. 1990. The onslaught of alien species, and other challenges in coming decades.

Conservation Biology 4:14-21. Vázquez DP, Simberloff D. 2002. Ecological specialization and susceptibility to disturbance:

Conjectures and refutations. The American Naturalist 159:606-623. Velázquez-Velázquez E & Schmitter-Soto JJ. 2004. Conservation status of the San Cristóbal

pupfish Profundulus hildebrandi (Teleostei: Profundiludae) in the face of urban growth

66

in Chiapas, Mexico. Aquatic Conservation: Marine and Freshwater Ecosystems 14:201-209.

Walsh SJ, Haney DC, Timmerman CM, Dorazio RM. 1998. Physiological tolerances of juvenile

robust redhorse, Moxostoma robustum: conservation implications for an imperiled species. Environmental Biology of Fishes 51: 429-444.

Wang L, Lyons J, Kanehl P. 2003. Impacts of urban land cover on trout streams in Wisconsin

and Minnesota. Transactions of the American Fisheries Society 132:825-839. Wischnath L. 1993. Atlas of livebearers of the world. T.F.H. Publications, Neptune City, NJ. Zar JH. 1999. Biostatistical Analysis, 4th edition. Prentice Hall, Upper Saddle River, NJ.

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Table 1. Source of information on fish collections in the Laja River. There may be more than

one sample per year. Sources for each of the collections are given as footnotes (UMMZ =

University of Michigan Museum of Zoology, ENCB-IPN = Escuela Nacional de Ciencias

Biológicas – Instituto Politécnico Nacional).

Site

Site Type

Time Range

1) Arroyo San Gabriel

River

1966§, 2003†.

2) Ignacio Allende Dam Reservoir 1969◊, 1986-1992§, 1996-1997§, 2003†. 3) Gallinero Dam Reservoir 1966§, 1978§, 1986§. 4) Empalme Escobedo River 1969◊, 1977°, 1986§, 1997§*, 2003† 5) Atotonilco River 1986§, 2003† 6) El Xote River 1997-1999‡

Sources: † Mercado-Silva (original data); ‡ Mercado-Silva et al. 2002; ◊ Barbour and Douglass

(UMMZ); ° Smith and Baker (UMMZ); § ENCB-IPN, *SIHGO – Sistema de Información

Hidalgo.

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Table 2. Fishes of the Laja River, their ecological attributes, and collection sites. For Trophic

position (T.P.): O = omnivore, C = carnivore, H = herbivore. For Origin: N = native, E = exotic.

For Tolerance: S = sensitive, M= medium tolerance, T = tolerant. For Habitat: W = water

column, B = benthic, EB = exotic benthic. Classification modified from Mercado-Silva et al.

2002. Sites where the species was collected in the 1966 – 2003 period are indicated (Ri = River

Site; Re = Reservoir Site). Asterisk identifies species reported for the Laja but not captured in the

2002-03 collections or the sites we studied here. Species collected in 2003 intensive samples are

identified.

Family and Species

T.P.

Origin

Tolerance

Habitat

Site where collected

Col. in 2003

Ri Re Cyprinidae Algansea tincella O N M W X X Carassius auratus O E T EB X X X Cyprinus carpio O E T EB X X X Notropis calientis C N S W X X Notropis sallaei C? N S? W X X Yuriria alta O N T W X X X Ctenopharyngodon idella

H E T W X X

Catostomidae Scartomyzon austrinus C N S B X Ictaluridae Ictalurus dugesi C N M B X Atherinopsidae Chirostoma jordani C N T W X X X Chirostoma arge C N M W X X X Chirostoma humboldtianum

C N S? W

Poecilidae Poecilia sphenops H/O E T W X X Poeciliopsis infans O N T W X X X Xiphophorus variatus O E T W X X X Goodeidae Goodea atripinnis O N T W X X X Xenotoca variata O N T W X X X Alloophorus robustus* C N M W

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Skiffia lermae* O N S W Centrarchidae Lepomis macrochirus C E M W X X X Lepomis cyanellus* C E T W Micropterus salmoides C E M W X X X Cichlidae Oreochromis mossambicus

O E T W X X X

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Table 3. Percentage occurrence (% of sites where encountered) and number of individuals

collected for species from the Laja River in 2003-04 (17 sites sampled).

Species

Percentage

Occurrence (%)

Number of Individuals Collected

Chirostoma jordani

94

3833

Yuriria alta 88 1032 Xenotoca variata 82 913 Goodea atripinnis 82 2156 Oreochromis mossambicus 64 187 Poeciliopsis infans 64 1043 Carassius auratus 58 108 Xiphophorus variatus 52 1418 Cyprinus carpio 52 26 Chirostoma arge 35 44 Lepomis macrochirus 35 154 Micropterus salmoides 23 238 Algansea tincella 23 19 Notropis calientis 5 3 Ctenopharyngodon idella 5 1

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Table 4. Changes perceived in the fish assemblage of the Laja River. These changes are

considered hypothesized predictions for other systems in Central Mexico when exposed to

similar human impacts (see text).

Community attributes (Number of:)

Response

Hypothesized Causes

Total Species

No Change

Exotic additions offset natives loss. Potential for increased richness exists as more exotics become established in the systems.

Tolerant Small increase Tolerant native species remain and exotic species (usually tolerant) are added to the community.

Omnivores Small increase This guild dominates fish communities but there is an addition of exotic omnivore species. Omnivory could also increase among other species as their preferred food items disappear (e.g., macroinvertebrates, other fishes) from the channel.

Exotics Increase New habitats, aquarium releases, fishery introductions contribute to an increase. In addition, species can be translocated from other basins in the region through channelization and connection of streams belonging to different basins.

Natives Decline Mainly sensitive and benthic native species are extirpated. Decline related to habitat loss, pollution, channel desiccation, and potentially from predation or competition interactions with exotics.

Sensitive and Medium-Tolerance Species

Decline Distribution reduced to isolated areas where water availability is permanent and human impacts are relatively low.

Carnivores Decline Prey habitat has been destroyed. Refuge areas disappear due to regulated flows and habitat destruction. Perhaps associated to fishery

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overexploitation. Remaining native carnivores are essentially zooplanktivores and exotics dominate this category. Benthivores sensitive to pollutants in sediments are extirpated.

Native Benthic Species

Decline Habitat change from rocky and gravel bottoms to silt and sand as a consequence of erosion. Lack of water in channels reduces availability of pools.

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FIGURE HEADINGS

Figure 1. The Laja River (Guanajauato, Mexico). Triangles mark sites for which historical data

were available (North to South: Presa el Gallinero, Arroyo San Gabriel, Atotonilco, El Xote,

Presa Ignacio Allende, Empalme Escobedo).

Figure 2. Trends (1960-present) for selected variables of the fish community in the Laja River.

All river and reservoir sites are considered in these plots. Variables that showed a significant

change (α=0.05) through time using RMANOVA are indicated with an asterisk. Variables

showing significance with Sign Test analysis indicated by a §.

Figure 3. Average percentage change per decade for the attributes of the fish community of the

Laja River. Positive value indicates increase, negative values indicate decline.

74

Figure 1.

75

0

2

4

6

8Native§

0

1

2

3Sensitive *§

0

1

2

3Exotic

0

1

2

3Carnivore §

0

0.5

1

1.5Benthic *§

0

0.4

0.8

1.2Medium Tolerance§

60s 70s 80s 90s 00s 60s 70s 80s 90s 00s

Figure 2

Decade

Mea

n N

umbe

r of S

peci

es

76

-60

-40

-20

0

20

40

60

Ave

rage

Per

cent

age

Cha

nge

/ Dec

ade

Tole

rant

Om

nivo

re

Exo

tic

Nat

ive

Sen

sitiv

e

Med

ium

To

lera

nce

Car

nivo

re

Ben

thic

Exo

tic

Ben

thic

Tota

l Spe

cies

Tolerance Trophic GroupOrigin / Habitat

Figure 3

77

CHAPTER IV

FOOD WEB STRUCTURE OF AN IMPACTED SEMI-DESERTIC FRESHWATER SYSTEM IN MEXICO’S

CENTRAL PLATEAU

ABSTRACT

Habitat modifications, invasive species and other anthropogenic impacts have restructured fish

communities in lotic freshwater ecosystems of central Mexico. Conservation of native fishes

requires understanding the trophic relationships resulting from the introduction of invasives,

natural flow regime alteration, and other human-derived impacts. This information needs to be

integrated to watershed scale management strategies. Using 13C and 15N stable isotope ratios of

491 fishes in 12 species and other food web components, we investigated the food web structure

in 11 sites along the Laja river (Guanajuato, central Mexico) to determine the role of invasive

species and the effects of reservoirs on food webs. Invasive largemouth bass (Micropterus

salmoides) were top predators in the system, but other invasive species affected natives through

resource competition, especially in riparian sites. Gradual enrichment of average δ13C from

reservoirs to downstream riparian sites suggested that these could be subsidizing food webs

throughout the system. Average trophic position of fishes was lower in reservoirs than in river

sites indicating that in reservoirs invasive and native omnivores, which comprise an important

component of the fish community, rely on items of lower energy content, than in riparian sites

downstream from reservoirs. In reservoirs food webs had well separated pelagic (zooplankton-

based) and benthic components, and species had a broad resource base compared to river sites,

where most species relied on similar basal resources. Since many rivers in central Mexico share

similar anthropogenic impacts, and functionally similar biotas, Laja food web characteristics

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potentially represent the trophic interactions of fish communities in other rivers systems in the

region.

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INTRODUCTION

Conservation of native fish communities relies importantly on the understanding of

trophic relationships among species in aquatic ecosystems. Individual species can be central in

structuring whole communities (Paine 1992, Power et al. 1996), but external drivers such as

exotic species, fishery exploitations, habitat modifications and other perturbations also play an

important role (Vanni et al. 1990, Wootton et al. 1996, Kitchell et al. 2000). Food web studies

can provide key elements to guide decision making processes, as they inform how species

interact with the rest of ecosystem components, and they allow the synthesis of multiple

ecological processes (Wootton et al. 1996, Vander Zanden et al. 2003).

Stable isotope analysis has become an important tool in the study of aquatic food webs

because it helps identify the dominant pathways of carbon and nutrient transfer in food webs, and

can be used to estimate assimilation of food resources over time (Minagawa and Wada 1984,

Peterson and Fry 1987). Although coarse in taxonomic resolution, isotope data can reveal

important feeding links among consumers and can overcome some of the difficulties associated

with more traditional methods such as stomach content analysis (Jepsen and Winemiller 2002).

Stable isotope data can trace energy flow pathway alterations arising from human induced

ecosystem changes and guide ecosystem management efforts to conserve or restore ecosystems.

In freshwater ecosystems of Mexico’s central plateau, native fish extirpations and

population declines have been caused by habitat deterioration, overexploitation, and invasive

species (Díaz-Pardo et al. 1993, Lyons et al. 1998, Soto-Galera et al. 1998, Mercado-Silva et al.

2005). The Lerma-Santiago river, which drains most of central Mexico, is considered one of the

most degraded river basins in the country (Soto-Galera et al.1998, Lyons et al. 1998). Few

isolated branches of this large system maintain environmental conditions required for the

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survival of a relatively intact fish fauna. The Laja River (Guanajuato, Mexico), although far

from being considered pristine, is one such tributary (Soto-Galera et al. 1998). Management of

the river has generally ignored fish communities, and has instead focused on the distribution of

water for crop irrigation and human consumption. The Laja has been proposed as representative

of the general trends occurring in fish communities in central Mexico (Mercado-Silva et al.

2005) and the study of its food webs could help understand how modification of flow regimes

and the introduction of exotic species are affecting other systems in this region. Further, this

information could help ongoing conservation and restoration efforts in the river.

Our objectives are to present a longitudinal stable isotope study of the food web of the

Laja taking into account habitat variability (lotic and lentic environments), and to understand

resource partitioning in fish communities, their interspecific interactions, and the possible effects

that reservoirs may have on food web dynamics. Knowledge of these food web interactions is

needed to integrate sound watershed management strategies that include conservation of

biological communities as a central objective.

Study Area

The Laja watershed (21°33’– 20°58’N; 101°28’- 100°30’W) covers 3,476km2 of the

State of Guanajuato in Central Mexico (Figure 1). The watershed has numerous intermittent and

semi-intermittent tributary streams draining into the Laja River. This semi-arid area is

characterized by numerous mountain ranges and high plateaus (mean altitude >1800m) that have

a relatively short rainy season during the summer (Jul.-Oct.) with total annual precipitation of

400-800mm (CONABIO, 2000). Although much of the basin has been impacted by agriculture,

the natural vegetation types include oak forests, chaparral, and xerophyte vegetation. Riparian

vegetation, where it occurs, consists of narrow strips of Salix sp. in small stretches of the river

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with rushes along river banks. Substrates in the main river channel are typically sand/silt-

dominated with interspersed rocky segments. Practically all streams and rivers in the watershed

are truncated by reservoirs or other water extraction and storage structures. Two major

reservoirs have been built in the Laja River, but at least 12 more reservoirs are located on

tributaries (CNA, 2002). The Ignacio Allende and Jesús María reservoirs were built in 1968 and

1982, respectively, to provide water for agriculture and flood control. The main stem of the Laja

is 154 km in length, of which 124 km are located upstream from the Ignacio Allende Dam

(Hernández-Javalera, 2002) and ≈15km are located above Jesús María. Dry land and irrigated

agriculture dictate water use in the watershed; the number of deep wells in the area has increased

significantly from 210 in 1971 (145 Mm3/year water extracted), when the first hydrological

studies were carried out in the region, to 781 in 1999 (≈ 210 Mm3/year water extracted) (CNA,

2002). Several other non-regulated water extraction operations are located along the river,

usually in the form of pumps that extract water directly from the Laja. Numerous springs in the

basin are used for recreation.

Except for the headwaters and a few canyons, the Laja has generally low to moderate

gradient and presents seasonal and dam regulated flows. Water is stored in reservoirs during the

rainy season, thus altering natural flow regimes. Especially during the later part of the rainy

season when dams release water, flow fluctuates widely from isolated pools separated by dry

river bed to fast runs exceeding 3m in depth. Even though a large portion of the Laja basin is

used for agriculture, its headwaters are considered as a priority area for watershed conservation

by CONABIO (2000) given their relatively high biodiversity. Water surface width throughout

the river varies from approximately 2 to 20 m depending on flow release from reservoirs and

season, and the geomorphology of the surrounding landscape.

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For this investigation, we sampled 11 sites in the Laja river during January, August and

November 2003 (2 reservoir, 3 reservoir-influenced and 6 river sites) (Table 1) (Figure 1). Sites

1 and 8 were the Jesus María and I. Allende reservoirs and sites 2 and 9 were located within 100

m of those bottom release dams. Sites 3 – 6 were located in a high plateau where the river is

surrounded by slight hills. Site 7 was located immediately upstream from the reservoir (Site 8)

and is regularly inundated by water backed up from the reservoir. Site 10 was located

downstream from an 11 km gorge downstream from site 9. Site 11 was located in a valley area

at the downstream end of our sampling reach and was heavily influenced by urban pollution.

Fish communities of the Laja

Twenty three fish species have been recorded from the Laja, of which 14 are native and 9

are introduced (Table 2). Of these, only fifteen persist today. The present-day fish community is

dominated by species that are tolerant and omnivorous, and with a high proportion of exotics.

Species sensitive to habitat deterioration and poor water quality as well as native benthivores

have been extirpated, and only a few representatives of these groups remain in isolated reaches

of the Laja (Mercado-Silva et al. 2005). At least 9 exotic species have been introduced to the

Laja as food resources in reservoirs or as accidental aquarium releases. Most exotic species are

common, and largemouth bass (Micropterus salmoides) and bluegill (Lepomis macrochirus) are

dominant in upper reaches of the Laja. Throughout the rest of the watershed, exotic livebearers,

cichlids, carp and goldfishes are common, but native livebearers and silversides still comprise an

important component of the community (López-López and Díaz-Pardo 1991, Mercado-Silva et

al. 2005).

METHODS

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Fish, benthic invertebrate and zooplankton samples were obtained for stable isotope

analysis from the 11 sites described above (Table 1, Figure 1) but not all sites or taxa were

sampled on every date (Table 2). Seines, DC backpack electroshockers, and gillnets, as required,

were used to obtain representative samples of the fish community in all habitats at each sampling

site. Approximately 1 g of dorsal muscle tissue was obtained from 1-5 fish of the same species

and size, and frozen for isotope analysis. D-nets, Eckman grab samplers and zooplankton (80

µm mesh) nets were used for invertebrate community sampling. Samples were sorted, identified

and frozen, until processed for stable isotope analysis. Samples were then dried at 60-75°C for

48 hours, ground into a fine powder with mortar and pestle, and packed (1 mg for fish tissues, 2-

3 mg for other samples) into acid-washed 5 X 8 tin capsules for 13C and 15N stable isotope

analysis (Vander Zanden et al. 1999, Vander Zanden 1999a, Vander Zanden et al. 2003). Stable

isotope analysis was performed in a Europa Hydra 20/20 continuous flow isotope ratio mass

spectrometer at the University of California –Davis Stable Isotope Facility.

Food web structure of the Laja river was investigated using carbon and nitrogen stable

isotope ratios (δ13C and δ15N). Stable isotope ratios are expressed in delta (δ) notation, which is

defined as the parts per thousand (‰) deviation from standard material; δ13C or δ15N = ([R sample

/R standard]-1) ∙1000, where R = 13C/12C or 15N/14N. There is a 3‰ – 4‰ increase in δ15N from

prey to predator, which makes this isotope ratio useful for determining consumer-resource

relationships. δ13C is conserved from prey to predator, and is thus useful in tracing the primary

source of energy for a food web (Hecky and Hesslein 1995, Vander Zanden et al. 1999).

δ15N from fish samples were converted to a continuous measure of trophic position (TP)

to standardize for among-site variation in δ15N at the base of the food web following Vander

Zanden and Rasmussen (1999): TP consumer = (δ15N consumer - δ15N baseline)/3.4 + 2 (Formula 1),

84

where 3.4 is the assumed per trophic level enrichment in δ15N. δ15N baseline was established

through a primary consumer δ13C - δ15N linear regression relationship which provided an

adequate fit for these data (Vander Zanden et al. 2003) (Figure 2a,b).

Primary consumers were obtained from 6 sites in the Laja. Three of these were river

sites, two were located immediately below reservoirs and one was a reservoir site (Table 1).

Primary consumers were identified as such using classification criteria in Merritt and Cummins

(1996) and Thorp and Covich (2001). Reservoir-influenced and reservoir site baseline

relationships were not significantly different using analysis of covariance (ANCOVA). There

were also no differences among baseline samples from the three river sites. Thus, two baseline

curves were developed for 1) 2 reservoir-influenced (2 and 9) and 1 reservoir sites (1)

(δ15Ncorrected = -0.673∙δ13Cconsumer – 2.9535), and 2) three river sites with primary consumers

(δ15Ncorrected = -0.5813∙δ13Cconsumer – 4.9188) (3, 4, and 11) (Figure 2). These curves were also

used for sites 7,8, and10 since their secondary invertebrate consumer curves were no different

than those from sites where primary consumers were available. Baseline δ15N was calculated for

each consumer using their δ13C value, the equation for a river or reservoir-influenced and

reservoir site, and a correction of the site-specific residuals from the general δ15N-δ13C

relationship (Vander Zanden and Rasmussen 1999) (Table 3). For sites 7,8 and 10 we used the

mean of site-specific deviations to calculate δ15Nbaseline . For two river sites (Sites 5 and 6) there

was no relationship between δ15N and δ13C for primary consumers. Thus, TP for fishes was

established by using the TP formula above, but using the average of all invertebrates to obtain

δ15N baseline.

No differences in δ15N values were found among fish samples collected from different

seasons (January, August and November) for individual sites (t-tests, all p>0.05, considering a

85

difference of 3‰ as threshold). Thus, the δ15N values for all individuals of a species from a

given site were averaged for analysis, regardless of season.

Food web structure determination is based on isotopic analysis of 395 fish specimens

from 12 species of the 15 currently present in the Laja. Of these, 6 are exotic and 6 are native.

Stable isotope samples were not obtained for one exotic (Ctenopharyngodon idella), and two

native species (Notropis calientis and Algansea tincella) that can still be found in the river. One

hundred and seven samples of invertebrates were used in building the present-day food web of

the Laja (Appendix 2). Two per cent of samples were analyzed in duplicate (Mean standard

error = 0.016‰ for δ13C and 0.042‰ for δ15N).

Food web analyses

To compare among-site food web differences in the Laja, we built plots depicting the

mean trophic position and mean δ13C for each species per site (± 1 SE) (Figure 3). Each food

web was analyzed separately. The specific food web position of exotic species in the food web

was studied with respect to natives.

To study the effect of reservoirs on the food webs of the Laja, we followed the

distributions of values of δ13C on an upstream-downstream gradient. Because δ13C traces the

primary source of energy for a food web, we searched for among site differences in δ13C

signatures of fishes. We analyzed these values to determine the dependency of fishes in river

sites on zooplankton from upstream reservoirs. Zooplankton usually has δ13C signatures that are

depleted relative to those of terrestrial and benthic invertebrates, as a consequence of primary

producer differences in fractionation relative to their inorganic carbon sources (Vander Zanden et

al. 2003). We expected declining dependence on pelagic zooplankton with distance from

reservoirs.

86

To test for differences among the basal resources for food webs in reservoir versus river

sites, sites were grouped into three categories: 1) reservoirs, 2) reservoir-influenced sites, and 3)

river sites (Table 1). Sites located within 100 meters downstream from dams were considered

reservoir-influenced as well as one site (site 7), which is a riverine section of the reservoir (Site

8) that becomes inundated when the reservoir is at full capacity. We then tested for differences

in the range of fish δ13C values (i.e. diversity of basal resources) among these groups.

To determine if reservoirs in the system affect the trophic position of the fish community

throughout the system, we analyzed the distribution of trophic position values on an upstream -

downstream gradient. Trophic positions of all fishes in the community were considered.

RESULTS

Food web plots of the 11 sites in the Laja river are depicted in Figure 3. These plots

show mean trophic position versus δ13C for each species in each site (mean ±1 SE). Mean δ15N,

δ13C, and trophic position values for each fish species in each site are included in Appendix 1.

Mean δ15N and δ13C for invertebrates collected can be found in Appendix 2. Native and exotic

species were present in all 11 sites. Stable isotope data for Yuriria alta was obtained from all

sampling sites, followed by Chirostoma jordani and Goodea atripinnis (10 sites), Poeciliopsis

infans (8), Xenotoca variata (7) and Chirostoma arge (1). Lepomis macrochirus and

Xiphophorus variatus were the exotics for which stable isotope data were obtained from the most

sites (6 sites), followed by Micropterus salmoides, Carassius auratus and Cyprinus carpio (5)

and Oreochromis sp. (4).

Individual sites

In reservoirs (Figure 3, Sites 1 and 8), food webs had relatively well separated benthic

and pelagic components (for δ13C values generally below -23‰), in which dominant pelagic

87

zooplaktivores (Chirostoma jordani) were separated from the rest of the species in the

community. In benthic food webs of site 1, Y. alta and the exotic bass, M. salmoides, were at the

top of the food web (TP ≈ 3.4, 3.7, respectively). Bass were also top predators in benthic food

webs of site 8. Bluegill (L. macrochirus) were also top consumers in the benthic food web of

site 1 (TP = 3.2) and G. atripinnis and goldfish (C. auratus) were at the bottom of the food web.

These two species relied more on food items with depleted δ13C signatures than Y. alta, bass and

bluegill.

In site 8 X. variata, were secondary consumers of benthic food webs (TP=3.2), and P.

infans was the species that relied most on δ13C enriched resources. Goldfish and tilapia

(Oreochromis sp.) had a TP of ≈ 2.4, but goldfish consumed food items with depleted δ13C

signatures relative to tilapia. Goodea atripinnis, X. variatus, and carp (C. carpio) were at the

bottom of the foodweb and relied on resources with a wide range of δ13C signatures (-20 to -

24‰).

Fishes in tailwater sites (Figure 3, sites 2 and 9) relied on resources depleted in δ13C,

which resembled pelagic resources found in reservoirs. In site 2, three species, bluegill, Y. alta,

and Ch. jordani had lower TP relative to conspecifics in the upstream reservoir. In addition, Ch.

jordani relied on δ13C enriched food items relative to the upstream reservoir. Chirostoma

jordani in these two sites remained well separated from the rest of the fishes in terms of the δ13C

signature of their food. In site 9 several species were clustered between δ13C -21.5 and -22.5‰,

indicating reliance on a common basal resource. In this cluster, the exotics X. variatus and

bluegill had the highest TP (≈ 3.5), but overlapped with natives X. variata, P. infans, and G.

atripinnis, and exotic tilapia which was at the bottom of the cluster. Yuriria alta and goldfish

were at the bottom of the food web, and depended on resources depleted in δ13C.

88

In river site 3, largemouth bass were at the top of the food web (TP = 4.5, δ13C = -

19.7‰), followed by bluegill and P. infans. Chirostoma jordani were again at the top of the

pelagic component of the food web (TP = 4, δ13C = -23.5‰) and separated from all other fishes.

Carp had the lowest trophic position (TP = 2.7). Overall, except for Y. alta, all species in this

site had a higher TP than in sites 1 and 2. In site 4, only 4 species were captured. G. atripinnis,

X. variata, Y. alta and goldfish had TP ≈ 3, and goldfish relied the most on δ13C enriched

resources.

River sites 5 and 6 showed relatively similar foodwebs. Contrary to sites 1-3, Ch. jordani

in these sites relied on resources similar to the rest of the fish community and enriched in δ13C.

In site 5 this species was still at a relatively high TP, similar to largemouth bass (TP ≈ 4), but

bass relied on resources with comparatively enriched δ13C. Bluegill, G. atripinnis, P. infans, X.

variatus, X. variata and Y. alta all had TP ≈ 3 and except for X. variata, relied on similar basal

resources. Tilapia and goldfish had the lowest TP in this food web (TP ≈ 2.6). Compared to site

5, in site 6 X. variata, P. infans and G. atripinnis had higher TP (≈3.8). Carp, Y. alta and tilapia

were at the bottom of this food web.

In site 7, a riverine portion of the reservoir in site 8, most species showed enriched δ13C

and higher TP relative to sites 5 and 6. Three natives, G. atripinnis, X. variata, and Y. alta

formed a cluster with TP ≈ 3.8 and δ13C ≈ -23.5‰. Goldfish and X. variatus had a high TP (4.1

and 4.5, respectively) relative to other sites in the Laja. Carp had the lowest TP in this site and

relied on depleted δ13C resources.

Site 10 showed a food web similar to site 9 (described above). A cluster was formed by

most species in this site. They all relied on resources with δ13C -22 and -24, and had TP: 3 – 3.2.

89

Two species were relatively separated from this cluster, Ch. jordani and Ch. arge. These had

higher TP (3.5 – 4), but shared the same basal resource as all other species.

Finally, in site 11 the fish community had Ch. jordani at the top of the food web (TP =

4.2) and separated from the rest of the species. Bluegill had the second highest TP and relied the

most on δ13C enriched resources among all fishes. P. infans, X. variatus, Y. alta and P. infans,

comprised the rest of the community with TP 3-3.2 and a reliance on enriched δ13C resources.

Grouped sites

In reservoirs (Figure 4a), food webs had a well separated benthic and pelagic component

(for δ13C values generally below -23‰), in which dominant pelagic zooplaktivores (Ch. jordani)

were separated from the rest of the species in the community which relied on a combination of

pelagic and benthic resources. In this benthic food web, top predators were largemouth bass (TP

≈ 3.4) and the cyprinid Y. alta. Both species relied on a combination of benthic and zooplankton

resources. Bluegill and X. variata also had relatively high trophic positions (TP≈3.2) but the

latter relied more on benthic resources. Poeciliopsis infans had an average TP of 2.7 and derived

resources from benthic habitats. One native, G. atripinnis and three exotic species (tilapias,

goldfish, and X. variatus) shared TP between 2 and 2.5. Goldfish relied the most on pelagic

resources in this group while tilapia derived their energy from benthic resources. Carp were

lowest in the food web (TP≈1.8) and showed more pelagic δ13C signature.

In reservoir-influenced sites TP and δ13C values showed lowered ranges relative to

reservoirs (Figure 4b). Chirostoma jordani still dominated the pelagic component of the food

web. The rest of the fish community had generally higher TP, and P. infans, Y. alta, G.

atripinnis, and X. variata had TPs between 2.9 and 3.6, along with the exotic bass and X.

variatus. Xiphophorus variatus increased their TP by approximately 1.5 TP between reservoirs

90

and reservoir-influenced sites. Carp and tilapia were at the bottom of the food web but also had

higher TP relative to reservoirs. Compared to reservoirs, fish from reservoir-influenced sites

relied on more pelagic derived resources.

Bass were top predators in river sites in the Laja (TP≈4.4) (Figure 4c). However, they

relied on less δ13C depleted resources than most of the fish community. Only bluegills and

goldfishes derived their energy from the same basal resources. Compared to the rest of the

species, Ch. jordani and Ch. arge had high TP and relied the most on δ13C depleted basal

resources. Yuriria alta, P. infans, X. variata and G. atripinnis formed a cluster of species that

had TP ≈ 3.3-3.5, and basal resources of very similar provenience. Carp and tilapia, again at the

bottom of the food web, as well as goldfish, bluegill and largemouth bass were reliant on less

depleted resources in river sites than in sites with reservoir influence. In general, fish showed

less depleted δ13C values in rivers compared to other site types.

Longitudinal trends

On a longitudinal gradient, there was a ≈2‰ δ 13C enrichment from the reservoir in site 1

to river site 6 (Figure 5). This trend was not repeated downstream from the reservoir in site 8.

The range of δ13C values in reservoir sites was significantly broader than that of river or

reservoir-influenced sites (Figure 6), but there was no difference between reservoir-influenced

and river sites. Average trophic position for the whole fish community increased with distance

from reservoirs (Sites 1 and 8) (Figure 7).

DISCUSSION

Many of the environmental impacts that have occurred in Mexican freshwater ecosystems have

resulted in important changes in the composition and structure of their communities (Lyons et al.

1998, Soto Galera et al. 1998, Mercado-Silva et al. 2005). Habitat modifications, pollution and

91

the introduction of exotic species in aquatic systems have caused the loss of native species

populations and extirpations that have been well documented (López-López and Díaz-Pardo

1991, Contreras –MacBeath et al. 1998, Lyons et al. 1998, Contreras-Balderas et al. 2003).

Understanding the implications of these changes on functional guilds in fish communities has

also increased (Lyons et al. 1995, Mercado-Silva et al. 2001, Contreras-Balderas et al. 2005) but

much less is known about how some of these impacts have altered ecological interactions in

aquatic communities.

Reservoirs are major modifications in river systems and have major detrimental effects

on native stream communities, fisheries and terrestrial riparian habitats (Havel et al. 2005). They

convert extensive reaches of stream habitat into standing water, flood large areas of land, change

the magnitude and timing of water flows, reduce sediment loads, form barriers to fish migration,

extirpate shallow water species through fluctuating water levels, aide in biological

homogenization, and accelerate biological invasions (Bain et al. 1988, Malmqvist and Rundle

2002, Rahel 2002, Havel et al. 2005). In the Laja River, reservoirs play a major role in

determining the trophic interactions among fishes.

Food webs in reservoirs presented the highest range in δ13C values in the Laja, indicating

the range of available resources relative to river and river-influenced sites. It was in reservoirs

where Ch. jordani, a well known zooplanktivore (Soto-Galera 1993), separated from the rest of

the fish community. All other species relied on nutrients mostly of benthic provenience. These

species were most likely relying on invertebrates living among macrophytes or other substrates,

inputs from terrestrial habitats, and detritus and algal mats that develop in shore areas.

Zooplanktivory of Ch. jordani is most likely not exclusive, but other species depended less on

pelagic resources. Largemouth bass and bluegills, for example, are known to feed on

92

zooplankton during their early life stages (Becker 1983). All of the native livebearers and Y. alta

(Cyprinidae), residing in the Laja are considered omnivores, feeding on algal mats and benthic

invertebrates (Wischnath 1993), but most likely also consume some pelagic resources, except

perhaps for P. infans which relied mostly on benthic productivity. In reservoirs, omnivorous

species such as carp, tilapia, G. atripinnis, and goldfish, had a lower trophic position than in

other site types. This suggests that reservoirs increase the availability of detritus and algae,

which are preferably preyed upon by these species, compared to other site types in the Laja,

where detrital resources and algae may not be as abundant. Particularly in tailwater reaches,

substrates are rocky and have a relatively low percentage of detritus, which is preferred by

species such as carp or goldfish. In these sites and in river areas, omnivores may have to shift to

zooplankton or benthic invertebrates for food, and food web length is thus increased relative to

reservoirs, where foodwebs are comparatively more detritus-based.

While the assemblage of fishes is constant along the Laja, food webs experience

important fluctuations from reservoirs to river areas that are located right below the bottom

release dams. Here, the whole fish community shared resources and only Ch. jordani separated

from the rest of the community. However, there was a shift for practically all species to a

dependence on resources that were similar in their δ13C to those found in pelagic areas of

reservoirs. This suggests that for all species there are less varied basal resources, and that

zooplanktivory could be common for all species. Most, if not all of this zooplankton would have

been produced in upstream lentic systems.

Similar to tailwaters, fish communities in river sites relied on a similar resource base. All

native species, including Ch. jordani, which was typically a zooplaktivore in reservoirs and

reservoir influenced sites, had enriched δ13C values that suggest that the whole community relies

93

on the same resources, with perhaps little input from pelagic nutrients. Thus inter-specific

interactions could be more intense than in reservoir areas. In river sites, while exotic top-

predator largemouth bass and exotic bluegills relied mostly on enriched δ13C resources, piscivory

effects from bass on natives are likely. Both species were introduced in tandem into reservoirs

as part of fishery management programs, but have now spread to river areas. As juveniles, these

two species most likely compete with native livebearers and cyprinids, but as adults, bluegills

undoubtedly are predators of these groups and atherinopsids. In reservoirs, atherinopsids

probably do not interact as much with these piscivores due to their preference for pelagic

habitats.

Other studies have found high variability in δ13C signatures in river systems, depending

on habitat characteristics (riffles vs pool) or flow intensity (Finlay et al. 1999, Finlay 2001). In

addition, in lotic systems different primary producers can acquire similar δ13C signatures,

reducing interpretability of C13 in food web studies. Thus, indicating the exact origin of

resources for consumers in river sites along the Laja is difficult. We make use of the existing

knowledge of the biology of a typically zooplanktivore species (Ch. jordani), and its δ13C

signature in reservoirs to follow pelagic resources through the river. However, in river sites

production may have several sources and we only use the position of each species relative to the

rest of the community in the food web to aid in understanding potential interspecific

relationships.

Reservoirs can subsidize food webs in downstream reaches of rivers (Petts 1984). As

water is released from reservoirs, it exports zooplankton that become part of lotic food webs. In

the Laja, we were able to detect δ13C that reflected reservoir pelagic resources in reservoir

influenced sites but not in some downstream river sites. δ13C enrichment of ≈1.5‰ occurred

94

between reservoir sites and river sites 3-6, suggesting that zooplankton may not constitute such

an important resource in these habitats. In other systems zooplankton abundances are known to

decline with distance from reservoirs (Ward 1975) depending on their size and form. Larger

zooplankton are more liable to mechanical destruction or predation, reducing their availability

further downstream in the Laja. This could result in the loss of the depleted pelagic δ13C

signature downstream from tailwaters (site 2). This trend was not observed in river sites 10-11,

which are located below the Ignacio Allende reservoir (Site 8). During our study, extensive

pools were formed in a gorge between site 9 (tailwaters) and 10, which may help explain why

fishes in this site have more depleted δ13C signatures than in reservoirs. These pools could have

provided adequate habitat for exported zooplankton.

Extirpation of native carnivores has been documented for the Laja (Mercado-Silva et al.

2005). Ictalurus dugesi, Notropis sallaei, Alloophorus robustus and Chirostoma humboldtianum

were carnivore species known from this system, but were not collected in our field efforts.

Undoubtedly, food webs have changed historically in the Laja as a consequence of the

disappearance of these species and the addition of a carnivorous invasive such as largemouth

bass. As a consequence of the presence of this invader, piscivory on native fishes is potentially

more common in the present-day Laja.

Our results suggest that there is high dietary overlap between native and invasive species

in the Laja river. Dietary overlap was lower in reservoir areas than in river areas and it is likely

that it becomes more significant under conditions of very low to no flow in the channel. Water

regulation in the Laja is driven by agricultural activities in the region, and often extensive

reaches of stream are reduced to low flows with little or no connection between isolated pools as

a consequence of upstream regulation. Under these circumstances, in addition to temperature

95

increases in these pools, competition and predation are likely to intensify. River management

should contemplate water flow increases in the river that allow fishes to better partition their

habitats and diversify their food resources. In addition, re-creation of natural flows could

improve the condition of riparian buffers and benthic communities, which could in turn provide

the fish community with additional and more diverse resources. Improving flows would not only

benefit aquatic ecosystems, but also terrestrial communities that rely on aquatic resources

(Nakano and Murakami 2001, Sabo 2002). Under the current water regulation regime, benthic

invertebrate communities may not be able to complete their life cycles and thus cannot

supplement in-river fish productivity.

Our study transcends the Laja river. Many freshwater systems in Mexico’s central

plateau and throughout the country share a common suite of exotics, functionally similar fish

assemblages, and similar environmental challenges. A study such as the one we present here

could bring insight to the structural changes that aquatic food webs have undergone in a broad

region, and could aid in predicting the consequences of future habitat modifications or species

introductions on not yet affected areas.

ACKNOWLEDGEMENTS

We thank G. Anderson, W. W. Fetzer, J Carcaño, M. Adams, A. Martínez-Aquino, M.G.

Kato, G. Cabañas-Carranza, C. Mendoza-Palmero, M.P. Ortega Olivares, C.P. Ornelas- García,

C. Pedraza –Lara, C. Barbour, R. Aguilar-Aguilar, J.G. Jiménez-Cortés, M.E. Reyna-Fabián, V.

E. Soto-Galera, J.T. Maxted, and D.W. Nelson for their assistance in the field, stable isotope

sample preparation, and the preparation of this manuscript. Institutional recognition to

Universidad Nacional Autónoma de México, Universidad Autónoma de Querétaro and Salvemos

al Laja A.C. Funding: Latin American and Iberian Studies Program of the University of

96

Wisconsin – Madison, UCMEXUS Grant Number K3233, and Proyecto CONACyT-

SIHGO2002020618 - Diagnóstico y propuesta de conservación y manejo de la ictiofauna

vivípara de las subcuencas Medio Lerma y Alto Pánuco-.

97

REFERENCES

Bain, M.B., J.T. Finn & H.E. Booke. 1988. Streamflow regulation and fish community structure. Ecology 69: 382-392.

Becker, G.C. 1983. Fishes of Wisconsin. University of Wisconsin Press, Madison, WI. CNA. 2002. Determinación de la disponibilidad de agua en el acuífero Cuanca Alta del Río Laja,

Comisión Nacional del Agua, Subdirección General Técnica, Gerencia de Aguas Subterráneas, Subgerencia de Evaluación y modelación Hidrológica, Mexico City.

CONABIO. 2000. Regiones hidrológicas prioritarias para la conservación de la biodiversidad.

Consejo Nacional para la Conservación de la Biodiversidad, México, D.F., México. Contreras-Balderas, S., P. Almada-Villela, M.d.L. Lozano-Vilano & M.E. García-Ramírez.

2003. Freshwater fish at risk or extinct in Mexico. Reviews in Fish Biology and Fisheries 12: 241-251.

Contreras-Balderas, S., M.d.L. Lozano -Vilano & M.E. García-Ramírez. 2005. Index of biological integrity, historical version, of the lower Rio Nazas, Coahuila, México: 2002. In: J. Rinne, R.M. Hughes & B. Calamusso (ed.) Historical changes in large river fish assemblages. American Fisheries Society. Bethesda, MD.

Contreras-Macbeath, T., H. Mejia-Mojica & R. Carrillo-Wilson. 1998. Negative impact on the

aquatic ecosystems of the state of Morelos, Mexico from introduced aquarium and other commercial fish. Aquarium Sciences and Conservation 2: 67-78.

Díaz-Pardo, E., M.A. Godínez-Rodríguez, E. López -López & E. Soto-Galera. 1993. Ecología de

los peces de la cuenca del río Lerma, México. Anales de la Escuela Nacional de Ciencias Biológicas 39: 103-127.

Finlay, J.C. 2001. Stable-carbon-isotope ratios of river biota:implications for energy flow in lotic

food webs. Ecology 82: 2001. Finlay, J.C., M.E. Power & G. Cabana. 1999. Effects of water velocity on algal carbon isotope

ratios: implications for river food web studies. Limnology and Oceanography 44: 1198-1203.

Havel, J.E., C.E. Lee & M.J. Vander Zanden. 2005. Do reservoirs facilitate invasions into

landscapes? BioScience 55: 518-525. Hecky, R.E. & R.H. Hesslein. 1995. Contributions of benthic algae to lake food webs as revealed

by stable isotope analysis. Journal of the North American Benthological Society 14: 631-653.

98

Hernández-Javalera, I.I. 2002. Stream priorization for natural resource recovery and development in the Rio Laja watershed, Guanajuato, Mexico. PhD Thesis, New Mexico State University, Las Cruces, NM. 124 pp.

Jepsen, D.B. & K.O. Winemiller. 2002. Structure of tropical rive food webs revealed by stable

isotope ratios. Oikos 96: 46-55. Kitchell, J.F., S.P. Cox, C.J. Harvey, T.B. Johnson, D.M. Mason, K.K. Schoen, K. Aydin, C.

Bronte, M.P. Ebener & M.J. Hansen. 2000. Sustainability of lake Superior fish community: interactions in a food web context. Ecosystems 3: 545-560.

López-López, E. & E. Díaz-Pardo. 1991. Cambios distribucionales en los peces del río de La

Laja (Cuenca Río Lerma), por efecto de disturbios ecológicos. Anales de la Escuela Nacional de Ciencias Biológicas 35: 91-116.

Lyons, J., S. Navarro-P., P.A. Cochran, E. Santana-C. & M. Guzmán-Arroyo. 1995. Index of

biotic integrity based on fish assemblages for the conservation of streams and rivers in West-Central Mexico. Conservation Biology 9: 569-584.

Lyons, J., G. González-Hernández, E. Soto-Galera & M. Guzmán-Arroyo. 1998. Decline of

freshwater fishes and fisheries in selected drainages of west central Mexico. Fisheries 23: 10-18.

Malmqvist, B. & S. Rundle. 2002. Threats to the running water ecosystems of the world.

Environmental Conservation 29: 134-153. Mercado-Silva, N., J. Lyons, E. Díaz-Pardo, A. Gutiérrez-Hernández, C.P. Ornelas-García, C.

Pedraza-Lara & M.J. Vander Zanden. In Press. Long -term changes in the fish assemblage of the Laja River, Guanajuato, central Mexico. Aquatic conservation: Marine and Freshwater Ecosystems.

Merritt, R.W. & K.W. Cummins. 1996. An introduction to the aquatic insects of North America.

Kendall/Hunt, Dubuque, Iowa. Minagawa, M. & E. Wada. 1984. Step-wise enrichment of 15N along food chains: further

evidence and the relation between δ15N and animal age. Geochimica et cosmochimica acta. 48: 1135-1140.

Nakano, S. & M. Murakami. 2001. Reciprocal subsidies: Dynamic interdependence between

terrestrial and aquatic food webs. Proceedings of the National Academy of Sciences USA 98: 166-170.

Paine, R.T. 1992. Food web analysis through field measurement of per capita interaction

strength. Nature 355: 73-75.

99

Peterson, B.J. & B. Fry. 1987. Stable studies in ecosystem studies. Annual Review of Ecology and Systematics 18: 293-320.

Power, M.E., D.G. Tilman, J.A. Estes, B.A. Menge, W.J. Bond, L.S. Mills, G. Daily, J.C.

Castilla, J. Lubchenco & R.T. Paine. 1996. Challenges in the quest for keystones. BioScience 46: 609-620.

Rahel, F.J. 2002. Homogenization of freshwater faunas. Annual Review of Ecology and

Systematics 33: 291-315. Sabo, J.L. & M.E. Power. 2002. River-watershed exchange: effects of riverine subsidies on

riparian lizards and their terrestial prey. Ecology 83: 1860-1869. Soto-Galera, E. 1993. Depredación selectiva de Chirostoma jordani sobre el zooplancton en el

Embalse Ignacio Alllende GTO. Master of Science, Instituto Poltécnico Nacional, Mexico City. 101 pp.

Soto-Galera, E., E. Diaz-Pardo, E. López -López & J. Lyons. 1998. Fish as indicators of

environmental quality in the Rio Lerma basin, México. Aquatic Ecosystem Health and Management 1: 267-276.

Thorp, J.H. & A.P. Covich. 2001. Ecology and classification of North American freshwater

invertebrates. Academic Press, San Diego, CA. 1056 pp. Vander Zanden, M.J., S. Chandra, B.C. Allen, J.E. Reuter & C.R. Goldman. 2003. Historical

food web structure and restoration of native aquatic communities in the Lake Tahoe (California-Nevada) basin. Ecosystems 6: 274-288.

Vander Zanden, M.J. & J.B. Rasmussen. 1999. Primary consumer δ13C and δ15N and the

trophic position of aquatic consumers. Ecology 80: 1395-1404. Vander Zanden, M.J., J.M. Casselman & J.B. Rasmussen. 1999. Stable isotopes evidence for the

food web consequences of species invasions in lakes. Nature 401: 464-467. Vander Zanden, M.J., J. Shuter Brian, N. Lester & J.B. Rasmussen. 1999a. Patterns of food chain

length in lakes: a stable isotope study. The American Naturalist 154: 406-415. Vanni, M.J., C. Luecke, J.F. Kitchell, Y. Allen, J. Temte & J.J. Magnuson. 1990. Effects on

lower trophic levels of massive fish mortality. Nature 344: 333-335. Ward, J.V. 1975. Downstream fate of zooplankton from a hypolimnial release mountain

reservoir. Verh. Internat. Verein. Limnol. 19: 1798-1804. Wischnath, L. 1993. Atlas of livebearers of the world. T.F.H. Publications, Neptune City, NJ.

336 pp.

100

Wootton, T.J., M.S. Parker & M.E. Power. 1996. Effects of disturbance on river food webs. Science 273: 1558-1560.

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Table 1. Sites sampled in 2003 and availability of primary consumer information per site.

------------------------------------------------------------------------------------------------------------ Site Site Name Site Type Primary Consumer Information ------------------------------------------------------------------------------------------------------------ 1 P. Jesús María Reservoir X 2 B.P. Jesús María Res. influenced X 3 La Quemada River X 4 La Laja River X 5 Adjuntas del Rio River NA 6 Atotonilco River NA 7 Cieneguilla Res. Influenced NA 8 P. Allende Reservoir NA 9 B.P. Allende Res. Influenced X 10 Rinconcillo Remedios River NA 11 Empalme Escobedo River X

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Table 2. Fishes of the Laja River (modified from Mercado-Silva et al. 2005). Acronym is used

as a reference for all figures. Origin and conservation status of the species is indicated: Native

(N), Introduced (I), Extirpated (EX) (not found in 2003 samples). ‘Field’ indicates that the

species was captured in 2003. S.I. indicates that stable isotopes samples were available for the

species. For Oreochromis sp. two species have been reported for the Laja (O. mossambicus and

O. aureus) but distinction among them is difficult; we thus make no distinction among them.

Family and Species

Acronym

Status

Field

S.I.

Cyprinidae Algansea tincella N X Carassius auratus CA I X X Cyprinus carpio CC I X X Notropis calientis N X Notropis sallaei EX Yuriria alta Y N X X Ctenopharyngodon idella I X Catostomidae Scartomyzon austrinus EX Ictaluridae Ictalurus dugesi EX Atherinopsidae Chirostoma jordani CJ N X X Chirostoma arge CR N X X Chirostoma humboldtianum EX Poecilidae Poecilia sphenops I Poeciliopsis infans P N X X Xiphophorus variatus XI I X X Goodeidae Goodea atripinnis G N X X Xenotoca variata XE N X X Alloophorus robustus EX Skiffia lermae EX Centrarchidae Lepomis macrochirus L I X X Lepomis cyanellus I (EX) Micropterus salmoides M I X X Cichlidae Oreochromis sp. O I X X

103

Table 3. Mean residual values used in the calculation of baselines for study sites. Values are

the site specific deviation from the river and reservoir general models. For sites 5 and 6, the

average of all invertebrates was used to obtain baseline.

------------------------------------------------------------------------------------------------------------

Site Mean residual (‰) SE of mean residual ------------------------------------------------------------------------------------------------------------ 1 -2.08 1.30 2 1.04 1.42 3 0.03 0.92 4 -0.01 0.62 5 - - 6 - - 7 -0.003 0.73 8 0.07 1.25 9 1.24 1.04 10 -0.003 0.73 11 -0.03 0.67

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FIGURE CAPTIONS

Figure 1. The Laja River (Guanajuato, Mexico). Marks indicate sites that were sampled for

stable isotopes. Site numbers correspond to those in Table 1.

Figure 2. Baseline curves and equations used to determine the trophic position of higher

consumers in the Laja river (Guanjauato, Mexico).

Figure 3. Food web diagrams for 11 sites in the Laja river (Guanajuato, Mexico). TP = trophic

position. δ13C values in ‰ . Acronyms correspond to species in Table 2. Values are mean TP

and δ13C ± 1 SE.

Figure 4. Food web diagrams of three different habitat types in the Laja river (Guanajuato,

Mexico). a) reservoirs, b) reservoir influenced, and c) river sites. Acronyms correspond to

species in Table 2. Values are mean TP and δ13C ± 1 SE

Figure 5. Distribution of fish δ13C values (‰ ) in 11 sites of the Laja river (Guanajuato,

Mexico) (± 1 SD). Site numbers correspond to those in Table 1. Sites arranged in upstream-

downstream order. Sites 1, 8 are reservoirs; sites 2,7,9 are reservoir-influenced; sites 3-6, 10-11

are river sites (see text).

Figure 6. Comparison of range of fish δ13C values (‰, ± 1 SE) among three site-types in the

Laja river (Guanajuato, Mexico).

Figure 7. Distribution of fish trophic position (TP) values (‰) and food web length (maximum

TP for fish) (triangles) in 11 sites of the Laja river (Guanajuato, Mexico). For TP distribution

mean ± 1 SD are shown. Site numbers correspond to those in Table 1. Sites arranged in

upstream-downstream order. Sites 1, 8 are reservoirs; sites 2,7,9 are reservoir-influenced; sites

3-6, 10-11 are river sites (see text).

105

Figure 1.

1 1

2 1

3 1

4 1

5 1

6 1

7

1

8

1

9 1 10

1 11

106

River Site Baseline

y = -0.5813x - 4.9188

56789

1011121314

-29 -27 -25 -23 -21 -19

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Reservoir Sites Baseline

y = -0.673x - 2.9535

6

8

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14

16

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-29 -27 -25 -23 -21 -19 -17 -15

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Figure 2

δ15Ncorrected = -0.5813 ∙ δ13Cfish – 4.9188

δ15Ncorrected = -0.673 ∙ δ13Cfish – 2.9535

δ 15

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107

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Figure

TPTP

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108

Figure 3 Continued

CACJ

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Trop

hic

posi

tion

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b) Reservoir influence

a) Reservoir

δ13C Figure 4

110

17181920212223242526

0 1 2 3 4 5 6 7 8 9 10 11

-

---------

Site Number

Upstream Downstream

δ 13

C

Figure 5

111

0

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4

6

8

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Reservoir ReservoirInfluenced

River

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ge o

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Figure 6

t = 8.325 p = 0.004

p > 0.05

112

1.8

2.3

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4.8

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5.8

0 1 2 3 4 5 6 7 8 9 10 11 12

Sites

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Posi

tion

Upstream Downstream

Figure 7

113

Appendix 1. Stable isotope values (δ13C and δ15 N) and trophic position (mean ± 1 SE) for

fishes in 11 sites of the Laja river. All samples from 2003.

Site TAXA n δ 15N SE TP SE δ 13C SE Site 1 Carassius auratus 4 11.46 0.15 2.10 0.09 -24.01 0.33 Chirostoma jordani 9 17.77 0.88 3.65 0.15 -25.55 0.60 Goodea atripinnis 5 12.51 0.64 2.46 0.15 -23.74 1.37 Lepomis macrochirus 12 13.72 0.37 3.15 0.21 -22.06 0.58 Micropterus salmoides 13 14.05 0.52 3.42 0.20 -21.19 0.71 Yuriria alta 6 15.01 0.75 3.72 0.08 -21.08 1.18 Site 2 Chirostoma jordani 6 16.21 0.17 2.56 0.06 -24.08 0.18 Cyprinus carpio 1 11.54 - 1.86 - -20.67 - Lepomis macrochirus 3 16.02 0.38 2.84 0.05 -22.42 0.40 Micropterus salmoides 6 17.07 0.38 3.26 0.10 -21.81 0.39 Yuriria alta 7 15.54 0.24 2.71 0.06 -22.33 0.27 Site 3 Chirostoma jordani 12 15.81 0.42 4.09 0.11 -23.40 0.16 Cyprinus carpio 1 9.84 - 2.79 - -20.74 - Goodea atripinnis 4 12.74 0.48 3.33 0.13 -22.52 0.40 Lepomis macrochirus 6 13.78 0.24 4.20 0.07 -19.22 0.30 Micropterus salmoides 7 15.20 1.02 4.55 0.15 -19.64 1.10 Poeciliopsis infans 1 13.49 - 3.89 - -20.58 - Yuriria alta 5 12.91 0.66 3.50 0.21 -21.87 0.42 Site 4 Carassius auratus 1 10.54 - 3.14 - -20.00 - Goodea atripinnis 6 10.70 0.61 2.96 0.11 -21.28 0.48 Xenotoca variata 6 11.83 0.28 3.11 0.06 -22.38 0.35 Yuriria alta 5 12.11 0.29 3.42 0.07 -21.00 0.51 Site 5 Carassius auratus 3 9.53 1.10 2.47 0.32 -18.95 0.29 Chirostoma jordani 8 14.33 0.68 3.88 0.20 -21.74 0.45 Goodea atripinnis 7 12.13 0.75 3.23 0.22 -21.45 1.16 Lepomis macrochirus 4 12.48 0.52 3.34 0.15 -20.23 0.28 Micropterus salmoides 6 14.75 0.31 4.00 0.09 -19.93 0.17 Oreochromis sp. 4 10.47 0.21 2.75 0.06 -20.53 0.24 Poeciliopsis infans 1 12.17 - 3.24 - -21.01 - Xenotoca variata 5 12.14 0.50 3.23 0.15 -19.84 0.53 Xiphophorus variatus 1 11.30 - 2.99 - -22.56 - Yuriria alta 5 11.66 0.66 3.09 0.19 -19.24 0.38 Site 6 Chirostoma jordani 2 16.10 1.08 3.63 0.32 -22.08 0.83

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Cyprinus carpio 2 13.87 0.06 2.97 0.02 -21.98 0.11 Goodea atripinnis 1 16.63 - 3.78 - -20.87 - Oreochromis sp. 2 12.97 2.79 2.71 0.82 -19.94 0.51 Poeciliopsis infans 6 16.80 0.11 3.83 0.03 -21.32 0.38 Xenotoca variata 6 17.29 0.21 3.98 0.06 -21.34 0.10 Yuriria alta 4 13.75 2.11 2.93 0.62 -21.17 0.81 Site 7 Carassius auratus 1 13.45 - 4.03 - -19.74 - Chirostoma jordani 4 17.96 0.14 4.45 0.05 -25.01 0.30 Cyprinus carpio 2 13.39 0.21 3.07 0.20 -25.25 0.81 Goodea atripinnis 3 14.92 1.02 3.84 0.54 -23.40 1.51 Poeciliopsis infans 6 12.76 0.21 3.36 0.13 -22.45 0.65 Xenotoca variata 9 14.89 0.46 3.84 0.19 -23.30 0.43 Xiphophorus variatus 1 16.18 - 4.51 - -21.60 - Yuriria alta 7 14.45 0.70 3.68 0.16 -23.49 0.75 Site 8 Carassius auratus 6 13.42 0.32 2.34 0.19 -22.52 0.69 Chirostoma jordani 10 17.97 0.19 3.33 0.06 -24.28 0.17 Cyprinus carpio 3 12.64 0.68 1.85 0.20 -23.82 1.62 Goodea atripinnis 10 11.04 0.20 2.06 0.15 -20.38 0.65 Micropterus salmoides 1 15.99 - 3.38 - -21.09 - Oreochromis sp. 5 12.38 0.86 2.50 0.12 -20.16 1.40 Poeciliopsis infans 5 12.26 0.67 2.80 0.20 -18.47 1.09 Xenotoca variata 10 15.18 0.38 3.26 0.13 -20.46 0.24 Xiphophorus variatus 6 11.50 0.72 2.01 0.25 -21.30 0.37 Yuriria alta 5 15.37 0.35 2.89 0.08 -22.61 0.29 Site 9 Carassius auratus 1 14.15 - 1.42 - -26.50 - Chirostoma jordani 6 19.51 0.20 3.36 0.08 -24.66 0.18 Goodea atripinnis 6 17.33 0.45 3.35 0.10 -21.50 0.21 Lepomis macrochirus 1 18.46 - 3.52 - -22.30 - Oreochromis sp. 3 15.44 1.19 2.64 0.31 -22.24 0.24 Poeciliopsis infans 6 17.56 0.25 3.28 0.09 -22.19 0.33 Xenotoca variata 6 16.79 1.19 3.00 0.54 -22.43 0.96 Xiphophorus variatus 6 18.43 0.53 3.47 0.21 -22.53 1.02 Yuriria alta 1 15.71 - 2.36 - -24.05 - Site 10 Chirostoma arge 6 14.14 0.71 3.62 0.15 -23.35 0.43 Chirostoma jordani 4 15.61 1.16 3.94 0.29 -23.98 0.37 Goodea atripinnis 9 12.86 0.34 3.30 0.08 -23.02 0.24 Poeciliopsis infans 8 12.14 0.31 3.20 0.07 -22.37 0.35 Xenotoca variata 7 12.83 0.37 3.25 0.15 -23.24 0.58 Xiphophorus variatus 7 12.15 0.33 3.06 0.09 -23.18 0.36 Yuriria alta 7 12.19 0.24 3.17 0.10 -22.63 0.32 Site 11 Chirostoma jordani 3 16.44 0.29 4.25 0.03 -23.68 0.35

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Goodea atripinnis 4 10.63 0.40 3.05 0.19 -20.71 0.56 Lepomis macrochirus 1 13.84 - 4.06 - -20.32 - Poeciliopsis infans 1 11.78 - 3.41 - -20.56 - Xiphophorus variatus 3 11.89 0.37 3.23 0.11 -21.79 0.61 Yuriria alta 1 11.25 - 3.02 - -21.91 -

116

Appendix 2. Stable isotope values (δ13C and δ15 N, mean ± 1 SE) for invertebrates in two

different habitat types (reservoir-reservoir influenced sites and river sites) of the Laja river.

Samples grouped by site.

Site type and number Taxa n δ15N SE δ13C se C Sample

Type Reservoir Sites Site 1 Amphipoda 1 7.87 - -17.75 - PC Coenagrionidae 1 12.12 - -20.79 - PI Corixidae 1 9.22 - -18.89 - PI Diptera 1 7.95 - -17.93 - PI Ephemerellidae 1 7.61 - -17.28 - PC Hydrophilidae larv. 1 9.68 - -18.80 - PC Naucoridae 1 10.48 - -19.41 - PI Zooplankton 2 10.68 3.88 -25.99 0.71 PC Site 2 Chironomidae 1 14.99 - -25.25 - PI Coleoptera 1 9.67 - -13.91 - PI Diptera 1 9.62 - -19.39 - PI Ephemeroptera 1 15.05 - -21.37 - PC Hemiptera 1 10.62 - -21.55 - PI Isopoda 1 7.92 - -20.23 - PC Simulidae 1 14.47 - -25.08 - PC Trichoptera 1 18.30 - -27.46 - PC Site 7 Coenagrionidae 1 13.33 - -29.04 - PI Corixidae 1 9.20 - -23.19 - PI Gastropoda 1 11.42 - -22.92 - PC Hemiptera 1 10.03 - -25.34 - PI Naucoridae 1 7.40 - -14.55 - PI Site 8 Hemiptera 1 14.23 - -22.71 - PI Zooplankton 1 9.52 - -24.45 - PC Site 9 Belostomatidae 1 19.14 - -21.73 - PI

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Diptera 1 12.32 - -28.79 - PI Elmidae 1 11.46 - -21.69 - PC Ephemeroptera 2 12.98 0.86 -23.11 2.34 PC Gerridae 1 11.38 - -21.72 - PI Nepidae 1 9.51 - -21.76 - PI Notonectidae 1 9.73 - -25.87 - PI Simulidae 1 16.45 - -23.45 - PC Trichoptera 1 13.99 - -22.15 - PC River Sites Site 3 Belostomatidae 2 10.78 2.17 -24.63 0.13 PI Chironomidae 1 11.49 - -27.74 - PI Corixidae 1 9.43 - -26.95 - PI Diptera 3 10.59 0.58 -25.12 0.89 PI Dytiscidae 1 11.71 - -27.25 - PI Ephemeroptera 3 10.43 1.67 -26.37 0.90 PC Gerridae 1 10.38 - -23.26 - PI Hemiptera 1 9.33 - -24.95 - PI Odonata 1 10.40 - -26.47 - PI Trichoptera 1 10.92 - -24.73 - PC Site 4 Belostomatidae 2 9.06 0.51 -22.47 0.73 PI Chironomidae 1 10.85 - -26.18 - PI Coleoptera 1 5.73 - -16.23 - PI Corixidae 1 8.53 - -24.51 - PI Elmidae 1 9.34 - -26.60 - PC Gyrinidae 1 6.88 - -23.50 - PI Hemiptera 1 8.57 - -23.89 - PI Simulidae 1 12.31 - -26.94 - PC Tadpole 1 7.07 - -19.96 - PC Site 5 Belostomatidae 1 10.42 - -20.96 - PI Dytiscidae 1 7.80 - -29.26 - PI Dytiscidae larvae 1 8.89 - -21.43 - PI Elmidae 1 9.51 - -20.56 - PC Ephemeroptera 1 7.92 - -22.80 - PC Hemiptera 1 9.81 - -26.97 - PI Homopteran 1 1.95 - -11.40 - PI Hydrophilidae larv. 1 8.27 - -21.59 - PI Lepidoptera 1 4.40 - -12.80 - PC Odonata 1 10.47 - -25.30 - PI

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Site 6 Belostomatidae 3 11.65 1.37 -23.43 0.56 PI Chaoborinae 1 12.00 - -23.73 - PI Corixidae 1 8.78 - -25.99 - PI Dytiscidae 1 5.41 - -25.50 - PI Ephemeroptera 1 5.97 - -27.33 - PC Gerridae 1 10.34 - -20.96 - PI Hydrometidae 1 10.17 - -21.12 - PI Mesoveliidae 1 10.39 - -27.04 - PI Notonectidae 1 6.57 - -28.52 - PI Odonata 2 14.09 2.15 -22.89 0.18 PI Site 10 Belostomatidae 1 8.69 - -24.89 - PI Chironomidae 1 7.07 - -26.21 - PI Coleoptera 1 7.62 - -25.11 - PI Gerridae 1 10.76 - -24.95 - PI Gomphidae 1 9.53 - -21.18 - PI Isopoda 1 6.46 - -26.65 - PC Odonata 2 11.46 0.52 -30.50 0.17 PI Simulidae 1 12.57 - -40.33 - PC Trichoptera 2 12.71 0.00 -36.46 1.30 PC Site 11 Chaoborinae 1 2.67 - -24.63 - PI Chironomidae 1 7.42 - -22.01 - PI Coenagrionidae 1 6.37 - -23.06 - PI Coleoptera 1 4.99 - -18.62 - PI Corixidae 1 8.02 - -21.16 - PI Culicidae 1 8.88 - -21.13 - PC Diptera 1 5.56 - -22.09 - PI Dytiscidae 1 5.46 - -22.08 - PI Ephemeroptera 1 7.44 - -23.63 - PC Gastropoda 1 4.96 - -20.83 - PC Notonectidae 2 9.10 3.41 -27.11 1.80 PI Syrphidae 1 5.51 - -19.56 - PC Zooplankton 1 6.18 - -21.94 - PC

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CHAPTER V

FORECASTING THE SPREAD OF INVASIVE RAINBOW SMELT (OSMERUS MORDAX) IN THE

LAURENTIAN GREAT LAKES REGION OF NORTH AMERICA

ABSTRACT

Rainbow smelt (Osmerus mordax) has invaded many North American lakes, often resulting in

the extirpation of native fish populations. Yet, their invasion is incipient and provides the

rationale for identifying ecosystems likely to be invaded, and where management and prevention

efforts should be focused. Using habitat data from 354 lakes in the native range for smelt in

southern Maine, we constructed a classification tree model predicting smelt presence/absence.

Maximum lake depth, lake area and Secchi depth (surrogate of lake productivity) were the most

important predictors. We then applied this model to identifying invasion-prone lakes in three

regions outside smelt’s native range: northern Maine (52 of 244 lakes in the non-native range),

Ontario (4,447 of 8,110), and Wisconsin (553 of 5,164). We further identified a subset of lakes

that are vulnerable to impact based on the presence of lake trout, brook trout, walleye, yellow

perch or Coregonids (i.e. cisco), species known to be impacted by rainbow smelt. Ninety four

percent of invasion-prone lakes in the non-native range in Maine have potential for impact, as do

94% and 58% of Ontario and Wisconsin invasion-prone lakes, respectively. This modeling

approach can be applied to other invaders and regions to identify invasion-prone ecosystems,

thus aiding in the management of exotic species and the efficient allocation of invasive species

mitigation and prevention resources.

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INTRODUCTION

The introduction of non-indigenous species is a major threat to freshwater ecosystems

and biodiversity (Richter et al. 1997; Claudi & Leach 1999), and the effects on ecosystem

services and associated economic impacts can be substantial (Pimentel et al. 2000; Leung et al.

2002). The freshwaters of North America are vulnerable to non-indigenous species

introductions, which pose serious threats to native biodiversity through competition, predation,

and hybridization (Taylor et al. 1984; Moyle 1986; Ricciardi & Rasmusen 1999; Lodge et al.

1998; Vander Zanden et al. 1999; Perry et al. 2002). Managing aquatic ecosystems in an age of

invaders will require new tools for forecasting invader spread. Success in predicting spread and

vulnerability will allow resources to be allocated to where they will achieve the greatest benefit

(Vander Zanden et al. 2004).

Rainbow smelt (Osmerus mordax Mitchill) is an anadromous fish species that is

indigenous to coastal freshwater and estuarine environments of northeastern North America.

Beginning in the early part of the 20th century, however, its freshwater range increased

dramatically through both intentional and accidental introductions (Nellbring 1989), resulting in

widespread invasion of the Laurentian Great Lakes by the 1940s, and secondary spread into

smaller, inland lakes throughout the Great Lakes, Mississippi and Hudson Bay watersheds during

the past 3 decades (Becker 1983; Evans & Loftus 1987; McLain 1991; Franzin et al. 1994;

Hrabik & Magnuson 1999). The main mechanisms of dispersal for these pelagic, carnivorous,

cool water fish are legal and illegal introductions, accidental bait bucket transfers, and dispersal

through drainage networks (Evans & Loftus 1987). Smelt have also been introduced as forage

fish for top carnivore game fishes (Johnston & Goettl 1999), but such introductions are now

uncommon. Negative impacts of rainbow smelt on native fauna include predation and

121

competition with native species (Selgeby et al. 1978; Loftus & Hulsman 1986; Evans & Loftus

1987; Evans & Waring 1987; McLain 1991; Franzin et al. 1994; Hrabik et al. 1998; Hrabik et al.

2001). Moreover, some native piscivores appear to exhibit strong preferences for rainbow smelt

over native forage species (Krueger & Hrabik 2005), and there are indications that changing to a

diet of rainbow smelt biomagnifies contaminants such as mercury and organochlorines (Vander

Zanden & Rasmussen 1996; Swanson et al. 2003, Tom Johnston, Queen’s University, pers.

com.).

In light of the well-documented impacts, the ongoing spread of rainbow smelt, and the

immense number of inland lakes in North America, it is imperative that lakes most prone to

invasion and impact by O. mordax are identified to help optimize the use of the scarce resources

dedicated to invasive species prevention, the conservation of native and endemic species, and the

protection of ecological services that lakes provide (Kolar & Lodge 2001). This is particularly

important given that once fish species invade and establish self-sustaining populations,

eradication is near impossible and impacts on native fish communities are common (Lodge

1993). In aquatic ecosystems, predictive models of species invasions have been used for

smallmouth bass, Micropterus dolomieu in Ontario lakes (Vander Zanden et al. 2004), and zebra

mussel Dreissena polymorpha (Koutnik & Padilla 1994) and spiny waterflea Bythothrephes

longimanus in North American inland lakes (MacIsaac et al. 2004). Hrabik & Magnuson (1999)

modeled the dispersal of rainbow smelt in a single watershed in Vilas County, Wisconsin based

on three previously published variables (pH, minimum lake depth, and lake surface area) which

appear to limit smelt distributions (Evans & Loftus 1987).

Our objectives were to develop a model for rainbow smelt presence/absence based on

their distribution in their native range in coastal regions of Maine (USA) and to apply model

122

predictions in other geographic areas to forecast future invasion potential. We worked explicitly

within the theoretical framework of ecological niche modeling by assuming that species will be

able to establish populations in areas that match the ecological conditions within their native

range (Peterson 2003). Based on the stenothermic nature of smelt we expected lake size and

depth to be important predictors of smelt occurrence, in addition to pH and shoreline perimeter.

We validated our model using areas outside smelt’s native range in Maine and applied it to lakes

of Ontario (Canada) and Wisconsin (USA) to identify lakes that are vulnerable to invasion, and

lakes with the greatest potential for ecological impact on native fish communities. To achieve

these objectives, we used large data sets containing information on fish species composition and

lake characteristics compiled by state and federal agencies in Maine, Ontario and Wisconsin.

METHODS

Study Systems

Rainbow smelt are native as glacial relict populations in lakes of south-southeastern

Maine, but have established in lakes throughout other areas of the state because of legal and

illegal introductions (David Halliwell and Peter Vaux, Maine Department of Environmental

Protection and University of Maine, pers. com.; Halliwell et al. 2001; Halliwell 2003) (Fig. 1).

Smelt occur naturally in 200 lakes and have been introduced into approximately 221 lakes

throughout Maine (Appendix). Many of these introductions were deliberately made by fisheries

managers to provide forage fish for landlocked Atlantic salmon stocks, but others were illegal or

accidental. In Maine, non-native smelt have negatively affected brook trout (Salvelinus

frontinalis) and lake whitefish populations (Coregonus clupeaformis) (Halliwell 2003).

Indigenous populations of rainbow smelt occurred in eastern Ontario (Evans & Loftus

1987). It is suggested that from these populations and from nonnative populations in the Great

123

Lakes, rainbow smelt have spread to new lakes as a result of human transport and dispersal

through connected waterways (Evans & Loftus 1987). In the late 1980s, rainbow smelt were

present in 194 Ontario lakes, but the number of invaded lakes has increased as smelt continue to

spread in Northwestern Ontario and Eastern Manitoba (Franzin, et al 1994; Swanson et al. 2003).

Our datasets include 126 Ontario lakes where smelt are currently present (Appendix). In

Ontario, smelt introductions have had adverse impacts on pelagic fishes, including lake whitefish

(Coregonus clupeaformis), cisco (Coregonus artedi), walleye (Sander vitreus), and lake trout

(Salvelinus namaycush) (Evans & Loftus 1987).

Rainbow smelt established in Lake Michigan and Lake Superior in the 1930’s, and by

1968 a number of inland lakes in Wisconsin had established non-native populations (Becker

1983). As of 2005, 24 lakes are known to support rainbow smelt in Wisconsin (Mercado-Silva,

unpublished data) (Appendix). These invasions have caused the possible extirpation of yellow

perch (Perca flavescens) and cisco (Coregonus artedi) in two well-studied lakes (McLain 1991;

Hrabik et al. 1998; Hrabik & Magnuson 1999), led to declines in walleye recruitment (Chapter 2,

this volume), and been associated with ecosystem-wide impacts (Beisner et al. 2003).

Predictors of Rainbow Smelt Occurrence

We used five environmental variables as predictors of smelt presence/absence: lake area

(mean = 265 ha, standard deviation [s.d.] = 634.78), maximum lake depth (mean = 12.93 m, s.d.

= 9.72), pH (mean 7.19 standard units, 95% C.I. = 6.04-7.34), Secchi depth (mean = 5.17 m, s.d.

= 1.80), and lake shoreline perimeter (mean 11.89 km, s.d. = 17.31). These variables were

available for lakes from the three study regions, and some have been identified as key predictors

of smelt presence in Maine (Halliwell et al. 2001), Ontario (Evans & Loftus 1987) and

Wisconsin (Hrabik & Magnuson 1999). Other variables likely to be important for smelt, such as

124

hypolimnion temperature and dissolved oxygen concentrations, were not available for a large

number of lakes. Thus, our analysis is limited to those few variables shared among data sets.

The distribution of Coregonids, lake trout, brook trout, walleye and yellow perch was

also examined in each study area. We focused on these taxa because they have similar thermal

and habitat requirements as rainbow smelt, and impacts of smelt have been previously

documented in the Great Lakes region and Maine (Evans & Loftus 1987; Page & Burr 1991;

Hrabik et al. 1998; Halliwell et al. 2001). Therefore, we were concerned with the distribution of

these five taxa and the degree of co-occurrence with predicted occurrence of rainbow smelt.

Data Sources

Maine - Limnological data and fish (rainbow smelt, lake trout, brook trout, walleye,

yellow perch and Coregonids [either Coregonus clupeaformis or Prosopium cylindraecum])

distribution data for Maine lakes were obtained from databases managed by the Public

Educational Access to Environmental Information in Maine (PEARL). PEARL is a data

repository maintained by the Senator George J. Mitchell Center (GMC) for Environmental and

Watershed Research and the Department of Spatial Information Science and Engineering,

University of Maine (UM). The native range of rainbow smelt in Maine was determined from

data in Halliwell et al. (2001), Halliwell (2003), maps created by the United States Geological

Survey (USGS, 2000) and personal communications with David Halliwell and Peter Vaux

(Maine Department of Environmental Protection [M-DEP] and UM, respectively). Thus, we

estimated 354 Maine lakes to be within the native range of rainbow smelt (Fig. 1). We assume

the native range of rainbow smelt to be saturated (i.e. smelt are present in all suitable lakes and

absent from non-suitable lakes; they have had the capacity for reaching all lakes within this

area).

125

For fish distribution data, we considered only lakes that had been sampled, and for which

at least one species was recorded in the available datasets. Lakes where fishes were not sampled

were removed from analysis.

Lake morphometry and physical-chemistry variables (lake area, maximum lake depth,

Secchi depth, shoreline perimeter, and pH) were obtained for 819 lakes from datasets in PEARL

(provided by the Maine State Department of Inland Fisheries and Wildlife, M-DEP, the

Volunteer Lake Monitoring Project, GMC, U.S. Environmental Protection Agency, and the

Acadia National Park Lake Monitoring project of the National Park Service) and from

geographic information provided by M-DEP. Shoreline perimeter and lake area were calculated

using geographic information systems (GIS) (ESRI 2002). Mean Secchi depth (available for 709

lakes) and pH for each lake were calculated from values in several PEARL datasets, prior to

which we checked for inter-annual and seasonal variations before calculating a final mean value

per lake.

Ontario - For species distribution, morphological and physical-chemical attributes of

Ontario lakes we used a database integrated by Vander Zanden et al. (2004) which was expanded

by obtaining species distribution and lake information from the Fish Species Distribution Data

System of the Ontario Ministry of Natural Resources (OMNR), N. Mandrak (OMNR,

unpublished data) and the OMNR Lake Inventory Database. Information was compiled for all

variables for 8,236 Ontario lakes. From fish presence/absence data, we obtained the distribution

of lake trout, brook trout, walleye, yellow perch and Coregonids. All species in the Coregoninae

subfamily that are known for Ontario were used to create this group (Coregonus sp., Coregonus

clupeaformis, C. artedi, C. hoyi, C. nipigon, and Prosopium cylindraecum).

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Wisconsin - Rainbow smelt, lake trout, brook trout, walleye, yellow perch and Coregonid

distributions in Wisconsin were obtained by reviewing published literature (Becker 1983; Colby

et al. 1987; Hrabik & Magnuson 1999; Lyons et al. 2000; Krueger & Hrabik 2005), interviews

with Wisconsin Department of Natural Resources (WDNR) regional fish managers (see

acknowledgement section), datasets in the Wisconsin Aquatic Gap Mapping Application

(WDNR) (http://web2.er.usgs.gov/wdnrfish/) and our own unpublished observations.

We compiled information for Wisconsin inland lakes from various sources. Maximum

lake depth, area and Secchi depth were obtained from an extensive lake dataset at the WDNR

(Kathy E. Webster [WDNR, pers. comm.]), pH data were obtained from the STORET database

of the US – Environmental Protection Agency (STORET-EPA), and shoreline perimeter was

calculated using GIS (ESRI 2002) from data obtained from the WDNR. We were able to obtain

information for as many as 5,188 lakes in Wisconsin, but data availability was uneven among

lakes (lake area = 5,188 lakes, Secchi depth = 3,891, pH =1,190, maximum depth = 5,142,

shoreline perimeter = 1,190).

Model Development and Predictions

We used classification trees (Breiman et al. 1984; CART 2002) to model rainbow smelt

presence and absence within their native range in Maine. This methodology uses a recursive

partitioning algorithm to repeatedly partition the data set according to the explanatory variables

into a nested series of mutually exclusive groups each of which is as homogeneous as possible

with respect to the presence or absence of rainbow smelt (see De’ath & Fabricius 2000, for a

comprehensive review). The outcome is a decision tree that represents the numerical

relationships in an interpretable, hierarchical model. We used the Gini impurity criterion to

determine the optimal variable splits, and determined the optimal size of the decision tree by

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constructing a series of cross-validated trees and selecting the smallest tree based on the 1-SE

rule (De’ath & Fabricius 2000). The relative importance of each habitat variable (s) was

estimated by summing the changes in misclassification (also called impurity) for each surrogate

split across all nodes and was expressed on a 0–100 scale (see Breiman et al. 1984). While there

are a number of statistical approaches available to model species presence/absence data, we

chose classification trees because of their demonstrated advantages over traditional approaches

(see Olden & Jackson 2002).

We used 10-fold cross validation to assess model predictive performance within rainbow

smelt’s native range of distribution (n=354 lakes), and applied the final model to information on

lakes outside the native range of smelt in Maine (n=465 lakes), Ontario (n=8,236 lakes), and

Wisconsin (n=5,188 lakes). Cohen’s kappa coefficient of agreement was used to assess the

classification performance of the classification tree compared to random expectations (Titus et

al. 1984). Following Fielding & Bell (1997) we partitioned the overall classification success of

the model into a confusion matrix that defines the total number of lakes where smelt were not

predicted by the model, but observed in our datasets (False Absence); the total number of lakes

where smelt were predicted by the model and observed (True Presence); the total number of

lakes where smelt were not predicted and not observed (True Absence); and the number of lakes

where smelt were predicted by the model but not observed (False Presence). Lakes classified as

false presence are considered vulnerable to invasion based on environmental suitability

according to the lake attributes. Next, we calculated three metrics of model performance: (1) the

overall classification performance of the model calculated as the percentage of sites where the

model correctly predicted the presence or absence of smelt, (2) the ability of the model to

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correctly predict smelt presence (termed model sensitivity), and (3) the ability of the model to

correctly predict smelt absence (termed model specificity).

For lakes categorized as false presences in each study area (i.e., lakes environmentally

suitable for smelt presence), we calculated the percentage of lakes that contained lake trout,

walleye, yellow perch, Coregonids, and/or brook trout. These lakes were considered highly

vulnerable to impact by rainbow smelt. We present the list of lakes that are classified as false

presences for each region and those with highest impact vulnerability in an electronic data

archive (see Appendix).

RESULTS

Of 354 lakes used for model development (rainbow smelt native range in Maine), 200

supported rainbow smelt. The model that best explained variability in rainbow smelt distribution

within their native range consisted of 6 splits and seven terminal nodes (Fig. 2). The most

important variable in defining the splits in the tree was maximum lake depth (score [s] = 100).

Closest competitor variables were Secchi depth (s = 52.26) and lake area (s = 51.41). Shoreline

length (s = 44.07), and pH (s = 12.59) were minor variables in defining tree architecture. The

tree had a low misclassification rate = 14%. The model correctly predicted smelt presence in 165

of 200 lakes (Sensitivity = 82.5%) and smelt absence in 139 of 154 lakes (Specificity = 90.3%).

Smelt presence was predicted for lakes deeper than 12.3 m and a lake surface area ≥ 21 ha, and

for lakes with a maximum depth between 8.9 and 12.3 m and a lake surface area ≥ 102.8 ha or a

Secchi depth ≥ 6.1 m. For lakes with area < 21.2 ha, smelt were predicted to be present if lakes

were deeper than 20 m. Smelt absence was predicted for lakes with maximum depth shallower

than 9 m, an area < 102.8 ha and Secchi depth < 6.1 m. However, smelt would also be absent if

lake area was ≥ 12.3 ha, but smaller than 21.2 ha, and shallower than 19.9m (Fig. 2).

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Predicted Distributions of Rainbow Smelt

The misclassification rate of the model was 20% when applied to Maine lakes outside of

the native range of rainbow smelt. The model accurately predicted smelt occurrences in 80% of

the lakes where they were present and 79% of lakes where they were absent (Sensitivity = 80%;

Specificity = 79%). Fifty-two lakes were categorized as false presences (Table 1) (Fig. 3,

Appendix).

Our Ontario dataset contained 126 lakes where smelt are currently present (Table 1) (Fig.

4, Appendix). For lakes in Ontario, the model predicted smelt presence in 87% of the smelt

lakes (Sensitivity = 87.30%). Specificity of the model was only 45%. In Ontario, the model

identified 4,447 new lakes that are capable of being invaded by smelt (false presences).

For Wisconsin lakes, the model had a misclassification rate of 11%. The model correctly

predicted presence and absence of smelt (Sensitivity = 87.5%; Specificity = 89%). The model

identified 553 new lakes with the potential to be invaded by smelt in Wisconsin (Table 1) (Fig. 5,

Appendix).

In both regions of Maine and in Ontario, roughly 55-60% of lakes currently contain, or

are predicted to support smelt. In contrast, only 10% of Wisconsin lakes contain, or are

predicted to support smelt (Table 2). In both native and non-native regions of Maine, the

landscape is ‘saturated’ with smelt: the majority of lakes that could potentially support smelt

presently do (Table 2). In Wisconsin and Ontario, less than 5% of lakes capable of supporting

smelt currently do, indicating vast potential for future spread in both areas (Table 2).

Potential for Impacts on Native Species

Of 4,447 Ontario lakes where smelt were predicted to occur (false presences), 94% have

at least one of the fish taxa known to be impacted by rainbow smelt (Coregonids, lake trout,

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walleye, yellow perch, and brook trout). These lakes are considered vulnerable to impacts by

future smelt invasion. Forty nine of the 52 (94%) predicted lakes in the non-native range of

Maine, and all 15 in the native range had at least one of these taxa. In Wisconsin, 58% of 553

predicted lakes (n = 323) had vulnerable taxa (Table 3). In Maine the most widely distributed

taxa in predicted lakes was brook trout. In Ontario, yellow perch were the most widely

distributed (in 3,118 of 4,447 predicted lakes), followed by Coregonids (2,337 lakes) and

walleye (1,607). In Wisconsin, most of the vulnerable lakes had yellow perch (n = 310) and

walleye (232), while Coregonids, brook trout, lake trout were present in 41, 26 and 9 lakes

respectively (Table 3).

DISCUSSION

A critical area of research in the field of invasion biology is the development of

predictive models that can assist in the prognosis of invasive species spread and impact on native

species and ecosystems (Vermeij 1996; Williamson 1996; Kolar & Lodge 2001; Peterson 2003).

In aquatic ecosystems, the management and control of invasive species hinges on our ability to

identify areas and specific water bodies that are vulnerable to invasion accurately. Ecological

forecasting of biological invasions is essential for the efficient allocation of prevention and

restoration efforts (Vander Zanden et al. 2004). In lake-rich regions such as our study area,

invasion-prone systems could become the target of focused management efforts for preventing

future introductions or limiting impact if they are already established.

Results from our study show that rainbow smelt presence/absence in their native range is

highly predictable as a function of morphological and physical-chemical variables. The model

exhibited very high specificity, indicating that environmentally-suitable lakes tend to support

populations of rainbow smelt, thus supporting the use of native range data in model

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development. Likewise, in the other study areas, the model correctly predicted smelt presence

for lakes where smelt have been observed, as well as accurately classified smelt absences in

lakes that have been sampled and in which smelt have not been collected. A notable exception

was the relatively higher misclassification rates for Ontario lakes, a consequence of a large

number of lakes where smelt are predicted to occur, but have not (yet) been observed. In

contrast to Ontario, the list of lakes with potential to be invaded in Maine and Wisconsin is low

relative to the total number of lakes in each area.

Using environmental information to model species distributions in their native range to

predict their potential future distribution has been an increasingly used tool in conservation

biology (see review by Peterson 2003), yet only a few studies have coupled these models with

predictions regarding the potential impacts of invaders on native communities (e.g., Hrabik &

Magnuson 1999; Vander Zanden et al. 2004). Our modeling efforts join these studies in trying to

identify specific water bodies where exotics are predicted to occur and cause impact, thereby

providing a multi-tiered management tool for invasive species prevention efforts. By using the

cumulative pool of literature that has documented the ecological impacts of rainbow smelt we

were able to identify not only lakes that are prone to smelt invasion, but also where impacts on

valued native fishes are likely to occur. Yellow perch, walleye, lake trout, brook trout, and

Coregonids have all been shown to be impacted by smelt introduction as a result of niche overlap

with the invader (Selgeby et al. 1978; Becker 1983; Evans & Loftus 1987; Franzin et al. 1994;

Hrabik et al 1998). Such overlap makes the presence of these species a useful indicator of

potential impact of rainbow smelt.

In Maine and Ontario the majority of lakes identified as suitable for rainbow smelt also

contained at least one native species considered vulnerable to invasion. In Wisconsin this

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approach identified a reduced number of lakes (323 of 553) where impacts on salmonids or

percids could be expected if invaded by rainbow smelt. Prevention efforts should be focused on

these lakes.

The most prevalent species that could be affected by rainbow smelt throughout our study

areas was yellow perch. However, given their societal value, vulnerability could be more

relevant in lakes with brook trout, walleye or lake trout populations. Lake trout and walleye

support important fisheries in Ontario and Wisconsin respectively (BIA 2003; Lester et al. 2003)

and are present in about half of the lakes vulnerable to smelt invasion in each region. In Maine,

brook trout supports a valuable fishery and most vulnerable lakes have the species. Coregonids

are found throughout all regions, but they are most prevalent in Ontario lakes. Using the

comparative dollar-value of fisheries in each region as criteria for the differential allocation of

invasion prevention efforts could further help direct which lakes to target.

We recognize that other species exist for which impacts from rainbow smelt have been

documented which we have not used to assess lake impact vulnerability (i.e. burbot [Lota lota]

[Brandt & Madon 1986]). We have only used species with reported impacts in inland lakes (i.e.

not the Laurentian Great Lakes) in our study. In addition, it is possible that even if invaded, the

fish communities in some lakes would not undergo significant changes. For example, rainbow

smelt impacts on walleye have been reported for a number of lakes in Wisconsin (Steve Gilbert

WDNR, pers. com.), but where walleye are heavily stocked, smelt impact on native fish

communities appears to be greatly reduced or curtailed (Roth 2005; Krueger & Hrabik 2005).

However, our approach is to provide a prioritization of those lakes that might be vulnerable to

rainbow smelt invasions based on available data.

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We have limited our invasion predictions to two large areas for which we were able to

obtain extensive data but our predictions can be easily applied to other areas of the Great Lakes

region (Michigan, Minnesota, Québec, etc.) and, in general, to other areas where rainbow smelt

could expand in the future (see Franzin et al. 1994). Rainbow smelt have been purposefully

introduced into reservoirs and lakes in other areas of the USA and Canada (e.g., Colorado,

[Johnson and Goettl 1999]; and Manitoba [Franzin, Fisheries and Oceans Canada, pers. com.])

from which secondary spread could occur.

Our results suggest that the majority of smelt habitable lakes (81%) have already been

invaded in Maine (high saturation) although it is possible that smelt exist in these lakes at low

densities or that local extinctions have occurred resulting from biotic or abiotic conditions. In

contrast, Wisconsin and Ontario have a low saturation of smelt lakes (4% and 3% respectively),

prompting efforts for invasion prevention in a large number of vulnerable lakes (Table 2). Fifty

six percent of all lakes in Ontario are currently invaded or predicted to support smelt. In

Wisconsin, with eleven percent of all lakes currently invaded or predicted to support smelt, and

only 24 lakes invaded thus far, it appears that the timescale for invasions is relatively long and

that the number of source populations is still low relative to the number of environmentally-

suitable lakes (Hrabik & Magnuson 1999). To aid in further focusing invasion prevention efforts

in both regions other factors need to be considered. Evans & Loftus (1987) suggest that urban

and cottage development is strongly associated with the presence of smelt. Vulnerable lakes

identified here could be ranked according to the degree of human impact and accessibility (i.e.

road access) or fisheries value to create a subset of lakes where monitoring efforts can be

directed.

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Our model relies on the assumption that the conditions that make a lake suitable for smelt

in their native range apply to areas where they are currently expanding. However, it is possible

that smelt in invaded regions may occupy lakes that are not ‘typical’ in their native range.

Environmental and phenotypic plasticity are well established traits of invasive species, and it is

possible that smelt can adapt to new conditions (Lodge 1993) in areas they invade. A related

assumption in our modeling strategy is that smelt have saturated their native range and that lakes

without smelt are really representative of lakes not suitable for smelt. Again, it is possible that

smelt could be present in lakes that do not share the morphological parameters with those we

have established here as determinants of smelt presence.

Given the nature of most of our variables, our analysis provides a static scenario for the

potential future distribution of smelt. However, two of the predictor variables we considered

have potential to change as a result of anthropogenic influence. Agricultural and urban runoff

can cause eutrophic conditions, reduced Secchi depth and hypolimnetic anoxia in summer

months (NRC 1992; Smith et al. 1999) making lakes inadequate for rainbow smelt survival, even

if they fit some of the depth and area considerations for lakes to be invaded. Similarly, lake pH

has changed as a consequence of environmental pollution and subsequent control measures

(Mills et al. 2000) and fish communities have responded to these changes (Yan et al. 2003;

Larssen et al. 2003). When making assessments of the potential invasion and establishment of

rainbow smelt, it will be important to consider temporal changes in ecosystem properties.

Variables we have used in our model are surrogates for other parameters that are relevant to

smelt but are not commonly measured in lakes. Lake area and maximum depth can be related to

lake stratification, which creates the necessary thermal habitat for smelt to survive during the

summer months. Secchi depth in turn, is a surrogate for water transparency and nutrient

135

concentration, and smelt prefer high water transparency to survive. Many other variables (e.g.,

lake interconnectivity, predator presence, etc.) that could help predict the presence of smelt in a

given lake were not used in this analysis. While this information was available for a number of

lakes, we favored those parameters that are known for a large number of lakes.

Taking into consideration the approximate time frame for smelt invasion in each region

(1900’s, 1930’s and 1960’s for Maine, Ontario and Wisconsin, respectively), the estimates of

spread rate are 4x higher in Maine compared to Wisconsin (2 invasions/year vs. 0.5

invasions/year, respectively) and ~1.5x higher compared to Ontario (1.68 invasions/year).

However, in Ontario, rates could be slightly higher if we consider the total number of invaded

lakes in Ontario (see Franzin et al. 1994). The rate of spread will depend on the number and

proximity of vulnerable lakes to invaded lakes, patterns of boat traffic, lakeshore development

trends, and the effectiveness of efforts to control the spread of invasive species. In a lake district

in northern Wisconsin, Hrabik & Magnuson (1999) modeled rainbow smelt dispersal and

concluded that with the aid of human intervention (e.g., human vectors) most of the 507 suitable

lakes in the district would be invaded in 1,000 years, which agrees closely with past spread rates

as estimated above for Wisconsin.

With increasing rates of exotic species introductions and associated negative impacts on

recipient ecosystems, the protection of unique native faunas require tools that allow the

identification of systems most vulnerable to invasion. Proactive management based on

predictions of lakes vulnerable to rainbow smelt will aid in avoiding the costs of eradication

incurred once a species has established. Our modeling approach has identified lakes that could

harbor rainbow smelt and lakes that could be impacted by this invader, aiding the identification

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of systems where monitoring, legislative and educational efforts should be concentrated to

prevent further damage to native fish communities and valuable fisheries.

ACKNOWLEDGEMENTS

For information on fish distribution, environmental datasets, GIS coverages, and

comments we thank S. Gilbert, J. Lyons, K. Webster, A. Neibur, R. Young, S. Toschner, F. Pratt,

J. McCarthy (Wisconsin); N. Mandrak, K. Armstrong, R. Mackereth, T. Johnston (Ontario); D.

B. Halliwell, P. Vaux, S. Harmon (Maine). We are thankful to all field crews that helped

assemble the datasets utilized in this project. Funding for this project and other support was

provided by U.S. National Science Foundation grant DEB-0217533 to the Center for Limnology,

University of Wisconsin-Madison for Long-Term Ecological Research on North Temperate

Lakes; the Wisconsin Department of Natural Resources; and the Anna Grant Birge Fund (CFL –

UW-Madison). Funding for NMS provided by Consejo Nacional de Ciencia y Tecnología,

Mexico City, Mexico. JDO was supported by The Nature Conservancy's David H. Smith Post-

doctoral Conservation Research Fellowship Program.

137

LITERATURE CITED

Becker, G. C. 1983. Fishes of Wisconsin. University of Wisconsin Press, Madison, WI, USA. Beisner, B. E., A. R. Ives and S. R. Carpenter. 2003. The effects of an exotic fish invasion on the

prey communities in two lakes. Journal of Animal Ecology 72: 331-342. (BIA), Bureau of Indian Affairs. 2003. Fishery status update in the Wisconsin treaty ceded

waters. Casting light upon the waters ~ a joint fishery assessment of the Wisconsin Ceded Territory. U.S. Department of the Interior, Bureau of Indian Affairs, Minneapolis, MN, USA.

Brandt, S. B., and S. P. Madon. 1986. Rainbow smelt (Osmerus mordax) predation on slimy

sculpin (Cottus cognatus) in Lake Ontario. Journal of Great Lakes Research 12:322-325. Breiman, L., J. H. Friedman, A. Olshen and C. G. Stone. 1984. Classification and regression

trees. Wadsworth Internation Group, Belmont, California, USA. CART. 2002. Salford Systems, California Statistical Software INC. San Diego California. USA. Claudi, R. and J. H. Leach. 1999. Nonindigenous freshwater organisms. Lewis Publishers, Boca

Raton, FL, USA. Colby, P. J., P. A. Ryan, D. H. Schupp and S. L. Serns. 1987. Interactions in north-temperate

lake fish communities. Canadian Journal of Fisheries and Aquatic Sciences 44:104-128. De'ath, G. and K. E. Fabricius. 2000. Classification and regression trees: A powerful yet simple

technique for ecological data analysis. Ecology 81:3178-3192. Evans, D. O. and D. H. Loftus. 1987. Colonization of inland lakes in the Great Lakes Region by

rainbow smelt, Osmerus mordax: Their freshwater niche and effects on indigenous fishes. Canadian Journal of Fisheries and Aquatic Sciences 44: 249-266.

Evans, D. O. and P. Waring. 1987. Changes in multispecies, winter angling fishery of Lake

Simcoe, Ontario, 1961-83: invasion by rainbow smelt, Osmerus mordax, and the roles of intra- and interspecific interactions. Canadian Journal of Fisheries and Aquatic Sciences 44:182-197.

(ESRI), Environmental Systems Research Institute, Inc. 2002. ArcGIS version 8.3. Redlands,

California, USA. Fielding, A. H. and J. F. Bell. 1997. A review of methods for the assessment of prediction errors

in conservation presence/absence models. Environmental Conservation 24:38-49. Franzin, W. G., B. A. Barton, R. A. Remnant, D. B. Wain and S. J. Pagel. 1994. Range

extension, present and potential distribution, and possible effects of rainbow smelt in

138

Hudson Bay Drainage waters of northwestern Ontario, Manitoba, and Minnesota. North American Journal of Fisheries Management 14:65-76.

Halliwell, D. B., T. R. Whittier and N. H. Ringler. 2001. Distributions of lake fishes of the

Northeast USA - III Salmonidae and associated coldwater species. Northeastern Naturalist 8:189-206.

Halliwell, D. B. 2003. Introduced fish in Maine. On line document Maine Department of

Environmental Protection Project Webpage: http://www.mainebiodiversity.org/introduced_fish.pdf, Maine DEP, Augusta, Maine, USA.

Hrabik, T. R. and J. Magnuson. 1999. Simulated dispersal of exotic rainbow smelt (Osmerus

mordax) in a northern Wisconsin lake district and implications for management. Canadian Journal of Fisheries and Aquatic Sciences 56:35-42.

Hrabik, T. R., J. Magnuson and A. S. McLain. 1998. Predicting the effects of rainbow smelt on

native fishes in small lakes: evidence from long term research in two lakes. Canadian Journal of Fisheries and Aquatic Sciences 55:1364-1371.

Hrabik, T. R., M. P. Carey and M. S. Webster. 2001. Interactions between young-of-the-year

exotic Rainbow Smelt and native Yellow Perch in a northern temperate lake. Transactions of the American Fisheries Society 130:568-582.

Johnson, B. M. and J. P. Goettl. 1999. Food web changes over fourteen years following

introduction of Rainbow Smelt into a Colorado reservoir. North American Journal of Fisheries Management 19:629-642.

Kolar, C. S. and D. M. Lodge, D. M. 2001. Progress in invasion biology: predicting invaders.

Trends in Ecology and Evolution 16:199-204. Koutnik, M. A. and D. K. Padilla. 1994. Predicting the spatial distribution of Dreissena

polymorpha (Zebra Mussel) among inland lakes of Wisconsin: Modeling with a GIS. Canadian Journal of Fisheries and Aquatic Sciences 51: 1189-1196.

Krueger, D. M. and T. R. Hrabik. 2005. Food web alterations that promote native species: the

recovery of native cisco (Coregonus artedi) populations through management of native piscivores. Canadian Journal of Fisheries and Aquatic Sciences 62:2177-2188.

Larssen, T., C. Brereton, and J. M. Gunn. 2003. Dynamic modeling of recovery from

acidification of lakes in Killarney Park, Ontario, Canada. Ambio 32:244-248. Lester, N. P., T. R. Marshall, K. Armstrong, W. I. Dunlop and B. Ritchie. 2003. A broad-scale

approach to management of Ontario's recreational fisheries. North American Journal of Fisheries Management 23:1312-1328.

139

Lodge, D. M. 1993. Biological invasions: Lessons for ecology. Trends in Ecology and Evolution 8:133-137.

Lodge, D. M., R. A. Stein, K. M. Brown, A. P. Covich, C. Brõnmark, J.E. Garvey and S.P.

Klosiewsky. 1998. Predicting impact of freshwater exotic species on native biodiversity: challenges in spatial scaling. Australian Journal of Ecology 23:53-67.

Loftus, D. H. and P. F. Hulsman. 1986. Predation on larval lake whitefish (Coregonus

clupeaformis) and lake herring (C. artedii) by adult rainbow smelt (Osmerus mordax). Canadian Journal of Fisheries and Aquatic Sciences 43:812-818.

Lyons, J., P. A. Cochran and D. Fago. 2000. Wisconsin fishes: status and distribution. University

of Wisconsin Sea Grant, Madison, WI, USA. MacIsaac, H. J., J. V. M. Borbely, J. R. Muirhead and P. A. Graniero. 2004. Backcasting and

forecasting biological invasions of inland lakes. Ecological Applications 14:773-783. McLain, A. S. 1991. Conceptual and empirical analyses of biological invasion: non-native fish

invasion into north temperate lakes. PhD. Thesis. University of Wisconsin – Madison. Mills, K. H., S. M. Chalanchuk and D. J. Allan. 2000. Recovery of fish populations in Lake 223

from experimental acidification. Canadian Journal of Fisheries and Aquatic Sciences 57:192-204.

Moyle, P. B. 1986. Fish introductions into North America: patterns and ecological impact. Pages

27-43 in H. A. Mooney and J. A. Drake, editors. Ecology of Biological Invasions of North America and Hawaii. Springer-Verlag, New York, USA.

(NRC) National Research Council. 1992. Restoration of aquatic ecosystems: science, technology

and public policy, National Academy Press, Washington (DC), USA. Olden, J. D. and D. A. Jackson. 2002. A comparison of statistical approaches for modeling fish

species distribution. Freshwater Biology 47:1976-1995. Page, L. M., and B. M. Burr. 1991. A field guide to freshwater fishes (North America, North of

Mexico). Houghton Mifflin Company, Boston, MA, USA. Perry, W. L., D. M. Lodge and J. L. Feder. 2002. Importance of hybridization between

indigenous and nonindigenous freshwater species: an overlooked threat to North American biodiversity. Systematic Biology 51:255-275.

Peterson, A. T. 2003. Predicting the geography of species' invasions via ecological niche

modeling. The Quarterly Review of Biology 78:419-433.

140

Peterson, A. T. and D. A. Viegalis. 2001. Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem. BioScience 51:363-371.

Pimentel, D., L. Lach, R. Zuniga and D. Morrison. 2000. Environmental and economic costs of

nonindigenous species in the United States. BioScience 50:53-65. Ricciardi, A. and J. B. Rasmussen. 1999. Extinction rates of North American freshwater fauna.

Conservation Biology 13:1220-1222. Richter, B. D., D. P. Braun, M. A. Mendelson and L. L. Master. 1997. Threats to imperiled

freshwater fauna. Conservation Biology 11:1081-1093. Roth, B. M. 2005. An investigation of exotic rust crayfish (Orconectes rusticus) and rainbow

smelt (Osmerus mordax) interactions: The Sparkling Lake biomanipulation. PhD Thesis, University of Wisconsin-Madison.

Selgeby, J. H., W. R. MacCallum and D. V. Swedberg. 1978. Predation by rainbow smelt

(Osmerus mordax) on lake herring in western Lake Superior. Journal of Fisheries Research Board of Canada 35:1457-1463.

Smith, V. H., G. D. Tilman and J. C. Nekola. 1999. Eutrophication: impacts of excess nutrient

inputs on freshwater, marine, and terrestrial ecosystems. Environmental Pollution 100:179-196.

STORET-EPA. STORET Database, U.S. Environmental Protection Agency.

http://www.epa.gov/storet/ Swanson, H. K., T. A. Johnston, W. C. Leggett, R. A. Bodaly, R. R. Doucett, and R. A. Cunjak.

2003. Trophic positions and mercury bioaccumulation in Rainbow Smelt (Osmerus mordax) and native forage fishes in Northwestern Ontario lakes. Ecosystems 6:289-299.

Taylor, J., W. R. Courtenay and J. A. McCann. 1984. Known impacts of exotic fishes in the

continental United States. Pages 323-373 in W. R. Courtenay, and J.R. Stauffer, editors. Distribution, biology and management of exotic fishes. Johns Hopkins University Press, Baltimore, MD, USA.

Titus, K., J. A. Mosher and B. K. Williams. 1984. Chance corrected classification for use of

discriminant analysis: ecological applications. American Midland Naturalist 111:1-7. (USGS) United States Geological Survey. 2000.

http://nas.er.usgs.gov/fishes/accounts/osmerida/os_morda.html Vander Zanden, M. J. and J. B. Rasmussen. 1996. A trophic position model of pelagic food

webs: Impact on contaminant bioaccumulation in lake trout. Ecological Monographs 66:451-477.

141

Vander Zanden, M. J., J. M. Casselman and J. B. Rasmussen. 1999. Stable isotopes evidence for

the food web consequences of species invasions in lakes. Nature 401:464-467. Vander Zanden, M. J., J. D. Olden, J. H. Thorne and N. E. Mandrak. 2004. Predicting

occurrences and impacts of smallmouth bass introductions in north temperate lakes. Ecological Applications 14:132-148.

Vermeij, G. L. 1996. An agenda for invasion biology. Biological Conservation 78: 3-9. Williamson, M. H. 1996. Biological Invasions. Chapman and Hall, London, UK. Yan, N. D., B. Leung, W. B. Keller, S. E. Arnott, J. M. Gunn and G. G. Raddum. 2003.

Developing conceptual frameworks for the recovery of aquatic biota from acidification. Ambio 32:165-169.

142

Table 1. Model results and predictions of lakes suitable for rainbow smelt invasion for Maine

lakes outside of its native range. ‘n lakes’ indicates the number of lakes in each group. ‘Total

N’ refers to the total number of lakes where smelt were actually observed or not observed.

Sensitivity: model correctly predicts presence of smelt. Specificity: model correctly predicts

absence. False absence = smelt not predicted, smelt observed. True presence = smelt predicted,

smelt observed. False presence = smelt predicted, smelt not observed. True absence = smelt not

predicted, smelt not observed. False presences are considered vulnerable based on

environmental suitability.

---------------------------------------------------------------------------------------------------------- Region n lakes Performance ----------------------------------------------------------------------------------------------------------- Maine (non-native range) False absence 45 Sensitivity = 80% Total N = 221 lakes True presence 176 False presence 52 Specificity = 79% Total N = 244 lakes True absence 192 ----------------------------------------------------------------------------------------------------------- Ontario False absence 16 Sensitivity = 87.3% Total N = 126 lakes True presence 110 False presence 4,447 Specificity = 45% Total N = 8,810 lakes True absence 3,663 --------------------------------------------------------------------------------------------------------- Wisconsin False absence 3 Sensitivity = 87.5% Total N = 24 lakes True presence 21

143

False presence 553 Specificity =89% Total N = 5164 lakes True absence 4,611 ---------------------------------------------------------------------------------------------------------------------

144

Table 2. Summary of lakes currently inhabited or predicted to inhabit rainbow smelt for each

study region. Column description: total number of lakes in study (# lakes); lakes currently with

smelt (# smelt); No. of vulnerable lakes (# vul), No. of not vulnerable lakes (# not vul); percent

of lakes supporting or predicted to support smelt (% smelt); percent saturation (refers to the

percent of smelt-suitable lakes that have a smelt population in each region) (% sat).

--------------------------------------------------------------------------------------------------------------------- Region # lakes # smelt # vul # not vul % smelt % sat --------------------------------------------------------------------------------------------------------------------- Maine (Native) 354 200 15 139 61% 93% Maine (Non-native) 465 221 52 192 59% 81% Ontario 8236 126 4447 3663 56% 3% Wisconsin 5188 24 553 4611 11% 4% ---------------------------------------------------------------------------------------------------------------------

145

Table 3. Impact-vulnerable lakes in each study area. ‘Vulnerability’ is established based on the

presence of fish taxa vulnerable to rainbow smelt. The percentage of lakes that were predicted

by the model and present the taxa that are most prone to be affected are indicated. N = number

of lakes within each category.

--------------------------------------------------------------------------------------------------------- Region ------------------------------------------------------------------------ Taxa Maine Maine Wisconsin Ontario (Native) (Non-Native) ------------------------------------------------------------------------ % n % n % n % n ---------- ------------ ------------ --------------- Coregonids 0 0 13 7 7 41 53 2,337 Brook Trout 47 7 69 36 5 26 16 704 Lake Trout 0 0 13 7 2 9 35 1,538 Walleye 0 0 0 0 42 232 36 1,607 Yellow Perch 93 14 56 29 56 310 70 3,118 At least 1 taxa 100 15 94 49 58 323 94 4,192 All taxa 0 0 0 0 0 2 0 7 ------------------------------------------------------------------------------------------------------------ Total lakes predicted 15 52 553 4,447 -------------------------------------------------------------------------------------------------------------

146

FIGURE LEGENDS

Figure 1. Distribution of rainbow smelt in Maine, USA. Shaded areas indicate native range.

Figure 2. Classification tree modeling the presence/absence of rainbow smelt (Osmerus

mordax). Tree constructed from morphological and physical-chemical variables for 354 lakes in

the native range of rainbow smelt in Maine, USA. Each of the 6 splits is labeled with the

variable and values that indicate the split-defining condition. Each of the 7 terminal nodes is

labeled with the number of lakes grouped in each node that have (non-shaded) or lack smelt

(shaded), and the model prediction (smelt presence/absence).

Figure 3. Predicted rainbow smelt presences for Maine lakes outside of their native distribution.

Black dots indicate known distribution; grey dots indicate lakes suitable for invasion (n = 52).

Figure 4. Current and predicted rainbow smelt distribution in Ontario, Canada.

Black dots indicate known occurrences. Grey dots indicate lakes suitable for invasion (n =

4,447).

Figure 5. Current and predicted rainbow smelt distribution in Wisconsin, USA. Black dots

indicate known occurrences. Grey dots indicate lakes suitable for invasion (n = 553).

147

Figure 1

148

Figure 2

8

129

8

23

Max. depth ≥ 8.9 m

Max. depth ≥ 12.3 m

Surface area ≥ 21.2 haSurface area ≥ 102.8 ha

Secchi depth

≥6.09 m

Max. depth

≥ 19.96 m

25

110

6

2

58

1

6

23

YES

YESYES

YES YES

YES

NO NO

NO

NO

NO

NO

Smelt Present

Smelt Absent

Smelt Present

Smelt Present

Smelt PresentSmelt AbsentSmelt Absent

154200

8

129

8

23

Max. depth ≥ 8.9 m

Max. depth ≥ 12.3 m

Surface area ≥ 21.2 haSurface area ≥ 102.8 ha

Secchi depth

≥6.09 m

Max. depth

≥ 19.96 m

25

110

6

2

58

1

6

23

YES

YESYES

YES YES

YES

NO NO

NO

NO

NO

NO

Smelt Present

Smelt Absent

Smelt Present

Smelt Present

Smelt PresentSmelt AbsentSmelt Absent

154200

149

Figure 3

150

Figure 4

151

Figure 5

152

APPENDIX

List of lakes invaded or prone to invasion by rainbow smelt in Maine, Ontario and Wisconsin.

Lake ID refers to the number assigned to each lake by management agencies in each region:

Maine N (native range) and NN (non-native range) = MIDAS number, Ontario = Lake ID,

Wisconsin = Water body identification code (WBIC). For ‘Smelt Known’: 1 = smelt are known

for this system; 0 = smelt not known. For ‘Smelt Predicted’: 1 smelt are predicted by our model

for this lake; 0 = smelt are not predicted for this system. For ‘Impact Vulnerability” 1 = lake

with at least one vulnerable taxa present (Coregonids, lake whitefish, brook trout, walleye,

yellow perch), 0 = taxa absent. Vulnerability was calculated for predicted lakes only; for

previously invaded lakes value: NA.

STUDY AREA

LAKE ID

SMELT KNOWN

SMELT PREDICTED

IMPACT VULNERABILITY

Maine Native 97 1 1 NA Maine Native 121 1 1 NA Maine Native 135 1 1 NA Maine Native 159 1 1 NA Maine Native 177 1 1 NA Maine Native 367 1 0 NA Maine Native 1063 1 1 NA Maine Native 1070 1 1 NA Maine Native 1086 1 1 NA Maine Native 1088 1 1 NA Maine Native 1096 1 1 NA Maine Native 1100 1 1 NA Maine Native 1104 1 0 NA Maine Native 1106 1 1 NA Maine Native 1110 1 1 NA Maine Native 1124 1 0 NA Maine Native 1150 1 1 NA Maine Native 1172 1 1 NA Maine Native 1196 1 0 NA Maine Native 1210 1 1 NA Maine Native 1228 1 1 NA Maine Native 1230 1 1 NA Maine Native 1236 1 1 NA Maine Native 1258 1 1 NA

153

Maine Native 1280 1 0 NA Maine Native 1284 1 1 NA Maine Native 1286 1 0 NA Maine Native 1288 1 1 NA Maine Native 1290 0 1 0 Maine Native 1302 1 0 NA Maine Native 1304 0 1 0 Maine Native 1332 1 1 NA Maine Native 1348 0 1 0 Maine Native 1352 1 1 NA Maine Native 1358 1 1 NA Maine Native 1364 1 0 NA Maine Native 1368 1 1 NA Maine Native 1374 1 0 NA Maine Native 1404 1 1 NA Maine Native 1418 1 1 NA Maine Native 1424 1 0 NA Maine Native 1428 1 1 NA Maine Native 3130 1 1 NA Maine Native 3132 1 1 NA Maine Native 3134 1 1 NA Maine Native 3136 1 1 NA Maine Native 3158 1 1 NA Maine Native 3160 1 1 NA Maine Native 3174 1 1 NA Maine Native 3176 1 1 NA Maine Native 3182 1 1 NA Maine Native 3188 1 0 NA Maine Native 3196 1 1 NA Maine Native 3201 0 1 1 Maine Native 3212 1 1 NA Maine Native 3224 1 0 NA Maine Native 3232 1 1 NA Maine Native 3234 1 1 NA Maine Native 3240 1 0 NA Maine Native 3254 1 1 NA Maine Native 3262 1 0 NA Maine Native 3264 1 0 NA Maine Native 3272 1 1 NA Maine Native 3374 1 1 NA Maine Native 3382 1 1 NA Maine Native 3390 1 1 NA Maine Native 3392 1 1 NA Maine Native 3408 1 1 NA Maine Native 3416 1 1 NA Maine Native 3418 1 1 NA Maine Native 3420 1 1 NA Maine Native 3424 1 1 NA Maine Native 3432 0 1 0 Maine Native 3434 1 1 NA Maine Native 3444 1 1 NA Maine Native 3446 1 1 NA

154

Maine Native 3448 1 1 NA Maine Native 3452 1 1 NA Maine Native 3454 0 1 0 Maine Native 3460 1 1 NA Maine Native 3464 1 1 NA Maine Native 3470 1 1 NA Maine Native 3472 1 0 NA Maine Native 3478 1 1 NA Maine Native 3480 1 1 NA Maine Native 3502 1 1 NA Maine Native 3504 0 1 1 Maine Native 3520 1 1 NA Maine Native 3594 1 1 NA Maine Native 3604 1 1 NA Maine Native 3608 1 1 NA Maine Native 3616 1 1 NA Maine Native 3624 1 0 NA Maine Native 3626 1 0 NA Maine Native 3672 1 1 NA Maine Native 3688 1 1 NA Maine Native 3690 1 1 NA Maine Native 3692 1 0 NA Maine Native 3694 1 1 NA Maine Native 3696 1 1 NA Maine Native 3700 1 1 NA Maine Native 3706 1 0 NA Maine Native 3708 1 1 NA Maine Native 3714 1 1 NA Maine Native 3734 1 1 NA Maine Native 3748 1 1 NA Maine Native 3750 1 1 NA Maine Native 3758 1 1 NA Maine Native 3760 0 1 0 Maine Native 3762 1 1 NA Maine Native 3772 1 0 NA Maine Native 3780 1 0 NA Maine Native 3784 1 1 NA Maine Native 3788 0 1 1 Maine Native 3800 0 1 0 Maine Native 3818 1 1 NA Maine Native 3822 1 1 NA Maine Native 3824 1 0 NA Maine Native 3836 1 1 NA Maine Native 4288 1 0 NA Maine Native 4290 1 1 NA Maine Native 4292 1 1 NA Maine Native 4294 1 1 NA Maine Native 4296 1 1 NA Maine Native 4300 1 1 NA Maine Native 4306 1 1 NA Maine Native 4308 1 1 NA Maine Native 4328 1 1 NA

155

Maine Native 4342 0 1 1 Maine Native 4344 1 1 NA Maine Native 4346 1 0 NA Maine Native 4350 1 1 NA Maine Native 4352 1 1 NA Maine Native 4370 1 1 NA Maine Native 4388 1 1 NA Maine Native 4406 1 1 NA Maine Native 4412 1 1 NA Maine Native 4414 1 1 NA Maine Native 4418 1 1 NA Maine Native 4422 1 0 NA Maine Native 4424 1 1 NA Maine Native 4434 1 1 NA Maine Native 4448 1 1 NA Maine Native 4450 1 1 NA Maine Native 4482 1 1 NA Maine Native 4492 1 1 NA Maine Native 4498 1 1 NA Maine Native 4522 1 1 NA Maine Native 4524 1 1 NA Maine Native 4538 1 1 NA Maine Native 4540 1 1 NA Maine Native 4598 1 1 NA Maine Native 4604 1 1 NA Maine Native 4640 1 1 NA Maine Native 4688 1 1 NA Maine Native 4690 0 1 1 Maine Native 4694 1 0 NA Maine Native 4700 1 1 NA Maine Native 4702 1 1 NA Maine Native 4708 1 1 NA Maine Native 4730 1 1 NA Maine Native 4786 1 0 NA Maine Native 4792 1 0 NA Maine Native 4798 1 1 NA Maine Native 4800 1 1 NA Maine Native 4806 1 0 NA Maine Native 4810 1 1 NA Maine Native 4814 1 1 NA Maine Native 4822 1 1 NA Maine Native 4832 1 1 NA Maine Native 4846 1 1 NA Maine Native 4848 0 1 1 Maine Native 4850 1 1 NA Maine Native 4852 1 1 NA Maine Native 4870 1 1 NA Maine Native 4886 1 1 NA Maine Native 4894 1 1 NA Maine Native 4896 1 1 NA Maine Native 5024 1 1 NA Maine Native 5032 1 1 NA

156

Maine Native 5040 1 1 NA Maine Native 5182 1 1 NA Maine Native 5186 1 1 NA Maine Native 5218 1 1 NA Maine Native 5330 1 0 NA Maine Native 5364 1 0 NA Maine Native 5384 1 0 NA Maine Native 5400 1 1 NA Maine Native 5492 1 1 NA Maine Native 5572 1 1 NA Maine Native 5654 1 1 NA Maine Native 5658 1 1 NA Maine Native 5664 1 0 NA Maine Native 5666 1 1 NA Maine Native 5682 1 1 NA Maine Native 5684 1 1 NA Maine Native 5686 0 1 0 Maine Native 5690 1 1 NA Maine Native 5694 1 0 NA Maine Native 5704 1 1 NA Maine Native 5706 1 1 NA Maine Native 5710 1 1 NA Maine Native 5712 1 1 NA Maine Native 5754 0 1 1 Maine Native 5780 1 1 NA Maine Native 5786 1 1 NA Maine Native 5812 1 1 NA Maine Native 5814 1 1 NA Maine Native 7437 1 1 NA Maine Native 9649 1 1 NA Maine Native 9661 1 1 NA Maine Native 9683 1 1 NA Maine Native 9685 1 1 NA Maine Native 9701 1 1 NA Maine Native 9971 1 1 NA Maine Non Native 7 0 1 0 Maine Non Native 9 1 1 NA Maine Non Native 12 1 1 NA Maine Non Native 48 1 1 NA Maine Non Native 62 0 1 1 Maine Non Native 78 1 1 NA Maine Non Native 82 1 1 NA Maine Non Native 86 1 1 NA Maine Non Native 98 1 1 NA Maine Non Native 103 1 1 NA Maine Non Native 150 0 1 1 Maine Non Native 152 1 1 NA Maine Non Native 155 0 1 0 Maine Non Native 170 1 1 NA Maine Non Native 202 1 1 NA Maine Non Native 224 1 1 NA Maine Non Native 227 1 1 NA

157

Maine Non Native 242 1 1 NA Maine Non Native 243 1 1 NA Maine Non Native 260 1 0 NA Maine Non Native 262 1 1 NA Maine Non Native 269 1 0 NA Maine Non Native 275 0 1 1 Maine Non Native 296 1 1 NA Maine Non Native 298 1 1 NA Maine Non Native 301 1 1 NA Maine Non Native 314 0 1 1 Maine Non Native 322 1 0 NA Maine Non Native 324 1 1 NA Maine Non Native 334 1 1 NA Maine Non Native 336 1 0 NA Maine Non Native 339 0 1 1 Maine Non Native 342 1 1 NA Maine Non Native 344 1 0 NA Maine Non Native 380 1 1 NA Maine Non Native 386 1 0 NA Maine Non Native 388 1 1 NA Maine Non Native 390 1 1 NA Maine Non Native 410 1 1 NA Maine Non Native 435 1 0 NA Maine Non Native 436 1 1 NA Maine Non Native 452 1 1 NA Maine Non Native 474 0 1 1 Maine Non Native 478 0 1 1 Maine Non Native 503 1 1 NA Maine Non Native 534 1 1 NA Maine Non Native 572 1 1 NA Maine Non Native 576 0 1 1 Maine Non Native 582 1 1 NA Maine Non Native 584 1 1 NA Maine Non Native 586 1 0 NA Maine Non Native 602 0 1 1 Maine Non Native 614 1 1 NA Maine Non Native 636 0 1 1 Maine Non Native 662 1 1 NA Maine Non Native 698 1 1 NA Maine Non Native 700 1 1 NA Maine Non Native 716 1 0 NA Maine Non Native 744 0 1 1 Maine Non Native 758 1 1 NA Maine Non Native 780 1 1 NA Maine Non Native 782 1 0 NA Maine Non Native 844 1 1 NA Maine Non Native 848 1 1 NA Maine Non Native 864 0 1 1 Maine Non Native 894 1 1 NA Maine Non Native 942 1 1 NA Maine Non Native 954 1 1 NA Maine Non Native 956 1 1 NA

158

Maine Non Native 982 1 1 NA Maine Non Native 984 1 1 NA Maine Non Native 1018 1 1 NA Maine Non Native 1036 1 1 NA Maine Non Native 1050 1 0 NA Maine Non Native 1470 1 1 NA Maine Non Native 1506 0 1 1 Maine Non Native 1514 0 1 1 Maine Non Native 1528 0 1 1 Maine Non Native 1598 1 1 NA Maine Non Native 1602 1 0 NA Maine Non Native 1610 1 1 NA Maine Non Native 1628 0 1 1 Maine Non Native 1634 1 1 NA Maine Non Native 1654 1 1 NA Maine Non Native 1672 1 1 NA Maine Non Native 1674 1 1 NA Maine Non Native 1680 1 0 NA Maine Non Native 1682 1 1 NA Maine Non Native 1686 1 1 NA Maine Non Native 1702 1 0 NA Maine Non Native 1728 1 1 NA Maine Non Native 1736 1 1 NA Maine Non Native 1750 1 0 NA Maine Non Native 1802 1 1 NA Maine Non Native 1820 1 0 NA Maine Non Native 1888 1 1 NA Maine Non Native 1892 1 1 NA Maine Non Native 1896 1 1 NA Maine Non Native 1906 0 1 1 Maine Non Native 1914 1 1 NA Maine Non Native 1918 1 1 NA Maine Non Native 1938 1 1 NA Maine Non Native 2004 1 0 NA Maine Non Native 2020 1 1 NA Maine Non Native 2064 0 1 1 Maine Non Native 2084 1 1 NA Maine Non Native 2102 1 1 NA Maine Non Native 2104 1 1 NA Maine Non Native 2140 0 1 1 Maine Non Native 2146 1 1 NA Maine Non Native 2178 1 1 NA Maine Non Native 2196 1 1 NA Maine Non Native 2198 1 0 NA Maine Non Native 2202 1 1 NA Maine Non Native 2216 1 1 NA Maine Non Native 2230 0 1 1 Maine Non Native 2232 1 1 NA Maine Non Native 2242 1 0 NA Maine Non Native 2250 0 1 0 Maine Non Native 2254 0 1 1 Maine Non Native 2264 1 1 NA

159

Maine Non Native 2286 1 0 NA Maine Non Native 2336 1 0 NA Maine Non Native 2374 1 1 NA Maine Non Native 2384 1 1 NA Maine Non Native 2398 0 1 1 Maine Non Native 2400 1 1 NA Maine Non Native 2488 0 1 1 Maine Non Native 2520 1 1 NA Maine Non Native 2536 1 1 NA Maine Non Native 2538 0 1 1 Maine Non Native 2544 1 1 NA Maine Non Native 2580 1 1 NA Maine Non Native 2590 1 1 NA Maine Non Native 2592 1 1 NA Maine Non Native 2608 1 1 NA Maine Non Native 2612 0 1 0 Maine Non Native 2614 1 1 NA Maine Non Native 2630 1 1 NA Maine Non Native 2650 1 0 NA Maine Non Native 2652 0 1 1 Maine Non Native 2682 1 1 NA Maine Non Native 2698 1 1 NA Maine Non Native 2702 1 1 NA Maine Non Native 2704 0 1 1 Maine Non Native 2710 1 1 NA Maine Non Native 2718 1 1 NA Maine Non Native 2726 1 1 NA Maine Non Native 2730 0 1 1 Maine Non Native 2752 1 1 NA Maine Non Native 2758 1 1 NA Maine Non Native 2766 0 1 1 Maine Non Native 2856 1 1 NA Maine Non Native 2858 1 1 NA Maine Non Native 2866 0 1 1 Maine Non Native 2882 0 1 1 Maine Non Native 2920 1 0 NA Maine Non Native 2936 1 1 NA Maine Non Native 2948 1 1 NA Maine Non Native 3004 1 1 NA Maine Non Native 3011 0 1 0 Maine Non Native 3038 1 1 NA Maine Non Native 3056 0 1 1 Maine Non Native 3102 1 1 NA Maine Non Native 3104 1 1 NA Maine Non Native 3276 1 1 NA Maine Non Native 3278 1 0 NA Maine Non Native 3290 1 1 NA Maine Non Native 3300 1 1 NA Maine Non Native 3302 1 1 NA Maine Non Native 3308 1 1 NA Maine Non Native 3312 1 0 NA Maine Non Native 3316 1 0 NA

160

Maine Non Native 3328 1 1 NA Maine Non Native 3332 1 1 NA Maine Non Native 3524 0 1 1 Maine Non Native 3526 1 0 NA Maine Non Native 3528 1 1 NA Maine Non Native 3532 1 1 NA Maine Non Native 3534 1 0 NA Maine Non Native 3562 1 1 NA Maine Non Native 3582 1 1 NA Maine Non Native 3636 1 1 NA Maine Non Native 3680 1 1 NA Maine Non Native 3682 1 1 NA Maine Non Native 3686 1 1 NA Maine Non Native 3814 1 0 NA Maine Non Native 3832 1 1 NA Maine Non Native 3838 1 1 NA Maine Non Native 3874 0 1 0 Maine Non Native 3876 0 1 0 Maine Non Native 3898 0 1 0 Maine Non Native 3916 0 1 0 Maine Non Native 3920 1 1 NA Maine Non Native 3922 1 1 NA Maine Non Native 3966 1 1 NA Maine Non Native 3980 0 1 0 Maine Non Native 3998 1 1 NA Maine Non Native 4012 1 1 NA Maine Non Native 4024 0 1 1 Maine Non Native 4048 1 0 NA Maine Non Native 4050 1 1 NA Maine Non Native 4120 1 0 NA Maine Non Native 4156 1 1 NA Maine Non Native 4180 1 1 NA Maine Non Native 4222 0 1 1 Maine Non Native 4260 1 1 NA Maine Non Native 4264 1 1 NA Maine Non Native 4282 0 1 0 Maine Non Native 4284 1 1 NA Maine Non Native 4318 1 0 NA Maine Non Native 4322 1 1 NA Maine Non Native 4332 1 1 NA Maine Non Native 4340 1 1 NA Maine Non Native 4452 1 1 NA Maine Non Native 4606 1 1 NA Maine Non Native 4608 1 1 NA Maine Non Native 4610 1 1 NA Maine Non Native 4612 1 1 NA Maine Non Native 4614 1 0 NA Maine Non Native 4622 1 1 NA Maine Non Native 4624 1 1 NA Maine Non Native 4630 1 1 NA Maine Non Native 4746 1 1 NA Maine Non Native 4756 1 1 NA

161

Maine Non Native 4766 1 1 NA Maine Non Native 5054 1 1 NA Maine Non Native 5064 1 1 NA Maine Non Native 5136 1 1 NA Maine Non Native 5138 1 1 NA Maine Non Native 5172 1 1 NA Maine Non Native 5176 0 1 0 Maine Non Native 5190 1 1 NA Maine Non Native 5216 1 0 NA Maine Non Native 5222 1 1 NA Maine Non Native 5236 1 1 NA Maine Non Native 5238 1 1 NA Maine Non Native 5240 1 1 NA Maine Non Native 5242 1 0 NA Maine Non Native 5244 1 0 NA Maine Non Native 5250 1 0 NA Maine Non Native 5254 1 0 NA Maine Non Native 5272 1 1 NA Maine Non Native 5274 1 1 NA Maine Non Native 5276 1 0 NA Maine Non Native 5280 1 1 NA Maine Non Native 5282 0 1 0 Maine Non Native 5286 0 1 0 Maine Non Native 5298 1 1 NA Maine Non Native 5302 1 1 NA Maine Non Native 5307 1 1 NA Maine Non Native 5310 1 1 NA Maine Non Native 5312 1 1 NA Maine Non Native 5336 1 0 NA Maine Non Native 5348 1 0 NA Maine Non Native 5349 1 0 NA Maine Non Native 5352 1 1 NA Maine Non Native 5408 1 1 NA Maine Non Native 5416 1 1 NA Maine Non Native 5434 0 1 0 Maine Non Native 5448 1 1 NA Maine Non Native 5464 1 0 NA Maine Non Native 5536 1 1 NA Maine Non Native 5538 0 1 1 Maine Non Native 5540 0 1 0 Maine Non Native 5550 1 0 NA Maine Non Native 5562 1 1 NA Maine Non Native 8065 1 0 NA Maine Non Native 9779 0 1 1 Maine Non Native 9787 1 1 NA Maine Non Native 9789 1 1 NA Maine Non Native 9931 1 1 NA Maine Non Native 9961 1 1 NA Ontario 11001 0 1 1 Ontario 11002 0 1 1 Ontario 11003 0 1 1 Ontario 11005 0 1 1

162

Ontario 11006 0 1 1 Ontario 11007 0 1 0 Ontario 11009 0 1 1 Ontario 11011 0 1 1 Ontario 11013 0 1 1 Ontario 11016 0 1 1 Ontario 11019 0 1 1 Ontario 11020 0 1 1 Ontario 11021 0 1 1 Ontario 11023 0 1 0 Ontario 11024 0 1 1 Ontario 11025 0 1 1 Ontario 11026 0 1 1 Ontario 11027 0 1 1 Ontario 11029 0 1 1 Ontario 11030 0 1 0 Ontario 11032 0 1 1 Ontario 11033 0 1 1 Ontario 11034 0 1 1 Ontario 11035 0 1 1 Ontario 11036 0 1 1 Ontario 11037 0 1 1 Ontario 11039 0 1 1 Ontario 11040 0 1 1 Ontario 11041 0 1 1 Ontario 11042 0 1 1 Ontario 11044 0 1 1 Ontario 11045 0 1 1 Ontario 11046 0 1 1 Ontario 11048 0 1 1 Ontario 11050 0 1 1 Ontario 11051 0 1 1 Ontario 11057 0 1 0 Ontario 11058 0 1 1 Ontario 11059 0 1 1 Ontario 11060 0 1 1 Ontario 11062 0 1 1 Ontario 11063 0 1 1 Ontario 11064 0 1 1 Ontario 11065 0 1 1 Ontario 11066 0 1 1 Ontario 11067 0 1 1 Ontario 11068 0 1 1 Ontario 11069 0 1 1 Ontario 11070 0 1 1 Ontario 11071 0 1 1 Ontario 11072 0 1 1 Ontario 11073 0 1 1 Ontario 11074 0 1 1 Ontario 11075 0 1 0 Ontario 11077 0 1 1 Ontario 11078 0 1 1

163

Ontario 11079 0 1 1 Ontario 11080 0 1 1 Ontario 11081 0 1 1 Ontario 11083 0 1 1 Ontario 11085 0 1 1 Ontario 11086 0 1 1 Ontario 11087 0 1 1 Ontario 11088 0 1 1 Ontario 11090 0 1 0 Ontario 11092 0 1 1 Ontario 11093 0 1 1 Ontario 11094 0 1 0 Ontario 11095 0 1 1 Ontario 11096 0 1 1 Ontario 11097 0 1 1 Ontario 11098 0 1 1 Ontario 11099 0 1 1 Ontario 11100 0 1 0 Ontario 11103 0 1 0 Ontario 11104 0 1 1 Ontario 11105 0 1 1 Ontario 11107 0 1 1 Ontario 11108 0 1 1 Ontario 11109 0 1 0 Ontario 11110 1 1 NA Ontario 11111 0 1 1 Ontario 11113 0 1 1 Ontario 11114 0 1 0 Ontario 11115 0 1 1 Ontario 11116 0 1 0 Ontario 11120 0 1 1 Ontario 11123 0 1 1 Ontario 11126 0 1 1 Ontario 11127 0 1 1 Ontario 11128 0 1 0 Ontario 11129 0 1 1 Ontario 11131 0 1 1 Ontario 11132 0 1 1 Ontario 11133 0 1 1 Ontario 11136 0 1 1 Ontario 11137 0 1 1 Ontario 11139 0 1 1 Ontario 11140 0 1 1 Ontario 11141 0 1 1 Ontario 11144 0 1 1 Ontario 11146 0 1 1 Ontario 11147 0 1 1 Ontario 11148 0 1 1 Ontario 11149 0 1 1 Ontario 11150 0 1 1 Ontario 11153 0 1 1 Ontario 11154 0 1 1

164

Ontario 11155 0 1 0 Ontario 11156 0 1 1 Ontario 11160 0 1 1 Ontario 11162 0 1 0 Ontario 11163 0 1 1 Ontario 11164 0 1 0 Ontario 11166 0 1 0 Ontario 11168 0 1 1 Ontario 11169 0 1 1 Ontario 11171 0 1 1 Ontario 11173 0 1 1 Ontario 11175 0 1 1 Ontario 11176 0 1 1 Ontario 11178 0 1 1 Ontario 11179 0 1 1 Ontario 11181 0 1 1 Ontario 11183 0 1 1 Ontario 11184 0 1 0 Ontario 11185 0 1 1 Ontario 11186 0 1 1 Ontario 11187 0 1 1 Ontario 11188 0 1 1 Ontario 11191 0 1 1 Ontario 11192 0 1 1 Ontario 11193 0 1 1 Ontario 11194 0 1 1 Ontario 11198 0 1 1 Ontario 11199 0 1 1 Ontario 11200 0 1 1 Ontario 11201 0 1 1 Ontario 11202 0 1 1 Ontario 11204 0 1 1 Ontario 11205 0 1 1 Ontario 11206 0 1 1 Ontario 11207 0 1 1 Ontario 11208 0 1 1 Ontario 11209 0 1 1 Ontario 11210 0 1 1 Ontario 11211 0 1 1 Ontario 11212 0 1 1 Ontario 11213 0 1 1 Ontario 11214 0 1 0 Ontario 11215 0 1 1 Ontario 11217 0 1 1 Ontario 11218 0 1 1 Ontario 11219 0 1 1 Ontario 11221 0 1 1 Ontario 11222 0 1 1 Ontario 11223 0 1 1 Ontario 11224 0 1 1 Ontario 11225 0 1 1 Ontario 11226 0 1 0

165

Ontario 11227 0 1 1 Ontario 11228 0 1 1 Ontario 11229 0 1 1 Ontario 11230 0 1 0 Ontario 11232 0 1 0 Ontario 11233 0 1 1 Ontario 11234 0 1 1 Ontario 11237 0 1 1 Ontario 11238 0 1 1 Ontario 11239 0 1 1 Ontario 11240 0 1 1 Ontario 11241 0 1 1 Ontario 12001 0 1 1 Ontario 12003 0 1 1 Ontario 12004 0 1 1 Ontario 12005 0 1 1 Ontario 12006 0 1 1 Ontario 12007 0 1 1 Ontario 12009 0 1 1 Ontario 12010 0 1 1 Ontario 12011 0 1 1 Ontario 12012 0 1 1 Ontario 12013 0 1 1 Ontario 12014 0 1 1 Ontario 12015 0 1 0 Ontario 12017 0 1 1 Ontario 12018 0 1 0 Ontario 12019 0 1 1 Ontario 12023 0 1 1 Ontario 12024 0 1 0 Ontario 12025 0 1 1 Ontario 12026 0 1 1 Ontario 12027 0 1 1 Ontario 12028 0 1 1 Ontario 12029 0 1 1 Ontario 12030 0 1 1 Ontario 12031 0 1 1 Ontario 12033 0 1 1 Ontario 12034 0 1 1 Ontario 12035 0 1 1 Ontario 12036 0 1 1 Ontario 12040 0 1 1 Ontario 12041 0 1 1 Ontario 12042 0 1 1 Ontario 12043 0 1 1 Ontario 12044 0 1 1 Ontario 12045 0 1 1 Ontario 12046 0 1 1 Ontario 12047 0 1 1 Ontario 12048 0 1 1 Ontario 12049 0 1 1 Ontario 12050 0 1 1

166

Ontario 12051 0 1 0 Ontario 12054 0 1 1 Ontario 12056 0 1 1 Ontario 12057 0 1 1 Ontario 12058 0 1 1 Ontario 12059 0 1 1 Ontario 12060 0 1 1 Ontario 12061 0 1 1 Ontario 12062 0 1 1 Ontario 12063 0 1 1 Ontario 12064 0 1 1 Ontario 12065 0 1 1 Ontario 12067 0 1 1 Ontario 12068 0 1 1 Ontario 12069 0 1 1 Ontario 12070 0 1 1 Ontario 12072 0 1 1 Ontario 12073 0 1 1 Ontario 12074 0 1 1 Ontario 12078 0 1 1 Ontario 12080 0 1 1 Ontario 12081 0 1 1 Ontario 12082 0 1 1 Ontario 12083 0 1 1 Ontario 12084 0 1 0 Ontario 12085 0 1 1 Ontario 12086 0 1 1 Ontario 12087 0 1 1 Ontario 12088 0 1 1 Ontario 12089 0 1 1 Ontario 12090 0 1 1 Ontario 12091 0 1 1 Ontario 12092 0 1 1 Ontario 12093 0 1 1 Ontario 12094 0 1 1 Ontario 12095 0 1 0 Ontario 12097 0 1 1 Ontario 12098 0 1 1 Ontario 12099 0 1 1 Ontario 12100 0 1 1 Ontario 12101 0 1 1 Ontario 12104 0 1 1 Ontario 12106 0 1 1 Ontario 12108 0 1 1 Ontario 12110 0 1 1 Ontario 12111 0 1 1 Ontario 12112 0 1 1 Ontario 12114 0 1 1 Ontario 12115 0 1 1 Ontario 12117 0 1 1 Ontario 12118 0 1 1 Ontario 12119 0 1 1

167

Ontario 12120 0 1 1 Ontario 12121 0 1 1 Ontario 12124 0 1 1 Ontario 12125 0 1 0 Ontario 12126 0 1 1 Ontario 12127 0 1 1 Ontario 12129 0 1 1 Ontario 12130 0 1 1 Ontario 12132 0 1 1 Ontario 12135 0 1 1 Ontario 12136 0 1 1 Ontario 12141 0 1 1 Ontario 12142 0 1 1 Ontario 12143 0 1 1 Ontario 12144 0 1 0 Ontario 12146 0 1 1 Ontario 12150 0 1 1 Ontario 12152 0 1 1 Ontario 12154 0 1 1 Ontario 12155 0 1 1 Ontario 12156 0 1 1 Ontario 12157 0 1 1 Ontario 12159 0 1 1 Ontario 12164 0 1 1 Ontario 12165 0 1 1 Ontario 12167 0 1 0 Ontario 12168 0 1 1 Ontario 12169 0 1 1 Ontario 12171 0 1 1 Ontario 12172 0 1 1 Ontario 12174 0 1 1 Ontario 12175 0 1 1 Ontario 12176 0 1 1 Ontario 12177 0 1 1 Ontario 12178 0 1 1 Ontario 12179 0 1 1 Ontario 12180 0 1 1 Ontario 12181 0 1 1 Ontario 12182 0 1 1 Ontario 12183 0 1 1 Ontario 12184 0 1 1 Ontario 12185 0 1 1 Ontario 12188 0 1 1 Ontario 12189 0 1 1 Ontario 12190 0 1 1 Ontario 12191 0 1 1 Ontario 12192 0 1 0 Ontario 12193 0 1 1 Ontario 12194 0 1 1 Ontario 12195 0 1 0 Ontario 12197 0 1 0 Ontario 12198 0 1 1

168

Ontario 12199 0 1 1 Ontario 12201 0 1 1 Ontario 12202 0 1 0 Ontario 12203 0 1 1 Ontario 12204 0 1 1 Ontario 12205 0 1 1 Ontario 12206 0 1 1 Ontario 12211 0 1 1 Ontario 12212 0 1 1 Ontario 12213 0 1 1 Ontario 12214 0 1 1 Ontario 12215 0 1 1 Ontario 12216 0 1 1 Ontario 12217 0 1 1 Ontario 13001 0 1 1 Ontario 13002 0 1 1 Ontario 13004 0 1 1 Ontario 13007 0 1 1 Ontario 13008 0 1 1 Ontario 13010 0 1 1 Ontario 13011 0 1 1 Ontario 13012 0 1 1 Ontario 13013 0 1 1 Ontario 13014 0 1 1 Ontario 13015 0 1 1 Ontario 13017 0 1 1 Ontario 13023 0 1 0 Ontario 13024 0 1 0 Ontario 13025 0 1 1 Ontario 13030 0 1 1 Ontario 13031 0 1 0 Ontario 13032 0 1 0 Ontario 13034 0 1 1 Ontario 13035 0 1 1 Ontario 13036 0 1 1 Ontario 13037 0 1 1 Ontario 13039 0 1 0 Ontario 13040 0 1 0 Ontario 13042 0 1 1 Ontario 13043 0 1 0 Ontario 13046 0 1 1 Ontario 13047 0 1 1 Ontario 13048 0 1 1 Ontario 13049 0 1 1 Ontario 13050 0 1 1 Ontario 13052 0 1 1 Ontario 13053 0 1 1 Ontario 13054 0 1 1 Ontario 13055 0 1 0 Ontario 13058 0 1 1 Ontario 13060 0 1 1 Ontario 13062 0 1 0

169

Ontario 13063 0 1 1 Ontario 13068 0 1 0 Ontario 13069 0 1 1 Ontario 13070 0 1 1 Ontario 13071 0 1 0 Ontario 13072 0 1 1 Ontario 13073 0 1 1 Ontario 13075 0 1 1 Ontario 13076 0 1 1 Ontario 13077 0 1 1 Ontario 13078 0 1 1 Ontario 13079 0 1 1 Ontario 13080 0 1 0 Ontario 13081 0 1 1 Ontario 13082 0 1 0 Ontario 13083 0 1 1 Ontario 13084 0 1 1 Ontario 13087 0 1 0 Ontario 13091 0 1 0 Ontario 13092 0 1 1 Ontario 13093 0 1 0 Ontario 13094 0 1 0 Ontario 13100 0 1 1 Ontario 13101 0 1 1 Ontario 13102 0 1 1 Ontario 13103 0 1 1 Ontario 13107 0 1 1 Ontario 13109 0 1 1 Ontario 13111 0 1 1 Ontario 13112 0 1 0 Ontario 13113 0 1 1 Ontario 13118 0 1 1 Ontario 13120 0 1 0 Ontario 13121 0 1 1 Ontario 13122 0 1 0 Ontario 13123 0 1 1 Ontario 13124 0 1 1 Ontario 13125 0 1 0 Ontario 13126 0 1 0 Ontario 13128 0 1 1 Ontario 13129 0 1 0 Ontario 13131 0 1 1 Ontario 13132 0 1 1 Ontario 13135 0 1 0 Ontario 13136 0 1 1 Ontario 13140 0 1 0 Ontario 13142 0 1 0 Ontario 13143 0 1 0 Ontario 13144 0 1 0 Ontario 13145 0 1 1 Ontario 13148 0 1 0 Ontario 13149 0 1 0

170

Ontario 13152 0 1 1 Ontario 13153 0 1 1 Ontario 13156 0 1 1 Ontario 13158 0 1 1 Ontario 13160 0 1 0 Ontario 13161 0 1 1 Ontario 13162 0 1 1 Ontario 13166 0 1 0 Ontario 13167 0 1 0 Ontario 13168 0 1 1 Ontario 13169 0 1 1 Ontario 13170 0 1 1 Ontario 13171 0 1 1 Ontario 13173 0 1 1 Ontario 13174 0 1 1 Ontario 13176 0 1 1 Ontario 13179 0 1 0 Ontario 13180 0 1 1 Ontario 13181 0 1 0 Ontario 13182 0 1 1 Ontario 13185 0 1 0 Ontario 13186 0 1 0 Ontario 13189 0 1 0 Ontario 13190 0 1 1 Ontario 13191 0 1 1 Ontario 13192 0 1 1 Ontario 13193 0 1 0 Ontario 13194 0 1 1 Ontario 13195 0 1 1 Ontario 13196 0 1 1 Ontario 13197 0 1 1 Ontario 13199 0 1 1 Ontario 13200 0 1 1 Ontario 13201 0 1 1 Ontario 13202 0 1 1 Ontario 13203 0 1 0 Ontario 13204 0 1 1 Ontario 13205 0 1 1 Ontario 13208 0 1 1 Ontario 13209 0 1 0 Ontario 13211 0 1 1 Ontario 13213 0 1 1 Ontario 13214 0 1 1 Ontario 13215 0 1 1 Ontario 13216 0 1 1 Ontario 13217 0 1 0 Ontario 13220 0 1 1 Ontario 13221 0 1 1 Ontario 13223 0 1 0 Ontario 13224 0 1 1 Ontario 13225 0 1 1 Ontario 13227 0 1 1

171

Ontario 13228 0 1 1 Ontario 13229 0 1 1 Ontario 14001 0 1 1 Ontario 14002 0 1 1 Ontario 14005 0 1 1 Ontario 14006 0 1 1 Ontario 14008 0 1 1 Ontario 14009 0 1 1 Ontario 14011 0 1 1 Ontario 14013 0 1 1 Ontario 14014 0 1 1 Ontario 14016 0 1 1 Ontario 14017 0 1 1 Ontario 14019 0 1 1 Ontario 14020 0 1 1 Ontario 14021 0 1 1 Ontario 14024 0 1 1 Ontario 14025 0 1 1 Ontario 14026 0 1 1 Ontario 14027 0 1 1 Ontario 14029 0 1 1 Ontario 14033 0 1 1 Ontario 14035 0 1 1 Ontario 14036 0 1 1 Ontario 14037 0 1 1 Ontario 14038 0 1 1 Ontario 14039 0 1 1 Ontario 14040 0 1 1 Ontario 14042 0 1 1 Ontario 14044 0 1 0 Ontario 14045 0 1 1 Ontario 14046 0 1 1 Ontario 14047 0 1 1 Ontario 14049 0 1 1 Ontario 14053 0 1 1 Ontario 14054 0 1 0 Ontario 14055 0 1 1 Ontario 14056 0 1 1 Ontario 14057 0 1 1 Ontario 14058 0 1 0 Ontario 14059 0 1 1 Ontario 14060 0 1 1 Ontario 14061 0 1 1 Ontario 14062 0 1 0 Ontario 14063 0 1 1 Ontario 14064 0 1 1 Ontario 14066 0 1 1 Ontario 14067 0 1 1 Ontario 14068 0 1 1 Ontario 14069 0 1 1 Ontario 14072 0 1 1 Ontario 14074 0 1 1

172

Ontario 14075 0 1 1 Ontario 14076 0 1 1 Ontario 14077 0 1 1 Ontario 14079 0 1 1 Ontario 14080 0 1 1 Ontario 14082 0 1 1 Ontario 14084 0 1 1 Ontario 14085 0 1 1 Ontario 14086 0 1 1 Ontario 14088 0 1 1 Ontario 14089 0 1 1 Ontario 14090 0 1 0 Ontario 14091 0 1 1 Ontario 14094 0 1 1 Ontario 14095 0 1 1 Ontario 14096 0 1 1 Ontario 14099 0 1 1 Ontario 14100 0 1 1 Ontario 14101 0 1 0 Ontario 14102 0 1 1 Ontario 14103 0 1 0 Ontario 14104 0 1 1 Ontario 14105 0 1 1 Ontario 14108 0 1 1 Ontario 14109 0 1 1 Ontario 14110 0 1 0 Ontario 14111 0 1 1 Ontario 14112 0 1 1 Ontario 14113 0 1 1 Ontario 14114 0 1 0 Ontario 14115 0 1 0 Ontario 14116 0 1 1 Ontario 14118 0 1 1 Ontario 14119 0 1 1 Ontario 14121 0 1 1 Ontario 14123 0 1 1 Ontario 14124 0 1 1 Ontario 14125 0 1 1 Ontario 14126 0 1 1 Ontario 14127 0 1 1 Ontario 14130 0 1 1 Ontario 14131 0 1 1 Ontario 14133 0 1 1 Ontario 14134 0 1 1 Ontario 14135 0 1 1 Ontario 14138 0 1 1 Ontario 14139 0 1 1 Ontario 14140 0 1 1 Ontario 14142 0 1 1 Ontario 14145 0 1 1 Ontario 14148 0 1 1 Ontario 14149 0 1 1

173

Ontario 14151 0 1 1 Ontario 14152 0 1 0 Ontario 14153 0 1 1 Ontario 14154 0 1 1 Ontario 14155 0 1 1 Ontario 14157 0 1 1 Ontario 14158 0 1 1 Ontario 14159 0 1 1 Ontario 14161 0 1 1 Ontario 14162 0 1 1 Ontario 14163 0 1 0 Ontario 14164 0 1 0 Ontario 14165 0 1 1 Ontario 14168 0 1 1 Ontario 14171 0 1 1 Ontario 14173 0 1 1 Ontario 14174 0 1 1 Ontario 14176 0 1 1 Ontario 14180 0 1 1 Ontario 14182 0 1 0 Ontario 14183 0 1 1 Ontario 14184 0 1 1 Ontario 14187 0 1 1 Ontario 14189 0 1 1 Ontario 14190 0 1 0 Ontario 14193 0 1 1 Ontario 14195 0 1 1 Ontario 14197 0 1 1 Ontario 14198 0 1 1 Ontario 14199 0 1 1 Ontario 14200 0 1 1 Ontario 14202 0 1 1 Ontario 14204 0 1 0 Ontario 14205 0 1 1 Ontario 14206 0 1 1 Ontario 14207 0 1 1 Ontario 14208 0 1 1 Ontario 14209 0 1 1 Ontario 14210 0 1 1 Ontario 14211 0 1 1 Ontario 14212 0 1 1 Ontario 14214 0 1 1 Ontario 14215 0 1 1 Ontario 14216 0 1 1 Ontario 14217 0 1 1 Ontario 14218 0 1 1 Ontario 14220 0 1 1 Ontario 14221 0 1 1 Ontario 14222 0 1 1 Ontario 14224 0 1 1 Ontario 14226 0 1 1 Ontario 14229 0 1 1

174

Ontario 14230 0 1 1 Ontario 14232 0 1 1 Ontario 14233 0 1 1 Ontario 14234 0 1 1 Ontario 14235 0 1 1 Ontario 14236 0 1 1 Ontario 14237 0 1 1 Ontario 14238 0 1 1 Ontario 14243 0 1 1 Ontario 14245 0 1 0 Ontario 14246 0 1 0 Ontario 14247 0 1 1 Ontario 14249 0 1 0 Ontario 14253 0 1 1 Ontario 14254 0 1 1 Ontario 14255 0 1 1 Ontario 14257 0 1 1 Ontario 14258 0 1 1 Ontario 14259 0 1 1 Ontario 14260 0 1 1 Ontario 14261 0 1 1 Ontario 14262 0 1 1 Ontario 14263 0 1 1 Ontario 14264 0 1 1 Ontario 14265 0 1 1 Ontario 14266 0 1 1 Ontario 14268 0 1 1 Ontario 14269 0 1 1 Ontario 14270 0 1 1 Ontario 14271 0 1 1 Ontario 14272 0 1 1 Ontario 14273 0 1 1 Ontario 14274 0 1 1 Ontario 14275 0 1 1 Ontario 14277 0 1 1 Ontario 14279 0 1 1 Ontario 14280 0 1 0 Ontario 14281 0 1 1 Ontario 14282 0 1 1 Ontario 14283 0 1 1 Ontario 14284 0 1 1 Ontario 14286 0 1 1 Ontario 14287 0 1 1 Ontario 14288 0 1 1 Ontario 14289 0 1 1 Ontario 14290 0 1 1 Ontario 14291 0 1 1 Ontario 14293 0 1 1 Ontario 14294 0 1 1 Ontario 14295 0 1 1 Ontario 15001 0 1 1 Ontario 15002 0 1 1

175

Ontario 15003 0 1 1 Ontario 15004 0 1 1 Ontario 15007 0 1 1 Ontario 15008 0 1 1 Ontario 15009 0 1 1 Ontario 15010 0 1 1 Ontario 15011 0 1 1 Ontario 15013 0 1 1 Ontario 15015 0 1 1 Ontario 15016 0 1 1 Ontario 15017 0 1 1 Ontario 15018 0 1 1 Ontario 15019 0 1 1 Ontario 15020 0 1 1 Ontario 15022 0 1 0 Ontario 15023 0 1 1 Ontario 15024 0 1 1 Ontario 15025 0 1 1 Ontario 15026 0 1 1 Ontario 15027 0 1 1 Ontario 15028 0 1 1 Ontario 15029 0 1 1 Ontario 15030 0 1 0 Ontario 15031 0 1 0 Ontario 15032 0 1 1 Ontario 15033 0 1 1 Ontario 15034 0 1 1 Ontario 15035 0 1 0 Ontario 15036 0 1 1 Ontario 15037 0 1 1 Ontario 15038 0 1 1 Ontario 15039 0 1 1 Ontario 15040 0 1 1 Ontario 15042 0 1 1 Ontario 15044 0 1 1 Ontario 15045 0 1 1 Ontario 15046 0 1 1 Ontario 15047 0 1 1 Ontario 15049 0 1 0 Ontario 15051 0 1 1 Ontario 15052 0 1 1 Ontario 15053 0 1 1 Ontario 15055 0 1 0 Ontario 15056 0 1 1 Ontario 15057 0 1 1 Ontario 15058 0 1 1 Ontario 15059 0 1 1 Ontario 15060 0 1 1 Ontario 15061 0 1 1 Ontario 15062 0 1 0 Ontario 15063 0 1 1 Ontario 15064 0 1 1

176

Ontario 15065 0 1 1 Ontario 15066 0 1 1 Ontario 15067 0 1 1 Ontario 15068 0 1 1 Ontario 15069 0 1 1 Ontario 15070 0 1 1 Ontario 15071 0 1 1 Ontario 15072 0 1 1 Ontario 15073 0 1 1 Ontario 15076 0 1 1 Ontario 15081 0 1 1 Ontario 15082 0 1 1 Ontario 15086 0 1 0 Ontario 15087 0 1 1 Ontario 15088 0 1 1 Ontario 15089 0 1 1 Ontario 15090 0 1 1 Ontario 15091 0 1 1 Ontario 15092 0 1 1 Ontario 15095 0 1 1 Ontario 15096 0 1 1 Ontario 15097 0 1 1 Ontario 15098 0 1 1 Ontario 15099 0 1 1 Ontario 15101 0 1 1 Ontario 15102 0 1 1 Ontario 15103 0 1 1 Ontario 15104 0 1 1 Ontario 15105 0 1 1 Ontario 15107 0 1 1 Ontario 15108 0 1 1 Ontario 15109 0 1 1 Ontario 15110 0 1 1 Ontario 15111 0 1 1 Ontario 15112 0 1 1 Ontario 15114 0 1 1 Ontario 15116 0 1 1 Ontario 15117 0 1 1 Ontario 15118 0 1 1 Ontario 15120 0 1 1 Ontario 15121 0 1 1 Ontario 15122 0 1 1 Ontario 15124 0 1 1 Ontario 15125 0 1 0 Ontario 15126 0 1 1 Ontario 15127 0 1 1 Ontario 15128 0 1 1 Ontario 15135 0 1 1 Ontario 15138 0 1 0 Ontario 15140 0 1 1 Ontario 15143 0 1 1 Ontario 15144 0 1 0

177

Ontario 15145 0 1 1 Ontario 15146 0 1 1 Ontario 15147 0 1 1 Ontario 15148 0 1 1 Ontario 15149 0 1 1 Ontario 15151 0 1 1 Ontario 15154 0 1 1 Ontario 15159 0 1 1 Ontario 15160 0 1 1 Ontario 15164 0 1 0 Ontario 15169 0 1 1 Ontario 15170 0 1 1 Ontario 15171 0 1 1 Ontario 15175 0 1 1 Ontario 15178 0 1 1 Ontario 15179 0 1 1 Ontario 15182 0 1 1 Ontario 15183 0 1 1 Ontario 15184 0 1 1 Ontario 15186 0 1 1 Ontario 15187 0 1 1 Ontario 15188 0 1 0 Ontario 15189 0 1 1 Ontario 15190 0 1 1 Ontario 15191 0 1 1 Ontario 15192 0 1 1 Ontario 15193 0 1 1 Ontario 15194 0 1 1 Ontario 15197 0 1 1 Ontario 15199 0 1 1 Ontario 15201 0 1 1 Ontario 15202 0 1 1 Ontario 15203 0 1 1 Ontario 15204 0 1 1 Ontario 15206 0 1 1 Ontario 15207 0 1 1 Ontario 15208 0 1 0 Ontario 15209 0 1 1 Ontario 15210 0 1 1 Ontario 15211 0 1 1 Ontario 15212 0 1 1 Ontario 15213 0 1 1 Ontario 15215 0 1 1 Ontario 15217 0 1 1 Ontario 15219 0 1 1 Ontario 15221 0 1 1 Ontario 15222 0 1 1 Ontario 15223 0 1 1 Ontario 15224 0 1 1 Ontario 15225 0 1 1 Ontario 15228 0 1 1 Ontario 15229 0 1 1

178

Ontario 15230 0 1 1 Ontario 15231 0 1 1 Ontario 15232 0 1 1 Ontario 15233 0 1 1 Ontario 15235 0 1 1 Ontario 15236 0 1 1 Ontario 15238 0 1 1 Ontario 15239 0 1 1 Ontario 15240 0 1 1 Ontario 15242 0 1 1 Ontario 15243 0 1 0 Ontario 15245 0 1 1 Ontario 15246 0 1 1 Ontario 15247 0 1 1 Ontario 15248 0 1 1 Ontario 15249 0 1 1 Ontario 15251 0 1 1 Ontario 15252 0 1 1 Ontario 15253 0 1 1 Ontario 15254 0 1 1 Ontario 15255 0 1 1 Ontario 15257 0 1 1 Ontario 15258 0 1 1 Ontario 15259 0 1 1 Ontario 15261 0 1 1 Ontario 15262 0 1 1 Ontario 15264 0 1 1 Ontario 15265 0 1 1 Ontario 15266 0 1 1 Ontario 15267 0 1 1 Ontario 15268 0 1 1 Ontario 15269 0 1 1 Ontario 15270 0 1 1 Ontario 15272 0 1 1 Ontario 15275 0 1 1 Ontario 15276 0 1 1 Ontario 15277 0 1 1 Ontario 15278 0 1 1 Ontario 15279 0 1 1 Ontario 15280 0 1 0 Ontario 15281 0 1 1 Ontario 15282 0 1 1 Ontario 15284 0 1 1 Ontario 16004 0 1 1 Ontario 16005 0 1 1 Ontario 16006 0 1 0 Ontario 16010 0 1 1 Ontario 16012 0 1 1 Ontario 16013 0 1 1 Ontario 16014 0 1 1 Ontario 16015 0 1 1 Ontario 16016 0 1 1

179

Ontario 16017 0 1 1 Ontario 16018 0 1 0 Ontario 16020 0 1 0 Ontario 16021 0 1 1 Ontario 16023 0 1 1 Ontario 16024 0 1 1 Ontario 16026 0 1 1 Ontario 16030 0 1 1 Ontario 16032 0 1 1 Ontario 16033 0 1 1 Ontario 16037 0 1 1 Ontario 16038 0 1 1 Ontario 16039 0 1 0 Ontario 16040 0 1 1 Ontario 16041 0 1 1 Ontario 16042 0 1 1 Ontario 16043 0 1 1 Ontario 16044 0 1 1 Ontario 16045 0 1 1 Ontario 16046 0 1 1 Ontario 16047 0 1 1 Ontario 16050 0 1 1 Ontario 16052 0 1 0 Ontario 16055 0 1 1 Ontario 16057 0 1 0 Ontario 16058 0 1 1 Ontario 16059 0 1 1 Ontario 16062 0 1 0 Ontario 16063 0 1 1 Ontario 16064 0 1 0 Ontario 16067 0 1 0 Ontario 16068 0 1 1 Ontario 16069 0 1 1 Ontario 16071 0 1 0 Ontario 16075 0 1 1 Ontario 16076 0 1 1 Ontario 16078 0 1 1 Ontario 16080 0 1 1 Ontario 16081 0 1 1 Ontario 16083 0 1 0 Ontario 16084 0 1 1 Ontario 16086 0 1 1 Ontario 16087 0 1 1 Ontario 16089 0 1 1 Ontario 16090 0 1 1 Ontario 16092 0 1 1 Ontario 16093 0 1 1 Ontario 16095 0 1 1 Ontario 16096 0 1 0 Ontario 16101 0 1 1 Ontario 16103 0 1 1 Ontario 16105 0 1 1

180

Ontario 16107 0 1 1 Ontario 16108 0 1 1 Ontario 16110 0 1 1 Ontario 16111 0 1 1 Ontario 16113 0 1 1 Ontario 16114 0 1 1 Ontario 16119 0 1 1 Ontario 16121 0 1 1 Ontario 16122 0 1 1 Ontario 16123 0 1 1 Ontario 16125 0 1 0 Ontario 16128 0 1 1 Ontario 16129 0 1 0 Ontario 16131 0 1 0 Ontario 16132 0 1 1 Ontario 16134 0 1 1 Ontario 16135 0 1 1 Ontario 16136 0 1 1 Ontario 16137 0 1 1 Ontario 16139 0 1 1 Ontario 16140 0 1 1 Ontario 16142 0 1 1 Ontario 16145 0 1 1 Ontario 16146 0 1 1 Ontario 16147 0 1 1 Ontario 16149 0 1 1 Ontario 16150 0 1 1 Ontario 16151 0 1 1 Ontario 16152 0 1 0 Ontario 16156 0 1 1 Ontario 16158 0 1 1 Ontario 16160 0 1 1 Ontario 16161 0 1 1 Ontario 16163 0 1 1 Ontario 16164 0 1 1 Ontario 16165 0 1 1 Ontario 16166 0 1 1 Ontario 16167 0 1 1 Ontario 16168 0 1 1 Ontario 16169 0 1 1 Ontario 16171 0 1 1 Ontario 16172 0 1 1 Ontario 16173 0 1 0 Ontario 16174 0 1 1 Ontario 16175 0 1 1 Ontario 16177 0 1 1 Ontario 16178 0 1 1 Ontario 16179 0 1 1 Ontario 16182 0 1 0 Ontario 16183 0 1 1 Ontario 16184 0 1 1 Ontario 16185 0 1 1

181

Ontario 16188 0 1 0 Ontario 16189 0 1 1 Ontario 16190 0 1 1 Ontario 16193 0 1 0 Ontario 16194 0 1 1 Ontario 16195 0 1 1 Ontario 16196 0 1 1 Ontario 16198 0 1 1 Ontario 16199 0 1 0 Ontario 16200 0 1 0 Ontario 16201 0 1 0 Ontario 16208 0 1 0 Ontario 16214 0 1 0 Ontario 16216 0 1 1 Ontario 16220 0 1 1 Ontario 16223 0 1 0 Ontario 16225 0 1 0 Ontario 16233 0 1 1 Ontario 16241 0 1 1 Ontario 16242 0 1 1 Ontario 16245 0 1 1 Ontario 16247 0 1 1 Ontario 16248 0 1 1 Ontario 16251 0 1 1 Ontario 16252 0 1 1 Ontario 16256 0 1 0 Ontario 16261 0 1 1 Ontario 16262 0 1 1 Ontario 16265 0 1 0 Ontario 16266 0 1 1 Ontario 16269 0 1 1 Ontario 16270 0 1 1 Ontario 16272 0 1 1 Ontario 16273 0 1 1 Ontario 16275 0 1 1 Ontario 16277 0 1 1 Ontario 16278 0 1 1 Ontario 16280 0 1 1 Ontario 16281 0 1 1 Ontario 16282 0 1 0 Ontario 16284 0 1 1 Ontario 16287 0 1 1 Ontario 16289 0 1 1 Ontario 16290 0 1 1 Ontario 16291 0 1 1 Ontario 16294 0 1 1 Ontario 16296 0 1 1 Ontario 16299 0 1 1 Ontario 16300 0 1 1 Ontario 16303 0 1 1 Ontario 16304 0 1 1 Ontario 16306 0 1 1

182

Ontario 16308 0 1 1 Ontario 16309 0 1 0 Ontario 16311 0 1 1 Ontario 16312 0 1 1 Ontario 16314 0 1 1 Ontario 16315 0 1 1 Ontario 16317 0 1 1 Ontario 16320 0 1 1 Ontario 16321 0 1 1 Ontario 16326 0 1 1 Ontario 16327 0 1 1 Ontario 16330 0 1 1 Ontario 16332 0 1 0 Ontario 16334 0 1 1 Ontario 16337 0 1 1 Ontario 16338 0 1 0 Ontario 16339 0 1 1 Ontario 16340 0 1 1 Ontario 16341 0 1 1 Ontario 16343 0 1 1 Ontario 16344 0 1 1 Ontario 16346 0 1 1 Ontario 16347 0 1 1 Ontario 16349 0 1 1 Ontario 16350 0 1 0 Ontario 16351 0 1 1 Ontario 16353 0 1 0 Ontario 16354 0 1 1 Ontario 16356 0 1 1 Ontario 16358 0 1 1 Ontario 16359 0 1 1 Ontario 21001 0 1 1 Ontario 21002 0 1 1 Ontario 21003 0 1 1 Ontario 21005 0 1 1 Ontario 21006 0 1 1 Ontario 21008 0 1 1 Ontario 21009 0 1 1 Ontario 21010 0 1 1 Ontario 21011 0 1 1 Ontario 21012 0 1 0 Ontario 21013 0 1 1 Ontario 21016 0 1 0 Ontario 21017 0 1 0 Ontario 21018 0 1 1 Ontario 21019 0 1 1 Ontario 21020 0 1 1 Ontario 21022 0 1 1 Ontario 21024 0 1 1 Ontario 21025 0 1 1 Ontario 21026 0 1 0 Ontario 21027 0 1 0

183

Ontario 21029 0 1 0 Ontario 21030 0 1 0 Ontario 21031 0 1 1 Ontario 21032 0 1 1 Ontario 21033 0 1 1 Ontario 21034 0 1 1 Ontario 21035 0 1 1 Ontario 21037 0 1 1 Ontario 21038 0 1 1 Ontario 21039 0 1 1 Ontario 21041 0 1 1 Ontario 21043 0 1 0 Ontario 21045 0 1 1 Ontario 21046 0 1 1 Ontario 21047 0 1 1 Ontario 21048 0 1 1 Ontario 21049 0 1 0 Ontario 21050 0 1 1 Ontario 21051 0 1 1 Ontario 21054 0 1 1 Ontario 21055 0 1 1 Ontario 21056 0 1 1 Ontario 21057 0 1 1 Ontario 21058 1 1 NA Ontario 21059 0 1 1 Ontario 21060 0 1 1 Ontario 21061 0 1 1 Ontario 21062 0 1 1 Ontario 21063 0 1 1 Ontario 21065 0 1 0 Ontario 21067 0 1 0 Ontario 21068 0 1 1 Ontario 21069 0 1 1 Ontario 21072 0 1 1 Ontario 21073 0 1 1 Ontario 21074 0 1 1 Ontario 21075 0 1 1 Ontario 21077 0 1 1 Ontario 21078 0 1 1 Ontario 21079 0 1 1 Ontario 21081 0 1 1 Ontario 21082 0 1 1 Ontario 21083 0 1 0 Ontario 21084 0 1 1 Ontario 21086 0 1 0 Ontario 21087 0 1 1 Ontario 21089 0 1 0 Ontario 21090 0 1 1 Ontario 21099 0 1 1 Ontario 21100 0 1 1 Ontario 21101 0 1 1 Ontario 21102 0 1 1

184

Ontario 21103 0 1 1 Ontario 21104 0 1 1 Ontario 21106 0 1 1 Ontario 21111 0 1 1 Ontario 21113 0 1 1 Ontario 21114 0 1 1 Ontario 21115 0 1 1 Ontario 21116 0 1 1 Ontario 21117 0 1 1 Ontario 21118 0 1 1 Ontario 21122 0 1 1 Ontario 21126 0 1 1 Ontario 21127 0 1 1 Ontario 21128 0 1 0 Ontario 21130 0 1 1 Ontario 21132 0 1 0 Ontario 21133 0 1 1 Ontario 21134 0 1 1 Ontario 21137 0 1 1 Ontario 21138 0 1 1 Ontario 21139 0 1 1 Ontario 21140 0 1 0 Ontario 21142 0 1 1 Ontario 21143 0 1 1 Ontario 21144 0 1 1 Ontario 21145 0 1 1 Ontario 21146 0 1 1 Ontario 21147 0 1 1 Ontario 21148 0 1 1 Ontario 21150 0 1 1 Ontario 21151 0 1 1 Ontario 21152 0 1 1 Ontario 21153 0 1 1 Ontario 21155 0 1 1 Ontario 21156 0 1 1 Ontario 21157 0 1 1 Ontario 21158 0 1 1 Ontario 21159 0 1 1 Ontario 21162 0 1 0 Ontario 21163 0 1 1 Ontario 21167 0 1 0 Ontario 21168 0 1 1 Ontario 21171 0 1 1 Ontario 21172 0 1 1 Ontario 21174 0 1 1 Ontario 21175 0 1 0 Ontario 21176 0 1 1 Ontario 21177 0 1 1 Ontario 21179 0 1 1 Ontario 21181 0 1 1 Ontario 21182 0 1 0 Ontario 21183 0 1 1

185

Ontario 21185 0 1 0 Ontario 21186 0 1 1 Ontario 21188 0 1 1 Ontario 21189 0 1 1 Ontario 21192 0 1 1 Ontario 21193 0 1 1 Ontario 21194 0 1 1 Ontario 21195 0 1 1 Ontario 21196 0 1 1 Ontario 21197 0 1 1 Ontario 21198 0 1 1 Ontario 21199 0 1 1 Ontario 21203 0 1 0 Ontario 21204 0 1 1 Ontario 21205 0 1 1 Ontario 21206 0 1 0 Ontario 21207 0 1 1 Ontario 21208 0 1 1 Ontario 21210 0 1 1 Ontario 21212 0 1 1 Ontario 21213 0 1 1 Ontario 21214 0 1 1 Ontario 21215 1 1 NA Ontario 21216 0 1 1 Ontario 21217 0 1 1 Ontario 21219 0 1 1 Ontario 21220 0 1 1 Ontario 21221 0 1 1 Ontario 21222 0 1 1 Ontario 21223 0 1 1 Ontario 21224 0 1 1 Ontario 21225 0 1 1 Ontario 21226 0 1 1 Ontario 21228 0 1 1 Ontario 21229 0 1 0 Ontario 21230 0 1 0 Ontario 21231 0 1 0 Ontario 21232 0 1 1 Ontario 22001 0 1 1 Ontario 22002 0 1 1 Ontario 22004 0 1 1 Ontario 22005 0 1 1 Ontario 22008 0 1 1 Ontario 22009 0 1 1 Ontario 22010 0 1 0 Ontario 22014 0 1 1 Ontario 22018 0 1 0 Ontario 22021 0 1 1 Ontario 22023 0 1 1 Ontario 22024 0 1 1 Ontario 22026 0 1 1 Ontario 22027 0 1 1

186

Ontario 22028 0 1 1 Ontario 22030 0 1 0 Ontario 22031 0 1 1 Ontario 22032 0 1 1 Ontario 22034 0 1 1 Ontario 22036 0 1 1 Ontario 22038 0 1 1 Ontario 22040 0 1 1 Ontario 22041 0 1 0 Ontario 22042 0 1 1 Ontario 22043 0 1 1 Ontario 22045 0 1 1 Ontario 22046 0 1 1 Ontario 22048 0 1 1 Ontario 22049 0 1 1 Ontario 22051 0 1 1 Ontario 22052 0 1 1 Ontario 22053 0 1 1 Ontario 22059 0 1 0 Ontario 22063 0 1 1 Ontario 22064 0 1 1 Ontario 22066 0 1 0 Ontario 22067 0 1 1 Ontario 22068 0 1 0 Ontario 22075 0 1 0 Ontario 22076 0 1 1 Ontario 22077 0 1 1 Ontario 22078 0 1 0 Ontario 22080 0 1 1 Ontario 22082 0 1 1 Ontario 22084 0 1 0 Ontario 22085 0 1 0 Ontario 22088 0 1 1 Ontario 22089 0 1 1 Ontario 22090 0 1 1 Ontario 22091 0 1 0 Ontario 22092 0 1 1 Ontario 22095 0 1 1 Ontario 22096 0 1 1 Ontario 22098 0 1 1 Ontario 22099 0 1 1 Ontario 22100 0 1 0 Ontario 22102 0 1 1 Ontario 22104 0 1 1 Ontario 22107 0 1 0 Ontario 22108 0 1 0 Ontario 22110 0 1 0 Ontario 22111 0 1 1 Ontario 22112 0 1 1 Ontario 22113 0 1 1 Ontario 22114 0 1 1 Ontario 22115 0 1 1

187

Ontario 22116 0 1 1 Ontario 22117 0 1 1 Ontario 22118 0 1 0 Ontario 22119 0 1 1 Ontario 22121 0 1 1 Ontario 22122 0 1 1 Ontario 22126 0 1 1 Ontario 22128 0 1 1 Ontario 22129 0 1 1 Ontario 22132 0 1 1 Ontario 22133 0 1 1 Ontario 22134 0 1 1 Ontario 22136 0 1 1 Ontario 22137 0 1 0 Ontario 22138 0 1 1 Ontario 22139 0 1 1 Ontario 22141 0 1 1 Ontario 22143 0 1 1 Ontario 22146 0 1 1 Ontario 22148 0 1 1 Ontario 22151 0 1 1 Ontario 22152 0 1 1 Ontario 22153 0 1 1 Ontario 22154 0 1 1 Ontario 22155 0 1 1 Ontario 22156 0 1 1 Ontario 22159 0 1 1 Ontario 22160 0 1 1 Ontario 22161 0 1 1 Ontario 22168 0 1 1 Ontario 22169 0 1 1 Ontario 22173 0 1 1 Ontario 22174 0 1 1 Ontario 22175 0 1 1 Ontario 22176 0 1 1 Ontario 22177 0 1 1 Ontario 22179 0 1 1 Ontario 22182 0 1 0 Ontario 22184 0 1 0 Ontario 22187 0 1 0 Ontario 22188 0 1 1 Ontario 22190 0 1 1 Ontario 22191 0 1 0 Ontario 22194 0 1 1 Ontario 22195 0 1 1 Ontario 22197 0 1 1 Ontario 22199 0 1 1 Ontario 22201 0 1 1 Ontario 22202 0 1 1 Ontario 22203 0 1 1 Ontario 22204 0 1 1 Ontario 22205 0 1 1

188

Ontario 22208 0 1 1 Ontario 22212 0 1 0 Ontario 22213 0 1 1 Ontario 22214 0 1 0 Ontario 22216 0 1 1 Ontario 22218 0 1 1 Ontario 22219 0 1 1 Ontario 22220 0 1 1 Ontario 22221 0 1 1 Ontario 22223 0 1 0 Ontario 22226 0 1 1 Ontario 22228 0 1 0 Ontario 22230 0 1 1 Ontario 22231 0 1 1 Ontario 22232 0 1 1 Ontario 22233 0 1 1 Ontario 22234 0 1 1 Ontario 22235 0 1 0 Ontario 22236 0 1 1 Ontario 22237 0 1 1 Ontario 23002 0 1 1 Ontario 23003 0 1 1 Ontario 23004 0 1 0 Ontario 23005 0 1 1 Ontario 23006 0 1 0 Ontario 23007 0 1 1 Ontario 23008 0 1 1 Ontario 23009 0 1 1 Ontario 23011 0 1 1 Ontario 23012 0 1 1 Ontario 23013 0 1 1 Ontario 23014 0 1 1 Ontario 23015 0 1 1 Ontario 23016 0 1 1 Ontario 23017 0 1 1 Ontario 23018 0 1 1 Ontario 23020 0 1 1 Ontario 23023 0 1 1 Ontario 23024 0 1 1 Ontario 23025 0 1 1 Ontario 23027 0 1 1 Ontario 23028 0 1 1 Ontario 23029 0 1 1 Ontario 23031 0 1 1 Ontario 23033 0 1 0 Ontario 23034 0 1 1 Ontario 23035 0 1 1 Ontario 23036 0 1 1 Ontario 23037 0 1 0 Ontario 23038 0 1 1 Ontario 23040 0 1 1 Ontario 23041 0 1 0

189

Ontario 23043 0 1 1 Ontario 23044 0 1 1 Ontario 23046 0 1 1 Ontario 23047 0 1 1 Ontario 23048 0 1 0 Ontario 23049 0 1 0 Ontario 23051 0 1 1 Ontario 23052 0 1 1 Ontario 23053 0 1 1 Ontario 23054 0 1 0 Ontario 23055 0 1 0 Ontario 23056 0 1 1 Ontario 23057 1 1 NA Ontario 23058 0 1 1 Ontario 23059 0 1 1 Ontario 23060 0 1 0 Ontario 23061 0 1 1 Ontario 23062 0 1 1 Ontario 23063 0 1 1 Ontario 23064 0 1 1 Ontario 23065 0 1 1 Ontario 23066 0 1 1 Ontario 23067 0 1 1 Ontario 23069 0 1 0 Ontario 23070 0 1 1 Ontario 23071 0 1 1 Ontario 23072 0 1 1 Ontario 23073 0 1 0 Ontario 23074 0 1 1 Ontario 23076 0 1 1 Ontario 23077 0 1 0 Ontario 23078 0 1 1 Ontario 23079 0 1 0 Ontario 23080 0 1 1 Ontario 23081 0 1 1 Ontario 23084 0 1 1 Ontario 23085 0 1 1 Ontario 23086 0 1 1 Ontario 23087 0 1 1 Ontario 23089 0 1 0 Ontario 23091 0 1 0 Ontario 23093 0 1 0 Ontario 23094 0 1 1 Ontario 23095 0 1 0 Ontario 23097 0 1 1 Ontario 23098 0 1 1 Ontario 23099 0 1 0 Ontario 23100 0 1 1 Ontario 23101 0 1 1 Ontario 23102 0 1 0 Ontario 23104 0 1 1 Ontario 23106 0 1 1

190

Ontario 23107 0 1 1 Ontario 23108 0 1 1 Ontario 23109 0 1 1 Ontario 23110 0 1 1 Ontario 23111 0 1 1 Ontario 23112 0 1 0 Ontario 23113 1 1 NA Ontario 23114 0 1 1 Ontario 23116 0 1 1 Ontario 23117 0 1 1 Ontario 23119 0 1 0 Ontario 23121 0 1 1 Ontario 23123 0 1 1 Ontario 23125 1 1 NA Ontario 23126 0 1 0 Ontario 23127 0 1 1 Ontario 23130 0 1 1 Ontario 23132 0 1 0 Ontario 23133 0 1 0 Ontario 23134 0 1 1 Ontario 23135 0 1 1 Ontario 23138 1 1 NA Ontario 23139 0 1 1 Ontario 23140 0 1 1 Ontario 23141 0 1 1 Ontario 23144 0 1 1 Ontario 23145 0 1 1 Ontario 23146 0 1 0 Ontario 23147 0 1 1 Ontario 23148 0 1 1 Ontario 23149 0 1 0 Ontario 23150 0 1 1 Ontario 23151 0 1 0 Ontario 23152 0 1 1 Ontario 23153 0 1 1 Ontario 23154 0 1 1 Ontario 23155 0 1 1 Ontario 23156 0 1 1 Ontario 23157 0 1 0 Ontario 23158 0 1 1 Ontario 23159 0 1 1 Ontario 23160 0 1 1 Ontario 23161 0 1 1 Ontario 23162 0 1 1 Ontario 23163 0 1 1 Ontario 24003 0 1 1 Ontario 24004 0 1 1 Ontario 24009 0 1 1 Ontario 24011 0 1 0 Ontario 24012 0 1 0 Ontario 24013 0 1 1 Ontario 24015 0 1 1

191

Ontario 24016 0 1 1 Ontario 24017 1 1 NA Ontario 24021 0 1 1 Ontario 24022 0 1 1 Ontario 24023 0 1 1 Ontario 24024 0 1 1 Ontario 24025 0 1 0 Ontario 24026 0 1 1 Ontario 24027 0 1 1 Ontario 24028 0 1 1 Ontario 24029 0 1 1 Ontario 24032 0 1 0 Ontario 24033 0 1 1 Ontario 24035 0 1 1 Ontario 24036 0 1 1 Ontario 24039 0 1 1 Ontario 24040 0 1 1 Ontario 24041 0 1 1 Ontario 24042 0 1 0 Ontario 24044 0 1 1 Ontario 24045 0 1 1 Ontario 24046 0 1 1 Ontario 24049 0 1 1 Ontario 24050 0 1 1 Ontario 24052 0 1 0 Ontario 24053 0 1 1 Ontario 24054 0 1 0 Ontario 24055 0 1 0 Ontario 24056 0 1 1 Ontario 24058 0 1 0 Ontario 24059 0 1 1 Ontario 24060 0 1 1 Ontario 24062 0 1 1 Ontario 24063 0 1 0 Ontario 24064 0 1 1 Ontario 24065 0 1 0 Ontario 24066 0 1 1 Ontario 24068 0 1 1 Ontario 24069 0 1 1 Ontario 24071 0 1 0 Ontario 24072 0 1 1 Ontario 24073 0 1 0 Ontario 24074 0 1 1 Ontario 24076 0 1 1 Ontario 24077 0 1 0 Ontario 24078 0 1 0 Ontario 24079 0 1 1 Ontario 24080 0 1 1 Ontario 24081 0 1 0 Ontario 24083 0 1 1 Ontario 24084 0 1 1 Ontario 24085 0 1 0

192

Ontario 24086 0 1 1 Ontario 24087 1 1 NA Ontario 24088 0 1 1 Ontario 24090 0 1 1 Ontario 24091 0 1 1 Ontario 24092 0 1 1 Ontario 24093 0 1 1 Ontario 24094 0 1 1 Ontario 24101 0 1 0 Ontario 24102 0 1 0 Ontario 24103 0 1 1 Ontario 24106 0 1 1 Ontario 24109 0 1 1 Ontario 24110 0 1 1 Ontario 24111 0 1 0 Ontario 24112 0 1 1 Ontario 24113 0 1 1 Ontario 24114 0 1 1 Ontario 24117 0 1 0 Ontario 24118 0 1 0 Ontario 24123 0 1 1 Ontario 24124 0 1 0 Ontario 24125 0 1 1 Ontario 24127 0 1 1 Ontario 24129 0 1 1 Ontario 24130 0 1 1 Ontario 24132 0 1 1 Ontario 24136 0 1 1 Ontario 24137 0 1 1 Ontario 24138 0 1 1 Ontario 24139 0 1 1 Ontario 24142 0 1 0 Ontario 24143 0 1 1 Ontario 24144 0 1 0 Ontario 24145 0 1 1 Ontario 24146 0 1 0 Ontario 24147 0 1 1 Ontario 24148 1 1 NA Ontario 24150 0 1 0 Ontario 24151 0 1 1 Ontario 24152 0 1 1 Ontario 24153 0 1 1 Ontario 24154 0 1 1 Ontario 24155 0 1 1 Ontario 24156 0 1 1 Ontario 24158 0 1 0 Ontario 24159 0 1 1 Ontario 24163 0 1 1 Ontario 24164 0 1 0 Ontario 24165 0 1 1 Ontario 24166 0 1 1 Ontario 24169 1 1 NA

193

Ontario 24170 0 1 1 Ontario 24171 0 1 1 Ontario 24172 0 1 1 Ontario 24173 0 1 1 Ontario 25001 0 1 1 Ontario 25007 0 1 1 Ontario 25009 0 1 0 Ontario 25010 0 1 1 Ontario 25011 0 1 1 Ontario 25012 0 1 1 Ontario 25013 0 1 1 Ontario 25014 0 1 0 Ontario 25015 0 1 1 Ontario 25017 0 1 1 Ontario 25018 0 1 0 Ontario 25021 0 1 1 Ontario 25024 0 1 1 Ontario 25035 0 1 1 Ontario 25036 0 1 1 Ontario 25038 0 1 1 Ontario 25039 0 1 1 Ontario 25040 0 1 1 Ontario 25043 0 1 1 Ontario 25044 0 1 1 Ontario 25045 0 1 1 Ontario 25046 0 1 1 Ontario 25048 0 1 1 Ontario 25049 0 1 1 Ontario 25052 0 1 1 Ontario 25057 0 1 1 Ontario 25059 0 1 0 Ontario 25061 0 1 1 Ontario 25062 0 1 1 Ontario 25066 1 1 NA Ontario 25068 0 1 1 Ontario 25072 0 1 1 Ontario 25074 0 1 1 Ontario 25077 0 1 1 Ontario 25078 0 1 1 Ontario 25079 0 1 1 Ontario 25080 0 1 1 Ontario 25083 0 1 0 Ontario 25086 0 1 1 Ontario 25090 0 1 1 Ontario 25091 0 1 0 Ontario 25097 0 1 1 Ontario 25098 0 1 1 Ontario 25099 0 1 1 Ontario 25100 0 1 1 Ontario 25101 0 1 1 Ontario 25102 0 1 1 Ontario 25104 0 1 1

194

Ontario 25105 0 1 0 Ontario 25108 0 1 1 Ontario 25112 0 1 1 Ontario 25114 0 1 1 Ontario 25115 0 1 1 Ontario 25118 0 1 0 Ontario 25120 0 1 1 Ontario 25121 0 1 1 Ontario 25122 0 1 1 Ontario 25123 0 1 0 Ontario 25124 0 1 1 Ontario 25127 1 1 NA Ontario 25128 0 1 1 Ontario 25131 0 1 1 Ontario 25133 0 1 1 Ontario 25134 0 1 1 Ontario 25139 0 1 1 Ontario 25140 0 1 1 Ontario 25141 0 1 1 Ontario 25143 0 1 0 Ontario 25144 0 1 1 Ontario 25145 0 1 1 Ontario 25149 0 1 1 Ontario 25153 0 1 1 Ontario 25154 0 1 1 Ontario 25155 0 1 1 Ontario 25162 0 1 1 Ontario 25163 0 1 1 Ontario 25166 0 1 1 Ontario 25169 0 1 1 Ontario 25171 1 0 NA Ontario 25172 0 1 1 Ontario 25173 0 1 1 Ontario 25175 0 1 1 Ontario 25178 0 1 1 Ontario 25179 0 1 1 Ontario 25182 0 1 1 Ontario 25188 0 1 1 Ontario 25190 0 1 1 Ontario 25192 0 1 0 Ontario 25193 0 1 1 Ontario 25197 0 1 1 Ontario 25199 0 1 0 Ontario 25200 0 1 1 Ontario 25203 0 1 1 Ontario 25207 0 1 1 Ontario 25208 0 1 1 Ontario 25213 0 1 1 Ontario 25214 0 1 0 Ontario 25215 0 1 1 Ontario 25216 0 1 1 Ontario 25218 0 1 1

195

Ontario 25219 1 0 NA Ontario 25220 0 1 1 Ontario 25221 0 1 1 Ontario 25224 0 1 1 Ontario 25225 0 1 1 Ontario 25226 0 1 1 Ontario 25227 0 1 1 Ontario 25229 0 1 0 Ontario 25231 0 1 1 Ontario 25232 0 1 1 Ontario 25233 0 1 1 Ontario 25234 0 1 1 Ontario 25235 0 1 1 Ontario 25236 1 0 NA Ontario 25238 0 1 1 Ontario 25246 0 1 0 Ontario 25247 0 1 1 Ontario 25251 0 1 0 Ontario 25253 0 1 0 Ontario 25255 0 1 0 Ontario 25258 0 1 1 Ontario 25259 0 1 1 Ontario 25260 0 1 0 Ontario 25262 0 1 1 Ontario 25265 0 1 1 Ontario 25266 0 1 1 Ontario 25268 0 1 1 Ontario 25269 1 1 NA Ontario 25270 0 1 1 Ontario 25272 0 1 1 Ontario 25273 0 1 1 Ontario 25275 0 1 0 Ontario 25280 0 1 1 Ontario 25281 0 1 0 Ontario 25282 0 1 1 Ontario 25285 0 1 1 Ontario 25288 0 1 1 Ontario 25289 0 1 1 Ontario 25290 0 1 1 Ontario 25291 0 1 0 Ontario 25293 0 1 1 Ontario 25295 0 1 1 Ontario 25296 0 1 1 Ontario 25300 0 1 1 Ontario 25301 0 1 1 Ontario 25304 0 1 1 Ontario 25305 0 1 0 Ontario 25306 0 1 1 Ontario 25308 0 1 1 Ontario 25313 0 1 1 Ontario 25315 0 1 0 Ontario 25316 0 1 1

196

Ontario 25320 0 1 1 Ontario 25322 0 1 1 Ontario 25323 0 1 1 Ontario 25324 0 1 1 Ontario 25325 0 1 1 Ontario 25327 0 1 1 Ontario 25330 0 1 1 Ontario 25331 0 1 1 Ontario 25332 0 1 1 Ontario 25333 0 1 1 Ontario 25335 0 1 1 Ontario 25337 0 1 0 Ontario 25338 0 1 0 Ontario 25339 0 1 1 Ontario 25341 0 1 0 Ontario 25346 0 1 1 Ontario 25347 0 1 1 Ontario 25350 0 1 1 Ontario 31005 0 1 0 Ontario 31007 0 1 1 Ontario 31011 0 1 1 Ontario 31012 0 1 1 Ontario 31014 0 1 1 Ontario 31018 0 1 1 Ontario 31019 0 1 1 Ontario 31020 0 1 1 Ontario 31022 0 1 1 Ontario 31023 0 1 1 Ontario 31027 0 1 0 Ontario 31028 0 1 1 Ontario 31030 0 1 1 Ontario 31031 0 1 0 Ontario 31033 0 1 1 Ontario 31035 0 1 1 Ontario 31039 0 1 0 Ontario 31042 0 1 0 Ontario 31043 0 1 1 Ontario 31049 0 1 1 Ontario 31054 0 1 0 Ontario 31058 0 1 0 Ontario 31059 0 1 0 Ontario 31060 0 1 1 Ontario 31062 0 1 1 Ontario 31063 0 1 0 Ontario 31065 0 1 1 Ontario 31066 0 1 0 Ontario 31068 0 1 1 Ontario 31069 0 1 1 Ontario 31070 0 1 1 Ontario 31071 0 1 1 Ontario 31076 0 1 1 Ontario 31079 0 1 1

197

Ontario 31080 0 1 1 Ontario 31082 0 1 1 Ontario 31083 0 1 1 Ontario 31084 0 1 1 Ontario 31086 0 1 1 Ontario 31087 0 1 1 Ontario 31089 0 1 1 Ontario 31090 0 1 1 Ontario 31091 0 1 1 Ontario 31093 0 1 1 Ontario 31094 0 1 0 Ontario 31096 0 1 1 Ontario 31097 0 1 1 Ontario 31098 0 1 1 Ontario 31099 0 1 1 Ontario 31101 0 1 0 Ontario 31104 0 1 1 Ontario 31105 0 1 0 Ontario 31107 0 1 1 Ontario 31108 0 1 1 Ontario 31137 0 1 0 Ontario 31141 0 1 0 Ontario 31177 0 1 1 Ontario 31180 0 1 0 Ontario 31185 0 1 1 Ontario 31186 0 1 0 Ontario 31187 0 1 1 Ontario 31188 0 1 1 Ontario 31194 0 1 1 Ontario 31196 0 1 1 Ontario 31197 0 1 1 Ontario 31198 0 1 0 Ontario 31199 0 1 0 Ontario 31200 0 1 1 Ontario 31201 0 1 0 Ontario 31203 0 1 1 Ontario 31205 0 1 1 Ontario 31206 0 1 1 Ontario 31207 0 1 1 Ontario 31211 0 1 1 Ontario 31212 0 1 1 Ontario 31213 0 1 0 Ontario 31216 0 1 1 Ontario 31217 0 1 1 Ontario 31218 0 1 1 Ontario 31221 0 1 1 Ontario 31226 0 1 0 Ontario 31228 0 1 0 Ontario 31229 0 1 1 Ontario 31232 0 1 0 Ontario 31233 0 1 1 Ontario 31234 0 1 1

198

Ontario 31235 0 1 1 Ontario 31236 0 1 1 Ontario 31237 0 1 1 Ontario 31238 0 1 1 Ontario 31239 0 1 1 Ontario 31243 0 1 1 Ontario 31244 0 1 1 Ontario 31246 0 1 0 Ontario 31249 0 1 1 Ontario 31251 0 1 1 Ontario 31252 0 1 1 Ontario 31253 0 1 1 Ontario 31254 0 1 0 Ontario 31255 0 1 1 Ontario 31257 0 1 0 Ontario 31260 0 1 1 Ontario 31268 0 1 1 Ontario 31271 0 1 1 Ontario 31279 0 1 0 Ontario 31281 0 1 1 Ontario 31285 0 1 1 Ontario 31286 0 1 1 Ontario 31287 0 1 1 Ontario 31288 0 1 1 Ontario 31290 0 1 1 Ontario 31291 0 1 1 Ontario 31292 0 1 1 Ontario 31293 0 1 1 Ontario 31294 0 1 0 Ontario 31295 0 1 1 Ontario 31296 0 1 0 Ontario 31297 0 1 1 Ontario 31298 0 1 1 Ontario 31300 0 1 1 Ontario 31301 0 1 1 Ontario 32002 0 1 1 Ontario 32007 0 1 0 Ontario 32009 0 1 0 Ontario 32010 0 1 0 Ontario 32019 0 1 1 Ontario 32020 0 1 0 Ontario 32022 0 1 1 Ontario 32023 0 1 0 Ontario 32024 0 1 1 Ontario 32029 0 1 0 Ontario 32033 0 1 1 Ontario 32043 0 1 0 Ontario 32046 0 1 1 Ontario 32050 0 1 1 Ontario 32053 0 1 0 Ontario 32058 0 1 1 Ontario 32059 0 1 1

199

Ontario 32060 0 1 0 Ontario 32066 0 1 0 Ontario 32068 0 1 1 Ontario 32069 0 1 1 Ontario 32071 0 1 1 Ontario 32072 0 1 0 Ontario 32078 0 1 0 Ontario 32081 0 1 1 Ontario 32083 0 1 1 Ontario 32084 0 1 0 Ontario 32090 0 1 1 Ontario 32094 0 1 0 Ontario 32096 0 1 1 Ontario 32104 0 1 1 Ontario 32111 0 1 1 Ontario 32115 0 1 1 Ontario 32117 0 1 1 Ontario 32122 0 1 0 Ontario 32134 0 1 0 Ontario 32136 0 1 0 Ontario 32137 0 1 0 Ontario 32141 0 1 0 Ontario 32143 0 1 0 Ontario 32144 0 1 0 Ontario 32151 0 1 1 Ontario 32152 0 1 1 Ontario 32154 0 1 0 Ontario 32155 0 1 1 Ontario 32156 0 1 1 Ontario 32157 0 1 1 Ontario 32158 0 1 1 Ontario 32159 0 1 1 Ontario 32160 0 1 1 Ontario 32163 0 1 1 Ontario 32164 0 1 0 Ontario 32165 0 1 1 Ontario 32166 0 1 1 Ontario 32168 0 1 0 Ontario 32171 0 1 1 Ontario 32174 0 1 1 Ontario 32175 0 1 1 Ontario 32182 0 1 0 Ontario 32192 0 1 1 Ontario 32193 0 1 1 Ontario 32196 0 1 1 Ontario 32199 0 1 1 Ontario 32200 0 1 0 Ontario 32202 0 1 1 Ontario 32203 0 1 1 Ontario 32205 0 1 0 Ontario 32206 0 1 0 Ontario 32207 0 1 1

200

Ontario 32211 0 1 1 Ontario 32213 0 1 1 Ontario 32225 0 1 0 Ontario 32226 0 1 1 Ontario 32229 0 1 0 Ontario 32236 0 1 1 Ontario 32237 0 1 0 Ontario 32238 0 1 1 Ontario 32241 0 1 1 Ontario 32243 0 1 1 Ontario 32247 0 1 1 Ontario 32257 0 1 1 Ontario 32259 0 1 1 Ontario 32267 0 1 0 Ontario 32269 0 1 0 Ontario 32270 0 1 1 Ontario 32271 0 1 0 Ontario 32275 0 1 1 Ontario 32277 0 1 0 Ontario 32279 0 1 1 Ontario 32281 0 1 0 Ontario 32282 0 1 1 Ontario 32283 0 1 1 Ontario 32286 0 1 1 Ontario 32288 0 1 1 Ontario 32291 0 1 1 Ontario 32293 0 1 1 Ontario 32294 0 1 0 Ontario 32298 0 1 1 Ontario 32305 0 1 0 Ontario 32306 0 1 1 Ontario 32308 0 1 0 Ontario 32312 0 1 0 Ontario 32315 0 1 1 Ontario 32320 0 1 0 Ontario 32321 0 1 1 Ontario 32329 0 1 1 Ontario 32330 0 1 0 Ontario 32333 0 1 1 Ontario 32336 0 1 1 Ontario 32337 0 1 0 Ontario 32340 0 1 1 Ontario 32349 0 1 1 Ontario 32355 0 1 1 Ontario 32356 0 1 1 Ontario 32357 0 1 1 Ontario 32358 0 1 1 Ontario 33002 0 1 1 Ontario 33006 0 1 1 Ontario 33007 0 1 1 Ontario 33008 0 1 1 Ontario 33009 0 1 1

201

Ontario 33011 0 1 1 Ontario 33015 0 1 1 Ontario 33016 0 1 1 Ontario 33018 0 1 1 Ontario 33019 0 1 1 Ontario 33021 0 1 1 Ontario 33023 0 1 1 Ontario 33025 0 1 0 Ontario 33027 0 1 0 Ontario 33029 0 1 0 Ontario 33031 0 1 1 Ontario 33034 0 1 1 Ontario 33035 0 1 1 Ontario 33039 0 1 0 Ontario 33041 0 1 0 Ontario 33044 0 1 1 Ontario 33047 0 1 1 Ontario 33048 0 1 1 Ontario 33049 0 1 0 Ontario 33050 0 1 1 Ontario 33051 0 1 1 Ontario 33052 0 1 1 Ontario 33055 0 1 0 Ontario 33057 0 1 1 Ontario 33058 0 1 0 Ontario 33060 0 1 1 Ontario 33062 0 1 0 Ontario 33066 0 1 1 Ontario 33067 0 1 1 Ontario 33068 0 1 1 Ontario 33070 0 1 1 Ontario 33071 0 1 1 Ontario 33072 0 1 1 Ontario 33073 0 1 0 Ontario 33074 0 1 0 Ontario 33084 0 1 1 Ontario 33086 0 1 1 Ontario 33087 0 1 0 Ontario 33088 0 1 1 Ontario 33089 0 1 1 Ontario 33090 0 1 1 Ontario 33091 0 1 0 Ontario 33092 0 1 1 Ontario 33093 0 1 1 Ontario 33094 0 1 1 Ontario 33095 0 1 1 Ontario 33096 0 1 0 Ontario 33097 0 1 1 Ontario 33098 0 1 0 Ontario 33099 0 1 1 Ontario 33100 0 1 1 Ontario 33105 0 1 1

202

Ontario 33112 0 1 1 Ontario 33117 0 1 1 Ontario 33126 0 1 1 Ontario 33127 0 1 0 Ontario 33130 0 1 0 Ontario 33133 0 1 1 Ontario 33134 0 1 1 Ontario 33137 0 1 1 Ontario 33138 0 1 0 Ontario 33139 0 1 1 Ontario 33141 0 1 1 Ontario 33143 0 1 0 Ontario 33146 0 1 1 Ontario 33147 0 1 1 Ontario 33148 0 1 0 Ontario 33149 0 1 1 Ontario 33150 0 1 0 Ontario 33151 0 1 1 Ontario 33152 0 1 1 Ontario 33153 0 1 1 Ontario 33155 0 1 0 Ontario 33159 0 1 0 Ontario 33160 0 1 1 Ontario 33161 0 1 1 Ontario 33162 0 1 1 Ontario 33164 0 1 1 Ontario 33166 0 1 1 Ontario 33167 0 1 0 Ontario 33170 0 1 1 Ontario 33171 0 1 1 Ontario 33172 0 1 1 Ontario 33173 0 1 1 Ontario 33174 0 1 0 Ontario 33176 0 1 1 Ontario 33177 0 1 1 Ontario 33178 0 1 1 Ontario 33179 0 1 1 Ontario 33181 0 1 1 Ontario 33183 0 1 1 Ontario 33185 0 1 1 Ontario 33187 0 1 1 Ontario 33188 0 1 1 Ontario 33190 0 1 1 Ontario 33192 0 1 1 Ontario 33193 0 1 1 Ontario 33197 0 1 1 Ontario 33199 0 1 1 Ontario 33202 0 1 1 Ontario 33203 0 1 1 Ontario 33204 0 1 1 Ontario 33206 0 1 1 Ontario 33207 0 1 1

203

Ontario 33208 0 1 1 Ontario 33209 0 1 0 Ontario 33211 0 1 1 Ontario 33213 0 1 1 Ontario 33216 0 1 1 Ontario 33218 0 1 1 Ontario 33221 0 1 1 Ontario 33222 0 1 0 Ontario 33224 0 1 1 Ontario 33225 0 1 1 Ontario 33227 0 1 1 Ontario 33230 0 1 1 Ontario 33231 0 1 0 Ontario 33232 0 1 0 Ontario 33234 0 1 0 Ontario 33236 0 1 1 Ontario 33237 0 1 0 Ontario 33238 0 1 1 Ontario 33242 0 1 1 Ontario 33244 0 1 1 Ontario 33245 0 1 1 Ontario 33246 0 1 1 Ontario 33252 0 1 1 Ontario 33253 0 1 0 Ontario 33255 0 1 1 Ontario 33256 0 1 0 Ontario 33258 0 1 1 Ontario 33259 0 1 1 Ontario 33261 0 1 1 Ontario 33263 0 1 1 Ontario 33264 0 1 1 Ontario 33265 0 1 0 Ontario 33270 0 1 1 Ontario 34001 0 1 1 Ontario 34004 0 1 1 Ontario 34005 0 1 0 Ontario 34006 0 1 1 Ontario 34007 0 1 1 Ontario 34009 0 1 1 Ontario 34010 0 1 1 Ontario 34012 0 1 1 Ontario 34014 0 1 0 Ontario 34015 0 1 1 Ontario 34016 0 1 1 Ontario 34019 0 1 1 Ontario 34025 0 1 0 Ontario 34027 0 1 1 Ontario 34029 0 1 0 Ontario 34030 0 1 0 Ontario 34032 0 1 0 Ontario 34035 0 1 1 Ontario 34036 0 1 1

204

Ontario 34038 0 1 0 Ontario 34039 0 1 0 Ontario 34043 0 1 1 Ontario 34051 0 1 0 Ontario 34052 0 1 1 Ontario 34058 0 1 1 Ontario 34060 0 1 1 Ontario 34063 0 1 0 Ontario 34067 0 1 0 Ontario 34071 0 1 0 Ontario 34075 0 1 1 Ontario 34077 0 1 1 Ontario 34078 0 1 1 Ontario 34079 0 1 0 Ontario 34081 0 1 1 Ontario 34082 0 1 1 Ontario 34085 0 1 1 Ontario 34089 0 1 0 Ontario 34091 0 1 1 Ontario 34094 0 1 1 Ontario 34102 0 1 1 Ontario 34103 0 1 1 Ontario 34109 0 1 1 Ontario 34110 0 1 1 Ontario 34112 0 1 1 Ontario 34114 0 1 1 Ontario 34115 0 1 1 Ontario 34117 0 1 0 Ontario 34118 0 1 0 Ontario 34119 0 1 1 Ontario 34123 0 1 0 Ontario 34127 0 1 0 Ontario 34132 0 1 0 Ontario 34140 0 1 0 Ontario 34141 0 1 1 Ontario 34143 0 1 0 Ontario 34152 0 1 0 Ontario 34153 0 1 1 Ontario 34156 0 1 0 Ontario 34160 0 1 1 Ontario 34161 0 1 1 Ontario 34162 0 1 1 Ontario 34164 0 1 1 Ontario 34166 0 1 1 Ontario 34169 0 1 1 Ontario 34174 0 1 1 Ontario 34182 0 1 1 Ontario 34183 0 1 0 Ontario 34191 0 1 1 Ontario 34192 0 1 1 Ontario 34193 0 1 1 Ontario 34195 0 1 1

205

Ontario 34196 0 1 1 Ontario 34202 0 1 0 Ontario 34204 0 1 1 Ontario 34209 0 1 1 Ontario 34210 0 1 1 Ontario 34214 0 1 1 Ontario 34215 0 1 0 Ontario 34217 0 1 1 Ontario 34218 0 1 0 Ontario 34221 0 1 0 Ontario 34224 0 1 0 Ontario 34226 0 1 0 Ontario 34227 0 1 1 Ontario 34229 0 1 1 Ontario 34231 0 1 0 Ontario 34233 0 1 0 Ontario 34235 0 1 0 Ontario 34236 0 1 1 Ontario 34239 0 1 1 Ontario 34247 0 1 1 Ontario 34248 0 1 1 Ontario 34250 0 1 1 Ontario 34251 0 1 0 Ontario 34258 0 1 0 Ontario 34259 0 1 1 Ontario 34260 0 1 0 Ontario 34266 0 1 1 Ontario 34268 0 1 1 Ontario 35001 0 1 0 Ontario 35004 0 1 1 Ontario 35005 0 1 1 Ontario 35011 0 1 0 Ontario 35015 0 1 0 Ontario 35016 0 1 0 Ontario 35017 0 1 1 Ontario 35018 0 1 0 Ontario 35019 0 1 1 Ontario 35022 0 1 1 Ontario 35031 0 1 1 Ontario 35032 0 1 0 Ontario 35035 0 1 0 Ontario 35040 0 1 0 Ontario 35049 0 1 0 Ontario 35053 0 1 1 Ontario 35056 0 1 0 Ontario 35060 0 1 0 Ontario 35063 0 1 0 Ontario 35066 0 1 0 Ontario 35072 0 1 0 Ontario 35073 0 1 0 Ontario 35075 0 1 1 Ontario 35079 0 1 1

206

Ontario 35080 0 1 1 Ontario 35081 0 1 0 Ontario 35082 0 1 1 Ontario 35111 0 1 0 Ontario 35123 0 1 1 Ontario 35129 0 1 0 Ontario 35130 0 1 0 Ontario 35132 0 1 1 Ontario 35146 0 1 0 Ontario 35147 0 1 0 Ontario 35148 0 1 0 Ontario 35149 0 1 0 Ontario 35151 0 1 1 Ontario 35153 0 1 1 Ontario 35161 0 1 1 Ontario 35162 0 1 1 Ontario 35168 0 1 0 Ontario 35170 0 1 1 Ontario 35172 0 1 0 Ontario 35174 0 1 1 Ontario 35179 0 1 1 Ontario 35186 0 1 0 Ontario 36002 0 1 1 Ontario 36006 0 1 0 Ontario 36008 0 1 1 Ontario 36009 0 1 0 Ontario 36018 0 1 1 Ontario 36020 0 1 1 Ontario 36023 0 1 1 Ontario 36024 0 1 1 Ontario 36029 0 1 0 Ontario 36030 0 1 0 Ontario 36033 0 1 1 Ontario 36041 0 1 1 Ontario 36043 0 1 0 Ontario 36045 0 1 1 Ontario 36048 0 1 1 Ontario 36050 0 1 1 Ontario 36051 0 1 1 Ontario 36052 0 1 1 Ontario 36058 0 1 1 Ontario 36062 0 1 1 Ontario 36063 0 1 1 Ontario 36077 0 1 0 Ontario 36087 0 1 1 Ontario 36088 0 1 1 Ontario 36096 0 1 1 Ontario 36102 0 1 1 Ontario 36107 0 1 1 Ontario 36111 0 1 1 Ontario 36112 0 1 1 Ontario 36117 0 1 1

207

Ontario 36120 0 1 1 Ontario 36125 0 1 1 Ontario 36133 0 1 0 Ontario 36135 0 1 0 Ontario 36136 0 1 0 Ontario 36139 0 1 1 Ontario 36140 0 1 0 Ontario 36142 0 1 1 Ontario 36143 0 1 1 Ontario 36144 0 1 1 Ontario 36145 0 1 1 Ontario 36147 0 1 1 Ontario 36153 0 1 1 Ontario 36155 0 1 0 Ontario 36156 0 1 1 Ontario 36182 0 1 0 Ontario 36183 0 1 1 Ontario 36184 0 1 0 Ontario 36204 0 1 0 Ontario 36218 0 1 0 Ontario 36224 0 1 1 Ontario 36226 0 1 1 Ontario 36231 0 1 0 Ontario 36239 0 1 1 Ontario 36242 0 1 1 Ontario 36244 0 1 1 Ontario 36247 0 1 1 Ontario 36255 0 1 0 Ontario 36258 0 1 1 Ontario 36263 0 1 1 Ontario 36272 0 1 1 Ontario 36274 0 1 1 Ontario 36278 0 1 1 Ontario 36287 0 1 1 Ontario 36288 0 1 1 Ontario 36292 0 1 1 Ontario 36300 0 1 0 Ontario 36308 0 1 1 Ontario 36312 0 1 1 Ontario 36323 0 1 1 Ontario 36327 0 1 0 Ontario 36328 0 1 1 Ontario 36331 0 1 1 Ontario 36334 1 1 NA Ontario 36345 0 1 1 Ontario 36349 0 1 1 Ontario 36350 0 1 0 Ontario 36351 0 1 1 Ontario 36352 0 1 0 Ontario 36358 0 1 0 Ontario 36361 0 1 1 Ontario 36369 0 1 1

208

Ontario 36371 0 1 1 Ontario 36376 0 1 0 Ontario 36378 0 1 0 Ontario 36383 0 1 1 Ontario 36385 0 1 1 Ontario 36386 0 1 0 Ontario 36390 0 1 0 Ontario 36393 0 1 1 Ontario 36399 0 1 1 Ontario 36407 0 1 1 Ontario 37002 0 1 1 Ontario 37014 0 1 0 Ontario 37016 0 1 1 Ontario 38001 0 1 0 Ontario 38004 0 1 0 Ontario 38011 0 1 0 Ontario 38012 0 1 0 Ontario 38018 0 1 0 Ontario 38020 0 1 0 Ontario 38022 0 1 1 Ontario 38024 0 1 1 Ontario 38028 0 1 1 Ontario 38030 0 1 1 Ontario 38032 0 1 1 Ontario 38033 0 1 1 Ontario 38042 0 1 0 Ontario 38049 0 1 0 Ontario 38052 0 1 0 Ontario 38058 0 1 1 Ontario 38059 0 1 0 Ontario 38063 0 1 0 Ontario 38064 0 1 1 Ontario 38066 0 1 1 Ontario 38071 0 1 0 Ontario 38072 0 1 1 Ontario 38093 0 1 1 Ontario 38111 0 1 1 Ontario 38126 0 1 0 Ontario 38135 0 1 0 Ontario 38147 0 1 0 Ontario 38162 0 1 1 Ontario 38171 0 1 0 Ontario 38179 0 1 0 Ontario 38180 0 1 1 Ontario 38186 0 1 0 Ontario 38189 0 1 0 Ontario 38191 0 1 0 Ontario 38193 0 1 1 Ontario 38197 0 1 0 Ontario 38205 0 1 1 Ontario 38210 0 1 1 Ontario 38212 0 1 1

209

Ontario 38217 0 1 0 Ontario 38218 0 1 1 Ontario 38222 0 1 0 Ontario 38227 0 1 0 Ontario 38228 0 1 1 Ontario 38229 0 1 1 Ontario 38230 0 1 0 Ontario 38239 0 1 0 Ontario 38244 0 1 0 Ontario 38247 0 1 0 Ontario 38250 0 1 0 Ontario 38256 0 1 1 Ontario 41001 0 1 1 Ontario 41002 1 0 NA Ontario 41005 0 1 1 Ontario 41006 0 1 1 Ontario 41007 0 1 1 Ontario 41010 0 1 1 Ontario 41013 0 1 1 Ontario 41014 0 1 0 Ontario 41016 0 1 1 Ontario 41017 0 1 0 Ontario 41019 0 1 1 Ontario 41020 0 1 1 Ontario 41021 1 1 NA Ontario 41023 0 1 1 Ontario 41024 0 1 1 Ontario 41025 0 1 1 Ontario 41026 0 1 1 Ontario 41027 0 1 1 Ontario 41028 0 1 1 Ontario 41029 0 1 0 Ontario 41030 0 1 0 Ontario 41031 0 1 1 Ontario 41032 0 1 1 Ontario 41033 0 1 1 Ontario 41034 0 1 1 Ontario 41035 0 1 1 Ontario 41037 0 1 1 Ontario 41038 0 1 1 Ontario 41040 0 1 1 Ontario 41041 0 1 0 Ontario 41042 0 1 1 Ontario 41044 0 1 1 Ontario 41047 0 1 0 Ontario 41049 0 1 1 Ontario 41051 0 1 1 Ontario 41054 1 1 NA Ontario 41056 0 1 1 Ontario 41057 0 1 0 Ontario 41058 0 1 1 Ontario 41059 0 1 1

210

Ontario 41060 0 1 1 Ontario 41061 0 1 1 Ontario 41063 0 1 1 Ontario 41064 0 1 1 Ontario 41065 0 1 1 Ontario 41068 0 1 1 Ontario 41069 0 1 1 Ontario 41070 0 1 1 Ontario 41071 1 1 NA Ontario 41072 1 1 NA Ontario 41074 0 1 1 Ontario 41076 0 1 1 Ontario 41078 0 1 1 Ontario 41079 1 1 NA Ontario 41080 1 1 NA Ontario 41081 0 1 1 Ontario 41082 1 1 NA Ontario 41083 0 1 1 Ontario 41085 0 1 1 Ontario 41086 0 1 1 Ontario 41088 0 1 1 Ontario 41089 0 1 1 Ontario 41091 0 1 1 Ontario 41092 0 1 1 Ontario 41093 0 1 1 Ontario 41095 1 1 NA Ontario 41096 0 1 0 Ontario 41097 0 1 1 Ontario 41099 0 1 1 Ontario 41100 0 1 1 Ontario 41101 1 1 NA Ontario 41102 0 1 1 Ontario 41103 0 1 1 Ontario 41104 1 1 NA Ontario 41105 0 1 1 Ontario 41106 0 1 1 Ontario 41107 0 1 1 Ontario 41109 0 1 1 Ontario 41110 0 1 1 Ontario 41113 0 1 1 Ontario 41114 0 1 1 Ontario 41116 0 1 1 Ontario 41119 0 1 1 Ontario 41120 0 1 0 Ontario 41121 1 1 NA Ontario 41123 0 1 1 Ontario 41125 0 1 1 Ontario 41126 0 1 1 Ontario 41127 0 1 0 Ontario 41129 0 1 0 Ontario 41132 0 1 1 Ontario 41133 0 1 1

211

Ontario 41134 0 1 1 Ontario 41135 0 1 1 Ontario 41136 0 1 0 Ontario 41138 0 1 1 Ontario 41139 0 1 1 Ontario 41140 0 1 1 Ontario 41142 0 1 1 Ontario 41146 0 1 1 Ontario 41147 1 1 NA Ontario 41148 0 1 1 Ontario 41149 0 1 1 Ontario 41150 0 1 1 Ontario 41151 0 1 1 Ontario 41154 0 1 0 Ontario 41155 0 1 0 Ontario 41156 0 1 0 Ontario 41162 0 1 1 Ontario 41164 0 1 0 Ontario 41166 0 1 0 Ontario 41177 1 1 NA Ontario 41178 0 1 1 Ontario 41181 0 1 0 Ontario 41183 0 1 1 Ontario 41186 1 1 NA Ontario 41187 0 1 1 Ontario 41188 0 1 1 Ontario 41190 0 1 1 Ontario 41195 0 1 0 Ontario 41197 0 1 1 Ontario 41198 0 1 1 Ontario 41199 0 1 0 Ontario 41200 0 1 1 Ontario 41202 0 1 0 Ontario 41203 0 1 1 Ontario 41204 1 1 NA Ontario 41205 0 1 1 Ontario 41206 0 1 1 Ontario 41207 1 1 NA Ontario 41208 0 1 1 Ontario 41209 0 1 1 Ontario 41210 1 1 NA Ontario 41213 0 1 1 Ontario 41215 0 1 1 Ontario 41220 0 1 1 Ontario 41221 0 1 1 Ontario 41222 0 1 1 Ontario 41223 0 1 1 Ontario 41225 0 1 1 Ontario 41226 0 1 0 Ontario 41227 0 1 1 Ontario 41228 0 1 1 Ontario 41229 0 1 0

212

Ontario 41230 0 1 1 Ontario 41231 0 1 0 Ontario 41232 0 1 1 Ontario 41233 0 1 0 Ontario 41235 0 1 1 Ontario 41238 0 1 0 Ontario 41239 0 1 0 Ontario 41241 0 1 1 Ontario 41242 0 1 1 Ontario 41246 0 1 1 Ontario 41248 0 1 1 Ontario 41249 0 1 1 Ontario 41251 0 1 1 Ontario 41252 0 1 1 Ontario 41253 0 1 1 Ontario 41255 0 1 1 Ontario 41256 1 0 NA Ontario 41257 1 1 NA Ontario 41258 0 1 1 Ontario 41260 0 1 1 Ontario 41261 0 1 1 Ontario 41263 0 1 1 Ontario 41264 0 1 1 Ontario 41265 0 1 1 Ontario 41268 0 1 1 Ontario 41269 0 1 1 Ontario 41271 0 1 1 Ontario 41272 0 1 1 Ontario 41273 0 1 1 Ontario 41274 0 1 1 Ontario 41276 0 1 1 Ontario 41278 0 1 1 Ontario 41281 0 1 1 Ontario 41282 0 1 1 Ontario 41284 1 1 NA Ontario 41285 0 1 1 Ontario 41286 0 1 1 Ontario 41287 0 1 1 Ontario 41288 0 1 1 Ontario 41290 0 1 1 Ontario 41292 0 1 0 Ontario 41293 0 1 1 Ontario 41294 0 1 1 Ontario 41295 0 1 1 Ontario 41296 0 1 1 Ontario 41298 0 1 1 Ontario 41299 0 1 0 Ontario 41300 0 1 1 Ontario 41303 0 1 1 Ontario 41305 0 1 1 Ontario 41306 0 1 1 Ontario 41307 0 1 1

213

Ontario 41308 0 1 1 Ontario 41309 0 1 0 Ontario 41310 0 1 1 Ontario 41312 0 1 1 Ontario 41313 0 1 1 Ontario 41314 0 1 1 Ontario 41320 1 1 NA Ontario 41321 0 1 1 Ontario 41322 0 1 1 Ontario 41324 0 1 1 Ontario 41325 0 1 1 Ontario 41326 1 1 NA Ontario 41327 0 1 1 Ontario 41329 0 1 1 Ontario 41333 0 1 1 Ontario 41334 0 1 1 Ontario 41335 0 1 1 Ontario 41339 0 1 1 Ontario 41340 0 1 0 Ontario 41341 0 1 1 Ontario 41342 0 1 1 Ontario 41343 0 1 1 Ontario 41344 0 1 1 Ontario 41345 0 1 1 Ontario 42001 0 1 1 Ontario 42003 0 1 1 Ontario 42005 0 1 1 Ontario 42011 0 1 1 Ontario 42012 0 1 1 Ontario 42013 0 1 1 Ontario 42014 0 1 1 Ontario 42015 0 1 1 Ontario 42016 0 1 1 Ontario 42017 0 1 1 Ontario 42021 0 1 1 Ontario 42022 0 1 1 Ontario 42024 0 1 1 Ontario 42025 0 1 1 Ontario 42026 0 1 1 Ontario 42029 0 1 1 Ontario 42030 0 1 1 Ontario 42031 0 1 0 Ontario 42032 0 1 0 Ontario 42035 0 1 1 Ontario 42037 0 1 1 Ontario 42038 0 1 1 Ontario 42039 0 1 1 Ontario 42040 0 1 1 Ontario 42041 0 1 1 Ontario 42042 0 1 1 Ontario 42043 0 1 1 Ontario 42044 0 1 0

214

Ontario 42045 0 1 1 Ontario 42047 0 1 1 Ontario 42048 0 1 0 Ontario 42050 0 1 1 Ontario 42054 0 1 0 Ontario 42056 0 1 1 Ontario 42057 0 1 0 Ontario 42058 0 1 1 Ontario 42059 0 1 1 Ontario 42060 0 1 1 Ontario 42061 0 1 0 Ontario 42062 1 1 NA Ontario 42063 0 1 1 Ontario 42064 0 1 0 Ontario 42065 0 1 0 Ontario 42066 1 1 NA Ontario 42068 0 1 1 Ontario 42069 0 1 1 Ontario 42070 0 1 1 Ontario 42071 0 1 1 Ontario 42072 0 1 1 Ontario 42073 0 1 1 Ontario 42074 0 1 1 Ontario 42075 0 1 1 Ontario 42089 0 1 0 Ontario 42091 0 1 1 Ontario 42093 0 1 1 Ontario 42103 0 1 0 Ontario 42115 0 1 1 Ontario 42116 0 1 0 Ontario 42119 0 1 1 Ontario 42120 0 1 1 Ontario 42121 0 1 1 Ontario 42122 0 1 1 Ontario 42126 0 1 0 Ontario 42127 0 1 1 Ontario 42129 0 1 1 Ontario 42130 0 1 1 Ontario 42132 0 1 0 Ontario 42133 0 1 1 Ontario 42139 0 1 1 Ontario 42140 1 1 NA Ontario 42141 0 1 1 Ontario 42143 0 1 1 Ontario 42145 0 1 1 Ontario 42150 0 1 1 Ontario 42151 0 1 1 Ontario 42152 0 1 1 Ontario 42153 0 1 1 Ontario 42154 0 1 1 Ontario 42158 0 1 1 Ontario 42161 0 1 1

215

Ontario 42162 0 1 1 Ontario 42163 0 1 1 Ontario 42165 0 1 0 Ontario 42166 0 1 1 Ontario 42169 0 1 1 Ontario 42171 0 1 0 Ontario 42172 0 1 1 Ontario 42174 0 1 1 Ontario 42175 1 1 NA Ontario 42176 0 1 0 Ontario 42178 0 1 1 Ontario 42179 0 1 1 Ontario 42181 0 1 1 Ontario 42183 0 1 1 Ontario 42184 0 1 1 Ontario 42185 0 1 1 Ontario 42186 0 1 1 Ontario 42187 0 1 1 Ontario 42188 0 1 1 Ontario 42191 0 1 1 Ontario 42193 0 1 1 Ontario 42194 0 1 0 Ontario 42195 0 1 0 Ontario 42196 0 1 1 Ontario 42197 0 1 1 Ontario 42198 0 1 1 Ontario 42199 1 1 NA Ontario 42202 0 1 1 Ontario 42203 0 1 1 Ontario 42205 0 1 1 Ontario 42207 0 1 1 Ontario 42211 0 1 0 Ontario 42212 0 1 1 Ontario 42216 0 1 1 Ontario 42217 0 1 1 Ontario 42219 0 1 1 Ontario 42220 0 1 1 Ontario 42221 0 1 1 Ontario 43002 0 1 1 Ontario 43006 0 1 0 Ontario 43009 0 1 1 Ontario 43010 0 1 0 Ontario 43012 0 1 1 Ontario 43014 0 1 0 Ontario 43019 0 1 1 Ontario 43022 0 1 1 Ontario 43024 0 1 1 Ontario 43026 0 1 0 Ontario 43028 0 1 1 Ontario 43029 0 1 0 Ontario 43032 0 1 0 Ontario 43033 0 1 1

216

Ontario 43035 0 1 0 Ontario 43036 0 1 1 Ontario 43038 0 1 1 Ontario 43040 0 1 1 Ontario 43041 0 1 1 Ontario 43043 0 1 1 Ontario 43046 0 1 0 Ontario 43048 0 1 1 Ontario 43049 0 1 1 Ontario 43053 0 1 1 Ontario 43055 0 1 1 Ontario 43057 0 1 1 Ontario 43058 0 1 1 Ontario 43059 0 1 0 Ontario 43060 0 1 0 Ontario 43061 0 1 1 Ontario 43063 0 1 1 Ontario 43064 0 1 1 Ontario 43067 0 1 1 Ontario 43069 0 1 1 Ontario 43070 0 1 1 Ontario 43071 0 1 1 Ontario 43073 0 1 0 Ontario 43075 0 1 1 Ontario 43076 0 1 1 Ontario 43084 0 1 1 Ontario 43087 0 1 0 Ontario 43091 0 1 1 Ontario 43095 0 1 1 Ontario 43096 0 1 0 Ontario 43098 0 1 1 Ontario 43100 0 1 1 Ontario 43101 0 1 0 Ontario 43102 0 1 1 Ontario 43103 0 1 1 Ontario 43104 0 1 1 Ontario 43106 0 1 0 Ontario 43113 0 1 0 Ontario 43120 0 1 1 Ontario 43125 0 1 1 Ontario 43126 0 1 1 Ontario 43127 0 1 0 Ontario 43128 0 1 0 Ontario 43129 0 1 0 Ontario 43132 0 1 1 Ontario 43134 0 1 1 Ontario 43135 0 1 1 Ontario 43136 0 1 0 Ontario 43140 0 1 1 Ontario 43141 0 1 0 Ontario 43144 0 1 1 Ontario 43147 0 1 1

217

Ontario 43148 0 1 0 Ontario 43149 0 1 1 Ontario 43152 0 1 0 Ontario 43153 0 1 1 Ontario 43156 0 1 1 Ontario 43160 0 1 1 Ontario 43161 0 1 1 Ontario 43163 0 1 1 Ontario 43164 0 1 1 Ontario 43165 0 1 0 Ontario 43167 0 1 1 Ontario 43168 0 1 1 Ontario 43170 0 1 1 Ontario 43172 0 1 1 Ontario 43173 0 1 0 Ontario 43175 0 1 1 Ontario 43177 0 1 1 Ontario 43179 0 1 1 Ontario 43180 0 1 1 Ontario 43181 0 1 0 Ontario 43183 0 1 1 Ontario 43184 0 1 1 Ontario 43185 0 1 1 Ontario 43187 1 1 NA Ontario 43188 0 1 1 Ontario 43189 0 1 1 Ontario 43192 0 1 1 Ontario 43197 0 1 1 Ontario 43200 0 1 1 Ontario 43201 0 1 1 Ontario 43203 0 1 1 Ontario 43210 0 1 1 Ontario 43211 0 1 0 Ontario 44001 0 1 1 Ontario 44002 0 1 1 Ontario 44004 0 1 1 Ontario 44009 0 1 1 Ontario 44015 0 1 1 Ontario 44016 0 1 1 Ontario 44019 0 1 1 Ontario 44021 0 1 1 Ontario 44022 0 1 1 Ontario 44025 0 1 1 Ontario 44030 0 1 1 Ontario 44031 0 1 1 Ontario 44035 0 1 1 Ontario 44036 0 1 1 Ontario 44038 0 1 1 Ontario 44039 0 1 1 Ontario 44040 0 1 1 Ontario 44041 0 1 0 Ontario 44042 0 1 1

218

Ontario 44043 0 1 1 Ontario 44044 0 1 0 Ontario 44045 0 1 1 Ontario 44046 0 1 1 Ontario 44048 0 1 1 Ontario 44049 1 0 NA Ontario 44050 1 1 NA Ontario 44051 0 1 1 Ontario 44053 0 1 0 Ontario 44054 0 1 1 Ontario 44056 0 1 0 Ontario 44060 0 1 1 Ontario 44061 0 1 1 Ontario 44062 0 1 1 Ontario 44063 0 1 1 Ontario 44064 0 1 0 Ontario 44065 0 1 1 Ontario 44066 0 1 0 Ontario 44070 0 1 1 Ontario 44077 0 1 1 Ontario 44079 0 1 1 Ontario 44080 0 1 1 Ontario 44081 0 1 1 Ontario 44082 0 1 1 Ontario 44084 0 1 1 Ontario 44086 0 1 1 Ontario 44087 0 1 1 Ontario 44088 0 1 1 Ontario 44099 0 1 1 Ontario 44101 0 1 0 Ontario 44102 0 1 1 Ontario 44107 0 1 1 Ontario 44112 0 1 1 Ontario 44114 0 1 1 Ontario 44117 0 1 1 Ontario 44119 1 1 NA Ontario 44120 0 1 1 Ontario 44121 0 1 1 Ontario 44125 0 1 1 Ontario 44126 1 1 NA Ontario 44127 0 1 1 Ontario 44130 0 1 1 Ontario 44131 0 1 1 Ontario 44132 0 1 1 Ontario 44133 0 1 1 Ontario 44134 0 1 0 Ontario 44135 0 1 1 Ontario 44136 0 1 1 Ontario 44137 1 1 NA Ontario 44138 0 1 1 Ontario 44140 0 1 1 Ontario 44142 0 1 1

219

Ontario 44143 0 1 1 Ontario 44144 0 1 1 Ontario 44147 0 1 1 Ontario 44148 0 1 1 Ontario 44149 0 1 1 Ontario 44152 0 1 1 Ontario 44154 1 1 NA Ontario 44155 0 1 1 Ontario 44156 0 1 1 Ontario 44157 0 1 0 Ontario 44161 0 1 1 Ontario 44165 1 0 NA Ontario 44166 0 1 1 Ontario 44167 0 1 1 Ontario 44168 0 1 1 Ontario 44169 0 1 1 Ontario 44170 0 1 1 Ontario 44171 0 1 1 Ontario 44172 0 1 1 Ontario 44173 0 1 0 Ontario 44174 1 0 NA Ontario 44180 0 1 1 Ontario 44181 0 1 1 Ontario 44183 0 1 1 Ontario 44185 0 1 1 Ontario 44186 0 1 1 Ontario 44189 1 1 NA Ontario 44193 0 1 1 Ontario 44196 0 1 1 Ontario 44197 0 1 1 Ontario 44198 0 1 1 Ontario 44200 0 1 1 Ontario 44201 0 1 1 Ontario 44204 0 1 1 Ontario 44206 1 1 NA Ontario 44207 0 1 1 Ontario 44208 0 1 1 Ontario 44211 0 1 0 Ontario 44214 0 1 1 Ontario 44215 0 1 1 Ontario 44216 0 1 1 Ontario 44217 0 1 1 Ontario 44219 0 1 1 Ontario 44222 0 1 1 Ontario 45001 0 1 1 Ontario 45005 0 1 1 Ontario 45007 0 1 1 Ontario 45008 0 1 0 Ontario 45009 0 1 1 Ontario 45011 0 1 0 Ontario 45013 0 1 1 Ontario 45016 0 1 1

220

Ontario 45017 0 1 1 Ontario 45018 0 1 1 Ontario 45023 0 1 1 Ontario 45024 0 1 0 Ontario 45026 0 1 0 Ontario 45027 0 1 1 Ontario 45030 0 1 0 Ontario 45031 0 1 1 Ontario 45033 0 1 1 Ontario 45036 0 1 1 Ontario 45040 0 1 1 Ontario 45041 0 1 1 Ontario 45044 0 1 1 Ontario 45046 0 1 0 Ontario 45052 0 1 1 Ontario 45053 0 1 1 Ontario 45055 0 1 1 Ontario 45056 0 1 0 Ontario 45058 0 1 1 Ontario 45059 0 1 1 Ontario 45060 0 1 1 Ontario 45061 0 1 1 Ontario 45062 0 1 1 Ontario 45063 0 1 1 Ontario 45065 0 1 0 Ontario 45070 0 1 1 Ontario 45072 0 1 1 Ontario 45073 0 1 0 Ontario 45076 0 1 0 Ontario 45077 0 1 1 Ontario 45078 0 1 1 Ontario 45082 0 1 1 Ontario 45083 0 1 0 Ontario 45085 0 1 1 Ontario 45086 0 1 0 Ontario 45088 0 1 0 Ontario 45089 0 1 1 Ontario 45093 0 1 0 Ontario 45094 0 1 1 Ontario 45095 0 1 0 Ontario 45098 0 1 1 Ontario 45099 0 1 1 Ontario 45101 0 1 1 Ontario 45104 0 1 0 Ontario 45105 0 1 1 Ontario 45108 0 1 1 Ontario 45109 0 1 1 Ontario 45113 0 1 1 Ontario 45114 0 1 1 Ontario 45115 0 1 0 Ontario 45122 0 1 0 Ontario 45125 0 1 0

221

Ontario 45126 0 1 0 Ontario 45133 0 1 1 Ontario 45134 0 1 1 Ontario 45136 0 1 1 Ontario 45138 0 1 1 Ontario 45139 0 1 0 Ontario 45140 1 1 NA Ontario 45144 0 1 0 Ontario 45145 0 1 0 Ontario 45147 0 1 0 Ontario 45149 0 1 1 Ontario 45150 0 1 0 Ontario 45151 0 1 1 Ontario 45152 0 1 1 Ontario 45155 0 1 1 Ontario 45156 0 1 1 Ontario 45159 0 1 1 Ontario 45160 0 1 1 Ontario 45161 0 1 1 Ontario 45162 0 1 1 Ontario 45163 0 1 0 Ontario 45165 0 1 1 Ontario 45166 0 1 0 Ontario 45167 0 1 1 Ontario 45168 0 1 1 Ontario 45169 0 1 0 Ontario 45173 0 1 1 Ontario 45180 0 1 1 Ontario 45182 0 1 0 Ontario 45184 0 1 1 Ontario 45185 0 1 1 Ontario 45186 0 1 1 Ontario 45188 0 1 1 Ontario 45191 0 1 1 Ontario 45192 0 1 1 Ontario 45194 0 1 1 Ontario 45195 0 1 1 Ontario 45198 0 1 1 Ontario 45201 0 1 1 Ontario 45202 0 1 1 Ontario 45203 0 1 1 Ontario 45209 0 1 0 Ontario 45210 0 1 1 Ontario 45213 0 1 0 Ontario 45216 0 1 0 Ontario 45217 0 1 1 Ontario 45223 0 1 1 Ontario 45229 0 1 1 Ontario 45230 0 1 1 Ontario 45231 0 1 1 Ontario 45232 0 1 0 Ontario 45236 0 1 0

222

Ontario 45237 0 1 1 Ontario 45246 0 1 1 Ontario 45251 0 1 1 Ontario 45256 0 1 1 Ontario 45257 0 1 0 Ontario 45258 0 1 0 Ontario 45260 0 1 1 Ontario 45262 0 1 1 Ontario 45263 0 1 1 Ontario 45264 0 1 1 Ontario 45265 0 1 1 Ontario 45266 0 1 1 Ontario 45268 0 1 1 Ontario 45269 0 1 0 Ontario 45271 0 1 1 Ontario 45272 0 1 1 Ontario 45273 0 1 0 Ontario 45274 0 1 1 Ontario 45275 0 1 1 Ontario 46002 0 1 1 Ontario 46003 0 1 1 Ontario 46004 0 1 1 Ontario 46005 0 1 0 Ontario 46007 0 1 1 Ontario 46008 0 1 0 Ontario 46009 0 1 1 Ontario 46011 0 1 1 Ontario 46012 0 1 1 Ontario 46015 0 1 1 Ontario 46017 0 1 1 Ontario 46018 0 1 1 Ontario 46019 0 1 0 Ontario 46021 0 1 1 Ontario 46022 0 1 1 Ontario 46023 0 1 0 Ontario 46024 0 1 1 Ontario 46026 0 1 1 Ontario 46027 0 1 0 Ontario 46028 0 1 0 Ontario 46029 0 1 1 Ontario 46030 0 1 1 Ontario 46032 0 1 1 Ontario 46033 0 1 0 Ontario 46038 0 1 1 Ontario 46039 0 1 1 Ontario 46040 0 1 0 Ontario 46044 0 1 1 Ontario 46048 0 1 1 Ontario 46050 0 1 0 Ontario 46053 0 1 1 Ontario 46056 0 1 1 Ontario 46058 0 1 1

223

Ontario 46059 0 1 0 Ontario 46061 0 1 1 Ontario 46065 0 1 1 Ontario 46066 0 1 1 Ontario 46067 0 1 1 Ontario 46069 0 1 1 Ontario 46074 0 1 1 Ontario 46076 0 1 1 Ontario 46079 0 1 0 Ontario 46081 0 1 1 Ontario 46083 0 1 0 Ontario 46084 0 1 0 Ontario 46085 0 1 1 Ontario 46086 0 1 1 Ontario 46087 0 1 1 Ontario 46088 0 1 1 Ontario 46091 0 1 0 Ontario 46092 0 1 1 Ontario 46093 0 1 0 Ontario 46095 0 1 1 Ontario 46097 0 1 1 Ontario 46098 0 1 1 Ontario 46099 0 1 1 Ontario 46100 0 1 1 Ontario 46101 0 1 1 Ontario 46102 0 1 1 Ontario 46103 0 1 0 Ontario 46104 0 1 0 Ontario 46106 0 1 1 Ontario 46110 0 1 1 Ontario 46111 0 1 0 Ontario 46112 0 1 0 Ontario 46115 0 1 0 Ontario 46119 0 1 1 Ontario 46120 0 1 1 Ontario 46122 0 1 1 Ontario 46123 0 1 1 Ontario 46124 0 1 0 Ontario 46126 0 1 0 Ontario 46128 0 1 1 Ontario 46129 0 1 1 Ontario 46130 0 1 1 Ontario 46132 0 1 1 Ontario 46133 0 1 1 Ontario 46135 0 1 0 Ontario 46138 0 1 1 Ontario 46139 0 1 1 Ontario 46140 0 1 1 Ontario 46141 0 1 1 Ontario 46142 0 1 1 Ontario 46143 0 1 1 Ontario 46144 0 1 1

224

Ontario 46145 0 1 1 Ontario 46146 1 1 NA Ontario 46147 0 1 0 Ontario 46149 0 1 1 Ontario 46153 0 1 0 Ontario 46155 0 1 0 Ontario 46159 0 1 0 Ontario 46166 0 1 1 Ontario 46167 0 1 0 Ontario 46168 0 1 1 Ontario 46172 0 1 1 Ontario 46177 0 1 0 Ontario 46178 0 1 1 Ontario 46179 0 1 1 Ontario 46180 0 1 1 Ontario 46182 0 1 1 Ontario 46183 0 1 0 Ontario 46184 0 1 1 Ontario 46185 0 1 1 Ontario 46186 0 1 1 Ontario 46187 0 1 1 Ontario 46188 0 1 0 Ontario 46189 0 1 1 Ontario 46190 0 1 0 Ontario 46191 0 1 1 Ontario 46192 0 1 1 Ontario 46193 0 1 1 Ontario 46194 0 1 1 Ontario 46195 0 1 1 Ontario 46197 0 1 1 Ontario 46198 0 1 1 Ontario 46199 0 1 1 Ontario 46200 0 1 1 Ontario 46202 0 1 0 Ontario 46206 0 1 1 Ontario 46208 0 1 1 Ontario 46209 0 1 1 Ontario 46211 0 1 1 Ontario 46213 0 1 1 Ontario 46215 0 1 1 Ontario 46216 0 1 1 Ontario 46218 0 1 0 Ontario 46220 0 1 1 Ontario 46223 0 1 0 Ontario 46224 0 1 0 Ontario 46226 0 1 1 Ontario 46228 0 1 1 Ontario 46229 0 1 1 Ontario 46230 0 1 1 Ontario 46231 0 1 0 Ontario 46232 0 1 1 Ontario 46233 0 1 0

225

Ontario 46234 0 1 1 Ontario 46235 0 1 1 Ontario 46236 0 1 0 Ontario 46238 0 1 1 Ontario 46239 0 1 0 Ontario 46240 0 1 1 Ontario 46241 0 1 1 Ontario 46244 0 1 1 Ontario 46245 0 1 1 Ontario 46246 0 1 0 Ontario 46247 0 1 1 Ontario 46248 0 1 1 Ontario 46249 0 1 1 Ontario 46251 0 1 1 Ontario 46252 0 1 1 Ontario 46255 0 1 1 Ontario 46261 0 1 1 Ontario 46262 0 1 1 Ontario 46263 0 1 0 Ontario 46264 0 1 1 Ontario 46266 0 1 1 Ontario 46271 0 1 0 Ontario 46272 0 1 1 Ontario 46273 0 1 1 Ontario 46275 0 1 1 Ontario 46276 0 1 0 Ontario 46277 0 1 1 Ontario 46278 0 1 1 Ontario 46279 0 1 1 Ontario 46283 0 1 1 Ontario 46284 0 1 0 Ontario 46285 0 1 1 Ontario 46286 0 1 0 Ontario 46287 0 1 1 Ontario 46290 0 1 0 Ontario 46291 0 1 1 Ontario 46292 0 1 1 Ontario 46293 0 1 0 Ontario 46297 0 1 1 Ontario 46298 0 1 1 Ontario 46302 0 1 1 Ontario 46303 0 1 1 Ontario 46304 0 1 1 Ontario 46305 0 1 0 Ontario 46306 0 1 0 Ontario 47003 0 1 1 Ontario 47006 0 1 1 Ontario 47007 0 1 1 Ontario 47009 0 1 0 Ontario 47010 0 1 0 Ontario 47011 0 1 1 Ontario 47012 0 1 1

226

Ontario 47013 0 1 1 Ontario 47014 0 1 1 Ontario 47016 0 1 1 Ontario 47020 0 1 1 Ontario 47021 0 1 0 Ontario 47023 0 1 1 Ontario 47025 0 1 1 Ontario 47026 0 1 1 Ontario 47027 0 1 0 Ontario 47028 0 1 0 Ontario 47029 0 1 1 Ontario 47030 0 1 1 Ontario 47031 0 1 1 Ontario 47032 0 1 1 Ontario 47033 0 1 1 Ontario 47034 0 1 0 Ontario 47038 0 1 1 Ontario 47039 0 1 1 Ontario 47040 0 1 1 Ontario 47041 0 1 0 Ontario 47043 0 1 0 Ontario 47046 0 1 0 Ontario 47047 0 1 1 Ontario 47049 0 1 1 Ontario 47050 0 1 0 Ontario 47051 0 1 1 Ontario 47053 0 1 0 Ontario 47054 0 1 1 Ontario 47055 0 1 0 Ontario 47056 1 1 NA Ontario 47058 0 1 1 Ontario 47059 0 1 1 Ontario 47061 0 1 1 Ontario 47063 0 1 0 Ontario 47064 0 1 1 Ontario 47065 0 1 0 Ontario 47066 0 1 0 Ontario 47067 0 1 1 Ontario 47068 0 1 0 Ontario 47069 0 1 0 Ontario 47070 0 1 0 Ontario 47071 0 1 0 Ontario 47073 0 1 0 Ontario 47075 0 1 0 Ontario 47076 0 1 1 Ontario 47077 0 1 1 Ontario 47079 0 1 1 Ontario 47081 0 1 1 Ontario 47082 0 1 1 Ontario 47083 0 1 1 Ontario 47085 0 1 1 Ontario 47087 0 1 1

227

Ontario 47089 0 1 0 Ontario 47090 0 1 0 Ontario 47091 0 1 1 Ontario 47092 0 1 1 Ontario 47093 0 1 0 Ontario 47094 0 1 1 Ontario 47097 0 1 1 Ontario 47098 0 1 0 Ontario 47099 0 1 1 Ontario 47100 0 1 1 Ontario 47102 0 1 0 Ontario 47103 0 1 0 Ontario 47104 0 1 1 Ontario 47106 0 1 1 Ontario 47107 0 1 0 Ontario 47109 0 1 1 Ontario 47110 0 1 1 Ontario 47111 0 1 1 Ontario 47112 0 1 1 Ontario 47113 0 1 1 Ontario 47114 0 1 1 Ontario 47119 0 1 1 Ontario 47121 0 1 0 Ontario 47122 0 1 1 Ontario 47123 0 1 0 Ontario 47124 0 1 1 Ontario 47125 0 1 0 Ontario 47126 0 1 1 Ontario 47128 0 1 1 Ontario 47129 0 1 1 Ontario 47130 0 1 1 Ontario 47133 0 1 1 Ontario 47135 0 1 0 Ontario 47137 0 1 0 Ontario 47139 0 1 1 Ontario 47140 0 1 1 Ontario 47142 0 1 1 Ontario 47143 0 1 1 Ontario 47144 0 1 1 Ontario 47147 0 1 1 Ontario 47150 0 1 0 Ontario 47151 0 1 1 Ontario 47152 0 1 1 Ontario 47153 0 1 1 Ontario 47155 0 1 0 Ontario 47158 0 1 0 Ontario 47159 0 1 0 Ontario 47160 0 1 1 Ontario 47161 0 1 1 Ontario 47162 0 1 1 Ontario 47164 0 1 1 Ontario 47166 0 1 0

228

Ontario 47167 0 1 1 Ontario 47169 0 1 1 Ontario 47170 0 1 1 Ontario 47171 0 1 0 Ontario 47172 0 1 1 Ontario 47173 0 1 1 Ontario 47174 0 1 0 Ontario 47176 0 1 1 Ontario 47177 0 1 1 Ontario 47178 0 1 1 Ontario 47180 0 1 1 Ontario 47181 0 1 1 Ontario 47184 0 1 1 Ontario 47185 0 1 1 Ontario 47192 0 1 0 Ontario 47205 0 1 1 Ontario 47207 0 1 0 Ontario 47211 0 1 0 Ontario 47212 0 1 1 Ontario 47214 0 1 0 Ontario 47217 0 1 1 Ontario 47218 0 1 0 Ontario 47219 0 1 1 Ontario 47220 0 1 1 Ontario 47221 0 1 0 Ontario 47224 0 1 1 Ontario 47227 0 1 1 Ontario 47228 0 1 0 Ontario 47229 0 1 1 Ontario 47231 0 1 1 Ontario 47232 0 1 1 Ontario 47233 0 1 1 Ontario 47234 0 1 1 Ontario 47235 0 1 1 Ontario 47236 0 1 1 Ontario 47237 0 1 1 Ontario 47238 0 1 0 Ontario 47239 0 1 1 Ontario 47241 0 1 1 Ontario 47242 0 1 0 Ontario 47243 0 1 0 Ontario 47245 0 1 1 Ontario 47249 0 1 1 Ontario 47252 0 1 1 Ontario 47253 0 1 0 Ontario 47255 0 1 1 Ontario 47258 0 1 0 Ontario 47259 0 1 1 Ontario 47260 0 1 1 Ontario 47261 0 1 1 Ontario 47262 0 1 0 Ontario 47268 0 1 0

229

Ontario 47276 0 1 0 Ontario 47281 0 1 1 Ontario 47282 0 1 1 Ontario 47285 0 1 1 Ontario 47286 0 1 1 Ontario 47288 0 1 1 Ontario 47289 0 1 1 Ontario 47290 0 1 1 Ontario 47291 0 1 1 Ontario 47292 0 1 1 Ontario 47293 0 1 1 Ontario 47294 0 1 1 Ontario 47295 0 1 1 Ontario 47296 0 1 0 Ontario 47301 0 1 1 Ontario 47302 0 1 1 Ontario 47304 0 1 1 Ontario 47305 0 1 1 Ontario 47306 0 1 1 Ontario 47308 0 1 1 Ontario 47310 0 1 1 Ontario 47311 0 1 1 Ontario 47313 0 1 0 Ontario 47315 0 1 1 Ontario 47317 0 1 0 Ontario 47319 0 1 1 Ontario 47320 0 1 0 Ontario 47324 0 1 1 Ontario 47328 0 1 0 Ontario 47331 0 1 1 Ontario 47334 0 1 1 Ontario 47335 0 1 1 Ontario 47336 0 1 1 Ontario 47337 0 1 0 Ontario 47338 0 1 1 Ontario 47341 0 1 1 Ontario 47343 0 1 1 Ontario 47344 0 1 1 Ontario 47345 0 1 1 Ontario 47347 0 1 1 Ontario 47353 0 1 0 Ontario 47354 0 1 1 Ontario 47356 0 1 1 Ontario 47357 0 1 1 Ontario 47358 0 1 0 Ontario 47359 0 1 1 Ontario 47360 0 1 1 Ontario 47361 0 1 1 Ontario 47365 0 1 1 Ontario 47366 0 1 1 Ontario 47368 0 1 1 Ontario 47369 0 1 1

230

Ontario 47371 0 1 1 Ontario 47372 0 1 1 Ontario 47375 0 1 1 Ontario 47376 0 1 1 Ontario 47379 0 1 1 Ontario 47380 0 1 0 Ontario 47382 0 1 1 Ontario 47383 0 1 0 Ontario 47385 0 1 1 Ontario 47387 1 1 NA Ontario 47388 0 1 0 Ontario 47389 0 1 0 Ontario 47390 0 1 0 Ontario 47391 0 1 1 Ontario 47396 0 1 1 Ontario 47397 0 1 1 Ontario 51002 0 1 1 Ontario 51003 0 1 1 Ontario 51004 0 1 1 Ontario 51014 0 1 0 Ontario 51015 0 1 1 Ontario 51018 0 1 1 Ontario 51019 0 1 1 Ontario 51021 0 1 0 Ontario 51022 0 1 1 Ontario 51023 0 1 1 Ontario 51030 0 1 1 Ontario 51035 0 1 1 Ontario 51036 0 1 1 Ontario 51037 0 1 1 Ontario 51040 0 1 1 Ontario 51043 0 1 1 Ontario 51045 0 1 1 Ontario 51046 0 1 1 Ontario 51047 0 1 1 Ontario 51048 0 1 1 Ontario 51049 0 1 1 Ontario 51050 0 1 1 Ontario 51051 0 1 1 Ontario 51052 0 1 1 Ontario 51053 0 1 1 Ontario 51054 0 1 1 Ontario 51055 0 1 1 Ontario 51058 0 1 1 Ontario 51059 0 1 1 Ontario 51060 0 1 1 Ontario 51061 0 1 1 Ontario 51064 0 1 1 Ontario 51067 0 1 1 Ontario 51069 0 1 0 Ontario 51070 0 1 1 Ontario 51071 0 1 1

231

Ontario 51074 0 1 1 Ontario 51076 0 1 1 Ontario 51077 0 1 1 Ontario 51078 0 1 1 Ontario 51079 0 1 0 Ontario 51081 0 1 1 Ontario 51083 0 1 1 Ontario 51085 0 1 1 Ontario 51086 0 1 1 Ontario 51089 0 1 1 Ontario 51092 0 1 1 Ontario 51093 0 1 1 Ontario 51097 0 1 1 Ontario 51098 0 1 1 Ontario 51100 0 1 1 Ontario 51103 0 1 1 Ontario 51104 0 1 1 Ontario 51107 0 1 1 Ontario 51108 0 1 1 Ontario 51110 0 1 1 Ontario 51112 0 1 1 Ontario 51113 0 1 1 Ontario 51117 0 1 1 Ontario 51119 0 1 1 Ontario 51120 0 1 1 Ontario 51123 0 1 1 Ontario 51131 0 1 1 Ontario 51132 0 1 1 Ontario 51137 0 1 1 Ontario 51142 0 1 1 Ontario 51144 0 1 1 Ontario 51145 0 1 1 Ontario 51146 0 1 1 Ontario 51149 0 1 1 Ontario 51150 0 1 1 Ontario 51151 0 1 1 Ontario 51152 0 1 1 Ontario 51154 0 1 0 Ontario 51157 0 1 1 Ontario 51158 0 1 1 Ontario 51161 0 1 0 Ontario 51163 0 1 1 Ontario 51164 0 1 1 Ontario 51165 0 1 1 Ontario 51168 0 1 1 Ontario 51169 0 1 0 Ontario 51170 0 1 1 Ontario 51175 0 1 1 Ontario 51178 0 1 1 Ontario 51179 0 1 1 Ontario 51182 0 1 1 Ontario 51183 0 1 1

232

Ontario 51185 0 1 1 Ontario 51186 0 1 1 Ontario 51189 0 1 1 Ontario 51190 0 1 0 Ontario 51192 0 1 1 Ontario 51193 0 1 1 Ontario 51195 0 1 1 Ontario 51196 0 1 1 Ontario 51199 0 1 1 Ontario 51201 0 1 1 Ontario 51204 0 1 1 Ontario 51205 0 1 1 Ontario 51206 0 1 1 Ontario 51207 0 1 1 Ontario 51210 0 1 1 Ontario 51215 0 1 1 Ontario 51216 0 1 1 Ontario 51221 0 1 1 Ontario 51226 0 1 0 Ontario 51227 0 1 1 Ontario 51245 0 1 1 Ontario 51246 0 1 1 Ontario 51247 0 1 1 Ontario 51248 0 1 1 Ontario 51250 0 1 1 Ontario 51252 0 1 0 Ontario 51253 0 1 1 Ontario 51254 0 1 1 Ontario 51256 0 1 1 Ontario 51259 0 1 1 Ontario 51265 0 1 1 Ontario 51266 0 1 1 Ontario 51268 0 1 1 Ontario 51269 0 1 1 Ontario 51276 0 1 1 Ontario 51278 0 1 0 Ontario 51279 0 1 1 Ontario 51280 0 1 1 Ontario 51281 0 1 1 Ontario 51285 0 1 0 Ontario 51292 0 1 1 Ontario 51293 0 1 1 Ontario 51296 0 1 1 Ontario 51298 0 1 1 Ontario 51299 0 1 1 Ontario 51304 0 1 1 Ontario 51306 0 1 1 Ontario 51308 0 1 1 Ontario 51309 0 1 1 Ontario 51313 0 1 1 Ontario 51315 0 1 1 Ontario 51317 0 1 1

233

Ontario 51318 0 1 1 Ontario 51322 0 1 0 Ontario 51323 0 1 1 Ontario 51325 0 1 1 Ontario 51326 0 1 1 Ontario 51327 0 1 1 Ontario 51328 0 1 1 Ontario 51329 0 1 1 Ontario 51331 0 1 1 Ontario 51333 0 1 1 Ontario 51335 0 1 1 Ontario 51337 0 1 1 Ontario 51338 0 1 1 Ontario 51339 0 1 1 Ontario 51341 0 1 1 Ontario 51342 0 1 1 Ontario 51349 0 1 1 Ontario 51350 0 1 1 Ontario 51352 0 1 1 Ontario 51353 0 1 1 Ontario 51356 0 1 1 Ontario 51357 0 1 1 Ontario 51359 0 1 0 Ontario 51361 0 1 1 Ontario 51362 0 1 1 Ontario 51363 0 1 1 Ontario 52002 0 1 1 Ontario 52004 0 1 0 Ontario 52007 0 1 1 Ontario 52008 0 1 0 Ontario 52011 0 1 0 Ontario 52012 0 1 1 Ontario 52014 0 1 1 Ontario 52016 0 1 0 Ontario 52017 0 1 1 Ontario 52018 0 1 0 Ontario 52019 0 1 1 Ontario 52020 0 1 0 Ontario 52021 0 1 0 Ontario 52025 0 1 1 Ontario 52030 0 1 1 Ontario 52031 0 1 1 Ontario 52032 0 1 0 Ontario 52036 0 1 0 Ontario 52037 0 1 1 Ontario 52040 0 1 1 Ontario 52042 0 1 1 Ontario 52043 0 1 0 Ontario 52044 0 1 1 Ontario 52046 0 1 1 Ontario 52049 0 1 1 Ontario 52052 0 1 0

234

Ontario 52054 0 1 1 Ontario 52056 0 1 1 Ontario 52059 0 1 0 Ontario 52064 0 1 0 Ontario 52065 0 1 0 Ontario 52067 0 1 1 Ontario 52070 0 1 1 Ontario 52071 0 1 0 Ontario 52073 0 1 1 Ontario 52077 0 1 1 Ontario 52078 0 1 1 Ontario 52079 0 1 1 Ontario 52082 0 1 1 Ontario 52083 0 1 1 Ontario 52084 0 1 0 Ontario 52087 0 1 1 Ontario 52088 0 1 1 Ontario 52090 0 1 1 Ontario 52093 0 1 1 Ontario 52094 0 1 0 Ontario 52096 0 1 0 Ontario 52098 0 1 1 Ontario 52099 0 1 1 Ontario 52100 0 1 1 Ontario 52107 0 1 1 Ontario 52109 0 1 1 Ontario 52113 0 1 1 Ontario 52119 0 1 1 Ontario 52121 0 1 1 Ontario 52122 0 1 1 Ontario 52126 0 1 1 Ontario 52127 0 1 0 Ontario 52128 0 1 0 Ontario 52130 0 1 1 Ontario 52134 0 1 1 Ontario 52136 0 1 1 Ontario 52140 0 1 1 Ontario 52141 0 1 1 Ontario 52145 0 1 1 Ontario 52151 0 1 1 Ontario 52152 0 1 1 Ontario 52154 0 1 0 Ontario 52158 0 1 1 Ontario 52161 0 1 1 Ontario 52162 0 1 1 Ontario 52163 0 1 0 Ontario 52165 0 1 1 Ontario 52172 0 1 1 Ontario 52174 0 1 1 Ontario 52177 0 1 0 Ontario 52179 0 1 1 Ontario 52180 0 1 0

235

Ontario 52185 0 1 1 Ontario 52188 0 1 0 Ontario 52196 0 1 0 Ontario 52197 0 1 0 Ontario 52199 0 1 0 Ontario 52200 0 1 0 Ontario 52203 0 1 0 Ontario 52205 0 1 1 Ontario 52207 0 1 1 Ontario 52209 0 1 0 Ontario 52218 0 1 1 Ontario 52219 0 1 1 Ontario 52223 0 1 1 Ontario 52225 0 1 1 Ontario 52226 0 1 1 Ontario 52227 0 1 1 Ontario 52232 0 1 0 Ontario 52241 0 1 0 Ontario 52244 0 1 1 Ontario 52246 0 1 0 Ontario 52249 0 1 0 Ontario 52257 0 1 0 Ontario 52260 0 1 1 Ontario 52261 0 1 1 Ontario 52262 0 1 0 Ontario 52263 0 1 1 Ontario 52269 0 1 1 Ontario 52276 0 1 1 Ontario 52283 0 1 0 Ontario 52286 0 1 0 Ontario 52289 0 1 1 Ontario 52295 0 1 1 Ontario 52298 0 1 0 Ontario 52299 0 1 1 Ontario 53007 0 1 1 Ontario 53012 1 1 NA Ontario 53013 0 1 1 Ontario 53016 1 1 NA Ontario 53018 0 1 0 Ontario 53019 1 1 NA Ontario 53024 0 1 1 Ontario 53031 0 1 1 Ontario 53037 0 1 1 Ontario 53039 0 1 1 Ontario 53041 0 1 1 Ontario 53043 0 1 1 Ontario 53046 0 1 1 Ontario 53047 0 1 1 Ontario 53055 0 1 0 Ontario 53056 0 1 1 Ontario 53059 0 1 0 Ontario 53067 0 1 0

236

Ontario 53069 0 1 1 Ontario 53071 0 1 1 Ontario 53072 1 1 NA Ontario 53073 0 1 1 Ontario 53075 0 1 1 Ontario 53081 0 1 1 Ontario 53084 0 1 0 Ontario 53086 0 1 1 Ontario 53087 0 1 1 Ontario 53088 0 1 0 Ontario 53089 0 1 1 Ontario 53090 0 1 1 Ontario 53094 0 1 0 Ontario 53102 0 1 1 Ontario 53108 1 1 NA Ontario 53116 0 1 1 Ontario 53117 0 1 1 Ontario 53121 0 1 0 Ontario 53122 1 1 NA Ontario 53124 0 1 0 Ontario 53125 0 1 1 Ontario 53129 0 1 1 Ontario 53132 0 1 1 Ontario 53133 0 1 1 Ontario 53135 0 1 0 Ontario 53137 0 1 1 Ontario 53143 0 1 1 Ontario 53145 0 1 1 Ontario 53146 0 1 1 Ontario 53151 0 1 1 Ontario 53157 0 1 1 Ontario 53158 0 1 1 Ontario 53160 0 1 1 Ontario 53163 0 1 1 Ontario 53169 0 1 0 Ontario 53171 0 1 1 Ontario 53173 0 1 1 Ontario 53179 0 1 1 Ontario 53180 0 1 1 Ontario 53184 0 1 1 Ontario 53192 0 1 1 Ontario 53193 0 1 1 Ontario 53195 0 1 0 Ontario 53197 0 1 1 Ontario 53203 1 1 NA Ontario 53205 1 1 NA Ontario 53206 1 1 NA Ontario 53207 1 1 NA Ontario 53208 1 1 NA Ontario 53218 0 1 0 Ontario 53219 0 1 1 Ontario 53226 0 1 1

237

Ontario 53227 0 1 0 Ontario 53228 0 1 1 Ontario 53235 0 1 0 Ontario 53242 0 1 1 Ontario 53245 0 1 0 Ontario 53246 0 1 1 Ontario 53249 0 1 1 Ontario 53250 0 1 1 Ontario 53251 0 1 1 Ontario 53255 0 1 1 Ontario 53256 0 1 1 Ontario 53257 0 1 1 Ontario 53259 0 1 1 Ontario 53261 0 1 1 Ontario 53262 0 1 1 Ontario 53264 0 1 1 Ontario 53265 0 1 1 Ontario 53272 1 1 NA Ontario 53273 0 1 0 Ontario 53274 0 1 0 Ontario 53276 0 1 1 Ontario 53279 0 1 0 Ontario 53283 0 1 1 Ontario 53286 0 1 1 Ontario 53296 0 1 1 Ontario 53298 0 1 1 Ontario 53302 0 1 1 Ontario 53308 0 1 0 Ontario 53317 0 1 0 Ontario 53319 0 1 1 Ontario 53324 0 1 1 Ontario 53325 0 1 1 Ontario 53338 0 1 1 Ontario 53339 0 1 1 Ontario 53340 0 1 1 Ontario 53341 0 1 1 Ontario 53342 0 1 1 Ontario 53343 0 1 1 Ontario 53344 1 0 NA Ontario 53345 0 1 1 Ontario 53346 0 1 1 Ontario 53347 0 1 1 Ontario 53351 1 1 NA Ontario 53352 0 1 1 Ontario 53354 0 1 1 Ontario 53355 0 1 1 Ontario 53356 0 1 1 Ontario 53359 0 1 1 Ontario 53369 0 1 0 Ontario 53370 0 1 0 Ontario 53372 0 1 1 Ontario 53374 0 1 1

238

Ontario 53375 0 1 0 Ontario 53380 0 1 0 Ontario 53382 0 1 0 Ontario 53383 1 1 NA Ontario 53384 1 1 NA Ontario 53386 0 1 1 Ontario 53387 1 1 NA Ontario 53388 0 1 1 Ontario 53389 0 1 1 Ontario 53392 0 1 1 Ontario 53394 0 1 0 Ontario 53397 0 1 0 Ontario 53402 1 1 NA Ontario 53410 0 1 1 Ontario 53412 0 1 1 Ontario 53415 0 1 1 Ontario 53416 0 1 1 Ontario 53422 0 1 1 Ontario 53423 0 1 1 Ontario 53427 0 1 0 Ontario 53429 0 1 0 Ontario 53431 1 1 NA Ontario 53432 1 0 NA Ontario 53436 0 1 1 Ontario 53440 0 1 1 Ontario 53441 0 1 1 Ontario 53442 0 1 1 Ontario 53445 0 1 1 Ontario 53446 0 1 1 Ontario 53450 0 1 0 Ontario 53451 0 1 1 Ontario 53457 0 1 0 Ontario 53460 0 1 1 Ontario 53461 0 1 1 Ontario 53462 0 1 1 Ontario 53463 0 1 1 Ontario 53467 0 1 1 Ontario 53468 0 1 1 Ontario 53469 0 1 1 Ontario 53472 0 1 0 Ontario 53475 0 1 0 Ontario 53477 0 1 1 Ontario 53478 0 1 1 Ontario 54001 0 1 0 Ontario 54002 0 1 1 Ontario 54005 0 1 0 Ontario 54008 0 1 1 Ontario 54009 0 1 0 Ontario 54011 0 1 1 Ontario 54012 0 1 1 Ontario 54013 0 1 0 Ontario 54016 0 1 1

239

Ontario 54017 0 1 0 Ontario 54018 0 1 1 Ontario 54019 0 1 1 Ontario 54020 1 1 NA Ontario 54023 0 1 1 Ontario 54026 0 1 1 Ontario 54027 1 1 NA Ontario 54028 0 1 0 Ontario 54030 0 1 1 Ontario 54033 0 1 1 Ontario 54034 0 1 1 Ontario 54038 0 1 1 Ontario 54039 0 1 1 Ontario 54041 0 1 1 Ontario 54042 0 1 1 Ontario 54044 0 1 0 Ontario 54049 0 1 1 Ontario 54050 0 1 0 Ontario 54051 0 1 1 Ontario 54052 0 1 0 Ontario 54053 0 1 1 Ontario 54054 0 1 0 Ontario 54055 0 1 1 Ontario 54056 0 1 1 Ontario 54058 0 1 1 Ontario 54060 0 1 1 Ontario 54061 0 1 1 Ontario 54062 0 1 1 Ontario 54064 0 1 1 Ontario 54065 0 1 1 Ontario 54066 0 1 1 Ontario 54067 0 1 1 Ontario 54068 0 1 1 Ontario 54070 0 1 1 Ontario 54071 0 1 0 Ontario 54074 0 1 1 Ontario 54075 0 1 1 Ontario 54076 0 1 1 Ontario 54080 0 1 0 Ontario 54081 0 1 0 Ontario 54083 0 1 0 Ontario 54084 1 1 NA Ontario 54086 0 1 1 Ontario 54087 0 1 1 Ontario 54088 0 1 1 Ontario 54093 1 1 NA Ontario 54098 0 1 1 Ontario 54099 0 1 1 Ontario 54100 0 1 1 Ontario 54101 0 1 1 Ontario 54102 0 1 1 Ontario 54103 1 1 NA

240

Ontario 54106 0 1 1 Ontario 54107 0 1 1 Ontario 54108 0 1 1 Ontario 54109 0 1 1 Ontario 54111 0 1 1 Ontario 54113 0 1 0 Ontario 54115 0 1 1 Ontario 54116 0 1 0 Ontario 54117 0 1 0 Ontario 54119 0 1 0 Ontario 54123 0 1 0 Ontario 54124 0 1 1 Ontario 54126 0 1 1 Ontario 54129 0 1 0 Ontario 54130 0 1 0 Ontario 54131 0 1 0 Ontario 54138 0 1 1 Ontario 54139 0 1 1 Ontario 54140 0 1 1 Ontario 54141 0 1 1 Ontario 54143 0 1 1 Ontario 54145 0 1 0 Ontario 54148 0 1 1 Ontario 54149 0 1 1 Ontario 54150 0 1 1 Ontario 54151 0 1 0 Ontario 54152 1 1 NA Ontario 54154 0 1 1 Ontario 54156 0 1 1 Ontario 54160 0 1 1 Ontario 54161 0 1 0 Ontario 54163 0 1 1 Ontario 54166 0 1 0 Ontario 54167 0 1 1 Ontario 54168 0 1 1 Ontario 54169 0 1 1 Ontario 54171 0 1 1 Ontario 54172 0 1 1 Ontario 54173 0 1 0 Ontario 54176 0 1 1 Ontario 54177 0 1 0 Ontario 54178 0 1 1 Ontario 54179 0 1 1 Ontario 54180 0 1 0 Ontario 54181 0 1 1 Ontario 54182 0 1 1 Ontario 54183 0 1 1 Ontario 54185 0 1 0 Ontario 54186 0 1 1 Ontario 54187 0 1 0 Ontario 54189 0 1 0 Ontario 54190 0 1 0

241

Ontario 54191 0 1 0 Ontario 54196 0 1 0 Ontario 54197 0 1 1 Ontario 54199 0 1 0 Ontario 54201 0 1 1 Ontario 54202 0 1 1 Ontario 54204 0 1 1 Ontario 54208 1 1 NA Ontario 54209 0 1 1 Ontario 54213 0 1 0 Ontario 54215 0 1 0 Ontario 54216 0 1 1 Ontario 54217 0 1 1 Ontario 54220 0 1 0 Ontario 55002 0 1 1 Ontario 55003 0 1 1 Ontario 55004 0 1 1 Ontario 55005 0 1 1 Ontario 55009 0 1 0 Ontario 55013 0 1 1 Ontario 55014 0 1 1 Ontario 55015 0 1 1 Ontario 55016 0 1 1 Ontario 55019 0 1 1 Ontario 55020 0 1 1 Ontario 55021 1 1 NA Ontario 55023 0 1 1 Ontario 55026 0 1 1 Ontario 55027 0 1 1 Ontario 55030 0 1 1 Ontario 55031 0 1 1 Ontario 55032 0 1 1 Ontario 55034 0 1 1 Ontario 55037 1 1 NA Ontario 55038 1 1 NA Ontario 55049 1 1 NA Ontario 55052 0 1 1 Ontario 55056 1 1 NA Ontario 55057 1 1 NA Ontario 55059 0 1 1 Ontario 55060 0 1 1 Ontario 55064 0 1 1 Ontario 55065 0 1 1 Ontario 55066 0 1 1 Ontario 55069 0 1 1 Ontario 55071 1 1 NA Ontario 55072 0 1 1 Ontario 55074 0 1 0 Ontario 55077 0 1 0 Ontario 55079 0 1 1 Ontario 55082 0 1 1 Ontario 55086 0 1 1

242

Ontario 55089 0 1 1 Ontario 55090 0 1 1 Ontario 55095 0 1 1 Ontario 55096 0 1 0 Ontario 55097 1 1 NA Ontario 55098 0 1 1 Ontario 55102 1 1 NA Ontario 55103 0 1 1 Ontario 55107 0 1 1 Ontario 55108 0 1 0 Ontario 55109 0 1 1 Ontario 55112 0 1 1 Ontario 55114 0 1 1 Ontario 55119 0 1 1 Ontario 55120 0 1 0 Ontario 55124 1 1 NA Ontario 55127 1 1 NA Ontario 55129 1 1 NA Ontario 55130 0 1 1 Ontario 55132 0 1 1 Ontario 55133 1 1 NA Ontario 55136 1 1 NA Ontario 55140 0 1 1 Ontario 55141 0 1 1 Ontario 55142 0 1 1 Ontario 55144 0 1 1 Ontario 55146 0 1 1 Ontario 55155 0 1 1 Ontario 55166 0 1 1 Ontario 55168 0 1 1 Ontario 55174 0 1 1 Ontario 55175 0 1 0 Ontario 55177 0 1 1 Ontario 55179 1 1 NA Ontario 55180 1 0 NA Ontario 55182 0 1 0 Ontario 55184 0 1 1 Ontario 55186 0 1 1 Ontario 55190 0 1 0 Ontario 55191 1 1 NA Ontario 55192 0 1 0 Ontario 55195 0 1 1 Ontario 55196 1 1 NA Ontario 55198 0 1 0 Ontario 55200 0 1 1 Ontario 55201 0 1 1 Ontario 55202 0 1 0 Ontario 55204 0 1 1 Ontario 55205 0 1 1 Ontario 55210 0 1 1 Ontario 55211 0 1 0 Ontario 55213 0 1 1

243

Ontario 55214 0 1 1 Ontario 55215 0 1 1 Ontario 55216 0 1 0 Ontario 55221 0 1 1 Ontario 55222 0 1 1 Ontario 55224 1 1 NA Ontario 55225 1 1 NA Ontario 55228 0 1 0 Ontario 55229 1 1 NA Ontario 55230 0 1 1 Ontario 55235 0 1 1 Ontario 55236 1 1 NA Ontario 55238 0 1 0 Ontario 55240 0 1 0 Ontario 55242 0 1 1 Ontario 55243 0 1 1 Ontario 55245 1 1 NA Ontario 55250 0 1 1 Ontario 55252 0 1 1 Ontario 55254 1 1 NA Ontario 55255 0 1 1 Ontario 55257 0 1 0 Ontario 55258 0 1 0 Ontario 55259 0 1 1 Ontario 55260 0 1 1 Ontario 55261 0 1 1 Ontario 55262 0 1 1 Ontario 56005 0 1 0 Ontario 56008 0 1 0 Ontario 56009 0 1 1 Ontario 56011 0 1 1 Ontario 56012 1 1 NA Ontario 56013 0 1 0 Ontario 56014 0 1 0 Ontario 56015 0 1 1 Ontario 56017 0 1 0 Ontario 56019 0 1 1 Ontario 56022 0 1 1 Ontario 56025 0 1 1 Ontario 56026 0 1 1 Ontario 56030 0 1 1 Ontario 56031 0 1 0 Ontario 56036 0 1 0 Ontario 56042 0 1 1 Ontario 56044 0 1 0 Ontario 56045 0 1 1 Ontario 56046 0 1 1 Ontario 56049 0 1 1 Ontario 56050 0 1 0 Ontario 56052 0 1 1 Ontario 56056 0 1 1 Ontario 56062 0 1 0

244

Ontario 56066 0 1 1 Ontario 56068 0 1 1 Ontario 56074 0 1 0 Ontario 56079 0 1 1 Ontario 56088 0 1 1 Ontario 56092 0 1 0 Ontario 56094 0 1 1 Ontario 56096 0 1 0 Ontario 56106 0 1 0 Ontario 56108 0 1 1 Ontario 56109 0 1 0 Ontario 56111 0 1 1 Ontario 56113 0 1 1 Ontario 56121 0 1 0 Ontario 56127 0 1 0 Ontario 56128 0 1 0 Ontario 56136 0 1 0 Ontario 56138 0 1 0 Ontario 56143 0 1 0 Ontario 56144 0 1 1 Ontario 56145 0 1 0 Ontario 56146 0 1 0 Ontario 56151 0 1 1 Ontario 56152 0 1 0 Ontario 56153 0 1 1 Ontario 56154 1 1 NA Ontario 56157 0 1 1 Ontario 56160 0 1 1 Ontario 56170 0 1 0 Ontario 56172 0 1 0 Ontario 56176 0 1 0 Ontario 56178 0 1 0 Ontario 56193 0 1 1 Ontario 56194 0 1 1 Ontario 56200 0 1 1 Ontario 56203 0 1 1 Ontario 56213 0 1 1 Ontario 56214 1 1 NA Ontario 56216 0 1 0 Ontario 56222 1 0 NA Ontario 56230 0 1 1 Ontario 56231 0 1 0 Ontario 56233 0 1 1 Ontario 56236 0 1 1 Ontario 56240 0 1 1 Ontario 56248 0 1 0 Ontario 56250 0 1 1 Ontario 56254 0 1 1 Ontario 56257 0 1 0 Ontario 56258 0 1 1 Ontario 56261 0 1 1 Ontario 56264 0 1 0

245

Ontario 56269 0 1 0 Ontario 56278 0 1 1 Ontario 56281 0 1 0 Ontario 56284 0 1 1 Ontario 56288 0 1 1 Ontario 56289 0 1 1 Ontario 56297 0 1 1 Ontario 56299 0 1 1 Ontario 56300 0 1 1 Ontario 56305 0 1 1 Ontario 56307 0 1 0 Ontario 56311 0 1 1 Ontario 57003 0 1 1 Ontario 57004 0 1 1 Ontario 57005 0 1 1 Ontario 57008 0 1 0 Ontario 57009 0 1 0 Ontario 57019 0 1 0 Ontario 57020 0 1 0 Ontario 57021 0 1 0 Ontario 57024 0 1 0 Ontario 57025 0 1 0 Ontario 57027 1 1 NA Ontario 57030 0 1 1 Ontario 57036 0 1 1 Ontario 57039 0 1 0 Ontario 57045 0 1 1 Ontario 57047 0 1 0 Ontario 57049 1 1 NA Ontario 57054 0 1 1 Ontario 57059 0 1 1 Ontario 57061 0 1 1 Ontario 57070 0 1 0 Ontario 61003 0 1 1 Ontario 61004 0 1 0 Ontario 61005 0 1 1 Ontario 61007 0 1 0 Ontario 61012 0 1 1 Ontario 61018 0 1 0 Ontario 61021 0 1 0 Ontario 61022 0 1 1 Ontario 61026 0 1 1 Ontario 61028 0 1 0 Ontario 61038 0 1 0 Ontario 61040 0 1 1 Ontario 61050 0 1 0 Ontario 61051 0 1 1 Ontario 61052 0 1 1 Ontario 61053 1 1 NA Ontario 61054 0 1 0 Ontario 61055 0 1 0 Ontario 61056 0 1 1

246

Ontario 61057 0 1 0 Ontario 61059 0 1 0 Ontario 61062 0 1 0 Ontario 62003 0 1 1 Ontario 63001 0 1 0 Ontario 63006 0 1 0 Ontario 63010 0 1 0 Ontario 63018 0 1 0 Ontario 63019 0 1 0 Ontario 63021 0 1 0 Ontario 63023 0 1 0 Ontario 63028 0 1 0 Ontario 63029 0 1 0 Ontario 63036 0 1 0 Ontario 63043 0 1 0 Ontario 63048 0 1 0 Ontario 63065 0 1 0 Ontario 63067 0 1 0 Ontario 63077 0 1 0 Ontario 63080 0 1 1 Ontario 63084 0 1 0 Ontario 63085 0 1 0 Ontario 63088 0 1 0 Ontario 63089 0 1 0 Ontario 63090 0 1 0 Ontario 63093 0 1 1 Ontario 63100 0 1 0 Ontario 63102 0 1 0 Ontario 63104 0 1 1 Ontario 63114 0 1 0 Ontario 64004 0 1 0 Ontario 64006 0 1 1 Ontario 64008 0 1 1 Ontario 64010 0 1 1 Ontario 64011 0 1 0 Ontario 64014 0 1 1 Ontario 64017 0 1 1 Ontario 64021 0 1 1 Ontario 64023 0 1 1 Ontario 64024 0 1 1 Ontario 64025 0 1 1 Ontario 64026 0 1 1 Ontario 64027 0 1 1 Ontario 64028 0 1 1 Ontario 64031 0 1 0 Ontario 64032 0 1 1 Ontario 64033 0 1 0 Ontario 64035 0 1 0 Ontario 64037 0 1 1 Ontario 64038 0 1 1 Ontario 64039 0 1 0 Ontario 64042 0 1 1

247

Ontario 64043 0 1 0 Ontario 64045 0 1 1 Ontario 64046 1 0 NA Ontario 64047 1 1 NA Ontario 64048 0 1 0 Ontario 64055 0 1 0 Ontario 64059 0 1 1 Ontario 64060 0 1 0 Ontario 64061 0 1 1 Ontario 64063 0 1 1 Ontario 64064 0 1 0 Ontario 64066 0 1 1 Ontario 64068 0 1 0 Ontario 64069 0 1 1 Ontario 64070 0 1 0 Ontario 64073 0 1 1 Ontario 66003 0 1 0 Ontario 66004 0 1 1 Ontario 66006 0 1 1 Ontario 66007 0 1 1 Ontario 66008 0 1 1 Ontario 66012 0 1 1 Ontario 66017 0 1 1 Ontario 66018 0 1 1 Ontario 66021 0 1 0 Ontario 66025 0 1 1 Ontario 66026 0 1 0 Ontario 66027 0 1 1 Ontario 66029 0 1 1 Ontario 66032 0 1 1 Ontario 66033 0 1 1 Ontario 66037 0 1 1 Ontario 66038 0 1 1 Ontario 66042 0 1 0 Ontario 66046 0 1 0 Ontario 66049 0 1 1 Ontario 66052 0 1 1 Ontario 66053 0 1 1 Ontario 66056 0 1 1 Ontario 66060 0 1 1 Ontario 66065 0 1 1 Ontario 66067 0 1 0 Ontario 66068 0 1 1 Ontario 66070 0 1 0 Ontario 66075 0 1 0 Ontario 66076 0 1 1 Ontario 66081 0 1 0 Ontario 66086 0 1 0 Ontario 66090 0 1 1 Ontario 66093 0 1 1 Ontario 66095 0 1 1 Ontario 66096 0 1 1

248

Ontario 66106 0 1 1 Ontario 66109 0 1 1 Ontario 66110 0 1 1 Ontario 66112 0 1 1 Ontario 66117 0 1 0 Ontario 66120 0 1 1 Ontario 66130 0 1 0 Ontario 66136 0 1 0 Ontario 66137 0 1 1 Ontario 66138 0 1 1 Ontario 66140 0 1 0 Ontario 66142 0 1 1 Ontario 66143 0 1 0 Ontario 66144 0 1 0 Ontario 66146 0 1 1 Ontario 66148 0 1 1 Ontario 66150 0 1 1 Ontario 66151 0 1 1 Ontario 66155 0 1 0 Ontario 66158 0 1 1 Ontario 66160 0 1 0 Ontario 66161 0 1 1 Ontario 66175 0 1 1 Ontario 66178 0 1 1 Ontario 66184 0 1 1 Ontario 66187 0 1 0 Ontario 66190 0 1 1 Ontario 66192 0 1 0 Ontario 66194 0 1 1 Ontario 66196 0 1 0 Ontario 66197 0 1 0 Ontario 66204 0 1 1 Ontario 66205 0 1 1 Ontario 66207 0 1 1 Ontario 66208 0 1 1 Ontario 66210 0 1 1 Ontario 66211 0 1 1 Ontario 66212 0 1 1 Ontario 66219 1 1 NA Ontario 66225 0 1 1 Ontario 66227 0 1 1 Ontario 66233 0 1 0 Ontario 66237 0 1 0 Ontario 66239 0 1 1 Ontario 66240 0 1 1 Ontario 66245 0 1 1 Ontario 66254 0 1 1 Ontario 66257 0 1 1 Ontario 66258 0 1 1 Ontario 66259 0 1 1 Ontario 71002 0 1 0 Ontario 71003 0 1 1

249

Ontario 71007 0 1 1 Ontario 71013 1 1 NA Ontario 71015 0 1 1 Ontario 71020 0 1 1 Ontario 71030 0 1 0 Ontario 71036 0 1 0 Ontario 71038 0 1 0 Ontario 71061 0 1 1 Ontario 71063 0 1 0 Ontario 71065 0 1 1 Ontario 71069 0 1 1 Ontario 71071 0 1 1 Ontario 71072 0 1 1 Ontario 72003 0 1 1 Ontario 72006 0 1 1 Ontario 73001 0 1 0 Ontario 73002 0 1 0 Ontario 73004 0 1 0 Ontario 73006 0 1 0 Ontario 73008 0 1 0 Ontario 73010 0 1 0 Ontario 73012 0 1 0 Ontario 73013 0 1 1 Ontario 73029 0 1 0 Ontario 73030 0 1 0 Ontario 73037 0 1 0 Ontario 73042 0 1 0 Ontario 73045 0 1 0 Ontario 73046 0 1 0 Ontario 74003 0 1 0 Ontario 74032 0 1 0 Ontario 74047 0 1 0 Ontario 75001 1 0 NA Ontario 75002 1 0 NA Ontario 75003 1 0 NA Ontario 81011 0 1 0 Ontario 81017 0 1 0 Ontario 83007 0 1 0 Ontario 83024 0 1 0 Ontario 83038 0 1 1 Ontario 83048 0 1 1 Ontario 83051 0 1 0 Ontario 83135 0 1 0 Ontario 85037 0 1 0 Ontario 85041 0 1 0 Wisconsin 400 0 1 0 Wisconsin 25100 0 1 0 Wisconsin 25300 1 1 NA Wisconsin 32500 0 1 0 Wisconsin 36200 0 1 0 Wisconsin 38700 0 1 1 Wisconsin 45000 0 1 0

250

Wisconsin 45200 0 1 1 Wisconsin 59300 0 1 1 Wisconsin 60800 0 1 0 Wisconsin 64000 0 1 0 Wisconsin 67400 0 1 0 Wisconsin 68100 0 1 0 Wisconsin 103300 0 1 0 Wisconsin 104000 0 1 0 Wisconsin 104800 0 1 0 Wisconsin 106900 0 1 0 Wisconsin 107700 0 1 0 Wisconsin 107900 0 1 0 Wisconsin 117600 0 1 0 Wisconsin 139300 0 1 0 Wisconsin 146100 0 1 1 Wisconsin 146500 0 1 0 Wisconsin 148100 0 1 0 Wisconsin 157300 0 1 0 Wisconsin 160600 0 1 0 Wisconsin 162500 0 1 0 Wisconsin 163970 0 1 0 Wisconsin 179800 0 1 0 Wisconsin 186400 0 1 0 Wisconsin 188000 0 1 0 Wisconsin 191100 0 1 0 Wisconsin 195400 0 1 0 Wisconsin 196100 0 1 0 Wisconsin 198000 0 1 0 Wisconsin 199700 0 1 1 Wisconsin 247200 0 1 0 Wisconsin 255000 0 1 0 Wisconsin 259600 0 1 0 Wisconsin 261200 0 1 0 Wisconsin 262400 0 1 0 Wisconsin 265000 0 1 0 Wisconsin 265100 0 1 1 Wisconsin 265300 0 1 1 Wisconsin 265500 0 1 0 Wisconsin 276300 0 1 0 Wisconsin 279700 0 1 1 Wisconsin 299200 0 1 0 Wisconsin 322800 0 1 0 Wisconsin 326400 0 1 1 Wisconsin 327800 0 1 1 Wisconsin 339600 0 1 0 Wisconsin 339800 0 1 0 Wisconsin 340500 0 1 0 Wisconsin 341000 0 1 0 Wisconsin 348700 0 1 0 Wisconsin 352200 0 1 0 Wisconsin 365500 0 1 0 Wisconsin 378400 0 1 0

251

Wisconsin 394400 0 1 0 Wisconsin 395900 0 1 0 Wisconsin 396500 1 1 NA Wisconsin 415000 0 1 0 Wisconsin 417300 0 1 0 Wisconsin 417400 0 1 0 Wisconsin 417500 0 1 0 Wisconsin 423100 0 1 0 Wisconsin 439800 0 1 0 Wisconsin 446600 0 1 0 Wisconsin 447200 0 1 0 Wisconsin 454200 0 1 0 Wisconsin 464700 0 1 0 Wisconsin 465000 0 1 0 Wisconsin 487500 0 1 0 Wisconsin 501200 0 1 0 Wisconsin 501700 0 1 0 Wisconsin 501800 0 1 0 Wisconsin 502300 0 1 1 Wisconsin 519500 0 1 0 Wisconsin 523300 0 1 0 Wisconsin 524700 0 1 0 Wisconsin 525900 0 1 1 Wisconsin 528500 0 1 0 Wisconsin 533600 0 1 1 Wisconsin 540600 0 1 1 Wisconsin 545400 0 1 0 Wisconsin 555500 0 1 0 Wisconsin 588000 0 1 0 Wisconsin 614200 0 1 0 Wisconsin 614900 0 1 0 Wisconsin 632800 0 1 1 Wisconsin 634300 0 1 0 Wisconsin 634500 0 1 0 Wisconsin 643800 0 1 0 Wisconsin 647500 0 1 0 Wisconsin 650200 0 1 0 Wisconsin 651600 0 1 0 Wisconsin 653700 0 1 1 Wisconsin 672000 0 1 0 Wisconsin 672300 0 1 1 Wisconsin 672900 1 1 NA Wisconsin 692400 0 1 0 Wisconsin 692900 0 1 1 Wisconsin 701900 0 1 1 Wisconsin 702500 0 1 0 Wisconsin 702600 0 1 0 Wisconsin 702700 0 1 0 Wisconsin 702800 0 1 0 Wisconsin 704200 0 1 1 Wisconsin 704400 0 1 1 Wisconsin 706000 0 1 0

252

Wisconsin 716800 0 1 0 Wisconsin 723100 0 1 0 Wisconsin 724000 0 1 0 Wisconsin 724600 0 1 0 Wisconsin 728100 0 1 0 Wisconsin 732200 0 1 0 Wisconsin 732300 0 1 0 Wisconsin 732600 0 1 0 Wisconsin 742800 0 1 0 Wisconsin 743000 0 1 0 Wisconsin 744200 0 1 0 Wisconsin 747900 0 1 0 Wisconsin 750300 0 1 0 Wisconsin 755600 0 1 0 Wisconsin 755700 0 1 0 Wisconsin 755800 0 1 0 Wisconsin 758300 0 1 1 Wisconsin 760900 0 1 0 Wisconsin 761300 0 1 0 Wisconsin 761700 0 1 0 Wisconsin 762700 0 1 0 Wisconsin 763600 0 1 0 Wisconsin 766600 0 1 1 Wisconsin 772000 0 1 0 Wisconsin 774400 0 1 0 Wisconsin 775900 0 1 0 Wisconsin 778100 0 1 0 Wisconsin 778400 0 1 0 Wisconsin 779200 0 1 1 Wisconsin 779800 0 1 1 Wisconsin 793600 0 1 0 Wisconsin 802600 0 1 0 Wisconsin 803700 0 1 0 Wisconsin 804600 0 1 1 Wisconsin 805400 0 1 1 Wisconsin 809600 0 1 0 Wisconsin 816800 0 1 0 Wisconsin 826300 0 1 0 Wisconsin 827000 0 1 1 Wisconsin 827100 0 1 1 Wisconsin 827300 0 1 0 Wisconsin 827500 0 1 1 Wisconsin 828000 0 1 1 Wisconsin 830700 0 1 0 Wisconsin 848800 0 1 1 Wisconsin 849400 0 1 1 Wisconsin 849600 0 1 1 Wisconsin 850300 0 1 1 Wisconsin 850800 0 1 0 Wisconsin 852400 0 1 0 Wisconsin 853200 0 1 0 Wisconsin 855200 0 1 0

253

Wisconsin 858300 0 1 0 Wisconsin 968500 1 0 NA Wisconsin 968800 0 1 0 Wisconsin 969600 0 1 0 Wisconsin 971600 0 1 1 Wisconsin 974000 0 1 0 Wisconsin 977500 0 1 0 Wisconsin 978200 0 1 0 Wisconsin 979200 0 1 0 Wisconsin 980900 0 1 0 Wisconsin 982500 0 1 0 Wisconsin 984000 0 1 0 Wisconsin 985100 0 1 0 Wisconsin 992400 0 1 0 Wisconsin 995200 0 1 0 Wisconsin 998500 0 1 0 Wisconsin 1001000 0 1 0 Wisconsin 1001300 0 1 0 Wisconsin 1001400 0 1 0 Wisconsin 1004600 0 1 0 Wisconsin 1007500 0 1 0 Wisconsin 1007600 0 1 0 Wisconsin 1008100 0 1 0 Wisconsin 1009400 0 1 0 Wisconsin 1013800 0 1 0 Wisconsin 1015500 0 1 0 Wisconsin 1018300 0 1 0 Wisconsin 1019500 0 1 0 Wisconsin 1020300 0 1 1 Wisconsin 1020400 0 1 0 Wisconsin 1020600 0 1 0 Wisconsin 1179600 0 1 0 Wisconsin 1242400 0 1 0 Wisconsin 1280400 0 1 0 Wisconsin 1299600 0 1 0 Wisconsin 1340200 0 1 0 Wisconsin 1340800 0 1 0 Wisconsin 1345700 0 1 0 Wisconsin 1346900 0 1 0 Wisconsin 1377100 0 1 0 Wisconsin 1377700 0 1 0 Wisconsin 1389800 0 1 0 Wisconsin 1412200 0 1 0 Wisconsin 1427400 0 1 0 Wisconsin 1437500 0 1 0 Wisconsin 1469700 0 1 0 Wisconsin 1494600 0 1 0 Wisconsin 1516500 0 1 0 Wisconsin 1517200 0 1 0 Wisconsin 1518200 0 1 0 Wisconsin 1519600 0 1 0 Wisconsin 1528300 0 1 1

254

Wisconsin 1535000 0 1 0 Wisconsin 1536300 0 1 0 Wisconsin 1538000 0 1 0 Wisconsin 1538600 0 1 0 Wisconsin 1539600 0 1 0 Wisconsin 1541100 0 1 0 Wisconsin 1541300 0 1 0 Wisconsin 1541500 0 1 0 Wisconsin 1542300 0 1 0 Wisconsin 1542400 0 1 1 Wisconsin 1542700 0 1 1 Wisconsin 1543300 0 1 0 Wisconsin 1543900 0 1 0 Wisconsin 1545300 0 1 0 Wisconsin 1545600 0 1 0 Wisconsin 1555400 0 1 0 Wisconsin 1555900 0 1 0 Wisconsin 1564200 0 1 0 Wisconsin 1564400 0 1 0 Wisconsin 1564600 0 1 0 Wisconsin 1569900 0 1 0 Wisconsin 1578400 0 1 1 Wisconsin 1579700 0 1 0 Wisconsin 1579900 0 1 0 Wisconsin 1580200 0 1 0 Wisconsin 1588200 0 1 0 Wisconsin 1589100 0 1 0 Wisconsin 1591100 0 1 1 Wisconsin 1592400 0 1 0 Wisconsin 1593100 0 1 0 Wisconsin 1595300 0 1 0 Wisconsin 1595800 0 1 0 Wisconsin 1596300 0 1 0 Wisconsin 1596900 0 1 0 Wisconsin 1600200 0 1 0 Wisconsin 1602300 1 1 NA Wisconsin 1602600 0 1 0 Wisconsin 1603400 0 1 0 Wisconsin 1603700 0 1 0 Wisconsin 1605800 0 1 0 Wisconsin 1606600 0 1 1 Wisconsin 1606700 0 1 0 Wisconsin 1609000 0 1 0 Wisconsin 1609100 0 1 0 Wisconsin 1610500 0 1 0 Wisconsin 1610600 0 1 0 Wisconsin 1610700 0 1 0 Wisconsin 1611700 0 1 1 Wisconsin 1612000 0 1 0 Wisconsin 1612200 0 1 1 Wisconsin 1614100 0 1 0 Wisconsin 1614300 0 1 0

255

Wisconsin 1616400 0 1 0 Wisconsin 1621800 0 1 0 Wisconsin 1623700 0 1 0 Wisconsin 1623800 0 1 0 Wisconsin 1629500 0 1 0 Wisconsin 1630100 0 1 1 Wisconsin 1631900 0 1 0 Wisconsin 1632200 0 1 0 Wisconsin 1667800 0 1 0 Wisconsin 1727700 0 1 0 Wisconsin 1831700 0 1 0 Wisconsin 1832200 0 1 0 Wisconsin 1833300 0 1 0 Wisconsin 1834100 0 1 0 Wisconsin 1834800 0 1 0 Wisconsin 1835100 0 1 0 Wisconsin 1835300 0 1 0 Wisconsin 1835700 0 1 0 Wisconsin 1836100 0 1 0 Wisconsin 1836200 0 1 0 Wisconsin 1836700 0 1 0 Wisconsin 1842000 0 1 0 Wisconsin 1842400 1 1 NA Wisconsin 1842500 0 1 1 Wisconsin 1843500 0 1 0 Wisconsin 1844500 0 1 0 Wisconsin 1844700 0 1 0 Wisconsin 1846000 0 1 0 Wisconsin 1848400 0 1 0 Wisconsin 1848500 0 1 0 Wisconsin 1852300 0 1 0 Wisconsin 1853700 0 1 0 Wisconsin 1856700 0 1 0 Wisconsin 1859900 0 1 0 Wisconsin 1862900 0 1 0 Wisconsin 1864500 0 1 0 Wisconsin 1866100 0 1 0 Wisconsin 1866500 0 1 0 Wisconsin 1869700 0 1 0 Wisconsin 1870200 0 1 0 Wisconsin 1870500 0 1 0 Wisconsin 1871100 0 1 0 Wisconsin 1872100 0 1 1 Wisconsin 1875100 0 1 0 Wisconsin 1876500 0 1 0 Wisconsin 1878500 0 1 0 Wisconsin 1878800 0 1 0 Wisconsin 1879800 0 1 0 Wisconsin 1880600 0 1 0 Wisconsin 1880900 0 1 0 Wisconsin 1881100 0 1 0 Wisconsin 1881900 1 1 NA

256

Wisconsin 1882500 0 1 0 Wisconsin 1882800 0 1 0 Wisconsin 1883800 0 1 0 Wisconsin 1884100 0 1 0 Wisconsin 1992300 0 1 0 Wisconsin 2046600 0 1 0 Wisconsin 2047300 0 1 0 Wisconsin 2053400 0 1 0 Wisconsin 2065900 0 1 1 Wisconsin 2068000 0 1 0 Wisconsin 2079000 0 1 1 Wisconsin 2079100 0 1 0 Wisconsin 2081200 1 1 NA Wisconsin 2092500 0 1 0 Wisconsin 2092900 0 1 0 Wisconsin 2098000 0 1 0 Wisconsin 2098200 0 1 0 Wisconsin 2105100 0 1 1 Wisconsin 2106800 0 1 0 Wisconsin 2107500 0 1 0 Wisconsin 2109300 0 1 0 Wisconsin 2109600 0 1 1 Wisconsin 2112800 0 1 0 Wisconsin 2113000 0 1 0 Wisconsin 2113300 0 1 0 Wisconsin 2149900 0 1 0 Wisconsin 2152600 0 1 0 Wisconsin 2152800 0 1 0 Wisconsin 2157000 0 1 0 Wisconsin 2166100 0 1 0 Wisconsin 2174700 0 1 0 Wisconsin 2178400 0 1 0 Wisconsin 2181400 0 1 0 Wisconsin 2184900 0 1 0 Wisconsin 2229200 0 1 0 Wisconsin 2230100 0 1 0 Wisconsin 2234900 0 1 0 Wisconsin 2239300 0 1 0 Wisconsin 2275100 0 1 0 Wisconsin 2275300 0 1 0 Wisconsin 2279800 0 1 0 Wisconsin 2282000 0 1 0 Wisconsin 2283300 0 1 0 Wisconsin 2294900 0 1 0 Wisconsin 2295200 0 1 0 Wisconsin 2299900 0 1 0 Wisconsin 2303500 0 1 0 Wisconsin 2304500 0 1 0 Wisconsin 2306300 0 1 0 Wisconsin 2307300 0 1 0 Wisconsin 2307600 0 1 0 Wisconsin 2308700 0 1 0

257

Wisconsin 2310400 0 1 0 Wisconsin 2311100 0 1 0 Wisconsin 2314300 0 1 0 Wisconsin 2316600 1 1 NA Wisconsin 2317600 0 1 0 Wisconsin 2317700 0 1 0 Wisconsin 2320500 1 1 NA Wisconsin 2320800 1 1 NA Wisconsin 2321100 0 1 0 Wisconsin 2321400 0 1 0 Wisconsin 2321600 1 1 NA Wisconsin 2321800 0 1 0 Wisconsin 2322200 0 1 0 Wisconsin 2322300 1 1 NA Wisconsin 2322500 1 0 NA Wisconsin 2322800 1 1 NA Wisconsin 2323000 1 1 NA Wisconsin 2323800 1 0 NA Wisconsin 2324000 1 1 NA Wisconsin 2327500 0 1 0 Wisconsin 2328700 0 1 0 Wisconsin 2328800 0 1 0 Wisconsin 2329000 0 1 0 Wisconsin 2329300 0 1 0 Wisconsin 2329400 0 1 1 Wisconsin 2329600 0 1 0 Wisconsin 2330800 0 1 0 Wisconsin 2331600 0 1 1 Wisconsin 2334300 0 1 0 Wisconsin 2334400 0 1 0 Wisconsin 2334700 0 1 0 Wisconsin 2335300 0 1 0 Wisconsin 2335400 0 1 0 Wisconsin 2336100 0 1 0 Wisconsin 2336800 0 1 0 Wisconsin 2338800 0 1 0 Wisconsin 2339100 0 1 0 Wisconsin 2343200 0 1 0 Wisconsin 2344000 0 1 0 Wisconsin 2348700 0 1 0 Wisconsin 2349500 0 1 0 Wisconsin 2350200 0 1 0 Wisconsin 2350500 0 1 0 Wisconsin 2350600 0 1 0 Wisconsin 2351000 0 1 0 Wisconsin 2351400 0 1 0 Wisconsin 2351800 0 1 0 Wisconsin 2352000 0 1 0 Wisconsin 2352500 0 1 0 Wisconsin 2353600 0 1 0 Wisconsin 2355300 0 1 0 Wisconsin 2358400 0 1 0

258

Wisconsin 2390500 0 1 0 Wisconsin 2390800 0 1 1 Wisconsin 2391200 0 1 0 Wisconsin 2392000 1 1 NA Wisconsin 2393200 0 1 0 Wisconsin 2393500 0 1 0 Wisconsin 2394100 0 1 0 Wisconsin 2395600 0 1 0 Wisconsin 2399700 0 1 0 Wisconsin 2406700 0 1 0 Wisconsin 2417000 0 1 0 Wisconsin 2435000 0 1 0 Wisconsin 2435700 0 1 0 Wisconsin 2448800 0 1 0 Wisconsin 2449200 0 1 0 Wisconsin 2450500 0 1 0 Wisconsin 2452200 0 1 0 Wisconsin 2454100 0 1 0 Wisconsin 2457300 0 1 0 Wisconsin 2457600 0 1 0 Wisconsin 2464500 0 1 0 Wisconsin 2467700 0 1 0 Wisconsin 2467800 0 1 0 Wisconsin 2467900 0 1 1 Wisconsin 2469900 0 1 0 Wisconsin 2470600 0 1 0 Wisconsin 2470800 0 1 0 Wisconsin 2474800 0 1 0 Wisconsin 2476000 0 1 0 Wisconsin 2476600 0 1 0 Wisconsin 2479900 0 1 0 Wisconsin 2482800 0 1 0 Wisconsin 2482900 0 1 0 Wisconsin 2483100 0 1 0 Wisconsin 2486100 0 1 0 Wisconsin 2486500 0 1 0 Wisconsin 2489200 0 1 0 Wisconsin 2489900 0 1 0 Wisconsin 2490500 0 1 0 Wisconsin 2492100 0 1 0 Wisconsin 2493100 0 1 0 Wisconsin 2493900 0 1 0 Wisconsin 2494900 1 1 NA Wisconsin 2495000 0 1 0 Wisconsin 2495100 0 1 0 Wisconsin 2496300 0 1 1 Wisconsin 2496800 0 1 0 Wisconsin 2499200 0 1 0 Wisconsin 2501700 1 1 NA Wisconsin 2524900 0 1 0 Wisconsin 2577600 0 1 0 Wisconsin 2584500 0 1 0

259

Wisconsin 2598600 0 1 0 Wisconsin 2599400 0 1 0 Wisconsin 2601500 0 1 0 Wisconsin 2615100 0 1 0 Wisconsin 2616100 0 1 0 Wisconsin 2618000 0 1 0 Wisconsin 2619400 0 1 0 Wisconsin 2620600 0 1 0 Wisconsin 2621100 0 1 0 Wisconsin 2628100 0 1 0 Wisconsin 2630100 0 1 0 Wisconsin 2631600 0 1 0 Wisconsin 2634400 0 1 0 Wisconsin 2638700 0 1 0 Wisconsin 2649500 0 1 0 Wisconsin 2649800 0 1 0 Wisconsin 2651800 0 1 0 Wisconsin 2654500 0 1 0 Wisconsin 2659300 0 1 0 Wisconsin 2661100 0 1 0 Wisconsin 2663800 0 1 0 Wisconsin 2664900 0 1 0 Wisconsin 2668200 0 1 0 Wisconsin 2668500 0 1 0 Wisconsin 2669300 0 1 0 Wisconsin 2671000 0 1 0 Wisconsin 2672500 0 1 0 Wisconsin 2675200 0 1 0 Wisconsin 2675700 0 1 0 Wisconsin 2676800 0 1 0 Wisconsin 2677200 0 1 0 Wisconsin 2678300 0 1 0 Wisconsin 2691500 0 1 0 Wisconsin 2691900 0 1 0 Wisconsin 2693700 0 1 0 Wisconsin 2693800 0 1 0 Wisconsin 2694000 1 1 NA Wisconsin 2695800 0 1 0 Wisconsin 2704200 0 1 0 Wisconsin 2705400 0 1 0 Wisconsin 2706500 0 1 0 Wisconsin 2706800 0 1 0 Wisconsin 2709400 0 1 0 Wisconsin 2712000 0 1 1 Wisconsin 2714700 0 1 0 Wisconsin 2726100 0 1 0 Wisconsin 2729500 0 1 0 Wisconsin 2729700 0 1 0 Wisconsin 2732600 0 1 1 Wisconsin 2733600 0 1 0 Wisconsin 2734000 0 1 0 Wisconsin 2740300 0 1 0

260

Wisconsin 2741600 0 1 1 Wisconsin 2742100 0 1 1 Wisconsin 2742500 0 1 1 Wisconsin 2742700 0 1 1 Wisconsin 2743700 0 1 1 Wisconsin 2755800 0 1 0 Wisconsin 2757000 0 1 0 Wisconsin 2757900 0 1 0 Wisconsin 2758600 0 1 0 Wisconsin 2760100 0 1 0 Wisconsin 2762100 0 1 0 Wisconsin 2762200 0 1 0 Wisconsin 2762800 0 1 0 Wisconsin 2762900 0 1 0 Wisconsin 2763300 0 1 0 Wisconsin 2766200 0 1 0 Wisconsin 2769700 0 1 0 Wisconsin 2770700 0 1 1 Wisconsin 2770800 0 1 0 Wisconsin 2771700 0 1 0 Wisconsin 2776000 0 1 0 Wisconsin 2823000 0 1 0 Wisconsin 2832300 0 1 0 Wisconsin 2858100 0 1 0 Wisconsin 2865000 0 1 0 Wisconsin 2866200 0 1 0 Wisconsin 2897100 1 1 NA Wisconsin 2898400 0 1 0 Wisconsin 2899200 1 1 NA Wisconsin 2899400 0 1 0 Wisconsin 2900200 0 1 1 Wisconsin 2901100 0 1 0 Wisconsin 2902600 0 1 0 Wisconsin 2902900 0 1 0 Wisconsin 2903100 0 1 0 Wisconsin 2903200 0 1 0 Wisconsin 2903700 0 1 0 Wisconsin 2903800 0 1 0 Wisconsin 2949200 0 1 0 Wisconsin 2952100 0 1 0 Wisconsin 2953200 0 1 0 Wisconsin 2953500 0 1 0 Wisconsin 2954500 0 1 0 Wisconsin 2954800 0 1 0 Wisconsin 2956500 0 1 0 Wisconsin 2957000 0 1 0 Wisconsin 2958500 0 1 0 Wisconsin 2962400 0 1 0 Wisconsin 2963800 0 1 0 Wisconsin 2964000 0 1 0