RESPONSE OF PUMAS (Puma concolor) TO THE MIGRATION...

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RESPONSE OF PUMAS (Puma concolor) TO THE MIGRATION OF GUANACOS (Lama guanicoe) IN PATAGONIA

By

MARIA LAURA GELIN SPESSOT

A THESIS PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2016

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©2016 Maria Laura Gelin Spessot

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ACKNOWLEDGMENTS

This study was financed by the Programa de becas BEC.AR Argentina, The

School of Natural Resources and Environment of the University of Florida, the

Conservation Food and Health Foundation, the Rufford Foundation, the Pittsburgh Zoo

& PPG Aquarium, Idea Wild, the Tropical Conservation and Development Program – U.

Florida, and the Sackler Institute for Comparative Genomics at the American Museum

of Natural History. The Argentinian program of the Wildlife Conservation Society and the

Dirección de Recursos Naturales Renovables de Mendoza graciously provided logistical

support.

I am deeply grateful to my advisor, Lyn Branch, for her helping and supporting

me every step of the way during my stay in Gainesville, sharing her knowledge and

making such valuable contributions to my academic training, and above all, for showing

me infinite patience. I want to thank my committee members, Dan Thornton and John

Blake, who provided valuable comments and suggestions to improve my work.

I am also grateful to Mariel, Maco and Andres Novaro, for giving me the

opportunity to work with them in such a beautiful landscape, and for their help and

support during field work. Special thanks to Andres for his valuable contributions to this

work, and to the Park rangers of Malargue, who opened the door to their home and

made my stay in “La Payunia” so comfortable. I would also like to thank my friend Nata

for being with me during the hardest stage of my fieldwork, as well as all of my field

assistants who withstood every difficulty during the fieldwork.

I would also like to acknowledge Oscar Murillo, Jose Soto, Andrew Noss and

Matt Gould for their important contributions to this Project, as well as Daniela Gomez,

Jose Priotto and Silvia Valdano for their helpful letters of support everytime I need them.

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My heartfelt thanks to my friends Juliana, Diego, Harry and Claudio for the thousands of

“mates” that we shared, and to Maca and Audrey for being the best roommates one

could ask for.

Finally, to Lucas for his unconditional love and support, and to my beloved family

for being always there and giving me the strength to never give up.

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TABLE OF CONTENTS

page

ACKNOWLEDGMENTS.............................................................................................................. 3

LIST OF TABLES ......................................................................................................................... 7

LIST OF FIGURES....................................................................................................................... 8

ABSTRACT ................................................................................................................................... 9

CHAPTER

1 INTRODUCTION................................................................................................................. 11

2 MATERIALS AND METHODS .......................................................................................... 14

Study Area............................................................................................................................ 14 Camera-trap Surveys ......................................................................................................... 15

Identification of Individual Pumas ..................................................................................... 16 Puma Density Estimates .................................................................................................... 16

Collection of Puma Scats................................................................................................... 20 Diet Analysis ........................................................................................................................ 21

3 RESULTS ............................................................................................................................. 25

Identification of Individual Pumas ..................................................................................... 25 Puma Density Estimates .................................................................................................... 25

Diet Analysis ........................................................................................................................ 27

4 DISCUSSION ...................................................................................................................... 29

APPENDIX

A PUMA CAPTURE HISTORIES ......................................................................................... 34

B ADDITIONAL MODELS EXAMINED WITH COVARIATES FOR DETECTION

PROBABILITY ..................................................................................................................... 40

C COUNTS OF GUANACOS AND PUMAS IN BOTH STUDY GRIDS ......................... 41

Index of guanaco abundance ............................................................................................ 41

D PLOT GENERATED BY SPATIAL MARK- RESIGHT TO DETERMINE APPROPIATE BUFFER SIZE........................................................................................... 44

E ADDITIONAL MODELS EXAMINED FOR DENSITY ................................................... 45

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LIST OF REFERENCES ........................................................................................................... 46

BIOGRAPHICAL SKETCH ....................................................................................................... 51

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LIST OF TABLES

Table page

2-1 Top five spatial mark-resight (SMR) models for density of pumas. Models

were ranked using Akaike’s Information Criterion adjusted for small sample sizes (AICc). .................................................................................................................... 24

2-2 Percent of occurrence of prey items in puma scats in summer (n =25 scats)

and winter (n =14 scats) in the north of La Payunia Reserve ................................. 24

3-1 Number of pumas identified to the individual level (n*), total number of puma

photographs that were labeled as individually identifiable (ID) and unidentifiable (non-ID). Distances moved and recaptures....................................... 28

3-2 Density estimates for pumas obtained from the five highest ranked spatial

mark-resight models ..................................................................................................... 28

4-1 Estimates of puma densities from other studies in Central and South America

and from studies in the western US with habitat similar to La Payunia. ................ 33

A-1 Capture histories for pumas individually identified on the north and south grids. ................................................................................................................................. 34

A-2 Capture histories for non-identified individuals for the north and south grids.. .... 36

B-1 Model comparison table to determine best fitting model for detection

probability parameter in multi-session density models using Akaike’s Information Criterion adjusted for small sample sizes (AICc). ................................ 40

B-2 Model comparison table to determine best-fitting model for detection

probability parameter with density constant. Ranking is based on Akaike’s Information Criterion adjusted for small sample sizes (AICc). ................................ 40

E-1 Model comparison table to determine best fitting model for density with constant detection probability, using Akaike’s Information Criterion adjusted for small sample sizes (AICc) ....................................................................................... 45

E-2 Model comparison table to determine influence of season in North Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc)....... 45

E-3 Model comparison table to determine influence of season in South Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc).. .... 45

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LIST OF FIGURES

Figure page

2-1 La Payunia Reserve....................................................................................................... 22

2-2 Grid layout and camera station locations (dots) in the La Payunia Reserve........ 23

C-1 Counts of guanacos in the north and south grids using 1 photograph per camera station per day as an independent record.. ................................................. 42

C-2 Counts of pumas in the north and south grids using 1 photograph per camera station per day as an independent record.................................................................. 43

D-1 Capture or detection probability as a function of distance from the center of a home range for the best-fitting SMR model. .............................................................. 44

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Master of Science

RESPONSE OF PUMAS (Puma concolor) TO THE MIGRATION OF GUANACOS (Lama guanicoe) IN PATAGONIA

By

Maria Laura Gelin Spessot

December 2016

Chair: Lyn C. Branch

Major: Interdisciplinary Ecology

Large-scale migrations can generate changes in the availability of prey for top

predators. In response to prey migration, predators can either track prey movements by

moving with them or they can modify their foraging behavior to focus on alternative

prey. My study was conducted in La Payunia Reserve, Argentina, where the puma

(Puma concolor) is the largest predator, and the guanaco (Lama guanicoe) is the

largest native ungulate. Guanacos exhibit a seasonal extensive migration at this site.

The goal of my project was to determine whether pumas in this site respond to

migration of guanacos by moving with the guanacos or by switching prey. Based on

data collected from camera traps, I used spatial mark–resight (SMR) models to estimate

density of pumas in summer and winter ranges of guanacos and analyzed the changes

in their density in response to the migration. Also, I analyzed puma scats to assess

changes in prey consumption in response to guanaco migration.

My results indicate that pumas do not follow the migration of guanacos in La

Payunia Reserve nor do they switch to alternative prey. Density estimates of pumas did

not increase significantly in the winter and summer range of guanacos when guanacos

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migrated to these areas. Analysis of puma diet showed that pumas feed on guanacos

throughout the year and do not switch to alternative prey when guanaco availability is

lower. Density estimates obtained in this study were within the range of estimates from

other sites with a similar landscape.

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CHAPTER 1 INTRODUCTION

Large-scale ungulate migrations, associated with changes in the availability of

resources and risk of predation (Fryxell and Sinclair 1988), are widespread phenomena

that influence mammal community composition and prey abundance for top predators.

Predators can either track prey movements by undertaking migrations with them or they

can modify their foraging behavior to focus on alternative prey sources (i.e., prey

switching; Bergerud 1983; Giroux et al. 2012; Elbroch et al. 2013). Either response can

have strong effects at the community level. For example, if predators move with prey,

the distribution of carcasses for scavengers and decomposers also shifts (Elbroch et al.

2013). On the other hand, if predators remain relatively stationary and switch prey in

response to the migration, they may have significant impacts on populations of

alternative prey (Ballard et al. 1997; Keehener et al. 2015). Migration of native prey also

may affect the frequency of predation on livestock and increase human-predator

conflict, if livestock serve as alternative prey sources for predators (Morehouse and

Boyce 2011).

The behavioral responses of carnivores to prey migration have been documented

in a variety of ecosystems for North America (Pierce et al. 1999; Giroux et al. 2012;

Nelson et al. 2012), but herbivore migrations and responses of top predator to these

migrations are poorly studied in South America. One predator that spans both of these

continents is the puma (Puma concolor), which has the largest geographic range of any

mammal in the western hemisphere and is a top predator from Canada to southern

South America. Although pumas rely on non-migratory prey in much of their range,

studies in the US have reported migration by pumas (Pierce et al. 1999) and prey

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switching (Elbroch et al. 2013) in systems where large ungulates, their primary prey,

migrate.

In the Patagonian region of southern South America, pumas prey heavily on

guanacos (Lama guanicoe), the largest native ungulate, at least in areas where

guanacos are still common (Martinez et al. 2012; Iriarte et al. 1991; Ortega and Franklin

1995). Although guanaco populations have declined throughout their range and

movement patterns likely have been altered, the longest known terrestrial migration for

mammals in South America is the seasonal migration of guanacos in Patagonia (Puig et

al. 2003; Mueller et al. 2011; Schroeder et al. 2014). The response of pumas to this

migration is unknown.

In this study, I examined the response of pumas to seasonal migration of

guanacos in La Payunia Reserve in northern Patagonia, Argentina, which is the site of

this extensive guanaco migration. Although La Payunia Reserve is one of the few sites

in South America where ungulate migrations have been well documented, shorter

seasonal movements of guanacos are known to occur in other areas (e.g., Tierra del

Fuego, Moraga et al. 2015). Migrations and other seasonal movements may have been

more common in the past when guanaco populations were large and fences were less

common.

I hypothesized that pumas respond to the migration of guanacos by switching to

alternative prey rather than migrating with them because pumas generally are territorial,

have generalist food habits (Logan and Sweanor 2001), and alternative prey are not

scarce in La Payunia and are available throughout the year. Also, I expected pumas to

include more livestock as well as native prey in their diet when guanacos migrated.

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Ranching of goats and cattle occurs within the reserve and on neighboring lands. A

higher frequency of attacks from pumas on livestock has been reported by local herders

in areas where the abundance of guanacos is lower, but this predation has not been

explicitly linked to the guanaco migration (Bolgeri and Novaro 2015).

To test my hypothesis, I examined density of pumas in the summer and winter

ranges of guanacos and analyzed the diet of pumas through analysis of scats to

determine whether pumas shift their diet when guanacos migrate. Alternatively, if

pumas move with guanacos, I expected to see changes in density of pumas that parallel

changes in abundance of guanacos with migration. I derived density estimates of

pumas from camera trapping data analyzed with spatial mark-resight models (SMR).

Spatial mark-resight models are a new technique developed to overcome important

limitations of capture-recapture models (CR) related to incorporation of unidentified

individuals in density estimates and the need to better define the effective area sampled

(Efford 2004; Chandler and Royle 2013; Rich et al. 2014; Efford 2016).

In addition to examining the response of pumas to prey migration, this study

provided an opportunity to compare densities of pumas from large open rangelands in

southern South America to estimates from similar habitats in North America (Logan et

al. 1996, Seidensticker et al. 1973, Lindzey et al. 1994, Logan et al. 1986). With the

exception of one study in Chile (Elbroch and Wittmer 2012), density estimates of pumas

from South America are limited to tropical and subtropical forests (Kelly et al. 2008;

Paviolo et al. 2009; Quiroga et al. 2016).

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CHAPTER 2 MATERIALS AND METHODS

Study Area

My study was conducted in La Payunia Reserve (36º10’S, 68º50’W, Figure 2-1),

a provincial reserve (6641 km2) in northern Patagonia, Mendoza Province, Argentina.

La Payunia Reserve has one of the largest remaining populations of guanacos (>

25,000 individuals, Puig et al. 2003; Schroeder et al. 2014; Bolgeri and Novaro 2015).

Guanacos in La Payunia move up to 70 km from north to south during the winter season

and return to the north of the reserve in the summer (Novaro et al. 2006). The reserve is

characterized by a high density (> 800) of ancient volcanoes (1200 - 2000 m above sea

level) dispersed across broad open plains. The low, open vegetation (~ 50 % cover) of

the area comprises shrubs interspersed with grasses. Dominant species include:

Neosparton aphyllum, Chuquiraga erinacea, Larrea divaricata, Cassia aphila, Panicum

urvilleanum, Poa spp., and Stipa spp. (Candia et al. 1993; Puig et al. 2003). Annual

precipitation averages 255 mm, occurring mostly during the summer months, and mean

seasonal temperatures range from 6ºC in winter to 20ºC in summer (Puig et al. 1996).

Approximately 55% of La Payunia Reserve is under government protection; the

rest is managed by private ranchers whose main economic activity is extensive

livestock ranching, principally of goats (Candia et al. 1993, Bolgeri and Novaro 2015).

Despite the protected status of the area, the reserve suffers from hunting pressure on a

variety of wildlife species, especially guanacos, the lesser rhea (Rhea pennata) and

pichi armadillo (Zaedyus pichiy), which also are potential prey for pumas (Pers. comm.

Lucas Aros, Park Ranger).

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Camera-trap Surveys

I conducted field surveys with remotely-triggered cameras (Browning BTC-5 HD

Strike Force) on two grids (each ~720 km2) from July 23, 2015- January 3, 2016, which

represents mid-winter 2015 to mid-summer 2016. For my study, I considered winter as

July 23 – October 5, 2015 and summer as October 6, 2015 – January 3, 2016. One grid

was located in the summer range of guanacos (north grid, Figure 2-2) and one grid was

located in the winter range (south grid). Each grid was divided into 17 cells of 6 x 6 km2.

I placed two camera-trap stations (hereafter stations) in each cell, except in one cell in

the south grid where I could not get permission from the landowner to access his land.

Instead, I placed four camera stations in the adjacent cell to the west (see Figure 2-2 for

empty cell and adjacent cell with four stations). Also one extra station was placed in the

north grid to maximize use of available cameras. Stations within a cell were separated

by at least 1 km and were placed in sites pumas were likely to use (e.g., drainages,

hills, etc.). I recorded the location of camera stations with a GPS (Garmin GPSMAP 64).

Each station consisted of two infrared cameras separated by an average of 5 meters.

Cameras were oriented facing each other to obtain photographs of both sides of

animals to facilitate identification of individuals from natural markings and for

redundancy in case of camera failure (Silver 2004; Silver et al. 2004; Rich et al. 2014).

Cameras were tied to wooden stakes approximately 50 cm off the ground and parallel to

it (Silver 2004; Negrões et al. 2010). To improve the quantity and quality of the

photographs of pumas in each station, I placed perfume (Obsession by Calvin Klein) as

an attractant on a stake approximately 50 cm tall between the two cameras (Marker and

Dickman 2003; Arroyo-Arce et al. 2014). Cameras were programmed to function 24

h/day, with an interval of five seconds between photographs.

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Cameras were checked every 1-2 months to verify that they were functioning

properly and to change memory cards, batteries and to replenish perfume. Pictures

were downloaded as jpeg files with the program FastStone Image Viewer 5.5. Data from

photographs (camera number, date and time) were extracted from the jpeg files to a

database using the camtrapR package (Niedballa et al. 2016) implemented in R.

Identification of Individual Pumas

Individual pumas were identified based on unique natural marks along the flanks,

scars, kinked tails and ear nicks (Kelly et al. 2008; Negrões et al. 2010). When possible,

I also recorded the sex of the individual. Identification of individual pumas from

photographs is more difficult than for species such as tigers (Panthera tigris) or jaguars

(Panthera onca) with strong natural markings (Kelly et al. 2008; Negrões et al. 2010).

Also, the long distances at which some animals were detected from the camera, poor

angles, and incomplete images hindered individual identification (Rich et al. 2014;

Thornton and Pekins 2015). Individuals could be identified in 43 % of the photographs.

Photographs of both identified and unidentified individuals were included in analyses.

Puma Density Estimates

In order to estimate puma density, I used spatial mark-resight models (SMR)

with a likelihood analysis framework (implemented in the R package ‘secr’, Efford 2016).

SMR models were developed as an alternative to capture-recapture models and as an

extension of spatially-explicit capture-recapture models (SECR) in cases where only

part of the population can be identified to the individual level (Efford 2004; Chandler and

Royle 2013; Rich et al. 2014). Unidentified individuals are incorporated in the SMR

models and treated as independent of identified sightings (Efford 2016), whereas

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standard capture-recapture models and SECR are used only with data sets where all

the individuals can be identified.

Another advantage of both SMR and SECR over earlier capture-recapture

models is that spatial data on captures informs density models. The spatial coordinates

of the camera traps where individuals are captured are used to provide information on

the location of the animal’s activity center (Chandler and Royle 2013; Rich et al. 2014).

The probability of an individual being captured is modeled as a function of distance

between the survey location and the animal’s activity center. The probability of capturing

an animal whose activity center is close to the camera will be higher than that of an

individual whose activity center is further away. This approach addresses one of the

primary criticisms of other methods of estimating density from camera trapping which is

the need to calculate a reliable ‘effective trapping area’ (Noss et al. 2012; Thornton and

Pekins 2015).

Input files for SMR include information on animal captures (i.e., photographs),

trap (i.e., camera) deployment, and potential home-range centers for the target species.

For photographs of identified individuals, I created encounter histories that included

individual animal identification, encounter occasion, and camera station in which the

individual was detected. To reduce processing time, I collapsed daily camera-trap data

into 10 time intervals (i.e., 10 encounter occasions), each comprising a 19-day sampling

period. I used one photograph per day per station as an independent record for

identified individuals and also for unidentified pumas. Then I summed counts of these

independent records separately for identified and unidentified animals for all days within

an occasion. If, for example, an individual was detected two times during the same

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occasion at the same station, this was registered twice in the capture history (e.g.,

puma4 in occasion 7 detected at site N7_27 twice for north_summer; Table A-1). For

unidentified individuals, encounter histories were similar but here the captures were

registered with the same identification name (i.e., UN), encounter occasion, and station

of animal detection (Table A-2).

Other input files necessary for analysis in SMR are the detector layout data (trap

locations and days the traps were active) and a grid of potential home range centers

(equally spaced point locations) for the study area. This grid is called the ‘mask’, and

each point represents a grid cell of potentially occupied habitat. The mask is

constructed by placing a buffer around the outer-most camera locations. The width of

the buffer is defined by a distance that ensures that no animals outside that distance

could be captured within the camera grid (Thornton and Pekins 2015, Efford 2016). I

used a buffer of 12 km. This buffer size was obtained from the function suggest.buffer

from package secr, which determines a suitable buffer width for the mask. I then

checked if this buffer was sufficiently large (i.e., detection probability was near 0 at the

edge of the buffer), using function esa.plot in SECR (Efford 2016). I generated potential

home range centers within this buffered area as a regular grid of points with 2-km

spacing.

SMR models contain parameters for each of the following: animal density (D),

detection (λ0- probability of detection at the center of the home range), and a spatial

scale parameter generated by SMR (σ- related to animal movement) (Royle et al. 2009;

Rich et al. 2014). I fit several models for the detection parameter λ0 including constant

detection (.), models with several covariates automatically generated, and one user-

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defined covariate (Table 2-1, Table B-1). For the automatically generated covariates, I

used a time factor model (‘t’, where probability of detection changes linearly with

occasion), a site learned response model (‘k’, where detection probability at the site

changes after an animal is recorded at that location), and a learned response model (‘b’,

where detection of an individual at all sites changes, dependent on previous capture)

(Efford 2016). I included an additional covariate to determine if the site where the station

was located (i.e., drainage or open plain) influenced detection probability. I expected

cameras in drainages to have higher detection probability because the ravine would

channel pumas between the two cameras.

To examine whether density of pumas changed seasonally in response to

guanaco migration, I fit a series of models of density, and compared the models with

Akaike’s Information Criteria corrected for finite sample size (AICc) (Thornton and

Pekins; Rich et al. 2014; Efford 2016). Models included a multi-session model where

each session was a different combination of trapping grid and season (north

grid_summer, north grid_winter, south grid_summer, south grid_winter) and reduced

models with single factors (season or grid), and a base model with no factors (Table 2-

1). Some models also included covariates for detection probability. Each season or

session contained 5 encounter occasions (i.e., 95 trapping days) and likely fulfilled the

assumption of a closed population during the survey (Kelly et al. 2008; Negrões et al.

2010; Quiroga et al. 2016). Models with grid only or the base model with no factors

generated density estimates based on 190 trapping days (i.e., all 10 occasions) and

may violate assumptions of closure.

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Collection of Puma Scats

Puma scats were collected along standardized transects as well as

opportunistically in drainages and other suitable puma habitat in the reserve. Collection

began prior to camera trapping but occurred in areas where grids eventually were

established. Eight 4-km transects were walked in the area occupied by each camera

grid repeatedly in mid-late summer 2015 (January-April) and winter 2015 (May-

September). From mid-winter 2015 through mid-summer 2016 (July 2015 - January

2016), I also searched for puma scats along the paths used when I was installing or

checking cameras. Puma scats were distinguished from those of other sympatric

predators (e.g., foxes and small cats) by their size and shape; identification was later

confirmed by DNA analysis. Scats were air-dried and stored with silica gel in labelled

paper envelopes, following the protocol of American Museum of Natural History (AMNH)

for preservation of scats for DNA analysis (conducted by AMNH). A small piece of each

sample was selected for DNA analysis and the remainder of the samples were washed

and sundried for lab analysis of hair and bones of consumed prey.

Prey items in scats were identified to species using identification keys for

mammalian hair for species from Patagonia (Chehébar and Martín 1989) and by

comparing hairs to known hair samples obtained from specimens from the Florida

Museum of Natural History and from dead animals found in the reserve. Hair samples

were analyzed macroscopically and microscopically. First, I characterized hairs by

shape, coloration, and presence of bands using a magnifying glass. Second, hair shafts

were observed under the microscope and dark hairs were cleared with commercial hair

clearer when necessary in order to observe the medulla. Third, I analyzed hair cuticles

(scale pattern) under the microscope. The scale patterns were obtained by pressing the

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hair on slides painted with transparent nail polish and observing the negative cast

(Weingart 1973; Rau and Jiménez 2010; Juárez-Sánchez et al. 2007).

Diet Analysis

To test for changes in the diet of pumas with the guanaco migration, prey items

in scats were divided in two major groups: guanacos and alternative prey, including

small and medium-sized mammals and livestock (Table 2-2). Because of the small

number of scats, particularly in the south, analyses were limited to scats collected in the

northern part of the study area (i.e., summer range of guanacos). I compared frequency

of prey items consumed between winter and summer using a Fisher’s Exact test (Zar

1999; Soto-Shoender and Giuliano 2011).

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Figure 2-1. La Payunia Reserve. A) Location of the reserve in Argentina, and B)

boundaries of the reserve and approximate layout of north and south grids within the reserve (red boxes).

A B

48 Km

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Figure 2-2. Grid layout and camera station locations (dots) in the La Payunia Reserve.

The north grid is located at the summer range of guanacos, and the south grid is located at their winter range. Each grid cell is 6 x 6 km.

Su

mm

er

ran

ge

of g

ua

na

co

s

Win

ter

ran

ge

of g

ua

na

co

s

29 Km

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Table 2-1. Top five spatial mark-resight (SMR) models for density of pumas. Models were ranked using Akaike’s Information Criterion adjusted for small sample

sizes (AICc). See text for explanation of covariates. Δ AICc= differences in AICc; σ = spatial scale parameter; K= number of parameters in the model; λ0 = probability of individual detection.

Table 2-2. Percent of occurrence of prey items in puma scats in summer (n =25 scats)

and winter (n =14 scats) in the north of La Payunia Reserve. Numbers in parentheses correspond to counts of prey items.

Model AICc Δ AICc AICc Weight σ (SE) K λ0

D(grid) λ0(site), σ (.) 1167 0 0.99 1485 (75) 5 0.011

D(.) λ0(site), σ (.) 1202 35 <0.01 1496 (76) 4 0.010

D(season) λ0(.), σ (.) 1305 138 <0.01 1326 (141) 4 0.029

D(season) λ0(site), σ (.) 1310 142 <0.01 1514 (180) 5 0.010

D(session) λ0(site), σ (.) 1432 256 <0.01 755 (28) 7 0.019

Percent of occurrence

Prey Winter Summer

Guanaco

Lama guanicoe 85.71 (12) 64 (16)

Alternative Prey

Lagostomus maximus 0 20 (5)

Lepus europaeus 14.29 (2) 12 (3)

Zaedius pichii 7.14 (1) 4 (1)

Leopardus geoffroy 0 4 (1)

Microcavia australis 0 4 (1)

Other small mammals 0 8 (2)

Cow (Bos taurus) 7.14 (1) 0

Goat (Capra hircus) 0 4 (1)

Unidentified mammal 0 12 (3)

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CHAPTER 3 RESULTS

Identification of Individual Pumas

I obtained 149 photographs of pumas on the north grid from which I identified 12

adult individuals (5 males, 6 females and 1 unsexed) (Table 3-1). These individuals

occurred in 59 of 149 photographs. For the south grid, I obtained 59 records of pumas

and identified 6 adults (4 males and 2 females). These individuals occurred in 23 of 59

photographs. Only one male was detected in both the north and south grids.

Puma Density Estimates

Density estimates from SMR models do not provide strong support for movement

of pumas with migration of guanacos. Puma density estimates for the north grid in

summer were about 19% higher than estimates for winter (Table 3-2), when many

guanacos had migrated to the winter range to the south, and counts of pumas in

photographs (uncorrected for detectability) peaked at the time guanaco abundance was

highest in the north grid (Figures C-1 and C-2). However, in the south grid, estimates of

puma density also were slightly higher for summer than in winter, opposite to the

expected pattern. Sample sizes were low, particularly in the south grid, and confidence

intervals were large in all cases. The model that included both season and grid (i.e.,

session) was not among the top-ranked models for density (Table 2-1).

The top-ranked model for density (i.e., the model with the lowest AICc value) was

one where density varied between grids, and capture probability was associated with

the location of the camera station (i.e., site in a drainage versus open habitat; Table 2-

1). However, because data from 190 days were used in the density estimate for each

grid, this model may violate the assumption of demographic closure. This problem also

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may occur in the second ranked model, which was the base model with no factors. The

next three models, which modeled density separately for winter and summer seasons

(95 days each season), likely do not violate the assumption of demographic closure if

pumas breed and disperse on an annual cycle. Based on AICc, the highest ranked

model that most certainly does not violate demographic closure included season, but

not grid, and included no effect of camera location on capture probability. However,

confidence intervals are high from this model. The poor fit of the model that included

season and grid (the 5th ranked model) was somewhat surprising for two reasons: 1) A

model with density as a function of grid was the highest ranked model, and raw counts

of photographs with pumas appears to support differences in grids (Figure C-2); 2)

Camera site is important in determining capture probability in all other models.

However, sample sizes, especially recaptures, were low when photographs were

divided by two grids and two seasons, and likely contributed to the poor fit of this model

(Table 3-1).

Density estimates of pumas ranged from 1.63/100 km2 (+0.52 SE) in the south

grid to 3.27/100 km2 (+0.71 SE) in the north grid for the best-fitting model, and from

1.50/100 km2 (+0.30 SE) to 1.93/100 km2 (+0.38 SE) in winter versus summer for the

best model restricted to a sampling period of 95 days (Table 3-2). Inclusion of site as a

covariate for capture probability only resulted in slightly higher density estimates, even

though site was an important factor in most models (Table 2-1 and Table 3-2). The 12-

km buffer incorporated around camera stations was sufficient; probability of detection

declined to near 0 by 12 km (Figure D-1).

27

Diet Analysis

I collected a total of 129 puma scats. However, only 43 scats (39 from north grid,

4 from south grid) were fairly fresh and thus could be clearly identified as being

deposited during the period when guanacos were present or absent in the area.

Because of the small sample size from south grid, I only analyzed scats collected in the

north grid.

I detected a total of 49 prey items corresponding to 9 different mammal species

(Table 2-2). In contrast to my prediction, pumas do not switch to alternative prey during

winter when guanaco availability is lower in the north grid (Fisher’s Exact test, p =

0.072, n = 39 scats). Remains of guanaco were found in 85% of puma scats during the

winter (n = 16) and only in 64% of scats during the summer (n = 33). Overall, guanacos

were the most common item in scats (71.8% of the scats), followed in order of

importance by plain vizcachas (Lagostomus maximus) and European hares (Lepus

europaeus), dwarf armadillos (Zaedyus pichiy), and small rodents. Livestock remains

(cow, Bos taurus and goat, Capra hircus) were found only in one scat in summer and

one in winter.

28

Table 3-1. The number of pumas identified to the individual level (n*), the total number

of puma photographs that were labeled as individually identifiable (ID) and unidentifiable (non-ID) per season and grid. MMDM= Mean Maximum

Distance Moved; RDM= Range Distance Moved; MNR= Mean Number of Recaptures; RNR= Range Number of Recaptures.

Table 3-2. Density estimates for pumas obtained from the five highest ranked spatial mark-resight models (i.e., models with the lowest AICc values).

Model

Density [pumas/100 km2 (SE)]

Season Grid

Grids

combined North South

D(grid) λ0(site), σ (.) Seasons combined 3.27 (0.71) 1.63 (0.52)

D(.) λ0(site), σ (.) Seasons combined 2.81 (0.46)

D(season) λ0(.), σ (.) Summer 1.93 (0.38)

Winter 1.50 (0.30)

D(season) λ0(site), σ (.) Summer 2.09 (0.43)

Winter 1.61 (0.34)

D(session) λ0(site), σ (.) Summer 4.27 (1.06) 2.25 (0.81)

Winter 3.59 (0.92) 1.80 (0.72)

n* ID non-ID MMDM (km) RDM (km) MNR RNR

North Grid 6.23 1.60 – 21.03 3.83 1 - 9

Summer 11 39 51

Winter 9 20 39

South Grid 6.84 2.17 – 11.13 3 1 - 5

Summer 5 12 20

Winter 4 11 16

29

CHAPTER 4 DISCUSSION

My results indicate that most pumas are not following the migration of guanacos

in La Payunia Reserve or switching to alternative prey. Density estimates of pumas did

not increase significantly in the winter and summer range of guanacos when guanacos

migrated to these areas, although counts of pumas in the summer range increased

slightly during the period when guanacos were most abundant and giving birth to

offspring. Only one of the 18 pumas individually identified was detected in the north and

south of the reserve. This puma was detected only once in the north grid in late

September and then was recorded 45 km away in the south grid in December. This

movement was in the opposite direction that would be expected if this puma was

following the guanaco migration. Although my sample of fresh scats was small, pumas

clearly feed primarily on guanacos throughout the year and, contrary to my predictions,

do not switch to alternative native prey or livestock when guanacos migrate. They

consume some small and medium-sized mammals in both winter and summer, and

livestock occurred at a very low frequency in the diet of pumas (5%) during both

seasons.

Migrations of large ungulates and other prey for top predators may include

movement of all individuals between seasonal ranges, or only part of the prey

population may migrate (Elbroch et al. 2013; Bolgeri and Novaro 2015). These two

patterns have very different implications for prey availability for predators. Even though

the majority of guanacos in La Payunia migrate south in winter (Ruiz Blanco and

Novaro, unpublished data), part of the population does not move (Bolgeri and Novaro

2015). These resident guanacos, together with some alternative prey, apparently

30

provide sufficient food so that pumas do not have to follow the migration of their primary

prey. In addition, pumas may specialize on killing guanacos in La Payunia, as has been

reported for southern Chile (Elbroch and Wittmer 2012), and, thus, may be efficient at

taking guanacos even when guanaco density is low.

Data on responses of pumas to migratory prey are still scarce but suggest that

pumas exhibit considerable flexibility (Fryxell and Sinclair 1988; Ballard et al. 1997;

Pierce et al. 1999; Elbroch et al. 2013). Similar to my study, pumas in Yellowstone

remain relatively stationary during seasonal migrations of their ungulate prey. However,

in Yellowstone, pumas have the option of feeding on a variety of ungulates and switch

prey as prey density changes with migrations (Elbroch et al. 2013). In contrast, in

California pumas move seasonally with their primary prey, mule deer (Odocoileus

hemionus), perhaps because few deer remain resident throughout the year and other

prey are scarce (Kucera or Weyhausen, personal communication – to be confirmed).

Density of pumas was lower in the southern part of my study area compared to

north. Several factors may explain this pattern, and landscape level factors that

potentially influence puma density (e.g., presence of volcanoes, distance from roads

and settlements, etc.) will be explored more thoroughly in future models by

incorporating density covariates. On the one hand, the habitat in the south and the north

of the reserve is somewhat different. The north grid has more rocky outcrops and large

volcanos than the south grid. In the south, volcanoes have gentler slopes with relatively

low height to diameter ratio (Llambías 2008). Also, vegetation cover is lower in the

south. Together these habitat differences may mean fewer refuges for pumas and less

cover for pumas stalking prey. Another potential explanation for the differences in puma

31

density is that human-induced mortality is higher for pumas in the south. This area

includes large amounts of private land and is poorly protected compared to northern

part of the reserve. This is cause for concern and points to the need to monitor puma

populations with methods that can detect mortality (e.g., telemetry) and to work with

private landowners in the south on puma conservation.

Density estimates for pumas obtained in this study (Table 3-2) are similar to the

only previous estimate from Patagonia and parts of the western US with large expanses

of open habitat as in Patagonia (e.g., Idaho and Wyoming; Table 4-1), even though

density estimates in these studies were based on very different methods (e.g., capture-

recapture and telemetry studies; Seidensticker et al. 1973; Logan et al. 1986; Elbroch

and Wittmer 2012). My estimates on puma density in the north of La Payunia, the area

with highest protection, are more than four times that reported for sites in northern

Argentina with high poaching pressure (Atlantic Forest, Kelly et al. 2008; semi-arid

Chaco, Quiroga et al. 2016; Table 4-1), but closer to estimates reported for sites with

low poaching pressure in the Atlantic Forest (Paviolo et al. 2009). Even though

poaching occurs in La Payunia, likely poaching is much lower than in most of the

Atlantic Forest and Chaco, which have much larger human populations (Núñez-

Regueiro et al. 2015; Altrichter et al. 2006; Kelly et al. 2008). Also, habitat in Payunia is

not fragmented as in the Atlantic forest and Chaco.

In addition to increasing understanding of the role of migration and predator-prey

dynamics in this system, baseline data on densities of pumas is important for

conservation and management. Pumas are heavily controlled outside protected areas in

Patagonia because of their impact on livestock, and sport hunting occurs in some

32

regions of Argentina (Novaro and Walker 2005). For elusive species such as pumas,

obtaining density estimates can be challenging. In my case, the lack of trails or other

features to guide pumas between cameras, made captures with cameras difficult. Many

of the photographs were taken with pumas at long distances from the camera and often

photos did not include both sides of the puma, which made detection of natural marks

difficult and hindered individual identification. The use of SMR helps overcome some of

these difficulties because unidentified individuals can be included in models. To date,

only one study has used SMR models to estimate densities for pumas. This study found

that different statistical techniques produce different estimates and that SMR models

increased precision compared to other analyses (Rich et al. 2014). Although obtaining

sufficiently large samples for robust estimates still remains a challenge, my study

demonstrates that the combination of camera traps and SMR analysis appears to be a

promising tool to help estimate population numbers for such elusive species, even in

open, non-forested habitat.

33

Table 4-1. Estimates of puma densities from other studies in Central and South America and from studies in the western US with habitat similar to La Payunia.

Country Habitat Adult pumas/

100 km2

Method

used Analysis

Argentina1 Atlantic Forest with high poaching pressure

0.47-0.87 Camera trapping

Capture

Argentina2 Atlantic Forest with high poaching

pressure

0.6 Camera trapping

SMR

Argentina3 semi-arid Chaco <1 Camera trapping

SECR- Capture

Argentina4 Atlantic Forest with low poaching

pressure

2.22 Camera trapping

Capture. Model Mh

Bolivia1 Chaco dry forest 5.3-8.3 Camera trapping

Capture

Bolivia2 Chaco dry forest 11.22 Camera trapping

SMR

Belize1 Tropical forest 2.12-4.72 Camera trapping

Capture

Belize2 Tropical forest 1.42 Camera

trapping SMR

Chile5 Patagonian steppe

and forest 3.44

radio-

telemetry

Home range

analysis

USA, New

Mexico6

San Andres Mountains,

Chihuahuan desert

1.7-3.9 radio-

telemetry

Home range

analysis

USA, Idaho7

Ponderosa pine

and desert shrublands

1.7-3.5 radio-

telemetry

Home range

analysis

USA, Utah8 Pinyon pine and

desert shrublands 0.58-1.4

radio-

telemetry

Home range

analysis

USA,

Wyoming9

Ponderosa pine

and desert grass and shrublands

3.5-4.6 radio-

telemetry

Overlapping

home ranges

1 Kelly et al. 2008, 2 Rich et al. 2014, 3 Quiroga et al. 2016, 4 Paviolo et al. 2009, 5 Elbroch and Wittmer

2012, 6 Logan et al. 1996, 7 Seidensticker et al. 1973, 8 Lindzey et al. 1994, 9 Logan et al. 1986.

34

APPENDIX A PUMAS CAPTURE HISTORIES

Table A-1. Capture histories for pumas individually identified on the north and south

grids. Data are in the format as used to model density by session. Session – grid location and sampling season; IndID – label for individually identified pumas; Occasion – time interval of animal capture; SiteID – identification

label for camera station.

#Session IndID Occasion SiteID

north_summer puma1 5 N5_18

































35

Tabla A-1. Continued








north_winter puma1 1 N1_142




















south_summer puma16 6 S5_124












south_winter puma13 2 S6_64



36

Table A-1. Continued










Table A-2. Capture histories for non-identified individuals for the north and south grids. Data are in the format as used to model density by session. Session – grid

location and sampling season; IndID – UN for unidentified pumas; Occasion – time interval of animal capture; SiteID – identification label for camera station.


north_summer UN 6 N10_101


























37




























north_winter UN 1 N10_48















38



























south_summer UN 6 S6_64
















39







south_winter UN 2 S9_87
















40

APPENDIX B ADDITIONAL MODELS EXAMINED WITH COVARIATES FOR DETECTION PROBABILITY

Table B-1. Model comparison table to determine best fitting model for detection probability parameter in multi -session

density models using Akaike’s Information Criterion adjusted for small sample sizes (AICc). See text for

explanation of covariates. Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection.

Table B-2. Model comparison table to determine best-fitting model for detection probability parameter with density

constant. Ranking is based on Akaike’s Information Criterion adjusted for small sample sizes (AICc). See text for explanation of covariates. Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection.

Model AICc Δ AICc σ (SE) K λ0

D(session) λ0(site), σ(.) 1432 0.00 755 (28) 7 0.019

D(session) λ0(.), σ(.) 1433 0.30 695 (20) 6 0.007

D(session) λ0(b), σ(.) 1551 118 733 (23) 7 0.001

D(session) λ0(t), σ(.) 1564 131 791 (24) 16 0.001

D(session) λ0(k), σ(.) 1719 287 1766 (187) 7 0.0000007


D(.) λ0(site), σ(.) 1203 0.00 771 (28) 4 0.001

D(.) λ0(.), σ(.) 1549 346 727 (24) 3 0.005

D(.) λ0(t), σ(.) 1624 421 834 (84) 13 0.001

D(.) λ0(b), σ(.) 1926 723 772 (25) 4 0.0007

D(.) λ0(k), σ(.) 2183 980 1712 (176) 4 0.0000007

41

APPENDIX C COUNTS OF GUANACOS AND PUMAS IN BOTH STUDY GRIDS

Index of guanaco abundance

Although the general pattern of guanaco migration is known for the reserve

(Schroeder et al. 2014) and additional seasonal censuses are under way (Ruiz Blanco

and Novaro pers. comm), data on seasonal changes in guanaco density were not

available for my grids. Therefore, I derived an index of guanaco abundance for each

grid in winter and summer based on photographs of guanacos at my camera stations.

From the database of photographic records obtained with camtrapR, I conducted a

count of the number of times guanacos were detected by cameras using 1

photograph/station/day as an independent record. Counts were summed for 14-day

intervals. I did not attempt to count the number of guanacos in photographs because I

was not certain that this would give me a good estimate of the real number of guanacos

in the site (photographs likely contained parts of large herds) and also due to time

constraints.

In summer, the number of photographic detections of guanacos increased in the

north of the reserve and decreased in the south during the same period, following the

observed seasonal migration of this species in the area (Figure C-1). A peak occurred in

detections of guanacos in the north during the period when guanacos give birth to their

offspring (11/22-12/4). In the winter season, detections of guanacos were much lower in

the north grid than during summer, but number of photographs with guanacos did not

increase greatly in the south. Detection may have underestimated guanaco abundance

in winter because guanacos are more aggregated in herds during this period.

42

Figure C-1. Counts of guanacos in the north and south grids using 1 photograph per camera station per day as an independent record. Counts were summed for

intervals of 14 days. The numbers above the columns correspond to the percentage of cameras in operation at that period. Periods with no number

above the columns had 100% of the cameras in operation.

0

10

20

30

40

50

60

70

80

90

100

Gu

an

aco

co

un

t

Count period

NORTH GRID SOUTH GRID

Winter Summer

9

88

70

94

20

43

Figure C-2. Counts of pumas in the north and south grids using 1 photograph per

camera station per day as an independent record. Solid bars represent

individuals that could be identified individually (ID). Bars with patterns indicate pumas that could not be identified individually (non-ID). Counts were summed

over 14-day intervals. The numbers above the columns correspond to the percentage of cameras in operation at that period. Periods with no number above the columns had 100% of the cameras in operation.

0

5

10

15

20

25P

um

a c

ou

nts

Count period

NORTH GRID ID NORTH GRID nonID SOUTH GRID ID SOUTH GRID nonID

9

88

70

94

20

Winter Summer

44

APPENDIX D PLOT GENERATED BY SPATIAL MARK- RESIGHT TO DETERMINE APPROPIATE

BUFFER SIZE

Figure D-1. Capture or detection probability as a function of distance from the center of a home range for the best-fitting SMR model. Note that capture probability is near 0 at 12000 m, which represents the edge of the buffer used in my

models.

45

APPENDIX E ADDITIONAL MODELS EXAMINED FOR DENSITY

Table E-1. Model comparison table to determine best fitting model for density with constant detection probability, using Akaike’s Information Criterion adjusted for small sample sizes (AICc). Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection.


D(grid) λ0(.), σ (.) 1174.41 0.00 1393 (67) 4 0.003

D(season) λ0 (.), σ (.) 1305.49 131.08 1326 (141) 4 0.002

D(session) λ0(.), σ (.) 1433.22 258.81 695 (20) 6 0.007

D(.) λ0(.), σ (.) 1549.20 374.79 727 (23) 3 0.005

Table E-2. Model comparison table to determine influence of season in North Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc). Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection; D= density estimates obtained for summer (s)

and winter (w).

Model AICc Δ AICc σ (SE) K λ0 D

D(.) λ0(.), σ(.) 867.91 0.00 1187 (142) 3 0.003 2.45

D(season) λ0(.), σ (.) 869.42 1.51 1185 (142) 4 0.003 s= 2.64 w=2.23

Table E-3. Model comparison table to determine influence of season in South Grid, using Akaike’s Information Criterion adjusted for small sample sizes (AICc). Δ AICc= differences in AICc; σ= spatial scale parameter; K= number of parameters in the model; λ0= probability of individual detection; D= density estimates obtained for summer (s)

and winter (w).

Model AICc Δ AICc σ (SE) K λ0 D

D(.) λ0(.), σ(.) 750.96 0.00 1480 (296) 3 0.000002 6.70

D(season) λ0(.), σ (.) 764.25 13.29 1480 (296) 4 0.000002 s= 7.41 w=5.93 Note: The detectability (λ) is extremely low and the density estimate is unrealistically high.

46

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BIOGRAPHICAL SKETCH

Maria Laura earned her Bachelor of Science in biology from Universidad

Nacional de Rio Cuarto, Argentina in 2011. Laura was supported by BEC.AR in

Argentina to begin her master’s degree in interdisciplinary ecology at the University of

Florida in August 2014 and graduated in December 2016.

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