10 Nov 2008 2008 WEPP Annual Potluck

Why We Monitor:“Learning to monitor ormonitoring to learn?”

- L. Gunderson, 2003

Gary S. CasperUW-Milwaukee Field Station,

Ozaukee Washington land Trust,and Great Lakes Ecological Services LLC

Some History• Wetland loss

concerns• Amphibian decline

meetings, late 1990s

• EPA ephemeral wetlands initiative (Ed and Jennifer Hammer)

Midwest Partners in Amphibian& Reptile Conservation

Ephemeral Wetlands : Monitoring Amphibians

Monitoring Programs Begin• Wisconsin Frog and Toad Survey• North American Amphibian Monitoring

Program• Breeding Bird Survey (nationwide)• Marsh Monitoring Program• National Park Service I&M Initiative• U.S. Forest Service indicator species• Many others nationwide

Inventory & Monitoring Objectives

• What’s there?– Species richness, communities, abiotic

conditions (pH, contaminants, etc.)• What are the trends?

– Species richness, population size, abiotic concentrations (i.e. mercury, PCBs)

• What does it mean?– Methods, interpretation– Use of data, informing conservation, public

health, quality of life

Ephemeral Wetlands : Monitoring : Education

• Wisconsin Ephemeral Ponds Project – Educate citizens to identify, inventory, and

monitor the ecology of ephemeral ponds– Interact with nature, care about it

Ephemeral Wetlands : Monitoring : Education• Applications

– Mapping, update Wisconsin Wetland Inventory– Improve I&M methodology through research– Improve land use planning and conservation

WEPP: Monitoring to learn• Find and describe

ephemeral wetlands• Improve wetlands

maps and use of them

Kate Barrett2, Tom Bernthal2, Marsha Burzynski2, Gary S. Casper1, and Joanne Kline2

1 - University of Wisconsin-Milwaukee, 2 - Wisconsin Department of Natural Resources

Milwaukee River Basin Wetlands and Wildlife Assessment Project

Using wetland science and GIS to better understand the roles wetlands play in watersheds and landscapes, and to improve wildlife habitat restoration and planning

Wisconsin Department of Natural Resourceswith funding from the USEPA

Some Objectives• Guide wetland restoration decision-

making by analyzing value and probability of success

• Improve water quality and flood control• Perform landscape level assessment

before site level assessment – Zoom Out• Provide a means to assess wildlife value

for wetland restorations

Meaningful to:• Local Planners: county, regional• Agencies

– Restoration: NRCS, DNR, USFWS– Regulation: DNR, County Zoning

• Conservation Organizations– Land trusts, restoration ecologists, NGOs

Select watershedand gather data

Select watershedand gather data

Apply Models forDecision making

Apply Models forDecision making

Water Quality andHydrology Tools

Water Quality andHydrology Tools

ID Potentially Restorable Wetlands

ID Potentially Restorable Wetlands

Develop Data(Drainage Ditches,

Reed Canary Grass)

Develop Data(Drainage Ditches,

Reed Canary Grass)

Wetland WildlifeHabitat Tool

Wetland WildlifeHabitat Tool

Overall Process in a Nutshell

Identifying Potentially Restorable Wetlands (PRWs)

PRWs = Hydric Soils - Existing WetlandsAND

Must be in agricultural or otherundeveloped rural land use

Existing and Potentially Restorable Wetlands

Milwaukee River BasinCedar CreekWatershed

Existing Wetlands

Potentially Restorable Wetlands

Surface Water

Watershed Boundary

Model for ephemeral wetland dependant wildlife in a forested matrix (wood frog)• Wetlands >= 0.5 acres size

• Wetlands within 10 m of forests

• Forests within 10 m of the wetlands

• Forests clipped (within 300 m of wetlands)

Wildlife Matrix Habitat

Milwaukee River BasinCedar CreekWatershed

All ForestsSuitable Wetlands

Surface WaterWatershed Boundary

AllSuitableHabitatAssociations

Wildlife Matrix Habitat

Milwaukee River BasinCedar CreekWatershed

All ForestsSuitable WetlandsPotential Wood Frog Forest HabitatPotential Wood Frog Wetland Habitat

Surface WaterWatershed Boundary

PerformProximityAnalysis

Wildlife Matrix Habitat

Milwaukee River BasinCedar CreekWatershed

Potential Wood Frog Forest HabitatPotential Wood Frog Wetland Habitat

Surface WaterWatershed Boundary

RemoveAreasFailingProximityCriteria------------PredictedSpeciesDistribution

Wildlife Matrix Habitat

Milwaukee River BasinCedar CreekWatershed

Restorable Wood Frog WetlandsSuitable Wood Frog Forest HabitatSuitable Wood Frog Wetland Habitat

Surface WaterWatershed Boundary

AddPRWs

Model Validation - Ongoing

• Use independent data sets of known occurrences• WEPP, Wisconsin Herp Atlas, WDNR Frog & Toad

Survey, personal observations

• Compare known occurrences to the predicted distribution

• Test to see if known occurrences fall within predicted habitat more often than do random localities

Model Validation - Ongoing• Initial wood frog model predictions were

significant (N=67, p=0.0000)

• Initial Blanding’s turtle model predictions were significant (N=47, p=0.0000)

• Initial chorus frog model predictions were not significant (N=63, p=0.1318)

WEPP: Learning to monitor• Monitoring protocols• Interpretation =

meaningfulness of data

Inventory vs. Monitoring• Inventory determines a species list for a

study area.– Usually short term discrete studies (surveys), but

sometimes long term cumulative programs (atlases).

• Monitoring addresses trends over time in occupancy or numbers.– Requires repeated sampling over time, generally

long term• Since one cannot monitor species without

knowing what species are there, inventory should always precede monitoring.

Citizen Monitors (i.e. volunteers)• May be performing inventory OR

monitoring OR both

A Fundamental Problem

• If a species is detected we know it is there

• If a species is not detected, either– It is not there, or– It was overlooked (false negative)

• Safe to assume no false positives

The False Negative

• False negatives can be ignored with “presence only” data analyses, but these have limitations

• Mackenzie et al developed a way to quantify false negatives, and correct for them in trend analyses, through Percent Area Occupied modeling

* MacKenzie et al. 2002, 2003 and others

7 Aug 2008 93rd ESA Annual Meeting, 2008 Milwaukee – Casper, Nadeau, Graff

Developing Monitoring Methods for Amphibians and Reptiles in the Great Lakes

Gary S. CasperUniv. WI Milwaukee Field Station and Great Lakes Ecological Services LLC

Stefanie M. Nadeau and Shawn GraffOzaukee Washington Land Trust, PO Box 917, West Bend, WI

Stephen J. Hecnar and Ashley E. SpenceleyLakehead University, Thunder Bay, Ontario

Research Program:

Lake SuperiorBasin

MilwaukeeRiver Basin

• Test a variety of standard survey methods

• Develop detection probability statistics

• Improve I&M programs through occupancy modeling

Percent Area Occupied (PAO) Synopsis

• Determines the fraction of the landscape occupied by the target species

• Models occupancy probability based on detection probabilities (DP)

• Treats changes in occupancy rates as trends

• Generally gives good occupancy estimates where DP > 0.3

The Allure of PAO Models• Only presence/absence data required• Can use any credible detection method• Detection probability stats make data from under-

sampled sites more useful (ex. frog call data)• Less costly than counts

WEPP data lends itself perfectly to regional analyses via PAO modeling!

Example PAO Modeling• Year 1 (20 sites)• Species 1 on 6 sites• If average DP = 0.5

then present on 7 of 14 negative sites

• Occupancy estimate = 13 sites

Example PAO Modeling

• Year 2 (20 sites)• Species 1 on 12 sites

+ 4 of 8 remaining• Occupancy est. = 16• Naïve trend 6 to 12

(+100%)• Corrected trend 13 to

16 (+23%)

First Determine Detection Probabilities• Define a standard sampling protocol• Over sample (5+ times)• DP = N times detected / N surveys• Do this a lot to get variance in DPs

Using DPs

Milwaukee River Basin

DETECTION PROBABILITY (P)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

MIN

IMU

M V

ISIT

S R

EQ

UIR

ED

(Nm

in)

0

10

20

30

40

50

60

70

80

90

100

Nmin=log(0.05)/log(1-P)

Using DPs

Milwaukee River Basin

DETECTION PROBABILITY (P)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

MIN

IMU

M V

ISIT

S R

EQU

IRE

D (N

min)

0

1

2

3

4

5

6

7

8

9

10

Nmin=log(0.05)/log(1-P)

6 Methods Tested• Calling surveys• Aquatic funnel traps• Turtle traps• Snake cover objects• Casual observations• Timed searches

PIRO Frog Calling Surveys

0.00.10.20.30.40.50.60.70.80.91.0

P1 P3 P4

Time Period

toadpeepertreefrogleopard froggreen frogwood frogmink frog

Results: Call Surveys

Results : Spring PeeperHigh DPs consistent among sites within a sampling area

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4Time Period

Moquah Calling Survey: Spring Peeper

Site 62

Site 63

Site 65

Site 69

Site 70

Site 71

Site 58P

Site 68P

Results : Spring PeeperHigh DPs consistent among areas

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4TIme Period

Calling Surveys: Spring Peeper

Mean Moquah

Mean Th. Bay

Mean LSPP

Mean PIRO

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4Time Period

Moquah Wood Frog Calling Survey: DPs by site (range in Period 1 is 0.17 - 1.00)

Site 63

Site 70

Site 71

Site 58P

Site 68P

Results: Wood Froglimited DP window, more variable DP

Calling Surveys: Wood Frog

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4

TIme Period

Ave MoquahAve Th. BayAve LSPPAve PIROAve Milw

Results : Wood FrogDP pattern consistent

among areas

PIRO Aquatic Funnel Traps

0.00.10.20.30.40.50.60.70.80.91.0

P1 P2 P3 P4

Time Period

toadpeepertreefroggreen frogwood frogmink frogblue-spot sallyspotted sallynewtgartersnake

Results: aquatic funnel traps

Results: Newtamong site consistency

0.00.20.40.60.81.0

Aquatic Funnel Traps: Central Newt2006 2007MRB data

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4Time Period

Aquatic Funnel Traps: Newt

Ave Moquah

Ave Th. Bay

Ave LSPP

Ave PIRO

Results: Newtvariation among areas

Results: Snake Boards

0.00.10.20.30.40.50.60.70.80.91.0

P1 P2 P3 P4

Time Period

Casual Observations: All Species PIROToadPeeperTreefrogLeopard FrogGreen FrogWood FrogMink FrogBlue-spot SalamanderSpotted SalamanderNewtGartersnakeRed-bellied SnakePainted Turtle

Results: Casual Observations

Amphibian Methods Comparison

0.00.10.20.30.40.50.60.70.80.91.0

Ave

. Det

. Pro

b.DPs by Method

Frog Calling Survey Aquatic Funnel Traps Casual Obs. Ponds

MRB data only

Calling Frog Survey(Cope's gray treefrog = 18)

0

1

2

3

Americantoad

springpeeper

chorusfrog

easterngray

treefrog

northernleopard

frog

northerngreenfrog

Americanbullfrog

wood frog

Min. N Samples at 95% Conf.

Application: How Many SamplesRequired for Confidence?

Added value

Standard

MRB data only =LOG(0.05)/LOG(1-DP)

PIRO Frog Calling Surveys: Min. N Samples (0.05)

0123456789

10

P1 P2 P3 P4

Time Period

toadpeepertreefrogleopard froggreen frogwood frog

Application: How Often?

Added value

Standard

Aquatic Funnel Traps

02468

101214161820

spotte

d salam

ander

blue-spo

tted s

alaman

dertig

er sa

laman

derea

stern

newt

America

n toad

sprin

g peeper

choru

s fro

g

easte

rn gray

tree

frog

norther

n leop

ard fr

og

norther

n gre

en fr

og

America

n bullfrog

wood fr

og

norther

n wate

rsnak

e

common gart

ersnak

e

Min. N Samples at 95% Conf.

How Often Must I Go When It’s Raining?

4 nights

MRB data only

PIRO Aquatic Funnel Traps: Min. N Samples (0.05)

02468

101214161820

P1 P2 P3 P4

Time Period

toadpeepertreefroggreen frogwood frogmink frogblue-spot sallyspotted sallynewtgartersnake

How Often: aquatic funnel traps?

How Often Must I Wrestle Snappers?

Turtle Traps: Min. N Samples (0.05)

-1

13

57

9

1113

15

painted snapperTime Period

PIROMoquahLSPPMilw

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4Time Period

Moquah Wood Frog Calling Survey: DPs by site (range in Period 1 is 0.17 - 1.00)

Site 63

Site 70

Site 71

Site 58P

Site 68P

Conclusions: use lowest DPs forconfidence in inventories

Conclusions: use species with consistently high DPs formonitoring programs

0.00.10.20.30.40.50.60.70.80.91.0

1 2 3 4TIme Period

Calling Surveys: Spring Peeper

Mean Moquah

Mean Th. Bay

Mean LSPP

…and that’s why we monitor!

Milwaukee River Basin

AcknowledgementsAll photos by G.S. Casper and OWLT crew

Funding: U.S. EPA, National Fish and Wildlife Foundation, National Sciences and Engineering Research Council of Canada, Ontario Ministry of Natural ResourcesOWLT Crew: Stefanie Nadeau, Mark Millar, Christopher Heston, Peter ZieglerLake Superior Crew: Jen Anderson, Ryne RutherfordProperty owners and partners: Pictured Rocks National Lakeshore, Chequamegon Nicollet National Forest, Lake Superior Provincial Park, Ozaukee Washington Land Trust, Kettle Moraine State Forest, Pike Lake State Park, City of Mequon, Concordia University, Wisconsin DNR