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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
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