predictive distribution models for marsh birds: occupancy and … · 2020. 6. 26. · predictive...
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Predictive Distribution Models for Marsh Birds: Occupancy and Spatial Modeling to
Facilitate Habitat Conservation
Bryan S. Stevens1 and Courtney J. Conway2
1Idaho Cooperative Fish and Wildlife Research Unit, Univ. of Idaho
2U.S. Geological Survey, Idaho Cooperative Fish and Wildlife Research Unit, Univ. of Idaho
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General Issue
• Predicting species distributions
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General Issue
• Predicting species distributions
• Binary range map• Uninformative
• Distribution ≠ uniform
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General Issue
• Predicting species distributions
• Binary range map• Uninformative
• Distribution ≠ uniform
• Important to refine predictions• Prioritizing management in
space
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General Approach
Field DataDistribution
Model
Habitat Suitability
Map
Prioritize Habitat
Management
Statistical Analyses
SpatialAnalyses
DecisionSupport
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Secretive Marsh Birds
• Wetland dependent
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Secretive Marsh Birds
• Wetland dependent
• Difficult to sample
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Secretive Marsh Birds
• Wetland dependent
• Difficult to sample
• Variable management status• Game birds
• T&E
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Secretive Marsh Birds
• Wetland dependent
• Difficult to sample
• Variable management status• Game birds
• T&E
• Populations declining• Habitat loss & degradation
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Question and Motivation
• Where should we prioritize breeding habitat conservation on lands managed by DoD and USFWS?
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Question and Motivation
• Where should we prioritize breeding habitat conservation on lands managed by DoD and USFWS?• DoD: include marsh birds in INRMPs
• USFWS: inform land acquisition for NWR system
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Decision Support for DoD
• INRMPs required at all 593 installations
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Decision Support for DoD
• INRMPs required at all 593 installations
• Which marsh birds should be included at each site?• Focus site-specific plans, while conserving species group
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Objectives
1. Develop optimally-predictive models of breeding distribution for marsh birds• First at range-wide extent
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Objectives
1. Develop optimally-predictive models of breeding distribution for marsh birds• First at range-wide extent
2. Predict optimal habitat for prioritizing conservation in space• DoD installations
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Objective 1: Develop Predictive Models
Field DataDistribution
ModelStatistical Analyses
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Field DataPied-Billed Grebe
American Bittern
Least Bittern
American Coot
Common Gallinule
Purple Gallinule
Limpkin
Black Rail
Clapper Rail
King Rail
Ridgeway’s Rail
Virginia Rail
Yellow Rail
Sora
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Pied-Billed Grebe
American Bittern
Least Bittern
American Coot
Common Gallinule
Purple Gallinule
Limpkin
Black Rail
Clapper Rail
King Rail
Ridgeway’s Rail
Virginia Rail
Yellow Rail
Sora
Field Data
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Field Data
• 903-8457 sites/species
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Field Data
• 903-8457 sites/species
• 10-14 yrs/species (1999-2012)
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Field Data
• 903-8457 sites/species
• 10-14 yrs/species (1999-2012)
• Detection-non-detection data
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Field Data
• 903-8457 sites/species
• 10-14 yrs/species (1999-2012)
• Detection-non-detection data
• Repeated call-broadcast surveys at each site
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Example: King Rail
• 3645 sites, 14 yrs
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Example: Black Rail
• 2885 sites, 13 yrs
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Example: Pied-Billed Grebe
• 8457 sites, 14 yrs
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Statistical Analyses
• Hierarchical occupancy models
𝒛𝒊,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝝍𝒊
𝒚𝒊,𝒋,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝒛𝒊,𝒕𝒑𝒊,𝒋,𝒕
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Statistical Analyses
• Hierarchical occupancy models• Multi-season 𝒛𝒊,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝝍𝒊
𝒚𝒊,𝒋,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝒛𝒊,𝒕𝒑𝒊,𝒋,𝒕
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Statistical Analyses
• Hierarchical occupancy models• Multi-season
• Model true distribution𝒛𝒊,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝝍𝒊
𝒚𝒊,𝒋,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝒛𝒊,𝒕𝒑𝒊,𝒋,𝒕
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Statistical Analyses
• Hierarchical occupancy models• Multi-season
• Model true distribution
• Bayesian implementation (R + JAGS)
𝒛𝒊,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝝍𝒊
𝒚𝒊,𝒋,𝒕~𝑩𝒆𝒓𝒏𝒐𝒖𝒍𝒍𝒊 𝒛𝒊,𝒕𝒑𝒊,𝒋,𝒕
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Model Selection
Training Data
Testing
Data
Site
Data
60% 40%
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Model Selection
Training Data
Testing
Data
Sites
• Log-scoring rule (Gelman et al. 2014, Hooten and Hobbs 2015, Broms et al. 2016)
• Newer method
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Model Selection
Training Data
Testing
Data
Sites
• Log-scoring rule (Gelman et al. 2014, Hooten and Hobbs 2015, Broms et al. 2016)
• Newer method
• “Gold standard”
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Model Selection
Training Data
Testing
Data
Sites
• Log-scoring rule (Gelman et al. 2014, Hooten and Hobbs 2015, Broms et al. 2016)
• Newer method
• “Gold standard”
• Fully Bayesian (parameter uncertainty)
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Habitat Predictors
• Wetland attributes (NWI)• Emergent wetlands, shrub-
scrub wetlands, etc.
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Habitat Predictors
• Wetland attributes (NWI)• Emergent wetlands, shrub-
scrub wetlands, etc.
• Land cover (GAP)• Agriculture, development,
etc.
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Scale-Specific Habitat Relations
• Animals respond to habitat composition at variety of scales (McGarigal et al. 2016)
• Nonlinear scaling (Hobbs 2003)
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Scale-Specific Habitat Relations
• Animals respond to habitat composition at variety of scales (McGarigal et al. 2016)
• Nonlinear scaling (Hobbs 2003)
𝝍
Habitat X
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Scale-Specific Habitat Relations
Habitat X
𝝍
• Animals respond to habitat composition at variety of scales (McGarigal et al. 2016)
• Nonlinear scaling (Hobbs 2003)
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Scale-Specific Habitat Relations
• Animals respond to habitat composition at variety of scales (McGarigal et al. 2016)
• Nonlinear scaling (Hobbs 2003)
• Developing predictive models• Multiple scales
• Scale optimization
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Habitat Predictors
• Multi-scale measurement• 100m, 224m, 500m
• Scale optimization
224m
500m
100m
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Selecting a Habitat Model
1. Select top detection model
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Selecting a Habitat Model
1. Select top detection model
Use top detection model for all further analyses
2. Select top scale for each local habitat variable
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Selecting a Habitat Model
1. Select top detection model
Use top detection model for all further analyses
2. Select top scale for each local habitat variable
Replicate for each of 30 local habitat variables
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Selecting a Habitat Model
1. Select top detection model
Use top detection model for all further analyses
2. Select top scale for each local habitat variable
3. Select top scale-optimized covariate from each category• NWI system-subsystem• NWI class• NWI water regime & modifiers• GAP land cover
Replicate for each of 30 local habitat variables
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Selecting a Habitat Model
1. Select top detection model
Use top detection model for all further analyses
2. Select top scale for each local habitat variable
3. Select top scale-optimized covariate from each category• NWI system-subsystem• NWI class• NWI water regime & modifiers• GAP land cover
4. Select top model with local-scale habitat variables
Fit all additive models with top covariates from each category
Replicate for each of 30 local habitat variables
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Broad-Scale Disturbance Variables
• Land cover (GAP)• Agriculture, development
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Broad-Scale Disturbance Variables
• Land cover (GAP)• Agriculture, development
• Hydrologic modification (NABD, NHDPlusV2)• Flow restriction, storage
capacity (upstream dams), storage fluctuation (upstream dams)
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Broad-Scale Disturbance Variables
• Land cover (GAP)• Agriculture, development
• Hydrologic modification (NABD, NHDPlusV2)• Flow restriction, storage
capacity (upstream dams), storage fluctuation (upstream dams)
• Multi-scale measurement• Catchment and network
• Scale optimization
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Catchment Scale
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Network Scale
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Incorporating Broad-Scale Disturbance
1. Select top scale for each broad-scale disturbance variable
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Incorporating Broad-Scale Disturbance
1. Select top scale for each broad-scale disturbance variable
Replicate for each of 10 broad-scale disturbance
variables
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Incorporating Broad-Scale Disturbance
1. Select top scale for each broad-scale disturbance variable
Replicate for each of 10 broad-scale disturbance
variables
2. Select top scale-optimized broad-scale disturbance variable
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Incorporating Broad-Scale Disturbance
1. Select top scale for each broad-scale disturbance variable
Replicate for each of 10 broad-scale disturbance
variables
2. Select top scale-optimized broad-scale disturbance variable
3. Combine with top local-habitat model and time trend covariate to create final model set
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Incorporating Broad-Scale Disturbance
1. Select top scale for each broad-scale disturbance variable
Replicate for each of 10 broad-scale disturbance
variables
2. Select top scale-optimized broad-scale disturbance variable
3. Combine with top local-habitat model and time trend covariate to create final model set
4. Select top model
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Modeling Summary
• Selected multi-scale distribution model using local habitat and land cover covariates• Accounted for imperfect detection
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Modeling Summary
• Selected multi-scale distribution model using local habitat and land cover covariates• Accounted for imperfect detection
• Incorporated watershed-level disturbances and temporal trends
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Modeling Summary
• Selected multi-scale distribution model using local habitat and land cover covariates• Accounted for imperfect detection
• Incorporated watershed-level disturbances and temporal trends
• Selected best overall model using spatial predictive ability
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Modeling Summary
• Selected multi-scale distribution model using local habitat and land cover covariates• Accounted for imperfect detection
• Incorporated watershed-level disturbances and temporal trends
• Selected best overall model using spatial predictive ability
• Replicated entire process for each species separately
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Results: King Rail
Developed
Permanently Flooded
ψ
ψ
ψScrub-shrub Wetland
Agriculture(Network)
ψ
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Results: Black Rail
Developed
Artificially Flooded
ψ
ψ
ψScrub-shrub Wetland
Time Trend
ψ
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Results: Pied-Billed Grebe
Developed
Artificially Flooded
ψ
ψ
ψScrub-shrub Wetland
RiverineLower-
perennial
ψ
Agriculture(Catchment)
ψ
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Objective 2: Spatial Analyses
DistributionModel
Habitat Suitability
Map
Prioritize Habitat
Management
SpatialAnalyses
DecisionSupport
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Spatial Analyses: Raster Regression
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Habitat Suitability Maps
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Decision Support for DoD
• Projected P(occupancy) for all 593 installations for each species
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Ranking DoD Installations
Area of optimal habitat at installation
Total area of optimal habitat
• Scaled area of optimal habitat• Fraction of all optimal
habitat located at each site
• Optimality threshold = top 30%
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Ranking DoD Installations
• Scaled area of optimal habitat• Fraction of all optimal
habitat located at each site
• Optimality threshold = top 30%
• Tested robustness of site rankings with other optimality thresholds• 20%, 40%
Area of optimal habitat at installation
Total area of optimal habitat
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Top 10 Installations: King Rail
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Top 10 Installations: Black Rail
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Top 10 Installations: Pied-Billed Grebe
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Top 10 Installations : All Species
• Species-specific installation rankings robust to optimality threshold changes
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Use of Output Results
• Species-specific inclusion of marsh birds in the INRMPs at relevant installations
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Summary and Conclusions
• National-scale policy problem• Where to prioritize breeding habitat conservation on DoD lands?
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Summary and Conclusions
• National-scale empirical assessment• Local-scale field data
• State-of-art predictive modeling
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Summary and Conclusions
• Map-based decision-support tools• Strategic habitat conservation over broad scales
• Focus local-scale management on appropriate species
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Acknowledgments
• Funding• DoD Legacy program• USFWS• Nebraska Game and
Parks Commission
• Colleagues• Chris Nadeau• Wes Glisson• Kathi Borgmann• Mindy Rice
• Contact• [email protected]