Strategic Habitat Conservation: Modeling to support cooperative, adaptive, science-based management

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Strategic Habitat ConservationModeling to
support cooperative, adaptive, science-based
management

• USGS-USFWS Science Support Partnership Ashton
  Drew

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Outline

• SSP project context objectives
• Building a tool to meet SHC science and
  management objectives
• Species-habitat modeling approach
• Future directions

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SSP SHC Challenge

• Move from static to dynamic thinking regarding
  how you collect, summarize, utilize, and share
  data
• Scaling stepping-down stepping-up
• Communicating science management
• Modeling general (what) specific (where)
• Management acting monitoring

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SHC Highlights

• Selecting species suitable for modeling
• Maximizing benefits from existing data
  expertise
• Knowledge summary communication tools

Biological Planning

• Decision support tools to evaluate alternative
  actions
• Integration of value systems into ecological
  model
• Decisions based on available science with
  documented assumptions and alternatives
  considered

• Hypotheses sampling design based on ecological
  assumptions and predicted management outcomes
• Regular maintenance of GIS and biological data
  layers
• Temporal cautionary note

Monitoring Research
Conservation Design
Delivery of Conservation Actions

• Multiple scales, on and off refuge lands
• Must be documented in a GIS

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Pilot Project Objective

• Aid with step-down of national population
  habitat objectives

Partners in Flight 2004 National Goals Bachmans
sparrow (250,000) Increase 100 Brown-headed
nuthatch (1.5 mil) Increase 50
Ecosystem?
National Wildlife Refuges? Other protected lands?
Errol Taskin www.birdsource.org
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Management Context Priorities

• State and refuge level planning documents
• Reference national and international plans
• Set management priorities in ecosystem context
• Partnership for coordinated management in time
  and space
• Shift from few to many species and habitats
• Quantitative goals measures of success

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RTNCF Pilot Model Guidelines

• Two spatial scales
• Terrestrial aquatic species
• Data-rich data-poor (expert opinion) scenarios
• Start with GAP products
• Design for adaptive management use

Bayesian Approach?
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Starting on the same page...

• Set population objectives for species
• Set abundance goals for RTNCF natural communities
• Convert population/abundance objectives into
  habitat objectives
• Map potential conservation areas where deficits
  exist
• Step down population/abundance objectives to
  individual refuges and partner lands

What do managers want? What can a model
provide? What are the objectives of SHC?
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Starting on the same page...

• Set population objectives for species
• Set abundance goals for RTNCF natural communities
• Convert population/abundance objectives into
  habitat objectives
• Map potential conservation areas where deficits
  exist
• Step down population/abundance objectives to
  individual refuges and partner lands

Models dont set targets... People do!
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Starting on the same page...

• Set population objectives for species
• Set abundance goals for RTNCF natural communities
• Convert population/abundance objectives into
  habitat objectives
• Map potential conservation areas where deficits
  exist
• Step down population/abundance objectives to
  individual refuges and partner lands

Managers starts with national goals... Modeling
starts with local knowledge
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Starting on the same page...

• Set population objectives for species
• Set abundance goals for RTNCF natural communities
• Convert population/abundance objectives into
  habitat objectives
• Map potential conservation areas where deficits
  exist
• Step down population/abundance objectives to
  individual refuges and partner lands

Is habitat acquisition the only management action
under consideration?
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Starting on the same page...

• Set population objectives for species
• Set abundance goals for RTNCF natural communities
• Convert population/abundance objectives into
  habitat objectives
• Map potential conservation areas where deficits
  exist
• Step down population/abundance objectives to
  individual refuges and partner lands

Single descriptive outcome knowledge
communication tool Multiple predictive
outcomes predictive decision support tool
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Starting on the same page...

• Set population objectives for species
• Set abundance goals for RTNCF natural communities
• Convert population/abundance objectives into
  habitat objectives
• Map potential conservation areas where deficits
  exist
• Step down population/abundance objectives to
  individual refuges and partner lands

STATIC vs. DYNAMIC OBJECTIVES

• Quantify refuge contributions to populations and
  habitats
• Identify where and how refuge-scale management
  actions may contribute to regional objectives
• Identify where and what additional research would
  be most beneficial
• Coordinate activities with partner agencies
  managers to step-down objectives and track
  regional progress

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Ecological Step-down
Policy Guidelines
Strategic Land Use Plans
SPACE
Refuge Management Plans
TIME
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Ecological Step-down
Policy Guidelines
Biogeographic Range
Strategic Land Use Plans
Habitat Distribution in Regional Landscape
SPACE
Refuge Management Plans
Patchy Resources within Habitat
TIME
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Knowledge AssumptionsVary with Scale
Policy Guidelines
Biogeographic Range
Strategic Land Use Plans
Habitat Distribution in Regional Landscape
SPACE
Refuge Management Plans
Good GIS data sources, limited knowledge
Patchy Resources within Habitat
TIME
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Knowledge AssumptionsVary with Scale
Policy Guidelines
Biogeographic Range
Reasonable knowledge, limited GIS
Strategic Land Use Plans
Habitat Distribution in Regional Landscape
SPACE
Refuge Management Plans
Patchy Resources within Habitat
TIME
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Effective Knowledge Transfer (Perera et al. 2007)
Policy Guidelines
Biogeographic Range
Strategic Land Use Plans
Habitat Distribution in Regional Landscape
SPACE
Refuge Management Plans
Patchy Resources within Habitat
TIME
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Species-Habitat Model
Amount of habitat, Number of individuals (total,
protected, spatially-explicit) Significant
sources of uncertainty
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Decision-Support Extension
Species-Habitat Model
Science Scenarios
Management Scenarios
Hypothesis Set A vs. B
Action Set A vs. B
Model habitat population under alternate
scenarios
Evaluate costs risks to compare value
Perform selected management action or research
Amount of habitat, Number of individuals (total,
protected, spatially-explicit) Significant
sources of uncertainty
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Species-Habitat Model
King Rail Rallus elegans
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Coarse Scale Habitat Models

• SE GAP provides Potential Occurrence in SE region

King Rail live in Fresh or Brackish Marsh Habitat
(red)
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Refuge-level Habitat Variability
King Rail Rallus elegans
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Bayesian Modeling Approach
Prior Probability (Model)
Likelihood (Data)
Posterior Probability (Model given the Data)
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Bayesian Belief Network
Prob ( )

• P (detect KIRA) varies within GAP predicted
  habitat
• Variables from literature and experts

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Bayesian Belief Network
Prob ( )
Foraging
Courting
Brooding
Wintering
Occurrence

• Occurrence patterns depend on activity and time
  of year
• Availability for detection varies by activity
  and time of year

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Bayesian Belief Network
Prob ( )
Foraging
Courting
Brooding
Wintering
Occurrence Habitat
Distance to Open Water
Landcover
Water Depth

• Hierarchical habitat selection macro and
  microhabitat
• Limited GIS data at relevant temporal spatial
  scale

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Bayesian Belief Network
Prob ( )
Foraging
Courting
Brooding
Wintering
Occurrence Habitat
Distance to Open Water
Landcover
Water Depth

• Relationships from literature and expert opinion

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Bayesian Belief Network
Prob ( )
Occurrence Habitat Management Choices
Foraging
Courting
Brooding
Wintering
Distance to Open Water
Landcover
Water Depth
Burning
Acquisition
Restoration
Flooding

• Management choices influence occurrence patterns
  via habitat
• Again, choices occur at multiple scales

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Bayesian Belief Network
Decision
Prob ( )
Foraging
Courting
Brooding
Wintering
Occurence Habitat Management Choices
Distance to Open Water
Landcover
Water Depth
Burning
Acquisition
Restoration
Flooding

• Manager defines potential habitat management
  actions
• Manager decides how to act in given situation
  based on probability and uncertainty associated
  with probability

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Model Validation Monitoring

• depends on
• patch size, cell context, distance from open
  water, salinity, water depth
• Stratify survey on GIS relevant assumptions
• Checking for ommission commission
• Collect microhabitat to distinguish false
  assumptions from inadequate data

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Science Management Feedback
SEGAP Marsh Patches gt1 acre
All SEGAP Marsh Patches

• Experts all suspect a minimum patch size, but
  disagree about how small is too small

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Science Management Feedback
SEGAP Marsh Patches gt1 acre
All SEGAP Marsh Patches

• Source of uncertainty in population and habitat
  estimates
• Uncertainty passes to management decisions

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Science Management Feedback
SEGAP Marsh Patches gt1 acre
All SEGAP Marsh Patches

• Take management action based on knowledge
• Select monitoring sites to test patch size
  hypothesis that underlies action

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Pilot Project Modelsvs.The Real Thing
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Future Directions?

• Five things I cant deliver (by June 2009)
• pretty GUI interface
• interactive decision support
• multi-year predictions
• population viability assessment
• GIS to track management actions
• but all are feasible additions to the framework
  I am developing

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Pilot Model Species

• King Rail
• USFWS Focal Species
• Fresh brackish wetlands
• Back Bay, Cedar Island, Currituck, MacKay Island,
  Pea Island, Swanquarter
• Swainsons Warbler
• PIF Priority Species
• Bottomland upland hardwood forest
• Alligator River, Great Dismal Swamp, Pocosin
  Lakes, Roanoke River
• Blueback Herring
• NOAA Species of Concern
• Anadromous fish
• Roanoke River, Alligator River

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Modeling Method to Support SHC

• Pilot project to establish protocol for
• Gathering, summarizing existing data
• Gathering, summarizing expert opinion
• Communally constructing a belief network
• Asking science and management what-ifs
• Designing a monitoring protocol to reduce
  uncertainty
• Updating model with new information
• Recommending adjustments to management and/or
  monitoring

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Bayesian Belief Network
Decision
Prob ( )
Foraging
Courting
Brooding
Wintering
Occurence Habitat Management Choices
Distance to Open Water
Landcover
Water Depth
Burning
Acquisition
Restoration
Flooding

• Manager defines potential habitat management
  actions
• Manager decides how to act in given situation
  based on probability and uncertainty associated
  with probability

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Bayesian Belief Network
Decision
Prob ( )
Occurrence Habitat Management Choices
Foraging
Spawning
Migrating
Water Quality
Shading
Substrate
Pool/Riffle
Fish Ladder
Riparian Mgmt.
Dam Removal
Landcover

• Ecological relationships from literature and
  experts
• Manager decides how to act in given situation
  based on probability and uncertainty associated
  with probability

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Bayesian Belief Network
Decision
Prob ( )
Breeding
Hybernating
Tadpoles
Occurrence Habitat Management Choices
Eggs
Dry Days
Water Quality
Shading
Landcover
Artificial Ponds
Restoration
Aqcuisition

• Ecological relationships from literature and
  experts
• Manager decides how to act in given situation
  based on probability and uncertainty associated
  with probability

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Many Thanks To

• GIS Data SE-GAP BaSIC
• Lit Review E. Laurent, Q. Mortell
• Expert Opinions Anonymous (USFWS, TNC, Natural
  Heritage Program, Wildlife Resources Commission,
  NC Museums)
• KIRA-CAP National cooperation on research,
  modeling, and funding
• Model and Validation Funding USGS USFWS

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RTNCF SSP Questions
Ashton Drew cadrew_at_ncsu.edu or
919-513-0506 Project Website www.basic.ncsu.edu/p
roj/SSP.html

• Quantify refuge contributions to populations and
  habitats
• Identify where and how refuge-scale management
  actions may contribute to regional objectives
• Identify where and what additional research would
  be most beneficial
• Coordinate activities with partner agencies
  managers to step-down objectives and track
  regional progress