Strategic Habitat Conservation: Modeling to support cooperative, adaptive, science-based management USGS-USFWS Science Support Partnership Ashton Drew

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

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

SHC Highlights Biological Planning Delivery of Conservation Actions Monitoring & Research Conservation Design ► Selecting species suitable for modeling ► Maximizing benefits from existing data & expertise ► Knowledge summary & communication tools ► Hypotheses & sampling design based on ecological assumptions and predicted management outcomes ► Regular maintenance of GIS and biological data layers ► Temporal cautionary note ► 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 ► Multiple scales, on and off refuge lands ► Must be documented in a GIS

Pilot Project Objective ► Aid with step-down of national population & habitat objectives Partners in Flight 2004 National Goals Bachman’s sparrow (250,000) – Increase 100% Brown-headed nuthatch (1.5 mil) – Increase 50% Errol Taskin Ecosystem? National Wildlife Refuges? Other protected lands?

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

RTNCF Pilot Model Guidelines Bayesian Approach? ► Two spatial scales ► Terrestrial & aquatic species ► Data-rich & data-poor (expert opinion) scenarios ► Start with GAP products ► Design for adaptive management use

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?

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 don’t set targets... People do!

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

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?

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

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 ► 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 STATIC vs. DYNAMIC OBJECTIVES

Ecological “Step-down” TIME SPACE Strategic Land Use Plans RefugeManagementPlans Policy Guidelines

Ecological “Step-down” TIME SPACE Strategic Land Use Plans RefugeManagementPlans BiogeographicRange PatchyResources within Habitat Habitat Distribution in Regional Landscape Policy Guidelines

Knowledge & Assumptions Vary with Scale TIME SPACE Strategic Land Use Plans RefugeManagementPlans BiogeographicRange PatchyResources within Habitat Habitat Distribution in Regional Landscape Good GIS data sources, limited knowledge Policy Guidelines

TIME SPACE Strategic Land Use Plans RefugeManagementPlans BiogeographicRange PatchyResources within Habitat Habitat Distribution in Regional Landscape Reasonable knowledge, limited GIS Policy Guidelines Knowledge & Assumptions Vary with Scale

Effective Knowledge Transfer (Perera et al. 2007) TIME SPACE BiogeographicRange Habitat Distribution in Regional Landscape Strategic Land Use Plans RefugeManagementPlans PatchyResources within Habitat Policy Guidelines

Amount of habitat, Number of individuals (total, % protected, spatially-explicit) Significant sources of uncertainty Model habitat location and quality based on expert opinion and literature review Field validation & model updating Validated and updated habitat model Species-Habitat Model

Amount of habitat, Number of individuals (total, % protected, spatially-explicit) Significant sources of uncertainty Model habitat location and quality based on expert opinion and literature review Field validation & model updating Validated and updated habitat model Species-Habitat Model Science Scenarios Management Scenarios Hypothesis Set A vs. B Action Set A vs. B Evaluate costs & risks to compare value Model habitat & population under alternate scenarios Decision-Support Extension Perform selected management action or research

Species-Habitat Model King Rail Rallus elegans

Coarse Scale Habitat Models ► SE GAP provides Potential Occurrence in SE region King Rail live in Fresh or Brackish Marsh Habitat (red)

Refuge-level Habitat Variability King Rail Rallus elegans

Bayesian Modeling Approach Prior Probability (Model) Likelihood(Data) Posterior Probability (Model given the Data) 400m grid cells containing GAP potential King Rail habitat Prob ( )

Bayesian Belief Network Prob ( ) ► P (detect KIRA) varies within GAP predicted habitat ► Variables from literature and experts

Bayesian Belief Network ForagingCourtingBroodingWinteringOccurrence ► Occurrence patterns depend on activity and time of year ► **Availability for detection varies by activity and time of year Prob ( )

Bayesian Belief Network ForagingCourtingBroodingWintering LandcoverWater Depth Distance to Open Water Occurrence Habitat ► Hierarchical habitat selection: macro and microhabitat ► Limited GIS data at relevant temporal & spatial scale Prob ( )

Bayesian Belief Network ForagingCourtingBroodingWintering LandcoverWater Depth Distance to Open Water Occurrence Habitat Prob ( ) ► Relationships from literature and expert opinion

Bayesian Belief Network Burning Flooding Acquisition Restoration Occurrence Habitat Management Choices ► Management choices influence occurrence patterns via habitat ► Again, choices occur at multiple scales ForagingCourtingBroodingWintering LandcoverWater Depth Distance to Open Water Prob ( )

Bayesian Belief Network ForagingCourtingBroodingWintering LandcoverWater Depth Distance to Open Water Occurence Habitat Management Choices Prob ( ) ► Manager defines potential habitat management actions ► Manager decides how to act in given situation based on probability and uncertainty associated with probability Decision Burning Flooding Acquisition Restoration

Model Validation & Monitoring 400m grid cells containing GAP potential King Rail habitat Prob ( ) ► …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

Science – Management Feedback All SEGAP Marsh Patches SEGAP Marsh Patches >1 acre ► Experts all suspect a minimum patch size, but disagree about how small is “too small”

Science – Management Feedback All SEGAP Marsh Patches SEGAP Marsh Patches >1 acre ► Source of uncertainty in population and habitat estimates ► Uncertainty passes to management decisions

Science – Management Feedback All SEGAP Marsh Patches SEGAP Marsh Patches >1 acre ► Take management action based on knowledge ► Select monitoring sites to test patch size hypothesis that underlies action

Pilot Project Models vs. “The Real Thing”

Future Directions? ► Five things I can’t 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

Pilot Model Species ► King Rail  USFWS Focal Species  Fresh & brackish wetlands  Back Bay, Cedar Island, Currituck, MacKay Island, Pea Island, & Swanquarter ► Swainson’s 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

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

Bayesian Belief Network ForagingCourtingBroodingWintering LandcoverWater Depth Distance to Open Water Occurence Habitat Management Choices Prob ( ) ► Manager defines potential habitat management actions ► Manager decides how to act in given situation based on probability and uncertainty associated with probability Decision Burning Flooding Acquisition Restoration

Occurrence Habitat Management Choices ► Ecological relationships from literature and experts ► Manager decides how to act in given situation based on probability and uncertainty associated with probability Prob ( ) Foraging Spawning Migrating Water Quality Substrate Shading Pool/Riffle Fish Ladder Landcover Riparian Mgmt. Dam Removal Bayesian Belief Network Decision

Occurrence Habitat Management Choices ► Ecological relationships from literature and experts ► Manager decides how to act in given situation based on probability and uncertainty associated with probability Prob ( ) Eggs Tadpoles Breeding Water Quality Shading # Dry Days Landcover Artificial Ponds Restoration Aqcuisition Bayesian Belief Network Decision Hybernating

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

RTNCF SSP Questions: Ashton Drew: or Project Website: ► 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