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Strategic Habitat Conservation: Modeling to support cooperative, adaptive, science-based management USGS-USFWS Science Support Partnership 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 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 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 Selecting species suitable for modeling ► Maximizing benefits from existing data & SHC Highlights Selecting species suitable for modeling ► Maximizing benefits from existing data & expertise ► Knowledge summary & communication tools ► Monitoring & Research Conservation Design Biological Planning ► Hypotheses & sampling design based on ecological assumptions and ► predicted management outcomes ► ► Regular maintenance of GIS and biological data layers Delivery of ► Temporal cautionary Conservation Actions note ► Multiple scales, on and off refuge lands ► Must be documented in a GIS ► 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

Pilot Project Objective ► Aid with step-down of national population & habitat objectives Partners 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% Ecosystem? National Wildlife Refuges? Other protected lands? Errol Taskin www. birdsource. org

Management Context & Priorities ► State and refuge level planning documents § Reference national 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 success goals & measures of

RTNCF Pilot Model Guidelines Two spatial scales ► Terrestrial & aquatic species ► Data-rich 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?

Starting on the same page. . . Set population objectives for species Set abundance 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 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 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 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 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 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 ► ►

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

Ecological “Step-down” Policy Guidelines Biogeographic Range SPACE Strategic Land Use Plans Habitat Refuge Management Ecological “Step-down” Policy Guidelines Biogeographic Range SPACE Strategic Land Use Plans Habitat Refuge Management Plans Distribution in Regional Landscape Patchy Resources within Habitat TIME

Knowledge & Assumptions Vary with Scale Policy Guidelines Biogeographic Range SPACE Strategic Land Use Knowledge & Assumptions Vary with Scale Policy Guidelines Biogeographic Range SPACE Strategic Land Use Plans Habitat Refuge Management Plans Distribution in Regional Landscape Patchy Resources within Habitat TIME Good GIS data sources, limited knowledge

Knowledge & Assumptions Vary with Scale SPACE Policy Guidelines Reasonable knowledge, limited GIS Refuge Knowledge & Assumptions Vary with Scale SPACE Policy Guidelines Reasonable knowledge, limited GIS Refuge Management Plans Biogeographic Range Strategic Land Use Plans Habitat Distribution in Regional Landscape Patchy Resources within Habitat TIME

Effective Knowledge Transfer (Perera et al. 2007) Policy Guidelines Biogeographic Range SPACE Strategic Land Effective Knowledge Transfer (Perera et al. 2007) Policy Guidelines Biogeographic Range SPACE Strategic Land Use Plans Habitat Refuge Management Plans Distribution in Regional Landscape Patchy Resources within Habitat TIME

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

Species-Habitat Model habitat location and quality based on expert opinion and literature review Field Species-Habitat Model habitat location and quality based on expert opinion and literature review Field validation & model updating Validated and updated habitat model Decision-Support Extension Management Scenarios Science Scenarios Action Set A vs. B Hypothesis 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

Species-Habitat Model King Rail Rallus elegans Species-Habitat Model King Rail Rallus elegans

Coarse Scale Habitat Models ► SE GAP provides Potential Occurrence in SE region King 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 Refuge-level Habitat Variability King Rail Rallus elegans

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

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

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

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

Bayesian Belief Network Prob ( Occurrence Habitat ► ) Foraging Landcover Courting Brooding Distance Bayesian Belief Network Prob ( Occurrence Habitat ► ) Foraging Landcover Courting Brooding Distance to Open Water Wintering Water Depth Relationships from literature and expert opinion

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

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

Model Validation & Monitoring Prob ( ) ► …depends on: § patch size, cell Model Validation & Monitoring 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 ► 400 m grid cells containing GAP potential King Rail habitat

Science – Management Feedback All SEGAP Marsh Patches ► SEGAP Marsh Patches >1 acre 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 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 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” Pilot Project Models vs. “The Real Thing”

Future Directions? ► Five things I can’t deliver (by June 2009)… § pretty GUI 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 ► Swainson’s Warbler ► Blueback Herring § USFWS Pilot Model Species ► King Rail ► Swainson’s Warbler ► Blueback Herring § USFWS Focal Species § Fresh & brackish wetlands § Back Bay, Cedar Island, Currituck, Mac. Kay Island, Pea Island, & Swanquarter § PIF Priority Species § Bottomland & upland hardwood forest § Alligator River, Great Dismal Swamp, Pocosin Lakes, Roanoke River § NOAA Species of Concern § Anadromous fish § Roanoke River, Alligator River

Modeling Method to Support SHC ► Pilot § § § § project to establish 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 Prob ( Occurence Habitat Management Choices ► ► ) Decision Foraging Bayesian Belief Network Prob ( Occurence Habitat Management Choices ► ► ) Decision Foraging Courting Landcover Burning Brooding Distance to Open Water Flooding Wintering Water Depth Acquisition Restoration Manager defines potential habitat management actions Manager decides how to act in given situation based on probability and uncertainty associated with probability

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

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

Many Thanks To… ► GIS Data: SE-GAP & Ba. SIC ► Lit Review: E. Many Thanks To… ► GIS Data: SE-GAP & Ba. SIC ► 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: cadrew@ncsu. edu or 919 -513 -0506 Project Website: www. RTNCF SSP Questions: Ashton Drew: cadrew@ncsu. edu or 919 -513 -0506 Project Website: www. basic. ncsu. edu/proj/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 ► ►