From Expert-based to Data-based Decision Support for Strategic Habitat Conservation Ashton Drew & Jaime Collazo NCSU Biology Department Biodiversity & Spatial Information Center USGS Fisheries & Wildlife Coop Unit

Step-down national population & habitat objectives USGS & USFWS Science Support Partnership Pilot project objective & planning unit Modeling approach Priority species Species-habitat relationships Limiting factors** Population objectives

National Population & Habitat Goals Southeast Region Waterbird Plan 2006 King Rail, SE Coastal Plain: 830 pair Increase to 6000 pair RTNCF Landscape? National Wildlife Refuges? Other protected lands? Regional Goals Local Goals & Actions National Plans, Local Actions

Step-down population and habitat objectives? Area based Brown-Headed Nuthatch Goal: 50% Increase, 1.5M pairs 80% habitat, so provide 80% pairs 20% habitat, so provide 20% pairs Who does the work?

Step-down population and habitat objectives? Area based Equal effort 10 pairs, so provide 15 pairs 100 pairs, so provide 150 pairs Who does the work? Brown-Headed Nuthatch Goal: 50% Increase, 1.5M pairs

Local gains equal national gains? Step-down population and habitat objectives? Area based Equal effort Increasing… or concentrating 100 pairs 50 pairs 100 pairs 10 pairs 40 pairs Brown-Headed Nuthatch Goal: 50% Increase, 1.5M pairs (Nationally)

Quantify current contribution How much habitat is in the landscape? How are individuals distributed within habitat? Where is the habitat in relation to protected lands? How certain are the estimates? Refuge & Landscape Models Identify opportunities to increase contribution Protection for high occupancy habitat? New management for low occupancy habitat? Individuals gained?

Biological Planning Unit Refuges & Partner Lands in Landscapes

Terrestrial & aquatic species Start with existing data products Utilize expert opinion, but aim for data-driven Design for use in adaptive management RTNCF Ecosystem & Refuges: ( ENC/SEVA SHC Team )

Regional Distribution Maps National plans based on potential habitat models Potential habitat different from occupancy Identify species and states for conservation action King Rail Rallus elegans Southeast Gap Analysis Program Bob Powell 2004

Regional Distribution Maps King Rail Rallus elegans Southeast Gap Analysis Program Bob Powell 2004 Not intended to support local decisions within conservation lands, nor to evaluate relative value of two potential sites Mackay Island NWR

Coarse Scale Habitat Models By design, ignore fine-scale habitat variability Fresh or Brackish Marsh (gold) = King Rail Habitat (red)

By design, ignore fine-scale habitat variability Fresh or Brackish Marsh (gold) = King Rail Habitat (red) Coarse Scale Habitat Models

Refuge-level Habitat Variability

How can we improve the predictive resolution of models, given the available GIS data and ecological knowledge? “Potential Habitat/Non-Habitat” “Low, Medium, High P(Occurrence)” with confidence intervals Refuge-level Management Decisions

Probability of Occupancy Mackay Island National Wildlife Refuge Occupancy Certainty

Modeling Approach Bayesian Belief Networks (Netica)

Models for Management Modeling approach designed to: initiate with diverse data sources function despite knowledge-data gaps document uncertainty to: 1. guide research and monitoring 2. support risk assessment update with new data or knowledge Bayesian Belief Networks: Expert-based to Data-based decision support

Begin with an Influence Diagram Depict hypotheses and assumptions about how the system works Why does the species occupy one place and not another? Variable 1 Food Shelter Threats Variable 2Variable 4 Variable 5 Variable 3 Probability of Occupancy

Bayesian Model Structure Model (Prior Probability) Data (Likelihood) Model given the Data (Posterior Probability) Prob ( ) Mackay Island National Wildlife Refuge

Priority Species

Pilot Model Species Benefit FWS but also fully test model approach Priority Trust species – little known, possibly declining, challenging to survey Diverse habitats – all refuges can participate and opportunity for collaboration Range of data challenges – ecological data, GIS data

Species-Habitat Relationships Biological & Data Limits

Species-Habitat Information Landscape Microhabitat Field/GIS DataLiteratureExperts Biological Limits Behavioral Preferences Threats Prob( )

Model Error & Uncertainty Landscape Microhabitat Field/GIS DataLiteratureExperts Multiple methods, Uneven sampling Not local, access bias, sensationalism Management bias, Micro focused Prob( )

Model Validation & Improvement Landscape Microhabitat GIS dataLiteratureExperts Locally collected data targets regionally important assumptions and knowledge gaps Prob( )

Uncertainty in Expert Opinion Experts differ experience histories priority habitat management concerns bias patterns Experts’ experience tends towards microhabitat observations, rather than landscape observations greater agreement on microhabitat associations lack of confidence on landscape associations

Experts: Distance to Open Water Disagreement as uncertainty? P (KIRA) Distance to Open Water (m)

Uncertainty depends on the question asked: A) What is probability at distance X? B) Where is the greatest probability? RelMax: P (KIRA) P (KIRA) Distance to Open Water (m) Experts: Distance to Open Water

Population Objectives

Occupancy Modeling Presence & Suitable Habitat Perfect detection is rare Presence does not always indicate suitability Suitability scores are difficult to validate Detection & Occupied Habitat “Failure to detect” vs. “True absence” Environment can influence detection and occupancy independently Confidence intervals included as measure of certainty

Use Detection History Distinguish probability of detection from probability of occupancy Prob ( )

Emigration Immigration Why would a King Rail arrive? (Regional Characteristics) Why would a King Rail stay? (Regional & Microhabitat Characteristics) P (Encounter Site) P (Select Site) Consider Pattern & Process

Influence Diagram & Belief Network

P (Encounter Site)

Suitable Unsuitable

Location = Suitable, Confident

Location = Unsuitable, Confident

Unsuitable, Less Confident

Pilot Model Summary Gather, summarize existing data Gather, summarize expert opinion Turn data & knowledge into model networks Turn model networks into maps & objectives

Pilot Model Summary Gather, summarize existing data Gather, summarize expert opinion Turn data & knowledge into model networks Turn model networks into maps & estimates Ask science and management “what-ifs” Guide monitoring to reduce uncertainty Update model with new information Recommend adjustments to management and/or monitoring

Many Thanks To… GIS Data & Support: SEGAP & BaSIC, D. Newcomb, S. Chappell Lit Review: E. Laurent, Q. Mortell Experts: USFWS, TNC, NHP, NCWRC, NC Museums Field Crew: J. Baker, H. Hareza, H. Smith, & R. Wise Research Assistants: L. Paine, N. Tarr KIRA-CAP: Cooperation on research, modeling, and funding under T. Cooper Admin Support: W. Moore Pilot Test Subjects: ENC/SEVA SHC Team Funding: USGS & USFWS

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