From Expert-based to Data-based Decision Support for

Strategic Habitat Conservation

Ashton Drew & Jaime CollazoNCSU Biology Department

Biodiversity & Spatial Information Center

USGS Fisheries & Wildlife Coop Unit

Step-down national population& habitat objectivesUSGS & 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 NuthatchGoal: 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 NuthatchGoal: 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 pairs40 pairs

Brown-Headed NuthatchGoal: 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 RailRallus elegans

Southeast Gap Analysis ProgramSoutheast Gap Analysis Program

Bob Powell 2004

Regional Distribution Maps

King RailRallus elegans

Southeast Gap Analysis ProgramSoutheast 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

Occ

upan

cy

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 monitoring2. 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 2 Variable 4

Variable 5Variable 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

LandscapeMicrohabitat

Field/GIS DataLiterature Experts

Biological LimitsBehavioral Preferences

Threats

Prob( )

Model Error & Uncertainty

LandscapeMicrohabitat

Field/GIS DataLiterature Experts

Multiple methods,Uneven sampling

Not local, access bias,

sensationalism

Management bias,Micro focused

Prob( )

Model Validation & Improvement

LandscapeMicrohabitat

GIS dataLiterature Experts

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 (

KIR

A)

Distance to Open Water (m)

Uncertainty depends on the question asked: A) What is probability at distance X? B) Where is the greatest probability?

RelM

ax:

P (

KIR

A)

P (

KIR

A)

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 ( )

00010

01010

00000

Emigration

ImmigrationWhy 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

Influence Diagram & Belief Network

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

For more information:

Contact Ashton Drew at: cadrew@ncsu.edu 919-513-0506

Project website with presentations, publications, and newsletters: www.basic.ncsu.edu/proj/SSP.html