Ashton DrewTom Kwak, Greg Cope, Tom Augspurger, Sarah McRae, and Tamara

Pandolfo

Hierarchical Landscape Models for Endemic Unionid Mussels: Building Strategic Habitat Conservation Tools for Mussel Recovery in the South Atlantic Landscape

Conservation Cooperative

Enable USFWS to identify candidate locations to:locate and protect extent populationsprioritize restoration areasidentify sites for augmentation or (re)introduction

Project Objective

Enable USFWS to identify candidate locations to:locate and protect extent populationsprioritize restoration areasidentify sites for augmentation or (re)introduction

Project Objective

Elliptio steinstansana

C. Eads, NCSU

Pilot Study: Tar River Spinymussel…but model design intended to apply broadly to other SALCC endemic unionid species

Bayesian Belief Network (BBN) model to:integrate available data and expert knowledge to

support present decisionsguide data collection and learning to support future

decisions

Hierarchical structure predicts probability of:suitable habitat from available GIS datasuccessful occupancy from field measurements

Project Method

Unique ChallengesUsually identify and protect the most suitable, occupied habitat,

but:we also need to identify unsuitable, restorable habitat and suitable,

unoccupied sites for possible (re)introduction

Usually define suitability based on similarity to other occupied sites, but:occupancy and suitability can be decoupled for endangered species,

especially if legacy effects

Unique ChallengesUsually identify and protect the most suitable, occupied habitat, but:

we also need to identify unsuitable, restorable habitat and suitable, unoccupied sites for possible (re)introduction

Usually define suitability based on similarity to other occupied sites, but:occupancy and suitability can be decoupled for endangered species,

especially if legacy effects

Separate habitat suitability and successful occupancy and hypothesize process rather than describe patternSuitability: geophysical processes – modified by anthropogenic threatsOccupancy: biological processes – modified by anthropogenic threats

Restore Habitat

Release Captive-Bred

Mussels

Translocate,(Re)Establish Population

ProtectAugment

Occupied

Unoccupied

Suitable

Unsuitable,Unrestorable

No Action

Unsuitable,Restorable

Field data

Probability of successful

mussel occupancy

Restore Habitat

Release Captive-Bred

Mussels

Translocate,(Re)Establish Population

ProtectAugment

Occupied

Unoccupied

GIS data

Probability of presence of

suitable habitat in 500 m reach Suitable

Unsuitable,Unrestorable

No Action

Unsuitable,Restorable

Conduct Habitat Survey

Conduct Mussel Survey

Key ecological attributesWater flowTemperatureSubstrateChemistry

EutrophicationToxicantsThermal stressFlashy hydrologyImpeded flow or reduced

flowSiltation

Expert Elicitation: Habitat Suitability

Direct threats

Bayesian Belief Network

Probability Suitable Habitat

Substrate Temp

To formalize experts’ hypotheses of how a system works, experts must define:• Key ecological attributes (what?)

Bayesian Belief Networks

Probability Suitable Habitat

Substrate Temp

To formalize experts’ hypotheses of how a system works, experts must define:• Key ecological attributes (what?)• Direct and indirect drivers of the system (why? how?)

Groundwater

ShadingDepth

Water Withdrawal

ThermalEffluent

Bayesian Belief Networks

Probability Suitable Habitat

Substrate Temp

To formalize experts’ hypotheses of how a system works, experts must define:• Key ecological attributes (what?)• Direct and indirect drivers of the system (why? how?)• Significant and observable levels of drivers (how much?)

Groundwater

ShadingDepth

ThermalEffluent

Present/Absent

<3 days per year exceed 25˚ in 5 year

average

<30%, 30-80%, >80% forested

riparian

Bayesian Belief Networks

Probability Suitable Habitat

Substrate Temp

To formalize experts’ hypotheses of how a system works, experts must define:• Key ecological attributes (what?)• Direct and indirect drivers of the system (why? how?)• Significant and observable levels of drivers (how much?)• Conditional relationships among drivers (when? where?)

Groundwater

ShadingDepth

Water Withdrawal

ThermalEffluent

Bayesian Belief NetworksTo formalize experts’ hypotheses of how a system works, experts must define:• Key ecological attributes (what?)• Direct and indirect drivers of the system (why? how?)• Significant and observable levels of drivers (how much?)• Conditional relationships among drivers (when? where?)

Thermal Effluent

Groundwater Depth Shading

P (Suitable Substrate Temp)

Present Significant < 1 m < 20% riparian

Present Negligible 1-2 m > 80% riparian

Absent Significant > 5 m 20-80% riparian

Absent Significant < 1 m > 80% riparian

Absent Negligible > 5 m < 20% riparian

Encoding with Elicitator: Questions & Answers Area of interest is ... A site is ... (size) We consider presence for timeframe ...

Encoding with Elicitator: Questions & Answers Area of interest is ... A site is ... (size) We consider presence for timeframe ... Imagine 100 sites with <30 % forested riparian, 2-5

m bankfull depth, significant groundwater input, no known thermal effluent ...

What is the minimum number of sites you would expect to maintain substrate temperatures within range suitable for TRSM?

Encoding with Elicitator: Questions & Answers Area of interest is ... A site is ... (size) We consider presence for timeframe ... Imagine 100 sites with <30 % forested riparian, 2-5

m bankfull depth, significant groundwater input, no known thermal effluent ...

What is the minimum number of sites you would expect to maintain substrate temperatures within range suitable for TRSM?

... and the maximum ... So you’re 100% sure ... Now bring in these limits – to be more informative –

so that you’re 95% sure. Bring in further so you’re 50% sure ... Now what’s your best estimate of ... So this means there’s a 1 in XX chance that the

number inhabited is in ... to ...

One expert, many scenarios

One expert, many scenariosEach level of each variable is represented multiple times but in different combinations

Internal consistency?Interaction effects?

Combine experts, add data

Variable 1

Variable 2

Variable 3

Red Line – Combined Expert Prior ProbabilityBlack Line – Data-informed Posterior Probability

Predicted Probabilityof Suitable Habitat

Confidence in Prediction

Flexibility for Other Species in SALCCElliptio steinstansana

C. Eads, NCSU

Substrate Temp

<3 days per year exceed 25˚ in 5 year

average

• Imagine 100 sites with <30 % forested riparian, 2-5 m bankfull depth, significant groundwater input, no known thermal effluent.

• What is the minimum number of sites you would expect to maintain substrate temperatures within range suitable for TRSM?

ENDAshton Drew – cadrew@ncsu.edu

Tom Kwak, Greg Cope, Tom Augspurger, Sarah McRae, and Tamara Pandolfo