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

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

Enable USFWS to identify candidate locations to : locate and protect extent populations prioritize restoration areas identify sites for augmentation or (re)introduction 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 decisions guide data collection and learning to support future decisions Hierarchical structure predicts probability of: suitable habitat from available GIS data successful occupancy from field measurements

Usually 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

Usually 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 pattern Suitability: geophysical processes – modified by anthropogenic threats Occupancy: biological processes – modified by anthropogenic threats

Restore Habitat Release Captive-Bred Mussels Translocate, (Re)Establish Population Protect Augment 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 Protect Augment 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 attributes Water flow Temperature Substrate Chemistry Eutrophication Toxicants Thermal stress Flashy hydrology Impeded flow or reduced flow Siltation Direct threats

Probability Suitable Habitat Substrate Temp To formalize experts’ hypotheses of how a system works, experts must define: Key ecological attributes (what?)

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 Shading Depth Water Withdrawal Thermal Effluent

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 Shading Depth Thermal Effluent Present/ Absent <3 days per year exceed 25˚ in 5 year average 80% forested riparian

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 Shading Depth Water Withdrawal Thermal Effluent

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?) Thermal Effluent Ground waterDepthShading P (Suitable Substrate Temp) PresentSignificant< 1 m< 20% riparian PresentNegligible1-2 m> 80% riparian AbsentSignificant> 5 m20-80% riparian AbsentSignificant< 1 m> 80% riparian AbsentNegligible> 5 m< 20% riparian

Area of interest is... A site is... (size) We consider presence for timeframe...

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?

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...

Each level of each variable is represented multiple times but in different combinations Internal consistency? Interaction effects?

Variable 1 Variable 2 Variable 3 Red Line – Combined Expert Prior Probability Black Line – Data-informed Posterior Probability Predicted Probability of Suitable Habitat Confidence in Prediction

Elliptio 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?

Ashton Drew – Tom Kwak, Greg Cope, Tom Augspurger, Sarah McRae, and Tamara Pandolfo