Ashton Drew Tom Kwak , Greg Cope, Tom Augspurger , Sarah McRae, and Tamara Pandolfo - PowerPoint PPT Presentation

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

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  1. Hierarchical Landscape Models for Endemic Unionid Mussels:Building Strategic Habitat Conservation Tools for Mussel Recovery in the South Atlantic Landscape Conservation Cooperative Ashton Drew Tom Kwak, Greg Cope, Tom Augspurger, Sarah McRae, and Tamara Pandolfo

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

  3. Project Objective Enable USFWS to identify candidate locations to: • locate and protect extent populations • prioritize restoration areas • identify sites for augmentation or (re)introduction Elliptiosteinstansana C. Eads, NCSU Pilot Study: Tar River Spinymussel …but model design intended to apply broadly to other SALCC endemic unionid species

  4. Project Method 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

  5. Unique Challenges • 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

  6. Unique Challenges • 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

  7. No Action Unsuitable, Unrestorable Restore Habitat Unsuitable, Restorable Suitable Translocate, (Re)Establish Population Release Captive-Bred Mussels Unoccupied Occupied Protect Augment

  8. No Action GIS data Unsuitable, Unrestorable Probability of presence of suitable habitat in 500 m reach Restore Habitat Unsuitable, Restorable Conduct Habitat Survey Suitable Translocate, (Re)Establish Population Field data Release Captive-Bred Mussels Unoccupied Probability of successful mussel occupancy Conduct Mussel Survey Occupied Protect Augment

  9. Expert Elicitation: Habitat Suitability Key ecological attributes Direct threats • Water flow • Temperature • Substrate • Chemistry • Eutrophication • Toxicants • Thermal stress • Flashy hydrology • Impeded flow or reduced flow • Siltation

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

  11. Bayesian Belief Networks 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 Water Withdrawal Depth Substrate Temp Probability Suitable Habitat Shading Thermal Effluent

  12. Bayesian Belief Networks 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?) <30%, 30-80%, >80% forested riparian Groundwater Depth Present/Absent Substrate Temp Probability Suitable Habitat Shading Thermal Effluent <3 days per year exceed 25˚ in 5 year average

  13. Bayesian Belief Networks 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 Water Withdrawal Depth Substrate Temp Probability Suitable Habitat Shading Thermal Effluent

  14. Bayesian Belief Networks 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?)

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

  16. 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?

  17. 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 ...

  18. One expert, many scenarios

  19. One expert, many scenarios Each level of each variable is represented multiple times but in different combinations Internal consistency? Interaction effects?

  20. Combine experts, add data Red Line – Combined Expert Prior Probability Black Line – Data-informed Posterior Probability Variable 1 Variable 2 Confidence in Prediction Predicted Probability of Suitable Habitat Variable 3

  21. Flexibility for Other Species in SALCC Substrate Temp Elliptiosteinstansana <3 days per year exceed 25˚ in 5 year average C. Eads, NCSU • 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?

  22. END Ashton Drew – cadrew@ncsu.edu Tom Kwak, Greg Cope, Tom Augspurger, Sarah McRae, and Tamara Pandolfo