From Expert-based to Data-based Decision Support for Strategic Habitat Conservation

1. 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

2. 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

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

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

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

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

7. Refuge & Landscape Models • 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? • Identify opportunities to increase contribution • Protection for high occupancy habitat? • New management for low occupancy habitat? • Individuals gained?

8. Biological Planning Unit Refuges & Partner Lands in Landscapes

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

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

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

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

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

14. Refuge-level Habitat Variability

15. Refuge-level Management Decisions 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

16. Occupancy Certainty Occupancy Probability of Occupancy Mackay Island National Wildlife Refuge

17. Modeling Approach Bayesian Belief Networks (Netica)

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

19. 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 3 Variable 5 Variable 1 Variable 2 Variable 4 Threats Shelter Food Probability of Occupancy

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

21. Priority Species

22. 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

23. Species-Habitat Relationships Biological & Data Limits

24. Species-Habitat Information Prob( ) Microhabitat Landscape Literature Experts Field/GIS Data Biological Limits Behavioral Preferences Threats

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

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

27. 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

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

29. Experts: Distance to Open Water • 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)

30. Population Objectives

31. 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

32. Use Detection History Prob ( ) • Distinguish probability of detection from probability of occupancy 00010 01010 00000

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

34. Influence Diagram & Belief Network

35. Influence Diagram & Belief Network

36. Influence Diagram & Belief Network P (Encounter Site)

37. Suitable Unsuitable

38. Location = Suitable, Confident

39. Location = Unsuitable, Confident

40. Unsuitable, Less Confident

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

42. 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 Pilot Model Summary

43. 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

44. 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