Spatially Explicit Individual-Based Models

1. Spatially Explicit Individual-Based Models What are they and can they be useful for habitat management? Gina K. Himes Boor Montana State University Christine Damiani Institute for Wildlife Studies March 2018 National Military Fish and Wildlife Association & North American Wildlife & Natural Resource Conference

2. Spatially Explicit Individual-Based Models SEIBMs IBMs Agent-based models Individual-based simulation models Spatially Explicit Population Models (SEPM)

3. Larger Project: Endangered butterflies as a model system for managing source-sink dynamics on Department of Defense lands Principle Investigator: Dr. Elizabeth Crone

4. Sent to: • 115 managers • 90 DoD installations • Responses from: • 27 managers • 26 installations SEIBM Managers Survey Map source: Congressional Research Service

5. Survey Responses:SEIBM Comments/Questions What can they do? Give us some examples! What data do we need? Map source: Congressional Research Service

6. Population-Level ModelsvsIndividual-Based Models

7. Population Models vs. Individual-Based Models Current Population Size Future Population Size?

8. Population Models vs. Individual-Based Models NTλ = NT+1 Growth Rate Population Size at Time T+1 Population Size at Time T

9. Population Models vs. Individual-Based Models NTλ = NT+1 Population-Level Population-Level

10. Population Models vs. Individual-Based Models Population or Landscape-Level Patterns Individuals

11. Population Models vs. Individual-Based Models

12. Population Models vs. Individual-Based Models

13. Red-Cockaded Woodpecker SEIBM Letcher, B. H., J. A. Priddy, J. R. Walters, and L. B. Crowder. 1998. An individual-based, spatially-explicit simulation model of the population dynamics of the endangered red-cockaded woodpecker, Picoides borealis. Biological Conservation 86:1-14.

14. Management questions • Restoration planning • Recovery planning • Impact assessment and mitigation • Habitat creation planning • Re-introduction or translocation planning • Fire regime planning

15. More Examples J Balke

16. Fender’s Blue Butterfly SEIBM What is the best fire regime for maintaining the habitat for this species? Lake

17. Fender’s Blue Butterfly SEIBM Lake

18. Fender’s Blue Butterfly SEIBM • Data Used • Habitat: • 3 habitat types (lupine, prairie, forest) • Individual butterfly data: • Lifespan • Move distance • Turn angle • Residence time • Population data: • Growth rate by time since fire

19. Fender’s Blue Butterfly SEIBM Best Smokey et al. (in prep for Landscape Ecology)

20. Taylor’s Checkerspot Butterfly SEIBM What is the best restoration strategy given this species’ boom-bust dynamics? D. Stinson

21. Taylor’s Checkerspot Butterfly SEIBM D. Stinson

22. Taylor’s Checkerspot Butterfly SEIBM • Data Used: • Habitat: • 3 habitat types (prairie, field/exurban, forest) • Individual butterfly data: • Move/rest probability S • Rest duration S • Move distance S • Turn angle S • Adult survival S • Egg & Larval survival S • Oviposition probability S • Nest size S • Population data: • Growth rate • General growth pattern and variability (i.e., boom-bust pattern) D. Stinson S = data from surrogate species

23. Taylor’s Checkerspot Butterfly SEIBM Best Restoration Scenarios Exogenous Endogenous D. Stinson Himes Boor et al. 2018 Ecological Applications

24. Can SEIBMs be useful to managers?

25. Can SEIBMs be useful to managers? Maybe

26. Can SEIBMs be useful to managers? • Data intensive • Address relevant questions • Becoming more common (lots of examples) • Ideal for species with social and/or spatial structure

27. Thank you! Gina K. Himes Boor (ghimesboor@montana.edu) Co-Author: Christine Damiani (longicarpus@yahoo.com) Collaborators: Elizabeth Crone (PI), Tufts University William Morris (co-PI), Duke University Cheryl Schultz (co-PI), Washington State University Brian Hudgens (co-PI), Institute for Wildlife Studies Funding:

29. Saved, not-used slides

30. Survey Responses:Types of Data Collected

31. Survey Responses:Experience & Interest in SEIBMs

34. Examples of SEIBMs • Chapron et al. 2016 – Scandinavian wolves, conversion factor, packs to total pop size • Stenglein et al. 2015 – MN wolves, simulated harvest scenarios to understand risk to population & identify an appropriate harvest level • Fedriani et al. 2017 – what spatial arrangement to plant trees to facilitate restoration through frugivore seed dispersal • Warwick-Evans et al. 2018 – predict the impacts of offshore wind farms on seabird populations • Letcher et al. 1998 - red-cockaded woodpecker; spatial distribution of territories was just as important as number of territories in predicting resilience to environmental perturbations

35. Population-Level Models Demographic Models Habitat Models Resource selection function (RSF) Habitat suitability index Ecological niche modeling • Exponential or logistic growth model • Demographic matrix model • Population viability analysis (PVA)

36. Survey Responses:CurrentHabitat Management Practices

37. Survey Responses:CurrentHabitat Management Questions