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Explore Spatially Explicit Individual-Based Models (SEIBMs) and their role in managing endangered species habitats like butterflies. Learn how SEIBMs help plan restoration, recovery, and impact mitigation strategies. Discover the importance of data in habitat creation and fire regime planning. Find out about best strategies for maintaining species habitats. See examples of SEIBMs in practice with Fender’s Blue Butterfly and Taylor’s Checkerspot Butterfly. Understand the relevance of SEIBMs to wildlife managers and their increasing use in addressing spatial and social structured species. Contact the authors for more information.
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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
Spatially Explicit Individual-Based Models SEIBMs IBMs Agent-based models Individual-based simulation models Spatially Explicit Population Models (SEPM)
Larger Project: Endangered butterflies as a model system for managing source-sink dynamics on Department of Defense lands Principle Investigator: Dr. Elizabeth Crone
Sent to: • 115 managers • 90 DoD installations • Responses from: • 27 managers • 26 installations SEIBM Managers Survey Map source: Congressional Research Service
Survey Responses:SEIBM Comments/Questions What can they do? Give us some examples! What data do we need? Map source: Congressional Research Service
Population Models vs. Individual-Based Models Current Population Size Future Population Size?
Population Models vs. Individual-Based Models NTλ = NT+1 Growth Rate Population Size at Time T+1 Population Size at Time T
Population Models vs. Individual-Based Models NTλ = NT+1 Population-Level Population-Level
Population Models vs. Individual-Based Models Population or Landscape-Level Patterns Individuals
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.
Management questions • Restoration planning • Recovery planning • Impact assessment and mitigation • Habitat creation planning • Re-introduction or translocation planning • Fire regime planning
More Examples J Balke
Fender’s Blue Butterfly SEIBM What is the best fire regime for maintaining the habitat for this species? Lake
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
Fender’s Blue Butterfly SEIBM Best Smokey et al. (in prep for Landscape Ecology)
Taylor’s Checkerspot Butterfly SEIBM What is the best restoration strategy given this species’ boom-bust dynamics? D. Stinson
Taylor’s Checkerspot Butterfly SEIBM D. Stinson
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
Taylor’s Checkerspot Butterfly SEIBM Best Restoration Scenarios Exogenous Endogenous D. Stinson Himes Boor et al. 2018 Ecological Applications
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
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:
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
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)
