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Multiscale consensus habitat modeling for landscape level conservation prioritization

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Poor, Erin E., Scheick, Brian K., Mullinax, Jennifer M.·2020·Scientific Reports
DOI 10.1038/s41598-020-74716-3PMC PMC7576151
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Abstract

Globally, wide-ranging carnivore populations are imperiled due to human-caused habitat fragmentation. Where populations are fragmented, habitat quantification is often the first step in conservation. Presence-only species distribution models can provide robust results when proper scales and data are considered. We aimed to identify habitat for a fragmented carnivore population at two scales and aid conservation prioritization by identifying potential future habitat fragmentation. We used location data and environmental variables to develop a consensus model using Maxent and Mahalanobis distance to identify black bear (Ursus americanus floridanus) habitat across Florida, USA. We compared areas of habitat to areas of predicted sea level rise, development, and protected areas. Local-scale models performed better than state-scale models. We identified 23,798 km 2 of habitat at the local-scale and 45,703 km 2 at the state-scale. Approximately 10% of state- and 14% of local-scale habitat may be inundated by 2100, 16% of state- and 7% of local-scale habitat may be developed, and 54% of state- and 15% of local-scale habitat is unprotected. Results suggest habitat is at risk of fragmentation. Lack of focused conservation and connectivity among bear subpopulations could further fragmentation, and ultimately threaten population stability as seen in other fragmented carnivore populations globally.

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Figure 1 Florida black bear (Ursus americanus floridanus) locations. Location of study bear locations, major roads, cities and bear management units (BMUs) throughout Florida. Created using ArcMap 10.4 (Esri 2015).
Figure 1 Florida black bear (Ursus americanus floridanus) locations. Location of study bear locations, major roads, cities and bear management units (BMUs) throughout Florida. Created using ArcMap 10.4 (Esri 2015).CC BY · PubMed Central
Figure 2 Modeled Florida black bear (Ursus americanus floridanus) habitat throughout Florida. Consensus model of black bear habitat suitability as modeled statewide (a) and at the bear management unit, or local-scale (b) using Maxent and Mahalanobis distance models. Cumulative frequency distribution values in 10% intervals, (each interval contained a cumulative percentage of the bear locations). For example, the 80% binned cells are 10% more likely to contain a bear location than the 70% bin and 70% more likely to contain a bear location than the 10% bin. Created using ArcMap 10.4 (Esri 2015).
Figure 2 Modeled Florida black bear (Ursus americanus floridanus) habitat throughout Florida. Consensus model of black bear habitat suitability as modeled statewide (a) and at the bear management unit, or local-scale (b) using Maxent and Mahalanobis distance models. Cumulative frequency distribution values in 10% intervals, (each interval contained a cumulative percentage of the bear locations). For example, the 80% binned cells are 10% more likely to contain a bear location than the 70% bin and 70% more likely to contain a bear location than the 10% bin. Created using ArcMap 10.4 (Esri 2015).CC BY · PubMed Central
Figure 3 Florida black bear (Ursus americanus floridanus) habitat and impacts of inundation, development, and projection. Florida black bear habitat identified at the state- and local-scale using an average maximum testing sensitivity plus specificity threshold from Maxent habitat models (a), areas of all combined habitat potentially inundated under 30 cm and 305 cm sea level scenarios (b), habitat that intersects with potential development, from a 2070, business-as-usual scenario (c), and habitat that is not under county, state, or federal protection (d). Created using ArcMap 10.4 (Esri 2015).
Figure 3 Florida black bear (Ursus americanus floridanus) habitat and impacts of inundation, development, and projection. Florida black bear habitat identified at the state- and local-scale using an average maximum testing sensitivity plus specificity threshold from Maxent habitat models (a), areas of all combined habitat potentially inundated under 30 cm and 305 cm sea level scenarios (b), habitat that intersects with potential development, from a 2070, business-as-usual scenario (c), and habitat that is not under county, state, or federal protection (d). Created using ArcMap 10.4 (Esri 2015).CC BY · PubMed Central

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Metadata

Authors
Poor, E. E., Scheick, B. K., & Mullinax, J. M.
Affiliations
  • University of Maryland, College Park (US)
  • Florida Fish and Wildlife Conservation Commission (US)
Corresponding author
Mullinax, Jennifer M.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2020-10-20
Volume
10
Issue
1
Pages
17783-17783
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-74716-3
PMC
PMC7576151
OpenAlex
W3094055716
Cited by
27
References
2
Citations by year
2026: 3 · 2025: 6 · 2024: 3 · 2023: 5 · 2022: 6 · 2021: 4
FWCI
6.71
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • University of Kentucky
  • University of Florida
Topics
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Physical Sciences › Environmental Science › Ecology › Wildlife-Road Interactions and Conservation
Keywords
Ursus americanus, habitat modeling, habitat fragmentation, conservation prioritization, multiscale analysis, sea level rise, development pressure, protected areas, carnivore conservation, habitat connectivity, species distribution modeling, Florida
MeSH terms
Animals, Ursidae, Computer Simulation, Conservation of Natural Resources, Humans, Population Dynamics, United States, Ecosystem, Animal Distribution

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Poor, E. E., Scheick, B. K., & Mullinax, J. M. (2020). Multiscale consensus habitat modeling for landscape level conservation prioritization. Scientific Reports. https://doi.org/10.1038/s41598-020-74716-3