Delineating important killer whale foraging areas using a spatiotemporal logistic model
Conservation management planning for highly mobile species requires an understanding of the distribution of areas that are biologically important to the species of concern. Collecting data on the locations of animal behaviors linked to biological characteristics, such as foraging, can be used to spa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Global Ecology and Conservation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235198942300361X |
| _version_ | 1797615909682020352 |
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| author | Eva H. Stredulinsky Scott Toews Joe Watson Dawn P. Noren Marla M. Holt Sheila J. Thornton |
| author_facet | Eva H. Stredulinsky Scott Toews Joe Watson Dawn P. Noren Marla M. Holt Sheila J. Thornton |
| author_sort | Eva H. Stredulinsky |
| collection | DOAJ |
| description | Conservation management planning for highly mobile species requires an understanding of the distribution of areas that are biologically important to the species of concern. Collecting data on the locations of animal behaviors linked to biological characteristics, such as foraging, can be used to spatially describe biological important areas. However, spatial modeling of free-ranging animal behavior can be challenging, as behavioral observations of animals are often clustered, and sampling is commonly conducted at a higher frequency than changes in behavioral states, resulting in data that are usually highly autocorrelated in space and time. Here, we fit latent Gaussian process models to observational behavioral data to generate spatially-explicit predictions of foraging behavior within the critical habitat of an endangered population of fish-eating killer whales (Orcinus orca) in southern British Columbia, Canada, and northern Washington State, USA. We compare spatial models treating temporal autocorrelation in behavior in three ways: (1) ignoring temporal autocorrelation entirely; (2) traditional data-thinning to remove temporal autocorrelation, and; (3) using a temporal Gaussian process to account for temporal autocorrelation. Comparisons of autocorrelative structures for each model and visual comparison of broad spatial patterns demonstrate that our third approach yields more accurate results than when ignoring temporal autocorrelation entirely and higher precision results than when applying data-thinning methods. Within the identified areas of critical habitat, our models indicate two primary regions of intense killer whale foraging activity, and we delineate areas wherein the probability of foraging was particularly high as candidate locations for conservation management actions. This study underscores the value of refining our understanding of high-use areas for at-risk species by incorporating animal behavior data to inform area-based conservation measures. |
| first_indexed | 2024-03-11T07:33:41Z |
| format | Article |
| id | doaj.art-304d88fd206843a19aaff0d017ce3980 |
| institution | Directory Open Access Journal |
| issn | 2351-9894 |
| language | English |
| last_indexed | 2024-03-11T07:33:41Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Ecology and Conservation |
| spelling | doaj.art-304d88fd206843a19aaff0d017ce39802023-11-17T05:27:12ZengElsevierGlobal Ecology and Conservation2351-98942023-12-0148e02726Delineating important killer whale foraging areas using a spatiotemporal logistic modelEva H. Stredulinsky0Scott Toews1Joe Watson2Dawn P. Noren3Marla M. Holt4Sheila J. Thornton5Marine Mammal Conservation Physiology program, Pacific Science Enterprise Centre, Fisheries and Oceans Canada, 4160 Marine Drive, West Vancouver, BC V7V 1N6, CanadaMarine Mammal Conservation Physiology program, Pacific Science Enterprise Centre, Fisheries and Oceans Canada, 4160 Marine Drive, West Vancouver, BC V7V 1N6, CanadaStock Assessment and Research Division, Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 6N7, CanadaConservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, WA 98112, United StatesConservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, WA 98112, United StatesMarine Mammal Conservation Physiology program, Pacific Science Enterprise Centre, Fisheries and Oceans Canada, 4160 Marine Drive, West Vancouver, BC V7V 1N6, Canada; Correspondence to: Fisheries and Oceans Canada, Pacific Science Enterprise Centre, 4160 Marine Drive, West Vancouver V7V 1N6 Canada.Conservation management planning for highly mobile species requires an understanding of the distribution of areas that are biologically important to the species of concern. Collecting data on the locations of animal behaviors linked to biological characteristics, such as foraging, can be used to spatially describe biological important areas. However, spatial modeling of free-ranging animal behavior can be challenging, as behavioral observations of animals are often clustered, and sampling is commonly conducted at a higher frequency than changes in behavioral states, resulting in data that are usually highly autocorrelated in space and time. Here, we fit latent Gaussian process models to observational behavioral data to generate spatially-explicit predictions of foraging behavior within the critical habitat of an endangered population of fish-eating killer whales (Orcinus orca) in southern British Columbia, Canada, and northern Washington State, USA. We compare spatial models treating temporal autocorrelation in behavior in three ways: (1) ignoring temporal autocorrelation entirely; (2) traditional data-thinning to remove temporal autocorrelation, and; (3) using a temporal Gaussian process to account for temporal autocorrelation. Comparisons of autocorrelative structures for each model and visual comparison of broad spatial patterns demonstrate that our third approach yields more accurate results than when ignoring temporal autocorrelation entirely and higher precision results than when applying data-thinning methods. Within the identified areas of critical habitat, our models indicate two primary regions of intense killer whale foraging activity, and we delineate areas wherein the probability of foraging was particularly high as candidate locations for conservation management actions. This study underscores the value of refining our understanding of high-use areas for at-risk species by incorporating animal behavior data to inform area-based conservation measures.http://www.sciencedirect.com/science/article/pii/S235198942300361XKiller whaleSpecies at riskSpatial analysisTemporal autocorrelationArea-based managementForaging |
| spellingShingle | Eva H. Stredulinsky Scott Toews Joe Watson Dawn P. Noren Marla M. Holt Sheila J. Thornton Delineating important killer whale foraging areas using a spatiotemporal logistic model Global Ecology and Conservation Killer whale Species at risk Spatial analysis Temporal autocorrelation Area-based management Foraging |
| title | Delineating important killer whale foraging areas using a spatiotemporal logistic model |
| title_full | Delineating important killer whale foraging areas using a spatiotemporal logistic model |
| title_fullStr | Delineating important killer whale foraging areas using a spatiotemporal logistic model |
| title_full_unstemmed | Delineating important killer whale foraging areas using a spatiotemporal logistic model |
| title_short | Delineating important killer whale foraging areas using a spatiotemporal logistic model |
| title_sort | delineating important killer whale foraging areas using a spatiotemporal logistic model |
| topic | Killer whale Species at risk Spatial analysis Temporal autocorrelation Area-based management Foraging |
| url | http://www.sciencedirect.com/science/article/pii/S235198942300361X |
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