Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species
Abstract Aim Advances in ecological and environmental modelling offer new opportunities for estimating dynamic habitat suitability for highly mobile species and supporting management strategies at relevant spatiotemporal scales. We used an ensemble modelling approach to predict daily, year‐round hab...
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Online Access: | http://dx.doi.org/10.1111/ddi.12940 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12940 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ddi.12940 |
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crwiley:10.1111/ddi.12940 2024-09-09T19:31:37+00:00 Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species Abrahms, Briana Welch, Heather Brodie, Stephanie Jacox, Michael G. Becker, Elizabeth A. Bograd, Steven J. Irvine, Ladd M. Palacios, Daniel M. Mate, Bruce R. Hazen, Elliott L. Beger, Maria Alfred P. Sloan Foundation Gordon and Betty Moore Foundation Office of Naval Research National Aeronautics and Space Administration David and Lucile Packard Foundation 2019 http://dx.doi.org/10.1111/ddi.12940 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12940 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ddi.12940 en eng Wiley http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ Diversity and Distributions volume 25, issue 8, page 1182-1193 ISSN 1366-9516 1472-4642 journal-article 2019 crwiley https://doi.org/10.1111/ddi.12940 2024-07-30T04:20:56Z Abstract Aim Advances in ecological and environmental modelling offer new opportunities for estimating dynamic habitat suitability for highly mobile species and supporting management strategies at relevant spatiotemporal scales. We used an ensemble modelling approach to predict daily, year‐round habitat suitability for a migratory species, the blue whale ( Balaenoptera musculus ), and demonstrate an application for evaluating the spatiotemporal dynamics of their exposure to ship strike risk. Location The California Current Ecosystem (CCE) and the Southern California Bight (SCB), USA. Methods We integrated a long‐term (1994–2008) satellite tracking dataset on 104 blue whales with data‐assimilative ocean model output to assess year‐round habitat suitability. We evaluated the relative utility of ensembling multiple model types compared to using single models, and selected and validated candidate models using multiple cross‐validation metrics and independent observer data. We quantified the spatial and temporal distribution of exposure to ship strike risk within shipping lanes in the SCB. Results Multi‐model ensembles outperformed single‐model approaches. The final ensemble model had high predictive skill (AUC = 0.95), resulting in daily, year‐round predictions of blue whale habitat suitability in the CCE that accurately captured migratory behaviour. Risk exposure in shipping lanes was highly variable within and among years as a function of environmental conditions (e.g., marine heatwave). Main conclusions Daily information on three‐dimensional oceanic habitats was used to model the daily distribution of a highly migratory species with high predictive power and indicated that management strategies could benefit by incorporating dynamic environmental information. This approach is readily transferable to other species. Dynamic, high‐resolution species distribution models are valuable tools for assessing risk exposure and targeting management needs. Article in Journal/Newspaper Balaenoptera musculus Blue whale Wiley Online Library Lanes ENVELOPE(18.933,18.933,69.617,69.617) Diversity and Distributions 25 8 1182 1193 |
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Open Polar |
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Wiley Online Library |
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crwiley |
language |
English |
description |
Abstract Aim Advances in ecological and environmental modelling offer new opportunities for estimating dynamic habitat suitability for highly mobile species and supporting management strategies at relevant spatiotemporal scales. We used an ensemble modelling approach to predict daily, year‐round habitat suitability for a migratory species, the blue whale ( Balaenoptera musculus ), and demonstrate an application for evaluating the spatiotemporal dynamics of their exposure to ship strike risk. Location The California Current Ecosystem (CCE) and the Southern California Bight (SCB), USA. Methods We integrated a long‐term (1994–2008) satellite tracking dataset on 104 blue whales with data‐assimilative ocean model output to assess year‐round habitat suitability. We evaluated the relative utility of ensembling multiple model types compared to using single models, and selected and validated candidate models using multiple cross‐validation metrics and independent observer data. We quantified the spatial and temporal distribution of exposure to ship strike risk within shipping lanes in the SCB. Results Multi‐model ensembles outperformed single‐model approaches. The final ensemble model had high predictive skill (AUC = 0.95), resulting in daily, year‐round predictions of blue whale habitat suitability in the CCE that accurately captured migratory behaviour. Risk exposure in shipping lanes was highly variable within and among years as a function of environmental conditions (e.g., marine heatwave). Main conclusions Daily information on three‐dimensional oceanic habitats was used to model the daily distribution of a highly migratory species with high predictive power and indicated that management strategies could benefit by incorporating dynamic environmental information. This approach is readily transferable to other species. Dynamic, high‐resolution species distribution models are valuable tools for assessing risk exposure and targeting management needs. |
author2 |
Beger, Maria Alfred P. Sloan Foundation Gordon and Betty Moore Foundation Office of Naval Research National Aeronautics and Space Administration David and Lucile Packard Foundation |
format |
Article in Journal/Newspaper |
author |
Abrahms, Briana Welch, Heather Brodie, Stephanie Jacox, Michael G. Becker, Elizabeth A. Bograd, Steven J. Irvine, Ladd M. Palacios, Daniel M. Mate, Bruce R. Hazen, Elliott L. |
spellingShingle |
Abrahms, Briana Welch, Heather Brodie, Stephanie Jacox, Michael G. Becker, Elizabeth A. Bograd, Steven J. Irvine, Ladd M. Palacios, Daniel M. Mate, Bruce R. Hazen, Elliott L. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
author_facet |
Abrahms, Briana Welch, Heather Brodie, Stephanie Jacox, Michael G. Becker, Elizabeth A. Bograd, Steven J. Irvine, Ladd M. Palacios, Daniel M. Mate, Bruce R. Hazen, Elliott L. |
author_sort |
Abrahms, Briana |
title |
Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
title_short |
Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
title_full |
Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
title_fullStr |
Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
title_full_unstemmed |
Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
title_sort |
dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species |
publisher |
Wiley |
publishDate |
2019 |
url |
http://dx.doi.org/10.1111/ddi.12940 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12940 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ddi.12940 |
long_lat |
ENVELOPE(18.933,18.933,69.617,69.617) |
geographic |
Lanes |
geographic_facet |
Lanes |
genre |
Balaenoptera musculus Blue whale |
genre_facet |
Balaenoptera musculus Blue whale |
op_source |
Diversity and Distributions volume 25, issue 8, page 1182-1193 ISSN 1366-9516 1472-4642 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1111/ddi.12940 |
container_title |
Diversity and Distributions |
container_volume |
25 |
container_issue |
8 |
container_start_page |
1182 |
op_container_end_page |
1193 |
_version_ |
1809900481032486912 |