Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping
Marine protected areas (MPAs) underpin the sustainable management of marine ecosystems but require accurate knowledge of species distributions. Recently, advances in tracking technology and habitat modelling have enabled the production of large-scale species distribution models (SDM), which provide...
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ftuglasgow:oai:eprints.gla.ac.uk:209651 2023-05-15T13:12:18+02:00 Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping Cleasby, Ian R. Owen, Ellie Wilson, Linda Wakefield, Ewan D. O'Connell, Peadar Bolton, Mark 2020-01 text https://eprints.gla.ac.uk/209651/ https://eprints.gla.ac.uk/209651/1/209651.pdf en eng Elsevier https://eprints.gla.ac.uk/209651/1/209651.pdf Cleasby, I. R., Owen, E., Wilson, L., Wakefield, E. D. <http://eprints.gla.ac.uk/view/author/31199.html> , O'Connell, P. and Bolton, M. (2020) Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping. Biological Conservation <https://eprints.gla.ac.uk/view/journal_volume/Biological_Conservation.html>, 241, 108375. (doi:10.1016/j.biocon.2019.108375 <https://doi.org/10.1016/j.biocon.2019.108375>) cc_by_4 CC-BY Articles PeerReviewed 2020 ftuglasgow https://doi.org/10.1016/j.biocon.2019.108375 2022-09-22T22:15:42Z Marine protected areas (MPAs) underpin the sustainable management of marine ecosystems but require accurate knowledge of species distributions. Recently, advances in tracking technology and habitat modelling have enabled the production of large-scale species distribution models (SDM), which provide the basis for hotspot mapping. In the UK, hotspot mapping to inform seabird MPA identification has involved converting observed or predicted distributions to polygons using either Maximum Curvature or Getis-Ord (Gi*) analysis. Here, we apply both mapping techniques to UK-wide, breeding season SDM predictions for four seabird species (Black-legged Kittiwakes Rissa tridactyla, Common Guillemots Uria aalge, Razorbills Alca torda and European Shags Phalacrocorax aristotelis) in order to compare their performance and inform seabird MPA. When using Maximum Curvature, grid cells within the identified maximum curvature boundaries were defined as hotspots. For Getis-Ord analysis, we defined hotspots as either (1) grid cells containing the top 1% or (2) the top 5% Gi* scores or (3) cells in which Gi* scores were statistically significant. Hotspots based upon Maximum Curvature or statistically significant Gi* scores covered the greatest area and were generally larger than current marine Special Protection Areas. Hotspots based on the top 1% or top 5% of Gi* scores were smaller and were concentrated around the largest breeding colonies. All hotspot methods consistently identified several high-density areas that should be prioritised for seabird conservation. Ultimately, the choice of hotspot identification method should be informed by considering species ecology alongside conservation goals to ensure hotspots are of sufficient size to protect target populations. Article in Journal/Newspaper Alca torda rissa tridactyla Uria aalge uria University of Glasgow: Enlighten - Publications Biological Conservation 241 108375 |
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University of Glasgow: Enlighten - Publications |
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ftuglasgow |
language |
English |
description |
Marine protected areas (MPAs) underpin the sustainable management of marine ecosystems but require accurate knowledge of species distributions. Recently, advances in tracking technology and habitat modelling have enabled the production of large-scale species distribution models (SDM), which provide the basis for hotspot mapping. In the UK, hotspot mapping to inform seabird MPA identification has involved converting observed or predicted distributions to polygons using either Maximum Curvature or Getis-Ord (Gi*) analysis. Here, we apply both mapping techniques to UK-wide, breeding season SDM predictions for four seabird species (Black-legged Kittiwakes Rissa tridactyla, Common Guillemots Uria aalge, Razorbills Alca torda and European Shags Phalacrocorax aristotelis) in order to compare their performance and inform seabird MPA. When using Maximum Curvature, grid cells within the identified maximum curvature boundaries were defined as hotspots. For Getis-Ord analysis, we defined hotspots as either (1) grid cells containing the top 1% or (2) the top 5% Gi* scores or (3) cells in which Gi* scores were statistically significant. Hotspots based upon Maximum Curvature or statistically significant Gi* scores covered the greatest area and were generally larger than current marine Special Protection Areas. Hotspots based on the top 1% or top 5% of Gi* scores were smaller and were concentrated around the largest breeding colonies. All hotspot methods consistently identified several high-density areas that should be prioritised for seabird conservation. Ultimately, the choice of hotspot identification method should be informed by considering species ecology alongside conservation goals to ensure hotspots are of sufficient size to protect target populations. |
format |
Article in Journal/Newspaper |
author |
Cleasby, Ian R. Owen, Ellie Wilson, Linda Wakefield, Ewan D. O'Connell, Peadar Bolton, Mark |
spellingShingle |
Cleasby, Ian R. Owen, Ellie Wilson, Linda Wakefield, Ewan D. O'Connell, Peadar Bolton, Mark Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
author_facet |
Cleasby, Ian R. Owen, Ellie Wilson, Linda Wakefield, Ewan D. O'Connell, Peadar Bolton, Mark |
author_sort |
Cleasby, Ian R. |
title |
Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
title_short |
Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
title_full |
Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
title_fullStr |
Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
title_full_unstemmed |
Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
title_sort |
identifying important at-sea areas for seabirds using species distribution models and hotspot mapping |
publisher |
Elsevier |
publishDate |
2020 |
url |
https://eprints.gla.ac.uk/209651/ https://eprints.gla.ac.uk/209651/1/209651.pdf |
genre |
Alca torda rissa tridactyla Uria aalge uria |
genre_facet |
Alca torda rissa tridactyla Uria aalge uria |
op_relation |
https://eprints.gla.ac.uk/209651/1/209651.pdf Cleasby, I. R., Owen, E., Wilson, L., Wakefield, E. D. <http://eprints.gla.ac.uk/view/author/31199.html> , O'Connell, P. and Bolton, M. (2020) Identifying important at-sea areas for seabirds using species distribution models and hotspot mapping. Biological Conservation <https://eprints.gla.ac.uk/view/journal_volume/Biological_Conservation.html>, 241, 108375. (doi:10.1016/j.biocon.2019.108375 <https://doi.org/10.1016/j.biocon.2019.108375>) |
op_rights |
cc_by_4 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1016/j.biocon.2019.108375 |
container_title |
Biological Conservation |
container_volume |
241 |
container_start_page |
108375 |
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1766251253875081216 |