Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models
Abstract Aim Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote‐sensing and ensemble ecological niche models ( EENM s)...
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crwiley:10.1111/ddi.12389 2024-09-30T14:33:12+00:00 Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models Scales, Kylie L. Miller, Peter I. Ingram, Simon N. Hazen, Elliott L. Bograd, Steven J. Phillips, Richard A. Thuiller, Wilfried Natural Environment Research Council 2015 http://dx.doi.org/10.1111/ddi.12389 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.12389 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12389 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Diversity and Distributions volume 22, issue 2, page 212-224 ISSN 1366-9516 1472-4642 journal-article 2015 crwiley https://doi.org/10.1111/ddi.12389 2024-09-17T04:49:47Z Abstract Aim Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote‐sensing and ensemble ecological niche models ( EENM s) to identify predictable foraging habitats for a globally important population of the grey‐headed albatross ( GHA ) Thalassarche chrysostoma . Location Bird Island, South Georgia; Southern Atlantic Ocean. Methods GPS and geolocation‐immersion loggers were used to track at‐sea movements and activity patterns of GHA over two breeding seasons ( n = 55; brood‐guard). Immersion frequency (landings per 10‐min interval) was used to define foraging events. EENM combining Generalized Additive Models ( GAM ), MaxEnt, Random Forest ( RF ) and Boosted Regression Trees ( BRT ) identified the biophysical conditions characterizing the locations of foraging events, using time‐matched oceanographic predictors (Sea Surface Temperature, SST chlorophyll a , chl‐ a thermal front frequency, TF req depth). Model performance was assessed through iterative cross‐validation and extrapolative performance through cross‐validation among years. Results Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl‐ a > 0.5 mg m −3 ) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi‐model ensembles was superior to that of single‐algorithm models, and cross‐validation among years indicated reasonable extrapolative performance. Main conclusions EENM ... Article in Journal/Newspaper Bird Island Wiley Online Library Bird Island ENVELOPE(-38.060,-38.060,-54.004,-54.004) Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Diversity and Distributions 22 2 212 224 |
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Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
Abstract Aim Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote‐sensing and ensemble ecological niche models ( EENM s) to identify predictable foraging habitats for a globally important population of the grey‐headed albatross ( GHA ) Thalassarche chrysostoma . Location Bird Island, South Georgia; Southern Atlantic Ocean. Methods GPS and geolocation‐immersion loggers were used to track at‐sea movements and activity patterns of GHA over two breeding seasons ( n = 55; brood‐guard). Immersion frequency (landings per 10‐min interval) was used to define foraging events. EENM combining Generalized Additive Models ( GAM ), MaxEnt, Random Forest ( RF ) and Boosted Regression Trees ( BRT ) identified the biophysical conditions characterizing the locations of foraging events, using time‐matched oceanographic predictors (Sea Surface Temperature, SST chlorophyll a , chl‐ a thermal front frequency, TF req depth). Model performance was assessed through iterative cross‐validation and extrapolative performance through cross‐validation among years. Results Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl‐ a > 0.5 mg m −3 ) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi‐model ensembles was superior to that of single‐algorithm models, and cross‐validation among years indicated reasonable extrapolative performance. Main conclusions EENM ... |
author2 |
Thuiller, Wilfried Natural Environment Research Council |
format |
Article in Journal/Newspaper |
author |
Scales, Kylie L. Miller, Peter I. Ingram, Simon N. Hazen, Elliott L. Bograd, Steven J. Phillips, Richard A. |
spellingShingle |
Scales, Kylie L. Miller, Peter I. Ingram, Simon N. Hazen, Elliott L. Bograd, Steven J. Phillips, Richard A. Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
author_facet |
Scales, Kylie L. Miller, Peter I. Ingram, Simon N. Hazen, Elliott L. Bograd, Steven J. Phillips, Richard A. |
author_sort |
Scales, Kylie L. |
title |
Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
title_short |
Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
title_full |
Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
title_fullStr |
Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
title_full_unstemmed |
Identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
title_sort |
identifying predictable foraging habitats for a wide‐ranging marine predator using ensemble ecological niche models |
publisher |
Wiley |
publishDate |
2015 |
url |
http://dx.doi.org/10.1111/ddi.12389 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fddi.12389 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ddi.12389 |
long_lat |
ENVELOPE(-38.060,-38.060,-54.004,-54.004) ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Bird Island Gam |
geographic_facet |
Bird Island Gam |
genre |
Bird Island |
genre_facet |
Bird Island |
op_source |
Diversity and Distributions volume 22, issue 2, page 212-224 ISSN 1366-9516 1472-4642 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/ddi.12389 |
container_title |
Diversity and Distributions |
container_volume |
22 |
container_issue |
2 |
container_start_page |
212 |
op_container_end_page |
224 |
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1811637169098326016 |