Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models
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 (EENMs) to identi...
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2016
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Online Access: | http://plymsea.ac.uk/id/eprint/6724/ http://plymsea.ac.uk/id/eprint/6724/1/KScales_D%2BD_2016_albatross_fronts_author.pdf https://doi.org/10.1111/ddi.12389 |
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ftplymouthml:oai:plymsea.ac.uk:6724 2023-05-15T15:44:44+02:00 Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models Scales, KL Miller, PI Ingram, SN Hazen, EL Bograd, SJ Phillips, RA 2016-02 text http://plymsea.ac.uk/id/eprint/6724/ http://plymsea.ac.uk/id/eprint/6724/1/KScales_D%2BD_2016_albatross_fronts_author.pdf https://doi.org/10.1111/ddi.12389 en eng http://plymsea.ac.uk/id/eprint/6724/1/KScales_D%2BD_2016_albatross_fronts_author.pdf Scales, KL; Miller, PI; Ingram, SN; Hazen, EL; Bograd, SJ; Phillips, RA. 2016 Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models. Diversity and Distributions, 22 (2). 212-224. https://doi.org/10.1111/ddi.12389 <https://doi.org/10.1111/ddi.12389> cc_by_nc_4 CC-BY-NC Conservation Earth Observation - Remote Sensing Ecology and Environment Oceanography Publication - Article PeerReviewed 2016 ftplymouthml https://doi.org/10.1111/ddi.12389 2022-09-13T05:48:45Z 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 (EENMs) 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, TFreq; 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 techniques are useful for ... Article in Journal/Newspaper Bird Island Plymouth Marine Science Electronic Archive (PlyMSEA - Plymouth Marine Laboratory, PML) 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 |
institution |
Open Polar |
collection |
Plymouth Marine Science Electronic Archive (PlyMSEA - Plymouth Marine Laboratory, PML) |
op_collection_id |
ftplymouthml |
language |
English |
topic |
Conservation Earth Observation - Remote Sensing Ecology and Environment Oceanography |
spellingShingle |
Conservation Earth Observation - Remote Sensing Ecology and Environment Oceanography Scales, KL Miller, PI Ingram, SN Hazen, EL Bograd, SJ Phillips, RA Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models |
topic_facet |
Conservation Earth Observation - Remote Sensing Ecology and Environment Oceanography |
description |
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 (EENMs) 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, TFreq; 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 techniques are useful for ... |
format |
Article in Journal/Newspaper |
author |
Scales, KL Miller, PI Ingram, SN Hazen, EL Bograd, SJ Phillips, RA |
author_facet |
Scales, KL Miller, PI Ingram, SN Hazen, EL Bograd, SJ Phillips, RA |
author_sort |
Scales, KL |
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 |
publishDate |
2016 |
url |
http://plymsea.ac.uk/id/eprint/6724/ http://plymsea.ac.uk/id/eprint/6724/1/KScales_D%2BD_2016_albatross_fronts_author.pdf https://doi.org/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_relation |
http://plymsea.ac.uk/id/eprint/6724/1/KScales_D%2BD_2016_albatross_fronts_author.pdf Scales, KL; Miller, PI; Ingram, SN; Hazen, EL; Bograd, SJ; Phillips, RA. 2016 Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models. Diversity and Distributions, 22 (2). 212-224. https://doi.org/10.1111/ddi.12389 <https://doi.org/10.1111/ddi.12389> |
op_rights |
cc_by_nc_4 |
op_rightsnorm |
CC-BY-NC |
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 |
_version_ |
1766379103256051712 |