Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea
1. The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. Safeguarding these ecosystems will require predictive, spatial studies of seabird foraging hotspots. Cu...
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Online Access: | https://hdl.handle.net/11250/3054345 https://doi.org/10.1002/2688-8319.12181 |
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ftninstnf:oai:brage.nina.no:11250/3054345 2023-05-15T13:12:20+02:00 Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea Hodges, Samuel Erikstad, Kjell E. Reiertsen, Tone Kristin Barents Sea 2022 application/pdf https://hdl.handle.net/11250/3054345 https://doi.org/10.1002/2688-8319.12181 eng eng Andre: Office of Naval Research Andre: National Ocean Partnership Program Andre: U.S. Navy Egen institusjon: Norwegian institute for nature research (NINA) Ecological Solutions and Evidence. 2022, 3 (4), . urn:issn:2688-8319 https://hdl.handle.net/11250/3054345 https://doi.org/10.1002/2688-8319.12181 cristin:2125402 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © 2022 The Authors CC-BY 0 3 Ecological Solutions and Evidence 4 e12181 Atlantic Puffin Barents Sea Brunnich’s Guillemot Common Guillemot ecological modelling MaxENT Razorbill spatial ecology VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 Peer reviewed Journal article 2022 ftninstnf https://doi.org/10.1002/2688-8319.12181 2023-03-01T23:46:24Z 1. The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. Safeguarding these ecosystems will require predictive, spatial studies of seabird foraging hotspots. Current research on seabird foraging hotspots has established a significant relationship between probability of presence and several environmental variables, including Sea Surface Temperature (SST). However, inter-annual, basin-wide variation has the potential to invalidate these models, which depend on seasonal mesoscale variability. 2. In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors, while reducing the influence of large-scale basin-wide variation. We model the Maximum Entropy (MaxENT) Model-derived relationship between Standardized Monthly SST (StdSST) and Habitat Suitability using Gaussian curve models, and then apply these models to independent StdSST data to produce heatmaps of predicted seabird presence. 3. In this study, we demonstrate StdSST to be a functional environmental predictor of seabird presence, within a Gaussian curve model framework. We demonstrate accurate predictions of the model’s training data and of independent seabird presence data to a high degree of accuracy (area under the receiver operator characteristic curve > 0.65) for four species of Auk: Common Guillemots (Uria aalge), Razorbills (Alca torda), Atlantic Puffins (Fratercula arctica) and Brunnich’s Guillemots (Uria lomvia). 4. We believe that the methodology we have developed and tested in this study can be used to guide ecosystem management practices by converting coupled-climate model predictions into predictions of future presence based on Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging seabird hotspots in the Barents Sea Atlantic Puffin, Barents Sea, Brunnich’s ... Article in Journal/Newspaper Alca torda Atlantic puffin Barents Sea common guillemot fratercula Fratercula arctica Razorbill Uria aalge Uria lomvia uria Norwegian Institute for Nature Research: Brage NINA Barents Sea Ecological Solutions and Evidence 3 4 |
institution |
Open Polar |
collection |
Norwegian Institute for Nature Research: Brage NINA |
op_collection_id |
ftninstnf |
language |
English |
topic |
Atlantic Puffin Barents Sea Brunnich’s Guillemot Common Guillemot ecological modelling MaxENT Razorbill spatial ecology VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 |
spellingShingle |
Atlantic Puffin Barents Sea Brunnich’s Guillemot Common Guillemot ecological modelling MaxENT Razorbill spatial ecology VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 Hodges, Samuel Erikstad, Kjell E. Reiertsen, Tone Kristin Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea |
topic_facet |
Atlantic Puffin Barents Sea Brunnich’s Guillemot Common Guillemot ecological modelling MaxENT Razorbill spatial ecology VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 |
description |
1. The conservation of seabirds is increasingly important for their role as indicator species of ocean ecosystems, which are predicted to experience increasing levels of exploitation this century. Safeguarding these ecosystems will require predictive, spatial studies of seabird foraging hotspots. Current research on seabird foraging hotspots has established a significant relationship between probability of presence and several environmental variables, including Sea Surface Temperature (SST). However, inter-annual, basin-wide variation has the potential to invalidate these models, which depend on seasonal mesoscale variability. 2. In this study, we present a novel solution to predict presence from spatially and temporally variable environmental predictors, while reducing the influence of large-scale basin-wide variation. We model the Maximum Entropy (MaxENT) Model-derived relationship between Standardized Monthly SST (StdSST) and Habitat Suitability using Gaussian curve models, and then apply these models to independent StdSST data to produce heatmaps of predicted seabird presence. 3. In this study, we demonstrate StdSST to be a functional environmental predictor of seabird presence, within a Gaussian curve model framework. We demonstrate accurate predictions of the model’s training data and of independent seabird presence data to a high degree of accuracy (area under the receiver operator characteristic curve > 0.65) for four species of Auk: Common Guillemots (Uria aalge), Razorbills (Alca torda), Atlantic Puffins (Fratercula arctica) and Brunnich’s Guillemots (Uria lomvia). 4. We believe that the methodology we have developed and tested in this study can be used to guide ecosystem management practices by converting coupled-climate model predictions into predictions of future presence based on Habitat Suitability for the species, allowing us to consider the possible effects of climate change and yearly variation of SST on foraging seabird hotspots in the Barents Sea Atlantic Puffin, Barents Sea, Brunnich’s ... |
format |
Article in Journal/Newspaper |
author |
Hodges, Samuel Erikstad, Kjell E. Reiertsen, Tone Kristin |
author_facet |
Hodges, Samuel Erikstad, Kjell E. Reiertsen, Tone Kristin |
author_sort |
Hodges, Samuel |
title |
Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea |
title_short |
Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea |
title_full |
Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea |
title_fullStr |
Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea |
title_full_unstemmed |
Predicting the foraging patterns of wintering Auks using a sea surface temperature model for the Barents Sea |
title_sort |
predicting the foraging patterns of wintering auks using a sea surface temperature model for the barents sea |
publishDate |
2022 |
url |
https://hdl.handle.net/11250/3054345 https://doi.org/10.1002/2688-8319.12181 |
op_coverage |
Barents Sea |
geographic |
Barents Sea |
geographic_facet |
Barents Sea |
genre |
Alca torda Atlantic puffin Barents Sea common guillemot fratercula Fratercula arctica Razorbill Uria aalge Uria lomvia uria |
genre_facet |
Alca torda Atlantic puffin Barents Sea common guillemot fratercula Fratercula arctica Razorbill Uria aalge Uria lomvia uria |
op_source |
0 3 Ecological Solutions and Evidence 4 e12181 |
op_relation |
Andre: Office of Naval Research Andre: National Ocean Partnership Program Andre: U.S. Navy Egen institusjon: Norwegian institute for nature research (NINA) Ecological Solutions and Evidence. 2022, 3 (4), . urn:issn:2688-8319 https://hdl.handle.net/11250/3054345 https://doi.org/10.1002/2688-8319.12181 cristin:2125402 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © 2022 The Authors |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/2688-8319.12181 |
container_title |
Ecological Solutions and Evidence |
container_volume |
3 |
container_issue |
4 |
_version_ |
1766251427921920000 |