Biologically informed ecological niche models for an example pelagic, highly mobile species
Background: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative mo...
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doaj-f5348cdc195e4ed0b43a5070fb5817ac2020-11-24T23:33:51ZengSciendoEuropean Journal of Ecology1339-84742017-03-0131557510.1515/eje-2017-0006eje-2017-0006Biologically informed ecological niche models for an example pelagic, highly mobile speciesIngenloff Kate0Biodiversity Institute, University of Kansas, Lawrence, KS, United States of America, Mailing Address: 1345 Jayhawk blvd, Lawrence, KS, 66045 United States of AmericaBackground: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change.http://www.degruyter.com/view/j/eje.2017.3.issue-1/eje-2017-0006/eje-2017-0006.xml?format=INTBoosted regression treesdigital accessible knowledgedistribution modellingMaxentminimum volume ellipsoidspelagic seabird distributionDiomedea exulans |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ingenloff Kate |
spellingShingle |
Ingenloff Kate Biologically informed ecological niche models for an example pelagic, highly mobile species European Journal of Ecology Boosted regression trees digital accessible knowledge distribution modelling Maxent minimum volume ellipsoids pelagic seabird distribution Diomedea exulans |
author_facet |
Ingenloff Kate |
author_sort |
Ingenloff Kate |
title |
Biologically informed ecological niche models for an example pelagic, highly mobile species |
title_short |
Biologically informed ecological niche models for an example pelagic, highly mobile species |
title_full |
Biologically informed ecological niche models for an example pelagic, highly mobile species |
title_fullStr |
Biologically informed ecological niche models for an example pelagic, highly mobile species |
title_full_unstemmed |
Biologically informed ecological niche models for an example pelagic, highly mobile species |
title_sort |
biologically informed ecological niche models for an example pelagic, highly mobile species |
publisher |
Sciendo |
series |
European Journal of Ecology |
issn |
1339-8474 |
publishDate |
2017-03-01 |
description |
Background: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change. |
topic |
Boosted regression trees digital accessible knowledge distribution modelling Maxent minimum volume ellipsoids pelagic seabird distribution Diomedea exulans |
url |
http://www.degruyter.com/view/j/eje.2017.3.issue-1/eje-2017-0006/eje-2017-0006.xml?format=INT |
work_keys_str_mv |
AT ingenloffkate biologicallyinformedecologicalnichemodelsforanexamplepelagichighlymobilespecies |
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