Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change
Species interactions are known to structure ecological communities. Still, the influence of climate change on biodiversity has primarily been evaluated by correlating individual species distributions with local climatic descriptors, then extrapolating into future climate scenarios. We ask whether pr...
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ftslunivuppsala:oai:pub.epsilon.slu.se:24339 2023-05-15T14:26:11+02:00 Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change Abrego, Nerea Roslin, Tomas Huotari, Tea Ji, Yinqiu Schmidt, Niels Martin Wang, Jiaxin Yu, Douglas W. Ovaskainen, Otso 2021 application/pdf https://pub.epsilon.slu.se/24339/ https://pub.epsilon.slu.se/24339/1/abrego_n_et_al_210601.pdf en eng eng https://pub.epsilon.slu.se/24339/1/abrego_n_et_al_210601.pdf Abrego, Nerea and Roslin, Tomas and Huotari, Tea and Ji, Yinqiu and Schmidt, Niels Martin and Wang, Jiaxin and Yu, Douglas W. and Ovaskainen, Otso (2021). Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change. Ecography. 44 , 885-896 [Research article] Ecology Climate Research Research article NonPeerReviewed info:eu-repo/semantics/article 2021 ftslunivuppsala 2022-01-09T19:16:19Z Species interactions are known to structure ecological communities. Still, the influence of climate change on biodiversity has primarily been evaluated by correlating individual species distributions with local climatic descriptors, then extrapolating into future climate scenarios. We ask whether predictions on arctic arthropod response to climate change can be improved by accounting for species interactions. For this, we use a 14-year-long, weekly time series from Greenland, resolved to the species level by mitogenome mapping. During the study period, temperature increased by 2 degrees C and arthropod species richness halved. We show that with abiotic variables alone, we are essentially unable to predict species responses, but with species interactions included, the predictive power of the models improves considerably. Cascading trophic effects thereby emerge as important in structuring biodiversity response to climate change. Given the need to scale up from species-level to community-level projections of biodiversity change, these results represent a major step forward for predictive ecology. Article in Journal/Newspaper Arctic Arctic Climate change Greenland Swedish University of Agricultural Sciences (SLU): Epsilon Open Archive Arctic Greenland |
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Open Polar |
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Swedish University of Agricultural Sciences (SLU): Epsilon Open Archive |
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ftslunivuppsala |
language |
English |
topic |
Ecology Climate Research |
spellingShingle |
Ecology Climate Research Abrego, Nerea Roslin, Tomas Huotari, Tea Ji, Yinqiu Schmidt, Niels Martin Wang, Jiaxin Yu, Douglas W. Ovaskainen, Otso Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
topic_facet |
Ecology Climate Research |
description |
Species interactions are known to structure ecological communities. Still, the influence of climate change on biodiversity has primarily been evaluated by correlating individual species distributions with local climatic descriptors, then extrapolating into future climate scenarios. We ask whether predictions on arctic arthropod response to climate change can be improved by accounting for species interactions. For this, we use a 14-year-long, weekly time series from Greenland, resolved to the species level by mitogenome mapping. During the study period, temperature increased by 2 degrees C and arthropod species richness halved. We show that with abiotic variables alone, we are essentially unable to predict species responses, but with species interactions included, the predictive power of the models improves considerably. Cascading trophic effects thereby emerge as important in structuring biodiversity response to climate change. Given the need to scale up from species-level to community-level projections of biodiversity change, these results represent a major step forward for predictive ecology. |
format |
Article in Journal/Newspaper |
author |
Abrego, Nerea Roslin, Tomas Huotari, Tea Ji, Yinqiu Schmidt, Niels Martin Wang, Jiaxin Yu, Douglas W. Ovaskainen, Otso |
author_facet |
Abrego, Nerea Roslin, Tomas Huotari, Tea Ji, Yinqiu Schmidt, Niels Martin Wang, Jiaxin Yu, Douglas W. Ovaskainen, Otso |
author_sort |
Abrego, Nerea |
title |
Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
title_short |
Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
title_full |
Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
title_fullStr |
Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
title_full_unstemmed |
Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
title_sort |
accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change |
publishDate |
2021 |
url |
https://pub.epsilon.slu.se/24339/ https://pub.epsilon.slu.se/24339/1/abrego_n_et_al_210601.pdf |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Arctic Climate change Greenland |
genre_facet |
Arctic Arctic Climate change Greenland |
op_relation |
https://pub.epsilon.slu.se/24339/1/abrego_n_et_al_210601.pdf Abrego, Nerea and Roslin, Tomas and Huotari, Tea and Ji, Yinqiu and Schmidt, Niels Martin and Wang, Jiaxin and Yu, Douglas W. and Ovaskainen, Otso (2021). Accounting for species interactions is necessary for predicting how arctic arthropod communities respond to climate change. Ecography. 44 , 885-896 [Research article] |
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1766298660776181760 |