Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species
The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its po...
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crwiley:10.1111/ecog.04630 2024-09-09T19:27:18+00:00 Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species Chardon, Nathalie Isabelle Pironon, Samuel Peterson, Megan Lynn Doak, Daniel Forest 2019 http://dx.doi.org/10.1111/ecog.04630 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fecog.04630 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.04630 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ecog.04630 en eng Wiley http://creativecommons.org/licenses/by/3.0/ Ecography volume 43, issue 1, page 60-74 ISSN 0906-7590 1600-0587 journal-article 2019 crwiley https://doi.org/10.1111/ecog.04630 2024-08-06T04:16:34Z The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic‐alpine cushion plant Silene acaulis , we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific‐level SDMs with a species‐level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic‐ and habitat‐informed SDMs are considerably more accurate than a species‐level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific‐level SDMs. We emphasize the need to carefully examine how to best define intraspecific‐level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring ... Article in Journal/Newspaper Arctic Climate change Silene acaulis Wiley Online Library Arctic Ecography 43 1 60 74 |
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English |
description |
The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic‐alpine cushion plant Silene acaulis , we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific‐level SDMs with a species‐level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic‐ and habitat‐informed SDMs are considerably more accurate than a species‐level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific‐level SDMs. We emphasize the need to carefully examine how to best define intraspecific‐level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring ... |
format |
Article in Journal/Newspaper |
author |
Chardon, Nathalie Isabelle Pironon, Samuel Peterson, Megan Lynn Doak, Daniel Forest |
spellingShingle |
Chardon, Nathalie Isabelle Pironon, Samuel Peterson, Megan Lynn Doak, Daniel Forest Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
author_facet |
Chardon, Nathalie Isabelle Pironon, Samuel Peterson, Megan Lynn Doak, Daniel Forest |
author_sort |
Chardon, Nathalie Isabelle |
title |
Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
title_short |
Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
title_full |
Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
title_fullStr |
Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
title_full_unstemmed |
Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
title_sort |
incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide‐spread plant species |
publisher |
Wiley |
publishDate |
2019 |
url |
http://dx.doi.org/10.1111/ecog.04630 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fecog.04630 https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.04630 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ecog.04630 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Silene acaulis |
genre_facet |
Arctic Climate change Silene acaulis |
op_source |
Ecography volume 43, issue 1, page 60-74 ISSN 0906-7590 1600-0587 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ |
op_doi |
https://doi.org/10.1111/ecog.04630 |
container_title |
Ecography |
container_volume |
43 |
container_issue |
1 |
container_start_page |
60 |
op_container_end_page |
74 |
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1809896758582444032 |