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Can species distribution models really predict the expansion of invasive species?

Predictive studies are of paramount importance for biological invasions, one of the biggest threats for biodiversity. To help and better prioritize management strategies, species distribution models (SDMs) are often used to predict the potential invasive range of introduced species. Yet, SDMs have b...

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Autores principales: Barbet-Massin, Morgane, Rome, Quentin, Villemant, Claire, Courchamp, Franck
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5839551/
https://www.ncbi.nlm.nih.gov/pubmed/29509789
http://dx.doi.org/10.1371/journal.pone.0193085
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author Barbet-Massin, Morgane
Rome, Quentin
Villemant, Claire
Courchamp, Franck
author_facet Barbet-Massin, Morgane
Rome, Quentin
Villemant, Claire
Courchamp, Franck
author_sort Barbet-Massin, Morgane
collection PubMed
description Predictive studies are of paramount importance for biological invasions, one of the biggest threats for biodiversity. To help and better prioritize management strategies, species distribution models (SDMs) are often used to predict the potential invasive range of introduced species. Yet, SDMs have been regularly criticized, due to several strong limitations, such as violating the equilibrium assumption during the invasion process. Unfortunately, validation studies–with independent data–are too scarce to assess the predictive accuracy of SDMs in invasion biology. Yet, biological invasions allow to test SDMs usefulness, by retrospectively assessing whether they would have accurately predicted the latest ranges of invasion. Here, we assess the predictive accuracy of SDMs in predicting the expansion of invasive species. We used temporal occurrence data for the Asian hornet Vespa velutina nigrithorax, a species native to China that is invading Europe with a very fast rate. Specifically, we compared occurrence data from the last stage of invasion (independent validation points) to the climate suitability distribution predicted from models calibrated with data from the early stage of invasion. Despite the invasive species not being at equilibrium yet, the predicted climate suitability of validation points was high. SDMs can thus adequately predict the spread of V. v. nigrithorax, which appears to be—at least partially–climatically driven. In the case of V. v. nigrithorax, SDMs predictive accuracy was slightly but significantly better when models were calibrated with invasive data only, excluding native data. Although more validation studies for other invasion cases are needed to generalize our results, our findings are an important step towards validating the use of SDMs in invasion biology.
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spelling pubmed-58395512018-03-23 Can species distribution models really predict the expansion of invasive species? Barbet-Massin, Morgane Rome, Quentin Villemant, Claire Courchamp, Franck PLoS One Research Article Predictive studies are of paramount importance for biological invasions, one of the biggest threats for biodiversity. To help and better prioritize management strategies, species distribution models (SDMs) are often used to predict the potential invasive range of introduced species. Yet, SDMs have been regularly criticized, due to several strong limitations, such as violating the equilibrium assumption during the invasion process. Unfortunately, validation studies–with independent data–are too scarce to assess the predictive accuracy of SDMs in invasion biology. Yet, biological invasions allow to test SDMs usefulness, by retrospectively assessing whether they would have accurately predicted the latest ranges of invasion. Here, we assess the predictive accuracy of SDMs in predicting the expansion of invasive species. We used temporal occurrence data for the Asian hornet Vespa velutina nigrithorax, a species native to China that is invading Europe with a very fast rate. Specifically, we compared occurrence data from the last stage of invasion (independent validation points) to the climate suitability distribution predicted from models calibrated with data from the early stage of invasion. Despite the invasive species not being at equilibrium yet, the predicted climate suitability of validation points was high. SDMs can thus adequately predict the spread of V. v. nigrithorax, which appears to be—at least partially–climatically driven. In the case of V. v. nigrithorax, SDMs predictive accuracy was slightly but significantly better when models were calibrated with invasive data only, excluding native data. Although more validation studies for other invasion cases are needed to generalize our results, our findings are an important step towards validating the use of SDMs in invasion biology. Public Library of Science 2018-03-06 /pmc/articles/PMC5839551/ /pubmed/29509789 http://dx.doi.org/10.1371/journal.pone.0193085 Text en © 2018 Barbet-Massin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Barbet-Massin, Morgane
Rome, Quentin
Villemant, Claire
Courchamp, Franck
Can species distribution models really predict the expansion of invasive species?
title Can species distribution models really predict the expansion of invasive species?
title_full Can species distribution models really predict the expansion of invasive species?
title_fullStr Can species distribution models really predict the expansion of invasive species?
title_full_unstemmed Can species distribution models really predict the expansion of invasive species?
title_short Can species distribution models really predict the expansion of invasive species?
title_sort can species distribution models really predict the expansion of invasive species?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5839551/
https://www.ncbi.nlm.nih.gov/pubmed/29509789
http://dx.doi.org/10.1371/journal.pone.0193085
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