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Predicting future invaders and future invasions
Invasive alien species are a great threat to biodiversity and human livelihoods worldwide. The most effective way to limit their impacts and costs is to prevent their introduction into new areas. Identifying invaders and invasions before their occurrence would arguably be the most efficient strategy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6475384/ https://www.ncbi.nlm.nih.gov/pubmed/30926662 http://dx.doi.org/10.1073/pnas.1803456116 |
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author | Fournier, Alice Penone, Caterina Pennino, Maria Grazia Courchamp, Franck |
author_facet | Fournier, Alice Penone, Caterina Pennino, Maria Grazia Courchamp, Franck |
author_sort | Fournier, Alice |
collection | PubMed |
description | Invasive alien species are a great threat to biodiversity and human livelihoods worldwide. The most effective way to limit their impacts and costs is to prevent their introduction into new areas. Identifying invaders and invasions before their occurrence would arguably be the most efficient strategy. Here, we provide a profiling method to predict which species—with which particular ecological characteristics—will invade, and where they could invade. We illustrate our approach with ants, which are among the most detrimental invasive species, as they are responsible for declines of numerous taxa, are involved in local extinctions, disturb ecosystem functioning, and impact multiple human activities. Based on statistical profiling of 992 ant species from an extensive trait database, we identify 18 native ant species with an ecological profile that matches that of known invasive ants. Even though they are not currently described as such, these species are likely to become the next global invaders. We couple these predictions with species distribution models to identify the regions most at risk from the invasion of these species: Northern and Central America, Brazil, Central Africa and Madagascar, Southeast Asia, Papua New Guinea Northeast Australia, and many islands worldwide. This framework, applicable to any other taxa, represents a remarkable opportunity to implement timely and specifically shaped proactive management strategies against biological invasions. |
format | Online Article Text |
id | pubmed-6475384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-64753842019-04-26 Predicting future invaders and future invasions Fournier, Alice Penone, Caterina Pennino, Maria Grazia Courchamp, Franck Proc Natl Acad Sci U S A Biological Sciences Invasive alien species are a great threat to biodiversity and human livelihoods worldwide. The most effective way to limit their impacts and costs is to prevent their introduction into new areas. Identifying invaders and invasions before their occurrence would arguably be the most efficient strategy. Here, we provide a profiling method to predict which species—with which particular ecological characteristics—will invade, and where they could invade. We illustrate our approach with ants, which are among the most detrimental invasive species, as they are responsible for declines of numerous taxa, are involved in local extinctions, disturb ecosystem functioning, and impact multiple human activities. Based on statistical profiling of 992 ant species from an extensive trait database, we identify 18 native ant species with an ecological profile that matches that of known invasive ants. Even though they are not currently described as such, these species are likely to become the next global invaders. We couple these predictions with species distribution models to identify the regions most at risk from the invasion of these species: Northern and Central America, Brazil, Central Africa and Madagascar, Southeast Asia, Papua New Guinea Northeast Australia, and many islands worldwide. This framework, applicable to any other taxa, represents a remarkable opportunity to implement timely and specifically shaped proactive management strategies against biological invasions. National Academy of Sciences 2019-04-16 2019-03-29 /pmc/articles/PMC6475384/ /pubmed/30926662 http://dx.doi.org/10.1073/pnas.1803456116 Text en Copyright © 2019 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Fournier, Alice Penone, Caterina Pennino, Maria Grazia Courchamp, Franck Predicting future invaders and future invasions |
title | Predicting future invaders and future invasions |
title_full | Predicting future invaders and future invasions |
title_fullStr | Predicting future invaders and future invasions |
title_full_unstemmed | Predicting future invaders and future invasions |
title_short | Predicting future invaders and future invasions |
title_sort | predicting future invaders and future invasions |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6475384/ https://www.ncbi.nlm.nih.gov/pubmed/30926662 http://dx.doi.org/10.1073/pnas.1803456116 |
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