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How Can Model Comparison Help Improving Species Distribution Models?

Today, more than ever, robust projections of potential species range shifts are needed to anticipate and mitigate the impacts of climate change on biodiversity and ecosystem services. Such projections are so far provided almost exclusively by correlative species distribution models (correlative SDMs...

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Autores principales: Gritti, Emmanuel Stephan, Gaucherel, Cédric, Crespo-Perez, Maria-Veronica, Chuine, Isabelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706317/
https://www.ncbi.nlm.nih.gov/pubmed/23874779
http://dx.doi.org/10.1371/journal.pone.0068823
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author Gritti, Emmanuel Stephan
Gaucherel, Cédric
Crespo-Perez, Maria-Veronica
Chuine, Isabelle
author_facet Gritti, Emmanuel Stephan
Gaucherel, Cédric
Crespo-Perez, Maria-Veronica
Chuine, Isabelle
author_sort Gritti, Emmanuel Stephan
collection PubMed
description Today, more than ever, robust projections of potential species range shifts are needed to anticipate and mitigate the impacts of climate change on biodiversity and ecosystem services. Such projections are so far provided almost exclusively by correlative species distribution models (correlative SDMs). However, concerns regarding the reliability of their predictive power are growing and several authors call for the development of process-based SDMs. Still, each of these methods presents strengths and weakness which have to be estimated if they are to be reliably used by decision makers. In this study we compare projections of three different SDMs (STASH, LPJ and PHENOFIT) that lie in the continuum between correlative models and process-based models for the current distribution of three major European tree species, Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L. We compare the consistency of the model simulations using an innovative comparison map profile method, integrating local and multi-scale comparisons. The three models simulate relatively accurately the current distribution of the three species. The process-based model performs almost as well as the correlative model, although parameters of the former are not fitted to the observed species distributions. According to our simulations, species range limits are triggered, at the European scale, by establishment and survival through processes primarily related to phenology and resistance to abiotic stress rather than to growth efficiency. The accuracy of projections of the hybrid and process-based model could however be improved by integrating a more realistic representation of the species resistance to water stress for instance, advocating for pursuing efforts to understand and formulate explicitly the impact of climatic conditions and variations on these processes.
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spelling pubmed-37063172013-07-19 How Can Model Comparison Help Improving Species Distribution Models? Gritti, Emmanuel Stephan Gaucherel, Cédric Crespo-Perez, Maria-Veronica Chuine, Isabelle PLoS One Research Article Today, more than ever, robust projections of potential species range shifts are needed to anticipate and mitigate the impacts of climate change on biodiversity and ecosystem services. Such projections are so far provided almost exclusively by correlative species distribution models (correlative SDMs). However, concerns regarding the reliability of their predictive power are growing and several authors call for the development of process-based SDMs. Still, each of these methods presents strengths and weakness which have to be estimated if they are to be reliably used by decision makers. In this study we compare projections of three different SDMs (STASH, LPJ and PHENOFIT) that lie in the continuum between correlative models and process-based models for the current distribution of three major European tree species, Fagus sylvatica L., Quercus robur L. and Pinus sylvestris L. We compare the consistency of the model simulations using an innovative comparison map profile method, integrating local and multi-scale comparisons. The three models simulate relatively accurately the current distribution of the three species. The process-based model performs almost as well as the correlative model, although parameters of the former are not fitted to the observed species distributions. According to our simulations, species range limits are triggered, at the European scale, by establishment and survival through processes primarily related to phenology and resistance to abiotic stress rather than to growth efficiency. The accuracy of projections of the hybrid and process-based model could however be improved by integrating a more realistic representation of the species resistance to water stress for instance, advocating for pursuing efforts to understand and formulate explicitly the impact of climatic conditions and variations on these processes. Public Library of Science 2013-07-09 /pmc/articles/PMC3706317/ /pubmed/23874779 http://dx.doi.org/10.1371/journal.pone.0068823 Text en © 2013 Gritti 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gritti, Emmanuel Stephan
Gaucherel, Cédric
Crespo-Perez, Maria-Veronica
Chuine, Isabelle
How Can Model Comparison Help Improving Species Distribution Models?
title How Can Model Comparison Help Improving Species Distribution Models?
title_full How Can Model Comparison Help Improving Species Distribution Models?
title_fullStr How Can Model Comparison Help Improving Species Distribution Models?
title_full_unstemmed How Can Model Comparison Help Improving Species Distribution Models?
title_short How Can Model Comparison Help Improving Species Distribution Models?
title_sort how can model comparison help improving species distribution models?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706317/
https://www.ncbi.nlm.nih.gov/pubmed/23874779
http://dx.doi.org/10.1371/journal.pone.0068823
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