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Model uncertainties do not affect observed patterns of species richness in the Amazon

BACKGROUND: Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical...

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Autores principales: Sales, Lilian Patrícia, Neves, Olívia Viana, De Marco, Paulo, Loyola, Rafael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638225/
https://www.ncbi.nlm.nih.gov/pubmed/29023503
http://dx.doi.org/10.1371/journal.pone.0183785
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author Sales, Lilian Patrícia
Neves, Olívia Viana
De Marco, Paulo
Loyola, Rafael
author_facet Sales, Lilian Patrícia
Neves, Olívia Viana
De Marco, Paulo
Loyola, Rafael
author_sort Sales, Lilian Patrícia
collection PubMed
description BACKGROUND: Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. METHODS: We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. RESULTS: The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. CONCLUSIONS: From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.
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spelling pubmed-56382252017-11-03 Model uncertainties do not affect observed patterns of species richness in the Amazon Sales, Lilian Patrícia Neves, Olívia Viana De Marco, Paulo Loyola, Rafael PLoS One Research Article BACKGROUND: Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. METHODS: We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. RESULTS: The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. CONCLUSIONS: From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. Public Library of Science 2017-10-12 /pmc/articles/PMC5638225/ /pubmed/29023503 http://dx.doi.org/10.1371/journal.pone.0183785 Text en © 2017 Sales 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
Sales, Lilian Patrícia
Neves, Olívia Viana
De Marco, Paulo
Loyola, Rafael
Model uncertainties do not affect observed patterns of species richness in the Amazon
title Model uncertainties do not affect observed patterns of species richness in the Amazon
title_full Model uncertainties do not affect observed patterns of species richness in the Amazon
title_fullStr Model uncertainties do not affect observed patterns of species richness in the Amazon
title_full_unstemmed Model uncertainties do not affect observed patterns of species richness in the Amazon
title_short Model uncertainties do not affect observed patterns of species richness in the Amazon
title_sort model uncertainties do not affect observed patterns of species richness in the amazon
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638225/
https://www.ncbi.nlm.nih.gov/pubmed/29023503
http://dx.doi.org/10.1371/journal.pone.0183785
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