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Predicting where species could go: climate is more important than dispersal for explaining the distribution of a South American turtle
by
Matheus Souza Lima-Ribeiro
, João Fabrício Mota Rodrigues
in
Algorithms
/ Bioclimatology
/ Biogeography
/ Biotic factors
/ Climate change
/ Dispersal
/ Dispersion
/ Distance
/ Distribution
/ Internet resources
/ Precipitation
/ Reptiles & amphibians
/ River networks
/ Rivers
/ Species
/ Surveys
/ Variables
/ Watersheds
2018
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Predicting where species could go: climate is more important than dispersal for explaining the distribution of a South American turtle
by
Matheus Souza Lima-Ribeiro
, João Fabrício Mota Rodrigues
in
Algorithms
/ Bioclimatology
/ Biogeography
/ Biotic factors
/ Climate change
/ Dispersal
/ Dispersion
/ Distance
/ Distribution
/ Internet resources
/ Precipitation
/ Reptiles & amphibians
/ River networks
/ Rivers
/ Species
/ Surveys
/ Variables
/ Watersheds
2018
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Predicting where species could go: climate is more important than dispersal for explaining the distribution of a South American turtle
by
Matheus Souza Lima-Ribeiro
, João Fabrício Mota Rodrigues
in
Algorithms
/ Bioclimatology
/ Biogeography
/ Biotic factors
/ Climate change
/ Dispersal
/ Dispersion
/ Distance
/ Distribution
/ Internet resources
/ Precipitation
/ Reptiles & amphibians
/ River networks
/ Rivers
/ Species
/ Surveys
/ Variables
/ Watersheds
2018
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Predicting where species could go: climate is more important than dispersal for explaining the distribution of a South American turtle
Journal Article
Predicting where species could go: climate is more important than dispersal for explaining the distribution of a South American turtle
2018
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Overview
Species distributions are determined by abiotic and biotic factors as well as dispersal, but most studies focus exclusively on abiotic (mainly climatic) components. In this study, we evaluated the influence of dispersal as a predictor for species distribution models (SDMs) using the turtle Mesoclemmys tuberculata as an example. We specifically tested whether dispersal is a better predictor of the distribution of M. tuberculata than climatic predictors. We sampled occurrence records of M. tuberculata to build SDMs and used the distance of each cell to the nearest river (river distance) as a predictor for dispersal. In addition, three bioclimatic predictors that quantify temperature and precipitation were used. We applied five different algorithms (BioClim, Domain, Maxent, SVM, and Random Forest) to model the distribution of M. tuberculata and evaluate the relative influence of each predictor variable. Although models including dispersal as a predictor performed slightly better than models omitting it, climatic predictors were found to be more important to describe species distribution across all SDMs. Our results suggest that although dispersal limits the potential geographic areas that the species may reach, abiotic parameters determine where M. tuberculata actually lives. Finally, we used consensus maps to prioritize areas for future field surveys.
Publisher
Springer Nature B.V
Subject
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