Incerteza nos modelos de distribuição de espécies

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dc.contributor.advisor-co1Rangel, Thiago Fernando
dc.contributor.advisor1Muñoz, Joaquin Hortal
dc.contributor.referee1Muñoz, Joaquin Hortal
dc.contributor.referee2Rangel, Thiago Fernando
dc.creatorTessarolo, Geiziane
dc.creator.Latteshttp://lattes.cnpq.br/1344166697425781por
dc.date.accessioned2014-11-17T15:10:55Z
dc.date.issued2014-04-29
dc.description.abstractAim Species Distribution Models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location Iberian Peninsula Methods We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using p-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species’ characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results Survey design has a small effect on prediction success. Characteristics of species’ ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions The species modelled – particularly the extent of its distribution – are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions.eng
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dc.description.provenanceMade available in DSpace on 2014-11-17T15:10:55Z (GMT). No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-04-29eng
dc.description.resumoAim Species Distribution Models (SDM) can be used to predict the location of unknown populations from known species occurrences. It follows that how the data used to calibrate the models are collected can have a great impact on prediction success. We evaluated the influence of different survey designs and their interaction with the modelling technique on SDM performance. Location Iberian Peninsula Methods We examine how data recorded using seven alternative survey designs (random, systematic, environmentally stratified by class and environmentally stratified using p-median, biased due to accessibility, biased by human density aggregation and biased towards protected areas) could affect SDM predictions generated with nine modelling techniques (BIOCLIM, Gower distance, Mahalanobis distance, Euclidean distance, GLM, MaxEnt, ENFA and Random Forest). We also study how sample size, species’ characteristics and modelling technique affected SDM predictive ability, using six evaluation metrics. Results Survey design has a small effect on prediction success. Characteristics of species’ ranges rank highest among the factors affecting SDM results: the species with lower relative occurrence area (ROA) are predicted better. Model predictions are also improved when sample size is large. Main conclusions The species modelled – particularly the extent of its distribution – are the largest source of influence over SDM results. The environmental coverage of the surveys is more important than the spatial structure of the calibration data. Therefore, climatic biases in the data should be identified to avoid erroneous conclusions about the geographic patterns of species distributions.por
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESpor
dc.formatapplication/pdf*
dc.identifier.citationTESSAROLO, Geiziane. Incerteza nos modelos de distribuição de espécies. 2014. 151 f. Tese (Doutorado em Ecologia e Evolução) - Universidade Federal de Goiás, Goiânia, 2014.por
dc.identifier.urihttp://repositorio.bc.ufg.br/tede/handle/tede/3615
dc.languageporpor
dc.publisherUniversidade Federal de Goiáspor
dc.publisher.countryBrasilpor
dc.publisher.departmentInstituto de Ciências Biológicas - ICB (RG)por
dc.publisher.initialsUFGpor
dc.publisher.programPrograma de Pós-graduação em Ecologia e Evolução (ICB)por
dc.rightsAcesso Abertopor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCaracterísticas das espéciespor
dc.subjectCobertura ambientalpor
dc.subjectDesenho amostralpor
dc.subjectIncertezapor
dc.subjectModelos de distribuição de espéciespor
dc.subjectSpecies traitseng
dc.subjectEnvironmental Completenesseng
dc.subjectSurvey designeng
dc.subjectUncertaintyeng
dc.subjectSpecies distribution modelseng
dc.subject.cnpqCIENCIAS BIOLOGICAS::ECOLOGIApor
dc.thumbnail.urlhttp://repositorio.bc.ufg.br/tede/retrieve/12329/Tese%20Geiziane%20Tessarolo%20-%202014.pdf.jpg*
dc.titleIncerteza nos modelos de distribuição de espéciespor
dc.title.alternativeUncertainty in species distribution modelseng
dc.typeTesepor

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Doutorado em Ecologia e Evolução (ICB)