Incerteza nos modelos de distribuição de espécies
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/3615 |
Resumo: | Aim 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. |
id |
UFG-2_23a80d1774707e67f5ad58e2979a8aa7 |
---|---|
oai_identifier_str |
oai:repositorio.bc.ufg.br:tede/3615 |
network_acronym_str |
UFG-2 |
network_name_str |
Repositório Institucional da UFG |
repository_id_str |
|
spelling |
Muñoz, Joaquin HortalRangel, Thiago FernandoMuñoz, Joaquin HortalRangel, Thiago Fernandohttp://lattes.cnpq.br/1344166697425781Tessarolo, Geiziane2014-11-17T15:10:55Z2014-04-29TESSAROLO, Geiziane. Incerteza nos modelos de distribuição de espécies. 2014. 151 f. Tese (Doutorado em Ecologia e Evolução) - Universidade Federal de Goiás, Goiânia, 2014.http://repositorio.bc.ufg.br/tede/handle/tede/3615ark:/38995/0013000008vscAim 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.Aim 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.Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2014-11-11T12:06:48Z No. of bitstreams: 2 Tese Geiziane Tessarolo - 2014.pdf: 5275889 bytes, checksum: fb092b496eb6eae85e89c28d423c44d9 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) 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)Made 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: 2014-04-29Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfhttp://repositorio.bc.ufg.br/tede/retrieve/12329/Tese%20Geiziane%20Tessarolo%20-%202014.pdf.jpgporUniversidade Federal de GoiásPrograma de Pós-graduação em Ecologia e Evolução (ICB)UFGBrasilInstituto de Ciências Biológicas - ICB (RG)http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessCaracterísticas das espéciesCobertura ambientalDesenho amostralIncertezaModelos de distribuição de espéciesSpecies traitsEnvironmental CompletenessSurvey designUncertaintySpecies distribution modelsCIENCIAS BIOLOGICAS::ECOLOGIAIncerteza nos modelos de distribuição de espéciesUncertainty in species distribution modelsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis-5361682850774351271600600600600-387277211782737340432634996052953650022075167498588264571reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://repositorio.bc.ufg.br/tede/bitstreams/d322cf90-4595-4003-9f01-785881e1e1aa/downloadbd3efa91386c1718a7f26a329fdcb468MD51CC-LICENSElicense_urllicense_urltext/plain; charset=utf-849http://repositorio.bc.ufg.br/tede/bitstreams/dde3011d-9ceb-4db4-8a1a-f45955083580/download4afdbb8c545fd630ea7db775da747b2fMD52license_textlicense_texttext/html; charset=utf-822302http://repositorio.bc.ufg.br/tede/bitstreams/1843d189-ec16-4c64-9357-9080cfdf2ad8/download1e0094e9d8adcf16b18effef4ce7ed83MD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-823148http://repositorio.bc.ufg.br/tede/bitstreams/e7ae8902-aa36-42fa-9244-0990cf527847/download9da0b6dfac957114c6a7714714b86306MD54ORIGINALTese Geiziane Tessarolo - 2014.pdfTese Geiziane Tessarolo - 2014.pdfapplication/pdf5275889http://repositorio.bc.ufg.br/tede/bitstreams/f6b48d06-afab-4926-a730-3dab3a1d41f3/downloadfb092b496eb6eae85e89c28d423c44d9MD55TEXTTese Geiziane Tessarolo - 2014.pdf.txtTese Geiziane Tessarolo - 2014.pdf.txtExtracted Texttext/plain226756http://repositorio.bc.ufg.br/tede/bitstreams/a1e439c1-5add-4f76-95d9-e161fd2344ec/downloadd302a2e9b8e80d4e6ed815d130cbaab7MD56THUMBNAILTese Geiziane Tessarolo - 2014.pdf.jpgTese Geiziane Tessarolo - 2014.pdf.jpgGenerated Thumbnailimage/jpeg2344http://repositorio.bc.ufg.br/tede/bitstreams/ef586797-2743-4417-b753-17a1ffde70ef/download1a80dfa94416a42d3e2594b749ecd80aMD57tede/36152014-11-18 03:02:01.428http://creativecommons.org/licenses/by-nc-nd/4.0/Acesso Abertoopen.accessoai:repositorio.bc.ufg.br:tede/3615http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2014-11-18T05:02:01Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.por.fl_str_mv |
Incerteza nos modelos de distribuição de espécies |
dc.title.alternative.eng.fl_str_mv |
Uncertainty in species distribution models |
title |
Incerteza nos modelos de distribuição de espécies |
spellingShingle |
Incerteza nos modelos de distribuição de espécies Tessarolo, Geiziane Características das espécies Cobertura ambiental Desenho amostral Incerteza Modelos de distribuição de espécies Species traits Environmental Completeness Survey design Uncertainty Species distribution models CIENCIAS BIOLOGICAS::ECOLOGIA |
title_short |
Incerteza nos modelos de distribuição de espécies |
title_full |
Incerteza nos modelos de distribuição de espécies |
title_fullStr |
Incerteza nos modelos de distribuição de espécies |
title_full_unstemmed |
Incerteza nos modelos de distribuição de espécies |
title_sort |
Incerteza nos modelos de distribuição de espécies |
author |
Tessarolo, Geiziane |
author_facet |
Tessarolo, Geiziane |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Muñoz, Joaquin Hortal |
dc.contributor.advisor-co1.fl_str_mv |
Rangel, Thiago Fernando |
dc.contributor.referee1.fl_str_mv |
Muñoz, Joaquin Hortal |
dc.contributor.referee2.fl_str_mv |
Rangel, Thiago Fernando |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1344166697425781 |
dc.contributor.author.fl_str_mv |
Tessarolo, Geiziane |
contributor_str_mv |
Muñoz, Joaquin Hortal Rangel, Thiago Fernando Muñoz, Joaquin Hortal Rangel, Thiago Fernando |
dc.subject.por.fl_str_mv |
Características das espécies Cobertura ambiental Desenho amostral Incerteza Modelos de distribuição de espécies |
topic |
Características das espécies Cobertura ambiental Desenho amostral Incerteza Modelos de distribuição de espécies Species traits Environmental Completeness Survey design Uncertainty Species distribution models CIENCIAS BIOLOGICAS::ECOLOGIA |
dc.subject.eng.fl_str_mv |
Species traits Environmental Completeness Survey design Uncertainty Species distribution models |
dc.subject.cnpq.fl_str_mv |
CIENCIAS BIOLOGICAS::ECOLOGIA |
description |
Aim 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. |
publishDate |
2014 |
dc.date.accessioned.fl_str_mv |
2014-11-17T15:10:55Z |
dc.date.issued.fl_str_mv |
2014-04-29 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
TESSAROLO, Geiziane. Incerteza nos modelos de distribuição de espécies. 2014. 151 f. Tese (Doutorado em Ecologia e Evolução) - Universidade Federal de Goiás, Goiânia, 2014. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/3615 |
dc.identifier.dark.fl_str_mv |
ark:/38995/0013000008vsc |
identifier_str_mv |
TESSAROLO, Geiziane. Incerteza nos modelos de distribuição de espécies. 2014. 151 f. Tese (Doutorado em Ecologia e Evolução) - Universidade Federal de Goiás, Goiânia, 2014. ark:/38995/0013000008vsc |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/3615 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
-5361682850774351271 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 |
dc.relation.department.fl_str_mv |
-3872772117827373404 |
dc.relation.cnpq.fl_str_mv |
3263499605295365002 |
dc.relation.sponsorship.fl_str_mv |
2075167498588264571 |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Ecologia e Evolução (ICB) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Ciências Biológicas - ICB (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
instname_str |
Universidade Federal de Goiás (UFG) |
instacron_str |
UFG |
institution |
UFG |
reponame_str |
Repositório Institucional da UFG |
collection |
Repositório Institucional da UFG |
bitstream.url.fl_str_mv |
http://repositorio.bc.ufg.br/tede/bitstreams/d322cf90-4595-4003-9f01-785881e1e1aa/download http://repositorio.bc.ufg.br/tede/bitstreams/dde3011d-9ceb-4db4-8a1a-f45955083580/download http://repositorio.bc.ufg.br/tede/bitstreams/1843d189-ec16-4c64-9357-9080cfdf2ad8/download http://repositorio.bc.ufg.br/tede/bitstreams/e7ae8902-aa36-42fa-9244-0990cf527847/download http://repositorio.bc.ufg.br/tede/bitstreams/f6b48d06-afab-4926-a730-3dab3a1d41f3/download http://repositorio.bc.ufg.br/tede/bitstreams/a1e439c1-5add-4f76-95d9-e161fd2344ec/download http://repositorio.bc.ufg.br/tede/bitstreams/ef586797-2743-4417-b753-17a1ffde70ef/download |
bitstream.checksum.fl_str_mv |
bd3efa91386c1718a7f26a329fdcb468 4afdbb8c545fd630ea7db775da747b2f 1e0094e9d8adcf16b18effef4ce7ed83 9da0b6dfac957114c6a7714714b86306 fb092b496eb6eae85e89c28d423c44d9 d302a2e9b8e80d4e6ed815d130cbaab7 1a80dfa94416a42d3e2594b749ecd80a |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFG - Universidade Federal de Goiás (UFG) |
repository.mail.fl_str_mv |
tasesdissertacoes.bc@ufg.br |
_version_ |
1811721456837459968 |