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: 2014-04-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / 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. / 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.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.bc.ufg.br:tede/3615 |
| Date | 29 April 2014 |
| Creators | Tessarolo, Geiziane |
| Contributors | Muñoz, Joaquin Hortal, Rangel, Thiago Fernando, Muñoz, Joaquin Hortal, Rangel, Thiago Fernando |
| Publisher | Universidade Federal de Goiás, Programa de Pós-graduação em Ecologia e Evolução (ICB), UFG, Brasil, Instituto de Ciências Biológicas - ICB (RG) |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFG, instname:Universidade Federal de Goiás, instacron:UFG |
| Rights | http://creativecommons.org/licenses/by-nc-nd/4.0/, info:eu-repo/semantics/openAccess |
| Relation | -5361682850774351271, 600, 600, 600, 600, -3872772117827373404, 3263499605295365002, 2075167498588264571 |
Page generated in 0.0049 seconds