How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end

Detalhes bibliográficos
Autor(a) principal: Terribile,LC
Data de Publicação: 2010
Outros Autores: Diniz-Filho,JAF, De Marco Jr.,P
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Biology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842010000200005
Resumo: The use of ecological niche models (ENM) to generate potential geographic distributions of species has rapidly increased in ecology, conservation and evolutionary biology. Many methods are available and the most used are Maximum Entropy Method (MAXENT) and the Genetic Algorithm for Rule Set Production (GARP). Recent studies have shown that MAXENT perform better than GARP. Here we used the statistics methods of ROC - AUC (area under the Receiver Operating Characteristics curve) and bootstrap to evaluate the performance of GARP and MAXENT in generate potential distribution models for 39 species of New World coral snakes. We found that values of AUC for GARP ranged from 0.923 to 0.999, whereas those for MAXENT ranged from 0.877 to 0.999. On the whole, the differences in AUC were very small, but for 10 species GARP outperformed MAXENT. Means and standard deviations for 100 bootstrapped samples with sample sizes ranging from 3 to 30 species did not show any trends towards deviations from a zero difference in AUC values of GARP minus AUC values of MAXENT. Ours results suggest that further studies are still necessary to establish under which circumstances the statistical performance of the methods vary. However, it is also important to consider the possibility that this empirical inductive reasoning may fail in the end, because we almost certainly could not establish all potential scenarios generating variation in the relative performance of models.
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spelling How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the endcoral snakesElapidaepotential distribution modelsGARPMAXENTThe use of ecological niche models (ENM) to generate potential geographic distributions of species has rapidly increased in ecology, conservation and evolutionary biology. Many methods are available and the most used are Maximum Entropy Method (MAXENT) and the Genetic Algorithm for Rule Set Production (GARP). Recent studies have shown that MAXENT perform better than GARP. Here we used the statistics methods of ROC - AUC (area under the Receiver Operating Characteristics curve) and bootstrap to evaluate the performance of GARP and MAXENT in generate potential distribution models for 39 species of New World coral snakes. We found that values of AUC for GARP ranged from 0.923 to 0.999, whereas those for MAXENT ranged from 0.877 to 0.999. On the whole, the differences in AUC were very small, but for 10 species GARP outperformed MAXENT. Means and standard deviations for 100 bootstrapped samples with sample sizes ranging from 3 to 30 species did not show any trends towards deviations from a zero difference in AUC values of GARP minus AUC values of MAXENT. Ours results suggest that further studies are still necessary to establish under which circumstances the statistical performance of the methods vary. However, it is also important to consider the possibility that this empirical inductive reasoning may fail in the end, because we almost certainly could not establish all potential scenarios generating variation in the relative performance of models.Instituto Internacional de Ecologia2010-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842010000200005Brazilian Journal of Biology v.70 n.2 2010reponame:Brazilian Journal of Biologyinstname:Instituto Internacional de Ecologia (IIE)instacron:IIE10.1590/S1519-69842010000200005info:eu-repo/semantics/openAccessTerribile,LCDiniz-Filho,JAFDe Marco Jr.,Peng2010-06-14T00:00:00Zoai:scielo:S1519-69842010000200005Revistahttps://www.scielo.br/j/bjb/https://old.scielo.br/oai/scielo-oai.phpbjb@bjb.com.br||bjb@bjb.com.br1678-43751519-6984opendoar:2010-06-14T00:00Brazilian Journal of Biology - Instituto Internacional de Ecologia (IIE)false
dc.title.none.fl_str_mv How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
title How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
spellingShingle How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
Terribile,LC
coral snakes
Elapidae
potential distribution models
GARP
MAXENT
title_short How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
title_full How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
title_fullStr How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
title_full_unstemmed How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
title_sort How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
author Terribile,LC
author_facet Terribile,LC
Diniz-Filho,JAF
De Marco Jr.,P
author_role author
author2 Diniz-Filho,JAF
De Marco Jr.,P
author2_role author
author
dc.contributor.author.fl_str_mv Terribile,LC
Diniz-Filho,JAF
De Marco Jr.,P
dc.subject.por.fl_str_mv coral snakes
Elapidae
potential distribution models
GARP
MAXENT
topic coral snakes
Elapidae
potential distribution models
GARP
MAXENT
description The use of ecological niche models (ENM) to generate potential geographic distributions of species has rapidly increased in ecology, conservation and evolutionary biology. Many methods are available and the most used are Maximum Entropy Method (MAXENT) and the Genetic Algorithm for Rule Set Production (GARP). Recent studies have shown that MAXENT perform better than GARP. Here we used the statistics methods of ROC - AUC (area under the Receiver Operating Characteristics curve) and bootstrap to evaluate the performance of GARP and MAXENT in generate potential distribution models for 39 species of New World coral snakes. We found that values of AUC for GARP ranged from 0.923 to 0.999, whereas those for MAXENT ranged from 0.877 to 0.999. On the whole, the differences in AUC were very small, but for 10 species GARP outperformed MAXENT. Means and standard deviations for 100 bootstrapped samples with sample sizes ranging from 3 to 30 species did not show any trends towards deviations from a zero difference in AUC values of GARP minus AUC values of MAXENT. Ours results suggest that further studies are still necessary to establish under which circumstances the statistical performance of the methods vary. However, it is also important to consider the possibility that this empirical inductive reasoning may fail in the end, because we almost certainly could not establish all potential scenarios generating variation in the relative performance of models.
publishDate 2010
dc.date.none.fl_str_mv 2010-05-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842010000200005
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842010000200005
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1519-69842010000200005
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Instituto Internacional de Ecologia
publisher.none.fl_str_mv Instituto Internacional de Ecologia
dc.source.none.fl_str_mv Brazilian Journal of Biology v.70 n.2 2010
reponame:Brazilian Journal of Biology
instname:Instituto Internacional de Ecologia (IIE)
instacron:IIE
instname_str Instituto Internacional de Ecologia (IIE)
instacron_str IIE
institution IIE
reponame_str Brazilian Journal of Biology
collection Brazilian Journal of Biology
repository.name.fl_str_mv Brazilian Journal of Biology - Instituto Internacional de Ecologia (IIE)
repository.mail.fl_str_mv bjb@bjb.com.br||bjb@bjb.com.br
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