How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , |
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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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 |
_version_ |
1752129878059122688 |