Are existing vegetation maps adequate to predict bird distributions?

Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of ex- isting maps used to derive predictors is in part res...

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Detalles Bibliográficos
Autores: Seoane, Javier, Bustamante, Javier, Díaz-Delgado, Ricardo
Tipo de recurso: artículo
Fecha de publicación:2003
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/47040
Acceso en línea:http://hdl.handle.net/10261/47040
Access Level:acceso abierto
Palabra clave:Map quality
Wildlife-habitat models
Bird distribution
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spelling Are existing vegetation maps adequate to predict bird distributions?Seoane, JavierBustamante, JavierDíaz-Delgado, RicardoMap qualityWildlife-habitat modelsBird distributionBird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of ex- isting maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km2 of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejer´ıa de Medio Ambiente, Junta de Andaluc´ıa). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by im- proving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation struc- ture maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distributionPeer reviewedElsevier201220122003info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/47040reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1016/j.ecolmodel.2003.10.011info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/470402026-05-22T06:33:51Z
dc.title.none.fl_str_mv Are existing vegetation maps adequate to predict bird distributions?
title Are existing vegetation maps adequate to predict bird distributions?
spellingShingle Are existing vegetation maps adequate to predict bird distributions?
Seoane, Javier
Map quality
Wildlife-habitat models
Bird distribution
title_short Are existing vegetation maps adequate to predict bird distributions?
title_full Are existing vegetation maps adequate to predict bird distributions?
title_fullStr Are existing vegetation maps adequate to predict bird distributions?
title_full_unstemmed Are existing vegetation maps adequate to predict bird distributions?
title_sort Are existing vegetation maps adequate to predict bird distributions?
dc.creator.none.fl_str_mv Seoane, Javier
Bustamante, Javier
Díaz-Delgado, Ricardo
author Seoane, Javier
author_facet Seoane, Javier
Bustamante, Javier
Díaz-Delgado, Ricardo
author_role author
author2 Bustamante, Javier
Díaz-Delgado, Ricardo
author2_role author
author
dc.subject.none.fl_str_mv Map quality
Wildlife-habitat models
Bird distribution
topic Map quality
Wildlife-habitat models
Bird distribution
description Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of ex- isting maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km2 of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejer´ıa de Medio Ambiente, Junta de Andaluc´ıa). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by im- proving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation struc- ture maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution
publishDate 2003
dc.date.none.fl_str_mv 2003
2012
2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/47040
url http://hdl.handle.net/10261/47040
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.ecolmodel.2003.10.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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