Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring

Autores
Cingolani, Ana María; Vaieretti, Maria Victoria; Gurvich, Diego Ezequiel; Giorgis, Melisa Adriana; Cabido, Marcelo Ruben
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We searched for predictive models for alpha, beta and gamma plant diversity based in easy to measure field indicators. The study was conducted on the upper belt of the Córdoba mountains (Argentina). We established 222 permanent plots of 4 × 4 m distributed on sites with different physiognomy, topography and management. At each plot we measured physical and physiognomic indicators and recorded the presence of all vascular plants. We estimated alpha diversity as the number of species detected in a plot, beta diversity as the floristic dissimilarity between two plots, and gamma diversity as the number of species detected in a landscape. Through linear regression we found predictive models for alpha and pair-wise beta diversity. Then we analysed if predicted average alpha and beta diversity were good estimators of gamma diversity. We recorded a total of 288 species (5–74 species per plot). Alpha diversity was highest in sites on shallow soils with high structural richness (i.e. high number of cover categories), half covered by lawns, at sunny slopes and rough landscapes (r2 = 0.66). For beta diversity, the difference between plots in structural richness and in cover of thick tussocks grasses and lawns were the best predictors (r2 = 0.45). For different sets of simulated landscapes, gamma diversity was well explained by predicted average alpha and beta diversity, plus the sampling effort (r2 = 0.92). We concluded that using easy to measure field indicators it is possible to estimate plant diversity at different levels with a good accuracy.
Fil: Cingolani, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Vaieretti, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Gurvich, Diego Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Giorgis, Melisa Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Cabido, Marcelo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Materia
Botanic Dissimilarity
Gamma Diversity
Growths-Forms
Landscape
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14994
Acceder
id CONICETDig_1ef01f8c06826df0c47990ac917d59c0
oai_identifier_str oai:ri.conicet.gov.ar:11336/14994
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoringCingolani, Ana MaríaVaieretti, Maria VictoriaGurvich, Diego EzequielGiorgis, Melisa AdrianaCabido, Marcelo RubenBotanic DissimilarityGamma DiversityGrowths-FormsLandscapehttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1We searched for predictive models for alpha, beta and gamma plant diversity based in easy to measure field indicators. The study was conducted on the upper belt of the Córdoba mountains (Argentina). We established 222 permanent plots of 4 × 4 m distributed on sites with different physiognomy, topography and management. At each plot we measured physical and physiognomic indicators and recorded the presence of all vascular plants. We estimated alpha diversity as the number of species detected in a plot, beta diversity as the floristic dissimilarity between two plots, and gamma diversity as the number of species detected in a landscape. Through linear regression we found predictive models for alpha and pair-wise beta diversity. Then we analysed if predicted average alpha and beta diversity were good estimators of gamma diversity. We recorded a total of 288 species (5–74 species per plot). Alpha diversity was highest in sites on shallow soils with high structural richness (i.e. high number of cover categories), half covered by lawns, at sunny slopes and rough landscapes (r2 = 0.66). For beta diversity, the difference between plots in structural richness and in cover of thick tussocks grasses and lawns were the best predictors (r2 = 0.45). For different sets of simulated landscapes, gamma diversity was well explained by predicted average alpha and beta diversity, plus the sampling effort (r2 = 0.92). We concluded that using easy to measure field indicators it is possible to estimate plant diversity at different levels with a good accuracy.Fil: Cingolani, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Vaieretti, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Gurvich, Diego Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Giorgis, Melisa Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Cabido, Marcelo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; ArgentinaElsevier2010-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14994Cingolani, Ana María; Vaieretti, Maria Victoria; Gurvich, Diego Ezequiel; Giorgis, Melisa Adriana; Cabido, Marcelo Ruben; Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring; Elsevier; Biological Conservation; 143; 11; 8-2010; 2570-25770006-3207enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.biocon.2010.06.026info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0006320710002922info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:25:51Zoai:ri.conicet.gov.ar:11336/14994instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:25:51.975CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
title Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
spellingShingle Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
Cingolani, Ana María
Botanic Dissimilarity
Gamma Diversity
Growths-Forms
Landscape
title_short Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
title_full Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
title_fullStr Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
title_full_unstemmed Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
title_sort Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring
dc.creator.none.fl_str_mv Cingolani, Ana María
Vaieretti, Maria Victoria
Gurvich, Diego Ezequiel
Giorgis, Melisa Adriana
Cabido, Marcelo Ruben
author Cingolani, Ana María
author_facet Cingolani, Ana María
Vaieretti, Maria Victoria
Gurvich, Diego Ezequiel
Giorgis, Melisa Adriana
Cabido, Marcelo Ruben
author_role author
author2 Vaieretti, Maria Victoria
Gurvich, Diego Ezequiel
Giorgis, Melisa Adriana
Cabido, Marcelo Ruben
author2_role author
author
author
author
dc.subject.none.fl_str_mv Botanic Dissimilarity
Gamma Diversity
Growths-Forms
Landscape
topic Botanic Dissimilarity
Gamma Diversity
Growths-Forms
Landscape
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We searched for predictive models for alpha, beta and gamma plant diversity based in easy to measure field indicators. The study was conducted on the upper belt of the Córdoba mountains (Argentina). We established 222 permanent plots of 4 × 4 m distributed on sites with different physiognomy, topography and management. At each plot we measured physical and physiognomic indicators and recorded the presence of all vascular plants. We estimated alpha diversity as the number of species detected in a plot, beta diversity as the floristic dissimilarity between two plots, and gamma diversity as the number of species detected in a landscape. Through linear regression we found predictive models for alpha and pair-wise beta diversity. Then we analysed if predicted average alpha and beta diversity were good estimators of gamma diversity. We recorded a total of 288 species (5–74 species per plot). Alpha diversity was highest in sites on shallow soils with high structural richness (i.e. high number of cover categories), half covered by lawns, at sunny slopes and rough landscapes (r2 = 0.66). For beta diversity, the difference between plots in structural richness and in cover of thick tussocks grasses and lawns were the best predictors (r2 = 0.45). For different sets of simulated landscapes, gamma diversity was well explained by predicted average alpha and beta diversity, plus the sampling effort (r2 = 0.92). We concluded that using easy to measure field indicators it is possible to estimate plant diversity at different levels with a good accuracy.
Fil: Cingolani, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Vaieretti, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Gurvich, Diego Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Giorgis, Melisa Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
Fil: Cabido, Marcelo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina; Argentina. Universidad Nacional de Córdoba; Argentina
description We searched for predictive models for alpha, beta and gamma plant diversity based in easy to measure field indicators. The study was conducted on the upper belt of the Córdoba mountains (Argentina). We established 222 permanent plots of 4 × 4 m distributed on sites with different physiognomy, topography and management. At each plot we measured physical and physiognomic indicators and recorded the presence of all vascular plants. We estimated alpha diversity as the number of species detected in a plot, beta diversity as the floristic dissimilarity between two plots, and gamma diversity as the number of species detected in a landscape. Through linear regression we found predictive models for alpha and pair-wise beta diversity. Then we analysed if predicted average alpha and beta diversity were good estimators of gamma diversity. We recorded a total of 288 species (5–74 species per plot). Alpha diversity was highest in sites on shallow soils with high structural richness (i.e. high number of cover categories), half covered by lawns, at sunny slopes and rough landscapes (r2 = 0.66). For beta diversity, the difference between plots in structural richness and in cover of thick tussocks grasses and lawns were the best predictors (r2 = 0.45). For different sets of simulated landscapes, gamma diversity was well explained by predicted average alpha and beta diversity, plus the sampling effort (r2 = 0.92). We concluded that using easy to measure field indicators it is possible to estimate plant diversity at different levels with a good accuracy.
publishDate 2010
dc.date.none.fl_str_mv 2010-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/14994
Cingolani, Ana María; Vaieretti, Maria Victoria; Gurvich, Diego Ezequiel; Giorgis, Melisa Adriana; Cabido, Marcelo Ruben; Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring; Elsevier; Biological Conservation; 143; 11; 8-2010; 2570-2577
0006-3207
url http://hdl.handle.net/11336/14994
identifier_str_mv Cingolani, Ana María; Vaieretti, Maria Victoria; Gurvich, Diego Ezequiel; Giorgis, Melisa Adriana; Cabido, Marcelo Ruben; Predicting alfa, beta and gamma diversity from physiognomic and physical indicators as a tool for ecosystem monitoring; Elsevier; Biological Conservation; 143; 11; 8-2010; 2570-2577
0006-3207
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.biocon.2010.06.026
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0006320710002922
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082698016194560
score 13.22299

Publicaciones similares