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A Generalized Approach to the Modeling of the Species-Area Relationship
This paper proposes a statistical generalized species-area model (GSAM) to represent various patterns of species-area relationship (SAR), which is one of the fundamental patterns in ecology. The approach enables the generalization of many preliminary models, as power-curve model, which is commonly u...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149369/ https://www.ncbi.nlm.nih.gov/pubmed/25171161 http://dx.doi.org/10.1371/journal.pone.0105132 |
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author | Conceição, Katiane Silva Ulrich, Werner Diniz, Carlos Alberto Ribeiro Rodrigues, Francisco Aparecido de Andrade, Marinho Gomes |
author_facet | Conceição, Katiane Silva Ulrich, Werner Diniz, Carlos Alberto Ribeiro Rodrigues, Francisco Aparecido de Andrade, Marinho Gomes |
author_sort | Conceição, Katiane Silva |
collection | PubMed |
description | This paper proposes a statistical generalized species-area model (GSAM) to represent various patterns of species-area relationship (SAR), which is one of the fundamental patterns in ecology. The approach enables the generalization of many preliminary models, as power-curve model, which is commonly used to mathematically describe the SAR. The GSAM is applied to simulated data set of species diversity in areas of different sizes and a real-world data of insects of Hymenoptera order has been modeled. We show that the GSAM enables the identification of the best statistical model and estimates the number of species according to the area. |
format | Online Article Text |
id | pubmed-4149369 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-41493692014-09-03 A Generalized Approach to the Modeling of the Species-Area Relationship Conceição, Katiane Silva Ulrich, Werner Diniz, Carlos Alberto Ribeiro Rodrigues, Francisco Aparecido de Andrade, Marinho Gomes PLoS One Research Article This paper proposes a statistical generalized species-area model (GSAM) to represent various patterns of species-area relationship (SAR), which is one of the fundamental patterns in ecology. The approach enables the generalization of many preliminary models, as power-curve model, which is commonly used to mathematically describe the SAR. The GSAM is applied to simulated data set of species diversity in areas of different sizes and a real-world data of insects of Hymenoptera order has been modeled. We show that the GSAM enables the identification of the best statistical model and estimates the number of species according to the area. Public Library of Science 2014-08-29 /pmc/articles/PMC4149369/ /pubmed/25171161 http://dx.doi.org/10.1371/journal.pone.0105132 Text en © 2014 Conceição et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Conceição, Katiane Silva Ulrich, Werner Diniz, Carlos Alberto Ribeiro Rodrigues, Francisco Aparecido de Andrade, Marinho Gomes A Generalized Approach to the Modeling of the Species-Area Relationship |
title | A Generalized Approach to the Modeling of the Species-Area Relationship |
title_full | A Generalized Approach to the Modeling of the Species-Area Relationship |
title_fullStr | A Generalized Approach to the Modeling of the Species-Area Relationship |
title_full_unstemmed | A Generalized Approach to the Modeling of the Species-Area Relationship |
title_short | A Generalized Approach to the Modeling of the Species-Area Relationship |
title_sort | generalized approach to the modeling of the species-area relationship |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149369/ https://www.ncbi.nlm.nih.gov/pubmed/25171161 http://dx.doi.org/10.1371/journal.pone.0105132 |
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