Temperature and precipitation as predictors of species richness in northern andean amphibians from Colombia
ABSTRACT: Our objective was to explore the spatial distribution patterns of amphibian species richness in Antioquia, as model for the tropical Andes, and determine how annual mean temperature, annual precipitation, and elevation range influence it. We also briefly compare local and global regression...
- Autores:
-
Ortiz Yusty, Carlos Eduardo
Páez Nieto, Vivian Patricia
Zapata Rivera, Fernando Alberto
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2013
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/22196
- Acceso en línea:
- http://hdl.handle.net/10495/22196
https://revistas.unal.edu.co/index.php/cal/article/view/39098
- Palabra clave:
- Amphibians
Anfibios
Especies
Species
Modelos de regresión
http://aims.fao.org/aos/agrovoc/c_359
http://aims.fao.org/aos/agrovoc/c_7280
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by/2.5/co/
Summary: | ABSTRACT: Our objective was to explore the spatial distribution patterns of amphibian species richness in Antioquia, as model for the tropical Andes, and determine how annual mean temperature, annual precipitation, and elevation range influence it. We also briefly compare local and global regression models for estimating the relation between environmental variables and species richness. Distribution maps for 223 amphibian species and environmental variables were generalized onto grid maps of 752 blocks each covering the entire Department of Antioquia. We explored the relationship between species richness and environment using two global regression models (the Ordinary Least Squares “OLS” and Generalized Linear Squares “GLS” models) and one local model (the Geographically Weighted Regression “GWR” model). We found a significant relationship between species richness and environmental variables (GLS r2: 0.869; GRW r2: 0.929). The GLS model efficiently incorporated the spatial autocorrelation effect and handled spatial dependence in the regression error terms while the GWR model showed the best fit (r2) and balance between number of parameters and fit (AICc). GWR parameters show wide variation within the study area, indicating that relationship between species richness and climate is spatially complex. Temperature was the most important variable in the GLS and GWR models, and altitude range the least significant. The strong relationship between environment and amphibian richness is possibly due to life history traits of amphibians, such as ectothermy and water dependency to complete the life cycle. |
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