Temperature and precipitation as predictors of species richness in northern andean amphibians from colombia

Our objective was to explore the spatial distribution patterns of amphibian speciesrichness in Antioquia, as model for the tropical Andes, and determine how annualmean temperature, annual precipitation, and elevation range influence it. We alsobriefly compare local and global regression models for e...

Full description

Autores:
Ortiz-Yusty, Carlos Eduardo
Páez, Vivian
Zapata, Fernando
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/72847
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/72847
http://bdigital.unal.edu.co/37322/
Palabra clave:
Amphibians
spatial pattern of species richness
spatial regression models
environmental variation
macroecology
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Our objective was to explore the spatial distribution patterns of amphibian speciesrichness in Antioquia, as model for the tropical Andes, and determine how annualmean temperature, annual precipitation, and elevation range influence it. We alsobriefly compare local and global regression models for estimating the relationbetween environmental variables and species richness. Distribution maps for 223amphibian species and environmental variables were generalized onto grid mapsof 752 blocks each covering the entire Department of Antioquia. We explored therelationship between species richness and environment using two global regressionmodels (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 andenvironmental variables (GLS r2: 0.869; GRW r2: 0.929). The GLS model efficientlyincorporated the spatial autocorrelation effect and handled spatial dependencein the regression error terms while the GWR model showed the best fit (r2) andbalance between number of parameters and fit (AICc). GWR parameters show widevariation within the study area, indicating that relationship between species richnessand climate is spatially complex. Temperature was the most important variablein the GLS and GWR models, and altitude range the least significant. The strongrelationship between environment and amphibian richness is possibly due to lifehistory traits of amphibians, such as ectothermy and water dependency to completethe life cycle