IDEA   23902
INSTITUTO DE DIVERSIDAD Y ECOLOGIA ANIMAL
Unidad Ejecutora - UE
artículos
Título:
Remote sensing variables as predictors of habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling mammal living in a desert environment.
Autor/es:
CAMPOS, V.E.; GATICA, GM; BELLIS, LM
Revista:
Mammal Research
Editorial:
Springer
Referencias:
Año: 2015 vol. 60 p. 117 - 117
ISSN:
2199-2401
Resumen:
Identifying high-quality habitats across large areas is a central goal in biodiversity conservation. Remotely sensed data provide the opportunity to study different habitat characteristics (e.g., landscape topography, soil, vegetation cover, climatic factors) that are difficult to identify at high spatial and temporal resolution on the basis of field studies. Our goal was to evaluate the applicability of remotely sensed information as a potential tool for modeling habitat suitability of the viscacha rat (Octomys mimax), a rock-dwelling species that lives in a desert ecosystem.We fitted models considering raw indices (i.e., green indices, Brightness Index (BI) and temperature) and their derived texture measures on locations used by and available for the viscacha rat. The habitat preferences identified in our models are consistent with results of field studies of landscape use by the viscacha rat. Rocky habitats were well differentiated by the second-order contrast of BI, instead of BI only, making an important contribution to the global model by capturing the heterogeneity of the substratum. Furthermore, rocky habitats are able to maintain more vegetation than much of the surrounding desert; hence, their availability might be estimated using SATVI (Soil Adjusted Total Vegetation Index) and its derived texture measures: second-order contrast and entropy. This is the first study that evaluates the usefulness of remotely sensed data for predicting and mapping habitat suitability for a small-bodied rock dwelling species in a desert environment. Our results may contribute to conservation efforts focused on these habitat specialist species by using good predictors of habitat quality.