Exploring spatial relationship between butterfly richness and environmental predictors at a local scale in North-Eastern Turkey

Abstract

Spatial distribution pattern of butterfly species richness were explored using geographically weighted regression (GWR) and ordinary least square (OLS) regression. These models were compared to assess their abilities in modelling butterfly species richness and, further the spatial variation in the relationship between butterfly species richness and environmental predictors was questioned. Data on the occurrence of butterflies from "Die Tagfalter der Türkei unter besonderer Berücksichtigung der angrenzenden Länder" (The Butterflies of Turkey with special attention to the adjacent countries) and three groups of environmental predictors (climatology, topology, and physical features) were incorporated in the analyses after eliminating highly correlated, redundant predictors. Furthermore, Monte Carlo permutation test was applied simultaneously to assess non-stationarity in the relationship between butterfly species richness and environmental predictors. The results indicated that GWR model predicted butterfly species richness better than the OLS model and also, demonstrated spatial non-stationarity in the relationship between butterfly species richness and environmental predictors. In addition, it was found that most of the variation in butterfly species richness was associated with minimum temperature in January, maximum temperature in July, diurnal range, and solar radiation. This result indicated that the distribution of butterfly species richness is mostly governed by climatic environmental predictors, particularly temperature related predictors, indicating that many butterfly species may respond to projected climate changes rapidly. © 2013, ALÖKI Kft., Budapest, Hungary.