Climate‐based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna

Abstract

Aim Identifying areas of high species richness is an important goal of conservation biogeography. In this study we compared alternative methods for generating climate‐based estimates of spatial patterns of butterfly and mammal species richness. Location Egypt. Methods Data on the occurrence of butterflies and mammals in Egypt were taken from an electronic database compiled from museum records and the literature. Using Maxent, species distribution models were built with these data and with variables describing climate and habitat. Species richness predictions were made by summing distribution models for individual species and by modelling observed species richness directly using the same environmental variables. Results Estimates of species richness from both methods correlated positively with each other and with observed species richness. Protected areas had higher species richness (both predicted and actual) than unprotected areas. Main conclusions Our results suggest that climate‐based models of species richness could provide a rapid method for selecting potential areas for protection and thus have important implications for biodiversity conservation.

Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques

“…However, in current study, among all the evaluation results from AUC, TSS and Kappa, there is no significant difference between the three evaluate methods, which interpret that the use of evaluation measures didn't interrupt the outcomes of statistical models, we can thus verified that the relationship between species characteristics and model performance was not artificial associated with use of accuracy measures (Newbold et al, 2009). However, to be more concisely evaluated the model performance, some recently studies presented one kind of global sensitivity and uncertainty analysis (GSUA) in SDMs (Convertino et al, 2014), compare with the traditional sensitivity analyses, GSUA is a robust method to globally investigate the uncertainty of SDMs and the importance of species distributions' drivers in space and time.…”

Effect of characteristics of butterfly species on the accuracy of distribution models in an arid environment

“…The species data were collected between 1829 and 2006, but most records date from the 20th Century (Newbold et al 2009). We used five environmental variables as predictors: four principal components, which describe altitude and 19 bioclimatic variables from WorldClim (Hijmans et al 2005), and a categorical land cover variable based on AVHRR satellite data (Hansen et al 2000).…”

Recorded and potential distributions on the iberian peninsula of speciesof Lepidoptera listed in the Habitats Directive

“…For the reasons discussed above (the broad knowledge of their present distributions, unequal sampling effort, gradual reduction in their ranges), Lepidoptera are good candidates for species distribution modelling (Fleishman et al, 2001;Scheingross, 2007;Newbold et al, 2009). Therefore, it seems appropriate to model the distribution of the species of Lepidoptera listed in the Habitats Directive and identify potential areas with suitable habitats for the priority species included in the Directive.…”