BackgroundShrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent.MethodsThis study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only.ResultsThe predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables.ConclusionsOur study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants.

A survey of the Blanford’s fox was established in Jabal Masuda (Southern Jordan), to increase the knowledge of its density. Live-trapping method was used and capture mark-recapture technique was applied. We captured a total of 27 specimens including nine re-captured specimens. Density was determined using two methods: the Bondrup-Nielsen formula which identified 8.5 individuals per km², and the ArcGIS tools which showed values, between 0.177 to 9 individuals per km². This paper is the first to give measured information on the Blanford’s fox density in Jordan. As well, it showed that ArcGIS 9.3 Spatial Analyst Extension is an effective tool in establishing information on density in large spatial areas. The information provided could serve as a base for future monitoring of the Blanford’s fox’s range of occurrence.