MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran

Abstract

Climate changes pose serious challenges to the persistence of plant species, especially those with narrow habitats. Failure to adopt timely measures would lead to the permanent loss of valuable endangered species. This study used maximum entropy to predict the geographical distribution of a medicinal and vulnerable plant species, Daphne mucronata Royle, under current and future climatic conditions (A2a/HadCM3) in central Iran. A total of 100 locations with the species occurrence were recorded. Three topographic variables (slope, elevation, and aspect) and 19 bioclimatic variables derived from monthly data (spatial resolution=1km) were used in the modeling process. The results showed that D. mucronata distribution was largely affected by elevation, annual precipitation, and precipitation of coldest quarter. According to species response curves, this species preferred habitats with mean precipitation of coldest quarter from 120 to 140mm, annual precipitation of 240–280mm, and elevation more than 2600m above sea level. The climate change projection (A2a/HadCM3) indicated that by 2030 and 2080, D. mucronata would disappear in sites located below 2000m, remain unchanged in elevations more than 3000m, and undergo a drastic change in sites located at 2000–3000m above sea level. This information is essential for conservation planners and rangeland managers who work on protecting the species from extinction. A similar approach can be adopted to identify sites with high extinction probability of endangered species and to protect susceptible habitats from the effects of climate change and future modifications.