Network‐ and distance‐based methods in bioregionalization processes at regional scale: An application to the terrestrial mammals of Iran

Aim In recent years, novel approaches have been proposed to improve current bioregionalization methods, but these have not been thoroughly compared. We assessed the applicability of the recently developed network‐based clustering method (Infomap algorithm) in bioregionalization analysis at regional spatial scales and compared the results with commonly used distance‐based methods (hierarchical clustering algorithm). We also identified climate regions by using a model‐based cluster analysis (Gaussian algorithm). Finally, we quantified the representation of climate regions and bioregions in current protected areas (PAs). Location Iran. Taxa Terrestrial mammals. Methods To define bioregions we used the Infomap algorithm and distance‐based clustering methods based on species distribution data (over 14,000 occurrence records for 188 species). The Infomap algorithm was applied using the interactive web application “INFOMAP BIOREGIONS” and the distance‐based clustering was based on unweighted pair‐group method using arithmetic averages (UPGMA). To identify climate regions we used principal components analysis and a model‐based cluster analysis both based on 15 climatic variables as well as a terrain ruggedness index. Results The Infomap algorithm detected nine biogeographical units: seven bioregions and two transition zones. The distance‐based method suggested five bioregions. The identified bioregions differed between methods with some consistent spatial patterns across methods. Temperature and precipitation explained 85.8% of the environmental variation. Eight climate regions were identified. In general, climate variation and bioregional patterns are currently poorly represented in PAs (