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

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

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YUSEFI, Gholam Hosein, Kamran SAFI, José Carlos BRITO, 2019. Network‐ and distance‐based methods in bioregionalization processes at regional scale : An application to the terrestrial mammals of Iran. In: Journal of Biogeography. 46(11), pp. 2433-2443. ISSN 0305-0270. eISSN 1365-2699. Available under: doi: 10.1111/jbi.13694

@article{Yusefi2019-11Netwo-46942, title={Network‐ and distance‐based methods in bioregionalization processes at regional scale : An application to the terrestrial mammals of Iran}, year={2019}, doi={10.1111/jbi.13694}, number={11}, volume={46}, issn={0305-0270}, journal={Journal of Biogeography}, pages={2433--2443}, author={Yusefi, Gholam Hosein and Safi, Kamran and Brito, José Carlos} }

Safi, Kamran 2019-11 2019-09-18T12:16:29Z Network‐ and distance‐based methods in bioregionalization processes at regional scale : An application to the terrestrial mammals of Iran eng Brito, José Carlos 2019-09-18T12:16:29Z Aim<br />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).<br /><br />Location<br />Iran.<br /><br />Taxa<br />Terrestrial mammals.<br /><br />Methods<br />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.<br /><br />Results<br />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 (<25% coverage).<br /><br />Main conclusions<br />The network‐based method allowed identifying bioregions at regional scale and was apparently more sensitive than the conventional distance‐based method. The detection of transition zones by the Infomap algorithm was an advantage, and stressed the fact that the distribution of Iranian mammalian fauna is complex, especially in the south‐eastern part where contact areas between several bioregions are found. The identified bioregions (especially the distance‐based bioregions) and climate regions tended to match well with previous bioregionalization studies and the global terrestrial ecoregions. When thoroughly compared and understood, bioregions and climate regions provide a framework for regional biodiversity conservation planning. Safi, Kamran Yusefi, Gholam Hosein Brito, José Carlos Yusefi, Gholam Hosein

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