The drivers of dark diversity in the Scandinavian mountains are metric‐dependent

• Published in: Journal of Vegetation Science
• Main Authors: Hostens, Lore, van Meerbeek, Koenraad, Wiegmans, Dymphna, Larson, Keith, Lenoir, Jonathan, Clavel, Jan, Wedegärtner, Ronja, Pirée, Amber, Nijs, Ivan, Lembrechts, Jonas
• Other Authors: Research Group of Plant and Vegetation Ecology, Universiteit Antwerpen = University of Antwerpen Antwerpen, Department of Earth and Environmental Sciences Leuven (EES), Catholic University of Leuven = Katholieke Universiteit Leuven (KU Leuven), KU Leuven Plant Institute (LPI), Climate Impacts Research Centre (CIRC), Umeå University, Sweden, Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 UPJV (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), Department of Biology Trondheim (IBI NTNU), Norwegian University of Science and Technology Trondheim (NTNU), Norwegian University of Science and Technology (NTNU)-Norwegian University of Science and Technology (NTNU)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: Beals' index;Co-occurrence model;Habitat characteristics;Method comparison;Niche model;Plant diversity;Plant ecology;Plant traits;Regional species pool; [SDE]Environmental Sciences; Abisko; Northern Sweden; Subarctic
• Online Access: https://hal.science/hal-04423954; https://doi.org/10.1111/jvs.13212

International audience Abstract Question Dark diversity refers to the set of species that are not observed in an area but could potentially occur based on suitable local environmental conditions. In this paper, we applied both niche‐based and co‐occurrence‐based methods to estimate the dark diversity of vascular plant species in the subarctic mountains. We then aimed to unravel the drivers explaining (a) why some locations were missing relatively more suitable species than others, and (b) why certain plant species were more often absent from suitable locations than others. Location The Scandinavian mountains around Abisko, northern Sweden. Methods We calculated the dark diversity in 107 plots spread out across four mountain trails using four different methods: two co‐occurrence‐based (Beals’ index and the hypergeometric method) and two niche‐based (the climatic niche model and climatic niche model followed by species‐specific threshold). We then applied multiple Generalized Linear Mixed‐Effects Models and General Linear Models to determine which habitat characteristics and species traits contributed the most to dark diversity. Results The study showed a notable divergence in the predicted drivers of dark diversity depending on the method used. Nevertheless, we can conclude that plot‐level dark diversity was generally 17% higher in areas at low elevations and 31% higher in areas with a low species richness. Conclusion Our findings call for caution when interpreting statistical findings of dark‐diversity estimates. Even so, all analyses point toward an important role for natural processes such as competitive dominance as the main driver of the spatial patterns found in dark diversity in the northern Scandes.