The drivers of dark diversity in the Scandinavian tundra are metric-dependent

• Main Authors: Hostens, Lore, van Meerbeek, Koenraad, Wiegmans, Dymphna, Larson, Keith, Lenoir, Jonathan, Clavel, Jan, Wedegärtner, Ronja, Pirée, Amber, Nijs, Ivan, Jonas J., Lembrechts
• Other Authors: Research Group of Plant and Vegetation Ecology, Universiteit Antwerpen = University of Antwerpen Antwerpen, Department of Earth and Environmental Sciences Leuven-Heverlee, 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 (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), University of Antwerp (UA), Research Group of Plant and Vegetation Ecology, Department of Biology
• Format: Report
• Language: English
• Published: HAL CCSD 2024
• Subjects: [SDE]Environmental Sciences; Abisko; Northern Sweden; Subarctic; Tundra
• Online Access: https://u-picardie.hal.science/hal-04517820; https://doi.org/10.1101/2023.02.17.528269

Aim 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 tundra. We then aimed to unravel the drivers explaining (1) why some locations were missing relatively more suitable species than others, and (2) why certain plant species were more often absent from suitable locations than others. Location The Scandinavian tundra around Abisko, northern Sweden. Methods We calculated the dark diversity in 107 plots spread out across four mountain trails using four different methods. Two niche-based (Beals’ index and hypergeometric method) and two co-occurrences-based (climatic niche model and climatic niche model followed by species-specific threshold) methods. This was then followed by multiple generalized linear mixed models and general linear models to determine which habitat characteristics and species traits contributed most to the 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 18% higher in areas at low elevations and 30% and 10% higher in areas with a low species richness or low levels of habitat disturbance, respectively. Conclusion Our findings call for caution when interpreting statistical findings of dark diversity estimates. Even so, all analyses point towards an important role for natural processes such as competitive dominance as main driver of the spatial patterns found in dark diversity in the northern Scandes.