Continent-Wide Tree Species Distribution Models May Mislead Regional Management Decisions: A Case Study in the Transboundary Biosphere Reserve Mura-Drava-Danube

• Main Authors: Markus Sallmannshofer, Debojyoti Chakraborty, Harald Vacik, Gábor Illés, Markus Löw, Andreas Rechenmacher, Katharina Lapin, Sophie Ette, Dejan Stojanović, Andrej Kobler, Silvio Schueler
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Forests
• Subjects: bioclimatic model; ecological niche model; forest management; tree species selection; riparian forest habitat; climate change adaptation
• Online Access: https://www.mdpi.com/1999-4907/12/3/330

The understanding of spatial distribution patterns of native riparian tree species in Europe lacks accurate species distribution models (SDMs), since riparian forest habitats have a limited spatial extent and are strongly related to the associated watercourses, which needs to be represented in the environmental predictors. However, SDMs are urgently needed for adapting forest management to climate change, as well as for conservation and restoration of riparian forest ecosystems. For such an operative use, standard large-scale bioclimatic models alone are too coarse and frequently exclude relevant predictors. In this study, we compare a bioclimatic continent-wide model and a regional model based on climate, soil, and river data for central to south-eastern Europe, targeting seven riparian foundation species—<i>Alnus glutinosa</i>,<i> Fraxinus angustifolia</i>,<i> F. excelsior</i>,<i> Populus nigra</i>,<i> Quercus robur</i>,<i> Ulmus laevis,</i> and <i>U. minor</i>. The results emphasize the high importance of precise occurrence data and environmental predictors. Soil predictors were more important than bioclimatic variables, and river variables were partly of the same importance. In both models, five of the seven species were found to decrease in terms of future occurrence probability within the study area, whereas the results for two species were ambiguous. Nevertheless, both models predicted a dangerous loss of occurrence probability for economically and ecologically important tree species, likely leading to significant effects on forest composition and structure, as well as on provided ecosystem services.