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Abstract
Majella National Park in central Italy is known to be an endemic-rich area, but distributions of its endemics have not been comprehensively studied. Endemics with 10 or more records and spatial uncertainties at <5 km were extracted from the Central-Apennine floristic geodatabase and the MNP Seed Index. Nine environmental predictor layers were prepared at 90 and 30 m resolution. A stepwise Maximum Entropy (Maxent) model was generated per endemic to achieve the most parsimonious result at an area under the curve > 0.8. Arctic-alpine elevation, edaphic barrens and low open-vegetation, individually or in pairs, were found to be predictive for endemics. Forty-eight endemics, 10 of which exclusive, were recorded and Maxent-predicted for the Majella massif. Subsets of 38 endemics were recorded on other mountains in proportion to their arctic-alpine area, thus conforming to the Island Theory. Maxent confirmed its strengths also at fine resolutions and, in addition, showed to be robust across predictor layers at both resolutions. A linear species-area relationship appeared superior to the Maxent model in predicting the number of endemics per arctic-alpine “island”. Our findings suggest the need for a proactive management of the botanical biodiversity contained in the alpine and montane barrens and low-open vegetation.
Acknowledgements
CRFA staff members, Daniela Tinti, Marco Iocchi and Fabrizio Bartolucci, are acknowledged for designing and populating the floristic geodatabase and their assistance in the extraction of records. The exploration of distribution models at various resolutions by Sylvia Nanyomo, Chia-Chi Chang and Arun Rai is gratefully remembered. Anton Vrieling (ITC) and David Rossiter (ITC) are acknowledged for theirreview of intermediate findings. Sincere thanks to the MNP for assistance with expertise, information and accommodation. Our research was financially supported by The Netherlands Organisation for International Cooperation in Higher Education (NUFFIC) and the European Cooperation in Science and Technology (COST) action: Expected Climate Change and Options for European Silvaculture (ECHOES). We are grateful to an anonymous reviewer for stimulating comments on an earlier version of the manuscript.