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.
Distribution modelling at fine resolutions of endemics in Majella National Park, Central Italy
CONTI, Fabio;
2012
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
