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EnglishMajella 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.
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| Titolo: | Distribution modelling at fine resolutions of endemics in Majella National Park, Central Italy |
| Autori: | |
| Data di pubblicazione: | 2012 |
| Rivista: | |
| 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. |
| Handle: | http://hdl.handle.net/11581/242106 |
| Appare nelle tipologie: | Articolo |
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