We present a statistically derived phytogeographic regionalization based on the spatial distribution of native woody flora, investigating environmental correlates and assessing congruence between the spatial patterns of species, genera, and families. A sector of central peninsular Italy (Lazio and Abruzzo regions) was selected as a case study. A rich georeferenced floristic database was compiled, including information from different sources. A total of 43,968 occurrence data, 290 10 × 10 km cells, 224 species, 103 genera, and 80 families was used; Ward’s clustering was performed to identify phytogeographic units. Three well-defined and relatively spatially coherent units were identified at the species, genus, and family levels: a Mediterranean unit, a Transition unit, and a Eurosiberian one. Congruence between taxonomic levels was well supported. Further divisions in subunits were detected using species data. The main environmental descriptors of the clusters were distance from the sea, elevation, temperature, and lithology.
Detecting phytogeographic units based on native woody flora: a case study in central peninsular Italy.
BARTOLUCCI F;CONTI F;
2017-01-01
Abstract
We present a statistically derived phytogeographic regionalization based on the spatial distribution of native woody flora, investigating environmental correlates and assessing congruence between the spatial patterns of species, genera, and families. A sector of central peninsular Italy (Lazio and Abruzzo regions) was selected as a case study. A rich georeferenced floristic database was compiled, including information from different sources. A total of 43,968 occurrence data, 290 10 × 10 km cells, 224 species, 103 genera, and 80 families was used; Ward’s clustering was performed to identify phytogeographic units. Three well-defined and relatively spatially coherent units were identified at the species, genus, and family levels: a Mediterranean unit, a Transition unit, and a Eurosiberian one. Congruence between taxonomic levels was well supported. Further divisions in subunits were detected using species data. The main environmental descriptors of the clusters were distance from the sea, elevation, temperature, and lithology.File | Dimensione | Formato | |
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