Spatial data integration for the environmental characterization of pasture macrotypes in the Italian Alps. (26th March 2015)

• Record Type: Journal Article
• Authors: Dibari, C.; Bindi, M.; Moriondo, M.; Staglianò, N.; Targetti, S.; Argenti, G.
• Is Part Of: Grass and forage science. Volume 71:Number 2(2016:Jun.)
• Page Start: 219
• Page End: 234
• Publication Date: 2015-03-26
• Subjects: semi‐natural grasslands -- spatial data integration -- Alps -- pasture macrotypes -- pasture characterization; Grasses -- Periodicals; Forage plants -- Periodicals; Grasslands -- Periodicals; 633.2
• DOI: 10.1111/gfs.12168
• Languages: English
• ISSNs: 0142-5242

Abstract

Abstract: In the Italian Alps, local climate, topography and soil play a dominant role in pasture distribution. In this study, pre‐existent layers of spatial data were used to identify the geographical locations of the main pasture communities (macrotypes) dominating these mountainous areas and to characterize them environmentally. The influence of the environment on macrotype distribution was assessed by principal component analysis (PCA) and a kernel density estimation (KDE) on nine explanatory variables extracted from soil–topography–climatic gridded maps. Results indicated the reliability of the method. The ecological description disentangled from PCA and KDE analysis fitted well with the ecological exigencies of seven macrotypes, identified as most representative in terms of their extent, ecological and pastoral importance across the Alps. This was more evident for high‐altitude communities and xeric species, but less evident for the more ubiquitous, mixed or fragmented macrotypes. The results show that three main environmental regions can be distinguished across the area: a relatively wet and cold region located at the highest altitudes dominated by mountainside pasture macrotypes; an intermediate area comprising macrotypes without any marked environmental needs; and relatively dry and warm areas at the lowest altitudes dominated by xeric species and rich semi‐natural grasslands. Method strengths, weaknesses and possible applications of the results are discussed.