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Abstract:Climatic data and bioclimatic indexes have been used to study plants, animals and ecosystem distribution. GIS-based maps of climatic and bioclimatic data can be obtained by interpolating values observed at measurement stations. Since no single method can be considered as optimal for all observed regions, a major task is to propose comparisons between results obtained using different methods applied to the same data set of climate variables. We compared three methods that have been proved to be useful at regional scale: 1 -a local interpolation method based on de-trended inverse distance weighting (D-IDW), 2 -universal kriging (i.e. simple kriging with trend function defined on the basis of a set of covariates) which is optimal (i.e. BLUP, best linear unbiased predictor) if spatial association is present, 3 -multilayer neural networks trained with backpropagation (representing a complex nonlinear fitting). Long-term (1955Long-term ( -1990 average monthly data were obtained from weather stations measuring precipitation (201 sites) and temperature (102 sites). We analysed twelve climatic variables (temperature and precipitation) and nine bioclimatic indexes. Terrain variables and geographical location have been used as predictors of the climate variables: longitude, latitude, elevation, aspect, slope, continentality and estimated solar radiation. Based on the root mean square errors from cross-validation tests, we ranked the best method for each variable data set. Universal kriging with external drift obtained the best performances for seventeen variables of the twenty-one analysed, neural network interpolator has proven to be more efficient for three variables and D-IDW for only one. Based on these results, we used the universal kriging estimates to produce the climatic and bioclimatic maps aimed at defining the bioclimatic envelope of species.

Question: How important are habitat configuration, quality, history and anthropic disturbance in determining nemoral plant species richness and distribution of fragmented forest patches in a Mediterranean region? Location: Agricultural landscape north of Rome, Italy. Methods: Sixty-nine woodland patches, identified through a stratified random sampling, were sampled for nemoral plant species. The homogeneity of woodlands was tested through a hierarchical classification of the floristic data and a Mann-Whitney test of dependent and independent variables. The importance of habitat configuration (area, isolation, shape), quality (soil properties, forest structure, anthropic disturbance) and history (age of woodland) in determining species richness was estimated through a Poisson regression model. Presence-absence of each species was analysed by logistic regression. Differences among plant life-trait types (life span, dispersal mode, habitat preference) were analysed by comparing their median beta-values through ANOVA models. Results: Through hierarchical classification, two woodland types were identified that differed in species composition, habitat quality and spatial configuration. Poisson regression showed that habitat configuration and history influenced species richness. Multiple logistic regression resulted in significant fits for 88 species/variable combinations: 38 are habitat quality variables, 25 are habitat configuration variables, and 13 are anthropic factors. Dispersal strategies varied significantly with respect to area, isolation and age, while generalist and specialist species differed according to age of the woodland. Conclusion: Our results show that habitat history and configuration are the key factors determining species richness of woodland. Together with habitat configuration, habitat quality (mainly soil acidity) appeared to influence species composition

Question: What is the effect of climate change on tree species abundance and distribution in the Italian peninsula? Location: Italian peninsula. Methods: Regression tree analysis, Random Forest, generalized additive model and geostatistical methods were compared to identify the best model for quantifying the effect of climate change on tree species distribution and abundance. Future potential species distribution, richness, local colonization, local extinction and species turnover were modelled according to two scenarios (A2 and B1) for 2050 and 2080. Results: Robust Random Forest proved to be the best statistical model to predict the potential distribution of tree species abundance. Climate change could lead to a shift in tree species distribution towards higher altitudes and a reduction of forest cover. Pinus sylvestris and Tilia cordata may be considered at risk of local extinction, while the other species could find potential suitable areas at the cost of a rearrangement of forest community composition and increasing competition. Conclusions: Geographical and topographical regional characteristics can have a noticeable influence on the impact of predicted climate change on forest ecosystems within the Mediterranean basin. It would be highly beneficial to create a standardized and harmonized European forest inventory in order to evaluate, at high resolution, the effect of climate change on forest ecosystems, identify regional differences and develop specific adaptive management strategies and plans

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