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Spatial probability modelling of forest productivity indicator in Italy

Abstract : The prediction of forest productivity is essential for sustainable forest management, particularly in countries, likeItaly, where forest is an important part of many protected areas.A spatial predictive probability model for forest productivity rates in Italy was developed over the period1961–1990, based on 135 annually-resolved records of site productivity and auxiliary variables measured at 219stations. Our analysis shows that the probability offinding high (> 7.3 m3ha−1yr−1) and low(< 5.8 m3ha−1yr−1) productivity rates changes across different regions of Italy. The generated spatial patternscontribute to a better understanding of the factors structuring the distribution of forest productivity in Italybecause they reflect the dependence of temperature and water availability conditions on the latitudinal andaltitudinal location of the study areas. We observed that the temperature control dominates forest productivity athigh elevations and latitudes, whereas low-elevation sites in central and southern Italy are more sensitive towater availability. The proposed spatial probability modelling should be further assessed for its possible in-corporation into forest management plans.
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Nazzareno Diodato, Gianni Bellocchi. Spatial probability modelling of forest productivity indicator in Italy. Ecological Indicators, Elsevier, 2020, 108, ⟨10.1016/j.ecolind.2019.105721⟩. ⟨hal-02320568⟩

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