Species Distribution Modelling (SDM) techniques were originally developed in the mid-1980s. In this century they are gaining increasing attention in the literature and in practical use as a powerful tool to support forest management strategies especially under climate change. In this review paper we consider species occurrence datasets, climatic and soil predictor variables, modelling algorithms, evaluation methods and widely used software for SDM studies. We describe several important and freely available sources for species occurrence and interpolated climatic data. We outline the use of both presence-only and presence/absence modelling algorithms including distance-based algorithms, machine learning algorithms and regression-based models. We conclude that SDM techniques provide a valuable asset for forest managers. However, it is essential to consider uncertainties behind the use of future climate change scenarios.
Species distribution modelling to support forest management. A literature review / Pecchi M.; Marchi M.; Burton V.; Giannetti F.; Moriondo M.; Bernetti I.; Bindi M.; Chirici G.. - In: ECOLOGICAL MODELLING. - ISSN 0304-3800. - ELETTRONICO. - 411:(2019), pp. 1-12. [10.1016/j.ecolmodel.2019.108817]
Species distribution modelling to support forest management. A literature review
Pecchi M.;Giannetti F.;Bernetti I.;Bindi M.;Chirici G.
2019
Abstract
Species Distribution Modelling (SDM) techniques were originally developed in the mid-1980s. In this century they are gaining increasing attention in the literature and in practical use as a powerful tool to support forest management strategies especially under climate change. In this review paper we consider species occurrence datasets, climatic and soil predictor variables, modelling algorithms, evaluation methods and widely used software for SDM studies. We describe several important and freely available sources for species occurrence and interpolated climatic data. We outline the use of both presence-only and presence/absence modelling algorithms including distance-based algorithms, machine learning algorithms and regression-based models. We conclude that SDM techniques provide a valuable asset for forest managers. However, it is essential to consider uncertainties behind the use of future climate change scenarios.File | Dimensione | Formato | |
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