<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Attorre, Fabio</style></author><author><style face="normal" font="default" size="100%">Francesconi, Fabio</style></author><author><style face="normal" font="default" size="100%">Sanctis, Michele</style></author><author><style face="normal" font="default" size="100%">Alfò, Marco</style></author><author><style face="normal" font="default" size="100%">Martella, Francesca</style></author><author><style face="normal" font="default" size="100%">Valenti, Roberto</style></author><author><style face="normal" font="default" size="100%">Vitale, Marcello</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Classifying and Mapping Potential Distribution of Forest Types Using a Finite Mixture Model</style></title><secondary-title><style face="normal" font="default" size="100%">Folia Geobotanica</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Finite Mixture Model</style></keyword><keyword><style  face="normal" font="default" size="100%">forest types</style></keyword><keyword><style  face="normal" font="default" size="100%">Italy</style></keyword><keyword><style  face="normal" font="default" size="100%">Potential distribution</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/index/10.1007/s12224-012-9139-8</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The present paper presents the application of a finite mixture model (FMM) to analyze spatially explicit data on forest composition and environmental variables to produce a high-resolution map of their current potential distribution. FMM provides a convenient yet formal setting for model-based clustering. Within this framework, forest data are assumed to come from an underlying FMM, where each mixture component corresponds to a cluster and each cluster is characterized by a different composition of tree species. An important extension of this model is based on including a set of covariates to predict class membership. These covariates can be climatic and topographical parameters as well as geographical coordinates and the class membership of neighbouring plots. FMM was applied to a national forest inventory of Italy consisting of 6,714 plots with a measure of abundance for 27 tree species. In this way, a map of potential forest types was produced. The limitations and usefulness of the proposed modelling approach were analyzed and discussed, comparing the results with an independently derived expert map</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Di Traglia, Mario</style></author><author><style face="normal" font="default" size="100%">Attorre, Fabio</style></author><author><style face="normal" font="default" size="100%">Francesconi, Fabio</style></author><author><style face="normal" font="default" size="100%">Valenti, Roberto</style></author><author><style face="normal" font="default" size="100%">Vitale, Marcello</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach</style></title><secondary-title><style face="normal" font="default" size="100%">Ecological Modelling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">2-d cellular automata</style></keyword><keyword><style  face="normal" font="default" size="100%">climate change</style></keyword><keyword><style  face="normal" font="default" size="100%">Importance Value</style></keyword><keyword><style  face="normal" font="default" size="100%">Logistic analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Mediterranean basin</style></keyword><keyword><style  face="normal" font="default" size="100%">Potential tree species shift</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://linkinghub.elsevier.com/retrieve/pii/S0304380010006587</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">222</style></volume><pages><style face="normal" font="default" size="100%">925 - 934</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper is presented a methodological approach which integrates statistic modelling and 2-D cellular automata (CA) in order to describe tree species shifts responding to the climate changes foreseen for Italy in the 21st century. Five Italian tree species populations of Abies alba, Pinus sylvestris, Fagus sylvatica, Acer campestris and Quercus suber and their actual potential distributions (PDs) – represented by Importance Value (IV), have been considered. Environmental and climatic relationships have been modelled through application of a new statistical methodology called extreme discretization, where the PD of a species was considered as a random ﬁeld. The IV-based PD has been spatialized through a probability function (A,S), which represented the spatio-temporal relationships between IV values and climatic (A) and geomorphological (S) variables. For each tree species = (A,S) has been estimated and inserted as rule in the 2-D cellular automata. The latter, acting by a Moore neighbouring, took in consideration also the suitability map for tree species, which has been obtained by land cover map. Two time frames (2050 and 2080) and two climatic scenarios (A2 and B1) have been considered. Results described a general reduction of the IV values and their distribution for A. alba, P. sylvestris and F. sylvatica, in both climatic scenarios, whereas an increase of IVs and distribution for Q. suber and only a slight increment of distribution for A. campestris was mainly observed under the B1 scenario, but not for the more limiting A2 scenario. Convergent results have been obtained with respect to other simulation systems concerning the shift of tree species responding to different climatic change scenarios but lacking of the description of dynamical paths. Our approach seems natural and practical to describe such phenomena. The transition rules for the CA and the parameters taken into account for the construction of the probabilistic models can be surely improved to obtain a more realistic pattern of tree species shifts. Future efforts should be made to take in account the inter-speciﬁc relationships inside the Italian forest ecosystems, in order to also consider the competiveness for resources that exert some effects on the plant distribution both in time and space.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><notes><style face="normal" font="default" size="100%">The following values have no corresponding Zotero field:&lt;br/&gt;publisher: Elsevier B.V.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Attorre, Fabio</style></author><author><style face="normal" font="default" size="100%">Alfò, Marco</style></author><author><style face="normal" font="default" size="100%">De Sanctis, Michele</style></author><author><style face="normal" font="default" size="100%">Francesconi, Fabio</style></author><author><style face="normal" font="default" size="100%">Valenti, Roberto</style></author><author><style face="normal" font="default" size="100%">Vitale, Marcello</style></author><author><style face="normal" font="default" size="100%">Bruno, Franco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating the effects of climate change on tree species abundance and distribution in the Italian peninsula</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Vegetation Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">generalized additive model</style></keyword><keyword><style  face="normal" font="default" size="100%">geostatistical</style></keyword><keyword><style  face="normal" font="default" size="100%">geostatistical methods</style></keyword><keyword><style  face="normal" font="default" size="100%">italian peninsula</style></keyword><keyword><style  face="normal" font="default" size="100%">methods</style></keyword><keyword><style  face="normal" font="default" size="100%">random forest</style></keyword><keyword><style  face="normal" font="default" size="100%">regression tree analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011///</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.wiley.com/10.1111/j.1654-109X.2010.01114.x</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">242 - 255</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record></records></xml>