Understorey phylogenetic diversity in thermophilous deciduous forests : overstorey species identity can matter more than species richness

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Linking forest diversity and tree health: preliminary insights from a large-scale survey in Italy

Forest health is currently assessed in Europe (ICP Forests monitoring program). Crown defoliation and dieback, tree mortality, and pathogenic damage are the main aspects considered in tree health assessment. The worsening of environmental conditions (i.e., increase of temperature and drought events) may cause large-spatial scale tree mortality and forest decline. However, the role of stand features, including tree species assemblage and diversity as factors that modify environmental impacts, is poorly considered. The present contribution reanalyses the historical dataset of crown conditions in Italian forests from 1997 to 2014 to identify ecological and structural factors that influence tree crown defoliation, highlighting in a special manner the role of tree diversity. The effects of tree diversity were explored using the entire data set through multivariate cluster analyses and on individual trees, analysing the influence of the neighbouring tree diversity and identity at the local (neighbour) level. Preliminary results suggest that each tree species shows a specific behaviour in relation to crown defoliation, and the distribution of crown defoliation across Italian forests reflects the distribution of the main forest types and their ecological equilibrium with the environment. The potentiality and the problems connected to the possible extension of this analysis at a more general level (European and North American) were discussed

Plant communities of travertine outcrops of the Saturnia area in southern Tuscany (central Italy).

Abstract A phytosociological survey was carried out in a poorly known travertine area of southern Tuscany harbouring a rich vegetation mosaic with chamaephytic garrigues, species-rich xerophytic grasslands, chasmophytic coenoses, annual species-dominated communities, shrublands and thermophilous deciduous forests. Field sampling and data analysis allowed to identify and characterize several community types, some of which of significant interest due to their ecological specificity and rarity in peninsular Italy. In particular, our data confirm the associations Pistacio terebinthi-Paliuretum spinosae and Pistacio terebinthi-Quercetum pubescentis, respectively a shrub and forest community type previously unknown for Tuscany. In addition, a new therophytic association of travertine debris named Sedetum hispanico-caespitosi and placed in the Hypochoerion achyrophori alliance (Brachypodietalia distachyi order, Tuberarietea class) is also described. Finally, dynamic relationships between the vegetation types are highlighted and the presence of conservation priority habitats in the area are pointed out

Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model

AbstractThis work belongs to the strand of literature that combines machine learning, optimization, and econometrics. The aim is to optimize the data collection process in a specific statistical model, commonly used in econometrics, employing an optimization criterion inspired by machine learning, namely, the generalization error conditioned on the training input data. More specifically, the paper is focused on the analysis of the conditional generalization error of the Fixed Effects Generalized Least Squares (FEGLS) panel data model, i.e., a linear regression model with applications in several fields, able to represent unobserved heterogeneity in the data associated with different units, for which distinct observations related to the same unit are corrupted by correlated measurement errors. The framework considered in this work differs from the classical FEGLS model for the additional possibility of controlling the conditional variance of the output variable given the associated unit and input variables, by changing the cost per supervision of each training example. Assuming an upper bound on the total supervision cost, i.e., the cost associated with the whole training set, the trade-off between the training set size and the precision of supervision (i.e., the reciprocal of the conditional variance of the output variable) is analyzed and optimized. This is achieved by formulating and solving in closed form suitable optimization problems, based on large-sample approximations of the generalization error associated with the FEGLS estimates of the model parameters, conditioned on the training input data. The results of the analysis extend to the FEGLS case and to various large-sample approximations of its conditional generalization error the ones obtained by the authors in recent works for simpler linear regression models. They highlight the importance of how the precision of supervision scales with respect to the cost per training example in determining the optimal trade-off between training set size and precision. Numerical results confirm the validity of the theoretical findings