Assessment of national parks affected by naturbanization processes in Southern Europe

This paper assesses land-use changes related to naturbanization processes on three biosphere reserves in Southern Europe. A comparative analysis has been done on the National Parks in Peneda-Ger^es in North Portugal, C_evennes in South France and Sierra Nevada in South Spain, using Corine Land Cover data from 1990 until 2006. Results indicate that the process of land-use intensification is taking place in the frame of naturbanization dynamics that could jeopardize the role of Protected Areas. Focusing on the trends faced by National Parks and their surrounding territories, the analysis demonstrates, both in quantitative and spatial terms, the intensification processes of land-use changes and how it is important to know them for coping with increasing threats. The article concludes that in the current context of increasing stresses, a broader focus on nature protection, encompassing the wider countryside, is needed if the initiatives for biodiversity protection are to be effective.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.