Understanding adaptive genetic responses to climate change is an issue of utmost importance to improve conservation policies and adaptive management. This study deeps on it, focusing on rear-edge silver fir ( Abies alba ) forests, where decline has been reported and linked to climatic stressors, such as warming and recurrent drought events. Hotspots and coldspots of forest decline and mortality were defined. Different sets of...

Accurate hourly streamflow prediction is crucial for managing water resources, particularly in smaller basins with short response times. This study evaluates six deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrids (CNN-LSTM, CNN-GRU, CNN-Recurrent Neural Network (RNN)), across two basins in Northwest Spain over a ten-year period....

Ensemble modelling is a numerical technique used to combine the results of a number of different individual models in order to obtain more robust, better-fitting predictions. The main drawback of ensemble modeling is the identification of the individual models that can be efficiently combined. The present study proposes a strategy based on the Random-Restart Hill-Climbing algorithm to efficiently build ANN-based hydrological e...

Purpose To evaluate time trends in the prevalence of antithrombotic and statin use in four European countries. Methods Using population-based data from the United Kingdom, Denmark, Spain and Italy between 2010 and 2018, we calculated standardized annual prevalence proportions of antithrombotics and statin use, and changes in prevalence proportions (2018 vs. 2010). Results Prevalence proportion of statins increased from 24.8% t...