Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …

Ecosystems are becoming damaged or degraded as a result of stresses especially
associated with human activities. A healthy ecosystem is essential to provide the services …

Abstract Input Variable Selection (IVS) is an essential step in the development of data-driven
models and is particularly relevant in environmental modelling. While new methods for …

Efficient methods to explore plant agro-biodiversity for climate change adaptive traits are
urgently required. The focused identification of germplasm strategy (FIGS) is one such …

Recent studies have shown that novel genetic variation for resistance to pests and diseases
can be detected in plant genetic resources originating from locations with an environmental …

This study aimed to set out a new methodology for habitat modeling in high‐gradient
streams. The methodology is based on the mesoscale approach of the MesoHABSIM …

Machine learning (ML) techniques have become important to support decision making in
management and conservation of freshwater aquatic ecosystems. Given the large number of …

The complexity of ecosystems is staggering, with hundreds or thousands of species
interacting in a number of ways from competition and predation to facilitation and mutualism …

In the context of water resources planning and management, the prediction of fish
distribution related to habitat characteristics is fundamental for the definition of …

In this paper, we use empirical modeling to predict and understand phytoplankton dynamics
in a reservoir affected by water transfers. Prediction of phytoplankton biovolume is central to …