Understanding the processes that shape forest functioning, structure, and diversity remains
challenging, although data on forest systems are being collected at a rapid pace and across …

Species distribution models (SDMs) are the primary tools used to model and predict
changes to species' ranges, and are often used to provide a quantitative baseline for …

Predicting contemporary and future species distributions is relevant for science and decision
making, yet the development of high‐resolution spatial predictions for numerous taxonomic …

Aim Groundwater ecosystems harbour a unique biodiversity, but remain poorly studied,
mainly due to difficulties in accessibility and imperfect species detection. Consequently …

The questions of how much abiotic environment contributes to explain species distributions,
and which abiotic factors are the most influential, are key when projecting species realized …

Species distribution models (SDMs) are powerful tools in ecology and conservation.
Choosing the right environmental drivers and filtering species' occurrences taking their …

Aim Assessing how different sampling strategies affect the accuracy and precision of
species response curves estimated by parametric species distribution models. Major Taxa …

The random forests (RF) algorithm is a superb learner and classifier in machine learning
applications. This ensemble model is also one of the most popular species distribution …

Species distribution models (SDMs) are widely used in ecology and conservation. However,
their performance is known to be affected by a variety of factors related to species …

To protect native biodiversity and habitats from the negative impacts of biological invasions,
comprehensive studies and measures to anticipate invasions are required, especially …