Specifying, assessing, and selecting among candidate statistical models is fundamental to
ecological research. Commonly used approaches to model selection are based on …

The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently,
the most available data for SDM are species presence‐only records (available through …

Species distribution models (SDMs) constitute the most common class of models across
ecology, evolution and conservation. The advent of ready‐to‐use software packages and …

Aim The idea of combining predictions from different models into an ensemble has gained
considerable popularity in species distribution modelling, partly due to free and …

Predictive performance is important to many applications of species distribution models
(SDMs). The SDM 'ensemble'approach, which combines predictions across different …

In this paper, we discuss an extension to two popular approaches to modeling complex
structures in ecological data: the generalized additive model (GAM) and the hierarchical …

The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

Soil salinization is the main source of global soil degradation. It has impeded progress
towards sustainable development goals (SDGs) by threatening 20% of irrigated areas …

Understanding crop responses to climate change is crucial for ensuring food security. Here,
we reviewed∼ 230 statistical crop modeling studies for major crops and summarized recent …