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 …

Climate change has become increasingly severe, threatening ecosystem stability and, in
particular, biodiversity. As a typical indicator of ecosystem evolution, vegetation growth is …

Species distribution models are commonly applied to predict species responses to
environmental conditions. A wide variety of models with different properties exist that vary in …

The Allerton Park workshop essentially defined landscape ecology as a field of study that
cuts across multiple natural and social sciences (Risser et al. 1984). The report on the …

Species distribution modeling has emerged as a foundational method to predict occurrence
and suitability of species in relation to environmental variables to advance ecological …

Species distribution modeling is widely used to quantify and predict species-environment
relationships. Most past applications and methods in species distribution modeling assume …

Abstract Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability
predictions relating species occurrence data with habitat features. Two of the most frequently …

The potential of artificial intelligence (AI) to shape research and education is a highly topical
issue. The recent release of ChatGPT (Chat Generative Pre‐trained Transformer) by OpenAI …

In the context of climate change, the decline in biodiversity and current socio-economic
trends, which support the processes of industrialization or abandonment of agricultural land …

Accurate digital mapping of farmland soil organic carbon (SOC) contributes to sustainable
agricultural development and climate change mitigation. Farmland landscape pattern has …