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 …

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology.
These technologies hold great potential for large-scale ecological understanding, but are …

A core challenge in global change biology is to predict how species will respond to future
environmental change and to manage these responses. To make such predictions and …

Species distribution models, also known as ecological niche models or habitat suitability
models, have become commonplace for addressing fundamental and applied biodiversity …

Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary
to ensure food, water, and human health and well-being. Understanding the distribution of …

Estimating the geographical range of a species from sparse observations is a challenging
and important geospatial prediction problem. Given a set of locations where a species has …

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 …

Traditional species distribution modelling relies on the links between species and their
environments, but often such information is unavailable or unreliable. The objective of our …

We propose a new active learning approach for efficiently estimating the geographic range
of a species from a limited number of on the ground observations. We model the range of an …

We present the Eco-ISEA3H database, a compilation of global spatial data characterizing
climate, geology, land cover, physical and human geography, and the geographic ranges of …