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 …

Self-powered sensing systems augmented with machine learning (ML) represent a path
toward the large-scale deployment of the internet of things (IoT). With autonomous energy …

Ecosystem services are essential for human well-being, but are currently facing many
natural and anthropogenic threats. Modeling and mapping ecosystem services helps us …

Abstract Machine learning covers a large set of algorithms that can be trained to identify
patterns in data. Thanks to the increase in the amount of data and computing power …

Conservation of marine ecosystems has been highlighted as a priority to ensure a
sustainable future. Effective management requires data collection over large spatio-temporal …

Abstract Context Artificial Intelligence (AI) has rapidly developed over the past several
decades. Several related AI approaches, such as Machine Learning (ML), have been …

Species distribution models (SDMs) are widely used in ecology, biogeography and
conservation biology to estimate relationships between environmental variables and …

Understanding and analyzing animal behavior is increasingly essential to protect
endangered animal species. However, the application of advanced computer vision …

Abstract Machine learning (ML) expands traditional data analysis and presents a range of
opportunities in ecosystem service (ES) research, offering rapid processing of 'big data'and …