Fig. 1. Examples of research acceleration by machine learning-based systems in animal ecology.

a The BirdNET algorithm⁶¹ was used to detect Carolina wren vocalizations in more than 35,000 h of passive acoustic monitoring data from Ithaca, New York, allowing researchers to document the gradual recovery of the population following a harsh winter season in 2015. b Machine-learning algorithms were used to analyze movement of savannah herbivores fitted with bio-logging devices in order to identify human threats. The method can localize human intruders to within 500 m, suggesting `sentinel animals' may be a useful tool in the fight against wildlife poaching¹⁴⁸. c TRex, a new image-based tracking software, can track the movement and posture of hundreds of individually-recognized animals in real-time. Here the software has been used to visualize the formation of trails in a termite colony¹⁴⁹. d, e Pose estimation software, such as DeepPoseKit (d)⁷⁵ and DeepLabCut (e)^74,142 allows researchers to track the body position of individual animals from video imagery, including drone footage, and estimate 3D postures in the wild. Panels b, c, and d are reproduced under CC BY 4.0 licenses. Panels b and d are cropped versions of the originals; the legend for panel b has been rewritten and reorganized. Panel e is reproduced with permission from Joska et al.¹⁴².