A general deep learning model for bird detection in high-resolution airborne imagery.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting.

Microclimate and resource quality determine resource use in a range-expanding herbivore.

The consequences of climate change for biogeographic range dynamics depend on the spatial scales at which climate influences focal species directly and indirectly via biotic interactions. An overlooked question concerns the extent to which microclimates modify specialist biotic interactions, with emergent properties for communities and range dynamics. Here, we use an in-field experiment to assess egg-laying behaviour of a range-expanding herbivore across a range of natural microclimatic conditions.