Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers from the University of Florida have uncovered the largest known nesting site for the threatened giant South American river turtle (Podocnemis expansa). How did they find over 41,000 nesting reptiles? The turtles were found gathered along the Amazon's Guaporé River between Brazil and Bolivia. This innovative use of drones opens up new avenues for conservationists, as detailed in a study recently published in the Journal of Applied Ecology.

Detecting flying animals (e.g., birds, bats, and insects) using weather radar helps gain insights into animal movement and migration patterns, aids in management efforts (such as biosecurity) and enhances our understanding of the ecosystem.The conventional approach to detecting animals in weather radar involves thresholding: defining and applying thresholds for the radar variables, based on expert opinion. More recently, Deep Learning approaches have been shown to provide improved performance in detection. However, obtaining sufficient labelled weather radar data for flying animals to build learning-based models is time-consuming and labor-intensive. To address the challenge of data labelling, we propose a self-supervised learning method for detecting animal movement. In our proposed method, we pre-train our model on a large dataset with noisy labels produced by a threshold approach. The key advantage is that the pre-trained dataset size is limited only by the number of radar images available. We then fine-tune the model on a small human-labelled dataset. Our experiments on Australian weather radar data for waterbird segmentation show that the proposed method outperforms the current state-of-the art approach by 43.53% in the dice co-efficient statistic.

Studying animal movements is essential for effective wildlife conservation and conflict mitigation. For aerial movements, operational weather radars have become an indispensable data source in this respect. However, partial measurements, incomplete spatial coverage, and poor understanding of animal behaviours make it difficult to reconstruct complete spatio-temporal movement patterns from available radar data. We tackle this inverse problem by learning a mapping from high-dimensional radar measurements to low-dimensional latent representations using a convolutional encoder. Under the assumption that the latent system dynamics are well approximated by a locally linear Gaussian transition model, we perform efficient posterior estimation using the classical Kalman smoother. A convolutional decoder maps the inferred latent system states back to the physical space in which the known radar observation model can be applied, enabling fully unsupervised training. To encourage physical consistency, we additionally introduce a physics-informed loss term that leverages known mass conservation constraints. Our experiments on synthetic radar data show promising results in terms of reconstruction quality and data-efficiency.

A drone camera films a herd of caribou as they migrate in Western Canada. The footage offers a unique look at the behavior of individuals within the herd. Flying cameras are giving biologists an all-encompassing view of migration that reveals how social interactions motivate the animals' every move. Ecologists Andrew Berdahl, a Santa Fe Institute fellow, Colin Torney of the University of Glasgow, and colleagues flew drones to capture footage of Dolphin and Union caribou, a Canadian herd, as the animals crossed from Victoria Island to the Canadian mainland in the last stage of their fall migration. Scientists have long pondered the dynamics of animal migrations, but they've had limited ways to study them.