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Extreme weather events perturb ecosystems and increasingly threaten biodiversity 1 . Ecologists emphasize the need to forecast and mitigate the impacts of these events, which requires knowledge of how risk is distributed among species and environments. However, the scale and unpredictability of extreme events complicate risk assessment 1-4 -especially for large animals (megafauna), which are ecologically important and disproportionately threatened but are wide-ranging and difficult to monitor 5 . Traits such as body size, dispersal ability and habitat affiliation are hypothesized to determine the vulnerability of animals to natural hazards 1,6,7 . Yet it has rarely been possible to test these hypotheses or, more generally, to link the short-term and long-term ecological effects of weather-related disturbance 8,9 . Here we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to changes in community composition nearly 2 years later. Animals responded behaviourally to rising floodwaters by moving upslope and shifting their diets. Body size and habitat association independently predicted population-level impacts: five of the smallest and most lowland-affiliated herbivore species declined by an average of 28% in the 20 months after landfall, while four of the largest and most upland-affiliated species increased by an average of 26%. We attribute the sensitivity of small-bodied species to their limited mobility and physiological constraints, which restricted their ability to avoid the flood and endure subsequent reductions in the quantity and quality of food. Our results identify general traits that govern animal responses to severe weather, which may help to inform wildlife conservation in a volatile climate., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species' morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN.