Social organization of a solitary carnivore: spatial behaviour, interactions and relatedness in the slender mongoose

The majority of carnivore species are described as solitary, but little is known about their social organization and interactions with conspecifics. We investigated the spatial organization and social interactions as well as relatedness of slender mongooses (Galerella sanguinea) living in the southern Kalahari. This is a little studied small carnivore previously described as solitary with anecdotal evidence for male associations. In our study population, mongooses arranged in spatial groups consisting of one to three males and up to four females. Male ranges, based on sleeping sites, were large and overlapping, encompassing the smaller and more exclusive female ranges. Spatial groups could be distinguished by their behaviour, communal denning and home range. Within spatial groups animals communally denned in up to 33% of nights, mainly during winter months, presumably to gain thermoregulatory benefits. Associations of related males gained reproductive benefits likely through increased territorial and female defence. Our study supports slender mongooses to be better described as solitary foragers living in a complex system of spatial groups with amicable social interactions between specific individuals. We suggest that the recognition of underlying ‘hidden' complexities in these apparently ‘solitary' organizations needs to be accounted for when investigating group living and social behaviour

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

Physics-informed inference of aerial animal movements from weather radar data

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.Comment: NeurIPS 2022, AI4Science worksho

Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems

Probabilistic inference in high-dimensional state-space models is computationally challenging. For many spatiotemporal systems, however, prior knowledge about the dependency structure of state variables is available. We leverage this structure to develop a computationally efficient approach to state estimation and learning in graph-structured state-space models with (partially) unknown dynamics and limited historical data. Building on recent methods that combine ideas from deep learning with principled inference in Gaussian Markov random fields (GMRF), we reformulate graph-structured state-space models as Deep GMRFs defined by simple spatial and temporal graph layers. This results in a flexible spatiotemporal prior that can be learned efficiently from a single time sequence via variational inference. Under linear Gaussian assumptions, we retain a closed-form posterior, which can be sampled efficiently using the conjugate gradient method, scaling favourably compared to classical Kalman filter based approachesComment: NeurIPS 2023; camera-ready versio

Exploratory Visual Analysis for Animal Movement Ecology

Movement ecologists study animals' movement to help understand their behaviours and interactions with each other and the environment. Data from GPS loggers are increasingly important for this. These data need to be processed, segmented and summarised for further visual and statistical analysis, often using predefined parameters. Usually, this process is separate from the subsequent visual and statistical analysis, making it difficult for these results to inform the data processing and to help set appropriate scale and thresholds parameters. This paper explores the use of highly interactive visual analytics techniques to close the gap between processing raw data and exploratory visual analysis. Working closely with animal movement ecologists, we produced requirements to enable data characteristics to be determined, initial research questions to be investigated, and the suitability of data for further analysis to be assessed. We design visual encodings and interactions to meet these requirements and provide software that implements them. We demonstrate these techniques with indicative research questions for a number of bird species, provide software, and discuss wider implications for animal movement ecology

Quantifying levels of animal activity using camera-trap data

1. Activity level (the proportion of time that animals spend active) is a behavioural and ecological metric that can provide an indicator of energetics, foraging effort and exposure to risk. However, activity level is poorly known for free-living animals because it is difficult to quantify activity in the field in a consistent, cost-effective and non-invasive way. 2. This paper presents a new method to estimate activity level with time-of-detection data from camera-traps (or more generally any remote sensors), fitting a flexible circular distribution to these data in order to describe the underlying activity schedule, and calculating overall proportion of time active from this. 3. Using simulations and a case study for a range of small to medium-sized mammal species, we find that activity level can reliably be estimated using the new method. 4. The method depends on the key assumption that all individuals in the sampled population are active at the peak of the daily activity cycle. We provide theoretical and empirical evidence suggesting that this assumption is likely to be met for many species, but may be less likely in large predators, or in high latitude winters. Further research is needed to establish stronger evidence on the validity of this assumption in specific cases, however, the approach has the potential to provide an effective, non invasive alternative to existing methods for quantifying population activity levels