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

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

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

Integrating animal movement with habitat suitability for estimating dynamic landscape connectivity

Context: High-resolution animal movement data are becoming increasingly available, yet having a multitude of trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species' potential to act as a link between patches, populations, or ecosystems are of importance. Objectives: We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of their ecological likelihood. With this approach, we estimated connectivity at the landscape level throughout the annual cycle of bar-headed geese (Anser indicus) in its native range. Methods: We used a tracking dataset of bar-headed geese to parameterise a multi-state movement model and to estimate temporally explicit habitat suitability within the species' range. We simulated migratory movements between range fragments, and estimated their ecological likelihood. The results are compared to expectations derived from published literature. Results: Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The estimated likelihood of simulated migrations was similar to that of empirical trajectories. We found that the predicted connectivity was higher within the breeding than in wintering areas, corresponding to previous findings for this species. Conclusions: We show how empirical tracking data and environmental information can be fused to make meaningful predictions about future animal movements. These are temporally explicit and transferable even outside the spatial range of the available data. Our integrative framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology

Moving in the anthropocene: global reductions in terrestrial mammalian movements

Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission

Modelling animal movement as Brownian bridges with covariates

The ability to observe animal movement and possible correlates has increased strongly over the past decades. Methods to analyze trajectories have developed in parallel, but many tools fail to make an immediate connection between a movement model, covariates of the movement, and animal space use. Methods Here I develop a novel method based on the Brownian Bridge Movement Model that facilitates investigating and testing covariates of movement. The model makes it possible to flexibly investigate different covariates including, for example, periodic movement patterns. Results I applied the Brownian Bridge Covariates Model (BBCM) to simulated trajectories demonstrating its ability to reproduce the parameters used for the simulation. I also applied the model to a GPS trajectory of a meerkat, showing its application to empirical data. The value of the model was shown by testing the interaction between maximal daily temperature and the daily movement pattern. Conclusion This model produces accurate parameter estimates for covariates of the movements and location error in simulated trajectories. Application to the meerkat trajectory also produced plausible parameter estimates. This new method opens the possibility to directly test hypotheses about the influence of covariates on animal movement while linking these to space-use estimates

Analysing animal movement in the environment

Movement is a crucial part in the life of animals. It determines their locations and their potential interactions. The interest in studying movement has increased in recent years. This increase is associated technological and methodological developments.In this thesis, methods to study movement are investigated and used to predict the location and evolution of migratory routes. Many other methods to study movement data rely on temporal independence of the observations. The temporal correlation however contains useful information and therefore should not be ignored or removed. In this thesis, I incorporate temporal information in the analysis of movement data.An accurate description of space use is essential to investigate movement. The Brownian Bridge movement model is a formal description of animal space use. It integrates the estimated position of the animal between observed locations over time, assuming continuous random movement. I extended the model to account for changes in the amount of movement to describe heterogeneous trajectories (Chapter 3). Using this technique it is possible to describe trajectories containing various behaviours and life history stages. For example, tracks that contain both migration and breeding behaviours, or trajectories with active and non-active periods. The Bivariate Gaussian Bridges further generalize the Brownian Bridges, but separate movement variance in two components on in the direction to the next location and one perpendicular to this direction. I can show that the decomposition of the movement variance describe correlated random walks better and produce an equal or better fit for trajectories of various species (Chapter 4). These models provide a more accurate description of space use. This in turn will make it possible to investigate a multitude of questions with higher accuracy and precision.Based on this work, I quantify the space use of migrating raptors. From an accurate description of the individual space use an environmental model for migration can be calculated per species. The model is used to predict species-wide space use during migration between winter and summer ranges. This creates a global map of migration diversity and intensity across species based on observed migrations (Chapter 5).Migration routes are not only shaped by static environmental conditions. To explore how migratory routes could be shaped by dynamic environmental conditions I use two decades of wind data to calculate the quickest migration route for a continuously flying bird. These routes are specific to one starting time, because wind conditions change continuously,and require knowledge of future wind conditions to be encountered. This makes it virtually impossible for individual animals to predict these routes at the time of departure. However, provided spatial and temporal persistence evolutionary processes could theoretically select at population level for adaptive adjustment of migratory paths. I investigated whether some of these routes could be followed every year on the basis of selection for the shortest travel time. There is nearly always an alternative route that is quicker than the shortest route over the years. This shows, that besides optimizing the timing of migration, optimization of the route is also important, that can be done by following a static route despite changing wind conditions (Chapter 6).With thesis I contribute to the study of animal movement by developing and applying various analytical techniques for movement research. The emphasis of these methods is especially on including time in the analysis. The methods developed here, together with other methods are used to investigate bird migration. These studies show how movement can be investigated on a global scale