Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups

The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of “universal” classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed

Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects

Active interactions between animals and technology:biohybrid approaches for animal behaviour research

Biohybrid approaches (where living and engineered components are combined) provide new opportunities for advancing animal behaviour research and its applications. This review article and accompanying special issue explores how different types of novel technologies can be used in the field of animal behaviour from three perspectives: (1) comprehension, (2) application and (3) integration. Under the perspective of ‘comprehension,’ we present examples of how technologies like virtual animals or robots can be used in experimental settings to interact with living animals in a standardized manner. Such interactions can advance our understanding of fundamental topics such as mate choice, social learning and collective behaviour. Under ‘application,’ we investigate the potential for technologies to monitor, react and interact with animals in a variety of scenarios. For example, we discuss how drones can be used to keep large herbivores away from valuable crops and robotic predators to deter invasive species. Under ‘integration,’ we discuss possibilities for the coexistence of engineered and biological systems, augmenting the capacity or resilience of either or both components. Integration can be physical, for example, livestock can have sensors sit in their inner body for temperature monitoring, or within the environment, where sensors or robots monitor and interact with animals, such as a short-term earthquake forecasting method. Based upon these three themes, we discuss and classify existing biohybrid animal behaviour research, including the four articles included in our special issue. We also consider the ethics of this emerging field, highlight the advantages and potential issues associated with using technologies to create biohybrid systems and emphasize how such technologies can support the advancement of animal behaviour research