Eikelboom, J. A. J. | Search bioRxiv

bioRxiv2024

Animal group size variation in a minimal attraction-repulsion agent-based model

Grouping behaviour of prey animals is thought to be mainly driven by fear of predation and resource scarcity. Fear of predation often leads to small inter-individual distances, while resource scarcity leads to the opposite. Consequently, it is believed that the number of individuals in a group (group size) is an emergent property of the trade-off between acquiring scarce resources and preventing predation. We analysed whether group size can be reliably used as a proxy for this trade-off, using a deterministic attraction-repulsion agent-based model in a homogeneous area. In our model, each individual experiences distancedependent attraction and repulsion to all others in the area, where varying degrees of grouping behaviour emerge from the number and distance of intersections between the attraction and repulsion functions. We show that the coefficient of variation of group size generally lies between 50 and 150%, depending on both animal density and the trade-off between resource scarcity and predation. Given that the variations of group size are already this large in homogeneous and deterministic scenarios, we urge researchers to be cautious in using group size as a proxy for the resources/predation trade-off and consider inter-individual distance as a more direct and potentially more reliable alternative.

ecology↗

bioRxiv2022

Movement of large African savanna herbivores is shaped by intricate interactions between behavioural modes and the spatial-temporal environment

O_LIAnimal movement arises from complex interactions between animals and their heterogeneous environment, making it challenging to capture the movement process in a single analysis. This has lead to a bias towards lower-dimensional representations of the process in such analyses. In order to better understand animal movement, its multiple components should be included and addressed simultaneously. C_LIO_LIWe present an analytic framework that integrates the behavioural, spatial, and temporal components of the movement process and their interactions, and allows for assessing the relative importance of these components. We propose a daily cyclic covariate to represent temporally cyclic movement patterns, for example diel variation in activity, and combine the three components in multi-modal Hidden Markov Models. We compare the statistical fits of models that include or exclude any of the behavioural, spatial and temporal components, and perform variance partitioning on the model that included all components to assess their relative importance to the movement process, both in isolation and in interaction. C_LIO_LIWe apply our framework to a case study on the movements of zebra, wildebeest and eland antelope in a South African reserve. Behavioural modes impacted movement the most, followed by diel rhythms and then the spatial environment (i.e. tree cover and terrain slope). Interactions between the components often explained more of the movement variation than the marginal effect of the spatial environment did on its own. Omitting components from the analysis led either to failure to detect relationships between input and response variables, resulting in overgeneralisations when drawing conclusions about the movement process, or to erroneously detecting spurious relationships, resulting in factually incorrect conclusions. C_LIO_LIOur analytic framework can be used to study animal movement by integrating the different components of the movement process, thereby preventing incomplete or overly generic ecological interpretations. We demonstrate that understanding the drivers of animal movement, and ultimately the ecological phenomena that emerge from it, critically depends on considering the various components of the movement process, and especially the interactions between them. C_LI Data/code for peer review statement: the data and code needed to reproduce the study and results are uploaded as a file with the initial submission.

ecology↗