Berthelot, G. | bioRxiv Science

bioRxiv2024

Stochastic foraging paths primarily drives within-species variations of prey consumption rates

The speed at which individuals interact, in particular prey and predators, affects ecological processes at all scales, including how fast matter and energy flow through ecosystems, and how stable communities are. Environmental heterogeneity and individual variabilities are generally believed to be the main factors underlying the variation of consumption rates of prey by predators. We challenge this view by comparing predicted variability from a stochastic model to experimental data. We first analyze a stochastic model of a simple random walk with elementary ecological processes involved in prey consumption, including prey depletion, predator movements and prey handling. We provide sharp approximations of the distribution of the consumption rate and a quantitative prediction of the coefficient of variation when stochastic foraging is the only source of variability. Predictions are then compared to the coefficients of variation estimated from data from dozens of various species and experimental contexts. We show that the predictions only accounting for intrinsic stochasticity in foraging are compatible with the range of observed values, in particular in 1 or 2 dimensional space. After evaluating the robustness of our models predictions through stochastic computer simulations, we conclude that the main driver of the variation of the consumption rate is the foraging process itself rather than environmental or between-individual variabilities. Our approach lays the foundations for unifying foraging theory and population ecology, and as such has many empirical and theoretical implications for both fields.

ecology↗

bioRxiv2020

How to use random walks for modeling the movement of wild animals

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. At the same time, technological improvements now allow for generating large datasets of high sampled GPS data over a long period of time. However, such datasets often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of rational or biological justifications in their design. We propose a simple model of individual movement with explicit parameters based on essential features of animal behavior. The main thrust was to stick to empirical observations, rather than using black-box models that could possibly perform very well while providing little insight from an ecological perspective. We used a simple model, based on a two-dimensional biased and correlated random walk with three forces related to advection, attraction and immobility of the animal. These forces can be directly estimated using individual raw GPS data. The performance of the model is assessed through 5 statistics that describe the spatial features of animal movement. We demonstrate the approach by using GPS data of 5 roe deer with high frequency sampling. We show that combining the three forces significantly improves the model performance. We also found that the models parameters are not affected by the sampling rate of the GPS, suggesting that our model could also be used with low frequency sampling GPS devices. Additionally, the practical design of the model was verified for detecting spatial feature abnormalities (such as voids) and for providing estimates of density and abundance of wild animals. Our results show that a simple and practicable random walk template can account for the spatial complexity of wild animals. Integrating even more additional features of animal movement, such as individuals interactions or environmental repellents, could help to better understand the spatial behavior of wild animals.

animal behavior and cognition↗