The aim of our study is to describe the dynamics of ant battles, with reference to laboratory experiments, by means of a chemical stochastic model. We focus on ants behavior as an interesting topic in order to predict the ecological evolution of invasive species and their spreading. In our work we want to describe the interactions between two groups of different ant species with different war strategies. Our model considers the single ant individuals and fighting groups in a way similar to atoms and molecules, respectively, considering that ant fighting groups remain stable for a relative long time. Starting from a system of differential non-linear equations (DE), derived from the chemical reactions, we obtain a mean field description of the system. The DE approach is valid when the number of individuals of each species is large in the considered unit, while we consider battles of at most 10 vs. 10 individuals, due to the difficulties in following the individual behavior in a large assembly. Therefore, we also adapt a Gillespie algorithm to reproduce the fluctuations around the mean field. The DE scheme is exploited to characterize the stochastic model. The set of parameters of chemical equations, obtained using a minimization algorithm, are used by the Gillespie algorithm to generate the stochastic trajectories. We then fit the stochastic paths with the DE, in order to analyze the variability of the parameters and their variance. Finally, we estimate the goodness of the applied methodology and we confirm that the stochastic approach must be considered for a correct description of the ant fighting dynamics. With respect to other war models, our chemical one considers all phases of the battle and not only casualties. Thus, we can count on more experimental data, but we also have more parameters to fit. In any case, our model allows a much more detailed description of the fights.
Centering Ecological Goals in Automated Identification of Individual Animals
University of West Bohemia in Pilsen（西波西米亚大学）；Massachusetts Institute of Technology（麻省理工学院）；LUT University（卢特大学）；Conservation X Labs（保护X实验室）；Queen Mary University of London（伦敦玛丽女王大学）；The Ohio State University（俄亥俄州立大学）；Giraffe Conservation Foundation（犀牛保护基金会）；University of Bristol（布里斯托大学）；Czech Technical University in Prague（布拉格捷克技术大学）；Cornell Lab of Ornithology（哥伦布鸟类实验室）；University of Pittsburgh（匹兹堡大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Princeton University（普林斯顿大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；CNR（意大利国家研究委员会）
