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Are Data on Predators Necessary When Modeling Prey Population Dynamics?

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Abstract

A new approach is proposed for building a model of the prey population in the “predator–prey” system without using data on the dynamics of the predator numbers. To replace these data, autoregressive models with a set of linear positive and negative feedbacks are considered, which replace data on the influence of predator populations. The proposed approach can be used to model local populations of animals, for which there are no complete data on their interactions with other species in the ecosystem. We used conjugate series of dynamics of prey and predator populations: “classical” data on the number of lynx and hare skins purchased by the Hudson’s Bay Company, as well as annual data on the number of moose and wolves on the Isle Royale on Lake Superior in North America. The studied populations were considered as auto-regulated (AR) systems with feedback. The regulation of the number of the prey populations (hares and moose) is characterized by the presence of two feedback loops: a positive feedback between the current population density and the population density in the previous season, and a negative feedback between the current population density and the population density 2 years before this. It is shown that in order to build a model of prey populations, there is no need to know how many species of predators (including humans) affect this population. Based on the data on the temporal dynamics of the studied populations, an assessment of the feedback coefficients of AR equations, their stability and stability margin is given. The coefficient of determination R2 for the considered models reaches the value R2 = 0.978.

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Funding

The work was supported by the Russian Science Foundation, grant no. 22-24-00148.

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Correspondence to V. G. Soukhovolsky.

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Soukhovolsky, V.G., Ivanova, Y.D. & Kovalev, A.V. Are Data on Predators Necessary When Modeling Prey Population Dynamics?. Biol Bull Rev 13, 216–227 (2023). https://doi.org/10.1134/S207908642303009X

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  • DOI: https://doi.org/10.1134/S207908642303009X

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