A review of computing models for studying population dynamics of giant panda ecosystems

Yingying Duan, Haina Rong, Gexiang Zhang, Sergey Gorbachev, Dunwu Qi, Luis Valencia-Cabrera and Mario J. Pérez-Jiménez

Ecological Modelling, 2024, vol. 487, issue C

Abstract: Computing models are a good and effective way to study population dynamics of endangered species like giant pandas. Until now, a variety of computing models were proposed for giant pandas, but no survey on computing models for population dynamics of giant panda ecosystems has yet appeared in the specialised literature. It is necessary to provide an overview of the state-of-the-art of this topic so as to allow newcomers to the area to obtain a clear understanding of developments, key research problems, properties of computing models in this field, including those that are currently under way. This paper proposes a unified framework to clearly summarise the computing models used for studying the population dynamics of threatened species with respect to theoretical and application aspects and presents a comprehensive and systematic survey of the state-of-the-art computing models. This paper also introduces basic concepts of computing models, surveys their theoretical developments and applications, sketches the differences between various computing model variants, and compares the advantages and limitations of the models. Comparing with single-factor computing models and double-factor computing models, multi-factor computing models, especially multi-environment population dynamics P systems, are more suitable for investigating giant panda ecosystems, because the use of bottom-up way to consider evolutionary behaviours influencing giant pandas’ population.

Keywords: Computing models; Population dynamics; Giant panda ecosystem (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:487:y:2024:i:c:s0304380023002739

DOI: 10.1016/j.ecolmodel.2023.110543

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