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Revive, Restore, Revitalize: An Eco-economic Methodology for Maasai Mara

Domaine:

environment and energysocioeconomic

Type de record:

modelpaper
Créateur:
Xu,SunZhu
Éditeur:
arXiv
Hôte:avatar
The Maasai Mara in Kenya, renowned for its biodiversity, is witnessing ecosystem degradation and species endangerment due to intensified human activities. Addressing this, we introduce a dynamic system harmonizing ecological and human priorities. Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types. The model employs the metabolic rate-mass relationship for animal energy dynamics, logistic curves for animal growth, individual interactions for food web simulation, and human intervention impacts. Algorithms like fitness proportional selection and particle swarm mimic organism preferences for resources. To guide preservation activities, we formulated 21 management strategies encompassing tourism, transportation, taxation, environmental conservation, research, diplomacy, and poaching, employing a game-theoretic framework. Using the TOPSIS method, we prioritized four key developmental indicators: environmental health, research advancement, economic growth, and security. The interplay of 16 factors determines these indicators, each influenced by our policies to varying degrees. By evaluating the policies' repercussions, we aim to mitigate adverse animal-human interactions and equitably address human concerns. We classified the policy impacts into three categories: Environmental Preservation, Economic Prosperity, and Holistic Development. By applying these policy groupings to our ecosystem model, we tracked the effects on the intricate animal-human-resource dynamics. Utilizing the entropy weight method, we assessed the efficacy of these policy clusters over a decade, identifying the optimal blend emphasizing both environmental conservation and economic progression. 25 pages, 16 figures

Visit

doi.orgarxiv.org

Languages

Maasai

Tags

Populations and Evolution (q-bio.PE)FOS: Biological sciencesFOS: Biological sciences

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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