Artificial Intelligence Evaluation of Forest Edge Effects on Small Mammal Assemblages

Abstract

Introduction: There is a lot of influence that can be seen on the habitat, microclimate, resource availability, and interactions of species due to the forest edge effects, which lead to noticeable changes in small mammal assemblages. It is important to understand this process for biodiversity conservation, but traditional approaches for its assessment suffer from fragmental observations and complex interactions. In this paper, the AI-based evaluation approach for the influence analysis of the forest edges on small mammal assemblages is proposed. Materials and Methods: The theoretical approach under discussion combines forest edges features, environment, and ecological features of small mammals for AI-based analysis. Habitat features like vegetation structure, edge effect, fragmentation of the landscape, temperature, humidity, and resources availability are the factors of the input, whereas species richness, abundance, diversity, and distributions are the ecological responses. Machine learning-driven evaluation is used to establish links between edge conditions and small mammal assemblage variability. Results: The AI-driven method helps to effectively determine critical ecological factors that affect small mammal population and predict possible changes in assemblage structure under various forest edge conditions. The approach contributes to better comprehension of habitat disturbances through the analysis of multidimensional relations between environmental factors. Conclusion: Artificial intelligence is an effective tool to evaluate forest edge impact by using multifaceted ecological data. The offered framework could help conservationists and forest managers to determine critical habitats, monitor ecosystems, and develop sustainable forest management approaches.