Artificial Intelligence Prediction of Microhabitat Selection by Terrestrial Vertebrates

Abstract

Introduction: Understanding the processes by which terrestrial vertebrates choose microhabitats is among the key issues of ecology that is particularly important regarding wildlife conservation, taking into account the dramatic fragmentation of natural habitats due to the anthropogenic effect. Microhabitat selection plays an important role in the foraging efficiency, predator avoidance, thermoregulation, and reproduction. Traditional methods of statistical analysis, such as generalized linear models and resource selection functions, do not have the capability of explaining the non-linear relationships between animals and fine-scale environmental features. Consequently, the creation of new computational methods becomes a necessity. Materials & Methods: In the present theoretical work, the modern artificial intelligence system for simulation, prediction, and analysis of microhabitat selection patterns among diverse assemblages of terrestrial vertebrates is offered. The method presupposes the design of the multilayer framework based on gradient boosting machine, random forests, and deep neural networks for handling high-dimensional data. The simulated environment includes topographic features, vegetation complexity, microclimatic conditions, and anthropogenic factors. The computational approach employs the algorithmic techniques of feature importance scoring and spatial mapping for the purpose of determining the probability of microhabitat utilization without relying on any parametric assumptions. Results: Based on the theoretical review, the application of machine learning and artificial intelligence greatly enhances the traditional statistical approaches in terms of modeling the microhabitat preferences of wildlife species. The evaluation of the output of the computation reveals that both tree-based ensemble methods and neural networks work effectively while analyzing the interactions among the microclimate, canopy cover, and substrate conditions. Besides, the approach stresses spatial predictability in terms of various disturbances of the environment in order to identify vital refugia and ecological corridors needed for the wildlife survival. Conclusion: The usage of artificial intelligence in the process of modeling microhabitat preferences is the highly efficient and adaptive theoretical approach to wildlife management and spatial conservation. Contrary to the traditional approaches based on the usage of linear models, the implementation of dynamic data-driven models allows ecologists to predict the reaction of wildlife to habitat changes. As a result, the computation improves the effectiveness of conservation measures and guarantees biodiversity preservation