Artificial Intelligence Modelling of Vegetation Complexity and Ground Faunal Richness

Abstract

Vegetation complexity is a key ecological factor affecting habitat structure, resource availability, microclimate, and distribution of ground fauna. Existing biodiversity assessment techniques are mostly based on field observation and conventional ecological observations, which are time-consuming and space-bound and are hard to implement for large landscapes. The use of artificial intelligence (AI) modelling allows studying complex interrelations between vegetation properties and faunal diversity using ecological observations, environmental parameters and spatial data. This paper develops the conceptual framework for using AI modelling in evaluating vegetation complexity and ground faunal richness through identification of ecological patterns related to habitat structure. Materials & Methods: The suggested theoretical framework is based on the integration of vegetation parameters, such as canopy density, vegetation diversity, variability of vegetation heights, coverage percentage and habitat heterogeneity with faunal parameters, such as species richness, abundance distribution and habitat preferences. Artificial intelligence modelling methods, including machine learning and deep learning techniques, are used for analysis of multi-dimensional ecological relations and ground faunal richness predictions. Information on remote sensing, field studies, and environmental parameters were viewed as the possible inputs in the development of predictive models that could be used for habitat evaluation. Results:

From the theoretical analysis it follows that the use of AI technology in the ecological modelling makes it possible to detect rather complicated patterns of interaction between vegetation structure and the distribution of fauna. The heterogeneity of vegetation, availability of shelters, and habitats were regarded as the important factors affecting the distribution and richness of fauna. Prediction methods based on machine learning can help to better understand the relationship between habitat and species taking into account several ecological variables at once. The suggested approach demonstrates the importance of the use of AI models for the biodiversity evaluation, detection of ecologically important habitats, and provision of predictive data. AI methods of analysis can help to avoid limitations in the traditional biodiversity surveys and make it possible to carry out repeatable and data-supported ecological evaluation on a large scale. Conclusion: Artificial Intelligence Modelling of Vegetation Complexity and Ground Faunal Richness.