APPLICATION OF HYBRID MODELS IN MONITORING ECOLOGICAL COMMUNITIES

Monitoring ecological communities in natural reserves is of critical importance for biodiversity conservation, especially in the context of climate change and human-induced pressures. Traditional methods, such as manually analysing camera trap data or using statistical modelling, have limitations. These include low image quality due to weather conditions or insufficient lighting, uncertainty when classifying similar species and a lack of adaptation to local ecosystems. Although modern approaches based on convolutional neural networks (CNNs) demonstrate high accuracy with global datasets, their effectiveness decreases in real-world conditions involving noise and limited training data, particularly with regard to regional peculiarities. The application of hybrid models combining CNNs with fuzzy logic offers a promising solution, enabling the handling of uncertainty and enhancing system robustness. However, these approaches remain underexplored, and their practical value for biodiversity monitoring requires detailed analysis.The purpose of this article is to review the current state of applying hybrid models in the monitoring of ecological communities; to assess their respective advantages and disadvantages compared to traditional methods; and to outline prospects for their future development. The study employs comparative analysis methods to evaluate the accuracy, computational complexity, and adaptability of models, alongside data synthesis to identify trends and gaps. Special attention is given to examples of hybrid approaches (CNN + fuzzy logic) in species classification and ecosystem dynamics forecasting tasks.The analysis conducted in the study indicates that hybrid models achieve an accuracy improvement in species classification of up to 85–90% under challenging conditions (low-quality images, species similarity), surpassing traditional CNNs (70–80%). Their ability to manage uncertainty through fuzzy logic makes them promising for local ecosystems, such as Ukrainian reserves. Nevertheless, gaps are identified: high computational complexity, limited training data, and insufficient integration with geographic information systems (GIS). Prospects include the development of optimized algorithms, adaptation to regional conditions, and the creation of cost-effective solutions for nature conservation. The findings of this article will contribute to further research and practical implementation of hybrid models in biodiversity monitoring.