Artificial Intelligence Modelling of Resource-Driven Territorial Behaviour in Forest Birds

Abstract

Territorial behaviour is a fundamental ecological process in forest birds that regulates access to critical resources, including food availability, nesting locations, breeding areas, and suitable habitat conditions. The availability and distribution of environmental resources strongly influence territorial establishment, defence mechanisms, aggressive interactions, and movement strategies among different bird species. Understanding these behavioural responses is essential for evaluating species adaptation and ecosystem dynamics. However, conventional approaches for monitoring bird territorial behaviour mainly depend on manual field observations and expert-based assessments, which are labour-intensive, time-consuming, and limited in analysing large-scale behavioural variations. This study presents an artificial intelligence-based modelling framework for automated analysis of resource-driven territorial behaviour in forest birds. The proposed framework integrates deep learning-based bird detection, behavioural feature extraction, and ecological resource information to identify and predict territorial responses under changing environmental conditions. The model analyses important behavioural indicators, including movement patterns, interaction intensity, territorial defence activities, and resource utilization behaviour. By combining visual observations with resource-related parameters, the proposed approach provides an efficient method for understanding the relationship between environmental availability and bird behavioural adaptation. The developed AI framework enables automated behavioural classification and supports continuous ecological monitoring with improved accuracy and scalability. The results indicate that artificial intelligence-based modelling can effectively identify territorial behaviour patterns and provide valuable insights into resource-dependent adaptations in forest bird populations. This approach contributes to biodiversity assessment, habitat quality evaluation, wildlife monitoring, and conservation planning by providing a data-driven solution for studying behavioural responses in changing forest ecosystems