Machine Learning Prediction of Waterbird Nest-Site Selection in Modified Wetlands

Abstract

Modification of the wetland environment due to expansion of cities, agriculture, and changes in the hydrology system has been a major cause of changes in the distribution of habitat and nesting sites for waterbirds. Proper prediction of nest-site suitability is necessary for proper conservation of the wetland environment and management of biodiversity. Conventional techniques used in the prediction of nest-site suitability have relied heavily on field-based methods that are laborious, time consuming, and limited in their spatial coverage. This paper outlines a machine-learning based framework for prediction of waterbird nest-site selection in modified wetland environments. Machine learning models are developed based on historical nest information along with environmental parameters, to estimate suitable sites for waterbird nesting. The suggested methodology studies the association between habitat features and nest site selection, by using automatic learning techniques. The model extracts important ecological features such as vegetation, water sources, habitat structure, and level of disturbance that affect the nesting suitability. The proposed methodology enhances prediction capabilities and assists in proper wetland conservation planning. It is an essential tool for ecological monitoring and habitat management of waterbird species in modified wetland ecosystems