Deep Learning Modelling of Migratory Bird Pathways and Avian Pathogen Dispersal

Abstract

Migration of birds is critical in ensuring that there is connectivity of ecosystems since birds move from one habitat to another during certain times of the year. Such movements enable the exchange of biological diversity, movement of nutrients, and interactions within the ecosystem but also provide a means for transferring diseases among birds as they move from one place to another. Traditional methods of monitoring bird migration and disease transmission largely depend on observation, tracking, and lab work, which have limitations in terms of geographical coverage, continuous monitoring capacity, and analysis of various environmental factors. In this research, a deep learning-based modelling approach for analysing migratory birds' paths and prediction of risks of avian pathogen dispersal is proposed. The proposed framework analyzes the movement of animals, the features of their habitat, climatic conditions, and disease transmission factors in order to find high risk regions where pathogens may be transmitted. Experimental evaluations show that the deep learning algorithm is more efficient than the standard machine learning techniques for pathogen prediction, being able to predict pathogen transmission more precisely. The results show that the availability of habitats, seasonality, bird abundance, and environmental disturbances are key parameters that influence the spread of pathogens. This approach offers an efficient decision-making system for wildlife health, migrations, and pathogen transmission