International migratory birds travel vast distances as they traverse countries and continents, with some species covering up to 70,000 km annually. These birds and their critical seasonal habitats are protected under multi-lateral international agreements, that form a chain of countries along the migratory routes, with each nation committed to conserving its part of the journey. Wetlands are often crucial stepping stones in these migratory journeys and effective conservation of these habitats requires a detailed understanding of the various species-specific habitat preferences.
In this study, we use detection records from the Atlas of Living Australia, an open-access data repository, and apply three machine learning algorithms to analyse the wetland preferences of key international migratory bird species. Our approach identifies species-specific preferences, offering new insights into how these birds select and use wetlands during migration, and how we can use this knowledge to create suitable habitats for conservation.
Our work highlights the importance of open-access data, and the value of machine learning approaches to inform conservation, but it also reveals the shortcomings of many existing monitoring frameworks.