A method to predict connectivity for nomadic waterbird species from tracking data

Abstract

ContextNomadic waterbird species move erratically, which makes it difficult to predict site use and connectivity over time. This is particularly pertinent for long-distance movements, during which birds may move between sites hundreds to thousands of kilometres apart.ObjectivesThis study aimed to understand how landscape and weather influence long-distance waterbird movements, to predict the probability of connectivity between locations and forecast short-term movements for a nomadic species, the straw-necked ibis (Threskiornis spinicollis) in Australia’s Murray–Darling basin.MethodsWe used 3.5 years of satellite tracking data together with high-resolution landscape and weather variables to model the expected distance travelled under environmental scenarios for long-distance movements. We generated least-cost paths between locations of interest and simulated the probability that birds could exceed the least cost-distance as a measure of connectivity. We also generated short-term forecasts (1–3 days; conditional on departure) of the probability of bird occurrence at a location given the expected environmental conditions.ResultsOur results suggested that wind is the dominant predictor of distance travelled during long-distance movements, with significant but smaller effects from month. Birds travelled further when wind benefit was higher and during summer. Further work is required to validate our forecasts of bird positions over short time periods.ConclusionsOur method infers the predictors of poorly understood movements of nomadic birds during flight. Understanding how partial migrants use landscapes at large scales will help to protect birds and the landscapes where they live.