Distinguishing local and global correlates of population change in migratory species
Published Web Locationhttps://doi.org/10.1111/ddi.12884
Aim: Understanding the processes driving population declines in migratory species can be challenging. Not only are monitoring data spatially and temporally sparse, but conditions in one location can carry over to indirectly (and disproportionately) affect the population in another location. Here, we explore whether remote factors can sequentially, and potentially cumulatively, influence local population fluctuations in declining populations of shorebirds. Location: Moreton Bay (Australia) and the East Asian–Australasian Flyway. Methods: We use N-mixture models to account for variable observer effort and estimate yearly population growth rate. We then use least squares regressions to correlate population growth rates with remotely sensed climate anomalies at different migratory stages. From this, we estimate species-specific climate sensitivity indices and explore whether species which are declining more rapidly, or which rely more heavily on areas undergoing rapid habitat loss, have higher climate sensitivity indices. Results: We find that species which rely more on the Yellow Sea during migratory stopover (a region which has undergone severe habitat loss) are more sensitive to rainfall anomalies in their Arctic breeding grounds, suggesting that habitat loss reduces the resilience of shorebirds to climate extremes. Furthermore, species with higher sensitivities to climatic conditions during stopover are also those which are declining quickest, suggesting that declining populations may also be less resilient to climate fluctuations at bottleneck sites. We also observed species-specific correlations between climate anomalies at all migratory stages and population growth rates, primarily for eastern curlew and lesser sand plover. Main conclusion: By applying methods in combination, it is possible to use citizen science data from a single location in a flyway of over 160 sites up to 11,680 km apart, to investigate how different stressors correlate with local population dynamics.