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.

Migratory animals are threatened by human-induced global change. However, little is known about how stopover habitat, essential for refuelling during migration, affects the population dynamics of migratory species. Using 20 years of continent-wide citizen science data, we assess population trends of ten shorebird taxa that refuel on Yellow Sea tidal mudflats, a threatened ecosystem that has shrunk by >65% in recent decades. Seven of the taxa declined at rates of up to 8% per year. Taxa with the greatest reliance on the Yellow Sea as a stopover site showed the greatest declines, whereas those that stop primarily in other regions had slowly declining or stable populations. Decline rate was unaffected by shared evolutionary history among taxa and was not predicted by migration distance, breeding range size, non-breeding location, generation time or body size. These results suggest that changes in stopover habitat can severely limit migratory populations.

As the United Nations develops a post-2020 global biodiversity framework for the Convention on Biological Diversity, attention is focusing on how new goals and targets for ecosystem conservation might serve its vision of 'living in harmony with nature'^1,2. Advancing dual imperatives to conserve biodiversity and sustain ecosystem services requires reliable and resilient generalizations and predictions about ecosystem responses to environmental change and management³. Ecosystems vary in their biota⁴, service provision⁵ and relative exposure to risks⁶, yet there is no globally consistent classification of ecosystems that reflects functional responses to change and management. This hampers progress on developing conservation targets and sustainability goals. Here we present the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology, a conceptually robust, scalable, spatially explicit approach for generalizations and predictions about functions, biota, risks and management remedies across the entire biosphere. The outcome of a major cross-disciplinary collaboration, this novel framework places all of Earth's ecosystems into a unifying theoretical context to guide the transformation of ecosystem policy and management from global to local scales. This new information infrastructure will support knowledge transfer for ecosystem-specific management and restoration, globally standardized ecosystem risk assessments, natural capital accounting and progress on the post-2020 global biodiversity framework.