Global climate change is expected to significantly affect coastal ecosystems worldwide. For tidal marsh birds of the Gulf of Mexico, the extent of these impacts on future population dynamics is unknown. Here, we present information on our current understanding of marsh bird responses to climate change, identify gaps in that understanding, and propose ways of improving our ability to predict impacts on avian populations. Our understanding of how Gulf Coast avian populations will respond to environmental drivers such as sea-level rise, precipitation patterns, and hurricanes is limited, and detailed local and regional studies linking avian biology to wetland processes are needed. Impacts of wetland change on marsh bird species will be optimally assessed and forecasted within an adaptive framework, making use of process-driven studies that include models designed to elucidate patterns in avian biology and wetland dynamics. Further, because management and conservation efforts are implemented at local or site-specific scales, we recommend that process-driven studies incorporate hierarchical structures, nesting local efforts within a regional context. Implementing this research program will prove fundamental in furthering our understanding of avian population dynamics within the changing Gulf of Mexico environment.

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Accelerating sea level rise (SLR) is likely to cause considerable changes to estuarine and other coastal wetlands. Efforts to forecast the effects of SLR on coastal wetland vegetation communities should be useful in making predictions for individual species that depend upon those communities. However, considerable uncertainty exists when predicting a chain of events that passes from the global climate to local effects to implications for a single species. One component of this uncertainty is the classification resolution used by SLR landscape change models such as the Sea Level Affects Marshes Model (SLAMM). To isolate and assess the effects of this kind of uncertainty on species‐level SLR prediction, we analyzed surveys of birds and plants in the lower Altamaha River and its estuary in Georgia, USA. For 19 marsh and forest bird species, we tested the predictive value of three classes of covariates of site occupancy: (1) field‐measured habitat variables and spatial information, (2) information available from a SLAMM map, including the spatial configuration of the SLAMM habitat classes, and (3) SLAMM habitat class alone. We found that the predictive ability of occupancy models built from these three kinds of information varies widely among species. We therefore suggest criteria for classifying species according to the amount of detail necessary to describe their habitat niche, and thus to maximize the accuracy of predictive models. We point out that for species with habitat requirements that can be represented well by SLAMM classes, such as the Clapper Rail, forecasts of SLR‐induced population change are probably feasible. For species with more narrow habitat needs, however, such as the Seaside Sparrow, reasonable predictions of SLR effects may not be possible without further refinement of SLR landscape change models. We suggest that improved thematic resolution of such models should be a priority, if the implications of SLR models for individual species are to be ascertained fully.

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