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We document the first recorded instance of a fish depredating a hatchling bird while in the nest. Some coastal marsh-nesting avian species, such as the Seaside Sparrow (Ammospiza maritima), experience [...]

Environmental heterogeneity is increasingly being used to select conservation areas that will provide for future biodiversity under a variety of climate scenarios. This approach, termed conserving nature's stage (CNS), assumes environmental features respond to climate change more slowly than biological communities, but will CNS be effective if the stage were to change as rapidly as the climate? We tested the effectiveness of using CNS to select sites in salt marshes for conservation in coastal Georgia (U.S.A.), where environmental features will change rapidly as sea level rises. We calculated species diversity based on distributions of 7 bird species with a variety of niches in Georgia salt marshes. Environmental heterogeneity was assessed across six landscape gradients (e.g., elevation, salinity, and patch area). We used 2 approaches to select sites with high environmental heterogeneity: site complementarity (environmental diversity [ED]) and local environmental heterogeneity (environmental richness [ER]). Sites selected based on ER predicted present-day species diversity better than randomly selected sites (up to an 8.1% improvement), were resilient to areal loss from SLR (1.0% average areal loss by 2050 compared with 0.9% loss of randomly selected sites), and provided habitat to a threatened species (0.63 average occupancy compared with 0.6 average occupancy of randomly selected sites). Sites selected based on ED predicted species diversity no better or worse than random and were not resilient to SLR (2.9% average areal loss by 2050). Despite the discrepancy between the 2 approaches, CNS is a viable strategy for conservation site selection in salt marshes because the ER approach was successful. It has potential for application in other coastal areas where SLR will affect environmental features, but its performance may depend on the magnitude of geological changes caused by SLR. Our results indicate that conservation planners that had heretofore excluded low-lying coasts from CNS planning could include coastal ecosystems in regional conservation strategies. [ABSTRACT FROM AUTHOR]

Species distribution models have been applied across a wide range of spatial scales to generate information for conservation planning. Understanding how well models transfer through space and time is important to promote effective species-habitat conservation. Here, we assess model transferability in coastal tidal marshes of the southeastern United States using count data of a widespread marsh bird: the Clapper Rail (Rallus crepitans). We developed species-habitat models at a state level in both South Carolina and Georgia, and then assessed how well top models from each state predicted abundance in the other state. Internally (locally) validated models performed well with reasonable fit (SC: R2 = 0.35, GA: R2 = 0.14), and high significance (P = 0.0005); however, both models performed poorly when predicting abundance from the other state (R2 = 0.03 and 0.003). To assess the consequences of this lack of transferability, we applied the South Carolina- and Georgia-derived parameter estimates to habitat features in South Carolina and identified the top 25% of tidal marsh habitat that each model predicted within the state. There was minimal overlap between model habitat quality predictions (<5%). Our results address the predictive power and uncertainties that arise from using habitat associations and climate models to predict species distributions or abundance in locations without training data. We discuss potential reasons model transferability was not successful and address the need for better regional datasets and the importance of intraspecific variability in response to environmental gradients. [ABSTRACT FROM AUTHOR]