Predictors of specialist avifaunal decline in coastal marshes.

Coastal marshes are one of the world's most productive ecosystems. Consequently, they have been heavily used by humans for centuries, resulting in ecosystem loss. Direct human modifications such as road crossings and ditches and climatic stressors such as sea-level rise and extreme storm events have the potential to further degrade the quantity and quality of marsh along coastlines. We used an 18-year marsh-bird database to generate population trends for 5 avian species (Rallus crepitans, Tringa semipalmata semipalmata, Ammodramus nelsonii subvirgatus, Ammodramus caudacutus, and Ammodramus maritimus) that breed almost exclusively in tidal marshes, and are potentially vulnerable to marsh degradation and loss as a result of anthropogenic change. We generated community and species trends across 3 spatial scales and explored possible drivers of the changes we observed, including marsh ditching, tidal restriction through road crossings, local rates of sea-level rise, and potential for extreme flooding events. The specialist community showed negative trends in tidally restricted marshes (-2.4% annually from 1998 to 2012) but was stable in unrestricted marshes across the same period. At the species level, we found negative population trends in 3 of the 5 specialist species, ranging from -4.2% to 9.0% annually. We suggest that tidal restriction may accelerate degradation of tidal marsh resilience to sea-level rise by limiting sediment supply necessary for marsh accretion, resulting in specialist habitat loss in tidally restricted marshes. Based on our findings, we predict a collapse of the global population of Saltmarsh Sparrows (A. caudacutus) within the next 50 years and suggest that immediate conservation action is needed to prevent extinction of this species. We also suggest mitigation actions to restore sediment supply to coastal marshes to help sustain this ecosystem into the future.

Combining multisensor images and social network data to assess the area flooded by a hurricane event.

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.