Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

Remote Sensing Data Reveal a Significant Reduction in the Area of the Nesting Habitat of Rafetus euphraticus in the Tigris River, Southeastern Turkey

The Euphrates Softshell Turtle (Rafetus euphraticus) is an endangered freshwater turtle native to the Tigris-Euphrates river system. Habitat destruction caused by dams and sand mining poses a major threat to the species. This study quantitatively assesses the occurrence of sandy areas in the upper Tigris in Turkey as a key component of their nesting habitat, utilizing remote sensing data. An ensemble approach was employed, applying and combining Generalized Additive Models (GAM), Generalized Boosting Models (GBM), Generalized Linear Models (GLM), and Random Forests (RF) for classification. The models indicate that, compared to 2018, the occurrence of sandbanks-assumed to be crucial nesting habitat for this species-was reduced by 41% in 2022 following the construction of a large dam. Additionally, sand mining and flooding of adjacent lands have fragmented and degraded the remaining sandbanks. The survival of the Turkish population of the Euphrates Softshell Turtle requires immediate and effective action.

Spatial and seasonal variation in macrozoobenthic density, biomass and community composition in a major tropical intertidal area, the Bijagós Archipelago, West-Africa

The coastal intertidal ecosystem of the Bijagós Archipelago, Guinea-Bissau, one of the largest and most important in West Africa, sustains a considerable proportion of the migratory shorebird populations of the East Atlantic Flyway and operates as a nursery area for benthic fish in the region. The macrozoobenthos in these mudflats constitute the main food source for both groups so that spatial and temporal variation in their abundance and community composition is likely to influence the abundance and distribution of fish and birds. In this study we described the spatial and temporal dynamics in the density, biomass, and community composition of macrozoobenthos across six intertidal flats in three islands of the Bijagós Archipelago. Overall, the Bijagós Archipelago was characterised by a highly species-rich macrozoobenthic community, with ca. 88 taxa identified across all sites, reaching a mean density of 1871 ± 58.3 ind.m-2 (mean ± SE) and mean biomass of 5.65 ± 0.41 g of AFDM.m-2 (ash-free dry mass per m2), values much lower than what was described for nearby intertidal areas, namely the Band d'Arguin, Mauritania. Density and biomass of major macrozoobenthos classes (Bivalvia, Polychaeta, Malacostraca and Gastropoda) differed across sites and months, displaying an overall increase in density towards the final months of the dry season (March and April). Similarly, community composition also differed significantly between sites and throughout the season. The site with most distinct community composition (Adonga) supported low diversity and high abundance of a few bivalve species, whilst other two sites that hosted the most diverse communities, were also the most similar between them (Anrumai and Abu). These spatial and temporal patterns constitute an important baseline to improve knowledge of this intertidal ecosystem and will contribute towards a better understanding of the spatial and temporal distribution patterns of their consumers.

Combining Multispectral and Radar Imagery with Machine Learning Techniques to Map Intertidal Habitats for Migratory Shorebirds

Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques.