With increasing threats from climate change, the vulnerability of coastal areas is heightened. One major challenge is the degradation of beaches due to flooding and erosion. Tropical and subtropical beaches are crucial nesting sites of sea turtles who are essential to marine ecosystems by supporting the preservation of coral reef and transporting nutrients from the ocean to coastal environments. Developing engineering maintenance or designing nature-based solutions can help mitigate the effects of flooding and erosion, preserving or expanding sea turtle nesting beaches. To effectively develop nature-based solutions that enhance sea turtle beaches by improving nesting suitability and reducing flooding and erosion, an understanding of the natural processes on small-scale environmental variables, such as sediment characteristics, are essential. The sediment characteristics are important for the survival of sea turtles because they influence the nesting behavior, egg survival and hatching success. Unfortunately, current data on sediment characteristics are scarce, especially at a global scale, which makes it difficult to identify global patterns of these characteristics across sea turtle nesting beaches.

The aim of this study was to analyze key sediment characteristics of sixteen globally significant sea turtle nesting beaches and to evaluate different experimental methods to investigate these sediment characteristics as a pilot study. The key sediment characteristics in this pilot study included color, particle size, particle grading, particle shape, bulk density, solid particle density, angle of repose, thermal retention, and porosity. This research served as a pilot study for a more extensive analysis that will be applied to approximately 2000 sediment samples from 209 sea turtle nesting beaches worldwide.
To achieve this, 32 sediment samples were examined from sixteen beaches, with one sample taken from the nesting line and one from the shoreline at each location. The nesting line was identified by visible nests and body pits, while the shoreline was defined as the highest point of debris left by the retreating tide. The sediment samples were analyzed using both qualitative and quantitative methods in different laboratories (TU Delft, Boskalis and Deltares). Qualitative methods included the Munsell color chart, Power scale, classification of sorting (well-sorted vs. poorly sorted), sand ruler and LEICA digital microscope.
Quantitative methods included dry sieving, Dynamic Image Analysis (DIA), Static Light Scattering (SLS), free-fall \& vibration table, volume of water displacement, helium gas displacement pycnometer, image-based angle of repose analysis and thermal retention analysis.

The results showed that all sixteen nesting beaches in this study were composed of well-sorted, medium-sized sand. These beaches may also be characterized by particles with moderate roundness and high circularity. Variations in bulk density and solid particle density suggested that sea turtles nest in diverse beach conditions, without a clear preference for specific density values. Results for angle of repose and thermal retention did not yield meaningful patterns related to sea turtle nesting preferences. Porosity values were consistent with those typically found in sandy environments. However, definitive conclusions about sea turtle nesting preferences can not be drawn at this stage, given the limited sample size and the lack of comparative data from non-nesting beaches. The findings should be considered suggestive rather than conclusive, and their main purpose is to guide future research directions.

This study also identified differences between key sediment characteristics at the nesting- and shoreline. While some variation was observed, no general statically significant differences were found between these two environments. Based on these findings, future large-scale studies may consider initially focusing on the sediment samples at the nesting line. No clear relationships were observed between sediment characteristics and latitude, and sea turtle species did not show distinct preferences for specific sediment characteristics. However, these findings should be further investigated with a large number of sampling sites.

For large-scale sediment analysis, DIA would be the most efficient alternative to analyze particle size and it also provides information on particle shape. Although dry sieving remained the most reliable method, it might be time-consuming for analyzing a large number of sediment samples. Regarding solid particle density analysis, the helium gas pycnometer seemed to be the most accurate option. However, it required professional expertise. Nevertheless, no significant differences were revealed between results obtained from the gas pycnometer and the volume of water displacement. The reliability of the water displacement method remained uncertain, and further testing was necessary to validate its accuracy or consider alternative methods.

Based on the outcomes of this pilot study, future research is recommended to pursue a more comprehensive and globally representative understanding of sediment characteristics at sea turtle nesting beaches. This will involve analyzing sediment samples from 209 beaches, focusing on the nesting line. The extended study will asses key characteristics, including color, particle size, particle shape, bulk density, solid particle density and porosity. The resulting dataset will provide essential information for researchers, ecologists and coastal engineers, and may contribute to the development of nature-based solutions, such as beach nourishment, to protect sea turtle nesting areas.

Galveston Island is crucial for sea turtle nesting but faces erosion and increasingwater levels, limiting nesting opportunities and posing dangers to nests in flood-prone areas. A numerical model was developed to estimate groundwater levels and hydrodynamics at specific locations on two onedimensional transects, located at the Tipsy Turtle and the Sea Wall. The model was used to assess the risk of turtle nest inundation at six fictitious nest locations and compared to a previous method used in a study by Ware et al. (2019), which used the 𝑅2% formulation developed by Stockdon et al. (2006).
The model was validated using data from a two-month field campaign in Galveston, Texas. Two five-day periods were simulated to represent different tidal and wind conditions. The model was also simulated for five different hydraulic conductivities.
In the nearshore, hydraulic conductivity did not influence the results. Although mean surface
elevations were captured well, the infragravity and incident wave heights were exaggerated in most cases, possibly due to wave spreading not being accounted for in 1D models and, therefore, concentrating energy transfer of incident waves to the infragravity waves.
The model demonstrated that small hydraulic conductivities performed better in estimating
groundwater levels in calmer conditions when the mean sea level was considerably lower than
the groundwater level, as in the second period. The RMSE close to the dune foot was 0.075 m for 𝐾 = 0.0001 m/s and 0.4 m for 𝐾 = 0.001 m/s in period 2 close to the dune foot at the Sea Wall. In contrast, the intermediate and higher hydraulic conductivities showed minor errors in more dynamic conditions close to the shoreline in period 1. The RMSE at TT4 (approximately halfway at the Tipsy Turtle) during period 1 was 0.164 m for 𝐾 = 0.001 m/s and 0.239 m for 𝐾 = 0.0001 m/s. In all cases, the best hydraulic conductivity decreased in landward direction.
Not a single hydraulic conductivity performed well in all conditions. Therefore, the results in a
test case assessing turtle nest inundation also varied. In most cases, the model overestimated turtle nest inundation, while the method by Ware et al. (2019) underestimated in all but one case. The run-up estimations of Stockdon et al. (2006) as used by Ware et al. (2019) were sensitive to changes in bed slope, with a steeper slope of 0.05 aligning better with modelled run-up during high tide in period 1 and a flatter slope of 0.01 in period 2. A slope of 0.0316 aligned mainly with the modelled run-up in intermediate conditions in period 1.
This report demonstrated that not one of the cases simulated is reliable enough for assessing turtle nest flooding accurately in all hydrodynamic conditions and is too conservative in most, but not all, cases. Therefore, the current model cannot be used as an alternative yet. Moreover, it concluded that the method using the empirical run-up formulation as in Ware et al. (2019) should also be used cautiously due to consistent underestimations in this experiment.