Variability of Beach Sediment and Sea Turtle Nesting, Hatching, and Emergence Patterns during the 2019 Nesting Season in Northern Palm Beach County, Florida, U.S.A. Free

Sandy beaches face threats of erosion due to sea-level rise (Bruun, 1962; Fish et al., 2008; Gornitz, 1991; Leatherman, Zhang, and Douglas, 2000), storms and hurricanes (Davis and Barnard, 2003; Elko and Wang, 2007; Stockdon et al., 2012; Zhang et al., 2005), and other climate change impacts (FitzGerald et al., 2008; Zhang, Douglas, and Leatherman, 2004). One of the most common erosion mitigation strategies is nourishment, or placing sediment, to restore the beaches, dunes, and other coastal habitat (Browder and Dean, 2000; Elko et al., 2021; Finkl, 1981). The goal of beach nourishment is to support wide beaches for recreation, economic vitality, provide storm protection, and provide habitat (Dean, 2005; Fish et al., 2008; Mazaris, Matsinos, and Pantis, 2009; Roberts and Wang, 2012).

The sediment used for nourishment can be from different sources such as offshore borrow sites, upland mines, or dredged from nearby inlets (Andrews and Khalil, 1997; Browder and Dean, 2000; Elko and Wang, 2007; Palaparthi et al., 2022). Sediment from these different borrow sources can have different characteristics, such as variable grain size and sorting ranging from well-sorted fine sand to poorly sorted coarse sand (Andrews and Khalil, 1997; Palaparthi et al., 2022; Stauble, 2005). According to USACE (2011), beach-compatible fill must maintain the general characteristics and functionality of the material naturally occurring on the beach and in the adjacent dune system; however, characteristics can vary on the basis of the borrow site (Brown and Briggs, 2020).

Sediment properties play an important role in the success of sea turtle clutch viability. Sea turtles are oviparous species and success of a sea-turtle clutch can be influenced by sediment characteristics like grain size, sorting, and color (Bladow and Briggs, 2017; Cisneros, Briggs, and Martin, 2017; Garmestani et al., 2000; Hays et al., 1995; Milton, Schulman, and Lutz, 1997; Mortimer, 1990). In addition, salinity, moisture content, mid-level beach slope, and carbonate content have also been shown to influence hatching success (HS) (Silva et al., 2020; Wood and Bjorndal, 2000). One of the most important factors in sea turtle sex determination is substrate temperature (Pieau, Dorizzi, and Richard-Mercier, 1999; Yntema and Mrosovsky, 1982), which also plays a vital role in HS (Ackerman, 2017; Matsuzawa et al., 2002). Increasing temperatures of sediment have been found to affect survivability of clutches and may result in egg mortality (Bladow and Milton, 2019; Hawkes et al., 2007; Martins et al., 2020). Sediment characteristics like grain size have been found to influence the reflectance of the subaerial beach, resulting in fluctuations of temperatures (Lucey et al., 2014; Milton, Schulman, and Lutz, 1997; Okin and Painter, 2004).

The east coast of Florida is home to the topmost active nesting aggregations for sea turtles every year. In 2019, Palm Beach County had recorded 28,790 of loggerhead (Caretta caretta) (FWC, 2023a), 12,612 of green (Chelonia mydas) (FWC, 2023b), and 397 of leatherback (Dermochelys coriacea) activity (FWC, 2023c). Although Plam Beach County hosts a significant number of sea turtle nests every year, the sandy beach habitat is threatened. Approximately 75% of Palm Beach County beaches are classified as critically eroded by the Florida Department of Environmental Protection (Clark, 2019). In response, Palm Beach County frequently constructs beach nourishment projects that use various borrow sources; the selection of borrow sources depends on quantity needed, source proximity, and a cost–benefit analysis. Various borrow sources can have variability in geotechnical properties and should be analyzed for enhanced or adverse impacts on sea turtle nesting. Therefore, the objective of this study is to (1) evaluate the sediment characteristics (grain size, sorting, carbonate content) of different borrow sites and conduct a statistical correlation with substrate temperatures. Additionally, include an assessment of how reflectance of surface sediments relates to variations in temperature; (2) compare the sediment characteristics and substrate temperatures with HS and emergence success (ES) of three sea turtle species; and (3) determine if there are statistically significant hot spots of locations particularly favorable for nesting.

The study area encompasses nine transects in northern Palm Beach County, covering both managed and non-nourished beaches (Figure 1). These transects feature the range “R” monuments, which are coastal survey markers situated approximately 1000 feet apart. The selected transects range from south of the Jupiter Inlet (R13A) to north of John D. McArthur Beach State Park (R51), covering an 11-km stretch. Different borrow sites (i.e. inlet, offshore, and upland mine) have been used for beach and dune restoration projects throughout this 11-km stretch (APTIM, 2020). Near-annual placement of inlet-dredged sediment occurs at a beach adjacent to the inlet (R13A to R13.5A), whereas beaches to the south have been nourished with sediment from an offshore borrow source (R13A to R18 and R26–R37) or with upland mine for dune restoration only (i.e. R19 to R25; Figure 1). The nine study sites considered for the study include R13A, R18, R21, R24, R27, R31, R34, R41, and R51 (Table 1).

Sea turtle nesting season typically occurs from March through October in Palm Beach County. Three sampling events (event 1, event 2, and event 3) were conducted in 2019 during early (May), mid- (June), and late (September) nesting season at the nine selected transects (Figure 1). Each sampling event included sediment samples taken at three different depths: the surface, 45 cm, and 75 cm to mimic the dimensions of a sea turtle clutch. Samples were collected at three locations along each transect: the back beach (high), mid-beach (mid), and mean high water (low). This resulted in a total of 81 samples collected during each event. In 2019, a total of 243 samples was collected; however, samples from low cross-shore areas and at a depth of 75 cm were not considered in this analysis because of the limited number of nests in these locations.

Sediment samples were processed following standard granulometric methodology for textural statistical properties (i.e. mean grain size and sorting) (Boggs, 2006) and analyzed for carbonate content using hydrochloric acid dissolution. The reflectance measurements for all surface samples for event 3 of the study area were obtained using an ASD FieldSpec 3 Spectroradiometer. Spectral reflectance data, covering wavelengths from 350 to 2500 nm with a 1-nm resolution, were collected. Five signatures were taken for each sample and a splice correction was applied to account for instrument sensitivity drift in the short-wave near-infrared region. The ASD binary files were then converted to ASCII format using the ASD ViewSpec Pro software. To eliminate electronic noise and potential lighting variations, the five signatures for each sample were averaged in Microsoft Excel.

Substrate temperatures throughout the 2019 nesting season were recorded using an Onset U23001A HOBO Pro v2 that records temperature and relative humidity with an internal sensor with an accuracy of +0.45 from −40 to 32°F and +0.36 from 32 to 158°F. The loggers were set at 15-minute intervals and buried at 45-cm and 75-cm depths at the high, mid, and low beach locations along each transect. Seasonal temperatures were averaged for the early (mid-April to May), mid (June to mid-July), and late (mid-July to August) nesting season for only the high and mid-cross-shore locations and only at 45-cm depth for all the transects. Temperature data from the low location and at 75-cm depth were not considered in this analysis because of a limited number of recorded nests at these locations. The results from the granulometric analysis were used to analyze correlation between grain size and substrate temperatures.

Sea turtle nesting data are collected by the Florida Fish and Wildlife Conservation Commission authorized surveyors that monitor nesting parameters in Palm Beach County for loggerhead, leatherback, and green sea turtles to gather critical data for population trends. Standard practice is to mark and document a subset of nests with information on location, chamber parameters, clutch size, and the total number of eggs and their outcome (e.g., hatched live or dead). Marked nests are then monitored for overwash, predation, and hatching activity. For this study, raw data were obtained from Florida Fish and Wildlife Conservation Commission (through a consent permit) to calculate nesting success (NS), HS, and ES. After collection of sea turtle data, nests and excavation records were categorized by GPS location and assigned to corresponding R monument areas. These R monuments are designated areas spaced 1000 feet apart, allowing for precise delineation on the basis of GPS coordinates. These areas correspond to sediment samples collected within the R-monument transects. The sediment data can then be analyzed alongside sea turtle nesting data to assess potential correlations.

All sediment properties and nesting data were tested for normality using the Shapiro–Wilk test. The results indicated that none of the variables showed a significant departure from normality. The statistical relationship between HS and each subsurface sediment property (mean grain size, sorting, carbonate percentage) and subsurface temperature was assessed using Pearson’s correlation coefficient. Correlation analysis was performed separately for each cross-shore location (high and mid) and three events.

A model was developed in ArcMap Toolbox to analyze nest location data and determine if there were statistically significant clusters, indicating nesting site preferences for each of the three species. The model was built using tools like integrate, collect events, and hot-spot analysis (Getis-Ord Gi*) as shown in Figure 2 (illustrating the model built for leatherbacks). Nesting data for all three species were analyzed separately using this model. Hot-spot analysis using the Getis-Ord Gi* statistical tool determines whether there are statistically significant hot spots or cold spots of nesting areas. This tool calculates the Getis-Ord Gi* statistic for each feature in the data set. The tool also creates a new output feature class with a z-score, p-value, and confidence level bin (GiZscores). The resultant z-scores and p-values indicate where features with either high or low values cluster spatially. Thus, this tool creates statistically significant spatial clusters of high nesting sites (hot spots) and low nesting sites (cold spots). Then, nearest-neighbor Kriging interpolation was executed on the GiZscores individually for each species to obtain a raster surface showing hot spots and cold spots of nesting patterns.

Sediment analysis included mean grain size (mm), sediment sorting (ϕ), carbonate content (%), and associated substrate temperature (°F) for the 2019 nesting season during three events at high and mid-cross-shore locations. All nine transects at the high cross-shore locations had an overall mean grain size that ranged from fine to medium sand (0.30 mm at R34 [event 2] to 0.75 mm at R13A [event 1]) (Table 2). During the entire 2019 nesting season, the lowest average grain size was measured at R34 at the high location, with an average grain size measuring 0.33 mm, which was associated with offshore borrow source sediment. However, the high location (R13A) measured, for the entire 2019 nesting season, the highest average grain size of 0.72 mm, where sediment was sourced from inlet and offshore dredging (Figure 3a). The sediment sorting (ϕ) at the high beach location ranged from well sorted to poorly sorted (0.49 ϕ at R34 [event 1] and 1.09 ϕ at R51 [event 2]) (Table 2). The average sorting across all three events showed that R34, nourished with offshore borrow material, exhibited moderately well sorting (0.54 ϕ). In contrast, R13A, which was nourished with inlet and offshore dredged material, measured moderate sorting (0.91 ϕ) (Figure 3a).

Carbonate content varied from 40% at R18 (event 1) to 82% at R13A (event 1) (Table 2). The average carbonate content across all three events was lowest at the offshore and upland mine-nourished location of R18 (44%) and highest at the inlet and offshore dredge-nourished site at R13A (91%). Substrate temperatures increased from event 1 (mean 86°F ± 1°F standard deviation [SD]) to event 3 (mean 89°F ± 1°F SD) across all nine study sites. The lowest average temperature was recorded at the non-nourished location at R51 (87°F) and the highest average temperature was recorded at the offshore borrow nourishment site at R31 (89°F) (Figure 3b).

At the mid-cross-shore location, the overall grain size varied from fine to medium sand (0.23 mm at R31 [event 3] to 0.72 mm at R13A [event 1]) (Table 3). Lowest average grain size for all three events was recorded at R31 (0.31 mm) and highest was recorded at R13A (0.65 mm) (Figure 4a). Sorting (ϕ) at the mid-cross-shore locations varied from well sorted to poorly sorted (0.36 ϕ at R31 [event 3] to 1.18 ϕ at R13A [event 2]) (Table 3). The average sorting across all three events indicated moderately well sorting (0.50 ϕ) at the offshore borrow site at R31 and poorly sorted (1.10 ϕ) at the inlet and offshore nourishment at R13A. Carbonate content varied from 34% at R27 (event 1) to 82% at R13A (event 1) (Table 3). The average carbonate content across all three events was lowest at the offshore borrow location at R27 (43.33%) and highest at R13A (77%). Substrate temperature for all nine transects at the mid-cross-shore locations increased from event 1 (86°F mean ± 1°F SD) to event 3 (89°F mean ± 1°F SD). The lowest average temperature was recorded as 87.60°F at the offshore borrow site at R34 and highest was 88.98°F at the offshore and upland mine-nourished site at R18 (Figure 4b).

Substrate sediment characteristics, such as grain size and carbonate content, did not show any statistically significant impact (r² < 0.5) on temperature in both the high and mid-cross-shore regions across all three events (Table 4). At the high cross-shore locations during event 2, sorting showed a negative correlation with temperature, indicating that poorly sorted sediments tend to result in lower average temperatures, whereas well-sorted sediments are associated with higher average temperatures (Table 4). Surface sediment characteristics resulted in no correlation (r² < 0.5) to temperature across all three events, but they exhibit better correlation in comparison with subsurface sediment properties (Table 5). The reflectance of surface sediment was compared with the sediment characteristics and temperature. Reflectance was higher for coarser-grained sands (R13A and R51) and lower for finer-grained sands (R31 and R24). Coarser grains are known to scatter more light, leading to higher reflectance (Okin and Painter, 2004). Additionally, enhanced reflectance is typically observed at lower temperatures (Lucey et al., 2014). Thus, locations with fine sand had a low surface reflectance and resulted in higher temperatures (R31) (Figure 5).

Total nesting attempts (combined for all three species) evaluated for the entire study area were 26,813, of which 10,645 of those attempts resulted in successful nests, or in other words a 40% NS rate. The total number of nests, false crawls, and NS rates varied for all three species during the entire nesting season (Table 6). Loggerheads had an average NS of about 41% during the 2019 nesting season (Table 6). The non-nourished site at R51 recorded the highest loggerhead NS rate for the entire study area, with a 40% success rate. A total of 1,810 successful loggerhead nests was recorded of the 4,573 total nesting attempts. The inlet and offshore dredged nourished location at R13A recorded the lowest loggerhead NS rates of 31%, with a total of 55 nests recorded of the 177 total attempts. Greens had an average nesting success of 40% across all study locations, with the highest NS rates calculated at the non-nourished location at R51, with 1,998 recorded nests of the 4,348 total attempts. The lowest NS rates for green sea turtles were calculated at the inlet and offshore borrow site at R13A, with only 6 recorded nests of the 59 total attempts, resulting in a 10% NS rate. Leatherback sea turtles recorded the highest average nesting success of all sea turtle species for the entire nesting season; however, a lower sample size for leatherback sea turtles yielded higher NS rates. During the 2019 nesting season, leatherback nests and false crawls were recorded at only a few transects. As a result, nesting success rates were calculated only at R13A (inlet sediment), R21 (upland mine), R34 (offshore), and R51 (non-nourished) (Table 6). Leatherback sea turtles had the highest number of recorded nests at the upland mine dune restoration site at R24, with 31 recorded nests of 34 total attempts, resulting in a 91% NS rate. In contrast, R13A recorded the lowest number of leatherback nests, with three recorded nests of a total of three total attempts during the nesting season. Despite R13A recording the fewest nests, the study location calculated the highest NS rate of 100%, which resulted in the highest NS rate even though R13A had the lowest recorded number of nests.

Each species of sea turtle is known to nest at certain times during the nesting season; generally, leatherback sea turtles tend to nest in the early season (event 1), loggerheads tend to nest during the mid-season (event 2), and greens nest in the late season (event 3) (Table 6). The lower HS and ES rates calculated in this study can be attributed to the typical nesting patterns of each species described above. To account for the nesting patterns of each species, HS and ES rates were calculated for nests at high and mid-cross-shore locations. These calculations were separated into early-, mid-, and late-season groups, with variable sample sizes, to assess HS and ES rates for each species throughout the season. The average HS for loggerheads during the mid-season (event 2) was approximately 75% at the high cross-shore locations and 74% at the mid-cross-shore locations, with the highest HS (87%) recorded at R27 (upland mine) at the mid-cross-shore location (Figure 6b). The highest ES (83%) for loggerheads during mid-season (event 2) occurred at R13A (inlet and offshore) at the high cross-shore location (Figure 6a), with an average of 70% and at R27 (offshore) at the mid-cross-shore location (Figure 6b), with an average 69%. The lowest HS (62%) for loggerheads occurred at R34 (offshore) high cross-shore location (Figure 6a) and lowest ES (51%) at R21 (upland mine) mid-cross-shore location (Figure 6b).

The average HS rates for greens for the mid- and late season (events 2 and 3) across the study area were 81% at the high cross-shore locations and 90% at mid-cross-shore locations. The highest HS (95%) for greens was observed at R24 (upland mine) and R34 (offshore) mid-cross-shore location (Figure 7b), whereas the lowest HS (52%) was recorded at R34 (offshore) at the high cross-shore location (Figure 7a). The average ES rates across the study area at the high and mid-cross-shore locations were 79% and 89%, respectively. The highest ES (97%) was at R24 (upland mine) mid-cross-shore locations, whereas the lowest ES (50%) occurred at R41 (non-nourished) at the high cross-shore location.

Leatherbacks during the early season (event 1) had an average HS of 63% across the study area at the high cross-shore locations and 54% at the mid-cross-shore locations. The R51 (non-nourished) study site recorded the highest HS (91%) at the high cross-shore location (Figure 8a) but at the mid-cross-shore location the HS was the lowest, at 15% (Figure 8b). The average ES for leatherbacks across the study area was 51% at the high cross-shore locations and 50% at the mid-cross-shore locations. The highest recorded ES was at R51 high cross-shore location (Figure 8a) and the lowest recorded at R51 mid-cross-shore location (Figure 8b).

Statistical correlation between subsurface sediment properties and HS was evaluated to determine the relationship between sediment characteristics and successful turtle nesting outcomes. For correlation analysis, events with sufficient data were analyzed to assess the relationship between sediment properties and HS. HS of loggerheads for event 2 at the high cross-shore location exhibited a very high positive correlation (r² > 0.5) with mean grain size (mm), sorting (ϕ), and carbonate content (Table 7) and negative correlation with temperature (r² > 0.5). HS of greens for event 2 at the high cross-shore location exhibit positive correlation (r² > 0.5) with mean grain size. Leatherback HS for event 2 had slightly negative correlation with sorting and carbonate (r² > 0.5) (Table 7). All three species for the entire nesting season at the mid-cross-shore locations had no correlation with sediment properties and temperature (Table 8).

The hot-spot analysis and the kriging interpolation determined if there were locations with high NS density (i.e. the hot spots) within a 1000-m radius. Loggerheads had the highest nesting density at R18 (offshore and upland mine), followed by R21 (upland mine) and between R41 and R51 (non-nourished). Greens had the highest nesting density between R41 and R51 (non-nourished). Leatherbacks did not show a specific location preference, as they were observed nesting throughout the study area, resulting in hot spots seen across the study area, except for R13A and R51 (Figure 9).

Sandy beaches are vital for sea turtle reproduction efforts in South Florida, but sea turtle nesting beaches are facing major threats due to coastal erosion. In recent years, studies have suggested that reduced sea turtle nesting and reproduction success on nourished beaches may be due to several factors including altered sediment type and grain size, altered beach profiles, and thermal impacts (ERDC, 2022; Shamblott, Reneker, and Kamel, 2021). Substrate temperature and sediment properties have an important role in sea turtle reproduction efforts by influencing sex determination (Pieau, Dorizzi, and Richard-Mercier, 1999), HS (Ackerman, 2017), and mortality of eggs (Bladow and Milton, 2019; Martins et al., 2020). The sandy beach substrate temperatures are heavily influenced by sediment type and characteristics (Milton, Schulman, and Lutz, 1997). To better understand the factors that influence substrate temperatures and provide optimal habitat for the success of sea turtle reproduction, a comprehensive analysis was completed. This study examined both substrate and surface sediment properties (i.e. mean grain size, sorting, carbonate content) across different cross-shore locations during the 2019 sea turtle nesting season. By evaluating these factors, the study aimed to identify the key determinants that affect substrate temperature and NS, HS, and ES for Palm Beach County, Florida.

Results showed that substrate sediment characteristics did not significantly influence the substrate temperature. However, surface sediment characteristics results suggest that coarser, more poorly sorted, and high-carbonate surface sediment provide cooler temperatures, whereas fine-grained, well-sorted, and low-carbonate surface sediments result in higher temperatures (Figure 10a). In certain locations, surface carbonate content showed a negative correlation with temperature; lower carbonate levels were associated with higher substrate temperatures (Figure 10b). However, the correlation between sediment characteristics and substrate temperature is <0.5. Although this study found correlation of sediment characteristics and substrate temperatures, substrate temperatures for in situ nests can vary significantly because of factors such as precipitation, which can mitigate extreme heat through evaporative cooling of the nest and eggs (Lolavar and Wyneken, 2015; Tezak, Sifuentes-Romero, and Wyneken, 2018). Other influences include humidity levels and the effects of seawater inundation, which can further affect the thermal environment of the nest (Gravelle and Wyneken 2022). The hot-spot analysis revealed that in 2019, loggerheads and greens were more likely to nest on non-nourished locations like R51, whereas leatherbacks preferred areas with upland mine-nourished sediment, such as R34. R13A, nourished with inlet dredged sediment and offshore sediment, was the least-preferable location for nesting, likely due to higher salinity compared with other beaches. However, since the nourishment period began in 2015, this location, along with others, has shown an increase in NS.

Sediment characteristics have also been found to influence HS and ES for sea turtles (Booth and Freeman, 2006; Mortimer, 1990; Stewart, Booth, and Rusli, 2019). This study evaluated the relationship between sediment properties (grain size, sorting, carbonate content) and substrate temperature in comparison with HS rates. Loggerhead sea turtles had a positive correlation with sediment characteristics and negative correlation with temperature. This suggests that for loggerheads, medium moderately sorted grains with high-carbonate-content sediment could improve HS rates. Green sea turtles had positive correlation with mean grain size, which suggests that medium to coarse grain sediment is suitable for green sea turtle HS. Leatherbacks had negative correlation with sorting and carbonate content. This means that well-sorted grains and lower carbonate content are favorable conditions for HS of leatherbacks. However, HS can vary because of many other factors like salinity, moisture content, oxygen flow, and porosity (Ackerman, 2017; Bustard and Greenham, 1968; Chen, Wang, and Cheng, 2010; Wood and Bjorndal, 2000). Future studies should combine sediment characteristics and HS with other factors like moisture content, porosity, and salinity to gain a better understanding of influences of HS rates for sea turtles. Although this study identifies favorable sediment characteristics that could enhance sea turtle nesting habitats, it has some limitations. The correlation analysis could benefit from a larger sample size, as some nests were excluded because of washouts and predation, reducing the data available for analysis. Additionally, in this study nests were categorized on the basis of R monuments spaced 1000 feet apart, but differences in surveying techniques among the permitted surveyors that recorded and marked the nests in the field led to the study sites in this analysis having limited sample sizes in some areas.

The results of this study suggest that variations in sediment granulometry from different sources may lead to differences in NS, HS, and ES across sea turtle species. Loggerheads and greens nested more frequently at mid-shore locations, whereas leatherback nests were more often at the high cross-shore locations. Examining cross-shore locations of nests, sediment near the base of the dune (high location) was found to have an influence on HS, whereas sediments at the mid-beach (mid-location) had no correlation with HS. In a typical beach cross-shore profile, finer sediments accumulate near the dunes because of aeolian transport, whereas coarser sediments remain closer to the high-tide line. Sediments with excessive fines (over 5–10% silt and clay) may cause beach compaction, hindering sea turtles' ability to dig proper egg chambers and potentially affecting HS (Bladow and Briggs, 2017; Milton et al., 1994; Montague, 1993; Mortimer, 1990). Additionally, finer sediments can impede water drainage and gas exchange, increasing the risk of embryonic mortality (Kramer and Bell 1980).

Although the study reveals that loggerheads and greens were more likely to nest on a non-nourished location, sea turtle nesting beaches are facing major threats because of coastal erosion, which is well documented (Fujisaki, Lamont, and Carthy, 2018; Hawkes et al., 2009; Reece et al., 2013). One of the most common responses is beach nourishment to mitigate long-term erosion and to bring back habitat lost from erosion. Results from this study suggest that beach nourishment sands that have moderately sorted medium sand with higher carbonate content can aid in improving HS. For green sea turtles, medium to coarse grains are ideal for HS. Overall, a beach nourishment that is sourced with medium-sized sand grains can provide an optimal suitable habitat for HS for loggerhead and green sea turtles.

Compared with the 2015 nesting season (Cisneros, Briggs, and Martin, 2017), the NS for all three species combined has shown improvement after the three cycles of nourishment events that have occurred since 2015 (Brock, Reece, and Ehrhart, 2009; Rumbold, Davis, and Perretta, 2001). R13A, situated south of the inlet, has also demonstrated an increase in NS. The average NS for the 2015 season ranged between 21% and 54%, whereas for the 2019 season it ranged between 47% and 62%. These findings emphasize the beneficial impact of nourishment activities on NS, illustrating the vital role that coastal management practices play in improving and maintaining wildlife habitats.

This study evaluated different sediment sources (offshore borrows, upland mine, and inlet dredged sediment) used for beach and dune restoration since 2015 in northern Palm Beach County, Florida and compared it with the NS, HS, and ES of loggerhead, green, and leatherback sea turtles during the 2019 nesting season. Sediment characteristics ranged from well-sorted to moderately sorted medium to coarse sand. Results showed no statistically significant influence between substrate sediment properties and temperature. Surface sediment samples that are well sorted fine sand and have a lower carbonate content were correlated with higher temperatures. Moderately sorted, medium-sized grains with high carbonate content are favorable for higher HS rates in loggerhead turtles. Medium to coarse grain size is suitable for green sea turtle nesting, whereas leatherback HS shows a slightly negative correlation with sorting and carbonate content. Overall, loggerheads exhibited poor NS (<50%) but good HS (>50%) and ES (>50%), particularly at managed beach sites, with the highest success recorded at R27, nourished with offshore sediments. Similarly, green turtles had poor NS but good HS and ES across all locations, with the highest at R13A, nourished with inlet and offshore sediments. Leatherbacks, however, had good NS but poor HS and ES, with the highest success at R51, a non-managed site.

The 2019 data set suggests that sand characteristics may influence NS and HS in certain sea turtle species, though further analysis is needed. Although this study focuses on the relationship between beach sedimentology and sea turtle nesting patterns, future research should also consider factors like salinity, moisture content, sand color, slope, and beach width. The results from this study indicate that different sediment sources used in beach and dune nourishment in South Florida do not adversely affect sea turtles but may actually enhance their nesting habitat.

This study was funded in part by the Palm Beach County Department of Environmental Resources Management. The authors thank Teal Kawana for reviewing the manuscript. The authors acknowledge Loggerhead Marine Life Center for collecting the nesting data. The authors are also grateful to the anonymous reviewers for suggestions improving this manuscript.