Modeling Sea Turtle Nesting Probability: Evaluating Loggerhead Nest Site Selection Using Presence–Pseudo-Absence Data

Modeling Sea Turtle Nesting Probability: Evaluating Loggerhead Nest Site Selection Using Presence–Pseudo-Absence Data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 August 2025 V1 Latest version Share on Modeling Sea Turtle Nesting Probability: Evaluating Loggerhead Nest Site Selection Using Presence–Pseudo-Absence Data Authors : Divina Cox 0009-0004-1822-6689 [email protected] , Phillip Schmutz 0000-0002-7243-4815 , and Samantha Seals Authors Info & Affiliations https://doi.org/10.22541/au.175525924.40059675/v1 253 views 117 downloads Contents Abstract Introduction References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Sea turtle nesting populations face growing threats from climate change, sea-level rise, and coastal development. Understanding how beach morphology influences nest-site selection is essential for effective conservation planning. This study examines loggerhead (Caretta caretta) nesting preferences on Santa Rosa Island and Perdido Key, Florida—an underutilized nesting region—using high-resolution LiDAR data from 2016 and 2020. Nest locations were paired with an equal number of pseudo-absence points, and five morphological variables were extracted: beach slope, foreshore slope, dune height, nest elevation, and nest distance from the mean higher high-water line. Logistic regression models (adjusted and unadjusted) identified nest elevation as the only variable consistently associated with nest presence, particularly in 2016. In contrast, elevation was not significant in 2020, highlighting annual variability in nesting cues. No other individual variable were significant across both years. Paired interaction analyses revealed a statistically significant and strong positive relationship between nest elevation and dune height, indicating that taller dunes increased the likelihood of nesting at higher elevations. Nest distance also significantly interacted with both dune height and beach slope, with inland nesting patterns more pronounced on steeper beaches and in areas with taller dunes. However, the positive effect of dune height weakened as beach slope increased, suggesting a trade-off between slope steepness and dune accessibility. Foreshore slope showed no meaningful direct or interactive effects. These findings highlight the complex and interrelated influences of beach morphology on nesting behavior, suggesting that loggerheads are flexible nesters across a range of physical settings but selectively respond to elevation—especially when paired with protective dune features. Conservation strategies that preserve or restore elevated nesting zones and dune structures may improve nesting success and enhance resilience in low-density nesting areas like the Florida Panhandle. Introduction Sea turtles, ancient marine reptiles that have traversed the world’s oceans for over 200 million years, now face escalating threats from anthropogenic climate change. Rising sea levels, more frequent and intense storms, and widespread coastal development are diminishing the availability and quality of suitable nesting habitats (Reneker and Kamel, 2016; Fuentes and Cinner, 2010). These pressures are forcing sea turtles to adapt their nesting behaviors and site selection patterns in real time. While environmental influences on nest placement are well documented in high-density nesting areas such as southeastern Florida, comparatively few studies have focused on turtle nesting site behaviors in low-density regions like the Florida Panhandle (e.g., Lamont and Carthy, 2007; Lamont and Houser, 2014; Lamont et al., 2023). A growing body of research has shown that beach morphology—including features such as slope, dune height, and beach width—can significantly affect nest placement and success (Eckert, 1987; Provancha and Ehrhart, 1987; Garmestani et al., 2000; Mazaris et al., 2006; Lamont and Carthy, 2007; Cuevas et al., 2010; Lamont and Houser, 2014; Fujisaki et al., 2018; Culver et al., 2020; Cuevas et al., 2021). Yet despite the importance of these physical characteristics, few studies incorporate them into spatially explicit modeling frameworks. Most rely instead on general site-level descriptions, limiting their predictive power and scalability. Understanding how species respond to environmental variation is further complicated by differences in behavioral plasticity across taxa. Some sea turtle species exhibit narrow environmental preferences and high site fidelity, while others tolerate a wider range of conditions and exhibit more flexible nesting behavior (Horrocks and Scott, 1991; Cuevas et al., 2010; Yamamoto, 2012; Liles et al., 2019; Culver et al., 2020). Loggerheads—the focal species of this study—are especially notable in this regard. Unlike more site-faithful species, loggerheads demonstrate considerable variability in nest site selection across regions and over time. They are known to return to nesting beaches at broad spatial scales but exhibit reduced fidelity at the microhabitat level (Talbert et al., 1980; Williams-Wallis et al., 1983; Lamont and Carthy, 2007; Pfaller et al., 2008; Martins et al., 2022). This behavioral flexibility makes loggerheads both a conservation challenge and an ideal subject for investigating how physical beach characteristics influence nesting behavior in dynamic or marginal habitats. Loggerheads’ reduced site fidelity complicates efforts to anticipate nesting behavior, particularly in low-density regions where social or historical cues may play a lesser role. However, this variability also presents an opportunity to uncover consistent environmental drivers of nest site selection across diverse conditions. Addressing this challenge requires analytical tools that can detect complex, nonlinear relationships between nesting activity and physical landscape features. Species distribution models (SDMs) offer a powerful framework for evaluating how environmental gradients influence species distributions, particularly in situations where direct experimentation is infeasible. Correlative SDMs estimate statistical associations between species occurrence and environmental variables (Guisan and Zimmermann, 2000; Elith and Leathwick, 2009), making them especially useful for studying elusive or low-density species, where presence data are limited and habitat associations may be subtle or spatially complex. SDMs have been widely used in both terrestrial and marine systems to guide habitat restoration, reserve design, and species recovery. For example, Su et al. (2021) used remote sensing variables in SDMs to identify suitable habitats for Asiatic black bears and red pandas in Nepal, while Ranjitkar et al. (2014) modeled Rhododendron arboreum distributions along Himalayan elevational gradients. Chefaoui et al. (2005) applied BIOMAPPER to explore niche overlap in Iberian dung beetles, revealing fine-scale ecological interactions. In marine contexts, Peavey (2010) used MAXENT to predict pelagic habitat suitability for olive ridley sea turtles using presence-only data, and Martino et al. (2021) combined systematic surveys with social media records in a Log Gaussian Cox process model to account for detection bias in dolphin distributions. These studies highlight the versatility of SDMs across taxa and environments, particularly for species with variable or fragmented habitats. However, the effectiveness of SDMs depends heavily on the quality and structure of the underlying species occurrence data. While presence-only models are widely used due to data accessibility, they often lack contrast and are subject to spatial bias (Phillips et al., 2006). Presence–absence models, on the other hand, generally offer stronger predictive power, especially for species with narrow habitat preferences (Brotons et al., 2004). But for many conservation-relevant species—such as sea turtles—confirmed absences are difficult to establish due to low detection probabilities, wide-ranging behavior, and variable nesting density (Araújo and Guisan, 2006). To address these limitations, many researchers turn to pseudo-absence (PA) modeling, which involves selecting background points from locations where the species is unlikely to occur based on spatial or ecological rules. This method increases environmental contrast and improves model reliability (Barbet-Massin et al., 2012), especially in systems where absence data are unavailable or unreliable. Pseudo-absence approaches are particularly well-suited to marine and coastal contexts, where confirming absence is inherently challenging (Elith and Leathwick, 2009). Although relatively underutilized in sea turtle nesting studies, pseudo-absence modeling has demonstrated strong potential. Culver et al. (2020), for example, combined LiDAR-derived morphology data with pseudo-absence points to assess nesting suitability for Kemp’s ridley turtles along the Texas coast, revealing clear avoidance of areas with steep or irregular terrain. Pseudo-absence techniques have also been shown to enhance model accuracy in other low-density applications, including those by Chefaoui et al. (2005) and Su et al. (2021). Building on this foundation, the present study applies a presence–pseudo-absence SDM approach to examine how loggerhead turtle nesting in the Florida Panhandle responds to fine-scale beach and dune morphology. In this research, we analyze how beach and dune morphology influence nesting probability in the Florida Panhandle—a low-density nesting region. Specifically, we (1) assess whether individual morphological metrics—such as foreshore slope, dune height, and beach width—predict loggerhead nest occurrence using presence and pseudo-absence data; (2) examine the independent effects of these morphological features; (3) quantify the relative importance of each variable; and (4) evaluate interaction effects among morphology characteristics. By incorporating high-resolution LiDAR data and statistically rigorous modeling techniques, this study offers insight into how spatial patterns of beach morphology structure sea turtle nesting. The results can inform site-specific management strategies and conservation planning, especially in areas undergoing beach nourishment, dune restoration, or future development. Study Site The study site includes Santa Rosa Island and Perdido Key in Escambia County, Florida, focusing on the Pensacola Beach community and Gulf Islands National Seashore (GINS). Santa Rosa Island is an 85 km wave-dominated barrier island along the Florida Gulf Coast, bordered by Pensacola Bay and Choctawhatchee Bay, and separated from the mainland by Santa Rosa Sound (Claudino-Sale et al., 2008). Perdido Key lies to the west, between Pensacola Pass and Alabama’s Perdido Pass (Browder and Dean, 2000). Both islands feature well-developed beaches and foredunes and are regularly impacted by hurricanes, which alter their beach and dune morphology (Houser, 2009). Geospatial Methods This study extracted key beach and dune morphological features using LiDAR-derived Digital Elevation Models (DEMs) sourced from NOAA’s Digital Coast platform, including datasets from the U.S. Geological Survey and the U.S. Army Corps of Engineers. Analyses focused on 2016 and 2020, years selected because: (1) LiDAR flights occurred during the loggerhead nesting season (mid-May to late August), ensuring temporal alignment; (2) both years recorded high nesting activity, offering stronger sample sizes; and (3) the DEMs were of high quality, with minimal voids or anomalies. Loggerhead nest locations were provided by Escambia County’s Natural Resources Department and Gulf Islands National Seashore. These GPS coordinates were overlaid on the DEMs to extract elevation-based metrics. Each year’s nesting area was delineated as a polygon to ensure consistent spatial boundaries and minimize extraction error. Elevation profiles were created using 3D line geometry in ArcGIS Pro, capturing slope, elevation, and nest position relative to shoreline features. These data provided the basis for analyzing geomorphic influences on nest site selection. Pseudo-absence Points Pseudo-absence points were generated using the Create Random Points tool in ArcGIS Pro, constrained to the same spatial extent as the presence data. A 1:1 ratio of pseudo-absence to presence points was used, consistent with established best practices that recommend this balance to enhance model contrast without introducing bias against true presences (Barbet-Massin et al., 2012; Culver et al., 2020). Morphological attributes for each pseudo-absence location were extracted using the same geospatial workflow as for presence points. This uniform extraction process ensured methodological consistency and strengthened the comparability of the datasets, providing a more robust foundation for statistical modeling of nest site selection. Data Analysis Morphological characteristics were computed relative to the 2018 National Tidal Datum. The Mean Higher High Water (MHHW) was set at 0.25 meters and the Mean Lower Low Water (MLLW) at -0.14 meters elevation (relative to NAVD88) (National Oceanic Atmospheric Administration, n.d.). Foreshore slope was calculated using the elevation difference between MHHW and MLLW (0.39 m) divided by the horizontal distance between those elevations. Beach slope was defined as the elevation difference from the nest to MHHW divided by the nest’s distance from MHHW. Dune height was recorded as the highest point in the foredune environment along the profile line, and nest elevation was extracted directly from the nesting coordinate. Continuous variables were summarized with means and standard deviations, categorical variables as counts and percentages. Two-sample t-tests assessed differences between years. Binary logistic regression evaluated the relationship between nesting status—presence or pseudo-absence—and beach characteristics, including interaction effects between morphological variables. Nesting observations were coded as 1 (presence) and pseudo-absence as 0. After data cleaning, all values were compiled into a single spreadsheet for statistical analysis. All models report odds ratios with 95% confidence intervals. Data were managed in Excel and analyzed in R, using base functions and packages from tidyverse, broom, janitor, and gsheet (Wickham et al., 2019; Conway, 2024; Firke, 2024; R Core Team, 2025; Robinson et al., 2025). Statistical significance was defined as p < 0.05. RESULTS Beach Morphology and Nesting Characteristics Summary statistics for beach morphology and nesting characteristics associated with presence-only nests are provided in Table 1. Beach and dune morphology varied significantly between 2016 and 2020 in ways that may influence nesting site selection. Nest elevation was significantly higher in 2016 than in 2020 ( p < 0.001), suggesting that turtles may have nested farther upslope in that year. Steeper beach slopes in 2016 and steeper foreshore slopes in 2020 point to interannual shifts in beach profile ( p = 0.019 and p < 0.001, respectfully). Table 2 reports the summary statistics for beach morphology and nesting characteristics associated with combined data of presence and pseudo-absence nests. Significant interannual differences were observed for nest elevation, beach slope, and foreshore slope. Mean nest elevation was higher in 2016 than in 2020 (1.90 m vs. 1.34 m, respectively; p < 0.001). Beach slope was also steeper in 2016, compared to 2020 (5.52° vs. 4.33°, respectively; p < 0.001). In contrast, foreshore slope was greater in 2020 than in 2016 (7.74° vs 6.18°, respectively; p < 0.001). No statistically significant differences were found for nest distance from the mean higher high-water line ( p = 0.609) or dune height ( p = 0.019). Summary statistics for pseudo-absence nest locations revealed several significant differences in beach morphology characteristics between the 2016 and 2020 nesting seasons (Table 3). Beach slope was significantly steeper in 2016 compared to 2020 ( p = 0.002), and dune heights were also slightly higher in 2016 than in 2020 ( p = 0.020). Similarly, nest elevation was greater in 2016 than in 2020 (p = 0.031), indicating vertical morphological differences between years. In contrast, the foreshore slope was significantly steeper in 2020 than in 2016 ( p < 0.001 ). These data possibly reflect broader seasonal or environmental changes in beach and dune profile. Nest distance from the high-water line, however, did not differ significantly between years ( p = 0.792 ). Table 4 presents the results of t-tests comparing mean values of beach morphology characteristics between Loggerhead nest sites and pseudo-absence points across the full dataset and by year (2016 and 2020). Statistically significant differences were observed primarily in nest elevation, with higher mean elevations at nest sites in both the full dataset (p = 0.027) and in 2016 (p < 0.001). This supports the idea that elevation may function as a key environmental cue, potentially offering protection from tidal inundation and storm surge. However, this pattern was not observed in 2020 (p = 0.198), suggesting possible interannual variation in habitat use or conditions influencing nest-site selection. No statistically significant differences were found for the other beach morphology characteristics—beach slope, dune height, foreshore slope, and nest distance. This lack of differentiation suggests that—within the scope of this study—slope, horizontal nesting position, and dune structure played a limited role in nest-site discrimination. Logistic Regression Modeling Table 5 summarizes results from unadjusted binomial probability models evaluating the independent effects of beach morphology characteristics on Loggerhead sea turtle nesting probability. These models, which incorporate both presence and pseudo-absence data, assess each variable individually without adjusting for covariates or interactions. Across the full dataset, nest elevation was found to be the only significant characteristic associated with nesting probability (OR = 1.36, p = 0.029). Beach slope (OR = 1.07, p = 0.075) and dune height (OR = 1.12, p = 0.185) showed weak positive associations with nesting probability, indicating that each unit increase in the respective characteristic was associated with an estimated 7% and 12% increase in the odds of nesting, though neither effect was statistically significant. Additionally, foreshore slope (OR = 1.01, p = 0.831) and nest distance (OR = 1.00, p = 0.838) had odds ratios near 1, with wide confidence intervals and high non-significant p-values, suggesting minimal to no influence on nesting probability. When analyzed by year, nest elevation in 2016 emerged as the only significant predictor (OR = 2.68, p < 0.001), indicating that turtles were more likely to nest at higher elevations during that season. This pattern did not persist in 2020, where the odds of nesting decreased with increasing elevation (OR = 0.76, p = 0.199). All other beach morphology variables were found to not be significant predictors. Table 6 presents the results of adjusted binomial probability models assessing the influence of beach morphology on Loggerhead nesting probability while accounting for the combined effects of multiple physical variables. These models incorporate both presence and pseudo-absence data, offering a more controlled evaluation of each characteristic’s role in shaping nest site selection. Across the full dataset, nest elevation was the only variable significantly associated with nesting probability ( p = 0.029), with an odds ratio of 1.62, indicating increased likelihood of nesting at higher elevations, independent of other beach features. This effect was especially pronounced in 2016, where the odds of nesting increased more than fivefold with elevation (OR = 5.67, p < 0.001). In contrast, the relationship was not significant in 2020 with a decreasing odds ratio (OR = 0.58, p = 0.141). None of the other morphological variables were statistically significant in the full dataset or individual years, though dune height and beach slope in 2020 were near the threshold for significance (p = 0.066 and p = 0.071, respectively), suggesting they may exert influence under certain environmental conditions warranting further study with larger sample sizes or alternative modeling approaches. Foreshore slope and nest distance remained consistently non-significant across all models. When considered together, the unadjusted and adjusted model results (Tables 5 and 6) offer a more complete view of the morphological factors influencing Loggerhead nest site selection. In both sets of models, nest elevation consistently emerged as the most important predictor of nesting probability, particularly in 2016. In the unadjusted model (Table 5), nest elevation had a moderate, significant positive effect in the full dataset (OR = 1.36, p = 0.029) and a strongly significant effect in 2016 (OR = 2.68, p < 0.001), indicating that higher elevations were more likely to contain nests. This relationship was further strengthened in the adjusted model (Table 6), where nest elevation remained the only statistically significant predictor in the full dataset (OR = 1.62, p = 0.029) and had an even larger effect in 2016 (OR = 5.67, p < 0.001). In contrast, the relationship was not significant in 2020 in either model, and in fact showed a reversed, though non-significant, trend (unadjusted OR = 0.76, p = 0.199; adjusted OR = 0.58, p = 0.141). In contrast, other variables—such as beach slope, dune height, and foreshore slope—remained non-significant in both unadjusted and adjusted analyses. The consistency of nest elevation’s effect across models and its magnitude in 2016 suggest that elevation may serve as a context-sensitive driver of nesting behavior, becoming particularly important in years where lower elevation sites may pose greater risk of tidal inundation or erosion. However, the absence of a significant relationship in 2020 and the reversal in direction reinforce that elevation is not a fixed preference but likely interacts with annual variability in beach conditions or broader management practices. Overall, these findings are consistent with existing literature that characterizes Loggerhead turtles as relatively low-selectivity nesters tolerating a wide range of beach conditions. Paired Interactions Results from adjusted logistic regression models examining pairwise interactions between beach morphology characteristics are summarized in Table 7. Several significant interactions were identified, indicating that the influence of one beach characteristic on nesting probability may be contingent upon the value of another. Most notably, nest distance displayed significant interactive effects with both dune height ( p < 0.001) and beach slope ( p < 0.001). For each one-meter increase in dune height, the association between nest distance and nesting probability became stronger (OR = 1.02), suggesting that turtles nested farther from the high-water line in areas with taller dunes. A similar pattern was observed for beach slope, where each one-degree increase in beach slope resulted in a 1 percent increase (OR = 1.01) in the slope of the nest distance to nesting status relationship, indicating that steeper beaches also strengthened the tendency for nests to occur farther inland. Nest elevation also interacted significantly with dune height ( p = 0.015). For each additional meter of dune height, the strength of the association between nest elevation and nesting probability increased substantially (OR = 1.37), suggesting that higher dunes may enhance the likelihood of nesting at greater elevations. Finally, dune height itself was moderated by beach slope ( p = 0.003), with an OR of 0.90. This indicates that as beach slope increases, the positive association between dune height and nesting probability weakens, potentially reflecting a trade-off between steeper foreshore profiles and dune accessibility. In contrast, several interactions were not statistically significant, including those involving foreshore slope, which showed no meaningful moderating effects on other characteristics (all p > 0.19 ). Similarly, interactions between nest elevation and beach slope, and nest distance and nest elevation, were not significant, indicating limited evidence that the interactive effects of these features influence nesting behavior. DISCUSSION From Low Selectivity to Adaptive Strategy The results of this study offer an empirically grounded yet ecologically nuanced perspective on Loggerhead sea turtle nest site selection. By combining presence and pseudo-absence data across two nesting seasons, we capture both the robustness and conditionality of nesting preferences, particularly in relation to nest elevation and beach morphology. While Loggerheads are often characterized as generalist or low-selectivity nesters, our findings point to more context-dependent behaviors. Nest elevation consistently emerged as the strongest predictor, especially in 2016, when it was significantly higher at nest sites. The odds of nesting increased more than fivefold for each additional meter of elevation, aligning with research identifying elevation as a critical factor linked to reduced flood risk, better drainage, and favorable incubation conditions (Wood and Bjorndal, 2000; Culver et al., 2020; Yamamoto et al., 2012). However, this relationship did not hold in 2020, when elevation showed no significant effect and even a slight negative trend. This contrast reinforces that elevation is not a universal cue but varies in importance depending on year-specific morphological conditions. Other variables—beach slope, dune height, foreshore slope, and nest distance—did not consistently influence nest placement across years. While some showed occasional significance, their effects were inconsistent in strength and direction. The lack of stable associations supports the idea that Loggerheads exhibit low microhabitat selectivity, nesting under a broad range of physical settings. Rather than relying on fixed cues, Loggerheads may adapt to broader-scale signals such as beach accessibility, tidal regime, or offshore orientation (Lamont and Cathy, 2007; Pfaller et al., 2008; Martins et al., 2022). Yet, this flexibility does not imply randomness. Nesting occurred within a narrower range of elevations, slopes, and dune heights compared to pseudo-absence locations. Accordingly, Loggerheads appear to respond to an acceptable range of environmental conditions, suggesting a filtering process that rules out unsuitable extremes. This aligns with studies noting low repeatability in microhabitat use (Williams-Wallis et al., 1983; Lamont and Carthy, 2007; Pfaller et al., 2008; Martins et al., 2022), while also revealing consistent spatial patterning across years. Our findings show that Loggerheads balance behavioral flexibility with selective nesting. Morphological cues like elevation may dominate under certain beach profiles, but turtles also integrate multiple environmental features when choosing a site. The presence of significant interaction effects—even when main effects were weak—supports a multi-cue framework. For example, nest distance was more predictive on shorelines with taller dunes and steeper slopes, while elevation interacted positively primarily with dune height. These interactions may reflect synergies between beach features. Dunes provide protection and visual orientation cues for hatchlings, helping to block artificial light and guide hatchlings toward the ocean (Witherington, 1992; Salmon et al., 1995; Weishampel et al., 2016; Hirama et al., 2021). Our results indicate that dune presence may enhance the attractiveness or effectiveness of elevated sites. Conversely, foreshore slope had minimal impact in any model or interaction, suggesting that while it may play a role offshore in beach recognition (Provancha and Ehrhart, 1987), they are less relevant once turtles are onshore. This complex interplay of cues supports a view of Loggerhead nesting behavior as conditional and integrative. Turtles appear to evaluate a suite of beach characteristics, likely using a hierarchical or threshold-based decision-making process that changes depending on environmental context. These findings lend additional weight to the idea that sea turtle nesting is not dictated by single variables but emerges from interactions among features that shift in influence over time and space (Wood and Bjorndal, 2000; Santos et al., 2006; Santos et al., 2015). Importantly, this study helps resolve the longstanding question of whether Loggerhead nest site selection is random. Our results confirm that although turtles do not strongly adhere to individual microhabitat features, their nesting behavior is not indiscriminate. Instead, turtles operated within bounded ranges of acceptable conditions, shaped by year-to-year variation and morphological context. Studies indicate that Loggerheads exhibit regional site fidelity (Lamont and Carthy, 2007) but distribute nests across varied microhabitats—likely a bet-hedging strategy that enhances survival in dynamic coastal systems (Patino-Martinez et al., 2022; Lamont et al., 2023). Our findings complement this interpretation by demonstrating that while Loggerheads may not use exact locations or cues each year, they remain sensitive to key environmental indicators like elevation and dune height. This adaptability reflects an evolved response to shifting beach morphology. In sum, Loggerhead turtles display both discernment and flexibility in nest site selection. Their choices reflect an ability to assess multiple morphological features simultaneously and to adapt nesting strategies based on the prevailing coastal environment. These results emphasize the importance of preserving a range of morphologically suitable nesting conditions to support Loggerhead populations amid rapid environmental change. Loggerhead Sea Turtle Conservation Management This study highlights the need for conservation strategies that consider fine-scale nesting behavior in response to changing coastal morphology. Loggerhead turtles depend on sandy beaches for reproduction and are especially vulnerable to coastal development, erosion, and sea-level rise (Hamann et al., 2010). One major threat is “coastal squeeze”—the erosion and narrowing of beaches due to sea-level rise and development—which reduces nesting area and increases nest density in limited high-elevation zones (Mazaris et al., 2009; Reece et al., 2013). While beach nourishment can temporarily expand nesting habitat, it may also alter physical conditions critical for nesting success (Parkinson et al., 1994; Crain et al., 1995; Gallaher, 2009; Reine, 2022). As Ware et al. (2021) emphasize, protecting undisturbed nesting areas remains vital to long-term population stability. Our findings suggest that the legacy effects of a major nourishment project in late 2015 remained evident during the 2016 nesting season. That year, nest elevation was the strongest predictor of nesting probability, with a more than fivefold increase in odds per additional meter of elevation. This likely reflects the added vertical relief from nourishment. However, the effect did not persist in 2020, when elevation was not significant and sometimes negatively associated with nesting. This contrast highlights the need to consider timing and design in nourishment efforts. If done during nesting or without attention to turtle behavior, nourishment can disrupt visual cues, alter sand temperature profiles, and reduce hatching success through skewed sex ratios (Parkinson et al., 1994; Mrosovsky et al., 2002). Other features such as dune height also play key roles. Dunes provide both physical protection and orientation cues for hatchlings. Studies show that hatchlings move toward taller silhouettes and that dunes and vegetation can shield nests from artificial light and buildings (Salmon et al., 1995; Weishampel et al., 2016; Stanley et al., 2020). Our analysis found interaction effects between nest elevation and dune height, suggesting that these features may work together to enhance nesting suitability and hatchling survival. Importantly, our data support the broader understanding that loggerheads exhibit low microhabitat fidelity but not random behavior. This behavioral flexibility may be adaptive in dynamic coastal settings. Populations that tolerate a range of beach conditions may be more resilient to environmental change (Hiebert et al., 2017; Santos et al., 2017). Still, general patterns can mask meaningful variation within populations, underscoring the need for locally responsive management (Liles et al., 2015). Effective conservation requires coastal strategies that account for turtle behavior and beach morphology. Nourishment projects should avoid nesting seasons and aim to replicate natural slopes and elevation patterns. Monitoring should assess impacts on nest placement, hatchling orientation, and thermal regimes across multiple years. Long-term conservation will depend on integrating geomorphological, ecological, and behavioral insights into planning. Our findings contribute to this effort by showing how elevation and its interactions with other beach features—especially after nourishment—shape nest placement. This kind of evidence is essential for designing adaptive strategies that anticipate environmental change and minimize negative effects on nesting habitat. Although loggerheads are the most common nesting turtle in the Florida Panhandle, this sub-region supports one of the smallest nesting populations in the Gulf (Valverde and Holzwart, 2017). Our study provides actionable guidance for protecting these marginal but significant beaches. In line with Ceriani et al. (2019), who emphasized Florida’s global importance for loggerheads and the need for adaptive management, our results underscore the value of preserving and restoring beach morphologies essential to nesting. By identifying fine-scale preferences within this understudied population, we provide site-specific data that Lamont et al. (2023) argues are critical for monitoring trends in low-density nesting populations. These findings support long-term, adaptive efforts to enhance habitat resilience and safeguard the Panhandle’s loggerhead population as environmental change accelerates. Conclusion This study demonstrates that loggerhead nest placement is influenced by beach morphology in context-specific ways, revealed through fine-scale topographic analysis and presence–pseudo-absence modeling across two seasons. Although loggerheads are often considered low-selectivity nesters, our results suggest they respond adaptively to certain physical cues under specific conditions. Nest elevation emerged as the only consistent predictor of nesting probability, especially in 2016—immediately following a major nourishment project. That year, turtles nested at higher elevations on nourished beaches, possibly responding to reduced inundation risk or steeper beach profiles. The relationship was absent in 2020, indicating that favorable thresholds shift over time and depend on seasonal conditions. While individual metrics like slope, dune height, and shoreline distance showed limited influence alone, interaction models revealed more nuanced patterns. Elevation became more predictive when paired with features like steeper foreshores or taller dunes, suggesting a multi-cue strategy. Loggerheads may exhibit low fidelity at fine scales, but within broader ranges, they appear to select sites that enhance clutch success under prevailing beach states. For coastal managers, these findings underscore the value of maintaining or restoring beach elevation and associated landforms. Nourishment can offer nesting benefits, but only if post-fill profiles are monitored and adaptively managed. Incorporating geomorphic criteria into project design and tracking turtle responses over time can help align coastal engineering with conservation goals. Even generalist nesters like loggerheads track key environmental signals—offering a pathway toward more responsive, habitat-aware management in dynamic coastal systems. Beach Characteristic Full Dataset Mean (SD) 2016 Mean (SD) 2020 Mean (SD) p-value Beach Slope 5.26 (2.98) 5.71 (3.09) 4.67 (2.73) 0.019 Dune Height 3.68 (1.09) 3.74 (0.95) 3.61 (1.26) 0.481 Foreshore Slope 6.97 (2.14) 6.10 (1.56) 7.93 (2.29) < 0.001 Nest Distance 23.45 (17.02) 24.59 (16.03) 21.93 (18.25) 0.301 Nest Elevation 1.74 (0.81) 2.10 (0.52) 1.26 (0.88) < 0.001 Table 2: Summary statistics (mean and standard deviation [SD]) for beach morphology characteristics based on combined presence and pseudo-absence nest data. Values are reported for the full dataset as well as separately for the 2016 and 2020 nesting seasons. Beach Characteristic Full Dataset Mean (SD) 2016 Mean (SD) 2020 Mean (SD) p-value Beach Slope 4.99 (2.93) 5.52 (3.05) 4.33 (2.64) < 0.001 Dune Height 3.60 (1.24) 3.74 (1.16) 3.43 (1.31) 0.019 Foreshore Slope 6.95 (2.22) 6.18 (1.76) 7.74 (2.37) < 0.001 Nest Distance 23.64 (17.72) 24.08 (17.74) 23.10 (17.70) 0.609 Nest Elevation 1.65 (0.79) 1.90 (0.72) 1.34 (0.76) < 0.001 Table 3: Summary statistics (mean and standard deviation [SD]) for beach morphology characteristics based on pseudo-absence nest data. Values are reported for the full dataset as well as separately for the 2016 and 2020 nesting seasons. Beach Characteristic Full Dataset Mean (SD) 2016 Mean (SD) 2020 Mean (SD) p-value Beach Slope 4.70 (2.85) 5.31 (3.00) 3.99 (2.51) 0.002 Dune Height 3.51 (1.38) 3.73 (1.37) 3.24 (1.35) 0.020 Foreshore Slope 6.92 (2.31) 6.27 (1.98) 7.53 (2.45) < 0.001 Nest Distance 23.84 (18.49) 23.49 (19.63) 24.25 (17.18) 0.792 Nest Elevation 1.55 (0.75) 1.67 (0.83) 1.42 (0.61) 0.031 Table 4: P -values comparing beach morphology characteristics between presence and pseudo-absence nest data for the full dataset and the 2016 and 2020 nest years. Beach Characteristic Full Dataset 2016 2020 Beach Slope 0.074 0.361 0.106 Dune Height 0.183 0.965 0.081 Foreshore Slope 0.831 0.557 0.290 Nest Distance 0.839 0.668 0.417 Nest Elevation 0.027 < 0.001 0.198 Table 5: Unadjusted binomial probability models incorporating both presence and pseudo-absence nest data. Odds ratios, 95% confidence intervals (in parentheses), and p-values are reported for each morphological characteristic for the full dataset and the 2016 and 2020 nest years. Models evaluate the effect of each variable on nesting probability independently, without adjusting for other factors. Beach Characteristic Full Dataset Odds Ratio (CI) p-value 2016 Odds Ratio (CI) p-value 2020 Odds Ratio (CI) p-value Beach Slope 1.07 (0.99, 1.15) p = 0.075 1.04 (0.95, 1.15) p = 0.359 1.11 (0.99, 1.25) p = 0.108 Dune Height 1.12 (0.95, 1.34) p = 0.185 1.01 (0.78, 1.29) p = 0.965 1.24 (0.98, 1.59) p = 0.082 Foreshore Slope 1.01 (0.92, 1.12) p = 0.831 0.95 (0.79, 1.13) p = 0.555 1.08 (0.94, 1.23) p = 0.289 Nest Distance 1.00 (0.99, 1.01) p = 0.838 1.00 (0.99, 1.02) p = 0.667 0.99 (0.97, 1.01) p = 0.415 Nest Elevation 1.36 (1.04, 1.79) p = 0.029 2.68 (1.68, 4.54) p < 0.001 0.76 (0.49, 1.15) p = 0.199 Table 6: Adjusted binomial probability models incorporating both presence and pseudo-absence nest data. Odds ratios, 95% confidence intervals (in parentheses), and p-values are reported for each morphological characteristic for the full dataset and the 2016 and 2020 nest years. Models assess the effect of individual morphological variables on the probability of nesting while controlling for the influence of other variables. Beach Characteristic Full Dataset Odds Ratio (CI) p-value 2016 Odds Ratio (CI) p-value 2020 Odds Ratio (CI) p-value Beach Slope 1.09 (0.97, 1.24) p = 0.157 0.99 (0.82, 1.19) p = 0.857 1.20 (0.99, 1.57) p = 0.071 Dune Height 1.06 (0.86, 1.29) p = 0.598 0.80 (0.55, 1.17) p = 0.252 1.28 (0.99, 1.67) p = 0.066 Foreshore Slope 1.00 (0.90, 1.11) p = 0.963 0.90 (0.73, 1.11) p = 0.329 1.05 (0.90, 1.23) p = 0.520 Nest Distance 1.00 (0.97, 1.02) p = 0.915 0.97 (0.93, 1.01) p = 0.126 1.03 (0.99, 1.07) p = 0.186 Nest Elevation 1.62 (1.06, 2.52) p = 0.029 5.67 (2.70, 13.24) p < 0.001 0.58 (0.27, 1.18) p = 0.141 Table 7: Matrix of paired interactions between beach morphology characteristics based on adjusted models incorporating presence and pseudo-absence nests. 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Keywords florida panhandle habitat management loggerhead sea turtle nest site selection presence–pseudo-absence data Authors Affiliations Divina Cox 0009-0004-1822-6689 [email protected] University of California Santa Barbara View all articles by this author Phillip Schmutz 0000-0002-7243-4815 University of West Florida View all articles by this author Samantha Seals University of West Florida View all articles by this author Metrics & Citations Metrics Article Usage 253 views 117 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Divina Cox, Phillip Schmutz, Samantha Seals. Modeling Sea Turtle Nesting Probability: Evaluating Loggerhead Nest Site Selection Using Presence–Pseudo-Absence Data. Authorea . 15 August 2025. DOI: https://doi.org/10.22541/au.175525924.40059675/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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