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Article

Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance

by
Molly Folkerts Caldwell
1,*,
Jorge E. López-Pérez
2,
Daniel A. Warner
1 and
Matthew E. Wolak
1
1
Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
2
Biology Department, Utah State University, Logan, UT 84322, USA
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(2), 275; https://doi.org/10.3390/d15020275
Submission received: 12 January 2023 / Revised: 10 February 2023 / Accepted: 11 February 2023 / Published: 14 February 2023
(This article belongs to the Special Issue Urban Ecology of the Amphibians and Reptiles)
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Abstract

:
Human disturbance impacts the breeding behavior of many species, and it is particularly important to understand how these human-caused changes affect vulnerable taxa, such as turtles. Habitat alteration can change the amount and quality of suitable nesting habitat, while human presence during nesting may influence nesting behavior. Consequently, both habitat alteration and human presence can influence the microhabitat that females choose for nesting. In the summer of 2019, we located emydid turtle nests in east-central Alabama, USA, in areas with varying levels of human disturbance (high, intermediate, low). We aimed to determine whether turtles selected nest sites based on a range of microhabitat variables comparing maternally selected natural nests to randomly chosen artificial nests. We also compared nest site choice across areas with different levels of human disturbance. Natural nests had less variance in canopy openness and average daily mean and minimum temperature than artificial nests, but microhabitat variables were similar across differing levels of disturbance. Additionally, we experimentally quantified nest predation across a natural to human-disturbed gradient. Nest predation rates were higher in areas with low and intermediate levels of disturbance than in areas with high human disturbance. Overall, these results show that turtles are not adjusting their choices of nest microhabitat when faced with anthropogenic change, suggesting that preserving certain natural microhabitat features will be critical for populations in human-disturbed areas.

1. Introduction

Urbanization dramatically changes the natural landscape and has numerous consequences on local climate, habitat structure, and biota. The damaging effects of urbanization on habitat quality and biodiversity are well documented [1,2,3,4] and can rapidly alter the behavior and survival of native species. A wide range of organismal responses to urbanization have been documented [5,6], including rapid acclimation, habituation, and even adaptation to human presence and human-modified environments [7]. For example, many mammal species have acclimated to urbanized habitats by shifting their behaviors, activity budgets and diet preferences [8]. Documenting these types of organismal responses to increasing urbanization is necessary to conserve wildlife populations and to determine ways to maintain key ecosystem functions [6].
Oviparous organisms typically rely on specific habitat characteristics for successful nesting. In most human-disturbed landscapes, however, habitat variables that are important for nest success (e.g., ground substrate, shade cover, temperature, predator densities) are heavily modified [9,10,11]. For example, urbanization can generate deviations from optimal thermal and hydric conditions in nests, leading to reduced offspring survival [12,13,14,15] and skewed population sex ratios for species with temperature-dependent sex determination (suggested by [16,17]). Urbanization can also alter nest-predator (e.g., racoons, dogs, and cats) densities and activity patterns, leading to increased predation rates ([18,19,20,21], but see [22]). Conversely, predator activity or movement may decrease or remain concentrated in pockets of urbanized areas in ways that may reduce rates of nest predation [23,24,25].
The environmental effects described above imply that nest site choice is under selective pressure, because it links female behavioral traits to the survival, hence fitness, of her offspring [26,27]. Females typically select nest sites with abiotic conditions that enhance egg hatching success and positively affect offspring phenotypes [28,29]. Females also choose nest environments that minimize the risk of predation either to themselves or their offspring [30,31,32,33,34]. Accordingly, nest site choice encapsulates multiple issues in urban ecology because it (1) has important fitness consequences, (2) is influenced by available habitat and various biotic and abiotic factors, and (3) is altered in human-disturbed habitats [9,35,36,37,38]. Given the dramatic changes in habitat variables in areas with intense human activity, nesting females must either shift their choice of nest microhabitat or seek out pockets of preferred nest microhabitat across a human-disturbed landscape.
Turtles are a globally imperiled taxon [39] and are particularly susceptible to rapid environmental changes associated with urbanization due to a variety of factors associated with their life history (e.g., low survival during early life stages, delayed sexual maturity, environmental sex determination) [40]. Moreover, predation on turtle nests is common [41] and can potentially hinder recruitment into adult age classes [42]. Threats caused by urbanization also extend to later life stages, as adult turtles are highly vulnerable to predators [43] and other urban-associated mortality during nesting forays and other overland migrations (e.g., road mortality [44,45]). Thus, the impacts of environmental change due to urbanization on turtles has received considerable research attention [46,47,48,49]. However, relatively few studies of reptiles have examined how varying degrees of human activity and infrastructure affect nesting habitat and its consequences on nest site choice and nest predation rates [9,17,50].
In this study, we examined nest microhabitats chosen by female emydid turtles across areas with varying degrees of human disturbance and urbanization in east-central Alabama, USA, which is within a global biodiversity hotspot for turtles [51]. Emydid turtles are abundant across all our study areas, which vary in both the degree of human disturbance as well as in available nesting habitat. Given this observation, we hypothesized that females seek and use more specific nest microhabitats (e.g., temperature, shade cover) than what is available across the landscape. This aspect of our study was designed to determine whether turtles discriminate among abiotic factors when selecting a nest site. We also predicted that the level of human disturbance at our study areas would influence maternally selected microhabitat variables. Additionally, we quantified nest predation to provide insight into the role of this biotic factor in shaping nest success across natural to human-disturbed areas. Specifically, we performed an experiment with artificially constructed nests to determine if nest predation rates differ among our study areas with respect to the level of human disturbance. Consistent with our observations, we predicted that rates of nest predation would be highest in areas with less human disturbance.

2. Methods

2.1. Field Data Collection

We located emydid turtle nests surrounding 13 ponds in Lee and Macon counties, Alabama, from May to July 2019 (Table 1). Although our study areas contained nearby forested areas, we focused our surveys in open habitat at each area because numerous studies show that emydid turtles choose open habitat for nesting [29,52,53]. We visited all study areas regularly during the nesting season to maximize discovery of recent nests. We did not formally quantify effort by study area. However, we searched for nests on average six, thirteen, and three times per month for the high, intermediate, and low disturbance study areas, respectively. The difference in visitation rate to the different study area types is due to differences in the number and size of potential nesting areas: high disturbance study areas total approximately 20.3 ha across nine locations, intermediate areas total approximately 24.6 ha across 16 locations, and low disturbance areas total approximately 1.3 ha across two locations. We found intact nests by observation of actively nesting females or by visual inspection of the ground for nests. Depredated nests, which were visually obvious as partially excavated cavities with eggshells scattered nearby, were also recorded during our survey. Depredated nests could have been from Trachemys scripta, Pseudemys concinna, Chrysemys picta, or Terrapene carolina. However, all nests for which the species could be confirmed were of T. scripta, the most common emydid turtle in our study areas. Here, we only report data from nests with obviously elliptical eggs characteristic of emydid turtles, as opposed to the spherical eggs of Chelydridae and Trionychidae.
Human disturbance and proximity to human infrastructure varied among our study areas, and as such, we ranked them as having high, intermediate, or low human disturbance (Table 1); we later confirmed these rankings with quantitative data on human census population size, amount of impervious surface, road density, and other variables (see details below). High disturbance areas were located at several city parks, as well as an apartment complex, in suburban areas of Auburn, Alabama. These areas were characterized as having infrastructure such as boardwalks and sidewalks adjacent to ponds and frequent human-related activity (e.g., pedestrians and pet dogs often swimming in ponds). Intermediate areas were located at Auburn University’s EW Shell Fisheries Center in Auburn, Alabama, which has many ponds located in large grassy fields. These ponds experience periodic management such as grass mowing around the perimeters, but overall infrequent human visitation and little infrastructure. Low disturbance areas were a naturally formed oxbow pond located in Tuskegee National Forest, Macon county, Alabama and a private property pond located in Notasulga, Macon county, Alabama. These areas were far from human infrastructure and experienced very little, if any, human traffic. All study areas were in relatively close proximity to each other (within 30 km) and therefore eliminated the potential for confounding effects of geographic or climatic variation.
We measured several microhabitat characteristics at each intact and depredated nest. We measured the distance between each nest and the water using a measuring tape or wheel, in a straight line to the nearest shoreline of the closest body of water. We measured canopy openness by taking hemispherical photographs above the nest. We used a Nikon Coolpix L30 with a magnetic Zykkor fish eye 0.2X 180 degree lens. Before each photo, we oriented the camera facing north and then set the camera facing lens up directly on top of the nest. We analyzed the photographs with GapLight Analyzer software to obtain percent canopy openness values [54]. We measured the slope of the ground surrounding the nest using the Apple Measure application.
To compare nest microhabitat to that available across the general landscape, we measured the same microhabitat variables described above at randomly selected artificial nest sites around each natural nest. We identified artificial nest site locations using a random number generator to obtain values between 1 and 10, indicating distance in meters from the natural nest, and values between 0 and 360, indicating the cardinal direction from the natural nest. Three artificial nests were created per natural nest. Henceforth, we refer to maternally selected nests as “natural nests” and randomly selected sites as “artificial nests”.
We placed an iButton temperature logger inside nest cavities (for natural nests and a subset of artificial nests; n = 47 total) for the duration of the nesting season. Early in the season, we placed an iButton in every artificial nest. Later in the season, we placed an iButton in one of every three artificial nests, due to time limitations. iButtons were buried at the approximate depth of the cavity of the natural nest and at the same depth for the associated artificial nests. Temperature was recorded hourly to the nearest 0.5 °C. We trimmed iButton data to a total of 67 days (the average incubation length for T. scripta eggs at 28 °C; unpublished data), starting on the day after the iButton was deployed. Average daily maximum, mean, and minimum temperatures were obtained from all iButtons. We then averaged these values across all 67 days of data to obtain a single average daily maximum, mean, and minimum temperature value for each natural and artificial nest. Additionally, the average daily temperature range value for each iButton (maximum–minimum) was calculated. Each of these temperature variables was then used as a microhabitat variable in our analyses.

2.2. Nest Predation Experiment

We conducted an experiment to assess variation in nest predation across different levels of human disturbance at our study areas. Our experiment was performed at ponds of similar size at two areas with high human disturbance, two with intermediate disturbance, and two with low disturbance (Table 1).
We measured the perimeter of each pond that contained typical nesting habitat (open, grassy), placed a flag at each 10 m section, and assigned each section a number. Five sections (marked with flags) were selected using a random number generator; an artificial nest was constructed at each section at 3 m from the edge of the pond. Artificial nests had a cavity similar to that of a natural nest and consisted of a chicken egg buried in the soil at 10 cm depth (comparable to the depth of an emydid turtle nest). Past studies have successfully used bird eggs to quantify rates of predation on turtle nests [21,22,55,56,57,58]. We moistened the disturbed soil with pond water to simulate when female turtles release water from their bladder before nesting. Because nest predators rely on olfactory and visual cues to locate turtle nests [59], we reasoned that disturbed soil and the pond water provided realistic cues that simulated those of natural nests [60]. To discreetly mark the location of each artificial nest, we removed the flags and placed two wooden dowels one meter away from each nest. After burying eggs, we checked each artificial nest once every 24–48 h for a total of 72 h (as most predation occurs within this timeframe [41,61,62]; but see [63]). On each visit, evidence of predation, such as an excavated cavity or the presence of eggshells, was recorded and remaining eggs were removed after 72 h. This process was performed 3 times at each location, totaling 90 eggs buried across all six study areas. We randomly selected different sections of the pond for each repetition and waited at least 48 h before burying a new set of eggs at each section.

2.3. Quantifying Urbanization

To quantify the level of human disturbance at each study area, we downloaded data from the United States Environmental Protection Agency StreamCat Dataset that contains standardized measures of natural and anthropogenic features of streams, their catchments, and watersheds [64]. For each pond where we measured nest microhabitat or conducted the predation experiment, we used the WATERS GeoViewer (https://www.epa.gov/waterdata/waters-geoviewer (accessed on 20 September 2022)) to select the closest stream segment to the ponds at our study areas and downloaded the StreamCat data associated with the catchment area draining into that segment. Five variables were extracted from the watershed dataset: mean imperviousness of anthropogenic surfaces within catchment, percentage of local catchment area classified as developed, average density of roads per square kilometer, mean of all housing units per square kilometer, and mean of 2010 census population per square kilometer. For ponds that were equidistant to two segments, we took the average of the two segments for each variable. We performed a principal components analysis (PCA) on these five variables across each of our study areas. The first principal component (PC1) explained 88.62% of the variation in the data and was used in our subsequent analyses as a continuous variable associated with the level of human disturbance. We multiplied PC1 scores by negative one to facilitate ease of interpretation (Table 2) so that positive PC1 scores indicated high human disturbance, while negative scores were indicative of more natural areas with low human disturbance. Importantly, because our original classification of study areas as “high disturbance” vs. “intermediate” vs. “low disturbance” were reflected in the results of the PCA (Figure 1), some of our analyses also used the study areas as their original designations (rather than use PC scores). Although our sample of nests was low in the high disturbance study area (see Results), we still distinguished this study area type from the others due to dramatically different surroundings, infrastructure, and levels of human activity. We will refer to the original designations as “study area type” and to PC1 as “urbanization level”.

2.4. Statistical Analysis

To broadly characterize high, intermediate, and low human disturbance study area microhabitats without respect to turtle nest site choice, we built linear models with a dataset that included artificial nests only, as these randomly located sites should provide a general description of the habitat at each study area. We used six models, each with a microhabitat variable (listed in Table 3) as the dependent variable, and study area type as a categorical fixed effect. We also included iButton depth as a continuous covariate in models that included a temperature-dependent variable. Distance from water was not included in this analysis since this variable is only meaningful in comparison with natural nests.
To determine if females discriminate among abiotic factors when selecting nest sites, and whether microhabitat measures varied with level of disturbance, we included a set of seven linear mixed-effects models, each with a microhabitat variable as the dependent variable. We included nest type (natural vs. artificial nest) as a categorical fixed effect, urbanization level (PC1 described above) as a continuous covariate, and the interaction between those two variables. For models of temperature-dependent variables, a continuous covariate of iButton depth (mean centered and standard deviation scaled) was also included. Statistical significance of individual fixed effect terms was evaluated with an analysis of variance implementing incremental sums of squares. Nest cluster (a natural nest and its associated artificial nests) was assigned as a random effect in each model to account for the non-independence of nests within clusters. To determine whether female turtles selected nest sites with more or less variance in microhabitat measures, we estimated separate residual variances for natural and artificial nests. We used the asreml package [65] in R for each model and obtained 95% confidence intervals for the residual variances using profile likelihoods [66] implemented in the nadiv package [67]. A likelihood ratio test was used to evaluate the statistical null hypothesis that there is no difference in residual variance between natural and artificial nests.
To determine if nest predation rate was associated with study area type, we performed a Pearson’s Chi-square test for independence, using the numbers of artificial nests that were depredated and the number that survived at each of the three study area types (high, intermediate, low disturbance). All statistical analyses were performed in R, version 4.2.1 [68].

3. Results

Slope of the ground and canopy openness measured at artificial nests (i.e., random locations representative of the landscape at each study area) varied with the level of human disturbance (Table 3). Ponds in more human-disturbed areas had steeper terrain and less canopy openness than those in natural areas. Mean and minimum ground temperatures did not change substantially with the level of human disturbance, but the daily maximum and daily range of ground temperature decreased with increasing human disturbance (Table 3).

3.1. Nest Site Choice

A total of 88 nests (11 intact, 77 depredated) were located during the study. Most nests (n = 84) were at intermediate or low disturbance areas, and only four were found in high disturbance areas. Due to logistical limitations, microhabitat data were collected on a subset of natural nests and their associated artificial nests (n = 43; 4 high, 25 intermediate, 14 low), and all results reported are from this set of nests. In addition, due to failure or loss of some iButtons, temperature data were available for 20 natural nests and 27 artificial nests.
Microhabitat variables measured at natural nests generally followed the same trends as those in artificial nests (Figure 2). Slope, daily mean, and daily maximum temperature were marginally significantly related to the level of urbanization (Table 4), and these relationships did not differ between natural and artificial nests. Nests in more urban areas were on steeper slopes than in natural areas (Figure 2B). Average nest temperature at natural areas (29.9 °C, n = 11) was about 2 °C warmer than that at intermediate areas (28.0 °C, n = 8), and daily maximum temperatures followed a similar trend; higher maxima in natural areas as compared to intermediate and high human disturbance locations (Figure 2). Although temperature data from only one natural nest iButton were available at a high human disturbance area, this trend was still observed when we compared just the intermediate to natural areas. The distance of natural nests to the nearest pond ranged from 2 to 241 m, with 88% of nests being within 50 m of a pond; this pattern did not vary with the level of human disturbance.
Although nest site microhabitat characteristics did not differ on average between natural and artificial nests, natural nest sites generally exhibited less variance compared to artificial nests. For instance, there was significantly (p < 0.05) less among-nest variance of natural nests in their distance to water, canopy openness, and average daily mean and minimum temperatures. However, the variance of daily temperature range of natural nests was greater than that of artificial nests. For other microhabitat measures, the best fit model indicated that residual variance was equal between natural and artificial nests.

3.2. Nest Predation Experiment

Of the 90 artificial nests, we documented six instances of nest predation, and nest predation rate was significantly greater in natural areas than the intermediate and high disturbance areas (chi-square = 7.5, p = 0.045). Five of the six nests depredated were at natural areas (17% predation rate), and the remaining was at an intermediate disturbance area; no predation occurred in high disturbance areas. Out of the five nests depredated in natural areas, four occurred at the oxbow pond in Tuskegee National Forest, while one occurred at the private property pond in Notasulga, AL. Similarly, during our nest surveys, more depredated nests were observed in low (n = 27) and intermediate (n = 49) human disturbance study areas than at high disturbance areas (n = 1).

4. Discussion

Human activity has altered natural landscapes in ways that have dramatic effects on wildlife populations. These effects of habitat alteration may be particularly pronounced when they directly impact habitat or other environmental factors that are important for reproduction [69,70], such as nesting areas of oviparous species. In this study, we quantified variation in nesting habitat for turtles, maternal nesting behaviors, and nest predation rates across a range of areas that vary in the level of human disturbance. We found that nesting landscapes in areas with greater human disturbance had steeper slopes and reduced canopy openness than in areas with less human disturbance. The reduced variance in some microhabitat variables for natural nests suggests that female turtles choose microhabitats with specific abiotic conditions, and these patterns remained consistent across the study. Nest predation also varied among study locations and was substantially lower in areas with high levels of human disturbance. These patterns illustrate that while human activities alter natural habitats, female turtles are still capable of finding nest sites with similar microhabitats across different levels of disturbance.
While the distance of nests to water and average canopy over nests tended to reflect what was available across study areas, female turtles nested in a narrower range of these variables than what was measured at artificial nest sites, suggesting that females are selective for distance from the water’s edge and canopy when choosing a nest site. However, we detected minimal differences for maternally selected nest microhabitat characteristics among different study area types on an urban gradient, suggesting that females discriminate among abiotic factors when selecting nest sites but generally select sites with similar characteristics at each level of human disturbance. Nesting turtles may not adjust their nest site choice when faced with anthropogenic change, highlighting the need for protecting areas with suitable habitat for nesting and egg incubation, particularly areas that are altered by humans. However, more investigation is needed to determine whether turtles are capable of adjusting their nesting strategy in these areas or whether they were able to find appropriate nest sites without substantial changes to their nesting behaviors.
Nest temperatures were relatively high in natural areas compared to high and intermediate disturbance areas. This result contrasts with those of studies on the urban heat island effect whereby urbanized areas are considerably warmer than surrounding natural areas due to a variety of factors (e.g., increased heat absorbing surface, decreased tree cover; [71]). Notably, our study areas with the highest levels of human disturbance were more suburban than urban and still contained substantial amounts of greenspace (i.e., city parks) that would reduce the likelihood of elevated temperatures comparable to those in large cities. The range of human disturbance in our study does not extend to the extremes seen in large cities [72]. Consequently, variation in temperature across our study areas is more subtle, especially with the limited temperature data collected for nests in our most human-disturbed areas. Nevertheless, we detected increased nest temperatures in natural areas, which could be driven by substrate differences; all nests at the natural location had pebbly substrate that may absorb more heat than the grassy/soil substrate at our more urban study areas. These results highlight the importance of considering local microhabitat characteristics in driving thermal patterns across urban to natural gradients, as major assumptions in urban ecology (e.g., urban areas are always relatively warm; [71]) may not always be met. Relying solely on these broad assumptions, especially when the gradient is relatively shallow, could impact the success of management efforts for wildlife species.
The artificial ponds at our high and intermediate human disturbance areas were surrounded by steeper terrain than ponds in natural areas, which is a common characteristic of human-made wetlands that were carved out by construction equipment. Consequently, turtles nested on relatively sloped ground at these areas, which reflects this feature of human-made ponds, rather than being indicative of turtle nest site choice. Nevertheless, a high occurrence of steep slopes in nesting areas could affect ambient conditions of the nest that have important consequences on embryo development. For example, sloped banks around the pond will affect radiant heating from the sun (especially on south-facing slopes in the northern hemisphere) and have impacts on nest temperature in ways that influence critical aspects of development [73,74]. Sloped banks could also influence water run-off and decrease the moisture absorbed in the soil at a nest site; these impacts on nest moisture could also influence development of turtle embryos [75,76]. Thus, while properly managed artificial wetlands can support healthy wildlife populations [77], the surrounding features of artificial ponds are often very different from that of natural ponds and may influence nesting behavior and embryo ecology of turtles in unique ways.
The abundance of nests was relatively low in areas with high human disturbance, despite a high abundance of turtles observed in ponds at these study areas. Over 70 adult turtles can readily be counted (within 1–2 min) in the ponds at city parks and apartment complexes (pers. obs.), whereas few, if any, turtles are observed within this short timeframe at our study areas with low human disturbance; these observations are mostly driven by turtle habituation to humans, as they are often fed by visitors at city parks (as seen in other wildlife; [78]) and not at the undisturbed areas. Given the high apparent densities of adult turtles in city parks, the lack of nest sites was unexpected, which may be due to several factors. First, females may choose nest sites far outside of our survey areas. While it is possible that females may travel farther distances across land in human-disturbed areas, we argue that this is unlikely because of barriers around the parks (roads, residential areas). Alternatively, females may nest in heavily shaded forest patches surrounding the city parks where we did not search, but a large amount of literature consistently shows that emydid turtles select open habitat for nesting [9,29,79], which was abundant at our human-disturbed study areas. Second, human disturbance may have caused diel shifts toward nocturnal nesting activity outside of our survey hours. Adaptive shifts toward nocturnal nesting activity at human-disturbed areas is unlikely given the long lifespans of turtles and the relatively short time since establishment of our city park study areas. In addition, other studies provide no evidence that emydid turtles shift the diel timing of nesting activity, as they readily nest during the day in areas with high human activity [80]. The lack of depredated nests at city parks also implies little nesting activity outside the times of our surveys. A third explanation involves potential physiological effects of human disturbance that may inhibit reproduction. Trapping efforts (in 2021 and 2022) show that almost no females in city park ponds are gravid during the reproductive season and smaller, juvenile turtles are rarely observed (unpubl. data), indicating low recruitment. Although urbanization has not previously been shown to affect abundance of immature turtles [16], previous work compared populations across a much larger geographic scale than our study. Frequent feeding of low-quality food (i.e., bread) by the public to wildlife can often result in disease and poor nutrition [81], which could be responsible for low fecundity, but these potential effects need to be further explored.
Nest predation varied across the study areas with respect to human disturbance. During our surveys, depredated nests were most common in natural and intermediate areas and declined with increasing levels of human disturbance. Although predation rates of simulated nests were low overall, our experimental study lessened concerns about our low detectability of nests in human-disturbed areas and suggests a negative relationship between nest predation and level of human disturbance. This pattern is consistent with studies that suggest that human presence may frighten potential predators [21,82], even though human disturbance can sometimes increase populations of nest predators [83]. The impact of human disturbance on nest predators can vary considerably and may have variable consequences on rates of nest predation. If female reproduction and nesting activity is low in areas of high human disturbance (e.g., city parks), then mammalian predators may not have had the same opportunity to develop a visual or olfactory search image for turtle nests at these locations. These predators can quickly learn how to locate food items when they become abundant [84,85], but if nest abundance remains low at human-disturbed areas, then it is unlikely predators would have been searching for nests during the 72 h period when simulated turtle nests were present during our experiment.
Increased human activity and alteration of landscapes has the potential to substantially change the nesting behavior and ecology of oviparous species [50]. Turtles are particularly vulnerable to changes in their nesting habitat since biotic and abiotic environments within and around nest sites largely determine egg hatching success [86,87,88,89], which in turn could impact population demographics. Understanding how human disturbance impacts nesting sites and changes in maternal nesting behaviors will provide important information for predicting how populations of oviparous animals will persist in human-disrupted environments. We show that maternal nest site choice is relatively consistent across areas with different levels of human disturbance, despite some differences in habitat features across our study areas. The general lack of behavioral shifts in human-disturbed areas suggests that preserving natural microhabitat features will be important for populations in areas with human activity. Moreover, limited evidence of reproduction in areas with high human activity is alarming and warrants more research. Future studies that quantify the effects of human disturbance on offspring development within nests [50,90] will provide further insight into how human activities affect recruitment and long-term persistence of populations in anthropogenically modified environments. Overall, our study highlights an important and relatively understudied aspect of reptile biology (i.e., nesting behavior) that warrants more attention in research programs aimed at understanding the impacts of anthropogenic environmental change. As urbanization and human populations continue to increase, it is crucial to document how urbanization impacts turtle nesting behavior, as well as nesting success across systems, species, and types of human disturbance.

Author Contributions

This study was conceived by M.F.C., D.A.W. and M.E.W., with assistance from J.E.L.-P.; M.F.C. and J.E.L.-P. conducted the field work; M.F.C. and M.E.W. conducted the data analyses; M.F.C. drafted the manuscript with edits and revisions from D.A.W., M.E.W. and J.E.L.-P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Auburn University Intramural Grants Program, NSF grant (DBI-1658694), the Alabama Agricultural Experiment Station and the Hatch program of the National Institute of Food and Agriculture, US Department of Agriculture.

Institutional Review Board Statement

This study was approved by the Auburn University Institutional Animal Care and Use Committee (protocol number: 2019-35020).

Data Availability Statement

All data and R code are freely available on Zenodo at https://doi.org/10.5281/zenodo.7630636.

Acknowledgments

We thank Debbie Folkerts, David Mitchell, Amélie Fargevieille, Josh Hall, Iwo Gross and Andrew Caldwell for their assistance in the field, Mike and Carolyn Williams for access to their property and Larry Lawson and Auburn University’s E.W. Shell Fisheries Center for permission to access fisheries ponds. Thanks to A. Wilson who made the Research Experience for Undergraduates Program possible for JELP.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. McKinney, M.L. The Impacts of Urbanization on Native Species Are Poorly Studied, but Educating a Highly Urbanized Human Population about These Impacts Can Greatly Improve Species Conservation in All Ecosystems. BioScience 2002, 52, 883–890. [Google Scholar] [CrossRef]
  2. Mcdonald, R.I.; Kareiva, P.; Forman, R.T.T. The Implications of Current and Future Urbanization for Global Protected Areas and Biodiversity Conservation. Biol. Conserv. 2008, 141, 1695–1703. [Google Scholar] [CrossRef]
  3. Pauchard, A.; Aguayo, M.; Peña, E.; Urrutia, R. Multiple Effects of Urbanization on the Biodiversity of Developing Countries: The Case of a Fast-Growing Metropolitan Area (Concepción, Chile). Biol. Conserv. 2006, 127, 272–281. [Google Scholar] [CrossRef]
  4. Czech, B.; Krausman, P.R.; Devers, P.K. Economic Associations among Causes of Species Endangerment in the United States. BioScience 2000, 50, 593. [Google Scholar] [CrossRef]
  5. Isaksson, C. Urbanization, Oxidative Stress and Inflammation: A Question of Evolving, Acclimatizing or Coping with Urban Environmental Stress. Funct. Ecol. 2015, 29, 913–923. [Google Scholar] [CrossRef]
  6. Alberti, M.; Correa, C.; Marzluff, J.M.; Hendry, A.P.; Palkovacs, E.P.; Gotanda, K.M.; Hunt, V.M.; Apgar, T.M.; Zhou, Y. Global Urban Signatures of Phenotypic Change in Animal and Plant Populations. Proc. Natl. Acad. Sci. USA 2017, 114, 8951–8956. [Google Scholar] [CrossRef]
  7. Szulkin, M.; Munshi-South, J.; Charmantier, A. Urban Evolutionary Biology; Oxford University Press: Oxford, UK, 2020. [Google Scholar]
  8. Ritzel, K.; Gallo, T. Behavior Change in Urban Mammals: A Systematic Review. Front. Ecol. Evol. 2020, 8, 576665. [Google Scholar] [CrossRef]
  9. Kolbe, J.J.; Janzen, F.J. Impact of Nest-Site Selection on Nest Success and Nest Temperature in Natural and Disturbed Habitats. Ecology 2002, 83, 269–281. [Google Scholar] [CrossRef]
  10. Buxton, V.L.; Santymire, R.M.; Benson, T.J. Mixed Effects of Urbanization on Density, Nest Survival, and Nestling Corticosterone of a Generalist Passerine. Ecosphere 2018, 9, e02517. [Google Scholar] [CrossRef]
  11. Hope, S.F.; Hopkins, W.A.; Angelier, F. Parenting in the City: Effects of Urbanization on Incubation Behaviour and Egg Temperature in Great Tits, Parus major. Anim. Behav. 2022, 194, 1–11. [Google Scholar] [CrossRef]
  12. Angilletta, M.J., Jr.; Winters, R.S.; Dunham, A.E. Thermal Effects on the Energetics of Lizard Embryos: Implications for Hatchling Phenotypes. Ecology 2000, 81, 2957–2968. [Google Scholar] [CrossRef]
  13. Christian, K.A.; Tracy, C.R.; Porter, W.P. The Effect of Cold Exposure during Incubation of Sceloporus undulatus Eggs. Copeia 1986, 1986, 1012–1014. [Google Scholar] [CrossRef]
  14. Muth, A. Physiological Ecology of Desert Iguana (Dipsosaurus dorsalis) Eggs: Temperature and Water Relations. Ecology 1980, 61, 1335–1343. [Google Scholar] [CrossRef]
  15. Bodensteiner, B.L.; Mitchell, T.S.; Strickland, J.T.; Janzen, F.J. Hydric Conditions during Incubation Influence Phenotypes of Neonatal Reptiles in the Field. Funct. Ecol. 2015, 29, 710–717. [Google Scholar] [CrossRef]
  16. Bowne, D.R.; Cosentino, B.J.; Anderson, L.J.; Bloch, C.P.; Cooke, S.; Crumrine, P.W.; Dallas, J.; Doran, A.; Dosch, J.J.; Druckenbrod, D.L.; et al. Effects of Urbanization on the Population Structure of Freshwater Turtles across the United States. Conserv. Biol. 2018, 32, 1150–1161. [Google Scholar] [CrossRef]
  17. Jackson, N.; Cristescu, R.H.; Piza-Roca, C.; Littleford-Colquhoun, B.L.; Strickland, K.; Frère, C.H. Maternal Nesting Behaviour in City Dragons: A Species with Temperature-Dependent Sex Determination. J. Urban Ecol. 2019, 5, juz005. [Google Scholar] [CrossRef]
  18. Wilcove, D.S. Nest Predation in Forest Tracts and the Decline of Migratory Songbirds. Ecology 1985, 66, 1211–1214. [Google Scholar] [CrossRef]
  19. Jokimäki, J.; Huhta, E. Artificial Nest Predation and Abundance of Birds Along an Urban Gradient. Condor 2000, 102, 838–847. [Google Scholar] [CrossRef]
  20. Feinberg, J.A.; Burke, R.L. Nesting Ecology and Predation of Diamondback Terrapins, Malaclemys terrapin, at Gateway National Recreation Area, New York. J. Herpetol. 2003, 37, 517–526. [Google Scholar] [CrossRef]
  21. Marchand, M.N.; Litvaitis, J.A. Effects of Landscape Composition, Habitat Features, and Nest Distribution on Predation Rates of Simulated Turtle Nests. Biol. Conserv. 2004, 117, 243–251. [Google Scholar] [CrossRef]
  22. Foley, S.M.; Price, S.J.; Dorcas, M.E. Nest-Site Selection and Nest Depredation of Semi-Aquatic Turtles on Golf Courses. Urban Ecosyst. 2012, 15, 489–497. [Google Scholar] [CrossRef]
  23. Prange, S.; Gehrt, S.D.; Wiggers, E.P. Influences of Anthropogenic Resources on Raccoon (Procyon lotor) Movements and Spatial Distribution. J. Mammal. 2004, 85, 483–490. [Google Scholar] [CrossRef]
  24. Vincze, E.; Seress, G.; Lagisz, M.; Nakagawa, S.; Dingemanse, N.J.; Sprau, P. Does Urbanization Affect Predation of Bird Nests? A Meta-Analysis. Front. Ecol. Evol. 2017, 5, 29. [Google Scholar] [CrossRef]
  25. Eötvös, C.B.; Magura, T.; Lövei, G.L. A Meta-Analysis Indicates Reduced Predation Pressure with Increasing Urbanization. Landsc. Urban Plan. 2018, 180, 54–59. [Google Scholar] [CrossRef]
  26. Brown, G.P.; Shine, R. Maternal Nest-Site Choice and Offspring Fitness in a Tropical Snake (Tropidonophis mairii, Colubridae). Ecology 2004, 85, 1627–1634. [Google Scholar] [CrossRef]
  27. Wilson, D.S. Nest-Site Selection: Microhabitat Variation and Its Effects on the Survival of Turtle Embryos. Ecology 1998, 79, 1884–1892. [Google Scholar] [CrossRef]
  28. Warner, D.A.; Andrews, R.M. Nest-site Selection in Relation to Temperature And Moisture by the Lizard Sceloporus undulatus. Herpetologica 2002, 58, 399–407. [Google Scholar] [CrossRef]
  29. Pruett, J.E.; Addis, E.A.; Warner, D.A. The Influence of Maternal Nesting Behaviour on Offspring Survival: Evidence from Correlational and Cross-Fostering Studies. Anim. Behav. 2019, 153, 15–24. [Google Scholar] [CrossRef]
  30. Warner, D.A.; Shine, R. Maternal Nest-Site Choice in a Lizard with Temperature-Dependent Sex Determination. Anim. Behav. 2008, 75, 861–870. [Google Scholar] [CrossRef]
  31. Refsnider, J.M.; Janzen, F.J. Putting Eggs in One Basket: Ecological and Evolutionary Hypotheses for Variation in Oviposition-Site Choice. Annu. Rev. Ecol. Evol. Syst. 2010, 41, 39–57. [Google Scholar] [CrossRef]
  32. Rand, A.S.; Dugan, B. Structure of Complex Iguana Nests. Copeia 1983, 1983, 705–711. [Google Scholar] [CrossRef]
  33. Madsen, T.; Shine, R. Life History Consequences of Nest-Site Variation in Tropical Pythons (Liasis fuscus). Ecology 1999, 80, 989–997. [Google Scholar] [CrossRef]
  34. Spencer, R.J. Experimentally Testing Nest Site Selection: Fitness Trade-Offs and Predation Risk in Turtles. Ecology 2002, 83, 2136–2144. [Google Scholar] [CrossRef]
  35. Ogden, J.C. Nesting by Wood Storks in Natural, Altered, and Artificial Wetlands in Central and Northern Florida. Colonial Waterbirds 1991, 14, 39–45. [Google Scholar] [CrossRef]
  36. Borges, F.J.A.; Marini, M.Â. Birds Nesting Survival in Disturbed and Protected Neotropical Savannas. Biodivers. Conserv. 2010, 19, 223–236. [Google Scholar] [CrossRef]
  37. James Reynolds, S.; Ibáñez-Álamo, J.D.; Sumasgutner, P.; Mainwaring, M.C. Urbanisation and Nest Building in Birds: A Review of Threats and Opportunities. J. Ornithol. 2019, 160, 841–860. [Google Scholar] [CrossRef]
  38. Xu, Y.; Cao, Z.; Wang, B. Effect of Urbanization Intensity on Nest-Site Selection by Eurasian Magpies (Pica pica). Urban Ecosyst. 2020, 23, 1099–1105. [Google Scholar] [CrossRef]
  39. Turtle Conservation Coalition; Rhodin, A.G.J.; Walde, A.D.; Horne, B.D.; van Dijk, P.P.; Blanck, T.; Hudson, R. Turtles in Trouble: The World’s 25+ Most Endangered Tortoises and Freshwater Turtles—2011; IUCN/SSC Tortoise and Freshwater Turtle Specialist Group, Turtle Conservation Fund, Turtle Survival Alliance, Turtle Conservancy, Chelonian Research Foundation, Conservation International, Wildlife Conservation Society, and San Diego Zoo Global: Lunenburg, MA, USA, 2011. [Google Scholar]
  40. Congdon, J.D.; Dunham, A.E.; Sels, R.C.V.L. Delayed Sexual Maturity and Demographics of Blanding’s Turtles (Emydoidea blandingii): Implications for Conservation and Management of Long-Lived Organisms. Conserv. Biol. 1993, 7, 826–833. [Google Scholar] [CrossRef]
  41. Congdon, J.D.; Tinkle, D.W.; Breitenbach, G.L.; van Loben Sels, R.C. Nesting Ecology and Hatching Success in the Turtle Emydoidea blandingi. Herpetologica 1983, 39, 417–429. [Google Scholar]
  42. Schwanz, L.E.; Spencer, R.J.; Bowden, R.M.; Janzen, F.J. Climate and Predation Dominate Juvenile and Adult Recruitment in a Turtle with Temperature-Dependent Sex Determination. Ecology 2010, 91, 3016–3026. [Google Scholar] [CrossRef]
  43. Refsnider, J.M.; Reedy, A.M.; Warner, D.A.; Janzen, F.J. Do Trade-Offs between Predation Pressures on Females versus Nests Drive Nest-Site Choice in Painted Turtles? Biol. J. Linn. Soc. 2015, 116, 847–855. [Google Scholar] [CrossRef] [Green Version]
  44. Vanek, J.P.; Glowacki, G.A. Assessing the Impacts of Urbanization on Sex Ratios of Painted Turtles (Chrysemys picta). Diversity 2019, 11, 72. [Google Scholar] [CrossRef]
  45. Anđelković, M.; Bogdanović, N. Amphibian and Reptile Road Mortality in Special Nature Reserve Obedska Bara, Serbia. Animals 2022, 12, 561. [Google Scholar] [CrossRef]
  46. Patrick, D.A.; Gibbs, J.P. Population Structure and Movements of Freshwater Turtles across a Road-Density Gradient. Landsc. Ecol. 2010, 25, 791–801. [Google Scholar] [CrossRef]
  47. Steen, D.A.; Gibbs, J.P. Effects of Roads on the Structure of Freshwater Turtle Populations. Conserv. Biol. 2004, 18, 1143–1148. [Google Scholar] [CrossRef]
  48. Witherington, B.E. Behavioral Responses of Nesting Sea Turtles to Artificial Lighting. Herpetologica 1992, 48, 31–39. [Google Scholar]
  49. Steen, D.A.; Aresco, M.J.; Beilke, S.G.; Compton, B.W.; Condon, E.P.; Kenneth Dodd Jr., C.; Forrester, H.; Gibbons, J.W.; Greene, J.L.; Johnson, G.; et al. Relative Vulnerability of Female Turtles to Road Mortality. Anim. Conserv. 2006, 9, 269–273. [Google Scholar] [CrossRef]
  50. Tiatragul, S.; Hall, J.M.; Warner, D.A. Nestled in the City Heat: Urban Nesting Behavior Enhances Embryo Development of an Invasive Lizard. J. Urban Ecol. 2020, 6, juaa001. [Google Scholar] [CrossRef]
  51. Mittermeier, R.A.; van Dijk, P.P.; Rhodin, A.G.J.; Nash, S.D. Turtle Hotspots: An Analysis of the Occurrence of Tortoises and Freshwater Turtles in Biodiversity Hotspots, High-Biodiversity Wilderness Areas, and Turtle Priority Areas. Chelonian Conserv. Biol. 2015, 14, 2–10. [Google Scholar] [CrossRef]
  52. Najbar, B.; Szuszkiewicz, E. Nest-Site Fidelity of the European Pond Turtle Emys orbicularis (LINNAEUS, 1758) (Testudines: Emydidae) in Western Poland. Acta Zool. Crac.-Ser. A Vertebr. 2007, 50, 1–8. [Google Scholar] [CrossRef]
  53. Roosenburg, W.M. Maternal Condition and Nest Site Choice: An Alternative for the Maintenance of Environmental Sex Determination? Am. Zool. 1996, 36, 157–168. [Google Scholar] [CrossRef]
  54. Doody, J.S.; Guarino, E.N.Z.O.; Harlow, P.; Corey, B. Quantifying Nest Site Choice in Reptiles Using Hemispherical Photography and Gap Light Analysis. Herpetol. Rev. 2006, 37, 49–51. [Google Scholar]
  55. Wilhoft, D.C.; Del Baglivo, M.G.; Del Baglivo, M.D. Observations on Mammalian Prediation of Snapping Turtle Nests (Reptilia, Testudines, Chelydridae). J. Herpetol. 1979, 13, 435–438. [Google Scholar] [CrossRef]
  56. Marchand, M.N.; Litvaitis, J.A.; Maier, T.J.; DeGraaf, R.M. Use of Artificial Nests to Investigate Predation on Freshwater Turtle Nests. Wildl. Soc. Bull. 2002, 30, 1092–1098. [Google Scholar]
  57. Ratnaswamy, M.J.; Warren, R.J.; Kramer, M.T.; Adam, M.D. Comparisons of Lethal and Nonlethal Techniques to Reduce Raccoon Depredation of Sea Turtle Nests. J. Wildl. Manag. 1997, 61, 368–376. [Google Scholar] [CrossRef]
  58. Dawson, S.J.; Adams, P.J.; Huston, R.M.; Fleming, P.A. Environmental Factors Influence Nest Excavation by Foxes. J. Zool. 2014, 294, 104–113. [Google Scholar] [CrossRef]
  59. Strickland, J.; Colbert, P.; Janzen, F.J. Experimental Analysis of Effects of Markers and Habitat Structure on Predation of Turtle Nests. J. Herpetol. 2010, 44, 467–470. [Google Scholar] [CrossRef]
  60. Buzuleciu, S.A.; Crane, D.P.; Parker, S.L. Scent of Disinterred Soil as an Olfactory Cue Used by Raccoons to Locate Nests of Diamond-Backed Terrapins. Herpetol. Conserv. Biol. 2016, 11, 539–551. [Google Scholar]
  61. Congdon, J.D.; Breitenbach, G.L.; van Loben Sels, R.C.; Tinkle, D.W. Reproduction and Nesting Ecology of Snapping Turtles (Chelydra serpentina) in Southeastern Michigan. Herpetologica 1987, 43, 39–54. [Google Scholar]
  62. Holcomb, S.R.; Carr, J.L. Mammalian Depredation of Artificial Alligator Snapping Turtle (Macrochelys temminckii) Nests in North Louisiana. Southeast. Nat. 2013, 12, 478–491. [Google Scholar] [CrossRef]
  63. Riley, J.L.; Litzgus, J.D. Cues Used by Predators to Detect Freshwater Turtle Nests May Persist Late into Incubation. Can. Field-Nat. 2014, 128, 179–188. [Google Scholar] [CrossRef]
  64. Hill, R.A.; Weber, M.H.; Leibowitz, S.G.; Olsen, A.R.; Thornbrugh, D.J. The Stream-Catchment (StreamCat) Dataset: A Database of Watershed Metrics for the Conterminous United States. J. Am. Water Resour. Assoc. 2016, 52, 120–128. [Google Scholar] [CrossRef]
  65. Butler, D.G.; Cullis, B.R.; Gilmour, A.R.; Gogel, B.J. Asreml: Asreml() Fits the Linear Mixed Model. R Package Version 3.0. 2009. Available online: www.vsni.co.uk (accessed on 22 November 2022).
  66. Meyer, K. Likelihood Calculations to Evaluate Experimental Designs to Estimate Genetic Variances. Heredity 2008, 101, 212–221. [Google Scholar] [CrossRef]
  67. Wolak, M.E. Nadiv: An R Package to Create Relatedness Matrices for Estimating Non-Additive Genetic Variances in Animal Models. Methods Ecol. Evol. 2012, 3, 792–796. [Google Scholar] [CrossRef]
  68. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2022. [Google Scholar]
  69. Reale, J.A.; Blair, R.B. Nesting Success and Life-History Attributes of Bird Communities Along an Urbanization Gradient. Urban Habitats 2005, 3, 1–14. [Google Scholar]
  70. Sumasgutner, P.; Nemeth, E.; Tebb, G.; Krenn, H.W.; Gamauf, A. Hard Times in the City–Attractive Nest Sites but Insufficient Food Supply Lead to Low Reproduction Rates in a Bird of Prey. Front. Zool. 2014, 11, 48. [Google Scholar] [CrossRef]
  71. Arnfield, A.J. Two Decades of Urban Climate Research: A Review of Turbulence, Exchanges of Energy and Water, and the Urban Heat Island. Int. J. Climatol. 2003, 23, 1–26. [Google Scholar] [CrossRef]
  72. Peng, S.; Piao, S.; Ciais, P.; Friedlingstein, P.; Ottle, C.; Bréon, F.-M.; Nan, H.; Zhou, L.; Myneni, R.B. Surface Urban Heat Island Across 419 Global Big Cities. Environ. Sci. Technol. 2012, 46, 696–703. [Google Scholar] [CrossRef]
  73. Breitenbach, G.L.; Congdon, J.D.; van Loben Sels, R.C. Winter Temperatures of Chrysemys picta Nests in Michigan: Effects on Hatchling Survival. Herpetologica 1984, 40, 76–81. [Google Scholar]
  74. Noble, D.W.A.; Stenhouse, V.; Schwanz, L.E. Developmental Temperatures and Phenotypic Plasticity in Reptiles: A Systematic Review and Meta-Analysis. Biol. Rev. 2018, 93, 72–97. [Google Scholar] [CrossRef]
  75. Packard, G.C.; Packard, M.J.; Miller, K.; Boardman, T.J. Influence of Moisture, Temperature, and Substrate on Snapping Turtle Eggs and Embryos. Ecology 1987, 68, 983–993. [Google Scholar] [CrossRef]
  76. Cagle, K.D.; Packard, G.C.; Miller, K.; Packard, M.J. Effects of the Microclimate in Natural Nests on Development of Embryonic Painted Turtles, Chrysemys picta. Funct. Ecol. 1993, 7, 653–660. [Google Scholar] [CrossRef]
  77. Chovanec, A. Man-Made Wetlands in Urban Recreational Areas—A Habitat for Endangered Species? Landsc. Urban Plan. 1994, 29, 43–54. [Google Scholar] [CrossRef]
  78. Orams, M.B. Feeding Wildlife as a Tourism Attraction: A Review of Issues and Impacts. Tour. Manag. 2002, 23, 281–293. [Google Scholar] [CrossRef]
  79. Spencer, R.J.; Thompson, M.B. The Significance of Predation in Nest Site Selection of Turtles: An Experimental Consideration of Macro- and Microhabitat Preferences. Oikos 2003, 102, 592–600. [Google Scholar] [CrossRef]
  80. Janzen, F.J.; Morjan, C.L. Repeatability of Microenvironment-Specific Nesting Behaviour in a Turtle with Environmental Sex Determination. Anim. Behav. 2001, 62, 73–82. [Google Scholar] [CrossRef]
  81. Murray, M.H.; Becker, D.J.; Hall, R.J.; Hernandez, S.M. Wildlife Health and Supplemental Feeding: A Review and Management Recommendations. Biol. Conserv. 2016, 204, 163–174. [Google Scholar] [CrossRef]
  82. Leighton, P.A.; Horrocks, J.A.; Kramer, D.L. Conservation and the Scarecrow Effect: Can Human Activity Benefit Threatened Species by Displacing Predators? Biol. Conserv. 2010, 143, 2156–2163. [Google Scholar] [CrossRef]
  83. Thorington, K.K.; Bowman, R. Predation Rate on Artificial Nests Increases with Human Housing Density in Suburban Habitats. Ecography 2003, 26, 188–196. [Google Scholar] [CrossRef]
  84. Dalgish, J.; Anderson, S. A Field Experiment on Learning by Raccoons. J. Mammal. 1979, 60, 620–622. [Google Scholar] [CrossRef]
  85. Pelech, S.A.; Smith, J.N.M.; Boutin, S. A Predator’s Perspective of Nest Predation: Predation by Red Squirrels Is Learned, Not Incidental. Oikos 2010, 119, 841–851. [Google Scholar] [CrossRef]
  86. Packard, G.C.; Packard, M.J.; Birchard, G.F. Sexual Differentiation and Hatching Success by Painted Turtles Incubating in Different Thermal and Hydric Environments. Herpetologica 1989, 45, 385–392. [Google Scholar]
  87. Tucker, J.K.; Paukstis, G.L. Hatching Success of Turtle Eggs Exposed to Dry Incubation Environment. J. Herpetol. 2000, 34, 529–534. [Google Scholar] [CrossRef]
  88. Temple, S.A. Predation on Turtle Nests Increases near Ecological Edges. Copeia 1987, 1987, 250–252. [Google Scholar] [CrossRef]
  89. Buhlmann, K.A.; Coffman, G. Fire Ant Predation of Turtle Nests and Implications for the Strategy of Delayed Emergence. J. Elisha Mitchell Sci. Soc. 2001, 117, 94–100. [Google Scholar]
  90. Lloyd, R.B.; Warner, D.A. Maternal Nest-Site Choice Does Not Affect Egg Hatching Success in an Invasive Turtle Population. Behaviour 2019, 156, 265–285. [Google Scholar] [CrossRef]
Diversity 15 00275 g001 550
Figure 1. Level of human disturbance across study areas. Initial designation of human disturbance included three categories based on our observations (high, intermediate, low levels of disturbance). Principal component axis 1 (PC1) combines different metrics of human disturbance across study areas (see Table 2). Colored ticks along the x-axis indicate actual PC1 scores of each study area (some study areas within a human disturbance category have the same PC1 score).
Figure 1. Level of human disturbance across study areas. Initial designation of human disturbance included three categories based on our observations (high, intermediate, low levels of disturbance). Principal component axis 1 (PC1) combines different metrics of human disturbance across study areas (see Table 2). Colored ticks along the x-axis indicate actual PC1 scores of each study area (some study areas within a human disturbance category have the same PC1 score).
Diversity 15 00275 g001
Diversity 15 00275 g002 550
Figure 2. Effects of urbanization level for natural (blue circles, solid lines) and artificial (orange squares, dashed lines) nests on microhabitat variables. (A) Distance of nests from the edge of water of the nearest pond. (B) Ground slope around nest sites. (C) Canopy openness over nests. (D) Daily mean nest temperature. (E) Daily maximum nest temperature. (F) Daily minimum nest temperature. (G) Daily temperature range.
Figure 2. Effects of urbanization level for natural (blue circles, solid lines) and artificial (orange squares, dashed lines) nests on microhabitat variables. (A) Distance of nests from the edge of water of the nearest pond. (B) Ground slope around nest sites. (C) Canopy openness over nests. (D) Daily mean nest temperature. (E) Daily maximum nest temperature. (F) Daily minimum nest temperature. (G) Daily temperature range.
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Table
Table 1. Study areas where turtle nesting was observed and where the nest predation experiments were performed. The coordinates for the pond on private property were omitted to maintain landowner privacy.
Table 1. Study areas where turtle nesting was observed and where the nest predation experiments were performed. The coordinates for the pond on private property were omitted to maintain landowner privacy.
Disturbance LevelStudy AreaPond Size (m2)CoordinatesNesting Study vs. Nest Predation Experiment
HighTown Creek Park456132.582539, −85.476735Both
HighKiesel Park74232.587040, −85.542433Nesting study
HighLongleaf Villas313132.570633, −85.506619Nesting study
HighAgricultural Heritage Park890732.594622, −85.675574Predation experiment
IntermediateFisheries pond S1011,55832.669121, −85.508862Both
IntermediateFisheries pond S1111,48532.671127, −85.507211Both
IntermediateFisheries pond S2722432.683346, −85.516154Nesting study
IntermediateFisheries pond S23560032.678296, −85.517820Nesting study
IntermediateFisheries pond S24708532.680441, −85.518099Nesting study
IntermediateFisheries pond S2911,71632.669498, −85.501004Nesting study
IntermediateFisheries pond S3038,26332.674933, −85.495792Nesting study
IntermediateFisheries pond S8 east559832.672734, −85.507651Nesting study
IntermediateFisheries pond S8 west37,51232.672084, −85.509432Nesting study
LowTuskegee National Forest oxbow pond734232.439472, −85.635536Both
LowNotasulga pond11,899-Predation experiment
Table
Table 2. Loadings and proportion of variance explained on different principal component (PC) axes from a principal components analysis.
Table 2. Loadings and proportion of variance explained on different principal component (PC) axes from a principal components analysis.
PC1PC2PC3PC4PC5
Impervious surface0.46240.0363−0.6312−0.6137−0.0988
Developed area within catchment0.47320.0675−0.0300−0.25630.8396
Density of roads0.40450.76920.41650.0355−0.2641
Housing units0.4270−0.59030.60120.3196−0.0742
Human population0.4649−0.2319−0.2562−0.6740−0.4583
Proportion of variance explained0.88620.086430.02150.00460.0011
Table
Table 3. Test statistics for comparisons of randomly selected locations (i.e., artificial nests) across three different types of study areas (high, intermediate, and low levels of human disturbance). Comparisons of temperature variables included iButton depth as an additional independent variable. For comparisons among study area types, effect sizes (β) were calculated using the low human disturbance area as the reference. Standard errors are indicated in parentheses, and statistically significant p values are in bold font.
Table 3. Test statistics for comparisons of randomly selected locations (i.e., artificial nests) across three different types of study areas (high, intermediate, and low levels of human disturbance). Comparisons of temperature variables included iButton depth as an additional independent variable. For comparisons among study area types, effect sizes (β) were calculated using the low human disturbance area as the reference. Standard errors are indicated in parentheses, and statistically significant p values are in bold font.
Dependent VariablesStudy Area TypeiButton Depth
Slope
Intermediate disturbance
High disturbance		F2,124 = 5.792, p = 0.004
β = 5.106 (1.602), p = 0.002
β = 6.233 (2.707), p = 0.023		-
Canopy openness (%)
Intermediate disturbance
High disturbance		F2,126 = 4.573, p = 0.012
β = 7.852 (4.238), p = 0.066
β = −10.898 (7.197), p = 0.133		-
Average daily mean temperature
Intermediate disturbance
High disturbance		F2,19 = 1.946, p = 0.170
β = −1.005 (1.168), p = 0.400
β = 1.179 (2.272), p = 0.610		β = −1.778 (1.246)
p = 0.170
Average daily maximum temperature
Intermediate disturbance
High disturbance		F2,19 = 3.565, p = 0.048
β = −2.627 (2.076), p = 0.221
β = 2.263 (4.039), p = 0.582		β = −3.877 (2.214)
p = 0.096
Average daily minimum temperature
Intermediate disturbance
High disturbance		F2,19 = 0.131, p = 0.878
β = −0.036 (0.645), p = 0.956
β = 0.588 (1.256), p = 0.645		β = −0.417 (0.688)
p = 0.552
Average daily temperature range
Intermediate disturbance
High disturbance		F2,19 = 5.587, p = 0.012
β = −2.591 (1.573), p = 0.116
β = 1.675 (3.061), p = 0.590		β = −3.460 (1.678)
p = 0.053
Table
Table 4. Effects of urbanization, nest type (natural vs. artificial), and their interaction on microhabitat variables. iButton depth was only included in the analyses of temperature data. The effect size for nest type was calculated with the natural nest as the intercept/reference. The rightmost two columns indicate whether residual variance differed between natural (N) and artificial (A) nests, and if so, what the two separate variances are (natural nests listed first, artificial nests second). Estimates and either standard errors or 95% confidence intervals are indicated in parentheses. For residual variances, we report lower confidence interval limits of 0 when the CIs cannot exclude zero. Statistically significant p values are in bold font.
Table 4. Effects of urbanization, nest type (natural vs. artificial), and their interaction on microhabitat variables. iButton depth was only included in the analyses of temperature data. The effect size for nest type was calculated with the natural nest as the intercept/reference. The rightmost two columns indicate whether residual variance differed between natural (N) and artificial (A) nests, and if so, what the two separate variances are (natural nests listed first, artificial nests second). Estimates and either standard errors or 95% confidence intervals are indicated in parentheses. For residual variances, we report lower confidence interval limits of 0 when the CIs cannot exclude zero. Statistically significant p values are in bold font.
Urbanization Level (PC1)Nest TypeUrbanization Level x Nest Type
Interaction		iButton DepthResidual VariancesEqual
Variance
Likelihood
Ratio Test
Distance from waterβ = −2.589 (4.504)
p = 0.567		β = 0.625 (0.523)
p = 0.186		β = 0.020 (0.328)
p = 0.951		-N = 4.875 (2.331 to 8.453)
A = 12.889 (10.507 to 16.048)		λ1 = 5.025
p = 0.012
Slopeβ = 1.377 (0.831)
p = 0.058		β = 0.428 (0.898)
p = 0.594		β = 0.006 (0.562)
p = 0.991		-N = 19.166 (12.147 to 29.477)
A = 23.457 (18.740 to 29.871)		λ1 = 0.263
p = 0.304
% canopy opennessβ = −2.189 (1.826)
p = 0.124		β = −2.931 (2.259)
p = 0.228		β = −0.704 (1.415)
p = 0.619		-N = 90.671 (49.070 to 153.127)
A = 241.750 (191.827 to 306.462)		λ1 = 4.712
p = 0.015
Average daily mean temp.β = −0.339 (0.372)
p = 0.095		β = −0.134 (0.409)
p = 0.522		β = 0.189 (0.262)
p = 0.470		β = −0.021 (0.707)
p = 0.973		N = 0.029 (0 to 1.228)
A = 2.659 (1.530 to 4.302)		λ1 = 2.781
p = 0.048
Average daily maximum temp.β = −0.333 (0.819)
p = 0.077		β = −0.871 (0.962)
p = 0.150		β = 0.472 (0.571)
p = 0.408		β = −1.435 (1.1494)
p = 0.335		N = 3.777 (0 to 20.031)
A = 8.985 (1.899 to 17.140)		λ1 = 0.321
p = 0.286
Average daily minimum temp.β = 0.032 (0.232)
p = 0.994		β = 0.291 (0.183)
p = 0.174		β = 0.100 (0.115)
p = 0.385		β = −0.072 (0.437)
p = 0.865		N = 0.008 (0 to 0.279)
A = 0.512 (0.317 to 0.835)		λ1 = 3.064
p = 0.040
Average daily temp. rangeβ = 0.482 (1.002)
p = 0.222		β = −0.793 (1.051)
p = 0.241		β = 0.382 (0.578)
p = 0.508		β = −3.296 (1.605)
p = 0.040		N = 11.348 (3.224 to 23.909)
A = 1.751 (0.828 to 7.223)		λ1 = 1.205
p = 0.136
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MDPI and ACS Style

Folkerts Caldwell, M.; López-Pérez, J.E.; Warner, D.A.; Wolak, M.E. Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance. Diversity 2023, 15, 275. https://doi.org/10.3390/d15020275

AMA Style

Folkerts Caldwell M, López-Pérez JE, Warner DA, Wolak ME. Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance. Diversity. 2023; 15(2):275. https://doi.org/10.3390/d15020275

Chicago/Turabian Style

Folkerts Caldwell, Molly, Jorge E. López-Pérez, Daniel A. Warner, and Matthew E. Wolak. 2023. "Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance" Diversity 15, no. 2: 275. https://doi.org/10.3390/d15020275

APA Style

Folkerts Caldwell, M., López-Pérez, J. E., Warner, D. A., & Wolak, M. E. (2023). Consistent Nest Site Selection by Turtles across Habitats with Varying Levels of Human Disturbance. Diversity, 15(2), 275. https://doi.org/10.3390/d15020275

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