Skip to main content

Satellite telemetry reveals space use of diamondback terrapins

Abstract

Movement and space use information of exploited and imperiled coastal species is critical to management and conservation actions. While satellite telemetry has been successfully used to document movements of marine turtles, the large tag sizes available have limited use on smaller turtle species. We used small Argos-based satellite tags to document movement patterns of diamondback terrapins (Malaclemys terrapin), the only estuarine turtle species in North America. Movement data from ten terrapins in St. Joseph Bay, Florida were gathered between July 13, 2018 and July 22, 2021. We estimated seasonal space use using the daily locations generated from a Bayesian hierarchical state-space model to calculate minimum convex polygons (95% MCP) and kernel density estimates (50% and 95% KDE). Mean tracking duration was 125 days and mean home range size was 9.4 km2 (95% MCP) and 8.1 km2 (95% KDE). Seagrass habitat comprised 55.8% of all home ranges on average, whereas salt marsh comprised a mean of 3.0%. Mean elevation used by terrapins was − 0.13 m (95% MCP) and -0.35 m (95% KDE). Satellite telemetry provided broad-scale spatiotemporal movement and space use data; however, Argos error produced considerable noise relative to true terrapin movements given their size, speed, and behavior. Terrapin home ranges were greater than previously reported and three of the ten terrapins exhibited repeated long-distance, directed movements within the bay. Small patches of salt marsh habitat were centralized within home ranges, despite comprising only a small percentage for each terrapin. Moreover, the percentage of salt marsh present in each core use area was positively correlated with terrapin mass. Although considered an estuarine species, seagrass habitat comprised a large portion of terrapin home ranges; however, our data did not provide the detail necessary to understand how terrapins were using this habitat. As northward-expanding mangroves continue to infringe upon salt marsh habitat, there is potential for negative impacts to terrapin populations across the northern Gulf of Mexico. As salt marsh habitat continues to be infringed upon by northward-expanding mangroves impacts to terrapins across the northern Gulf of Mexico.

Background

Animal movement data can be used to infer behaviors and resource use of imperiled and exploited species, and as such are necessary for identifying effective conservation strategies. This is particularly critical for species that inhabit at-risk systems, such as seagrass meadows and wetlands [1, 2]. The locations of these habitats, linking terrestrial and neritic systems, make them vulnerable to threats from coastal development, sea-level rise, declining water quality, and propeller scarring [2, 3]. Though gathering space use data for imperiled species in these shallow-water systems can be challenging [4], it is essential as data gaps for estuarine and seagrass dependent species hamper population modeling, critical habitat assessments, and development of recovery plans for these species [4, 5].

The diamondback terrapin (Malaclemys terrapin) is a relatively small turtle that may play an important role in salt marsh habitats by foraging on the periwinkle snail (Littoraria irrorate; [6]), an herbivore that has potential to over-graze salt marsh vegetation [7]. Terrapins are distributed along the Atlantic and Gulf of Mexico coasts of the U.S.A. [6, 8]. Listed as vulnerable on the IUCN Red List [9], terrapin populations are declining throughout their range due to overexploitation in the pet trade, road mortality, and drowning in crab traps [10, 11]. Loss of habitat from sea-level rise and human development of coastal areas has also contributed to fragmented terrapin populations [8, 11, 12].

Movement data for marine turtles have primarily been gathered through satellite [13, 14] and acoustic telemetry [15, 16]. While these techniques are ideal for large turtles that use relatively deepwater (> 1 m) habitats and travel long distances, historically they have not been adequate for smaller turtle species, such as terrapins. Until recently, satellite tags were too large and heavy for small turtle species; even small juvenile sea turtles have been difficult to track for more than a few weeks using this technique [17, 18]. While acoustic tags are smaller than satellite tags, they are only detectable while underwater and as such, are less useful for species that remain primarily in intertidal or shallow water habitats and emerge frequently onshore [19]. Because of these limitations, home range and movement data for aquatic and terrestrial turtle species, including diamondback terrapins, have primarily been gathered by use of VHF radio telemetry and mark-recapture methods [12, 20,21,22,23]. Results of these studies suggested terrapins moved less than 10 km from their capture sites and used home ranges that were generally < 1 km2.

Recent advances in satellite tag technology have resulted in tags small enough to use on large terrapins, generally females. Using satellite telemetry, [24] tracked two adult female diamondback terrapins in Northwest Florida for nearly 150 days each and documented one individual traveling almost 50 km from her original capture site. Home range sizes for these terrapins were also much larger than previously reported for the species. However, the small sample size in that study (n = 2) was insufficient for a broader scale (i.e., population-level) understanding of terrapin movements. We have expanded on this previous investigation to include nine additional terrapins equipped with satellite telemetry transmitters, revealing some of the variation in individual habitat use and challenges still to overcome with tracking small semi-aquatic species.

Methods

Data collection

St. Joseph Bay is located in northwest Florida in the northern Gulf of Mexico and covers approximately 260 km2 (Fig. 1). Seagrass beds, dominated by Thalassia testudinum, cover approximately one-sixth of the bay (43 km2) and are most abundant in the shallow southern end [25]. Tidal marshes cover approximately 3 km2 and are comprised primarily of black needlerush (Juncus roemerianus) and smooth cordgrass (Spartina alterniflora).

Fig. 1
figure 1

Habitat map of St. Joseph Bay, FL, including polygons of salt marsh and seagrass habitat overlain on a digital elevation model (DEM) raster

Because terrapins are frequently taken by poachers, we do not provide specific capture methods or locations. We conducted monthly surveys for terrapins at five sites in St. Joseph Bay from March to November 2018–2021 as part of a long-term mark-recapture program. The five capture location sites (A–E, Additional file 1: Table S1) ranged from 2.0 to 10.9 km apart and were located on the east side of St. Joseph Bay.

We measured straight plastron length (SPL) and weighed all captured terrapins following [5], then sexed and aged each individual following processing protocols outlined by [26]. To allow individual identification, we individually marked each turtle with unique notches in their marginal scutes [27, 28]. We also inserted a passive integrated transponder (PIT) behind the left bridge of each terrapin after first cleaning the application site with isopropyl alcohol [29].

We adhered satellite transmitters (Wildlife Computers SPOT-275 86 mm x 17 mm x 18 mm; SPOT-387 59 mm x 29 mm x 23 mm) to the anterior carapace using fast-setting epoxy (Superbond). Briefly, the anterior portion of the carapace was cleaned using rough grit sandpaper to remove dirt and epibionts, and to create more surface area for tag adhesion. A small amount of epoxy (approximately 15 g) was then placed on the carapace either directly along the midline of the vertebral scutes or slightly off-set near the margin of the vertebral and costal scutes, depending on the individual carapace (i.e., how well the tag fit on the carapace). After letting the epoxy set for approximately 15 min, we placed the tag into the epoxy and then covered most of the tag (except necessary sensors) with a thin coating (i.e., another approximately 15 g) of epoxy. Because we were initially concerned about possible antenna damage, the transmitter on the first satellite tagged terrapin was deployed with the antenna facing the posterior of the terrapin. All subsequent transmitters were deployed with the antenna facing the terrapins’ anterior. The combined mass of the transmitter and epoxy was less than 5% of terrapin mass. Each tag was programmed to be active for 24 h d−1 and to transmit 250 transmissions d−1. We released all tagged individuals at the site of capture.

Analysis

Location information was downloaded from Wildlife Computers Inc. which uses the satellite-based Argos system to generate locations and assign location quality based on accuracy estimates using Kalman filtering. When a satellite tag’s antenna breaks the surface of the water, a saltwater switch signals the tag to send messages. Those messages are used to estimate locations and the number of messages successfully received affects the error associated with each location. Argos assigns accuracy estimates for locations ranging from < 250 m for the most accurate location class (LC 3) to > 1500 m for LC 0. The estimated accuracy is unknown for LCs A and B and locations failing Argos’s plausibility test are assigned to LC Z ([30, 31]. We filtered out all LC equal to Z. Similarly, large temporal gaps in location data at the beginning or end of some terrapin tracks lead to greater uncertainty in ssm locations. Thus, we censored the head or tail end of the data for some individuals when satellite communications failed for more than 5 days, generally removing transmissions that occurred after November for four terrapins and before May 22 for one terrapin. This time period coincides with the portion of the year when terrapins have limited activity and are brumating in mud [6]. An exception was made for PTT 176033 which had very sparse data throughout the tag duration and is likely due to the posterior-facing antenna on this one individual.

In addition to error associated with Argos locations generated from satellite tags, these telemetry data also often contain large temporal gaps. Filtering these data is often insufficient to account for the location errors and may require dropping a significant amount (e.g., 80%) of transmitted locations. Bayesian hierarchical state-space models are frequently used to smooth Argos tracking data and account for LC error ([32, 33]). Similarly, we used the ‘bsam’ package [34, 35] in R (version 4.2.1; R Core Team 2022) to estimate a location for each individual every 24 h using a Bayesian hierarchical state-space model (‘fit_ssm’ function). Terrapin tracks were fitted using a hierarchical first difference correlated random walk (hDCRW) to improve movement parameter estimates by estimating them jointly across all individuals. Two independent MCMC chains were run in parallel by calling JAGS (package rjags; [36] using 10,000 adaptive samples and 30000 samples drawn from the posterior distribution and thinned by 20 to reduce within chain autocorrelation. We assessed for convergence using the ‘diag_ssm’ function in the ‘bsam’ package which utilizes Gelman and Rubin’s shrink factor [37, 38]. Applying the state-space model (ssm) to the Argos-derived location data refines estimates by accounting for location class error and reduces the autocorrelation inherent in movement data.

We estimated seasonal space use using the daily locations generated from the ssm to calculate minimum convex polygons (MCP) and kernel density estimates (KDE) for each individual using the ‘adehabitatHR’ [39] R package. We used the least-squares cross-validation method to determine the bandwidth of the utilization density, ensuring convergence for each individual. As a consequence of their size and speed, terrapins occupy relatively small areas characterized by mostly tortuous movements which, combined with the error structure inherent in Argos-derived positions, tend to result in an overestimation of home range metrics [40,41,42]. While the ssm appeared to account for much of that error, the output presented some uncertainty with several outlying location estimates for a few individuals. For this reason, we estimated MCPs using 95% of the locations closest to the centroid to define individual space use areas. Using all locations within each 95% MCP, we determined which point was farthest from individual centroids. For each terrapin, we then calculated the distance of each location along a terrapin’s track from the associated farthest location to assess patterns of directed movements for each terrapin.

We estimated polygon area overlap of each MCP and KDE with seagrass and salt marsh habitat [43, 44]. We also summarized elevation use within each MCP and KDE area from cells (3 m x 3 m) within a digital elevation model (DEM) of St. Joseph Bay [45]. We tested the relationship between space use area (MCP and KDE) and terrapin size (mass and SPL), as well as the relationship between tracking duration and terrapin size, capture location, and capture day (i.e., day of year) using linear regression and bivariate Pearson’s correlation coefficient. In addition, we tested site fidelity using “adehabitatLT” [39] by generating 1000 correlated random walks starting from the true first location of each individual, then randomizing turn angles and step distances, and returning the same number of relocations as the ssm-derived original track. We then ran a one-sided Monte-Carlo test from the “ade4” R package [46] to determine whether the terrapin movement tracks were more spatially constrained than random by computing the “less” alternate hypothesis p value using both 95% MCP and 95% KDE from randomized and observed values.

Results

We captured and tagged 10 terrapins between July 13, 2018 and July 22, 2021 (Additional file 1: Table S1). Terrapins ranged in size from 13.7 to 16.4 cm SPL and in mass from 670 to 1200 g. We obtained 14,018 locations from satellite data, after censoring 212 locations and two Z LCs, which provided a mean tracking duration of 125 d (range 39–186 days). There was no relationship between tracking duration and terrapin size, tagging date, or capture location within the bay. Though we censored all location data after November, most tags (n = 7) ceased transmitting between October and December. One tag (PTT 202456) that was deployed in May 2020 continued transmitting until February 2021, while another (PTT 202455) only transmitted for 40 days from June to July. Resultant censored location data still included a high proportion of LC B (68.6%) to LC 3 transmissions (10.3%, highest class), but large temporal gaps in location data were removed to improve the certainty in estimates (Additional file 1: Table S1).

Three terrapins were more mobile than expected, undertaking long-distance, directed movements, two of which (PTT 202453 and PTT 202458) traveled in a cyclical pattern (i.e., traveling repeatedly between two approximate locations) with a mean periodicity of 13 (6–20) days (Fig. 2). Mean seasonal space use area was 9.4 (3.1–14.8) km2 and 8.1 (0.5–17.1) km2 for 95% MCP and 95% KDE, respectively, with a core area (i.e., 50% KDE) mean of 1.3 (0.0007–2.79) km2 (1, Fig. 3). There was no relationship between terrapin size or mass and space use area; however, the largest terrapin (PTT 202457) used the smallest area, calculated using KDE (50% and 95%), while the smallest terrapin (PTT 215716) used the second smallest area. The resulting terrapin 95% MCPs and 95% KDEs were more constrained than random for each individual (p < 0.01), indicating that all terrapins in this study exhibited site fidelity during the study period.

Fig. 2
figure 2

Calculated 95% MCPs for ten terrapins and the associated farthest ssm estimated relocation point from the centroid. Distance from the farthest point from the centroid of the 95% MCP based on ssm estimated relocations for each of ten terrapins. The colored point represents the farthest location and the dashed line represents the centroid distance from the farthest location. The black points represent the distance of each relocation point from the farthest point traveled by each turtle over time (black line)

Fig. 3
figure 3

Calculated 95% MCPs, 95% KDE, and 50% KDEs for ten terrapins on the same spatial scale. The black polygon represents the 95% MCP, light red represents the 95% KDE, and darker red outlined with a dashed line represents the 50% KDE (core area)

Seagrass habitat was present in all space use areas, comprising 10.3–94.1% (mean: 55.8%) and 8.9–91.1% (mean: 58.0%) of terrapin 95% MCP and 95% KDE areas, respectively. Whereas terrapin core use areas encompassed a greater percentage of seagrass, ranging from 21.7% to 98.2% (mean: 70.7%). In general, the percentage of seagrass area overlap increased as kernel density areas reduced from 95% to 50%, except for terrapin PTT 202457 which had a very small (< 0.0008 km2) core area comprised primarily of one isolated patch that included 50.4% of salt marsh habitat. Salt marsh made up a smaller portion of each terrapin area than seagrass, largely due to availability (~ 3 km2 total area, or 1% of St. Joseph Bay), which included 0.28–6.15% (mean: 3.0%) and 0.18–6.48% (mean: 3.4%) of 95% MCP and 95% KDE terrapin use areas, respectively. Core use areas included 0–50.4% (mean: 6.7%) salt marsh habitat. The percentage of salt marsh present in each core use area was positively correlated (rho: 0.76) with terrapin mass (p < 0.05).

Derived from DEMs that were calibrated to mean high water, mean elevations used by terrapins were all below sea level at − 0.13 m (± 2.3), − 0.35 m (± 2.5), and − 0.27 m (± 1.19) for 95% MCP, 95% KDE, and 50% KDE, respectively. Two terrapin space use areas, belonging to PTT 202454 and PTT 202455, encompassed a wide range of positive and negative values with 95% of pixels between − 7.01 and 6.77 m, while 95% of elevations used by the other eight terrapins were between − 4.91 and 2.52 m, with a core area composition between − 0.91 and 0.42 m. Excluding PTT 202454, PTT 202455 and PTT 202457 (50% KDE only due to use of a small, isolated patch), the most frequently used elevation was -0.30 to -0.61 m, accounting for an average of 49.3% (± 19.3) of pixels within each terrapin space use area (Fig. 4).

Fig. 4
figure 4

Proportion of elevation (DEM raster) cells (3 × 3 m) contained within each space use area and binned at a 0.5 m resolution

Discussion

Satellite telemetry provided broad-scale spatiotemporal movement and space use data for diamondback terrapins inhabiting a coastal bay in Northwest Florida; however, because they are small, slow-moving semi-aquatic animals that often seek cover in vegetation or are submerged, the error inherent in Argos-derived telemetry data produced considerable noise in the location data relative to true terrapin movement [47, 48]. In addition, there were punctuated periods of missing information during sheltering or inactivity, especially from November to February when terrapins most likely remain buried in the mud [21]. Though the ssm appeared to perform adequately in estimating most terrapin locations, some locations did not seem biologically realistic (e.g., in water > 6 m deep in the center of the bay as opposed to crossing a channel to access additional salt marsh sites) and not all tags appeared to perform with the same precision in Argos locations, possibly due to the inclusion of November locations which appeared to have more error. Moreover, these satellite tags contain a saltwater switch that triggers the tag to transmit location messages when removed from the water (i.e., as the animal surfaces). When submerged in freshwater (e.g., if a terrapin temporarily moves into a freshwater source), the tag will continually transmit messages even when underwater thereby resulting in poor location quality and reduced battery life, as was most likely observed with PTT 202454 and PTT 202455, where their home ranges included a large freshwater source (Wildlife Computers SPOT User Guide v.202004; https://static.wildlifecomputers.com/SPOT-User-Guide-5.pdf). Despite these challenges, we were able to estimate reasonable broad-scale habitat use areas for 10 individuals captured within St. Joseph Bay. Because area overestimation remains a possibility [49], we included three habitat use metrics (i.e., 95% MCP, 50% KDE, and 95% KDE) for comparison and limited our MCP analysis to 95% of ssm estimated locations nearest to the centroid.

While there were two general movement patterns exhibited by terrapins in this study, stationary and migratory, when we attempted to run the behavioral hierarchical first difference correlated random walk (hDCRWS) switching model, it was unable to differentiate movement parameters between the two groups given the small movements of terrapins relative to the large error in Argos-derived telemetry data (i.e., directed movements contained short step-lengths and positional noise similar to stationary behaviors). However, when examining movement patterns based on distances relative to the farthest individual location (Fig. 2), three terrapins made long-distance (3–7 km), directed movements. For two of those migratory individuals, those long-distance movements were repeated multiple times, separated by fairly short time periods (6–20 days) within the tracking season.

Female terrapins are known to undertake relatively long-distance movements during the nesting season [12, 21, 24, 50]; however, the movements we documented occurred through October, whereas in most locations in the Gulf of Mexico, terrapins only nest through August [51, 52]. The extent of the terrapin nesting season in Northwest Florida is unknown, so perhaps these are indeed late-season nesting movements. Alternatively, these individuals could be making movements to freshwater sources [53, 54] or in search of prey. The predominate osmoregulatory strategy used by terrapins are behavioral adjustments, such as basking or terrestrial shuttling [53, 54]. This may explain movement patterns undertaken by some of our tagged terrapins (e.g., PTT 202454, PTT 202455) who appeared to spend considerable time in or near freshwater sources. The use of freshwater is a challenge to satellite tags that are built for the marine environment, and this may have contributed to poor tag transmission quality for PTT 202454 and lower tracking durations for PTT 202455 (Additional file 1: Table S1). Importantly, movements of this scale and frequency would unlikely be observed if using traditional VHF tracking. Although the remaining seven individuals did not exhibit the same directed movement, all 10 appeared to oscillate at consistent distances around the centroid of each 95% MCP (Fig. 2) and exhibited site fidelity, suggesting that the space use information collected encompasses the extent of seasonal home ranges for these terrapins.

The comparatively large home ranges documented in this study support preliminary findings presented by [24] who suggested terrapins in St. Joseph Bay may use larger areas than previously documented in other locations [20, 21, 23]. Although, despite reporting smaller home ranges, terrapins in those studies were reported traveling 1.5–12.5 km from known locations [12, 20, 21, 55]. Similarly, home ranges of aquatic turtles are typically estimated to be small at < 1 km2 [56,57,58,59], while home range size may increase with latitude [56] or waterbody size [60] and in less productive habitats, where turtles must roam to find resources [57]. Estimates of home range size are also affected by tracking methods. Home ranges of yellow-spotted river turtles (Podocnemis unifilis) tracked using VHF telemetry were 10 × smaller than those tracked using satellite telemetry [61, 62], and green turtle (Chelonia mydas) home ranges were also smaller when data were collected using active acoustic telemetry (i.e., manual tracking; [15] than satellite telemetry [18]. It is possible that the limited frequency of location fixes obtained via hand-telemetry using VHF and acoustic tags may result in underestimates of home range size; these species may thus have larger home ranges than currently believed. This underestimation could impact conservation decisions; for example, when quantifying appropriate habitat for management decisions [63, 64] or ranking threats to terrapins [11]. Conversely, the use of satellite telemetry has the potential to overestimate habitat use areas [40,41,42]. Thus, it is important to consider the scientific questions being addressed when deciding which method to use to gather location information for smaller species [47]. Satellite telemetry in this study provided valuable information on broad-scale terrapin use of the bay but is not sufficient for addressing fine-scale ecological concerns (Tables 1, 2 and 3).

Table 1 Use area calculations for ten terrapins, including percentage of area overlap with salt marsh and seagrass habitats
Table 2 Summary of elevation cells within terrapin space use areas, including the mean and quantiles (0.025, 0.975)
Table 3 Space use by diamondback terrapins at sites across the southeastern U.S., including results presented in [24] for a female terrapin tracked in St. Joseph Bay (also included in this study) and in Santa Rosa Sound, FL

Although home ranges were larger than expected, terrapins in St. Joseph Bay remained within the bay throughout the entire tracking period, using a small amount of the bay. Despite using a small proportion (1–16% of 95% MCP) of available salt marsh (~ 3 km2), this habitat covers only 1% of the entire bay and patches were centralized within the 95% MCPs or 95% KDEs of all terrapins in this study. This is not surprising as terrapins use salt marsh for thermoregulation, salinity regulation, and predator avoidance [20, 54, 65] and typically forage on salt marsh species, particularly the periwinkle snail (Littornia sp; [66,67,68]. In fact, in our study, heavier terrapins had a greater proportion of salt marsh in their home ranges. It has been suggested that the diamondback terrapin is an area-sensitive species that requires a minimum proportion of salt marsh area within its home range [64]. Areas with disjunct fringing marshes may support dispersing individuals or individuals making long-distance movements but not be sufficient for core terrapin habitat [64]. This may explain the disparity in home range sizes between this and other studies, as habitats in other studied regions are generally dominated by salt marsh and are confined, tidal waterway systems [20, 21, 23]. Although salt marsh is distributed along the coastline throughout the southern end of St. Joseph Bay, most of the mainland marshes are multi-species assemblages [69] comprised of some species that are not favored by periwinkle snails [70] and also increasingly invaded by expanding mangrove habitat [71]. Terrapins in St. Joseph Bay may more heavily use marsh patches that are dominated by Spartina alterniflora most likely because it is preferred by periwinkle snails [72]. In addition, the configuration (i.e., narrow fringe) of salt marsh in the bay does not make a large amount of it available to terrapins relative to the size of terrapin home ranges. Interestingly, the core use area for the largest female in this study contained 50% salt marsh habitat; however, she also used the smallest areas (50% and 95% KDE).

Although considered an estuarine species, seagrass habitat comprised a large portion of the terrapin home ranges in St. Joseph Bay. The southern end of the bay is dominated by seagrasses and as such, any movements away from salt marsh habitat would take terrapins over seagrasses. Our data do not provide the resolution necessary to determine how, or if, terrapins are using seagrass habitat, other than as corridors; however, it is likely that terrapins use seagrass habitat as protection from predators, similar to small green turtles [73]. In addition, terrapins are considered foraging generalists [67, 74] and may select prey in seagrass beds or forage directly on seagrass [75]. In the Chesapeake Bay, terrapins foraged on eelgrass (Zostera marina) and widgeon grass (Ruppia maritima; [75]), which is likely also occurring with Thalassia testudinum that dominates St. Joseph Bay [25]. In fact, terrapins may help disperse eelgrass seeds thereby contributing to the maintenance of seagrass habitats [76, 77]. If a similar process occurs in St. Joseph Bay, the relatively large home ranges we documented in this study would allow for increased seed dispersal distances by terrapins, and as such, would highlight another important role diamondback terrapins play in maintaining coastal habitats.

Conclusion

Understanding the spatial needs of coastal species is critical to their conservation, particularly as human populations increase in these regions and climate change threatens coastal habitats [78]. Urbanization and landscape alteration can constrain landward migration of coastal habitats due to sea-level rise [79], thereby reducing available habitat for terrapins. Anthropogenic structures such as bulkheads and riprap block the connection between the terrestrial and estuarine environment and can prevent terrapin movements [64]. Using a variety of movement ecology tools (e.g., satellite telemetry) can provide invaluable data that lead to conservation actions, such as establishing Marine Protected Areas [80, 81], defining critical habitat [82], and implementing activity closures, such as commercial fishing or vessel activity [83, 84]. However, the choice of satellite telemetry, particularly with smaller species, such as diamondback terrapins, needs to be made with project objectives in mind [13, 85]. Although there are satellite tags that can document locations with submeter accuracy (e.g., [86]), the large size required for such data collection prohibit their use with small species. Development of such tags would provide the ability to identify relationships between terrapin movements, environmental parameters, and habitat characteristics; data that are necessary for design of effective conservation actions to benefit terrapins. While terrapin home ranges in this study were considerably larger than in previous studies, it is unclear whether those sizes were due solely to the method of data collection or whether reduced habitat quality and sparse marsh distribution combined with anthropogenic spatial limitations, forced terrapins to cover larger areas to meet life history requirements. Moreover, there is a growing concern that as mangrove habitat expands northwards [71] it will alter or out-compete salt marsh habitat in this area, displacing many of the plant and prey species relied upon by diamondback terrapins.

Disclaimer

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Availability of data and materials

Because diamondback terrapins are an exploited species, the authors will not release location information publicly. Data requests can be sent to the lead author at: USGS Wetland and Aquatic Research Center, 7920 NW 71st St., Gainesville, FL 32653 or via email at mlamont@usgs.gov.

References

  1. Vitousek S, Barnard PL, Fletcher CH, Frazer N, Erikson L, Storlazzi CD. Doubling of coastal flooding frequency within decades due to sea-level rise. Sci Rep. 2017;7:1399.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dee SG, Torres MA, Martindale RC, Weiss A, DeLong KL. The future of reef ecosystems in the Gulf of Mexico: insights from coupled climate model simulations and ancient hot-house reefs. Front Mar Sci. 2019;6:691.

    Article  Google Scholar 

  3. Leo KL, Gillies CL, Fitzsimons JA, Hale LZ, Beck MW. Coastal habitat squeeze: a review of adaptation solutions for saltmarsh, mangrove and beach habitats. Ocean Coast Manag. 2019;175:180–90.

    Article  Google Scholar 

  4. Stott ND, Faust MD, Vandergoot CS, Miner JG. Acoustic telemetry detection probability and location accuracy in a freshwater wetland embayment. Anim Biotelemetry. 2021;9:19.

    Article  Google Scholar 

  5. Hart KM, McIvor CC. Demography and ecology of mangrove Diamondback Terrapins in a Wilderness Area of Everglades National Park, Florida. USA Copeia. 2008;2008:200–8.

    Article  Google Scholar 

  6. Roosenburg WM, Kennedy VS. Ecology and conservation of the diamond-backed terrapin. Baltimore, Maryland: Johns Hopkins University Press; 2018.

    Google Scholar 

  7. Silliman BR, Bertness MD. A trophic cascade regulates salt marsh primary production. Proc Natl Acad Sci. 2002;99:10500–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hart KM, Hunter ME, King TL. Regional differentiation among populations of the Diamondback terrapin (Malaclemys terrapin). Conserv Genet. 2014;15:593–603.

    Article  Google Scholar 

  9. IUCN. Malaclemys terrapin: Roosenburg, W.M., Baker, P.J., Burke, R., Dorcas, M.E. & Wood, R.C.: The IUCN Red List of Threatened Species 2019: e.T12695A507698. 2018 [cited 2023 Jun 9]. Available from: https://www.iucnredlist.org/species/12695/507698

  10. Crawford BA, Maerz JC, Nibbelink NP, Buhlmann KA, Norton TM, Albeke SE. Hot spots and hot moments of diamondback terrapin road-crossing activity. J Appl Ecol. 2014;51:367–75.

    Article  Google Scholar 

  11. Levy B, Windoloski K, Ludlam J. Matrix and agent-based modeling of threats to a diamond-backed terrapin population. Math Biosci. 2021;340: 108672.

    Article  PubMed  Google Scholar 

  12. Gibbons JW, Lovich JE, Tucker AD, Fitzsimmons NN, Greene JL. Demographic and ecological factors affecting conservation and management of the Diamondback Terrapin (Malaclemys terrapin) in South Carolina. Chelonian Conserv Biol. 2001;4.

  13. Sequeira AMM, Heupel MR, Lea M-A, Eguíluz VM, Duarte CM, Meekan MG, et al. The importance of sample size in marine megafauna tagging studies. Ecol Appl. 2019. https://doi.org/10.1002/eap.1947.

    Article  PubMed  Google Scholar 

  14. Ferreira RL, Ceia FR, Borges TC, Ramos JA, Bolten AB. Size-based differences in isotopic niche width (δ 13 C and δ 15 N) of green turtles (Chelonia mydas) nesting on Príncipe Island. Gulf of Guinea Mar Ecol. 2021. https://doi.org/10.1111/maec.12636.

    Article  Google Scholar 

  15. Lamont MM, Fujisaki I, Stephens BS, Hackett C. Home range and habitat use of juvenile green turtles (Chelonia mydas) in the northern Gulf of Mexico. Anim Biotelemetry. 2015;3:53.

    Article  Google Scholar 

  16. Hardin EE, Fuentes MMPB. A systematic review of acoustic telemetry as a tool to gain insights into marine turtle ecology and aid their conservation. Front Mar Sci. 2021;8: 765418.

    Article  Google Scholar 

  17. Seney EE, Higgins BM, Landry AM. Satellite transmitter attachment techniques for small juvenile sea turtles. J Exp Mar Biol Ecol. 2010;384:61–7.

    Article  Google Scholar 

  18. Lamont M, Iverson A. Shared habitat use by juveniles of three sea turtle species. Mar Ecol Prog Ser. 2018;606:187–200.

    Article  Google Scholar 

  19. Biggs CR, Erisman BE. Transmission loss of fish spawning vocalizations and the detection range of passive acoustic sampling in very shallow estuarine environments. Estuaries Coasts. 2021;44:2026–38.

    Article  Google Scholar 

  20. Spivey PB. Home range, habitat selection, and diet of the Diamondback Terrapin (Malaclemys terrapin) in a North Carolina estuary. Athens, Georgia: University of Georgia; 1998.

    Google Scholar 

  21. Butler JA. Population ecology, home range, and seasonal movements of the Carolina Diamondback Terrapin, Malaclemys terrapin centrata, in Northeastern Florida. Florida Fish and Wildlife Conservation Commission; 2002. Report No.: NG96–007.

  22. Szerlag-Egger S, McRobert SP. Northern Diamondback Terrapin occurrence, movement, and nesting activity along a salt marsh access road. Chelonian Conserv Biol. 2007;6:295.

    Article  Google Scholar 

  23. Harden L, Williard A. Using spatial and behavioral data to evaluate the seasonal bycatch risk of diamondback terrapins Malaclemys terrapin in crab pots. Mar Ecol Prog Ser. 2012;467:207–17.

    Article  Google Scholar 

  24. Lamont MM, Johnson D, Catizone DJ. Home ranges and movements of two diamondback terrapins (Malaclemys terrapin macrospilota) in Northwest Florida. Estuaries Coasts. 2021;44:1484–91.

    Article  Google Scholar 

  25. Florida Department of Environmental Protection. St. Joseph Bay Aquatic Preserve Management Plan. Florida Department of Environmental Protection; p. 192. Report No.: MS #235.

  26. Dorcas ME, Willson JD, Gibbons JW. Crab trapping causes population decline and demographic changes in diamondback terrapins over two decades. Biol Conserv. 2007;137:334–40.

    Article  Google Scholar 

  27. Cagle FR. A Louisiana terrapin population (Malaclemys). Copeia. 1952;1952:74.

    Article  Google Scholar 

  28. Sexton OJ. A method of estimating the age of painted turtles for use in demographic studies. Ecology. 1959;40:716–8.

    Article  Google Scholar 

  29. Buhlmann KA, Tuberville TD. Use of passive integrated transponder (PIT) tags for marking small freshwater turtles. Chelonian Conserv Biol. 1998;3:102–4.

    Google Scholar 

  30. CLS. Argos user’s manual: worldwide tracking and environmental monitoring by satellite. 19 August 2011 update. CLS, Toulouse. Available: http://www.argossystem.org/web/en/76-user-s-manual.php. Accessed 2023 Jan 23.

  31. Hart KM, Lamont MM, Sartain AR, Fujisaki I. Migration, foraging, and residency patterns for northern Gulf loggerheads: Implications of local threats and international movements. PLoS ONE. 2014;9:e103453.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Jonsen ID, Myers RA, James MC. Robust hierarchical state–space models reveal diel variation in travel rates of migrating leatherback turtles. J Anim Ecol. 2006;75:1046–57.

    Article  PubMed  Google Scholar 

  33. Hart KM, Lamont MM, Sartain AR, Fujisaki I, Stephens BS. Movements and habitat-use of loggerhead sea turtles in the northern Gulf of Mexico during the reproductive period. PLoS ONE. 2013;8:e66921.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jonsen I. Joint estimation over multiple individuals improves behavioural state inference from animal movement data. Sci Rep. 2016;6:20625.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jonsen ID, Flemming JM, Myers RA. Robust state-space modeling of animal movement data. Ecology. 2005;86:2874–80.

    Article  Google Scholar 

  36. Plummer M. rjags: Bayesian Graphical Models using MCMC. 2022.

  37. Jonsen ID, Patterson TA, Costa DP, Doherty PD, Godley BJ, Grecian WJ, et al. A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags. Mov Ecol. 2020;8:31.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Brooks S. Markov chain Monte Carlo method and its application. J R Stat Soc Ser Stat. 1998;47:69–100.

    Google Scholar 

  39. Calenge C. The package “adehabitat” for the R software: a tool for the analysis of space and habitat use by animals. Ecol Model. 2006;197:516–9.

    Article  Google Scholar 

  40. Moser BW, Garton EO. Effects of telemetry location error on space-use estimates using a fixed-kernel density estimator. J Wildl Manag. 2007;71:2421–6.

    Article  Google Scholar 

  41. Witt MJ, Åkesson S, Broderick AC, Coyne MS, Ellick J, Formia A, et al. Assessing accuracy and utility of satellite-tracking data using Argos-linked Fastloc-GPS. Anim Behav. 2010;80:571–81.

    Article  Google Scholar 

  42. Hoenner X, Whiting SD, Hindell MA, McMahon CR. Enhancing the use of Argos satellite data for home range and long distance migration studies of marine animals. PLoS ONE. 2012;7:e40713.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Florida Fish and Wildlife Conservation Commission. Saltwater Marsh Florida. 2022. Available from: https://geodata.myfwc.com/datasets/salt-marshes-in-florida/explore.

  44. Florida Fish and Wildlife Conservation Commission. Seagrass Florida. 2022. Available from: https://geodata.myfwc.com/datasets/seagrass-habitat-in-florida/explore?location=27.466248%2C-83.788150%2C6.92.

  45. Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, Boulder. Continuously Updated Digital Elevation Model (CUDEM) - 1/9 Arc-Second Resolution Bathymetric-Topographic Tiles. NOAA National Centers for Environmental Information; 2014.

  46. Thioulouse J, Dray S, Dufour A-B, Siberchicot A, Jombart T, Pavoine S. Multivariate Analysis of Ecological Data with ade4. New York, NY: Springer; 2018.

    Book  Google Scholar 

  47. Douglas DC, Weinzierl R, C. Davidson S, Kays R, Wikelski M, Bohrer G. Moderating Argos location errors in animal tracking data. Methods Ecol Evol. 2012;3:999–1007.

    Article  Google Scholar 

  48. Nicholls DG, Robertson CJR, Murray MD. Measuring accuracy and precision for CLS: Argos satellite telemetry locations. Notornis. 2007;54:137–57.

    Google Scholar 

  49. Powell RA, Mitchell MS. What is a home range? J Mammal. 2012;93:948–58.

    Article  Google Scholar 

  50. Sheridan CM. Mating system and dispersal patterns in the diamondback terrapin (Malaclemys terrapin) [Doctor of Philosophy]. Philadelphia: Drexel University; 2010.

    Google Scholar 

  51. Roberge T. Evaluating the reproductive ecology of the diamondback terrapin in Alabama saltmarshes: implications for the recovery of a depleted species. Birmingham: University of Alabama; 2012.

    Google Scholar 

  52. Pearson SH, Wiebe JJ. Considering diamond-backed terrapin (Malaclemys terrapin) nesting habitat and reproductive productivity in the restoration of Gulf of Mexico coastal ecosystems. Ocean Coast Manag. 2018;155:8–14.

    Article  Google Scholar 

  53. Davenport J, MaGill SH. Thermoregulation or osmotic control? Some preliminary observations on the function of emersion in the diamondback terrapin Malaclemys terrapin (Latreille). Herpetol J. 1996;6:26–9.

    Google Scholar 

  54. Williard AS, Harden LA, Jones TT, Midway SR. Effects of temperature and salinity on body fluid dynamics and metabolism in the estuarine Diamond-backed Terrapin (Malaclemys terrapin). J Exp Biol. 2019. https://doi.org/10.1242/jeb.202390.

    Article  Google Scholar 

  55. Hurd LE, Smedes GW, Dean TA. An ecological study of a natural population of Diamondback Terrapins (Malaclemys t. terrapin) in a Delaware salt marsh. Estuaries. 1979;2:28.

    Article  Google Scholar 

  56. Jones MT, Willey LL. Status and conservation of the wood turtle in the Northeastern United States. 2015 p. 288.

  57. Latham SR, Sirén APK, Reitsma LR. Space use and resource selection of Wood Turtles (Glyptemys insculpta) in the northeastern part of its range. Can J Zool. 2023;101:20–31.

    Article  Google Scholar 

  58. O’Dell DI, Karberg JM, Beattie KC, Omand KA, Buck EC. Changes to spotted turtle (Clemmys guttata) habitat selection in response to a salt marsh restoration. Wetl Ecol Manag. 2021;29:301–13.

    Article  Google Scholar 

  59. Smith LM, Cherry RP. Movement, seasonal activity, and home range of an isolated population of Glyptemys muhlenbergii, Bog Turtle, in the Southern Appalachians. Southeast Nat. 2016;15:207–19.

    Article  Google Scholar 

  60. Schubauer JP, Gibbons JW, Spotila JR. Home range and movement patterns of slider turtles inhabiting Par Pond Life Hist Ecol Slider Turt. Washington, DC: Smithsonian Institution Press; 1990. p. 223–32.

    Google Scholar 

  61. Hinderaker SE. Spatio-temporal ecology of Amazonian freshwater turtles [MS Thesis]. [Oslo, Norway]: Norges-miljo-og biovitenskapelige universitet; 2021.

  62. De Leao SP, Famelli S, Vogt RC. Home range of Yellow-Spotted Amazon River Turtles (Podocnemis unifilis) (Testudines: Podocnemididae) in the Trombetas river biological reserve, Pará. Brazil Chelonian Conserv Biol. 2019;18:10–8.

    Article  Google Scholar 

  63. Browne JP, Kanonik A, Vanek JP, Crown CA, Burke RL. Quantifying New York’s Diamondback Terrapin habitat. Northeast Nat. 2015;22:630–42.

    Article  Google Scholar 

  64. Isdell RE, Chambers RM, Bilkovic DM, Leu M. Effects of terrestrial-aquatic connectivity on an estuarine turtle. Divers Distrib. 2015;21:643–53.

    Article  Google Scholar 

  65. Ashley EA, Davis AK, Terrell VK, Lake C, Carden C, Head L, et al. Effects of salinity on hatchling Diamond-Backed Terrapin (Malaclemys terrapin) growth, behavior, and stress physiology. Herpetologica. 2021. https://doi.org/10.1655/Herpetologica-D-20-00028.1.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Tucker AD, Fitzsimmons NN, Gibbons JW. Resource partitioning by the estuarine turtle Malaclemys terrapin: trophic, spatial, and temporal foraging constraints. Herpetologica. 1995;1:167.

    Google Scholar 

  67. Butler JA, Heinrich GL, Mitchell ML. Diet of the Carolina Diamondback Terrapin (Malaclemys terrapin centrata) in Northeastern Florida. Chelonian Conserv Biol. 2012;11:124–8.

    Article  Google Scholar 

  68. Denton MJ, Demopoulos AWJ, Baldwin JD, Smith BJ, Hart KM. Stable isotope analysis enhances our understanding of diamondback terrapin (Malaclemys terrapin) foraging ecology. Estuaries Coasts. 2019;42:596–611.

    Article  CAS  Google Scholar 

  69. Hughes A, Lotterhos K. Genotypic diversity at multiple spatial scales in the foundation marsh species. Spartina alterniflora Mar Ecol Prog Ser. 2014;497:105–17.

    Article  Google Scholar 

  70. Zerebecki RA, Crutsinger GM, Hughes AR. Spartina alterniflora genotypic identity affects plant and consumer responses in an experimental marsh community. J Ecol. 2017;105:661–73.

    Article  Google Scholar 

  71. Snyder CM, Feher LC, Osland MJ, Miller CJ, Hughes AR, Cummins KL. The distribution and structure of mangroves (Avicennia germinans and Rhizophora mangle) near a rapidly changing range limit in the Northeastern Gulf of Mexico. Estuaries Coasts. 2022;45:181–95.

    Article  Google Scholar 

  72. Hughes AR. A neighboring plant species creates associational refuge for consumer and host. Ecology. 2012;93:1411–20.

    Article  PubMed  Google Scholar 

  73. MichaelR H, Wirsing AJ, Dill LM. The ecological importance of intact top-predator populations: a synthesis of 15 years of research in a seagrass ecosystem. Mar Freshw Res. 2012;63:1039.

    Article  Google Scholar 

  74. Breininger DR, Stolen ED, Breininger DJ, Breininger RD. Sampling rare and elusive species: Florida east coast diamondback terrapin population abundance. Ecosphere. 2019. https://doi.org/10.1002/ecs2.2824.

    Article  Google Scholar 

  75. Tulipani D. Foraging ecology and habitat use of the northern diamondback terrapin (Malaclemys terrapin terrapin) in southern Chesapeake Bay. The College of William and Mary; 2013

  76. Sumoski S, Orth R. Biotic dispersal in eelgrass Zostera marina. Mar Ecol Prog Ser. 2012;471:1–10.

    Article  Google Scholar 

  77. Tulipani DC, Lipcius RN. Evidence of eelgrass (Zostera marina) seed dispersal by northern diamondback terrapin (Malaclemys terrapin terrapin) in Lower Chesapeake Bay. PLoS ONE. 2014;9:e103346.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Grieger R, Capon SJ, Hadwen WL, Mackey B. Between a bog and a hard place: a global review of climate change effects on coastal freshwater wetlands. Clim Change. 2020;163:161–79.

    Article  Google Scholar 

  79. Borchert SM, Osland MJ, Enwright NM, Griffith KT. Coastal wetland adaptation to sea level rise: quantifying potential for landward migration and coastal squeeze. Appl Ecol. 2018;55:2876–87.

    Article  Google Scholar 

  80. Thompson CDH, Meeuwig JJ, Brown J, Richardson AJ, Friedlander AM, Miller PI, et al. Spatial variation in pelagic wildlife assemblages in the Ascension Island Marine Protected Area: implications for monitoring and management. Front Mar Sci. 2021;8: 634599.

    Article  Google Scholar 

  81. Metcalfe K, White L, Lee ME, Fay JM, Abitsi G, Parnell RJ, et al. Fulfilling global marine commitments; lessons learned from Gabon. Conserv Lett. 2022. https://doi.org/10.1111/conl.12872.

    Article  Google Scholar 

  82. Zanella I, López-Garro A, Cure K. Golfo Dulce: critical habitat and nursery area for juvenile scalloped hammerhead sharks Sphyrna lewini in the Eastern Tropical Pacific Seascape. Environ Biol Fishes. 2019;102:1291–300.

    Article  Google Scholar 

  83. Tickler DM, Carlisle AB, Chapple TK, Curnick DJ, Dale JJ, Schallert RJ, et al. Potential detection of illegal fishing by passive acoustic telemetry. Anim Biotelemetry. 2019;7:1.

    Article  Google Scholar 

  84. Aschettino JM, Engelhaupt DT, Engelhaupt AG, DiMatteo A, Pusser T, Richlen MF, et al. Satellite telemetry reveals spatial overlap between vessel high-traffic areas and humpback whales (Megaptera novaeangliae) near the mouth of the Chesapeake Bay. Front Mar Sci. 2020;7:121.

    Article  Google Scholar 

  85. Johansson Ö, Kachel S, Weckworth B. Guidelines for telemetry studies on Snow Leopards. Animals. 2022;12:1663.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Forrest SW, Recio MR, Seddon PJ. Moving wildlife tracking forward under forested conditions with the SWIFT GPS algorithm. Anim Biotelemetry. 2022;10:19.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Joseph Alday, Carson Arends, and Samantha Snow for field support. We are grateful to Kathy Gault, Justin Johnson, and Bruce Hagedorn from Eglin Air Force Base for continued support. We also thank Daniel Slone for his review.

Funding

The authors have no funding to declare.

Author information

Authors and Affiliations

Authors

Contributions

ML acquired resources for this project. ML and DC designed the study and conducted field investigations. ML, DC, and MP conceptualized manuscript objectives. MP analyzed data. ML and MP led writing of the manuscript. All authors contributed to revisions and final submission approval.

Corresponding author

Correspondence to Margaret M. Lamont.

Ethics declarations

Ethics approval and consent to participate

All turtle handling and sampling was conducted under State of Florida Scientific Collection Permits #33447 and #73692 and performed according to the Institutional Animal Care Protocol USGS/WARC/GNV 2019–15 and USGS/WARC.GNV 2018–04.

Competing interests

The authors declare no competing or conflicts of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

: Terrapin capture date and satellite transmitter deployment information, including the first and last day transmissions were received from the tag, for 10 diamondback terrapins tracked using satellite telemetry in St. Joseph Bay, FL. PTT = Platform Terminal Transponder, SPL = straight plastron length; Argos location classes = number of transmissions (after data were censored) in each location class.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lamont, M.M., Price, M.E. & Catizone, D.J. Satellite telemetry reveals space use of diamondback terrapins. Anim Biotelemetry 11, 42 (2023). https://doi.org/10.1186/s40317-023-00354-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40317-023-00354-x

Keywords