Spatial Ecology and Habitat Use of the Reeves’ Turtle (Mauremys reevesii) in Lentic and Lotic Systems

Spatial Ecology and Habitat Use of the Reeves’ Turtle (Mauremys reevesii) in Lentic and Lotic Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Ecology and Habitat Use of the Reeves’ Turtle (Mauremys reevesii) in Lentic and Lotic Systems Chang-Deuk Park, Kwanik Kwon, Jeongwoo Yoo, Nakyung Yoo, Chang-Yong Choi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8363107/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background The Reeves' turtle ( Mauremys reevesii ), a globally endangered species listed on CITES Appendix Ⅲ, faces significant threats in South Korea, including habitat fragmentation and competition with invasive species. Effective conservation requires a deep understanding of its spatial ecology, particularly how it differs between distinct habitat types. We compared the spatial ecology of M. reevesii in lentic (reservoir) and lotic (river) ecosystems to elucidate how their spatial behavior and habitat use are shaped by these distinct environments. Methods We used GPS telemetry to track 12 adult turtles (6 in a reservoir, 6 in rivers) in South Korea from April to November 2022. We analyzed differences in home range (Minimum Convex Polygon [MCP], Kernel Density Estimation [KDE 95%], Local Convex Hull [LoCoH 99%]), daily movement distance (Generalized Linear Model [GLM] testing habitat, sex, and season), diel terrestrial activity (circular statistics), road-crossing behavior (GLM), and seasonal displacement (Generalized Additive Model [GAM]). Results (1) Home range size (LoCoH 99%) did not differ significantly between habitats, but males exhibited significantly larger home ranges than females (MCP: p = 0.015). (2) Daily movement distance was significantly greater in the reservoir than in rivers (t=-5.92, p < 0.001) and significantly greater in spring than in autumn (t=-2.67, p = 0.008). (3) Diel terrestrial activity patterns differed significantly between habitats (Watson’s u 2 test, p < 0.001); while both groups peaked activity around 14:00 h, the activity of the river population was significantly more concentrated at this time. (4) The rate of road crossing was significantly positively correlated with the area of adjacent paddy fields, but not the length of roads within the home range. (5) GAM analysis revealed distinct movement patterns: reservoir turtles exhibited stable, non-linear displacement (100–400 m), while river turtles showed erratic, linear movements (100–900 m, max 1300 m) with a distinct peak in August. Conclusions M. reevesii displays significant plasticity in spatial ecology, adopting divergent movement patterns in response to lentic versus lotic environments. We recommend habitat-specific conservation buffers (e.g., 400 m radius for reservoirs, 1300 m linear distance for rivers) and prioritizing road mitigation measures (e.g., eco-passages, fences) in areas adjacent to paddy fields. These findings underscore the necessity of connectivity within these defined core habitats for effective population management. Connectivity Home range Daily movement Road ecology Seasonal displacement pattern Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Turtles are among the most threatened vertebrate taxa globally, with more than half of all species facing extinction [ 1 ]. Due to their low fecundity and slow recruitment rates, turtle populations are ecologically vulnerable; the loss of even a few adults can lead to rapid population decline and local extirpation [ 2 , 3 ]. Despite this universal threat, ecological and genetic research on many turtle species is hindered by the scarcity of their populations, leading to a significant lack of understanding, particularly for species inhabiting Asia [ 4 ]. Amidst this global crisis, the Reeves’ turtle, Mauremys reevesii (Gray, 1831), faces severe threats in South Korea. Internationally, M. reevesii is also listed on CITES Appendix Ⅲ, reflecting global conservation concerns. M. reevesii plays a crucial role in freshwater ecosystems as both a top predator and a scavenger, contributing significantly to ecosystem stability [ 5 , 6 ]. However, its populations have drastically declined due to habitat destruction, road mortality, and competition with invasive species. Consequently, M. reevesii was designated as a Class II Endangered Species by the Ministry of Climate, Energy and Environment and as a Natural Monument by the Cultural Heritage Administration [ 7 – 10 ]. Effective conservation hinges on a detailed understanding of a species' spatial ecology. Habitat selection provides critical information for effective habitat planning and management, particularly for the conservation of endangered species [ 11 – 13 ]. Information on wildlife movement patterns is also essential for understanding a species' ecology [ 14 ]. A key factor influencing these spatial patterns is the ecosystem structure. Freshwater ecosystems vary greatly in size and structure. Specifically, lentic systems (reservoirs with minimal flow) and lotic systems (rivers with continuous flow) present fundamental differences in hydraulic characteristics, food resource availability, and thermal environments [ 15 , 16 ]. These environmental differences are known to drive distinct spatial use patterns in other freshwater turtles, such as the Northern map turtle ( Graptemys geographica ) and the Western pond turtle ( Actinemys marmorata ) [ 17 , 18 ]. Therefore, these environmental factors are also expected to be key drivers of distinct spatial use patterns in M. reevesii populations. Despite this clear need, research specific to M. reevesii movement remains limited. Most existing research has focused on genetic identification [ 19 , 20 ] or VHF-based tracking limited to specific seasons [ 21 , 22 ]. Consequently, detailed behavioral data regarding spatial use and home range throughout the entire active season remain scarce. Enhancing the understanding of the behavioral ecology of wild turtles, which are often difficult to observe due to their rarity, is particularly fundamental to effective conservation efforts [ 23 ]. Therefore, this study aims to characterize the spatial use of M. reevesii by habitat type (reservoir and river) using GPS telemetry. By tracking released individuals, we investigated home range, daily movement, road use, and seasonal displacement patterns to propose tailored conservation recommendations based on the spatial ecology of the species. Methods Study site and period This study was conducted at study sites in Jeollanam-do and Gyeongsangnam-do, South Korea. Fieldwork involving turtle capture and GPS tracking occurred from April to November 2022. To compare spatial ecology based on habitat type, study sites were divided into reservoirs and rivers. The reservoir site selected was Dongsanje Reservoir in Gurye-gun, Jeollanam-do (35°11'20.09"N, 127°27'10.31"E). The river sites selected were Yangcheon River in Sancheong-gun (35°17'16.39"N, 127°57'46.09"E) and Imcheon River in Hamyang-gun, Gyeongsangnam-do (35°26'26.1"N, 127°44'48"E) (Fig. 1 ). Turtle captures and tagging M. reevesii , an omnivorous, 25–45 cm freshwater turtle found in Korea, prefers shallow, stagnant water with abundant cover and is active from April before hibernating in November. Distinguished by its three-keeled carapace, the species mates in autumn, and females lay 4–15 eggs in terrestrial nests around June-July [ 8 ]. Custom traps were fabricated for capturing M. reevesii . The basic design, referenced from an open patent (Kangwon National University Industry-University Cooperation Foundation, 10-2011-0085184), consisted of a 70 cm (L) × 70 cm (W) × 80 cm (H) stainless steel frame covered with a 3.5 × 3.5 cm mesh net. A critical design feature was the prevention of drowning if turtles were submerged for extended periods. To achieve this, the netting was shaped into an inverted square pyramid, with the apex tied to the top of a central support post, creating an internal air space. Captured turtles were attached with GPS transmitters, released, and monitored from April to November 2022. A total of 50 turtles were captured from April to June. Traps were typically set and checked 24 hours later. Captured individuals were held at the capture site for transmitter attachment and were released at their point of capture the next day to minimize disruption. This study analyzes data from the 12 individuals (6 reservoir, 6 river) from which sufficient tracking data were recovered (Table 2 ). Table 2 Transmitter ID, sex and home range and movement for each Mauremys reevesii individual. Habitat type Transmitter No. Sex Telemetry period (days) Points Home range (㎡) Daily moved distance (m) MCP KDE 95% LoCoH 99% mean max min Reservoir 10511 M 19 May– 23 Oct. (158) 1,072 49,050 11,541 10,732 209.71 ± 167.37 662.49 2.74 10486 F 18 May − 9 Oct. (145) 655 74,058 11,885 23,960 110.37 ± 108.87 578.26 2.93 10516 F 11 June − 15 Oct. (127) 849 77,003 11,667 18,274 160.91 ± 137.80 840.44 3.07 10514 F 16 June − 25 July (40) 56 26,582 1,565 11,480 79.96 ± 110.52 365.33 4.85 9443 F 21 Apr. − 7 July (78) 599 12,366 4,898 6,259 166.30 ± 127.95 494.35 10.2 10512 F 10 June − 23 June (14) / 21 Aug. − 29 Aug. (9) 185 10,351 4,311 5,913 205.6 ± 129.80 469.25 14.27 River 11336 M 27 Apr. − 8 Sep. (135) 227 50,062 2,607 20,563 125.88 ± 142.08 551.94 1.84 11342 M 21 May − 19 June (30) 33 96,381 1,044 44,347 158.16 ± 245.42 647.71 3.56 11341 F 18 May − 15 Aug. (90) 119 9,936 1,489 7.225 76.05 ± 85.14 315.29 1.22 11343 M 22 June − 12 Nov. (144) 327 204,242 7,702 22,311 78.77 ± 79.40 372.42 2.56 11311 M 23 June − 28 Oct. (128) 262 83,133 3,977 13,448 50.85 ± 70.20 311.59 3.91 11348 F 22 June − 21 Sep. (92) 91 8,049 2,268 2,590 51.09 ± 53.73 162.34 4.65 For monitoring, we used Debut YAWL (7.75 g) and LEGO (20.4 g) GPS transmitters from Druid (China) with an expected battery life of 12 months. GPS transmitters, which integrate GPS technology with conventional transmitters to enhance location accuracy, are highly effective for tracking the continuous, fine-scale movement paths of animals, thereby elucidating habitat use and temporal movement patterns [ 24 ]. Furthermore, GPS transmitters can remotely collect location data at predefined intervals without the researcher being present, ensuring operator safety and preventing accidents. To minimize interference with natural behaviors post-release, transmitters were attached only to turtles for which the device weight was less than 5% of the turtle's body mass. Transmitters were affixed to the highest central point of the carapace using epoxy and non-toxic silicone. This placement was intended to maximize the potential for satellite acquisition during shallow swimming and to increase the charging rate during basking. Data analysis We analyzed home range size and daily movement distance, stratified by habitat type (reservoir vs. river) and sex (male vs. female). Daily movement was further analyzed by season. Diel terrestrial activity was analyzed using mean activity time and variance. We also examined the influence of adjacent paddy field area and road length on road use by turtles. Home range size was calculated using three methods: Minimum Convex Polygon (MCP), Kernel Density Estimation 95% (KDE 95%), and Local Convex Hull 99% (LoCoH 99%) [ 25 – 27 ]. To define the necessary habitat scale for M. reevesii conservation, we utilized the LoCoH 99% method, as it is frequently used for home range estimations that must account for geographical features such as conservation area boundaries, rivers, lakes, and unsuitable habitat [ 28 , 29 ]. We analyzed the home ranges of GPS-tagged turtles by habitat type (reservoir, n = 6; river, n = 6) and sex (male, n = 5; female, n = 7) using Aligned Rank Transform (ART) ANOVA [ 30 ]. Daily movement distance for M. reevesii was calculated by summing the total distance moved by an individual per day, from which we derived the mean, standard deviation, maximum, and minimum daily movement distances for each individual. We used Generalized Linear Models (GLM) to analyze the effects of habitat type, sex, and season (Spring: Apr-May; Summer: Jun-Aug; Autumn: Sep-Nov) on movement distance. We performed circular statistics analysis to examine diel terrestrial activity patterns using the circular package [ 31 ]. We then used the Watson’s u 2 test, which compares the entire distributions of two groups (mean, variance, shape), to compare activity patterns between habitat types. Terrestrial activity in this study included both terrestrial locomotion and static basking on logs or rocks. To understand road (paddy roads in reservoir, dike roads in river) mortality risk, we analyzed road-crossing frequency relative to monitoring period, road length, and paddy field area within each turtle's home range. A road-crossing event was operationally defined as any two consecutive GPS points found on opposite sides of a road or a GPS point located directly on a road surface. We used a GLM to determine the influence of road length and paddy area on the road-crossing rate. The varying monitoring duration for each individual was standardized by using an offset term [+ offset(log(telemetry_period))] in the GLM function, allowing us to analyze the effects of the variables on the crossing rate per unit time. To compare seasonal movement patterns between habitat types, we performed a Generalized Additive Model (GAM) analysis using the mgcv package [ 32 ]. After filtering location data, the initial recorded point for each individual was set as the "original point". We then visualized the movement and non-linear trendline for each individual by habitat type, with time (X-axis) plotted against displacement from the original point (Y-axis). All statistical analyses were performed using R (ver. 4.4.3) [ 33 ]. All spatial analyses and map production were conducted using QGIS (ver 3.34) [ 34 ]. Results Home ranges Using the MCP method, home range size did not differ significantly between habitat types. However, males (96,574 ± 63,360 ㎡) had significantly larger home ranges than females (31,192 ± 30,913 ㎡) (Table 1 ; F = 9.43, p = 0.015). For the KDE 95% method, no significant differences were found for either habitat or sex (Table 1 , Table 2 , Fig. 1 , Fig. 2 ). Similarly, the LoCoH method showed no statistically significant differences by habitat or sex, although a trend was observed where male home ranges (22,280 ± 13,241 ㎡) were larger than female home ranges (10,814 ± 7,683 ㎡) (Table 1 ; F = 5.06, p = 0.055). Table 1 Results of the aligned rank transform (ART) ANOVA built to evaluate the effects of habitat type and sex on the minimum convex polygon (MCP), the kernel density estimation (KDE) 95% and the local convex hull (LoCoH) 99% home range size of Mauremys reevesii . Variable df MCP KDE LoCoH F p F p F p Habitat type 1 1.06 0.333 3.59 0.095 1.06 0.333 Sex 1 9.43 0.015 0.01 0.941 5.06 0.055 Habitat type × Sex 1 4.09 0.078 0.06 0.811 4.49 0.067 Daily and seasonal movement Individual daily movement distances (mean ± SD) were 291 ± 157 m (max: 840.44 m, min: 2.74 m) in the reservoir (n = 318 days) and 200 ± 180 m (max: 647.71 m, min: 1.22 m) in the rivers (n = 190 days) (Table 2 ). The GLM analysis revealed that habitat type and season had statistically significant effects on daily movement distance (Table 3 ). Habitat type was the most influential factor; river turtles moved, on average, 79.33 m less per day than reservoir turtles, a statistically significant difference (Table 3 ; t=-5.92, p < 0.001). Season also had a significant effect; compared to the reference season (Spring), movement in Autumn decreased significantly by an average of 49.26 m (Table 3 ; t=-2.67, p = 0.008). Movement in Summer also showed a tendency to decrease (by 24.62 m), though this was not statistically significant (Table 3 ; t=-.1.58, p = 0.114). Regarding sex, males tended to move 22.48 m more than females, but this difference did not reach statistical significance (Table 3 ; t=-1.71, p = 0.087). Analysis of diel terrestrial activity patterns revealed a significant difference in the variance of activity timing between habitat types (Wallraff test, p < 0.05). Consequently, a Watson’s u2 test confirmed that the overall distribution of diel activity patterns differed significantly by habitat. While the mean activity time was nearly identical for reservoir (13:54 h) and river (13:55 h) turtles, the degree of variance was distinct (Circular SD: reservoir = 1.56, river = 0.78). Turtles in rivers exhibited a much more concentrated activity pattern around 14:00 h compared to the reservoir population (F = 8.99, p < 0.001). Table 3 Results of the generalized linear models (GLM) built to evaluate the variables affecting the daily moved distances (m) of Mauremys reevesii . Variable Estimate Standard Error t value p (Intercept) 170.01 14.67 11.59 < 0.001 Habitat type River -79.33 13.39 -5.92 < 0.001 Sex Male 22.48 13.12 1.71 0.087 Season Summer -24.64 15.57 -1.58 0.114 Fall -49.26 18.46 -2.67 0.008 Road crossing We analyzed the effects of road length and paddy field area within a turtle's home range on its road-crossing rate, using the data summarized in Table 4 . The results indicated that the area of paddy fields within the home range had a significant effect on the road-crossing rate (GLM analysis, z = 2.51, p = 0.01), whereas road length did not (z=-1.11, p = 0.27). This suggests that road crossings are influenced less by the characteristics of the road itself and more by the surrounding habitat features, specifically paddy fields. In other words, turtles with more paddy fields near their habitat tended to cross adjacent roads more frequently. Table 4 Variables used in the generalized linear models (GLM) to analyze road crossing behavior of Mauremys reevesii . The table shows the monitoring period, number of road crossings, and total road length and paddy field area within each individual's home range. Transmitter ID Telemetry period (days) No. of road crossings Length of road (m) within home range Area of paddy field (m 2 ) within home range 10511 158 7 154.44 21,189.87 10486 145 5 477.62 28,429.99 10516 127 8 712.98 30,362.90 10514 40 2 83.81 747.18 9443 78 0 43.48 0 10512 23 0 0 0 11336 135 0 0 0 11342 30 0 0 0 11341 90 0 0 0 11343 144 10 1,075.07 42,523.48 11311 128 9 822.64 33,964.97 11348 92 0 34.20 0 Seasonal displacement patterns by habitat type We conducted a GAM analysis on all 12 monitored turtles (6 reservoir, 6 river). The analysis of movement patterns by habitat type showed that reservoir turtles concentrated their displacement distances between 100–400 m, following a relatively consistent and smooth non-linear curve, with peak movement concentrated between June and August. In contrast, river turtles exhibited a wider range of displacements (100–900 m, max 1300 m) in a more erratic and irregular pattern, with a distinct movement peak observed in August (Fig. 4 ). The GAM summary statistics showed that, compared to the reference group (reservoir), river turtles had a mean displacement distance that was approximately 66.07 m shorter, a statistically significant difference (Table 5 ). Furthermore, the dependent variable (displacement) represents the relative distance from the individual's origin point. Therefore, sections where the Y-axis value approaches zero indicate the individual has returned to its original location. This suggests homing behavior or settlement after a period of exploration. This pattern was clearly observed in both habitats and is interpreted as an ecological strategy to return to an initial location to prepare for hibernation. However, some individuals in the rivers were observed to be distributed hundreds of meters away from their origin points even after October, during the typical hibernation period (Fig. 4 ). Table 5 Parametric coefficient estimates from the generalized additive model (GAM) analyzing the effect of habitat type on the displacement of Mauremys reevesii . The intercept represents the baseline for the reservoir. Variable Estimate Standard Error t value p (Intercept) 181.44 2.58 70.44 < 0.001 Habitat type River -66.07 5.53 -11.95 < 0.001 Discussion A comparison of home range size by habitat type revealed no significant difference between reservoirs and rivers. The mean MCP home range size in this study was 41,568 ㎡ for reservoirs and 75,301 ㎡ for rivers. These values are much closer to the average MCP home range size reported for semi-aquatic turtles (12.08 ha) than for aquatic turtles (41.29 ha), suggesting M. reevesii aligns numerically with semi-aquatic species [ 35 ]. Furthermore, Song et al. [ 36 ], studying M. reevesii in the same reservoir as our study, reported KDE 95% home ranges between 28,003 ㎡ and 61,379 ㎡. This is considerably larger than our KDE 95% findings (mean ± SD: 7,645 ± 4,482 ㎡) and reinforces the conclusion that home range sizes are not consistently different between lentic and lotic habitats. Regarding sex, males had significantly larger MCP and LoCoH home ranges than females. This difference is likely attributable to reproductive behaviors; males typically patrol larger areas to locate mates, resulting in larger home ranges [ 37 ]. The analysis of daily movement distance showed that environmental factors (habitat type, season) had a greater influence than intrinsic factors (sex). Reservoir turtles moved more than river turtles, and spring movement was greater than autumn. To date, no studies have reported significant comparisons of daily movement distance by habitat type and sex for M. reevesii or other semi-aquatic turtles. Related research on the Mexican mud turtle ( Kinosternon integrum ) found an average movement of 51.44 ± 4.50 m, with 87.3% of movements under 100 m; movement was greater in the wet season than the dry season, and longer in water than on land [ 38 ]. The Alligator snapping turtle ( Macrochelys temminckii ) had an average daily movement of 82.3 ± 12.5 m, with most movements under 100 m [ 39 ]. In our study, 46.9% (238 of 508 days total) of M. reevesii movements were under 100 m, suggesting they are somewhat more mobile than K. integrum or M. temminckii . Further research comparing climatic patterns and aquatic vs. terrestrial movements is needed. Seasonally, movement increased in spring compared to autumn, consistent with findings from the National Park Research Institute [ 40 ]. This is likely due to rising water temperatures increasing metabolic activity and foraging efforts to replenish energy stores depleted during hibernation [ 40 ]. Diel terrestrial activity patterns, while differing in concentration (variance), showed a shared peak activity time around 14:00 h in both habitats, indicating a common diurnal activity pattern. This finding, coupled with Kim's [ 41 ] study showing that "resting" was the most frequent behavior for M. reevesii (peaking at 12:00–13:00 h), suggests this mid-day "resting" is not mere inactivity, but rather essential basking behavior for thermoregulation and metabolic activation [ 42 – 44 ]. Our analysis reveals a critical link between landscape-scale resource availability and movement patterns. We found that the road-crossing rate was not significantly influenced by the length of roads within the home range, but was significantly and positively correlated with the area of adjacent paddy fields. This suggests that road-crossing is not a random event but a consequence of resource-driven movement. Paddy fields function as important supplemental habitats, providing M. reevesii with high food availability and refuge sites, which in turn encourages activity and expands their movement radius [ 22 , 45 ]. The GAM analysis clearly demonstrates that M. reevesii employs heterogeneous spatial patterns and seasonal rhythms contingent on habitat type. This plasticity manifests as two distinct movement strategies: reservoir turtles exhibited stable, non-linear displacement, while river turtles showed more erratic, linear, and long-distance movements. The significantly shorter mean displacement in rivers compared to reservoirs is likely attributable to the physical structure of the habitats. Unlike the non-linear, open space of a reservoir, a river is a linear ecosystem that constrains movement to upstream or downstream paths. This structural difference appears to be the key factor shaping the spatial ecology in rivers [ 17 ]. The observation that some river individuals remained highly displaced from their origin points into the hibernation period (post-October) is interpreted as an optimal hibernaculum search strategy in response to the dynamic lotic environment. For semi-aquatic turtles, overwinter survival depends on securing microhabitats with stable temperatures and dissolved oxygen [ 46 ]. While lentic environments are relatively uniform, allowing turtles to find suitable hibernacula within their summer range, lotic environments are characterized by fluctuating flow and water levels. In rivers, suitable sites (e.g., deep pools, silted areas) are likely patchy. Thus, river turtles face greater ecological pressure to leave their summer range to find these optimal sites, resulting in the late-season long-distance movements observed in this study [ 47 ]. This movement may also be linked to pre-hibernation mating (Oct-Nov), where males searching for females along the river corridor overlap their search for hibernacula [ 48 ]. Consequently, the late-season movement of the river population is a complex, active behavioral adaptation to solve the dual challenges of overwinter survival and reproduction. Our analysis of seasonal displacement found that from June to September, turtles moved up to 400 m from their origin point in reservoirs and up to 1,300 m in rivers. These data provide the first quantitative metrics for defining conservation buffers in these distinct landscapes. Finally, with the exception of one individual that moved to a nearby reservoir and ceased transmitting, all turtles exhibited homing behavior. Previous research indicates turtle navigation operates via chemical signals, spatial memory, and sun compass navigation [ 49 , 50 ]. This high site fidelity, even after extensive seasonal movements, underscores the paramount importance of their core territory [ 51 ]. Conclusions This study reveals that M. reevesii exhibits significant behavioral plasticity, adopting divergent movement patterns in response to the fundamental structure of lentic versus lotic environments. As M. reevesii also exhibits high site fidelity and homing behavior, the conservation of its specific core territory is paramount [ 51 ]. Our findings provide the first evidence-based metrics for this conservation: we recommend conservation buffers should extend 400 m from reservoir core habitats and 1,300 m along river core habitats to encompass their maximum seasonal displacement range. Furthermore, our analysis identifies the key driver of human-wildlife conflict for this species. Given that the area of adjacent paddy fields, not road length, was the primary driver of road-crossing risk, mitigation strategies must evolve beyond simply managing roads and instead focus on landscape connectivity. We recommend prioritizing road sections that transect major agricultural areas for the installation of fences and eco-passages. Ultimately, effective conservation for M. reevesii requires management that not only preserves the core spatial scale (home range) but also maintains the connectivity between aquatic habitats and adjacent terrestrial resources, such as paddy fields, managing them as a single, functional habitat unit. Declarations Ethics approval This study was conducted in strict accordance with the Act on Conservation and Utilization of Natural Heritage. We obtained permits for the alteration of state-designated cultural heritage from the Korea Heritage Service for the periods of 2022–2023 (Permit Nos. 2022-0507, 2022-0959, and 2022-0510). All animal handling and experimental procedures complied with the Animal Protection Act and the Laboratory Animal Act of the Republic of Korea. The study protocol was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of the National Institute of Ecology (Approval Nos. NIEIACUC-R-2021-011 and NIEIACUC-R-2022-003; period: 2021–2023). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Institute of Ecology (NIE), funded by the Ministry of Climate, Energy and Environment (MCEE) of the Republic of Korea (Grant number: NIE- B-2025-47). Authors' contributions CDP conceived and designed the study, performed the fieldwork, analyzed the data, and wrote the original draft of the manuscript. JDY supervised the project, provided critical feedback, and reviewed and edited the manuscript. KK, JY and NY assisted with data collection and field surveys. CYC supervised the data analysis and critically reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgements We are grateful to the staff of the National Institute of Ecology for their administrative support and assistance during the fieldwork. We also thank Moon Seong Heo and Eun-Mi Jo for their valuable help with data collection. References Stanford CB, Iverson JB, Rhodin AG, van Dijk PP, Mittermeier RA, Kuchling G, et al. Turtles and tortoises are in trouble. Curr Biol. 2020;30(12):R721–35. Innes RJ, Babbitt KJ, Kanter JJ. Home range and movement of Blanding's Turtles ( Emydoidea blandingii ) in New Hampshire. Northeast Nat. 2008;15(3):431–44. Keevil MG, Brooks RJ, Litzgus JD. Post-catastrophe patterns of abundance and survival reveal no evidence of population recovery in a long‐lived animal. 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Anim Behav. 1995;49(4):977–87. Roth TC, Krochmal AR. The role of age-specific learning and experience for turtles navigating a changing landscape. Curr Biol. 2015;25(3):333–7. Xiao F, Bu R, Lin L, Wang J, Shi H. Home-site fidelity and homing behavior of the big‐headed turtle Platysternon megacephalum . Ecol Evol. 2021;11(11):5803–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 17 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 15 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8363107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562545229,"identity":"acb56a72-c5d3-48f6-ab5b-8bf2ce3828ca","order_by":0,"name":"Chang-Deuk Park","email":"","orcid":"","institution":"Research Center for Endangered Species, National Institute of Ecology","correspondingAuthor":false,"prefix":"","firstName":"Chang-Deuk","middleName":"","lastName":"Park","suffix":""},{"id":562545232,"identity":"4a438fd9-58c6-4e55-a577-1b5a1455c56c","order_by":1,"name":"Kwanik Kwon","email":"","orcid":"","institution":"Research Center for 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1","display":"","copyAsset":false,"role":"figure","size":1601676,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location and landscape of study sites. The blue represents the reservoir while the red indicates the river.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8363107/v1/1308c8a3dbf7b3d1ec7e0daa.jpeg"},{"id":98643809,"identity":"2676fbb7-30dc-42a9-9cbb-89d9f712b6ee","added_by":"auto","created_at":"2025-12-19 19:34:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1764586,"visible":true,"origin":"","legend":"\u003cp\u003eHome ranges of \u003cem\u003eMauremys reevesii \u003c/em\u003eindividual tracked with GPS transmitters in the reservoir. Each number corresponds to the unique transmitter ID, m = male, f = female, a = adult.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8363107/v1/7a982b481ad468512988319f.png"},{"id":98643808,"identity":"47ec7c0f-e464-4cbf-93bb-bc49af1bd0d4","added_by":"auto","created_at":"2025-12-19 19:34:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1612819,"visible":true,"origin":"","legend":"\u003cp\u003eHome ranges of \u003cem\u003eMauremys reevesii \u003c/em\u003eindividual tracked with GPS transmitters in the river. Each number corresponds to the unique transmitter ID, m = male, f = female, a = adult.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8363107/v1/319e423ffcba6b4698e738ad.png"},{"id":98775124,"identity":"49a7f835-e19f-4c85-bf6c-e9e239852577","added_by":"auto","created_at":"2025-12-22 12:18:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":248144,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized additive model (GAM) analysis result plot. Displacement refers to the distance moved from the original points by \u003cem\u003eMauremys reevesii\u003c/em\u003e over time, distinguished by habitat type.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8363107/v1/a4f14cefc45f9fb7daf71208.png"},{"id":98782633,"identity":"ef69b875-cf86-4e9d-bd00-937bba4b1017","added_by":"auto","created_at":"2025-12-22 12:40:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6141419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8363107/v1/3a8737e3-66c8-4151-9f9b-ebf61f632148.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Ecology and Habitat Use of the Reeves’ Turtle (Mauremys reevesii) in Lentic and Lotic Systems","fulltext":[{"header":"Background","content":"\u003cp\u003eTurtles are among the most threatened vertebrate taxa globally, with more than half of all species facing extinction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to their low fecundity and slow recruitment rates, turtle populations are ecologically vulnerable; the loss of even a few adults can lead to rapid population decline and local extirpation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite this universal threat, ecological and genetic research on many turtle species is hindered by the scarcity of their populations, leading to a significant lack of understanding, particularly for species inhabiting Asia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmidst this global crisis, the Reeves\u0026rsquo; turtle, \u003cem\u003eMauremys reevesii\u003c/em\u003e (Gray, 1831), faces severe threats in South Korea. Internationally, \u003cem\u003eM. reevesii\u003c/em\u003e is also listed on CITES Appendix Ⅲ, reflecting global conservation concerns. \u003cem\u003eM. reevesii\u003c/em\u003e plays a crucial role in freshwater ecosystems as both a top predator and a scavenger, contributing significantly to ecosystem stability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, its populations have drastically declined due to habitat destruction, road mortality, and competition with invasive species. Consequently, \u003cem\u003eM. reevesii\u003c/em\u003e was designated as a Class II Endangered Species by the Ministry of Climate, Energy and Environment and as a Natural Monument by the Cultural Heritage Administration [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective conservation hinges on a detailed understanding of a species' spatial ecology. Habitat selection provides critical information for effective habitat planning and management, particularly for the conservation of endangered species [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Information on wildlife movement patterns is also essential for understanding a species' ecology [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A key factor influencing these spatial patterns is the ecosystem structure. Freshwater ecosystems vary greatly in size and structure. Specifically, lentic systems (reservoirs with minimal flow) and lotic systems (rivers with continuous flow) present fundamental differences in hydraulic characteristics, food resource availability, and thermal environments [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These environmental differences are known to drive distinct spatial use patterns in other freshwater turtles, such as the Northern map turtle (\u003cem\u003eGraptemys geographica\u003c/em\u003e) and the Western pond turtle (\u003cem\u003eActinemys marmorata\u003c/em\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, these environmental factors are also expected to be key drivers of distinct spatial use patterns in \u003cem\u003eM. reevesii\u003c/em\u003e populations.\u003c/p\u003e \u003cp\u003eDespite this clear need, research specific to \u003cem\u003eM. reevesii\u003c/em\u003e movement remains limited. Most existing research has focused on genetic identification [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] or VHF-based tracking limited to specific seasons [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Consequently, detailed behavioral data regarding spatial use and home range throughout the entire active season remain scarce. Enhancing the understanding of the behavioral ecology of wild turtles, which are often difficult to observe due to their rarity, is particularly fundamental to effective conservation efforts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, this study aims to characterize the spatial use of \u003cem\u003eM. reevesii\u003c/em\u003e by habitat type (reservoir and river) using GPS telemetry. By tracking released individuals, we investigated home range, daily movement, road use, and seasonal displacement patterns to propose tailored conservation recommendations based on the spatial ecology of the species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and period\u003c/h2\u003e \u003cp\u003eThis study was conducted at study sites in Jeollanam-do and Gyeongsangnam-do, South Korea. Fieldwork involving turtle capture and GPS tracking occurred from April to November 2022. To compare spatial ecology based on habitat type, study sites were divided into reservoirs and rivers. The reservoir site selected was Dongsanje Reservoir in Gurye-gun, Jeollanam-do (35\u0026deg;11'20.09\"N, 127\u0026deg;27'10.31\"E). The river sites selected were Yangcheon River in Sancheong-gun (35\u0026deg;17'16.39\"N, 127\u0026deg;57'46.09\"E) and Imcheon River in Hamyang-gun, Gyeongsangnam-do (35\u0026deg;26'26.1\"N, 127\u0026deg;44'48\"E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTurtle captures and tagging\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eM. reevesii\u003c/em\u003e, an omnivorous, 25\u0026ndash;45 cm freshwater turtle found in Korea, prefers shallow, stagnant water with abundant cover and is active from April before hibernating in November. Distinguished by its three-keeled carapace, the species mates in autumn, and females lay 4\u0026ndash;15 eggs in terrestrial nests around June-July [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Custom traps were fabricated for capturing \u003cem\u003eM. reevesii\u003c/em\u003e. The basic design, referenced from an open patent (Kangwon National University Industry-University Cooperation Foundation, 10-2011-0085184), consisted of a 70 cm (L) \u0026times; 70 cm (W) \u0026times; 80 cm (H) stainless steel frame covered with a 3.5 \u0026times; 3.5 cm mesh net. A critical design feature was the prevention of drowning if turtles were submerged for extended periods. To achieve this, the netting was shaped into an inverted square pyramid, with the apex tied to the top of a central support post, creating an internal air space. Captured turtles were attached with GPS transmitters, released, and monitored from April to November 2022. A total of 50 turtles were captured from April to June. Traps were typically set and checked 24 hours later. Captured individuals were held at the capture site for transmitter attachment and were released at their point of capture the next day to minimize disruption. This study analyzes data from the 12 individuals (6 reservoir, 6 river) from which sufficient tracking data were recovered (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransmitter ID, sex and home range and movement for each \u003cem\u003eMauremys reevesii\u003c/em\u003e individual.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHabitat\u003c/p\u003e \u003cp\u003etype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTransmitter\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTelemetry period (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eHome range (㎡)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003eDaily moved distance (m)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKDE\u003c/p\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLoCoH\u003c/p\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003emax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003emin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eReservoir\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 May\u0026ndash; 23 Oct. (158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11,541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10,732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e209.71\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;167.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e662.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 May \u0026minus;\u0026thinsp;9 Oct. (145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11,885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23,960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e110.37\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;108.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e578.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 June \u0026minus;\u0026thinsp;15 Oct. (127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e11,667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18,274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e160.91\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;137.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e840.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 June \u0026minus;\u0026thinsp;25 July (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1,565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e79.96\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;110.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e365.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 Apr. \u0026minus;\u0026thinsp;7 July (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e166.30\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;127.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e494.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 June \u0026minus;\u0026thinsp;23 June (14) / 21 Aug. \u0026minus;\u0026thinsp;29 Aug. (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4,311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e205.6\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;129.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e469.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e14.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eRiver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 Apr. \u0026minus;\u0026thinsp;8 Sep. (135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2,607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20,563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e125.88\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;142.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e551.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 May \u0026minus;\u0026thinsp;19 June (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96,381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44,347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e158.16\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;245.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e647.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 May \u0026minus;\u0026thinsp;15 Aug. (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e76.05\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;85.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e315.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 June \u0026minus;\u0026thinsp;12 Nov. (144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204,242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7,702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22,311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e78.77\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;79.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e372.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 June \u0026minus;\u0026thinsp;28 Oct. (128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3,977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e50.85\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;70.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e311.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 June \u0026minus;\u0026thinsp;21 Sep. (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e51.09\u003c/p\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;53.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e162.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor monitoring, we used Debut YAWL (7.75 g) and LEGO (20.4 g) GPS transmitters from Druid (China) with an expected battery life of 12 months. GPS transmitters, which integrate GPS technology with conventional transmitters to enhance location accuracy, are highly effective for tracking the continuous, fine-scale movement paths of animals, thereby elucidating habitat use and temporal movement patterns [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, GPS transmitters can remotely collect location data at predefined intervals without the researcher being present, ensuring operator safety and preventing accidents. To minimize interference with natural behaviors post-release, transmitters were attached only to turtles for which the device weight was less than 5% of the turtle's body mass. Transmitters were affixed to the highest central point of the carapace using epoxy and non-toxic silicone. This placement was intended to maximize the potential for satellite acquisition during shallow swimming and to increase the charging rate during basking.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eWe analyzed home range size and daily movement distance, stratified by habitat type (reservoir vs. river) and sex (male vs. female). Daily movement was further analyzed by season. Diel terrestrial activity was analyzed using mean activity time and variance. We also examined the influence of adjacent paddy field area and road length on road use by turtles.\u003c/p\u003e \u003cp\u003eHome range size was calculated using three methods: Minimum Convex Polygon (MCP), Kernel Density Estimation 95% (KDE 95%), and Local Convex Hull 99% (LoCoH 99%) [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To define the necessary habitat scale for \u003cem\u003eM. reevesii\u003c/em\u003e conservation, we utilized the LoCoH 99% method, as it is frequently used for home range estimations that must account for geographical features such as conservation area boundaries, rivers, lakes, and unsuitable habitat [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We analyzed the home ranges of GPS-tagged turtles by habitat type (reservoir, n\u0026thinsp;=\u0026thinsp;6; river, n\u0026thinsp;=\u0026thinsp;6) and sex (male, n\u0026thinsp;=\u0026thinsp;5; female, n\u0026thinsp;=\u0026thinsp;7) using Aligned Rank Transform (ART) ANOVA [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDaily movement distance for \u003cem\u003eM. reevesii\u003c/em\u003e was calculated by summing the total distance moved by an individual per day, from which we derived the mean, standard deviation, maximum, and minimum daily movement distances for each individual. We used Generalized Linear Models (GLM) to analyze the effects of habitat type, sex, and season (Spring: Apr-May; Summer: Jun-Aug; Autumn: Sep-Nov) on movement distance.\u003c/p\u003e \u003cp\u003eWe performed circular statistics analysis to examine diel terrestrial activity patterns using the circular package [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We then used the Watson\u0026rsquo;s u\u003csup\u003e2\u003c/sup\u003e test, which compares the entire distributions of two groups (mean, variance, shape), to compare activity patterns between habitat types. Terrestrial activity in this study included both terrestrial locomotion and static basking on logs or rocks.\u003c/p\u003e \u003cp\u003eTo understand road (paddy roads in reservoir, dike roads in river) mortality risk, we analyzed road-crossing frequency relative to monitoring period, road length, and paddy field area within each turtle's home range. A road-crossing event was operationally defined as any two consecutive GPS points found on opposite sides of a road or a GPS point located directly on a road surface. We used a GLM to determine the influence of road length and paddy area on the road-crossing rate. The varying monitoring duration for each individual was standardized by using an offset term [+\u0026thinsp;offset(log(telemetry_period))] in the GLM function, allowing us to analyze the effects of the variables on the crossing rate per unit time.\u003c/p\u003e \u003cp\u003eTo compare seasonal movement patterns between habitat types, we performed a Generalized Additive Model (GAM) analysis using the mgcv package [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. After filtering location data, the initial recorded point for each individual was set as the \"original point\". We then visualized the movement and non-linear trendline for each individual by habitat type, with time (X-axis) plotted against displacement from the original point (Y-axis).\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R (ver. 4.4.3) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All spatial analyses and map production were conducted using QGIS (ver 3.34) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHome ranges\u003c/h2\u003e \u003cp\u003eUsing the MCP method, home range size did not differ significantly between habitat types. However, males (96,574\u0026thinsp;\u0026plusmn;\u0026thinsp;63,360 ㎡) had significantly larger home ranges than females (31,192\u0026thinsp;\u0026plusmn;\u0026thinsp;30,913 ㎡) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; F\u0026thinsp;=\u0026thinsp;9.43, p\u0026thinsp;=\u0026thinsp;0.015). For the KDE 95% method, no significant differences were found for either habitat or sex (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, the LoCoH method showed no statistically significant differences by habitat or sex, although a trend was observed where male home ranges (22,280\u0026thinsp;\u0026plusmn;\u0026thinsp;13,241 ㎡) were larger than female home ranges (10,814\u0026thinsp;\u0026plusmn;\u0026thinsp;7,683 ㎡) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; F\u0026thinsp;=\u0026thinsp;5.06, p\u0026thinsp;=\u0026thinsp;0.055).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the aligned rank transform (ART) ANOVA built to evaluate the effects of habitat type and sex on the minimum convex polygon (MCP), the kernel density estimation (KDE) 95% and the local convex hull (LoCoH) 99% home range size of \u003cem\u003eMauremys reevesii\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMCP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eKDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eLoCoH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHabitat type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHabitat type \u0026times; Sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDaily and seasonal movement\u003c/h2\u003e \u003cp\u003eIndividual daily movement distances (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) were 291\u0026thinsp;\u0026plusmn;\u0026thinsp;157 m (max: 840.44 m, min: 2.74 m) in the reservoir (n\u0026thinsp;=\u0026thinsp;318 days) and 200\u0026thinsp;\u0026plusmn;\u0026thinsp;180 m (max: 647.71 m, min: 1.22 m) in the rivers (n\u0026thinsp;=\u0026thinsp;190 days) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The GLM analysis revealed that habitat type and season had statistically significant effects on daily movement distance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Habitat type was the most influential factor; river turtles moved, on average, 79.33 m less per day than reservoir turtles, a statistically significant difference (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; t=-5.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Season also had a significant effect; compared to the reference season (Spring), movement in Autumn decreased significantly by an average of 49.26 m (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; t=-2.67, p\u0026thinsp;=\u0026thinsp;0.008). Movement in Summer also showed a tendency to decrease (by 24.62 m), though this was not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; t=-.1.58, p\u0026thinsp;=\u0026thinsp;0.114). Regarding sex, males tended to move 22.48 m more than females, but this difference did not reach statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; t=-1.71, p\u0026thinsp;=\u0026thinsp;0.087). Analysis of diel terrestrial activity patterns revealed a significant difference in the variance of activity timing between habitat types (Wallraff test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Consequently, a Watson\u0026rsquo;s u2 test confirmed that the overall distribution of diel activity patterns differed significantly by habitat. While the mean activity time was nearly identical for reservoir (13:54 h) and river (13:55 h) turtles, the degree of variance was distinct (Circular SD: reservoir\u0026thinsp;=\u0026thinsp;1.56, river\u0026thinsp;=\u0026thinsp;0.78). Turtles in rivers exhibited a much more concentrated activity pattern around 14:00 h compared to the reservoir population (F\u0026thinsp;=\u0026thinsp;8.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the generalized linear models (GLM) built to evaluate the variables affecting the daily moved distances (m) of \u003cem\u003eMauremys reevesii\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e170.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRiver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-79.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-5.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSummer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-24.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-49.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-2.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRoad crossing\u003c/h3\u003e\n\u003cp\u003eWe analyzed the effects of road length and paddy field area within a turtle's home range on its road-crossing rate, using the data summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results indicated that the area of paddy fields within the home range had a significant effect on the road-crossing rate (GLM analysis, z\u0026thinsp;=\u0026thinsp;2.51, p\u0026thinsp;=\u0026thinsp;0.01), whereas road length did not (z=-1.11, p\u0026thinsp;=\u0026thinsp;0.27). This suggests that road crossings are influenced less by the characteristics of the road itself and more by the surrounding habitat features, specifically paddy fields. In other words, turtles with more paddy fields near their habitat tended to cross adjacent roads more frequently.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables used in the generalized linear models (GLM) to analyze road crossing behavior of \u003cem\u003eMauremys reevesii\u003c/em\u003e. The table shows the monitoring period, number of road crossings, and total road length and paddy field area within each individual's home range.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransmitter ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelemetry period (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of road crossings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLength of road (m) within home range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eArea of paddy field (m\u003csup\u003e2\u003c/sup\u003e) within home range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e154.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21,189.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e477.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e28,429.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e712.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e30,362.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e83.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e747.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e43.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1,075.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e42,523.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e822.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e33,964.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e34.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSeasonal displacement patterns by habitat type\u003c/h3\u003e\n\u003cp\u003eWe conducted a GAM analysis on all 12 monitored turtles (6 reservoir, 6 river). The analysis of movement patterns by habitat type showed that reservoir turtles concentrated their displacement distances between 100\u0026ndash;400 m, following a relatively consistent and smooth non-linear curve, with peak movement concentrated between June and August. In contrast, river turtles exhibited a wider range of displacements (100\u0026ndash;900 m, max 1300 m) in a more erratic and irregular pattern, with a distinct movement peak observed in August (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The GAM summary statistics showed that, compared to the reference group (reservoir), river turtles had a mean displacement distance that was approximately 66.07 m shorter, a statistically significant difference (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, the dependent variable (displacement) represents the relative distance from the individual's origin point. Therefore, sections where the Y-axis value approaches zero indicate the individual has returned to its original location. This suggests homing behavior or settlement after a period of exploration. This pattern was clearly observed in both habitats and is interpreted as an ecological strategy to return to an initial location to prepare for hibernation. However, some individuals in the rivers were observed to be distributed hundreds of meters away from their origin points even after October, during the typical hibernation period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParametric coefficient estimates from the generalized additive model (GAM) analyzing the effect of habitat type on the displacement of \u003cem\u003eMauremys reevesii\u003c/em\u003e. The intercept represents the baseline for the reservoir.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e181.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e70.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRiver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-66.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-11.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA comparison of home range size by habitat type revealed no significant difference between reservoirs and rivers. The mean MCP home range size in this study was 41,568 ㎡ for reservoirs and 75,301 ㎡ for rivers. These values are much closer to the average MCP home range size reported for semi-aquatic turtles (12.08 ha) than for aquatic turtles (41.29 ha), suggesting \u003cem\u003eM. reevesii\u003c/em\u003e aligns numerically with semi-aquatic species [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, Song et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], studying \u003cem\u003eM. reevesii\u003c/em\u003e in the same reservoir as our study, reported KDE 95% home ranges between 28,003 ㎡ and 61,379 ㎡. This is considerably larger than our KDE 95% findings (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 7,645\u0026thinsp;\u0026plusmn;\u0026thinsp;4,482 ㎡) and reinforces the conclusion that home range sizes are not consistently different between lentic and lotic habitats. Regarding sex, males had significantly larger MCP and LoCoH home ranges than females. This difference is likely attributable to reproductive behaviors; males typically patrol larger areas to locate mates, resulting in larger home ranges [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis of daily movement distance showed that environmental factors (habitat type, season) had a greater influence than intrinsic factors (sex). Reservoir turtles moved more than river turtles, and spring movement was greater than autumn. To date, no studies have reported significant comparisons of daily movement distance by habitat type and sex for \u003cem\u003eM. reevesii\u003c/em\u003e or other semi-aquatic turtles. Related research on the Mexican mud turtle (\u003cem\u003eKinosternon integrum\u003c/em\u003e) found an average movement of 51.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50 m, with 87.3% of movements under 100 m; movement was greater in the wet season than the dry season, and longer in water than on land [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The Alligator snapping turtle (\u003cem\u003eMacrochelys temminckii\u003c/em\u003e) had an average daily movement of 82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5 m, with most movements under 100 m [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In our study, 46.9% (238 of 508 days total) of \u003cem\u003eM. reevesii\u003c/em\u003e movements were under 100 m, suggesting they are somewhat more mobile than \u003cem\u003eK. integrum\u003c/em\u003e or \u003cem\u003eM. temminckii\u003c/em\u003e. Further research comparing climatic patterns and aquatic vs. terrestrial movements is needed. Seasonally, movement increased in spring compared to autumn, consistent with findings from the National Park Research Institute [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This is likely due to rising water temperatures increasing metabolic activity and foraging efforts to replenish energy stores depleted during hibernation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiel terrestrial activity patterns, while differing in concentration (variance), showed a shared peak activity time around 14:00 h in both habitats, indicating a common diurnal activity pattern. This finding, coupled with Kim's [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] study showing that \"resting\" was the most frequent behavior for \u003cem\u003eM. reevesii\u003c/em\u003e (peaking at 12:00\u0026ndash;13:00 h), suggests this mid-day \"resting\" is not mere inactivity, but rather essential basking behavior for thermoregulation and metabolic activation [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis reveals a critical link between landscape-scale resource availability and movement patterns. We found that the road-crossing rate was not significantly influenced by the length of roads within the home range, but was significantly and positively correlated with the area of adjacent paddy fields. This suggests that road-crossing is not a random event but a consequence of resource-driven movement. Paddy fields function as important supplemental habitats, providing \u003cem\u003eM. reevesii\u003c/em\u003e with high food availability and refuge sites, which in turn encourages activity and expands their movement radius [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GAM analysis clearly demonstrates that \u003cem\u003eM. reevesii\u003c/em\u003e employs heterogeneous spatial patterns and seasonal rhythms contingent on habitat type. This plasticity manifests as two distinct movement strategies: reservoir turtles exhibited stable, non-linear displacement, while river turtles showed more erratic, linear, and long-distance movements. The significantly shorter mean displacement in rivers compared to reservoirs is likely attributable to the physical structure of the habitats. Unlike the non-linear, open space of a reservoir, a river is a linear ecosystem that constrains movement to upstream or downstream paths. This structural difference appears to be the key factor shaping the spatial ecology in rivers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The observation that some river individuals remained highly displaced from their origin points into the hibernation period (post-October) is interpreted as an optimal hibernaculum search strategy in response to the dynamic lotic environment. For semi-aquatic turtles, overwinter survival depends on securing microhabitats with stable temperatures and dissolved oxygen [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. While lentic environments are relatively uniform, allowing turtles to find suitable hibernacula within their summer range, lotic environments are characterized by fluctuating flow and water levels. In rivers, suitable sites (e.g., deep pools, silted areas) are likely patchy. Thus, river turtles face greater ecological pressure to leave their summer range to find these optimal sites, resulting in the late-season long-distance movements observed in this study [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This movement may also be linked to pre-hibernation mating (Oct-Nov), where males searching for females along the river corridor overlap their search for hibernacula [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Consequently, the late-season movement of the river population is a complex, active behavioral adaptation to solve the dual challenges of overwinter survival and reproduction.\u003c/p\u003e \u003cp\u003eOur analysis of seasonal displacement found that from June to September, turtles moved up to 400 m from their origin point in reservoirs and up to 1,300 m in rivers. These data provide the first quantitative metrics for defining conservation buffers in these distinct landscapes. Finally, with the exception of one individual that moved to a nearby reservoir and ceased transmitting, all turtles exhibited homing behavior. Previous research indicates turtle navigation operates via chemical signals, spatial memory, and sun compass navigation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This high site fidelity, even after extensive seasonal movements, underscores the paramount importance of their core territory [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study reveals that \u003cem\u003eM. reevesii\u003c/em\u003e exhibits significant behavioral plasticity, adopting divergent movement patterns in response to the fundamental structure of lentic versus lotic environments. As \u003cem\u003eM. reevesii\u003c/em\u003e also exhibits high site fidelity and homing behavior, the conservation of its specific core territory is paramount [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our findings provide the first evidence-based metrics for this conservation: we recommend conservation buffers should extend 400 m from reservoir core habitats and 1,300 m along river core habitats to encompass their maximum seasonal displacement range.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis identifies the key driver of human-wildlife conflict for this species. Given that the area of adjacent paddy fields, not road length, was the primary driver of road-crossing risk, mitigation strategies must evolve beyond simply managing roads and instead focus on landscape connectivity. We recommend prioritizing road sections that transect major agricultural areas for the installation of fences and eco-passages. Ultimately, effective conservation for \u003cem\u003eM. reevesii\u003c/em\u003e requires management that not only preserves the core spatial scale (home range) but also maintains the connectivity between aquatic habitats and adjacent terrestrial resources, such as paddy fields, managing them as a single, functional habitat unit.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict accordance with the Act on Conservation and Utilization of Natural Heritage. We obtained permits for the alteration of state-designated cultural heritage from the Korea Heritage Service for the periods of 2022–2023 (Permit Nos. 2022-0507, 2022-0959, and 2022-0510). All animal handling and experimental procedures complied with the Animal Protection Act and the Laboratory Animal Act of the Republic of Korea. The study protocol was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of the National Institute of Ecology (Approval Nos. NIEIACUC-R-2021-011 and NIEIACUC-R-2022-003; period: 2021–2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Institute of Ecology (NIE), funded by the Ministry of Climate, Energy and Environment (MCEE) of the Republic of Korea (Grant number: NIE-\u0026nbsp;B-2025-47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCDP conceived and designed the study, performed the fieldwork, analyzed the data, and wrote the original draft of the manuscript. JDY supervised the project, provided critical feedback, and reviewed and edited the manuscript. KK, JY and NY assisted with data collection and field surveys. CYC supervised the data analysis and critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are grateful to the staff of the National Institute of Ecology for their administrative support and assistance during the fieldwork. We also thank Moon Seong Heo and Eun-Mi Jo for their valuable help with data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStanford CB, Iverson JB, Rhodin AG, van Dijk PP, Mittermeier RA, Kuchling G, et al. Turtles and tortoises are in trouble. Curr Biol. 2020;30(12):R721\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInnes RJ, Babbitt KJ, Kanter JJ. 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Ecol Evol. 2021;11(11):5803\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Connectivity, Home range, Daily movement, Road ecology, Seasonal displacement pattern","lastPublishedDoi":"10.21203/rs.3.rs-8363107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8363107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Reeves' turtle (\u003cem\u003eMauremys reevesii\u003c/em\u003e), a globally endangered species listed on CITES Appendix Ⅲ, faces significant threats in South Korea, including habitat fragmentation and competition with invasive species. Effective conservation requires a deep understanding of its spatial ecology, particularly how it differs between distinct habitat types. We compared the spatial ecology of \u003cem\u003eM. reevesii\u003c/em\u003e in lentic (reservoir) and lotic (river) ecosystems to elucidate how their spatial behavior and habitat use are shaped by these distinct environments.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used GPS telemetry to track 12 adult turtles (6 in a reservoir, 6 in rivers) in South Korea from April to November 2022. We analyzed differences in home range (Minimum Convex Polygon [MCP], Kernel Density Estimation [KDE 95%], Local Convex Hull [LoCoH 99%]), daily movement distance (Generalized Linear Model [GLM] testing habitat, sex, and season), diel terrestrial activity (circular statistics), road-crossing behavior (GLM), and seasonal displacement (Generalized Additive Model [GAM]).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e(1) Home range size (LoCoH 99%) did not differ significantly between habitats, but males exhibited significantly larger home ranges than females (MCP: p\u0026thinsp;=\u0026thinsp;0.015). (2) Daily movement distance was significantly greater in the reservoir than in rivers (t=-5.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significantly greater in spring than in autumn (t=-2.67, p\u0026thinsp;=\u0026thinsp;0.008). (3) Diel terrestrial activity patterns differed significantly between habitats (Watson\u0026rsquo;s u\u003csup\u003e2\u003c/sup\u003e test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); while both groups peaked activity around 14:00 h, the activity of the river population was significantly more concentrated at this time. (4) The rate of road crossing was significantly positively correlated with the area of adjacent paddy fields, but not the length of roads within the home range. (5) GAM analysis revealed distinct movement patterns: reservoir turtles exhibited stable, non-linear displacement (100\u0026ndash;400 m), while river turtles showed erratic, linear movements (100\u0026ndash;900 m, max 1300 m) with a distinct peak in August.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eM. reevesii\u003c/em\u003e displays significant plasticity in spatial ecology, adopting divergent movement patterns in response to lentic versus lotic environments. We recommend habitat-specific conservation buffers (e.g., 400 m radius for reservoirs, 1300 m linear distance for rivers) and prioritizing road mitigation measures (e.g., eco-passages, fences) in areas adjacent to paddy fields. These findings underscore the necessity of connectivity within these defined core habitats for effective population management.\u003c/p\u003e","manuscriptTitle":"Spatial Ecology and Habitat Use of the Reeves’ Turtle (Mauremys reevesii) in Lentic and Lotic Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 19:34:43","doi":"10.21203/rs.3.rs-8363107/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T19:43:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T15:02:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93008298640955117449349162992010646228","date":"2026-01-28T18:35:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T22:48:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312057081578601931048310668366041082723","date":"2025-12-18T17:15:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39389804526644079233819179173616024068","date":"2025-12-18T14:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T00:04:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T23:54:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T04:23:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Biotelemetry","date":"2025-12-15T07:26:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-biotelemetry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abit","sideBox":"Learn more about [Animal Biotelemetry](http://animalbiotelemetry.biomedcentral.com)","snPcode":"40317","submissionUrl":"https://submission.nature.com/new-submission/40317/3","title":"Animal Biotelemetry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"340b6fb1-1ab5-4bf4-8d4b-9aa48ef9efdf","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T01:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 19:34:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8363107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8363107","identity":"rs-8363107","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}