Seasonal utilization distributions, site fidelity, and habitat use of the Black-tailed Gull (Larus crassirostris) in the Yellow Sea

Seasonal utilization distributions, site fidelity, and habitat use of the Black-tailed Gull (Larus crassirostris) in the Yellow Sea | 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 Article Seasonal utilization distributions, site fidelity, and habitat use of the Black-tailed Gull (Larus crassirostris) in the Yellow Sea Dae-Han Cho, Sang-Min Jung, Dal-Ho Kim, Si-Wan Lee, Ga-Young Kim, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8831504/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Movement shapes biodiversity patterns and ecosystem functioning, and recent advances in animal tracking have improved our ability to quantify space usage. Utilization distributions, site fidelity, and habitat use provide complementary perspectives on where animals concentrate their activity and how consistently they reuse key areas, informing conservation and habitat management. Black-tailed Gulls ( Larus crassirostris ) are abundant in the Yellow Sea, yet their fine-scale seasonal space use and fidelity patterns remain poorly quantified. Here, we estimated their seasonal home ranges and core areas and evaluated site fidelity, comparing them among seasons. From 2020 to 2024, we tracked 15 adult Black-tailed Gulls with GPS loggers on the west coast of the Korean Peninsula for 496–850 days per individual. Breeding, non-breeding, and wintering sites were delineated using hierarchical density-based spatial clustering of applications with noise (HDBSCAN), and seasonal utilization distributions were estimated using weighted autocorrelated kernel density estimation (wAKDE). Site fidelity was quantified as the breeding-site return rate, inter-annual home-range overlap, and habitat-use similarity, and habitat use was compared among annual stages. Home ranges and core area sizes differed among annual stages, with wintering ranges larger than breeding ranges. Ten individuals could be tracked across consecutive breeding seasons, all of which returned to the same breeding colonies, yielding a 100% breeding-site return rate. Inter-annual home-range overlap and habitat-use similarity were high and did not differ among annual stages. In contrast, habitat use differed among stages. Together, strong fidelity and seasonal habitat switching suggest reliance on repeatedly used key areas that may become maladaptive under the rapid environmental change present in the Yellow Sea, increasing the risk of fidelity-induced ecological traps. Conservation planning should prioritize repeatedly used key habitats and incorporate season-specific spatial and habitat requirements, including the protection of breeding colonies and the intertidal and marine habitats used during non-breeding and wintering periods. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology Black-tailed Gull Movement ecology Tracking Utilization distribution Site fidelity Habitat use Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Movement, defined as the change in an individual’s spatial position over time, is a key mechanism influencing biodiversity patterns and ecosystem processes 1 – 3 . With the advent of new tracking technologies and advanced data-processing techniques, detailed observations of an animal’s movements have become possible, leading to the establishment of an integrated theoretical framework for movement ecology 3 . Traditional research methods (e.g., re-capturing, banding, and flagging) have provided only limited spatial information, constraining species management and conservation decisions 4 . Recent advances in wildlife-tracking technologies have revolutionized our ability to obtain fine-scale spatiotemporal data across entire species ranges. In particular, GPS-based tracking devices have become widely used in movement ecology, as they enable the collection of continuous, high-precision, and remotely acquired location data, even from previously inaccessible regions, allowing the detailed monitoring of animal movements 5 . Such information greatly enhances our understanding of how individual animals move within their natural environments and species adjust their movements across seasons and life stages, crucial information for population management and habitat conservation 6 , 7 . Notably, home ranges and habitat selection are key aspects of animal movement processes in natural environments, reflecting how individuals utilize and respond to spatially distributed resources 8 , 9 . The home range of an animal was defined by Burt (1943) 10 as “the area traversed by the individual in its normal activities of food gathering, mating and caring for young.” This concept has been widely accepted and, in modern movement ecology, the notion of the utilization distribution (UD), which represents the probabilistic distribution of an animal’s space use within its geographic range, has emerged as a complement to the home range 11 , 12 . Estimating an animal's home range using UD, which indicates how frequently an individual uses a space, can provide valuable insights into the species' ecological niche 13 . Identifying high-use areas within an animal’s home range can reveal core habitats that are essential for survival and reproduction, which can help delineate priority areas for effective conservation and habitat management 6 , 14 , 15 . The tendency of individuals to revisit familiar areas is a widespread movement strategy observed across diverse taxa (e.g., mammals, birds, fish, and reptiles 13 , 16 – 18 ) and is commonly referred to as site fidelity 19 . The fitness benefits of site fidelity arise from an animal’s familiarity with the physical and social characteristics of its environment 19 – 22 . This familiarity can provide numerous advantages, including efficient resource acquisition 23 , 24 , lower movement costs 25 , 26 , and greater breeding success 27 . Moreover, fidelity to previously used sites can be particularly advantageous during critical life-history stages, such as during breeding and wintering, when site quality is directly linked to individual fitness 28 , 29 . Site fidelity can be particularly strong when an individual's accumulated knowledge of local environmental conditions provides greater benefits than the risks associated with exploring unfamiliar and potentially low-quality sites 19 , 24 , 30 , 31 . In such situations, the decision to revisit a site is shaped by an individual’s previous experiences at that location 32 , 33 . One mechanism that may explain this pattern is the “win–stay, lose–switch” rule 22 , in which animals continue returning to a familiar site until they experience a negative outcome, after which they shift to an alternative site. This behavioral rule is particularly evident in long-lived seabirds and provides strong empirical support for experience-based site fidelity. For example, Monteiro’s Storm-Petrels ( Hydrobates monteiroi ) that experience successful breeding typically reuse the same nest sites in subsequent years 33 , whereas Black-legged Kittiwakes ( Rissa tridactyla ) that experience breeding failure are more likely to relocate to alternative nest sites or even shift to entirely new breeding areas 34 . Such contrasting patterns offer compelling evidence that past reproductive outcomes shape decisions to revisit or abandon previously used sites. Because this rule represents a conditional response to environmental variation in site quality, the strength of its effect on site fidelity can vary in a scale-dependent manner, manifesting differently at different spatial (e.g., home range, habitat type, foraging site, or breeding site) and temporal (e.g., seasons or years) scales. The Black-tailed Gull ( Larus crassirostris ) is a medium-sized seabird widely distributed across the East Asian–Australasian Flyway (EAAF), with all life-history stages (e.g., breeding site, wintering site, non-breeding site) occurring in coastal or marine areas in Korea, China, Japan, and Russia 35 – 38 . The population within the EAAF is estimated at 1,100,000 individuals 39 , and the species is widely regarded as one of the most common seabirds observed along marine and coastal waters throughout the Yellow Sea 40 – 42 . Previous movement ecology studies on the Black-tailed Gull have largely focused on migration behavior 38 , 43 and flight characteristics 44 , 45 , and there is a need for finer-scale analyses of utilization distributions, site fidelity, and habitat use to better inform conservation management. The aim of this study is to estimate the seasonal home range and core area of Black-tailed Gulls using the Yellow Sea. We further quantify intra-annual home range overlap, habitat use similarity, and breeding-site return to characterize their site fidelity patterns. Finally, we compare habitat use among seasons. These findings will provide essential baseline information for the effective conservation and management of the Black-tailed Gull. Results HDBSCAN identified a total of 102 distinct residency clusters across the 15 tracked Black-tailed Gulls, with clusters per individual ranging from 4 to 12. These clusters were assigned to three annual stages: 23 to the breeding stage, 26 to the wintering stage, and 53 to the non-breeding stage. Across all clusters, individuals remained for an average of 100 days. Mean residency duration differed slightly among annual stages, averaging 98 ± 66 days during the breeding stage, 82 ± 52 days during the wintering stage, and 109 ± 86 days during stage non-breeding stage. The mean home range, based on the 95% AKDE, and the mean core area, based on the 50% AKDE, were 583.28 ± 904.94 km² and 77.22 ± 140.96 km² during breeding. During wintering, the mean home range was 3,807.02 ± 9,293.31 km², and the mean core area was 888.29 ± 2,323.64 km². During non-breeding, the mean home range was 1,347.67 ± 4,249.51 km², and the mean core area was 197.55 ± 612.04 km². The annual stages differed significantly in both home range size (χ² = 6.03, df = 2, p = 0.04; Table 2 ) and core area (χ² = 15.98, df = 2, p < 0.001; Table 2 ). Tukey post hoc tests indicated that home ranges and core areas during wintering were significantly larger than those during breeding (Fig. 4 A and Fig. 4 B). On the other hand, body mass had no effect on home range (χ² = 0.08, df = 1, p = 0.78; Table 2 ), and core area (χ² = 0.19, df = 1, p = 0.66; Table 2 ). The breeding site return rate was observable in 10 of the 15 individuals, all of which returned to the same breeding site in two consecutive years, resulting in a 100% return rate among all individuals monitored across two years. These individuals bred on Napdaekido Island (n = 1; 35° 15′ 54″N, 126° 13′ 19″E), Yuksando Island (n = 2; 35° 19′ 20″N, 126° 16′ 34″E), Miyeodo Island (n = 3; 37° 38′ 16″N, 125° 40′ 50″E), Bulmugido Island (n = 3; 34° 45′ 32″N, 126° 13′ 25″E), and Gujido Island (n = 1; 35° 32′ 39″N, 126° 26′ 31″E). Mean home range overlap and habitat use similarity were both 0.80 ± 0.15 during the breeding stage; 0.75 ± 0.23 and 0.85 ± 0.14, respectively, during non-breeding; and 0.59 ± 0.27 and 0.89 ± 0.09, respectively, during wintering. However, the annual stages did not differ significantly in either home range overlap (χ² = 3.46, df = 2, p = 0.18; Table 2 and Fig. 4 C) or habitat-use similarity (χ² = 2.37, df = 2, p = 0.31; Table 2 and Fig. 4 D), and body mass had no effect on habitat use similarity either (χ² = 0.31, df = 1, p = 0.58; Table 2 ). Habitat-type use differed significantly among annual stages (χ² = 15,404, df = 14, p < 0.001; Fig. 5 ). Post hoc pairwise tests indicated that all stage pairs differed significantly in habitat-type composition (p < 0.001 for breeding vs non-breeding, p < 0.001 for breeding vs wintering, and p < 0.001 for non-breeding vs wintering). During breeding, grassland and tree cover were used at substantially higher-than-expected frequencies (standardized residuals of + 72.16 and + 57.09, respectively; Fig. 5 ), whereas intertidal areas were strongly overrepresented during non-breeding (standardized residual of + 61.23; Fig. 5 ), as were marine waters during wintering (standardized residual of + 68.60; Fig. 5 ). Discussion Black-tailed Gulls exhibited clear differences in home ranges and core area sizes among annual stages, with space use being significantly smaller during breeding than during wintering. This pattern is consistent with those seen in previous studies. According to Park et al . (2024) 45 , Black-tailed Gulls breeding on five uninhabited islands in the Yellow Sea had substantially smaller home ranges during the breeding period (mean KDE 95% = 1,111.47 km²) than during the post-breeding period (mean KDE 95% = 4,940.47 km²), indicating an expansion of spatial use after the breeding season. Similarly, 46 reported that core areas (KDE 50%) were larger during the wintering period (December–February) than during the breeding season (March–July), during both daytime and nighttime. However, because different home-range estimation methods were used, KDE in previous studies versus AKDE in the present study, direct quantitative comparisons of absolute area sizes should be interpreted with caution 47 , 48 . In our study, home range and core area sizes tended to be larger during the non-breeding stage than during the breeding stage, though this contrast was not statistically significant. Taken together, these results indicate that Black-tailed Gulls progressively broaden their spatial use from the breeding to non-breeding to wintering stages. Such seasonal variation in space use likely reflects changes in movement constraints and prey availability, as well as environmental seasonality. According to the central place foraging theory, during incubation and chick-rearing, birds act as central-place foragers that must regularly return to the nest, limiting how far they can travel while provisioning themselves or their offspring 49 . Thus, their feeding areas and foraging decisions are constrained by how far they can fly from a colony before needing to return to incubate eggs or feed offspring 50 . In fact, Lesser Black-backed Gulls (Larus fuscus ) reduced their foraging range during the core breeding period, which covers incubation and chick-rearing, when compared with the pre- and post-breeding periods 51 . In addition, Black-tailed Gulls breeding on islands located in the Yellow Sea exhibited significantly smaller foraging ranges than those breeding on the east sea 37 . These findings demonstrate that colony-breeding gulls conform strongly to the predictions of the central place foraging theory. In our study, all breeding sites (n = 23) were located on islands within the Yellow Sea, where individuals were constrained by incubation and chick-rearing duties. Consequently, the relatively small home range and core area sizes observed at breeding sites are consistent with the movement limitations imposed by central place foraging during the breeding season. Conversely, spatial constraints associated with central-place foraging are mitigated at non-breeding and wintering sites, allowing Black-tailed Gulls to use a broader spatial extent. During winter, Black-tailed Gulls spend most of their time at sea and are known to forage by following fishing vessels, exploiting discards from nets, or capturing small fish that surface due to vessel-induced disturbance 38 , 52 . Although the precise cause of this wintering behavior remains unclear, coastal intertidal and inland habitats are likely less profitable during winter due to waters freezing. This appears to prompt gulls to shift toward marine environments where food availability is more predictable and accessible. Indeed, many gulls are known to forage predominantly in ice-free marine habitats during winter 53 – 55 , and such behavior typically involves exploring wider areas, resulting in larger home ranges 56 , 57 . Consistent with these patterns, habitat type analysis revealed that Black-tailed Gulls primarily utilize marine waters at their wintering sites (Fig. 5 ). Collectively, these behavioral and environmental factors likely explain the substantially larger home ranges and core areas observed at wintering sites in this study. Home ranges during the non-breeding stage tended to be larger than breeding stage, although the difference was not statistically significant. Black-tailed Gulls often remain in waters adjacent to breeding areas or along coastal regions during pre- and post-breeding periods to recover their body condition and prepare for the subsequent movements 36 . Habitat type analyses further indicated that Black-tailed Gulls predominantly used intertidal habitat at their non-breeding site. Intertidal environments in the Yellow Sea are characterized by strong tidal dynamics 58 , providing extensive foraging opportunities during low tide and restricted opportunities during high tide, when gulls frequently shift to alternative resting areas 59 , 60 . Such tidal-driven shifts in habitat availability may contribute to increased spatial use and influence both the home range and core area size during the non-breeding stage. Consequently, spatial use during the non-breeding period may be moderately expanded relative to the breeding season but does not reach the broad extent observed during winter. Among the 15 tracked Black-tailed Gulls, all 10 that were tracked for two breeding seasons used the same breeding site in two consecutive years, resulting in a 100% breeding site return rate. Of the remaining six individuals, three (G1, G9, G14) were tracked for only a single breeding season, while the other two (G8, G13) did not use a breeding site despite being adults, suggesting they were in a sabbatical period. According to Kazama et al . (2013) 36 , some Black-tailed Gulls skip breeding in certain years, i.e., sabbatical periods, presumably to recover their body condition, and therefore do not return to breeding colonies. Breeding site reuse behavior in seabirds is known to facilitate pair reunion and enhance reproductive success through familiarity with the breeding environment 61 , 62 . In addition, fidelity patterns in gulls are strongly associated with the physical characteristics and long-term stability of breeding sites. Long-established and environmentally stable habitats, such as rocky cliffs and offshore islands, tend to exhibit high return rates, while habitats subject to substantial environmental fluctuation, including sandy substrates or newly formed colonies, typically engender lower fidelity 63 , 64 . In this study, Black-tailed Gulls bred on Napdaekido, Yuksando, Miyeodo, Bulmugido, and Gujido Islands, all of which are uninhabited offshore islands where rocky cliffs dominate the outer margins and vegetated habitats occur inland. The cliff-dominated outer margins restrict human access and reduce disturbance, likely contributing to the observed high breeding site fidelity. Previous research has also documented high fidelity (94–100%) in environmentally similar breeding sites and on Bulmugido Island specifically 65 . Taken together, the results suggest that Black-tailed Gulls exhibit strong fidelity to previously used breeding sites, except during the sabbatical period, highlighting the ecological importance of stable and undisturbed breeding habitats for this species. Black-tailed Gulls showed consistently high levels of both home range overlap and habitat use similarity, with no significant differences among annual stages, indicating strong spatial and habitat use fidelity throughout the annual cycle. In gulls, returning to previously used sites for breeding, wintering, and energy replenishment during similar periods is a well-documented strategy 38 , 65 – 67 . Species exhibiting such site fidelity typically also show high similarity in space use and habitat composition across years. For example, Clark et al. (2016) 67 reported relatively high inter-annual home range overlap during winter among Ring-billed Gulls ( Larus delawarensis ) and Herring Gulls (Larus argentatus ), ranging from 0.31–0.78 and 0.38–0.79, respectively. Additionally, Fernandes et al. (2025) 68 reported high habitat-use similarity in Yellow-legged Gulls ( Larus michahellis ), with values ranging from 0.53–0.87 at breeding sites and 0.53–0.71 at wintering sites. Collectively, these findings support a general tendency for gulls to show marked fidelity in both space and habitat use. Nonetheless, the degree and intensity of fidelity may vary across geographical locations, as an individual’s movement is shaped by the complex interactions of various extrinsic factors, including resource distribution, disturbance, and competition 69 , 70 . Many seabirds show route and site fidelity but may adjust routes or switch areas when conditions deteriorate 32 . Such switching is consistent with a “win–stay, lose–switch” rule, whereby individuals are less likely to revisit a previously used area after unfavorable outcomes 22 . In the current study, Black-tailed Gulls in the Yellow Sea showed consistently high home-range overlap and habitat-use similarity, suggesting that the key areas used during the study period experienced relatively low external disturbance and/or provided relatively predictable conditions that maintained the benefits of repeated use. Black-tailed Gulls exhibited clear differences in habitat-type use across annual stages, with the breeding stage characterized by a high use of tree cover and grassland. This pattern reflects the physical structure of the uninhabited islands used for breeding, where the interior areas consist mainly of grassland, open soil, shrubs, and scattered trees. According to the WorldCover 2 classifications, tree cover includes areas with at least 10% tree canopy cover, and other land cover types can be present below the canopy 71 . In our field observations, Black-tailed Gulls breeding on uninhabited islands rarely select areas dominated by dense tree cover. Thus, although tree cover appeared dominant at breeding sites, this likely represents mixed habitats where grasses, open soil, and low vegetation are interspersed with sparse trees. Previous studies have shown that Black-tailed Gulls preferentially breed on cliff habitats rather than grass-dominated areas, with higher breeding success observed on cliffs 72 . However, since WorldCover 2 data is derived from satellite imagery, steep cliff environments on small offshore islands may be difficult to classify accurately. Consequently, the use of cliff habitats (considered the bare /sparse vegetation type) may be underestimated in the habitat-use analysis, while tree cover and grassland usage may be overrepresented. This limitation should be considered when interpreting habitat use patterns on uninhabited islands. During the non-breeding stage, Black-tailed Gulls predominantly used intertidal habitats. The pre- and post-breeding periods are energetically demanding phases during which individuals remain near breeding areas to recover their body condition and prepare for subsequent movements 54 , 73 . Larus gulls, including the Black-tailed Gull, are known to commonly forage in intertidal habitats during the non-breeding stage 38 , 74 . Intertidal habitats provide abundant and predictable food resources, such as benthic invertebrates and fish, which contribute to improved body conditions and enhanced breeding success 75 . In contrast, we found that Black-tailed Gulls primarily used the marine water type during the wintering stage. Similar winter shifts toward offshore habitats have been reported in several gull species, suggesting that increased reliance on marine resources during winter is a common strategy among Larus species 53 , 76 , 77 . During winter, inland and nearshore coastal habitats are likely less accessible due to ice cover, which can restrict access to prey. In the Yellow Sea, where wild fishery activity is intensive, Black-tailed Gulls frequently associate with fishing vessels. Such vessel-associated foraging provides predictable and easily accessible food resources, reducing search effort and increasing foraging efficiency compared to non-vessel-associated prey hunting 77 – 79 . Black-tailed Gulls inhabiting the Yellow Sea exhibited marked differences in spatial extent and habitat use across annual stages. Nevertheless, they maintained consistently high site fidelity throughout the annual cycle. In general, site fidelity is strengthened when environmental conditions are temporally and spatially predictable, allowing individuals to repeatedly revisit high-quality sites based on prior experience 80 . Such fidelity can confer fitness advantages under stable or predictable environmental conditions, promoting individual survival and population growth 19 . Despite these potential benefits, strong site fidelity can also lead to maladaptive outcomes when environmental conditions change rapidly 81 . The Yellow Sea, which is extensively used by Black-tailed Gulls as well as many other seabirds, is currently undergoing rapid environmental change driven by climate change, offshore wind-farm development, and land reclamation 82 – 84 . Under such conditions, the continued selection of historically used sites, movement routes, or areas whose suitability has declined may expose individuals to fidelity-induced ecological traps 81 . Fidelity-induced ecological traps have now been empirically demonstrated across a wide range of taxa 85 , 86 and can ultimately reduce population resilience and long-term viability 87 . Accordingly, conservation strategies for Black-tailed Gulls and other seabirds in marine and coastal habitats should prioritize the identification of repeatedly used key habitats and ensure the long-term environmental stability and appropriate management of these areas. Methods From 2020 to 2024, a total of 15 Black-tailed Gulls were captured along the coastal regions of the Korean Peninsula (Table 1 ). All captures targeted adult individuals and were achieved using a cannon net. Birds were placed individually into bags and immediately transported to the closest handling station. Following the methods of Kenward (2000) 88 , each bird was weighed, and to minimize potential negative effects, a solar-powered GPS logger (WT-300, Koeco Inc., Daejeon, Korea) weighing less than 5% of the bird’s body mass was mounted as a backpack using a Teflon ribbon harness. All birds were released within 30 minutes of capture and behaved normally after release. The procedures for capturing, handling, and tagging the birds complied with applicable national laws and relevant guidelines and regulations and were approved by the Institutional Animal Care and Use Committee (IACUC). Animal ethics training was completed prior to fieldwork (KoEco certificate nos. 2022-001, 2023-001, and 2024-001). For all 15 individuals, GPS fix intervals were set at 2 h. Individuals were tracked for a minimum of 496 days and a maximum of 850 days. We removed GPS position data with a dilution of precision (DOP) value > 5 to improve the accuracy of location data 89 , 90 . The dataset used in this study included only latitude, longitude coordinates and time. Table 1 Detailed information on the Black-tailed Gull monitored from 2020 to 2024 and its associated GPS tracking data. Tracker ID Capture site Body mass (g) First tracking date Last tracking date Duration (days) Fix interval G1 Jaeun Island (34° 51′ 36″N, 126° 1′ 46″E) 430 2022-06-30 2024-03-03 612 2 h G2 Songi Island (35° 16′ 25″N, 126° 9′ 2″E) 530 2021-10-02 2023-06-27 633 2 h G3 Bigeum Island (34° 43′ 11″N, 125° 55′ 36″E) 460 2022-03-08 2023-05-19 437 2 h G4 Gyeokpo port (35° 37′ 18″N, 126° 27′ 40″E) 520 2022-07-11 2024-11-07 850 2 h G5 Gusipo beach (35° 26′ 49″N, 126° 26′ 6″E) 450 2022-07-12 2024-09-11 792 2 h G6 Dongho beach (35° 30′ 43″N, 126° 28′ 42″E) 520 2022-07-12 2024-08-19 769 2 h G7 Dongho beach (35° 30′ 43″N, 126° 28′ 42″E) 460 2022-07-12 2024-05-15 673 2 h G8 Dongho beach (35° 30′ 43″N, 126° 28′ 42″E) 460 2022-07-12 2024-01-22 559 2 h G9 Wi island (35° 34′ 35″N, 126° 15′ 59″E) 550 2022-07-19 2024-03-26 616 2 h G10 Aphae Island (34° 50′ 52″N, 126° 15′ 59″E) 550 2023-04-22 2024-09-03 500 2 h G11 Aphae Island (34° 50′ 54″N, 126° 14′ 8″E) 495 2023-04-26 2024-10-12 535 2 h G12 Aphae Island (34° 50′ 54″N, 126° 14′ 8″E) 550 2023-04-26 2024-12-27 611 2 h G13 Anma Island (35° 20′ 55″N, 126° 1′ 13″E) 450 2022-08-02 2023-12-11 496 2 h G14 Anma Island (35° 20′ 55″N, 126° 1′ 13″E) 580 2022-08-02 2024-03-16 592 2 h G15 Chunjangdae Beach (36° 9′ 31″N, 126° 31′ 6″E) 650 2020-09-16 2022-04-20 581 2 h We applied the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to the GPS tracking data to identify breeding, wintering, and non-breeding sites. The HDBSCAN algorithm forms clusters based on data density without requiring a predefined number of clusters and can effectively filter out low-density regions, making it suitable for analyzing ecological location data like that of birds in migration 91 . Nevertheless, the conventional HDBSCAN algorithm measures the proximity of birds mainly by the Euclidean distance between two points and does not take time information into account 92 . To overcome the limitations of clustering based solely on spatial proximity, we included temporal information as an additional clustering feature and applied HDBSCAN to a three-dimensional dataset (longitude, latitude, and time). This approach enabled the detection of clusters that reflect not only spatial aggregation but also patterns of prolonged residency within a given period, allowing the effective identification of ecologically significant habitats. In addition, HDBSCAN requires setting a minimum cluster size (p), a parameter that defines a lower bound on the number of data points required to define a cluster 93 , 94 . Based on the methods of Xia et al . (2023) 38 , clusters were classified as habitat when an individual remained in the same location for at least eight consecutive days (192 h). Given that our GPS tracking data were collected at 2-hour intervals, we set the minimum cluster size to 96. We identified residency sites using temporal and spatial criteria and then assigned these sites to three annual stages (breeding, non-breeding, and wintering). Breeding sites were defined as locations where an individual remained for at least one month during the breeding season. Wintering sites were defined as the location with the longest residency duration among the lowest recorded latitudes during the winter period. Non-breeding sites were defined as all other clusters not classified as breeding or wintering sites. All clustering analyses were performed using the “hdbscan” function of the dbscan package 95 . When estimating home ranges and core areas using tracking devices, it is essential to account for autocorrelation in the data 48 . Conventional kernel density estimation (KDE) does not incorporate the autocorrelation structure of tracking data and can produce biased estimates, often underestimating the sizes of home ranges and core areas 96 , 97 . Autocorrelated kernel density estimation (AKDE) is particularly appropriate for movement behavior studies, as it explicitly accounts for autocorrelation in tracking data, minimizing biases associated with inconsistent tracking time lags 48 , 96 . In this study, we specifically applied the weighted AKDE (wAKDE) to address irregular sampling schedules and missing data, as prolonged periods of poor weather conditions reduced solar charging efficiency and occasionally prevented data collection for several consecutive days. Missing data equate to a loss of information, and these errors can propagate into biased results 98 . By compensating for temporal sampling bias—upweighting observations from under-sampled periods and downweighting those from over-sampled periods—wAKDE mitigates this issue, producing utilization distributions that more accurately represent the animals’ space use 99 . Home ranges and core areas were calculated separately for each year within each annual stage (i.e., breeding, wintering, and non-breeding). The home range was defined as the 95% contour of the utilization distribution and the core area as the 50% contour 12 , 100 . Following the method described in Calabrese et al . (2016) 101 , the autocorrelation structure in the tracking data was estimated by fitting multiple continuous-time movement models, selecting the one with the lowest Akaike information criterion (AIC) value as the most appropriate model 48 , 101 . The Ornstein–Uhlenbeck (OU) process is characterized by correlated positions but not correlated velocities 102 , whereas the Ornstein–Uhlenbeck Foraging (OUF) process accounts for correlation in both position and velocity 103 . In contrast, the independent and identically distributed (IID) process assumes no correlation in either positions or velocities. Movement models, including the OU, OUF, and IID processes, were fitted to the tracking data using the “ctmm.fit” function of the ctmm package within the wAKDE calculations. Home range and core area estimations were performed using the “akde” function of the ctmm package 101 . The breeding site return rate was calculated as the proportion of individuals that used the same breeding site in their first and second year of monitoring 65 . We assessed annual site fidelity for each annual stage by calculating the intra-individual 95% wAKDE home range overlap using Bhattacharyya’s affinity overlap 104 . Conventional home range overlap methods, which are based solely on the area of overlap, do not incorporate UDs and may therefore overestimate spatial overlap. In contrast, Bhattacharyya’s affinity quantifies the similarity between the UDs of two home ranges, yielding values between zero (no overlap) and 1 (identical UDs), making it better for evaluating overlap 105 . All analyses of home range overlap were conducted only when the home ranges from the first and second years overlapped within the same annual stage. All home range overlap analyses were performed using the “overlap” function of the ctmm package 101 . Habitat use was quantified for each habitat type based on GPS tracking locations within the 95% home range. For each location, a land cover type was assigned based on the WorldCover Version 2 (2021) land cover database (10 m resolution) 71 . Our tracking data indicated that Black-tailed Gull individuals utilized coastal regions of the Korean Peninsula, southeastern China, northern Japan, and the Sakhalin region of Russia. Based on these locations, the following land cover types were identified within the gulls’ home ranges: tree cover, grassland, cropland, built-up, bare/sparse vegetation, and permanent water bodies. Considering the significant ecological differences among aquatic environments and their importance for Black-tailed Gull habitat use, the “permanent water bodies” type was further subdivided into freshwater, marine water, and intertidal cover types. Freshwater comprised inland water bodies located landward of the coastline, while marine water included coastal seas beyond the shoreline. The intertidal cover type was identified within marine waters based on the 2019 dataset of the Global Intertidal Map v1.2 (30 m resolution) 106 . Habitat use similarity was assessed only when the home ranges from the first and second years overlapped within the same habitat type, remaining consistent with the criteria applied in the overlap analysis. We assessed habitat use similarity for each annual stage using the Bray–Curtis similarity index 107 . Following 108 , we calculated the index as $$\:{\varvec{S}\varvec{i}\varvec{m}\varvec{i}\varvec{l}\varvec{a}\varvec{r}\varvec{i}\varvec{t}\varvec{y}}_{\varvec{h},1-2}\:=\:1\:-\:\frac{{\sum\:}_{\varvec{i}}|{\varvec{p}}_{\varvec{h},\mathbf{i},\:\mathbf{f}\mathbf{i}\mathbf{r}\mathbf{s}\mathbf{t}\:\mathbf{y}\mathbf{e}\mathbf{a}\mathbf{r}}\:-\:{\varvec{p}}_{\varvec{h},\varvec{i},\:\varvec{s}\varvec{e}\varvec{c}\varvec{o}\varvec{n}\varvec{d}\:\varvec{y}\varvec{e}\varvec{a}\varvec{r}}|}{2}$$ where \(\:{\varvec{p}}_{\varvec{h},\varvec{i}}\) represents the fraction of GPS locations within land cover type \(\:\varvec{i}\) (tree cover, grassland, cropland, built-up areas, bare/sparse vegetation, freshwater, marine water, and intertidal) for a given habitat type h. A value of 1 indicated identical habitat use, whereas a value of zero indicated complete segregation. Habitat similarity analyses were performed using the “vegdist” function of the vegan package 109 . We examined differences in home ranges, core areas, overlap, and habitat use similarity among the annual stages. The distribution of each response variable was assessed using the “chooseDist” function of the gamlss package 110 , which indicated that home range and core area followed a gamma distribution, whereas overlap and habitat use similarity followed a beta distribution. Accordingly, we fitted generalized linear mixed models (GLMMs) with gamma distributions and log link functions when home range and core area were the response variables, and GLMMs with beta distributions and logit link functions for home range overlap and habitat use using the “glmmTMB” function of the glmmTMB package 111 . For the home range and core area analyses, annual stage, body mass (g), and year were included as independent variables. For the overlap and habitat use similarity analyses, annual stage and body mass were included as independent variables. In all analyses, the Tracker ID (each GPS tracking device was assigned a unique ID) was included as a random effect in the model to control for individual variation. We used the “dredge” function of the Mumin package to select the best models based on the AIC 112 , and the model with the least number of independent variables was selected as the best model when the ΔAIC value between the models with the lowest and second-lowest AIC values was less than two. Following each analysis, we used the “Anova” function of the car package with type Ⅱ Wald chi-square tests used to identify significant differences in the response variables among the annual stages 113 . When significant effects were detected, we used the “emmeans” function in the emmeans package to compute estimated marginal means for each level of annual stage 114 , followed by pairwise comparisons using Tukey’s post hoc test with adjusted p-values. We performed a chi-square test of independence to assess whether habitat type composition differed among annual stages, and when significant differences were detected, we conducted pairwise post-hoc comparisons using the “pairwiseNominalIndependence” function in the rcompanion package to identify which annual stages differed in habitat type use 115 . All analyses were conducted in R software v4.4.2 Declarations Acknowledgements We thank the research team at the Korea Environment & Ecology Institute for their assistance with the capture of Black-tailed Gulls. This research was supported by the Animal and Plant Quarantine Agency (APQA) through the “Risk Assessment of Highly Pathogenic Avian Influenza (HPAI) Outbreaks Caused by Wild Birds (Migratory Birds)” project. Author contributions Dae-Han Cho : Writing – Original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Sang-Min Jung : Validation, Investigation. Dal-Ho Kim : Methodology, Data curation. Si-Wan Lee : Funding acquisition, Project administration. Ga-Young Kim : Formal analysis, Visualization. Kee-Sung Hong : Project administration. Chang-Ho Cho : Project administration. Ha-Cheol Sung : Writing – Review & Editing, Supervision. Funding The authors declare that no external funding was received for this study. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no known competing financial interests of personal relationships that could have appeared to influence the work reported in this paper. References Fischer, C. Challenges and opportunities when studying movement ecology in science and practical conservation. Basic Appl. Ecol. 81 , 59–65 (2024). Jeltsch, F. et al. 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Cran Repos . 20 , 1–71 (2017). Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Editor invited by journal 04 Mar, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 18 Feb, 2026 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. 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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-8831504","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604656261,"identity":"280f90ea-1b28-4f4d-b62a-dfd0cf2d788d","order_by":0,"name":"Dae-Han Cho","email":"","orcid":"","institution":"Chonnam National University","correspondingAuthor":false,"prefix":"","firstName":"Dae-Han","middleName":"","lastName":"Cho","suffix":""},{"id":604656262,"identity":"ca3cbe2b-5898-4508-9f1f-11df602b5dce","order_by":1,"name":"Sang-Min Jung","email":"","orcid":"","institution":"Korea Institute of Environmental Ecology","correspondingAuthor":false,"prefix":"","firstName":"Sang-Min","middleName":"","lastName":"Jung","suffix":""},{"id":604656264,"identity":"bef39f4d-c2a3-446a-a056-009f82066420","order_by":2,"name":"Dal-Ho Kim","email":"","orcid":"","institution":"Korea Institute of Environmental Ecology","correspondingAuthor":false,"prefix":"","firstName":"Dal-Ho","middleName":"","lastName":"Kim","suffix":""},{"id":604656266,"identity":"3fc933ac-10f2-41e3-975a-8d8bb852fc0f","order_by":3,"name":"Si-Wan Lee","email":"","orcid":"","institution":"Korea Institute of Environmental Ecology","correspondingAuthor":false,"prefix":"","firstName":"Si-Wan","middleName":"","lastName":"Lee","suffix":""},{"id":604656271,"identity":"1d8a3e86-3256-4518-909b-42c0579d3a2c","order_by":4,"name":"Ga-Young Kim","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Ga-Young","middleName":"","lastName":"Kim","suffix":""},{"id":604656272,"identity":"e98e79d3-1f78-4685-a104-9bd698c68cec","order_by":5,"name":"Kee-Sung Hong","email":"","orcid":"","institution":"Animal and Plant Quarantine Agency","correspondingAuthor":false,"prefix":"","firstName":"Kee-Sung","middleName":"","lastName":"Hong","suffix":""},{"id":604656274,"identity":"2d124ba4-3e84-4fc6-9b30-e8e39c414c20","order_by":6,"name":"Chang-Ho Cho","email":"","orcid":"","institution":"Animal and Plant Quarantine Agency","correspondingAuthor":false,"prefix":"","firstName":"Chang-Ho","middleName":"","lastName":"Cho","suffix":""},{"id":604656275,"identity":"243344d7-4d6a-4f97-96c2-8a04f5132139","order_by":7,"name":"Ha-Cheol Sung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3QMQrCMBSA4QdCXZ50fUWwV4h0LXqVF4S4eADBwYpQFw8g6GEKAV10r+AQEZxcvIDYoqBTo5tgfsgS8pGXALhcvxgVywwJ/XrCjx38hPA2bgXz7BsiUxWJ/CmsJFxOj4Y9LZO9OZ8QOiHgzlQScVhHglHLyZL7EUKvnTRmoprQwCMmLadNVk2EGoPvWQZblERomQZZScZ2AnlJWEVIUBLN0EirhciLz+IsbhGyClZi005xbRusdzTXG2F3s1V0GY5CH5VlsFfIxaUAtpe8V8++OOxyuVz/1B0uwD11/7u9DwAAAABJRU5ErkJggg==","orcid":"","institution":"Chonnam National University","correspondingAuthor":true,"prefix":"","firstName":"Ha-Cheol","middleName":"","lastName":"Sung","suffix":""}],"badges":[],"createdAt":"2026-02-09 14:24:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8831504/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8831504/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104526133,"identity":"5bb28fe3-8d62-4585-afbe-52f49d987af5","added_by":"auto","created_at":"2026-03-12 22:47:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":868902,"visible":true,"origin":"","legend":"\u003cp\u003eMigratory and movement trajectories of the 15 GPS-tracked Black-tailed Gulls (G1–G15) monitored between 2018 and 2024. Each colored line represents the full movement path of an individual, with colors corresponding to Tracker IDs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/1eb0757ab608b74c31769293.png"},{"id":104526130,"identity":"fca8a72f-c334-4ff3-9593-5d4b77f333b2","added_by":"auto","created_at":"2026-03-12 22:47:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":907476,"visible":true,"origin":"","legend":"\u003cp\u003eIndividual movement trajectories and residency site clusters identified by HDBSCAN for the 15 GPS-tracked Black-tailed Gulls (G1–G15). The white lines indicate the tracking path for each individual. Colored points (Cluster IDs 1–12) represent residency site clusters classified as breeding, non-breeding, or wintering sites following criteria based on time of year and location. Cluster 0 (White points) represents locations that did not meet the required temporal or spatial thresholds for defining residency sites and was therefore excluded from residency site classification.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/d0b17ef3756935c11dfb4937.png"},{"id":104526132,"identity":"72942d99-5838-4192-83d8-978e10e59d10","added_by":"auto","created_at":"2026-03-12 22:47:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74149012,"visible":true,"origin":"","legend":"\u003cp\u003eExample analysis of annual home-range overlap and habitat-use similarity for Black-tailed Gull G7. Green points and polygons represent GPS locations and the 95% AKDE home range from the first year, whereas red points and polygons indicate those of the second year. Panels on the left illustrate home-range overlap between consecutive years for the breeding (top), non-breeding (middle), and wintering (bottom) stages. Panels on the right show the corresponding habitat type distributions used to calculate habitat use similarity. For individual G7, home range overlap values were 0.82 (breeding), 0.55 (non-breeding), and 0.67 (wintering), and habitat use similarity values were 0.58, 0.81, and 0.84, respectively.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/d9a5d2c34f73e293a4f773fb.png"},{"id":104526131,"identity":"98ed66ac-11f4-4b9f-846e-cefc06aae220","added_by":"auto","created_at":"2026-03-12 22:47:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4671085,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in (A) home range sizes (95% AKDE), (B) core area sizes (50% AKDE), (C) annual home range overlaps, and (D) habitat use similarities among the three annual stages (breeding, non-breeding, wintering). Asterisks indicate significant differences (*, p \u0026lt; 0.05, and ***, p \u0026lt; 0.001) between annual stages according to Tukey’s post hoc test.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/0138daca94507f54e892dd84.png"},{"id":104526129,"identity":"ade07707-a7bc-4a18-8eec-364dd2118032","added_by":"auto","created_at":"2026-03-12 22:47:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1514640,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the standardized residuals from the Chi-squared test based on the cross table between annual stages and habitat types. The Pearson residuals of habitat use within each annual stage are shown within the cells, with blue cells indicating positive residuals (observed \u0026gt; expected), near-white cells indicating near-zero residuals (observed ≒ expected), and red cells indicating negative residuals (observed \u0026lt; expected). The intensity of the color increases with the absolute value of the residual, reflecting the contribution of each cell to the overall Chi-squared statistic.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/06556be59bbfc5ea3e2885d4.png"},{"id":104786098,"identity":"e18f438b-e8ed-4da1-8eed-8fefbeab4edc","added_by":"auto","created_at":"2026-03-17 08:15:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":68089511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/1935c319-04e1-4c5f-9632-c6f92c2ff1d8.pdf"},{"id":104781256,"identity":"1a8f55ff-d3c9-444a-9a39-2195ff43cb6f","added_by":"auto","created_at":"2026-03-17 07:55:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20960,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8831504/v1/13173b0466986d506479651a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal utilization distributions, site fidelity, and habitat use of the Black-tailed Gull (Larus crassirostris) in the Yellow Sea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMovement, defined as the change in an individual\u0026rsquo;s spatial position over time, is a key mechanism influencing biodiversity patterns and ecosystem processes \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. With the advent of new tracking technologies and advanced data-processing techniques, detailed observations of an animal\u0026rsquo;s movements have become possible, leading to the establishment of an integrated theoretical framework for movement ecology \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Traditional research methods (e.g., re-capturing, banding, and flagging) have provided only limited spatial information, constraining species management and conservation decisions \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent advances in wildlife-tracking technologies have revolutionized our ability to obtain fine-scale spatiotemporal data across entire species ranges. In particular, GPS-based tracking devices have become widely used in movement ecology, as they enable the collection of continuous, high-precision, and remotely acquired location data, even from previously inaccessible regions, allowing the detailed monitoring of animal movements \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Such information greatly enhances our understanding of how individual animals move within their natural environments and species adjust their movements across seasons and life stages, crucial information for population management and habitat conservation \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Notably, home ranges and habitat selection are key aspects of animal movement processes in natural environments, reflecting how individuals utilize and respond to spatially distributed resources \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe home range of an animal was defined by Burt (1943) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e as \u0026ldquo;the area traversed by the individual in its normal activities of food gathering, mating and caring for young.\u0026rdquo; This concept has been widely accepted and, in modern movement ecology, the notion of the utilization distribution (UD), which represents the probabilistic distribution of an animal\u0026rsquo;s space use within its geographic range, has emerged as a complement to the home range \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Estimating an animal's home range using UD, which indicates how frequently an individual uses a space, can provide valuable insights into the species' ecological niche \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Identifying high-use areas within an animal\u0026rsquo;s home range can reveal core habitats that are essential for survival and reproduction, which can help delineate priority areas for effective conservation and habitat management \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe tendency of individuals to revisit familiar areas is a widespread movement strategy observed across diverse taxa (e.g., mammals, birds, fish, and reptiles \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e) and is commonly referred to as site fidelity \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The fitness benefits of site fidelity arise from an animal\u0026rsquo;s familiarity with the physical and social characteristics of its environment \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This familiarity can provide numerous advantages, including efficient resource acquisition \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, lower movement costs \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and greater breeding success \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Moreover, fidelity to previously used sites can be particularly advantageous during critical life-history stages, such as during breeding and wintering, when site quality is directly linked to individual fitness \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSite fidelity can be particularly strong when an individual's accumulated knowledge of local environmental conditions provides greater benefits than the risks associated with exploring unfamiliar and potentially low-quality sites \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In such situations, the decision to revisit a site is shaped by an individual\u0026rsquo;s previous experiences at that location \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. One mechanism that may explain this pattern is the \u0026ldquo;win\u0026ndash;stay, lose\u0026ndash;switch\u0026rdquo; rule \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, in which animals continue returning to a familiar site until they experience a negative outcome, after which they shift to an alternative site. This behavioral rule is particularly evident in long-lived seabirds and provides strong empirical support for experience-based site fidelity. For example, Monteiro\u0026rsquo;s Storm-Petrels (\u003cem\u003eHydrobates monteiroi\u003c/em\u003e) that experience successful breeding typically reuse the same nest sites in subsequent years \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, whereas Black-legged Kittiwakes (\u003cem\u003eRissa tridactyla\u003c/em\u003e) that experience breeding failure are more likely to relocate to alternative nest sites or even shift to entirely new breeding areas \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Such contrasting patterns offer compelling evidence that past reproductive outcomes shape decisions to revisit or abandon previously used sites. Because this rule represents a conditional response to environmental variation in site quality, the strength of its effect on site fidelity can vary in a scale-dependent manner, manifesting differently at different spatial (e.g., home range, habitat type, foraging site, or breeding site) and temporal (e.g., seasons or years) scales.\u003c/p\u003e \u003cp\u003eThe Black-tailed Gull (\u003cem\u003eLarus crassirostris\u003c/em\u003e) is a medium-sized seabird widely distributed across the East Asian\u0026ndash;Australasian Flyway (EAAF), with all life-history stages (e.g., breeding site, wintering site, non-breeding site) occurring in coastal or marine areas in Korea, China, Japan, and Russia \u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The population within the EAAF is estimated at 1,100,000 individuals \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and the species is widely regarded as one of the most common seabirds observed along marine and coastal waters throughout the Yellow Sea \u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Previous movement ecology studies on the Black-tailed Gull have largely focused on migration behavior \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and flight characteristics \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and there is a need for finer-scale analyses of utilization distributions, site fidelity, and habitat use to better inform conservation management.\u003c/p\u003e \u003cp\u003eThe aim of this study is to estimate the seasonal home range and core area of Black-tailed Gulls using the Yellow Sea. We further quantify intra-annual home range overlap, habitat use similarity, and breeding-site return to characterize their site fidelity patterns. Finally, we compare habitat use among seasons. These findings will provide essential baseline information for the effective conservation and management of the Black-tailed Gull.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eHDBSCAN identified a total of 102 distinct residency clusters across the 15 tracked Black-tailed Gulls, with clusters per individual ranging from 4 to 12. These clusters were assigned to three annual stages: 23 to the breeding stage, 26 to the wintering stage, and 53 to the non-breeding stage. Across all clusters, individuals remained for an average of 100 days. Mean residency duration differed slightly among annual stages, averaging 98\u0026thinsp;\u0026plusmn;\u0026thinsp;66 days during the breeding stage, 82\u0026thinsp;\u0026plusmn;\u0026thinsp;52 days during the wintering stage, and 109\u0026thinsp;\u0026plusmn;\u0026thinsp;86 days during stage non-breeding stage.\u003c/p\u003e \u003cp\u003eThe mean home range, based on the 95% AKDE, and the mean core area, based on the 50% AKDE, were 583.28\u0026thinsp;\u0026plusmn;\u0026thinsp;904.94 km\u0026sup2; and 77.22\u0026thinsp;\u0026plusmn;\u0026thinsp;140.96 km\u0026sup2; during breeding. During wintering, the mean home range was 3,807.02\u0026thinsp;\u0026plusmn;\u0026thinsp;9,293.31 km\u0026sup2;, and the mean core area was 888.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2,323.64 km\u0026sup2;. During non-breeding, the mean home range was 1,347.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4,249.51 km\u0026sup2;, and the mean core area was 197.55\u0026thinsp;\u0026plusmn;\u0026thinsp;612.04 km\u0026sup2;. The annual stages differed significantly in both home range size (χ\u0026sup2; = 6.03, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.04; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and core area (χ\u0026sup2; = 15.98, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Tukey post hoc tests indicated that home ranges and core areas during wintering were significantly larger than those during breeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). On the other hand, body mass had no effect on home range (χ\u0026sup2; = 0.08, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.78; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and core area (χ\u0026sup2; = 0.19, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.66; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe breeding site return rate was observable in 10 of the 15 individuals, all of which returned to the same breeding site in two consecutive years, resulting in a 100% return rate among all individuals monitored across two years. These individuals bred on Napdaekido Island (n\u0026thinsp;=\u0026thinsp;1; 35\u0026deg; 15\u0026prime; 54\u0026Prime;N, 126\u0026deg; 13\u0026prime; 19\u0026Prime;E), Yuksando Island (n\u0026thinsp;=\u0026thinsp;2; 35\u0026deg; 19\u0026prime; 20\u0026Prime;N, 126\u0026deg; 16\u0026prime; 34\u0026Prime;E), Miyeodo Island (n\u0026thinsp;=\u0026thinsp;3; 37\u0026deg; 38\u0026prime; 16\u0026Prime;N, 125\u0026deg; 40\u0026prime; 50\u0026Prime;E), Bulmugido Island (n\u0026thinsp;=\u0026thinsp;3; 34\u0026deg; 45\u0026prime; 32\u0026Prime;N, 126\u0026deg; 13\u0026prime; 25\u0026Prime;E), and Gujido Island (n\u0026thinsp;=\u0026thinsp;1; 35\u0026deg; 32\u0026prime; 39\u0026Prime;N, 126\u0026deg; 26\u0026prime; 31\u0026Prime;E).\u003c/p\u003e \u003cp\u003eMean home range overlap and habitat use similarity were both 0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 during the breeding stage; 0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 and 0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, respectively, during non-breeding; and 0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 and 0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09, respectively, during wintering. However, the annual stages did not differ significantly in either home range overlap (χ\u0026sup2; = 3.46, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.18; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) or habitat-use similarity (χ\u0026sup2; = 2.37, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.31; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), and body mass had no effect on habitat use similarity either (χ\u0026sup2; = 0.31, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.58; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHabitat-type use differed significantly among annual stages (χ\u0026sup2; = 15,404, df\u0026thinsp;=\u0026thinsp;14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Post hoc pairwise tests indicated that all stage pairs differed significantly in habitat-type composition (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for breeding vs non-breeding, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for breeding vs wintering, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for non-breeding vs wintering). During breeding, grassland and tree cover were used at substantially higher-than-expected frequencies (standardized residuals of +\u0026thinsp;72.16 and +\u0026thinsp;57.09, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), whereas intertidal areas were strongly overrepresented during non-breeding (standardized residual of +\u0026thinsp;61.23; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), as were marine waters during wintering (standardized residual of +\u0026thinsp;68.60; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBlack-tailed Gulls exhibited clear differences in home ranges and core area sizes among annual stages, with space use being significantly smaller during breeding than during wintering. This pattern is consistent with those seen in previous studies. According to Park \u003cem\u003eet al\u003c/em\u003e. (2024) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, Black-tailed Gulls breeding on five uninhabited islands in the Yellow Sea had substantially smaller home ranges during the breeding period (mean KDE 95% = 1,111.47 km\u0026sup2;) than during the post-breeding period (mean KDE 95% = 4,940.47 km\u0026sup2;), indicating an expansion of spatial use after the breeding season. Similarly, \u003csup\u003e46\u003c/sup\u003e reported that core areas (KDE 50%) were larger during the wintering period (December\u0026ndash;February) than during the breeding season (March\u0026ndash;July), during both daytime and nighttime. However, because different home-range estimation methods were used, KDE in previous studies versus AKDE in the present study, direct quantitative comparisons of absolute area sizes should be interpreted with caution \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In our study, home range and core area sizes tended to be larger during the non-breeding stage than during the breeding stage, though this contrast was not statistically significant. Taken together, these results indicate that Black-tailed Gulls progressively broaden their spatial use from the breeding to non-breeding to wintering stages.\u003c/p\u003e \u003cp\u003eSuch seasonal variation in space use likely reflects changes in movement constraints and prey availability, as well as environmental seasonality. According to the central place foraging theory, during incubation and chick-rearing, birds act as central-place foragers that must regularly return to the nest, limiting how far they can travel while provisioning themselves or their offspring \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Thus, their feeding areas and foraging decisions are constrained by how far they can fly from a colony before needing to return to incubate eggs or feed offspring \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In fact, Lesser Black-backed Gulls \u003cem\u003e(Larus fuscus\u003c/em\u003e) reduced their foraging range during the core breeding period, which covers incubation and chick-rearing, when compared with the pre- and post-breeding periods \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In addition, Black-tailed Gulls breeding on islands located in the Yellow Sea exhibited significantly smaller foraging ranges than those breeding on the east sea \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These findings demonstrate that colony-breeding gulls conform strongly to the predictions of the central place foraging theory. In our study, all breeding sites (n\u0026thinsp;=\u0026thinsp;23) were located on islands within the Yellow Sea, where individuals were constrained by incubation and chick-rearing duties. Consequently, the relatively small home range and core area sizes observed at breeding sites are consistent with the movement limitations imposed by central place foraging during the breeding season.\u003c/p\u003e \u003cp\u003eConversely, spatial constraints associated with central-place foraging are mitigated at non-breeding and wintering sites, allowing Black-tailed Gulls to use a broader spatial extent. During winter, Black-tailed Gulls spend most of their time at sea and are known to forage by following fishing vessels, exploiting discards from nets, or capturing small fish that surface due to vessel-induced disturbance \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Although the precise cause of this wintering behavior remains unclear, coastal intertidal and inland habitats are likely less profitable during winter due to waters freezing. This appears to prompt gulls to shift toward marine environments where food availability is more predictable and accessible. Indeed, many gulls are known to forage predominantly in ice-free marine habitats during winter \u003csup\u003e\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, and such behavior typically involves exploring wider areas, resulting in larger home ranges \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Consistent with these patterns, habitat type analysis revealed that Black-tailed Gulls primarily utilize marine waters at their wintering sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Collectively, these behavioral and environmental factors likely explain the substantially larger home ranges and core areas observed at wintering sites in this study.\u003c/p\u003e \u003cp\u003eHome ranges during the non-breeding stage tended to be larger than breeding stage, although the difference was not statistically significant. Black-tailed Gulls often remain in waters adjacent to breeding areas or along coastal regions during pre- and post-breeding periods to recover their body condition and prepare for the subsequent movements \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Habitat type analyses further indicated that Black-tailed Gulls predominantly used intertidal habitat at their non-breeding site. Intertidal environments in the Yellow Sea are characterized by strong tidal dynamics \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, providing extensive foraging opportunities during low tide and restricted opportunities during high tide, when gulls frequently shift to alternative resting areas \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Such tidal-driven shifts in habitat availability may contribute to increased spatial use and influence both the home range and core area size during the non-breeding stage. Consequently, spatial use during the non-breeding period may be moderately expanded relative to the breeding season but does not reach the broad extent observed during winter.\u003c/p\u003e \u003cp\u003eAmong the 15 tracked Black-tailed Gulls, all 10 that were tracked for two breeding seasons used the same breeding site in two consecutive years, resulting in a 100% breeding site return rate. Of the remaining six individuals, three (G1, G9, G14) were tracked for only a single breeding season, while the other two (G8, G13) did not use a breeding site despite being adults, suggesting they were in a sabbatical period. According to Kazama \u003cem\u003eet al\u003c/em\u003e. (2013) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, some Black-tailed Gulls skip breeding in certain years, i.e., sabbatical periods, presumably to recover their body condition, and therefore do not return to breeding colonies. Breeding site reuse behavior in seabirds is known to facilitate pair reunion and enhance reproductive success through familiarity with the breeding environment \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In addition, fidelity patterns in gulls are strongly associated with the physical characteristics and long-term stability of breeding sites. Long-established and environmentally stable habitats, such as rocky cliffs and offshore islands, tend to exhibit high return rates, while habitats subject to substantial environmental fluctuation, including sandy substrates or newly formed colonies, typically engender lower fidelity \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. In this study, Black-tailed Gulls bred on Napdaekido, Yuksando, Miyeodo, Bulmugido, and Gujido Islands, all of which are uninhabited offshore islands where rocky cliffs dominate the outer margins and vegetated habitats occur inland. The cliff-dominated outer margins restrict human access and reduce disturbance, likely contributing to the observed high breeding site fidelity. Previous research has also documented high fidelity (94\u0026ndash;100%) in environmentally similar breeding sites and on Bulmugido Island specifically \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Taken together, the results suggest that Black-tailed Gulls exhibit strong fidelity to previously used breeding sites, except during the sabbatical period, highlighting the ecological importance of stable and undisturbed breeding habitats for this species.\u003c/p\u003e \u003cp\u003eBlack-tailed Gulls showed consistently high levels of both home range overlap and habitat use similarity, with no significant differences among annual stages, indicating strong spatial and habitat use fidelity throughout the annual cycle. In gulls, returning to previously used sites for breeding, wintering, and energy replenishment during similar periods is a well-documented strategy \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Species exhibiting such site fidelity typically also show high similarity in space use and habitat composition across years. For example, Clark et al. (2016) \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e reported relatively high inter-annual home range overlap during winter among Ring-billed Gulls (\u003cem\u003eLarus delawarensis\u003c/em\u003e) and Herring Gulls \u003cem\u003e(Larus argentatus\u003c/em\u003e), ranging from 0.31\u0026ndash;0.78 and 0.38\u0026ndash;0.79, respectively. Additionally, Fernandes et al. (2025) \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e reported high habitat-use similarity in Yellow-legged Gulls (\u003cem\u003eLarus michahellis\u003c/em\u003e), with values ranging from 0.53\u0026ndash;0.87 at breeding sites and 0.53\u0026ndash;0.71 at wintering sites. Collectively, these findings support a general tendency for gulls to show marked fidelity in both space and habitat use.\u003c/p\u003e \u003cp\u003eNonetheless, the degree and intensity of fidelity may vary across geographical locations, as an individual\u0026rsquo;s movement is shaped by the complex interactions of various extrinsic factors, including resource distribution, disturbance, and competition \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Many seabirds show route and site fidelity but may adjust routes or switch areas when conditions deteriorate \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Such switching is consistent with a \u0026ldquo;win\u0026ndash;stay, lose\u0026ndash;switch\u0026rdquo; rule, whereby individuals are less likely to revisit a previously used area after unfavorable outcomes \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In the current study, Black-tailed Gulls in the Yellow Sea showed consistently high home-range overlap and habitat-use similarity, suggesting that the key areas used during the study period experienced relatively low external disturbance and/or provided relatively predictable conditions that maintained the benefits of repeated use.\u003c/p\u003e \u003cp\u003eBlack-tailed Gulls exhibited clear differences in habitat-type use across annual stages, with the breeding stage characterized by a high use of tree cover and grassland. This pattern reflects the physical structure of the uninhabited islands used for breeding, where the interior areas consist mainly of grassland, open soil, shrubs, and scattered trees. According to the WorldCover 2 classifications, tree cover includes areas with at least 10% tree canopy cover, and other land cover types can be present below the canopy \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In our field observations, Black-tailed Gulls breeding on uninhabited islands rarely select areas dominated by dense tree cover. Thus, although tree cover appeared dominant at breeding sites, this likely represents mixed habitats where grasses, open soil, and low vegetation are interspersed with sparse trees. Previous studies have shown that Black-tailed Gulls preferentially breed on cliff habitats rather than grass-dominated areas, with higher breeding success observed on cliffs \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. However, since WorldCover 2 data is derived from satellite imagery, steep cliff environments on small offshore islands may be difficult to classify accurately. Consequently, the use of cliff habitats (considered the bare /sparse vegetation type) may be underestimated in the habitat-use analysis, while tree cover and grassland usage may be overrepresented. This limitation should be considered when interpreting habitat use patterns on uninhabited islands.\u003c/p\u003e \u003cp\u003eDuring the non-breeding stage, Black-tailed Gulls predominantly used intertidal habitats. The pre- and post-breeding periods are energetically demanding phases during which individuals remain near breeding areas to recover their body condition and prepare for subsequent movements \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eLarus\u003c/em\u003e gulls, including the Black-tailed Gull, are known to commonly forage in intertidal habitats during the non-breeding stage \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Intertidal habitats provide abundant and predictable food resources, such as benthic invertebrates and fish, which contribute to improved body conditions and enhanced breeding success \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. In contrast, we found that Black-tailed Gulls primarily used the marine water type during the wintering stage. Similar winter shifts toward offshore habitats have been reported in several gull species, suggesting that increased reliance on marine resources during winter is a common strategy among Larus species \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. During winter, inland and nearshore coastal habitats are likely less accessible due to ice cover, which can restrict access to prey. In the Yellow Sea, where wild fishery activity is intensive, Black-tailed Gulls frequently associate with fishing vessels. Such vessel-associated foraging provides predictable and easily accessible food resources, reducing search effort and increasing foraging efficiency compared to non-vessel-associated prey hunting \u003csup\u003e\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlack-tailed Gulls inhabiting the Yellow Sea exhibited marked differences in spatial extent and habitat use across annual stages. Nevertheless, they maintained consistently high site fidelity throughout the annual cycle. In general, site fidelity is strengthened when environmental conditions are temporally and spatially predictable, allowing individuals to repeatedly revisit high-quality sites based on prior experience \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Such fidelity can confer fitness advantages under stable or predictable environmental conditions, promoting individual survival and population growth \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Despite these potential benefits, strong site fidelity can also lead to maladaptive outcomes when environmental conditions change rapidly \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The Yellow Sea, which is extensively used by Black-tailed Gulls as well as many other seabirds, is currently undergoing rapid environmental change driven by climate change, offshore wind-farm development, and land reclamation \u003csup\u003e\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Under such conditions, the continued selection of historically used sites, movement routes, or areas whose suitability has declined may expose individuals to fidelity-induced ecological traps \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Fidelity-induced ecological traps have now been empirically demonstrated across a wide range of taxa \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e and can ultimately reduce population resilience and long-term viability \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e. Accordingly, conservation strategies for Black-tailed Gulls and other seabirds in marine and coastal habitats should prioritize the identification of repeatedly used key habitats and ensure the long-term environmental stability and appropriate management of these areas.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFrom 2020 to 2024, a total of 15 Black-tailed Gulls were captured along the coastal regions of the Korean Peninsula (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All captures targeted adult individuals and were achieved using a cannon net. Birds were placed individually into bags and immediately transported to the closest handling station. Following the methods of Kenward (2000) \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, each bird was weighed, and to minimize potential negative effects, a solar-powered GPS logger (WT-300, Koeco Inc., Daejeon, Korea) weighing less than 5% of the bird\u0026rsquo;s body mass was mounted as a backpack using a Teflon ribbon harness. All birds were released within 30 minutes of capture and behaved normally after release. The procedures for capturing, handling, and tagging the birds complied with applicable national laws and relevant guidelines and regulations and were approved by the Institutional Animal Care and Use Committee (IACUC). Animal ethics training was completed prior to fieldwork (KoEco certificate nos. 2022-001, 2023-001, and 2024-001). For all 15 individuals, GPS fix intervals were set at 2 h. Individuals were tracked for a minimum of 496 days and a maximum of 850 days. We removed GPS position data with a dilution of precision (DOP) value\u0026thinsp;\u0026gt;\u0026thinsp;5 to improve the accuracy of location data \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. The dataset used in this study included only latitude, longitude coordinates and time.\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\u003eDetailed information on the Black-tailed Gull monitored from 2020 to 2024 and its associated GPS tracking data.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eTracker ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapture site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBody mass\u003c/p\u003e \u003cp\u003e(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst tracking date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLast tracking date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003cp\u003e(days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFix interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaeun Island\u003c/p\u003e \u003cp\u003e(34\u0026deg; 51\u0026prime; 36\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 1\u0026prime; 46\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-06-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-03-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSongi Island\u003c/p\u003e \u003cp\u003e(35\u0026deg; 16\u0026prime; 25\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 9\u0026prime; 2\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2021-10-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2023-06-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBigeum Island\u003c/p\u003e \u003cp\u003e(34\u0026deg; 43\u0026prime; 11\u0026Prime;N,\u003c/p\u003e \u003cp\u003e125\u0026deg; 55\u0026prime; 36\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-03-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2023-05-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGyeokpo port\u003c/p\u003e \u003cp\u003e(35\u0026deg; 37\u0026prime; 18\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 27\u0026prime; 40\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-07-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-11-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGusipo beach\u003c/p\u003e \u003cp\u003e(35\u0026deg; 26\u0026prime; 49\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 26\u0026prime; 6\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-07-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-09-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDongho beach\u003c/p\u003e \u003cp\u003e(35\u0026deg; 30\u0026prime; 43\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 28\u0026prime; 42\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-07-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-08-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDongho beach\u003c/p\u003e \u003cp\u003e(35\u0026deg; 30\u0026prime; 43\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 28\u0026prime; 42\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-07-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-05-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDongho beach\u003c/p\u003e \u003cp\u003e(35\u0026deg; 30\u0026prime; 43\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 28\u0026prime; 42\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-07-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-01-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWi island\u003c/p\u003e \u003cp\u003e(35\u0026deg; 34\u0026prime; 35\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 15\u0026prime; 59\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-07-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-03-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAphae Island\u003c/p\u003e \u003cp\u003e(34\u0026deg; 50\u0026prime; 52\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 15\u0026prime; 59\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2023-04-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-09-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAphae Island\u003c/p\u003e \u003cp\u003e(34\u0026deg; 50\u0026prime; 54\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 14\u0026prime; 8\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2023-04-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-10-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAphae Island\u003c/p\u003e \u003cp\u003e(34\u0026deg; 50\u0026prime; 54\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 14\u0026prime; 8\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2023-04-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-12-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnma Island\u003c/p\u003e \u003cp\u003e(35\u0026deg; 20\u0026prime; 55\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 1\u0026prime; 13\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-08-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2023-12-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnma Island\u003c/p\u003e \u003cp\u003e(35\u0026deg; 20\u0026prime; 55\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 1\u0026prime; 13\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2022-08-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2024-03-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChunjangdae Beach\u003c/p\u003e \u003cp\u003e(36\u0026deg; 9\u0026prime; 31\u0026Prime;N,\u003c/p\u003e \u003cp\u003e126\u0026deg; 31\u0026prime; 6\u0026Prime;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e2020-09-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e2022-04-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 h\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\u003eWe applied the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to the GPS tracking data to identify breeding, wintering, and non-breeding sites. The HDBSCAN algorithm forms clusters based on data density without requiring a predefined number of clusters and can effectively filter out low-density regions, making it suitable for analyzing ecological location data like that of birds in migration \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the conventional HDBSCAN algorithm measures the proximity of birds mainly by the Euclidean distance between two points and does not take time information into account \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. To overcome the limitations of clustering based solely on spatial proximity, we included temporal information as an additional clustering feature and applied HDBSCAN to a three-dimensional dataset (longitude, latitude, and time). This approach enabled the detection of clusters that reflect not only spatial aggregation but also patterns of prolonged residency within a given period, allowing the effective identification of ecologically significant habitats. In addition, HDBSCAN requires setting a minimum cluster size (p), a parameter that defines a lower bound on the number of data points required to define a cluster \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e,\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Based on the methods of Xia \u003cem\u003eet al\u003c/em\u003e. (2023) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, clusters were classified as habitat when an individual remained in the same location for at least eight consecutive days (192 h). Given that our GPS tracking data were collected at 2-hour intervals, we set the minimum cluster size to 96. We identified residency sites using temporal and spatial criteria and then assigned these sites to three annual stages (breeding, non-breeding, and wintering). Breeding sites were defined as locations where an individual remained for at least one month during the breeding season. Wintering sites were defined as the location with the longest residency duration among the lowest recorded latitudes during the winter period. Non-breeding sites were defined as all other clusters not classified as breeding or wintering sites. All clustering analyses were performed using the \u0026ldquo;hdbscan\u0026rdquo; function of the dbscan package \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen estimating home ranges and core areas using tracking devices, it is essential to account for autocorrelation in the data \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Conventional kernel density estimation (KDE) does not incorporate the autocorrelation structure of tracking data and can produce biased estimates, often underestimating the sizes of home ranges and core areas \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e,\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Autocorrelated kernel density estimation (AKDE) is particularly appropriate for movement behavior studies, as it explicitly accounts for autocorrelation in tracking data, minimizing biases associated with inconsistent tracking time lags \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. In this study, we specifically applied the weighted AKDE (wAKDE) to address irregular sampling schedules and missing data, as prolonged periods of poor weather conditions reduced solar charging efficiency and occasionally prevented data collection for several consecutive days. Missing data equate to a loss of information, and these errors can propagate into biased results \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. By compensating for temporal sampling bias\u0026mdash;upweighting observations from under-sampled periods and downweighting those from over-sampled periods\u0026mdash;wAKDE mitigates this issue, producing utilization distributions that more accurately represent the animals\u0026rsquo; space use \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHome ranges and core areas were calculated separately for each year within each annual stage (i.e., breeding, wintering, and non-breeding). The home range was defined as the 95% contour of the utilization distribution and the core area as the 50% contour \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Following the method described in Calabrese \u003cem\u003eet al\u003c/em\u003e. (2016) \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e, the autocorrelation structure in the tracking data was estimated by fitting multiple continuous-time movement models, selecting the one with the lowest Akaike information criterion (AIC) value as the most appropriate model \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. The Ornstein\u0026ndash;Uhlenbeck (OU) process is characterized by correlated positions but not correlated velocities \u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e, whereas the Ornstein\u0026ndash;Uhlenbeck Foraging (OUF) process accounts for correlation in both position and velocity \u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. In contrast, the independent and identically distributed (IID) process assumes no correlation in either positions or velocities. Movement models, including the OU, OUF, and IID processes, were fitted to the tracking data using the \u0026ldquo;ctmm.fit\u0026rdquo; function of the ctmm package within the wAKDE calculations. Home range and core area estimations were performed using the \u0026ldquo;akde\u0026rdquo; function of the ctmm package \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe breeding site return rate was calculated as the proportion of individuals that used the same breeding site in their first and second year of monitoring \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. We assessed annual site fidelity for each annual stage by calculating the intra-individual 95% wAKDE home range overlap using Bhattacharyya\u0026rsquo;s affinity overlap \u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Conventional home range overlap methods, which are based solely on the area of overlap, do not incorporate UDs and may therefore overestimate spatial overlap. In contrast, Bhattacharyya\u0026rsquo;s affinity quantifies the similarity between the UDs of two home ranges, yielding values between zero (no overlap) and 1 (identical UDs), making it better for evaluating overlap \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. All analyses of home range overlap were conducted only when the home ranges from the first and second years overlapped within the same annual stage. All home range overlap analyses were performed using the \u0026ldquo;overlap\u0026rdquo; function of the ctmm package \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHabitat use was quantified for each habitat type based on GPS tracking locations within the 95% home range. For each location, a land cover type was assigned based on the WorldCover Version 2 (2021) land cover database (10 m resolution) \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Our tracking data indicated that Black-tailed Gull individuals utilized coastal regions of the Korean Peninsula, southeastern China, northern Japan, and the Sakhalin region of Russia. Based on these locations, the following land cover types were identified within the gulls\u0026rsquo; home ranges: tree cover, grassland, cropland, built-up, bare/sparse vegetation, and permanent water bodies. Considering the significant ecological differences among aquatic environments and their importance for Black-tailed Gull habitat use, the \u0026ldquo;permanent water bodies\u0026rdquo; type was further subdivided into freshwater, marine water, and intertidal cover types. Freshwater comprised inland water bodies located landward of the coastline, while marine water included coastal seas beyond the shoreline. The intertidal cover type was identified within marine waters based on the 2019 dataset of the Global Intertidal Map v1.2 (30 m resolution) \u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHabitat use similarity was assessed only when the home ranges from the first and second years overlapped within the same habitat type, remaining consistent with the criteria applied in the overlap analysis. We assessed habitat use similarity for each annual stage using the Bray\u0026ndash;Curtis similarity index \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. Following \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e, we calculated the index as\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{S}\\varvec{i}\\varvec{m}\\varvec{i}\\varvec{l}\\varvec{a}\\varvec{r}\\varvec{i}\\varvec{t}\\varvec{y}}_{\\varvec{h},1-2}\\:=\\:1\\:-\\:\\frac{{\\sum\\:}_{\\varvec{i}}|{\\varvec{p}}_{\\varvec{h},\\mathbf{i},\\:\\mathbf{f}\\mathbf{i}\\mathbf{r}\\mathbf{s}\\mathbf{t}\\:\\mathbf{y}\\mathbf{e}\\mathbf{a}\\mathbf{r}}\\:-\\:{\\varvec{p}}_{\\varvec{h},\\varvec{i},\\:\\varvec{s}\\varvec{e}\\varvec{c}\\varvec{o}\\varvec{n}\\varvec{d}\\:\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r}}|}{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{p}}_{\\varvec{h},\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the fraction of GPS locations within land cover type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{i}\\)\u003c/span\u003e\u003c/span\u003e (tree cover, grassland, cropland, built-up areas, bare/sparse vegetation, freshwater, marine water, and intertidal) for a given habitat type h. A value of 1 indicated identical habitat use, whereas a value of zero indicated complete segregation. Habitat similarity analyses were performed using the \u0026ldquo;vegdist\u0026rdquo; function of the vegan package \u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe examined differences in home ranges, core areas, overlap, and habitat use similarity among the annual stages. The distribution of each response variable was assessed using the \u0026ldquo;chooseDist\u0026rdquo; function of the gamlss package \u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e, which indicated that home range and core area followed a gamma distribution, whereas overlap and habitat use similarity followed a beta distribution. Accordingly, we fitted generalized linear mixed models (GLMMs) with gamma distributions and log link functions when home range and core area were the response variables, and GLMMs with beta distributions and logit link functions for home range overlap and habitat use using the \u0026ldquo;glmmTMB\u0026rdquo; function of the glmmTMB package \u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. For the home range and core area analyses, annual stage, body mass (g), and year were included as independent variables. For the overlap and habitat use similarity analyses, annual stage and body mass were included as independent variables. In all analyses, the Tracker ID (each GPS tracking device was assigned a unique ID) was included as a random effect in the model to control for individual variation. We used the \u0026ldquo;dredge\u0026rdquo; function of the Mumin package to select the best models based on the AIC \u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e, and the model with the least number of independent variables was selected as the best model when the ΔAIC value between the models with the lowest and second-lowest AIC values was less than two. Following each analysis, we used the \u0026ldquo;Anova\u0026rdquo; function of the car package with type Ⅱ Wald chi-square tests used to identify significant differences in the response variables among the annual stages \u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. When significant effects were detected, we used the \u0026ldquo;emmeans\u0026rdquo; function in the emmeans package to compute estimated marginal means for each level of annual stage \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e, followed by pairwise comparisons using Tukey\u0026rsquo;s post hoc test with adjusted p-values. We performed a chi-square test of independence to assess whether habitat type composition differed among annual stages, and when significant differences were detected, we conducted pairwise post-hoc comparisons using the \u0026ldquo;pairwiseNominalIndependence\u0026rdquo; function in the rcompanion package to identify which annual stages differed in habitat type use \u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e. All analyses were conducted in R software v4.4.2\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the research team at the Korea Environment \u0026amp; Ecology Institute for their assistance with the capture of Black-tailed Gulls. This research was supported by the Animal and Plant Quarantine Agency (APQA) through the \u0026ldquo;Risk Assessment of Highly Pathogenic Avian Influenza (HPAI) Outbreaks Caused by Wild Birds (Migratory Birds)\u0026rdquo; project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDae-Han Cho\u003c/strong\u003e: Writing \u0026ndash; Original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eSang-Min Jung\u003c/strong\u003e: Validation, Investigation. \u003cstrong\u003eDal-Ho Kim\u003c/strong\u003e: Methodology, Data curation. \u003cstrong\u003eSi-Wan Lee\u003c/strong\u003e: Funding acquisition, Project administration. \u003cstrong\u003eGa-Young Kim\u003c/strong\u003e: Formal analysis, Visualization. \u003cstrong\u003eKee-Sung Hong\u003c/strong\u003e: Project administration. \u003cstrong\u003eChang-Ho Cho\u003c/strong\u003e: Project administration. \u003cstrong\u003eHa-Cheol Sung\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no external funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests of personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFischer, C. 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Package \u0026lsquo;rcompanion\u0026rsquo;. \u003cem\u003eCran Repos\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, 1\u0026ndash;71 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Black-tailed Gull, Movement ecology, Tracking, Utilization distribution, Site fidelity, Habitat use","lastPublishedDoi":"10.21203/rs.3.rs-8831504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8831504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMovement shapes biodiversity patterns and ecosystem functioning, and recent advances in animal tracking have improved our ability to quantify space usage. Utilization distributions, site fidelity, and habitat use provide complementary perspectives on where animals concentrate their activity and how consistently they reuse key areas, informing conservation and habitat management. Black-tailed Gulls (\u003cem\u003eLarus crassirostris\u003c/em\u003e) are abundant in the Yellow Sea, yet their fine-scale seasonal space use and fidelity patterns remain poorly quantified. Here, we estimated their seasonal home ranges and core areas and evaluated site fidelity, comparing them among seasons. From 2020 to 2024, we tracked 15 adult Black-tailed Gulls with GPS loggers on the west coast of the Korean Peninsula for 496\u0026ndash;850 days per individual. Breeding, non-breeding, and wintering sites were delineated using hierarchical density-based spatial clustering of applications with noise (HDBSCAN), and seasonal utilization distributions were estimated using weighted autocorrelated kernel density estimation (wAKDE). Site fidelity was quantified as the breeding-site return rate, inter-annual home-range overlap, and habitat-use similarity, and habitat use was compared among annual stages. Home ranges and core area sizes differed among annual stages, with wintering ranges larger than breeding ranges. Ten individuals could be tracked across consecutive breeding seasons, all of which returned to the same breeding colonies, yielding a 100% breeding-site return rate. Inter-annual home-range overlap and habitat-use similarity were high and did not differ among annual stages. In contrast, habitat use differed among stages. Together, strong fidelity and seasonal habitat switching suggest reliance on repeatedly used key areas that may become maladaptive under the rapid environmental change present in the Yellow Sea, increasing the risk of fidelity-induced ecological traps. Conservation planning should prioritize repeatedly used key habitats and incorporate season-specific spatial and habitat requirements, including the protection of breeding colonies and the intertidal and marine habitats used during non-breeding and wintering periods.\u003c/p\u003e","manuscriptTitle":"Seasonal utilization distributions, site fidelity, and habitat use of the Black-tailed Gull (Larus crassirostris) in the Yellow Sea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 22:47:13","doi":"10.21203/rs.3.rs-8831504/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T08:23:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T11:45:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T06:22:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91933349871649880618417476225344561982","date":"2026-03-12T02:27:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301895100339413033180535333775528954330","date":"2026-03-11T15:46:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T14:39:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T14:22:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-04T11:38:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T10:19:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-18T10:12:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9738f4ba-3b3c-4679-a802-6f80cec47d2c","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64351138,"name":"Biological sciences/Ecology"},{"id":64351139,"name":"Earth and environmental sciences/Ecology"},{"id":64351140,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-04-16T01:53:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 22:47:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8831504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8831504","identity":"rs-8831504","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}