Weak migration connectivity and high genetic admixture of urban-adapted Eastern Rooks (Corvus frugilegus pastinator) wintering in Korea

Weak migration connectivity and high genetic admixture of urban-adapted Eastern Rooks (Corvus frugilegus pastinator) wintering in Korea | 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 Weak migration connectivity and high genetic admixture of urban-adapted Eastern Rooks (Corvus frugilegus pastinator) wintering in Korea Eun-Jeong Kim, Who-Seung Lee, Chang-Wan Kang, Seoyun Choi, Chang-Yong Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7921474/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 Global habitat loss and change reshape human-wildlife interactions, with animal migrations generating dynamic patterns of conflict. Thus, effective management of these issues requires knowledge of migratory ecology, particularly for migratory birds that strongly influence human activities, yet such information is often limited. We investigated the migration connectivity, migration patterns, and population genetic structure of the Eastern Rook ( Corvus frugilegus pastinator ), a conflict-associated urban population increasingly adapting to East Asian cities. Through hydrogen stable isotope (δ 2 H) and mitochondrial DNA analyses, we examined 66 carcasses from four wintering sites in Korea. Feather δ 2 H values did not differ among sites and showed no clear latitudinal trend. However, adults consistently had higher δ 2 H values than first winter individuals, likely reflecting age-related differences in molt timing and location. Genetic analyses revealed high intermixing among wintering populations, with no evidence of geographically distinct breeding origins. These findings suggest that Korean wintering rooks constitute a diffuse population, implying that localized lethal control may be ineffective. Non-lethal management strategies, such as habitat modification, providing alternative roosts, and deterrence measures, should be considered to mitigate conflicts. Further research on age structure and breeding ecology is essential for identifying the causes of conflict and developing sustainable management solutions. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology Rook Nuisance animal Stable isotope Migration connectivity Migration pattern Population genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The reduction of wild habitats, the expansion of human areas, and increased human activity in nature have led to more perpetual and severe conflicts between wildlife and humans, occurring globally [ 1 , 2 ]. Urbanization is one of the major drivers of rapid habitat changes and results in frequent conflicts with wildlife due to a high density of humans and man-made structures [ 3 , 4 ]. As these conflicts can lead to direct and indirect harm to humans; e.g. collisions with anthropogenic objects including vehicles [ 5 , 6 ], an accompanying property damage and human casualties, threat to human well-being and health by increased risk of spreading zoonosis [ 7 ], sanitation and esthetic issue caused by feces, and aggressive behavior toward humans to protect nests [ 8 ], immediate action often required when wildlife-human conflicts occur in urban areas. Although non-lethal methods, such as supporting alternative habitats, relocation, and eviction, are sometimes used as management strategies [ 9 ], lethal methods that are believed to be more effective than non-lethal approaches are also implemented [ 2 , 10 ]. However, to determine the timing, intensity, and area of lethality, as well as to predict the outcomes of these methods, ecological information about the target species is crucial. For migratory birds that cover large geographical areas, their spatial and temporal distribution, migration connectivity [ 11 ], and migration patterns [ 12 ] must be considered. Without knowledge of the migration ecology of these birds, predicting the impact on their population becomes even more difficult. Recently, Asia has been experiencing faster habitat changes and urban growth than other continents [ 13 ]. Therefore, migratory birds must adapt to these environments to survive, and as contact with humans gradually increases, conflicts and collisions with people are expected to rise further. However, comprehensive research on migratory birds, which is fundamentally needed to address this issue, is very limited in Asia [ 14 ]. Although studies are ongoing on specific taxa of interest, many species still have unknown migratory ecologies, and collecting and understanding ecological information remains essential to reduce human-wildlife conflicts, promote coexistence, and conserve biodiversity and species. Rook Corvus frugilegus , a common passerine bird widely distributed from Europe to East Asia, is divided into two subspecies. The Eastern Rook C. frugilegus pastinator in East Asia breeds in northern Mongolia, northeastern Russia, and China, then migrates to the eastern coast of China, Korea, and Kyushu in Japan for wintering [ 15 ]. However, the distribution of the rooks in Japan expanded to the Shikoku and Hokuriku regions in the 1980s and later to northern Honshu in the 1990s [ 16 ]. Recently, rooks wintering in Japan and Korea have increasingly adapted to urban areas as roosting sites [ 17 , 18 ]. In Korea, the largest wintering population is found in Ulsan, where numbers have increased from 30,000 to 40,000 individuals in 2010 to 70,000 in recent years [ 19 , 20 ], while large flocks (up to 30,000 individuals) are also observed in Gyeonggi and Jeonbuk Provinces. This adaptation has caused inconvenience for residents due to noise, fecal pollution, safety and disease concerns, and aesthetic damage. To mitigate these issues, lasers have been used to drive flocks away from cities, but conflicts with humans remain unresolved and continue to intensify. Consequently, the governments of Korea designated the rook as a “Harmful Wildlife”, under the Wildlife Protection and Management Act, and allowed hunting to control their populations [ 17 , 21 ]. Rooks wintering in Korea can be broadly classified into four geographically distinct populations: Gyeonggi, Jeonbuk (especially at Gimje), Ulsan, and Jeju Island (Fig. 1 ). If these populations exhibit strong migration connectivity, meaning that individuals at each wintering site originate from a distinct breeding population, localized harvesting to specific regions could be effective in controlling nuisance individuals. Conversely, weak connectivity would imply extensive mixing among breeding populations, rendering localized control ineffective. However, little is known about the rook’s migration ecology, and research has mainly focused on wintering areas [ 17 , 18 , 22 ]. Therefore, for effective management of this species, information about their origins and the strength and pattern of connectivity between wintering and breeding sites is essential. In this study, we investigated whether geographically distinct wintering populations of Eastern Rooks in Korea originate from separate breeding areas. Specifically, we examined migration connectivity, latitudinal migration patterns, and analyzed population genetic structure using intrinsic markers, hydrogen stable isotope analysis, and mitochondrial DNA sequences from Cytochrome c oxidase subunit I (COI) and the Control Region (CR). Based on the clear geographic separation and latitudinal gradient of wintering sites, we tested the following hypotheses: 1) populations with distinct wintering areas will also have geographically separate breeding areas, indicating strong migration connectivity; 2) northern wintering populations will originate from more northern breeding locations, demonstrating a chain migration pattern; and 3) genetically distinct population structures will be evident among wintering groups if their breeding origins are geographically separated. Results Migration connectivity & Migration pattern A Bayesian GLM analysis of migration connectivity revealed that the Jeju (Estimate = 3.72, 95% CI = -1.00–8.27) and Jeonbuk (Estimate = 4.26, 95% CI = -0.94–9.45) populations had posterior estimates with credible intervals overlapping zero, indicating no clear differences from the reference Gyeonggi population. In contrast, the Ulsan population (Estimate = 8.27, 95% CI = 2.88–13.73) showed a positive effect with its 95% credible interval excluding zero, indicating strong posterior support for higher hydrogen stable isotope values. However, Ulsan did not clearly differ from the Jeonbuk and Jeju populations. Age was also supported as an important covariate; adults (A) had higher isotope values than first winter individuals (Fig. 2 a, Table 1 ). Table 1 Posterior estimates from Bayesian generalized linear models of migration connectivity and migration pattern in the Eastern Rook ( Corvus frugilegus pastination ). Posterior mean estimates (Estimate), standard errors (Est. Error), and 95% credible intervals (Lower 95% CI, Upper 95% CI) are shown for each predictor. Rhat values close to 1.0 indicate model convergence, and Bulk_ESS and Tail_ESS denote effective sample sizes for bulk and tail estimates, respectively. Migration connectivity model compares stable isotope values (δ 2 H f ) among wintering sites (reference: Gyeonggi), with age (adult vs. first winter), included as a covariate. The migration pattern model includes latitude as a main predictor and age as covariate. Model Estimate Est.Error Lower 95% CI Upper 95% CI Rhat Bulk_ESS Tail_ESS Migration connectivity Intercept -86.53 2.68 -91.84 -81.20 1 17425 11854 Jeju 3.72 2.34 -1.00 8.27 1 15788 12508 Jeonbuk 4.26 2.67 -0.94 9.45 1 16105 12648 Ulsan 8.27 2.76 2.88 13.73 1 18352 12517 AgeA 7.58 2.47 2.75 12.44 1 21609 12558 Migration pattern Intercept -45.09 24.01 -95.24 1.24 1 16889 11946 Latitude 1.07 0.68 -2.39 0.27 1 16908 11783 AgeA 7.29 2.51 2.36 12.23 1 18508 11996 Regarding migration patterns, the effect of latitude on hydrogen stable isotope values (Estimate = 1.07, 95% CI = -2.39–0.27) showed a wide credible interval overlapping zero, indicating no clear evidence of a latitudinal trend. In contrast, age again showed strong posterior support for higher isotope values in adults compared to first winter individuals (Estimate = 7.29, 95% CI = 2.36–12.23; Fig. 2 b, Table 1 ). Geographic assignment Based on the migration connectivity and pattern analyses, we identified the breeding origins of wintering populations and age groups of rooks in Korea (Fig. 3). Overall, breeding origins centered around the border regions of Mongolia–Russia and China–Russia, including the Amur River Basin in northeastern China and the Baikal region. Adults from the Gyeonggi, Jeonbuk, and Jeju populations showed similar distribution patterns, with the centroid of the 50% probability region located in Zabaykalsky Krai, Russia. In contrast, the centroid of the Ulsan population was shifted toward lower latitudes, near Bayantu’men, Mongolia, and Hulunbuir, China. For first winter individuals, the centroids of the Ulsan, Jeonbuk, and Jeju populations were also in Zabaykalsky Krai, Russia, while those from Gyeonggi were further north, in the Mogochinskiy Rayon of Zabaykalsky Krai. Although the latitudinal distribution of breeding origins varied slightly by region and age, the probability regions largely overlapped, showing no geographically distinct breeding grounds among wintering groups. Population genetic structure Genetic diversity indices based on concatenated COI and CR sequences are summarized in Table 2 . Across all wintering populations, haplotype diversity was high (h = 0.887), whereas nucleotide diversity remained low (π = 0.00172), indicating moderate overall diversity. At the population level, haplotype diversity ranged from 0.833 (Ulsan) to 0.933 (Gyeonggi), while nucleotide diversity ranged from 0.00108 (Jeju) to 0.00215 (Gyeonggi). Neutrality tests revealed negative Tajima’s D values across all populations, with the Gyeonggi population showing a significant deviation from neutrality (D = -4.001, p < 0.05). Fu’s F s values were generally negative, but none were statistically significant. Overall, concatenated mtDNA sequences indicated moderate haplotype diversity, low nucleotide diversity, and little evidence for recent population expansion, except for a potential signal of demographic change in the Gyeonggi population. Table 2 Summary of genetic diversity of Eastern Rooks ( Corvus frugilegus pastination ) wintering in Korea. For COI, CR, and concatenated sequences, we calculated sample size (n), number of haplotypes (No. haps), haplotype diversity (h), nucleotide diversity (π), average number of nucleotide differences (k), Fu’s Fs (Fs), and Tajima’s D (D). The significance of Fs and D was tested using 1000 random permutations, and values with significance (p < 0.05) are shown in bold. Population DNA n No. haps h k π F s D Gyeonggi COI + CR 10 8 0.933 2.644 0.00215 -1.432 -4.001 Jeonbuk 7 5 0.905 1.905 0.00155 -0.330 -1.352 Ulsan 9 6 0.833 2.611 0.00212 -0.973 -1.496 Jeju 10 5 0.844 1.333 0.00108 -0.219 -1.547 Total 36 17 0.887 2.116 0.00172 - - Gyeonggi COI 10 5 0.667 1.2 0.00195 -1.796 -1.803 Jeonbuk 7 3 0.524 0.857 0.00139 -1.358 -0.237 Ulsan 9 6 0.833 1.5 0.00244 -1.398 -2.978 Jeju 10 3 0.378 0.4 0.00065 -1.401 -1.164 Total 36 12 0.595 0.975 0.00158 - - Gyeonggi CR 10 6 0.844 1.444 0.00236 -0.733 -2.735 Jeonbuk 8 4 0.786 1.0 0.00163 1.104 -0.785 Ulsan 9 5 0.722 1.111 0.00181 0.025 -2.231 Jeju 10 3 0.733 0.933 0.00152 1.033 0.345 Total 37 9 0.767 1.131 0.00184 - - Genetic differentiation among wintering populations was low (Table 3 ). Pairwise F st values estimated from concatenated COI and CR sequences ranged from − 0.053 to 0.040, with none being significant (p > 0.05). Analysis of molecular variance (AMOVA) further supported this pattern, showing that the vast majority of genetic variation (100.65%) was distributed within populations, whereas variation among populations was negligible (–0.65%). The overall fixation index was close to zero (F st = − 0.0065), and permutation tests confirmed non-significance (p = 0.538). Table 3 F st values estimated using concatenated COI and CR sequences among wintering populations. Values with significance (p < 0.05) are shown in bold. Population Gyeonggi Jeonbuk Ulsan Jeju Gyeonggi 0 Jeonbuk -0.036 0 Ulsan -0.028 -0.053 0 Jeju 0.011 0.033 0.040 0 Table 4 Information on collected samples and used samples for analysis. A denotes an adult, while 1W indicates a young bird in its first calendar year (a first winter bird) of the Eastern Rook ( Corvus frugilegus pastination ). Location Sample size δ 2 H DNA Period Coordinates A 1W Total Gyeonggi 21 2 23 23 10 25 January 2022 27–31 January 2025 27–31 January 2025 37°17'15.4"N 126°51'48.9"E 37°06'25.2"N 127°02'45.6"E 37°00'07.2"N 126°59'31.2"E Jeonbuk 8 6 14 14 7 17 March 2023 35°50'03.7"N 126°50'16.9"E Ulsan 8 1 9 9 9 03 March 2023 35°32'52.0"N 129°17'46.4"E Jeju 15 5 20 20 10 03 December 2021 33°31'43.6"N 126°38'42.0"E Discussion In this study, we integrated stable hydrogen isotope analyses and mitochondrial DNA sequencing to examine the migration connectivity, migration patterns, and population genetic structure of rook populations wintering in South Korea. The results revealed weak migratory connectivity, an absence of latitudinal migration patterns, and no detectable genetic differentiation among wintering populations. Despite the clear geographic separation of wintering sites, the breeding origins of these populations largely overlapped, suggesting that rooks wintering in Korea constitute a diffuse, panmictic population rather than distinct subpopulations. Stable isotope analyses showed that the Ulsan population had higher δ 2 H values than the Gyeonggi population, suggesting that individuals in Ulsan likely originated from relatively lower latitude breeding grounds compared to the Gyeonggi population. However, Jeju and Jeonbuk populations did not differ significantly from either Gyeonggi or Ulsan, indicating extensive overlap in breeding origins among wintering sites. Geographic assignment analyses further supported this pattern, with broad overlap in the core breeding probability regions. No spatially structured migration pattern, such as chain migration, where northern breeders winter further north, or leapfrog migration, where they winter further south, was detected. Although Ulsan, a relatively southern wintering site, showed breeding origins at slightly lower latitudes compared to Gyeonggi, this trend was not consistent across all wintering grounds. Along with stable isotope results, the genetic evidence reinforces the idea that Rooks in Korea represent a diffuse population with low migration connectivity. Although limited by small sample sizes, in mitochondrial DNA analyses, negative or near-zero values in pairwise F st and AMOVA results provided no evidence of significant population structure, indicating that Korean wintering rooks belong to large, panmictic breeding population, and there is a high level of mixing among wintering populations [ 23 ]. Migration connectivity can be determined by two components, spreading and mixing [ 24 ]. Spreading describes how widely individuals from a single breeding site disperse across wintering areas, whereas mixing reflects the extent to which individuals from multiple breeding sites overlap within a single wintering area. When the degree of spreading is high, population mixing is more likely to occur, thereby weakening connectivity. Conversely, when spreading is limited, mixing decreases and connectivity becomes stronger. However, when the wintering range is relatively small compared to the breeding range, spatial constraints can still promote mixing, even if spreading is limited, thereby reducing connectivity. As we didn’t use detailed tracking data, which can provide information on the degree of individual spatial spread, we were unable to define whether this species exhibits high or low spreading characteristics. Nevertheless, the breeding range of Eastern Rook covers a much larger area than its relatively limited wintering range (southern China, Korea, and Japan; Fig. 1 ), and thus population mixing may occur regardless of the degree of spreading. The only tracking study on the pastinator subspecies of rook in East Asia [ 25 ] identified a few migration routes and breeding grounds of the Hokkaido population in Japan, revealing that tracked individuals bred around Lake Khanka and the Amur River basin. Although the number of tracked individuals was limited, the gregarious behavior of this species during both migration and wintering suggests that these results reasonably represent the Hokkaido population. Among Korean wintering grounds, Ulsan is located on the east coast and is relatively close to Japan. Therefore, it is plausible that the Ulsan population breeds in areas similar to the Japanese population, with their relatively higher δ 2 H values compared to other Korean populations reflecting the presence of individuals breeding at lower latitudes (e.g., Lake Khanka). Additional studies tracking rooks in Korea, encompassing multiple wintering locations, will help identify their migration routes and specific breeding sites, as well as understand the factors driving these lower stable isotope values. Another factor influencing isotope values was age. Adults consistently exhibited higher δ 2 H values than first winter individuals. In species that undergo molting after the breeding season (post-breeding molt) [ 26 ], arid conditions or poor resources at the end of the breeding season make birds leave before molting, and this causes individuals to replace feathers during southward migration after raising their offspring, a strategy often referred to as molt migration [ 11 ]. Rook is the species that performs a post-breeding molt in which adults replace their entire plumage, whereas first winter birds replace only the body feathers and wing coverts, retaining their juvenile primaries and secondaries in Europe [ 26 ]. Consequently, the wing feathers of first winter birds reflect the isotopic signature of their natal breeding grounds, while the feathers of adults may incorporate isotopic signals from lower latitudes where molting occurs after breeding [ 27 , 28 ]. Another possibility is that, in some species (e.g., Cooper’s Hawk Astur cooperii , American Redstart Setophaga ruticilla ), adults exhibit higher isotope values than their offspring even when both molt at the same location [ 29 , 30 ]. Therefore, it would be valuable to further analyze the isotopic differences between adults and juveniles at the same breeding sites in Eastern Rooks. During rapid population growth, new haplotypes are generated and retained, leading to relatively high haplotype diversity. However, the short evolutionary timescale is insufficient for the accumulation of large numbers of substitutions among haplotypes, resulting in low nucleotide diversity. This pattern is often observed in populations that have recently undergone a bottleneck or founder effect, followed by expansion [ 31 , 32 , 33 ]. Consequently, populations may exhibit an excess of low-frequency polymorphisms, which is reflected in negative values of Tajima’s D and Fu’s Fs. Although most neutrality tests were not statistically significant in our data, a combination of moderate haplotype diversity and low nucleotide diversity, and generally negative neutrality statistics across wintering populations could suggest the possibility of recent, rapid demographic expansion from an ancestral group [ 31 ]. The significant negative Tajima’s D observed in the Gyeonggi population may also indicate a localized or recent demographic expansion event. The Nationwide Winter Waterbird Census (NWWC) in South Korea, conducted across key habitats for waterbirds, also surveys wintering passerines, though they are not among the target species [ 19 ]. Population data on rooks are primarily available for the Ulsan region, which appears to be experiencing an increasing trend. Since the wintering populations originate from a single breeding population, similar increases are likely occurring in other regions as well, and such demographic changes may also be reflected in the DNA results. Although intrinsic markers such as stable isotopes and mitochondrial DNA sequences have certain limitations, they remain widely used tools in studies of avian migration. Stable isotope analyses can be influenced by confounding factors such as local diet, isotopic variance within isoscapes, and differences in molting strategies, which may reduce spatial resolution and complicate the assignment of breeding origins [ 34 , 35 ]. Similarly, mtDNA markers provide valuable insights into population structure and demographic history but may fail to detect fine-scale differentiation due to maternal inheritance and reduced effective population size relative to nuclear DNA [ 36 ]. Despite these constraints, intrinsic markers continue to be powerful approaches for investigating migratory connectivity, particularly when combined with other methods such as extrinsic tracking and multi-isotope or genomic analyses [ 11 ]. Furthermore, in stable isotope studies, increasing the number of samples has been shown to narrow the predicted breeding origin areas and improve assignment accuracy [ 37 ]. Therefore, our study demonstrates that stable isotope analysis provides an efficient and non-invasive alternative for investigating population migration, including migration connectivity, migration patterns, compared to methods that require capturing a small number of individuals and attaching costly Global Positioning System (GPS) telemetry devices. Our findings suggest that localized lethal control at particular wintering sites is unlikely to effectively reduce overall rook populations in Korea. Because wintering groups originate from overlapping breeding populations, controlling numbers at one site may simply be offset by immigration from other breeding sources. Moreover, information essential for evaluating the effectiveness of lethal control, including total wintering population size in Korea, demographic structure (age and sex ratios), and harvest data, is currently lacking. Given these uncertainties, non-lethal management approaches may provide more sustainable solutions. Potential strategies include habitat modification to reduce roost attractiveness, provision of alternative roosting sites, active disturbance and deterrence, and public education to increase tolerance and awareness of rooks in urban areas [ 38 ]. Additionally, future research on rooks will be essential to refine our understanding of their migration ecology and population dynamics. Tracking studies are needed to directly determine breeding origins and to compare these data with our isotope-based assignments, particularly to test whether the Ulsan population indeed breeds at lower latitudes such as Lake Khanka. It will also be important to investigate age-related differences in molting locations to clarify how molt timing influences isotopic signals. In addition, long-term monitoring of the population trends of rooks wintering in Korea is required to assess whether the genetic signatures of demographic expansion observed in this study correspond to actual increases in population size. Further research on breeding ecology at the breeding grounds would also help to connect demographic processes with migration connectivity. More broadly, our study highlights the need for ecological information not only on rooks but also on other migratory bird species that are increasingly becoming the focus of management due to human–wildlife conflicts. A deeper understanding of their migration strategies, demographic history, and breeding–wintering linkages will be crucial for developing science-based management and conservation measures in rapidly changing, human-dominated landscapes. Methods Sample collection For stable isotope and DNA analysis, feathers and tissue samples (pectoral muscle) were collected. In Gimjae and Ulsan, carcasses found at roosting sites were used. For the Jeju population, carcasses that had died from pesticide poisoning were obtained from the Jeju branch of the Korean Association for Bird Protection. For the Gyeonggi population, individuals harvested by the Pyeongtaek branch of the Wildlife Management Association, under permission from Pyeongtaek City, as part of the municipal pest control program, were used (Fig. 1 , Table 1 ). We used only harvested or salvaged carcasses, and no bird was harmed or killed for this study. When collecting the carcasses, we measured, aged, and collected the samples following the procedures below. Since adults undergo a complete post-breeding molt during or after the breeding season, and juveniles retain their natal wing feathers, we collected wing feathers for stable isotope analysis [ 26 ]. Because isotope ratios can differ depending on the feather location [ 29 , 39 , 40 ], the third secondary feather of the right wing (SR3) was sampled from all individuals. Feathers suspected to have been replaced outside the breeding grounds were excluded from sampling to avoid isotopic bias. All collected feathers and tissue samples were stored at -20°C until analysis. Hydrogen stable isotope analysis Many elements in nature are a mixture of two or more isotopes due to differences in neutrons [ 41 ]. Specifically, stable isotopes remain in a stable state in the environment, leading to variations in isotope ratios across different geographic and trophic levels. Therefore, stable isotopes found in tissues can serve as valuable chemical tracers in ecological research. Notably, the hydrogen stable isotope ratio in precipitation (δ 2 H p ) creates a spatial gradient across the Northern Hemisphere, generally increasing from higher to lower latitudes, which makes it useful for estimating geographic origins. Bird feathers are inert tissues that preserve the stable isotope composition during feather growth [ 34 , 42 ], so δ 2 H of feathers (δ 2 H f ) from migratory birds has been used to understand migration characteristics such as connectivity, and spatial and temporal patterns. Stable isotope analysis was performed using about 3 cm of the tip of collected feathers at Iso-Analytical Limited and Sercon Analytical Limited (Crewe, UK) between 2022 and 2025. All samples were equilibrated with ambient laboratory moisture for 14 days before analysis. Each feather sample was first washed in 0.25 M sodium hydroxide, rinsed twice with purified water, and then dried overnight at 60°C. The cleaned feathers were finely chopped, and about 1 ± 0.1 mg was weighed into silver capsules for hydrogen isotope analysis using Elemental Analyzer–Isotope Ratio Mass Spectrometry (EA-IRMS). IA-R002 (δ 2 H V−SMOW = − 111.2‰) served as the reference material, with an additional IA-R002 (–148.61‰) used for quality control. These standards were calibrated against NBS-22 and IAEA-CH-7. Four non-exchangeable hydrogen standards (USGS42, USGS43, USGS CBS, and USGS KHS) were used for calibration and control. A three-point linear calibration with USGS42, CBS, and KHS corrected for exchangeable hydrogen, while USGS43 was used to validate this calibration. Final δ 2 H values are reported both uncorrected (total hydrogen) and corrected for exchangeable hydrogen. We used a non-exchangeable hydrogen value for further analysis. Migration connectivity & Migration pattern We used a Bayesian generalized linear model to determine whether stable hydrogen isotope values varied across wintering sites and to assess spatial trends in breeding origins based on the latitude of wintering sites. This model accounts for limited sample sizes by incorporating prior information. In this study, the age variable, used as a covariate, had fewer first winter (1W) samples than adults, so we aimed to reduce the influence of this small sample size on the model. The Bayesian GLM was implemented in R (version 4.4.2) with the brms package, which interfaces with Stan [ 43 , 44 ]. In the migration connectivity analysis, the stable hydrogen isotope values of each individual’s feathers served as the dependent variable, while the wintering site where the sample was collected was the independent variable. Individual age was included as a covariate. To understand latitudinal migration patterns, the latitude of each wintering site was treated as the independent variable, with age included as a covariate. Weakly informative priors were specified for the model parameters. The independent variables and intercept were modeled with normal distributions (𝑁(− 80, 20 2 ), 𝑁(0, 10 2 )), and sigma was assigned an exponential distribution (Exp(1)). The Markov Chain Monte Carlo (MCMC) method was used to estimate the posterior distribution from the Bayesian model. Four chains were run, each with 6,000 iterations. The warm-up period for model convergence was 2,000 iterations. Model convergence was assessed by examining R-hat statistics (< 1.01), effective sample sizes, and visual inspection of trace plots. To verify the effectiveness of the Bayesian model, the 95% credible interval of the estimated value was examined. If the CI included 0, it was interpreted as considerable uncertainty in the direction of the effect. Therefore, variables that did not include 0 were regarded as having an effect on the dependent variable. Isoscape assignment To visualize the breeding origins based on the hypotheses we tested, we performed geographic assignments of breeding origins using the assignR package in R [ 45 ] environment based on the hydrogen isotope ratio. We used a raster map of hydrogen stable isotope values (δ 2 H p ) for global mean annual precipitation provided by the Global Network of Isotopes in Precipitation (GNIP) of the International Atomic Energy Agency (IAEA). The grid resolution of the global δ 2 H p raster was 5 arc-minutes (~ 10 km at the equator). Since the calibration equation, which relates feather hydrogen to precipitation isotopes, has not been confirmed for the Asian region, a simple linear regression model was derived from data on songbirds with known origins in Europe and North America (δ 2 H f = 0.613 × δ 2 H p - 16.983; using the CalRaster function), and we calibrated the raster map of hydrogen stable isotope values for annual average precipitation into a feather hydrogen stable isotope raster. We created an individual probability density raster of breeding origin using the pdRaster function, applying a geographic mask based on the known species range [ 15 ]. A posterior probability density raster was then generated for variables with explanatory power (site, latitude, or age) using the JointP function, assuming that individuals with the same variable information originated from the same breeding population. Finally, probability contour (50%, 90%, and 100% areas of the probability) were calculated to estimate the approximate origins, and the centroids for the 50% area were generated. DNA extraction and sequencing All DNA extraction, polymerase chain reaction (PCR), and sequencing were conducted at Macrogen Incorporated (Korea). Most analytical methods followed those described in the earlier study [ 46 , 47 ]. DNA extracts were obtained using an AxenTM Total DNA Mini Kit (Macrogen, Korea) following the manufacturer’s protocol. The CR and COI regions in mitochondrial DNA were amplified using specific primers: for CR, CR-Cor+ (5’-ACCCTTCAAGTGCGTAGCAG-3’) and Phe-Cor- (5’-(TTGACATCTTCAGTGTCATGC-3’); for COI, BirdF1 (5’-ACGTGGGAGATAATTCCAAATCCTGG-3’) and BirdR2 (5’-ACTACATGTGAGATGATTCCGAATCCAG-3’). If amplification of COI was of low quality, an alternative reverse primer, Vertebrate R1 (5’-TAGACTTCTGGGTGGCCAAAGAATCA-3’), was used. Target gene-specific primer pairs and Dr.MAX DNA Polymerase (Doctor Protein Inc., South Korea) were used for PCR reactions. CR PCR was performed on a DNA Engine Tetrad 2 Peltier Thermal Cycler (Eppendorf, Germany) in 25 µl reactions with 0.5 units of Dynazyme DNA polymerase (Finnzyme OY, Finland), 1 µM of each primer, and 0.2 mM of each dNTP (Boehringer Mannheim, Germany). The annealing temperature was 58°C, with 35 reaction cycles. The PCR amplification conditions included: 1 min at 94°C, followed by six cycles of 1 min at 94°C, 1.5 min at 45°C, and 1.5 min at 72°C; then 35 cycles of 1 min at 94°C, 1.5 min at 55°C, and 1.5 min at 72°C; ending with a 5-minute final extension for CR. PCR products were purified using a Millipore plate MSNU030 (Millipore SAS, France). The purified PCR products were subsequently Sanger-sequenced with the BigDye Terminator v3.1 sequencing kit and an A3730xl automated sequencer (Applied Biosystems, USA). Population genetics The nucleotide sequences of mitochondrial COI and CR genes were aligned using MEGA (version 11) [ 48 ] and BioEdit (version 7.2) [ 49 ], then visually checked. The number of haplotypes (No. haps), haplotype diversity (h), the average number of nucleotide differences (k), and nucleotide diversity (π) were calculated separately for COI and CR, as well as for the concatenated sequences, using DNASP (version 6.12.03) [ 50 ]. To examine the population genetic structure, we calculated pairwise genetic distance F st values and performed an analysis of molecular variance (AMOVA) with Arlequin (version 3.5.2.2) using 10,000 permutations to assess genetic variance among groups and populations [ 51 ]. We also assessed population expansion using Fu’s and Tajima’s neutrality tests with 1,000 random permutation tests to evaluate statistical significance [ 52 , 53 ]. Finally, to visualize the genetic relationships among populations, haplotype network analysis was conducted in PopART (version 1.7) using a median-joining network [ 54 ]. Declarations Author contribution E.K. and C.C. conceptualized the study. E.K., C.K. and C.C. conducted field work and collected data. E.K. performed statistical analysis and prepared the original draft. W.L., C.K., S.C. and C.C. reviewed and edited the manuscript. W. L. and C.C. acquired funding. C.C. supervised the project. All authors approved the final manuscript. Data availability statement The DNA data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB102121 (https://www.ebi.ac.uk/ena/browser/view/PRJEB102121). The stable isotope datasets generated and analyzed during the current study are not publicly available due to data ownership and management restrictions, but are available from the corresponding author on reasonable request. Funding The study was supported by the Korea Environment Institute through a research program (GP2022-14) entitled “The analysis of biogeographical and phenological migration pattern of birds as vectors of avian influenza (0525–202300058)”. References Redpath, S. M. et al. Understanding and managing conservation conflicts. Trends Ecol. Evol. 28 , 100–109 (2013). Nyhus, P. J. Human–wildlife conflict and coexistence. Annu. Rev. Environ. Resour. 41 , 143–171 (2016). Soulsbury, C. D. & White, P. C. L. Human–wildlife interactions in urban areas: a review of conflicts, benefits and opportunities. Wildl. Res. 42 , 541 (2015). Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. 109, 16083–16088 (2012). Loss, S. R., Will, T., Loss, S. S. & Marra, P. P. Bird–building collisions in the United States: Estimates of annual mortality and species vulnerability. Condor 116 , 8–23 (2014). 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C. brms: An r package for bayesian multilevel models using stan. J Stat. Softw 80 , (2017). R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. (2024). Available at: https://www.R-project.org/ Ma, C., Vander Zanden, H. B., Wunder, M. B. & Bowen, G. J. assignR: An r package for isotope-based geographic assignment. Methods Ecol. Evol. 11 , 996–1001 (2020). Kerr, K. C. R. et al. Comprehensive DNA barcode coverage of North American birds. Mol. Ecol. Notes . 7 , 535–543 (2007). Haring, E., Gamauf, A. & Kryukov, A. Phylogeographic patterns in widespread corvid birds. Mol. Phylogenet Evol. 45 , 840–862 (2007). Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38 , 3022–3027 (2021). Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT Hall 1999. Nucleic Acids Symp. Ser. 41, 95–98 (1999). Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34 , 3299–3302 (2017). Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10 , 564–567 (2010). Fu, Y. X. Statistical tests of neutrality of mutations against population growth, Hitchhiking and background selection. Genetics 147 , 915–925 (1997). Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123 , 585–595 (1989). Leigh, J. W., Bryant, D. & PopArt Full-feature software for haplotype network construction. Methods Ecol. Evol. 6 , 1110–1116 (2015). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers invited by journal 18 Nov, 2025 Editor invited by journal 12 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 10 Nov, 2025 First submitted to journal 10 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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17:54:24","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143218,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7921474/v1/4e574130de2bf120d212a07b.html"},{"id":97284318,"identity":"fa228588-e852-4449-9510-c3515de83205","added_by":"auto","created_at":"2025-12-02 17:54:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":797773,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic range and study sites of the Eastern Rook (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e). The geographic range was modified from that of BirdLife International [15] based on a previous study [15]. Colors indicate breeding, wintering, and year-round areas, with additional wintering areas shown as stripes. Black points mark the study sites in Korea.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7921474/v1/428b2597c8b18163d37c626c.png"},{"id":97368783,"identity":"7e10907a-c74a-4333-9e4c-43b317194017","added_by":"auto","created_at":"2025-12-03 16:22:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129836,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior expected δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ef\u003c/sub\u003e values for migration connectivity (a) and migration pattern (b) of the Eastern Rook (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e), predicted from the Bayesian models. (a) Horizontal density plots show posterior distributions across four wintering sites (Jeju, Ulsan, Gimjae, and Gyeonggi), with thick and thin bars indicating 50% and 95% credible intervals, respectively. Colors represent age classes (adult, A: purple; first winter, 1W: pink). (b) Posterior predictions along the latitude gradient, with dashed lines indicating posterior mean trends that provide no strong posterior support, as the 95% CI of the slope includes zero. Shaded ribbons display 95% credible intervals, and overlaid points show observed δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ef\u003c/sub\u003e values colored by age.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7921474/v1/7c0038982aedf8a223fd6552.png"},{"id":97284323,"identity":"fc9329f3-fa3e-49d5-8c43-99e7714f7ac5","added_by":"auto","created_at":"2025-12-02 17:54:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1433542,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 4 Predicted breeding origins of rooks based on wintering sites (Gyeonggi, Jeonbuk \u0026amp; Jeju, Ulsan) and age groups (adult, A; first winter, 1W) using hydrogen stable isotope assignment. Raster shading indicates the probability of origin. Dark and light green areas represent the 50% (core) and 95% (extent) probability regions, respectively, while the broadest background layer (100%) shows the known breeding range of the the Eastern Rook (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e). Circles mark the centroids of the 50% probability areas, with sample sizes (n) shown for each panel.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7921474/v1/08ec61c2cbe5d01874ace691.png"},{"id":97369182,"identity":"fc7118e2-ea21-4f51-a090-d6c230afb551","added_by":"auto","created_at":"2025-12-03 16:23:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211675,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 5 Median-joining network for concatenated sequences (a) and separate COI (b) and CR (c) in the Eastern Rook (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e). Circles represent haplotypes, and the size of each circle indicates the number of individuals. Network branches show a single nucleotide change, with additional mutations marked by bars on the branches. Black dots denote unsampled haplotypes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7921474/v1/eba44282298d2a4978bf49d0.png"},{"id":97664681,"identity":"a7888f67-bb1e-4ce4-b1d8-c0d49fd215bc","added_by":"auto","created_at":"2025-12-08 09:12:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3255730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7921474/v1/ebfe1ee0-0ffb-4156-9627-a3baf9ec6af2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Weak migration connectivity and high genetic admixture of urban-adapted Eastern Rooks (Corvus frugilegus pastinator) wintering in Korea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe reduction of wild habitats, the expansion of human areas, and increased human activity in nature have led to more perpetual and severe conflicts between wildlife and humans, occurring globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Urbanization is one of the major drivers of rapid habitat changes and results in frequent conflicts with wildlife due to a high density of humans and man-made structures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As these conflicts can lead to direct and indirect harm to humans; e.g. collisions with anthropogenic objects including vehicles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], an accompanying property damage and human casualties, threat to human well-being and health by increased risk of spreading zoonosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], sanitation and esthetic issue caused by feces, and aggressive behavior toward humans to protect nests [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], immediate action often required when wildlife-human conflicts occur in urban areas.\u003c/p\u003e\u003cp\u003eAlthough non-lethal methods, such as supporting alternative habitats, relocation, and eviction, are sometimes used as management strategies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], lethal methods that are believed to be more effective than non-lethal approaches are also implemented [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, to determine the timing, intensity, and area of lethality, as well as to predict the outcomes of these methods, ecological information about the target species is crucial. For migratory birds that cover large geographical areas, their spatial and temporal distribution, migration connectivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and migration patterns [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] must be considered. Without knowledge of the migration ecology of these birds, predicting the impact on their population becomes even more difficult.\u003c/p\u003e\u003cp\u003eRecently, Asia has been experiencing faster habitat changes and urban growth than other continents [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, migratory birds must adapt to these environments to survive, and as contact with humans gradually increases, conflicts and collisions with people are expected to rise further. However, comprehensive research on migratory birds, which is fundamentally needed to address this issue, is very limited in Asia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although studies are ongoing on specific taxa of interest, many species still have unknown migratory ecologies, and collecting and understanding ecological information remains essential to reduce human-wildlife conflicts, promote coexistence, and conserve biodiversity and species.\u003c/p\u003e\u003cp\u003eRook \u003cem\u003eCorvus frugilegus\u003c/em\u003e, a common passerine bird widely distributed from Europe to East Asia, is divided into two subspecies. The Eastern Rook \u003cem\u003eC. frugilegus pastinator\u003c/em\u003e in East Asia breeds in northern Mongolia, northeastern Russia, and China, then migrates to the eastern coast of China, Korea, and Kyushu in Japan for wintering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the distribution of the rooks in Japan expanded to the Shikoku and Hokuriku regions in the 1980s and later to northern Honshu in the 1990s [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recently, rooks wintering in Japan and Korea have increasingly adapted to urban areas as roosting sites [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In Korea, the largest wintering population is found in Ulsan, where numbers have increased from 30,000 to 40,000 individuals in 2010 to 70,000 in recent years [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], while large flocks (up to 30,000 individuals) are also observed in Gyeonggi and Jeonbuk Provinces. This adaptation has caused inconvenience for residents due to noise, fecal pollution, safety and disease concerns, and aesthetic damage. To mitigate these issues, lasers have been used to drive flocks away from cities, but conflicts with humans remain unresolved and continue to intensify. Consequently, the governments of Korea designated the rook as a \u0026ldquo;Harmful Wildlife\u0026rdquo;, under the Wildlife Protection and Management Act, and allowed hunting to control their populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRooks wintering in Korea can be broadly classified into four geographically distinct populations: Gyeonggi, Jeonbuk (especially at Gimje), Ulsan, and Jeju Island (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). If these populations exhibit strong migration connectivity, meaning that individuals at each wintering site originate from a distinct breeding population, localized harvesting to specific regions could be effective in controlling nuisance individuals. Conversely, weak connectivity would imply extensive mixing among breeding populations, rendering localized control ineffective. However, little is known about the rook\u0026rsquo;s migration ecology, and research has mainly focused on wintering areas [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, for effective management of this species, information about their origins and the strength and pattern of connectivity between wintering and breeding sites is essential.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn this study, we investigated whether geographically distinct wintering populations of Eastern Rooks in Korea originate from separate breeding areas. Specifically, we examined migration connectivity, latitudinal migration patterns, and analyzed population genetic structure using intrinsic markers, hydrogen stable isotope analysis, and mitochondrial DNA sequences from Cytochrome c oxidase subunit I (COI) and the Control Region (CR). Based on the clear geographic separation and latitudinal gradient of wintering sites, we tested the following hypotheses: 1) populations with distinct wintering areas will also have geographically separate breeding areas, indicating strong migration connectivity; 2) northern wintering populations will originate from more northern breeding locations, demonstrating a chain migration pattern; and 3) genetically distinct population structures will be evident among wintering groups if their breeding origins are geographically separated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMigration connectivity \u0026amp; Migration pattern\u003c/p\u003e\u003cp\u003eA Bayesian GLM analysis of migration connectivity revealed that the Jeju (Estimate\u0026thinsp;=\u0026thinsp;3.72, 95% CI = -1.00\u0026ndash;8.27) and Jeonbuk (Estimate\u0026thinsp;=\u0026thinsp;4.26, 95% CI = -0.94\u0026ndash;9.45) populations had posterior estimates with credible intervals overlapping zero, indicating no clear differences from the reference Gyeonggi population. In contrast, the Ulsan population (Estimate\u0026thinsp;=\u0026thinsp;8.27, 95% CI\u0026thinsp;=\u0026thinsp;2.88\u0026ndash;13.73) showed a positive effect with its 95% credible interval excluding zero, indicating strong posterior support for higher hydrogen stable isotope values. However, Ulsan did not clearly differ from the Jeonbuk and Jeju populations. Age was also supported as an important covariate; adults (A) had higher isotope values than first winter individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePosterior estimates from Bayesian generalized linear models of migration connectivity and migration pattern in the Eastern Rook (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e). Posterior mean estimates (Estimate), standard errors (Est. Error), and 95% credible intervals (Lower 95% CI, Upper 95% CI) are shown for each predictor. Rhat values close to 1.0 indicate model convergence, and Bulk_ESS and Tail_ESS denote effective sample sizes for bulk and tail estimates, respectively. Migration connectivity model compares stable isotope values (δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ef\u003c/sub\u003e) among wintering sites (reference: Gyeonggi), with age (adult vs. first winter), included as a covariate. The migration pattern model includes latitude as a main predictor and age as covariate.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEst.Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUpper 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRhat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBulk_ESS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTail_ESS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eMigration connectivity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-86.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-91.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-81.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eJeju\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eJeonbuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12648\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eUlsan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAgeA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e21609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eMigration pattern\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-45.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-95.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLatitude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eAgeA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11996\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\u003eRegarding migration patterns, the effect of latitude on hydrogen stable isotope values (Estimate\u0026thinsp;=\u0026thinsp;1.07, 95% CI = -2.39\u0026ndash;0.27) showed a wide credible interval overlapping zero, indicating no clear evidence of a latitudinal trend. In contrast, age again showed strong posterior support for higher isotope values in adults compared to first winter individuals (Estimate\u0026thinsp;=\u0026thinsp;7.29, 95% CI\u0026thinsp;=\u0026thinsp;2.36\u0026ndash;12.23; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeographic assignment\u003c/p\u003e\u003cp\u003eBased on the migration connectivity and pattern analyses, we identified the breeding origins of wintering populations and age groups of rooks in Korea (Fig.\u0026nbsp;3). Overall, breeding origins centered around the border regions of Mongolia\u0026ndash;Russia and China\u0026ndash;Russia, including the Amur River Basin in northeastern China and the Baikal region. Adults from the Gyeonggi, Jeonbuk, and Jeju populations showed similar distribution patterns, with the centroid of the 50% probability region located in Zabaykalsky Krai, Russia. In contrast, the centroid of the Ulsan population was shifted toward lower latitudes, near Bayantu\u0026rsquo;men, Mongolia, and Hulunbuir, China. For first winter individuals, the centroids of the Ulsan, Jeonbuk, and Jeju populations were also in Zabaykalsky Krai, Russia, while those from Gyeonggi were further north, in the Mogochinskiy Rayon of Zabaykalsky Krai. Although the latitudinal distribution of breeding origins varied slightly by region and age, the probability regions largely overlapped, showing no geographically distinct breeding grounds among wintering groups.\u003c/p\u003e\u003cp\u003ePopulation genetic structure\u003c/p\u003e\u003cp\u003eGenetic diversity indices based on concatenated COI and CR sequences are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Across all wintering populations, haplotype diversity was high (h\u0026thinsp;=\u0026thinsp;0.887), whereas nucleotide diversity remained low (π\u0026thinsp;=\u0026thinsp;0.00172), indicating moderate overall diversity. At the population level, haplotype diversity ranged from 0.833 (Ulsan) to 0.933 (Gyeonggi), while nucleotide diversity ranged from 0.00108 (Jeju) to 0.00215 (Gyeonggi). Neutrality tests revealed negative Tajima\u0026rsquo;s D values across all populations, with the Gyeonggi population showing a significant deviation from neutrality (D = -4.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Fu\u0026rsquo;s F\u003csub\u003es\u003c/sub\u003e values were generally negative, but none were statistically significant. Overall, concatenated mtDNA sequences indicated moderate haplotype diversity, low nucleotide diversity, and little evidence for recent population expansion, except for a potential signal of demographic change in the Gyeonggi population.\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of genetic diversity of Eastern Rooks (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e) wintering in Korea. For COI, CR, and concatenated sequences, we calculated sample size (n), number of haplotypes (No. haps), haplotype diversity (h), nucleotide diversity (π), average number of nucleotide differences (k), Fu\u0026rsquo;s Fs (Fs), and Tajima\u0026rsquo;s D (D). The significance of Fs and D was tested using 1000 random permutations, and values with significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in bold.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo. haps\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ek\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eπ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eF\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGyeonggi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCOI\u0026thinsp;+\u0026thinsp;CR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-4.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeonbuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.352\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUlsan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeju\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.547\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGyeonggi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e-1.796\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-1.803\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeonbuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUlsan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-2.978\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeju\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e-1.401\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-1.164\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGyeonggi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-2.735\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeonbuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUlsan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-2.231\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeju\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGenetic differentiation among wintering populations was low (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Pairwise F\u003csub\u003est\u003c/sub\u003e values estimated from concatenated COI and CR sequences ranged from \u0026minus;\u0026thinsp;0.053 to 0.040, with none being significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Analysis of molecular variance (AMOVA) further supported this pattern, showing that the vast majority of genetic variation (100.65%) was distributed within populations, whereas variation among populations was negligible (\u0026ndash;0.65%). The overall fixation index was close to zero (F\u003csub\u003est\u003c/sub\u003e = \u0026minus;\u0026thinsp;0.0065), and permutation tests confirmed non-significance (p\u0026thinsp;=\u0026thinsp;0.538).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eF\u003csub\u003est\u003c/sub\u003e values estimated using concatenated COI and CR sequences among wintering populations. Values with significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in bold.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGyeonggi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJeonbuk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUlsan\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJeju\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGyeonggi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeonbuk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUlsan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eJeju\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInformation on collected samples and used samples for analysis. A denotes an adult, while 1W indicates a young bird in its first calendar year (a first winter bird) of the Eastern Rook (\u003cem\u003eCorvus frugilegus pastination\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003eH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePeriod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCoordinates\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1W\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGyeonggi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25 January 2022\u003c/p\u003e\u003cp\u003e27\u0026ndash;31 January 2025\u003c/p\u003e\u003cp\u003e27\u0026ndash;31 January 2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37\u0026deg;17'15.4\"N 126\u0026deg;51'48.9\"E\u003c/p\u003e\u003cp\u003e37\u0026deg;06'25.2\"N 127\u0026deg;02'45.6\"E\u003c/p\u003e\u003cp\u003e37\u0026deg;00'07.2\"N 126\u0026deg;59'31.2\"E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJeonbuk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17 March 2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e35\u0026deg;50'03.7\"N 126\u0026deg;50'16.9\"E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlsan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e03 March 2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e35\u0026deg;32'52.0\"N 129\u0026deg;17'46.4\"E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJeju\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e03 December 2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33\u0026deg;31'43.6\"N 126\u0026deg;38'42.0\"E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we integrated stable hydrogen isotope analyses and mitochondrial DNA sequencing to examine the migration connectivity, migration patterns, and population genetic structure of rook populations wintering in South Korea. The results revealed weak migratory connectivity, an absence of latitudinal migration patterns, and no detectable genetic differentiation among wintering populations. Despite the clear geographic separation of wintering sites, the breeding origins of these populations largely overlapped, suggesting that rooks wintering in Korea constitute a diffuse, panmictic population rather than distinct subpopulations.\u003c/p\u003e\u003cp\u003eStable isotope analyses showed that the Ulsan population had higher δ\u003csup\u003e2\u003c/sup\u003eH values than the Gyeonggi population, suggesting that individuals in Ulsan likely originated from relatively lower latitude breeding grounds compared to the Gyeonggi population. However, Jeju and Jeonbuk populations did not differ significantly from either Gyeonggi or Ulsan, indicating extensive overlap in breeding origins among wintering sites. Geographic assignment analyses further supported this pattern, with broad overlap in the core breeding probability regions. No spatially structured migration pattern, such as chain migration, where northern breeders winter further north, or leapfrog migration, where they winter further south, was detected. Although Ulsan, a relatively southern wintering site, showed breeding origins at slightly lower latitudes compared to Gyeonggi, this trend was not consistent across all wintering grounds. Along with stable isotope results, the genetic evidence reinforces the idea that Rooks in Korea represent a diffuse population with low migration connectivity. Although limited by small sample sizes, in mitochondrial DNA analyses, negative or near-zero values in pairwise F\u003csub\u003est\u003c/sub\u003e and AMOVA results provided no evidence of significant population structure, indicating that Korean wintering rooks belong to large, panmictic breeding population, and there is a high level of mixing among wintering populations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMigration connectivity can be determined by two components, spreading and mixing [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Spreading describes how widely individuals from a single breeding site disperse across wintering areas, whereas mixing reflects the extent to which individuals from multiple breeding sites overlap within a single wintering area. When the degree of spreading is high, population mixing is more likely to occur, thereby weakening connectivity. Conversely, when spreading is limited, mixing decreases and connectivity becomes stronger. However, when the wintering range is relatively small compared to the breeding range, spatial constraints can still promote mixing, even if spreading is limited, thereby reducing connectivity. As we didn\u0026rsquo;t use detailed tracking data, which can provide information on the degree of individual spatial spread, we were unable to define whether this species exhibits high or low spreading characteristics. Nevertheless, the breeding range of Eastern Rook covers a much larger area than its relatively limited wintering range (southern China, Korea, and Japan; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and thus population mixing may occur regardless of the degree of spreading.\u003c/p\u003e\u003cp\u003eThe only tracking study on the pastinator subspecies of rook in East Asia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] identified a few migration routes and breeding grounds of the Hokkaido population in Japan, revealing that tracked individuals bred around Lake Khanka and the Amur River basin. Although the number of tracked individuals was limited, the gregarious behavior of this species during both migration and wintering suggests that these results reasonably represent the Hokkaido population. Among Korean wintering grounds, Ulsan is located on the east coast and is relatively close to Japan. Therefore, it is plausible that the Ulsan population breeds in areas similar to the Japanese population, with their relatively higher δ\u003csup\u003e2\u003c/sup\u003eH values compared to other Korean populations reflecting the presence of individuals breeding at lower latitudes (e.g., Lake Khanka). Additional studies tracking rooks in Korea, encompassing multiple wintering locations, will help identify their migration routes and specific breeding sites, as well as understand the factors driving these lower stable isotope values.\u003c/p\u003e\u003cp\u003eAnother factor influencing isotope values was age. Adults consistently exhibited higher δ\u003csup\u003e2\u003c/sup\u003eH values than first winter individuals. In species that undergo molting after the breeding season (post-breeding molt) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], arid conditions or poor resources at the end of the breeding season make birds leave before molting, and this causes individuals to replace feathers during southward migration after raising their offspring, a strategy often referred to as molt migration [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Rook is the species that performs a post-breeding molt in which adults replace their entire plumage, whereas first winter birds replace only the body feathers and wing coverts, retaining their juvenile primaries and secondaries in Europe [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consequently, the wing feathers of first winter birds reflect the isotopic signature of their natal breeding grounds, while the feathers of adults may incorporate isotopic signals from lower latitudes where molting occurs after breeding [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Another possibility is that, in some species (e.g., Cooper\u0026rsquo;s Hawk \u003cem\u003eAstur cooperii\u003c/em\u003e, American Redstart \u003cem\u003eSetophaga ruticilla\u003c/em\u003e), adults exhibit higher isotope values than their offspring even when both molt at the same location [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, it would be valuable to further analyze the isotopic differences between adults and juveniles at the same breeding sites in Eastern Rooks.\u003c/p\u003e\u003cp\u003eDuring rapid population growth, new haplotypes are generated and retained, leading to relatively high haplotype diversity. However, the short evolutionary timescale is insufficient for the accumulation of large numbers of substitutions among haplotypes, resulting in low nucleotide diversity. This pattern is often observed in populations that have recently undergone a bottleneck or founder effect, followed by expansion [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Consequently, populations may exhibit an excess of low-frequency polymorphisms, which is reflected in negative values of Tajima\u0026rsquo;s D and Fu\u0026rsquo;s Fs. Although most neutrality tests were not statistically significant in our data, a combination of moderate haplotype diversity and low nucleotide diversity, and generally negative neutrality statistics across wintering populations could suggest the possibility of recent, rapid demographic expansion from an ancestral group [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The significant negative Tajima\u0026rsquo;s D observed in the Gyeonggi population may also indicate a localized or recent demographic expansion event. The Nationwide Winter Waterbird Census (NWWC) in South Korea, conducted across key habitats for waterbirds, also surveys wintering passerines, though they are not among the target species [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Population data on rooks are primarily available for the Ulsan region, which appears to be experiencing an increasing trend. Since the wintering populations originate from a single breeding population, similar increases are likely occurring in other regions as well, and such demographic changes may also be reflected in the DNA results.\u003c/p\u003e\u003cp\u003eAlthough intrinsic markers such as stable isotopes and mitochondrial DNA sequences have certain limitations, they remain widely used tools in studies of avian migration. Stable isotope analyses can be influenced by confounding factors such as local diet, isotopic variance within isoscapes, and differences in molting strategies, which may reduce spatial resolution and complicate the assignment of breeding origins [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Similarly, mtDNA markers provide valuable insights into population structure and demographic history but may fail to detect fine-scale differentiation due to maternal inheritance and reduced effective population size relative to nuclear DNA [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Despite these constraints, intrinsic markers continue to be powerful approaches for investigating migratory connectivity, particularly when combined with other methods such as extrinsic tracking and multi-isotope or genomic analyses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, in stable isotope studies, increasing the number of samples has been shown to narrow the predicted breeding origin areas and improve assignment accuracy [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, our study demonstrates that stable isotope analysis provides an efficient and non-invasive alternative for investigating population migration, including migration connectivity, migration patterns, compared to methods that require capturing a small number of individuals and attaching costly Global Positioning System (GPS) telemetry devices.\u003c/p\u003e\u003cp\u003eOur findings suggest that localized lethal control at particular wintering sites is unlikely to effectively reduce overall rook populations in Korea. Because wintering groups originate from overlapping breeding populations, controlling numbers at one site may simply be offset by immigration from other breeding sources. Moreover, information essential for evaluating the effectiveness of lethal control, including total wintering population size in Korea, demographic structure (age and sex ratios), and harvest data, is currently lacking. Given these uncertainties, non-lethal management approaches may provide more sustainable solutions. Potential strategies include habitat modification to reduce roost attractiveness, provision of alternative roosting sites, active disturbance and deterrence, and public education to increase tolerance and awareness of rooks in urban areas [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, future research on rooks will be essential to refine our understanding of their migration ecology and population dynamics. Tracking studies are needed to directly determine breeding origins and to compare these data with our isotope-based assignments, particularly to test whether the Ulsan population indeed breeds at lower latitudes such as Lake Khanka. It will also be important to investigate age-related differences in molting locations to clarify how molt timing influences isotopic signals. In addition, long-term monitoring of the population trends of rooks wintering in Korea is required to assess whether the genetic signatures of demographic expansion observed in this study correspond to actual increases in population size. Further research on breeding ecology at the breeding grounds would also help to connect demographic processes with migration connectivity.\u003c/p\u003e\u003cp\u003eMore broadly, our study highlights the need for ecological information not only on rooks but also on other migratory bird species that are increasingly becoming the focus of management due to human\u0026ndash;wildlife conflicts. A deeper understanding of their migration strategies, demographic history, and breeding\u0026ndash;wintering linkages will be crucial for developing science-based management and conservation measures in rapidly changing, human-dominated landscapes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSample collection\u003c/p\u003e\u003cp\u003eFor stable isotope and DNA analysis, feathers and tissue samples (pectoral muscle) were collected. In Gimjae and Ulsan, carcasses found at roosting sites were used. For the Jeju population, carcasses that had died from pesticide poisoning were obtained from the Jeju branch of the Korean Association for Bird Protection. For the Gyeonggi population, individuals harvested by the Pyeongtaek branch of the Wildlife Management Association, under permission from Pyeongtaek City, as part of the municipal pest control program, were used (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We used only harvested or salvaged carcasses, and no bird was harmed or killed for this study. When collecting the carcasses, we measured, aged, and collected the samples following the procedures below.\u003c/p\u003e\u003cp\u003eSince adults undergo a complete post-breeding molt during or after the breeding season, and juveniles retain their natal wing feathers, we collected wing feathers for stable isotope analysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Because isotope ratios can differ depending on the feather location [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the third secondary feather of the right wing (SR3) was sampled from all individuals. Feathers suspected to have been replaced outside the breeding grounds were excluded from sampling to avoid isotopic bias. All collected feathers and tissue samples were stored at -20\u0026deg;C until analysis.\u003c/p\u003e\u003cp\u003eHydrogen stable isotope analysis\u003c/p\u003e\u003cp\u003eMany elements in nature are a mixture of two or more isotopes due to differences in neutrons [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Specifically, stable isotopes remain in a stable state in the environment, leading to variations in isotope ratios across different geographic and trophic levels. Therefore, stable isotopes found in tissues can serve as valuable chemical tracers in ecological research. Notably, the hydrogen stable isotope ratio in precipitation (δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ep\u003c/sub\u003e) creates a spatial gradient across the Northern Hemisphere, generally increasing from higher to lower latitudes, which makes it useful for estimating geographic origins. Bird feathers are inert tissues that preserve the stable isotope composition during feather growth [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], so δ\u003csup\u003e2\u003c/sup\u003eH of feathers (δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ef\u003c/sub\u003e) from migratory birds has been used to understand migration characteristics such as connectivity, and spatial and temporal patterns.\u003c/p\u003e\u003cp\u003eStable isotope analysis was performed using about 3 cm of the tip of collected feathers at Iso-Analytical Limited and Sercon Analytical Limited (Crewe, UK) between 2022 and 2025. All samples were equilibrated with ambient laboratory moisture for 14 days before analysis. Each feather sample was first washed in 0.25 M sodium hydroxide, rinsed twice with purified water, and then dried overnight at 60\u0026deg;C. The cleaned feathers were finely chopped, and about 1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mg was weighed into silver capsules for hydrogen isotope analysis using Elemental Analyzer\u0026ndash;Isotope Ratio Mass Spectrometry (EA-IRMS). IA-R002 (δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003eV\u0026minus;SMOW\u003c/sub\u003e = \u0026minus;\u0026thinsp;111.2\u0026permil;) served as the reference material, with an additional IA-R002 (\u0026ndash;148.61\u0026permil;) used for quality control. These standards were calibrated against NBS-22 and IAEA-CH-7. Four non-exchangeable hydrogen standards (USGS42, USGS43, USGS CBS, and USGS KHS) were used for calibration and control. A three-point linear calibration with USGS42, CBS, and KHS corrected for exchangeable hydrogen, while USGS43 was used to validate this calibration. Final δ\u003csup\u003e2\u003c/sup\u003eH values are reported both uncorrected (total hydrogen) and corrected for exchangeable hydrogen. We used a non-exchangeable hydrogen value for further analysis.\u003c/p\u003e\u003cp\u003eMigration connectivity \u0026amp; Migration pattern\u003c/p\u003e\u003cp\u003eWe used a Bayesian generalized linear model to determine whether stable hydrogen isotope values varied across wintering sites and to assess spatial trends in breeding origins based on the latitude of wintering sites. This model accounts for limited sample sizes by incorporating prior information. In this study, the age variable, used as a covariate, had fewer first winter (1W) samples than adults, so we aimed to reduce the influence of this small sample size on the model. The Bayesian GLM was implemented in R (version 4.4.2) with the brms package, which interfaces with Stan [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the migration connectivity analysis, the stable hydrogen isotope values of each individual\u0026rsquo;s feathers served as the dependent variable, while the wintering site where the sample was collected was the independent variable. Individual age was included as a covariate. To understand latitudinal migration patterns, the latitude of each wintering site was treated as the independent variable, with age included as a covariate. Weakly informative priors were specified for the model parameters. The independent variables and intercept were modeled with normal distributions (\u0026#119873;(\u0026minus;\u0026thinsp;80, 20\u003csup\u003e2\u003c/sup\u003e), \u0026#119873;(0, 10\u003csup\u003e2\u003c/sup\u003e)), and sigma was assigned an exponential distribution (Exp(1)). The Markov Chain Monte Carlo (MCMC) method was used to estimate the posterior distribution from the Bayesian model. Four chains were run, each with 6,000 iterations. The warm-up period for model convergence was 2,000 iterations. Model convergence was assessed by examining R-hat statistics (\u0026lt;\u0026thinsp;1.01), effective sample sizes, and visual inspection of trace plots.\u003c/p\u003e\u003cp\u003eTo verify the effectiveness of the Bayesian model, the 95% credible interval of the estimated value was examined. If the CI included 0, it was interpreted as considerable uncertainty in the direction of the effect. Therefore, variables that did not include 0 were regarded as having an effect on the dependent variable.\u003c/p\u003e\u003cp\u003eIsoscape assignment\u003c/p\u003e\u003cp\u003eTo visualize the breeding origins based on the hypotheses we tested, we performed geographic assignments of breeding origins using the assignR package in R [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] environment based on the hydrogen isotope ratio. We used a raster map of hydrogen stable isotope values (δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ep\u003c/sub\u003e) for global mean annual precipitation provided by the Global Network of Isotopes in Precipitation (GNIP) of the International Atomic Energy Agency (IAEA). The grid resolution of the global δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ep\u003c/sub\u003e raster was 5 arc-minutes (~\u0026thinsp;10 km at the equator). Since the calibration equation, which relates feather hydrogen to precipitation isotopes, has not been confirmed for the Asian region, a simple linear regression model was derived from data on songbirds with known origins in Europe and North America (δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ef\u003c/sub\u003e = 0.613\u0026thinsp;\u0026times;\u0026thinsp;δ\u003csup\u003e2\u003c/sup\u003eH\u003csub\u003ep\u003c/sub\u003e- 16.983; using the CalRaster function), and we calibrated the raster map of hydrogen stable isotope values for annual average precipitation into a feather hydrogen stable isotope raster.\u003c/p\u003e\u003cp\u003eWe created an individual probability density raster of breeding origin using the pdRaster function, applying a geographic mask based on the known species range [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A posterior probability density raster was then generated for variables with explanatory power (site, latitude, or age) using the JointP function, assuming that individuals with the same variable information originated from the same breeding population. Finally, probability contour (50%, 90%, and 100% areas of the probability) were calculated to estimate the approximate origins, and the centroids for the 50% area were generated.\u003c/p\u003e\u003cp\u003eDNA extraction and sequencing\u003c/p\u003e\u003cp\u003eAll DNA extraction, polymerase chain reaction (PCR), and sequencing were conducted at Macrogen Incorporated (Korea). Most analytical methods followed those described in the earlier study [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDNA extracts were obtained using an AxenTM Total DNA Mini Kit (Macrogen, Korea) following the manufacturer\u0026rsquo;s protocol. The CR and COI regions in mitochondrial DNA were amplified using specific primers: for CR, CR-Cor+ (5\u0026rsquo;-ACCCTTCAAGTGCGTAGCAG-3\u0026rsquo;) and Phe-Cor- (5\u0026rsquo;-(TTGACATCTTCAGTGTCATGC-3\u0026rsquo;); for COI, BirdF1 (5\u0026rsquo;-ACGTGGGAGATAATTCCAAATCCTGG-3\u0026rsquo;) and BirdR2 (5\u0026rsquo;-ACTACATGTGAGATGATTCCGAATCCAG-3\u0026rsquo;). If amplification of COI was of low quality, an alternative reverse primer, Vertebrate R1 (5\u0026rsquo;-TAGACTTCTGGGTGGCCAAAGAATCA-3\u0026rsquo;), was used. Target gene-specific primer pairs and Dr.MAX DNA Polymerase (Doctor Protein Inc., South Korea) were used for PCR reactions. CR PCR was performed on a DNA Engine Tetrad 2 Peltier Thermal Cycler (Eppendorf, Germany) in 25\u0026emsp;\u0026micro;l reactions with 0.5 units of Dynazyme DNA polymerase (Finnzyme OY, Finland), 1\u0026emsp;\u0026micro;M of each primer, and 0.2\u0026emsp;mM of each dNTP (Boehringer Mannheim, Germany). The annealing temperature was 58\u0026deg;C, with 35 reaction cycles. The PCR amplification conditions included: 1 min at 94\u0026deg;C, followed by six cycles of 1 min at 94\u0026deg;C, 1.5 min at 45\u0026deg;C, and 1.5 min at 72\u0026deg;C; then 35 cycles of 1 min at 94\u0026deg;C, 1.5 min at 55\u0026deg;C, and 1.5 min at 72\u0026deg;C; ending with a 5-minute final extension for CR. PCR products were purified using a Millipore plate MSNU030 (Millipore SAS, France). The purified PCR products were subsequently Sanger-sequenced with the BigDye Terminator v3.1 sequencing kit and an A3730xl automated sequencer (Applied Biosystems, USA).\u003c/p\u003e\u003cp\u003ePopulation genetics\u003c/p\u003e\u003cp\u003eThe nucleotide sequences of mitochondrial COI and CR genes were aligned using MEGA (version 11) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and BioEdit (version 7.2) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], then visually checked. The number of haplotypes (No. haps), haplotype diversity (h), the average number of nucleotide differences (k), and nucleotide diversity (π) were calculated separately for COI and CR, as well as for the concatenated sequences, using DNASP (version 6.12.03) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. To examine the population genetic structure, we calculated pairwise genetic distance F\u003csub\u003est\u003c/sub\u003e values and performed an analysis of molecular variance (AMOVA) with Arlequin (version 3.5.2.2) using 10,000 permutations to assess genetic variance among groups and populations [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We also assessed population expansion using Fu\u0026rsquo;s and Tajima\u0026rsquo;s neutrality tests with 1,000 random permutation tests to evaluate statistical significance [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Finally, to visualize the genetic relationships among populations, haplotype network analysis was conducted in PopART (version 1.7) using a median-joining network [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.K. and C.C. conceptualized the study. E.K., C.K. and C.C.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;conducted field work and collected data. E.K. performed statistical analysis and prepared the original draft. W.L., C.K., S.C. and C.C. reviewed and edited the manuscript. W. L. and C.C. acquired funding. C.C. supervised the project. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DNA data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB102121 (https://www.ebi.ac.uk/ena/browser/view/PRJEB102121). The stable isotope datasets generated and analyzed during the current study are not publicly available due to data ownership and management restrictions, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Korea Environment Institute through a research program (GP2022-14) entitled \u0026ldquo;The analysis of biogeographical and phenological migration pattern of birds as vectors of avian influenza (0525\u0026ndash;202300058)\u0026rdquo;.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRedpath, S. M. et al. Understanding and managing conservation conflicts. \u003cem\u003eTrends Ecol. Evol.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 100\u0026ndash;109 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNyhus, P. J. 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Evol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 1110\u0026ndash;1116 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Rook, Nuisance animal, Stable isotope, Migration connectivity, Migration pattern, Population genetics","lastPublishedDoi":"10.21203/rs.3.rs-7921474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7921474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal habitat loss and change reshape human-wildlife interactions, with animal migrations generating dynamic patterns of conflict. Thus, effective management of these issues requires knowledge of migratory ecology, particularly for migratory birds that strongly influence human activities, yet such information is often limited. We investigated the migration connectivity, migration patterns, and population genetic structure of the Eastern Rook (\u003cem\u003eCorvus frugilegus pastinator\u003c/em\u003e), a conflict-associated urban population increasingly adapting to East Asian cities. Through hydrogen stable isotope (δ\u003csup\u003e2\u003c/sup\u003eH) and mitochondrial DNA analyses, we examined 66 carcasses from four wintering sites in Korea. Feather δ\u003csup\u003e2\u003c/sup\u003eH values did not differ among sites and showed no clear latitudinal trend. However, adults consistently had higher δ\u003csup\u003e2\u003c/sup\u003eH values than first winter individuals, likely reflecting age-related differences in molt timing and location. Genetic analyses revealed high intermixing among wintering populations, with no evidence of geographically distinct breeding origins. These findings suggest that Korean wintering rooks constitute a diffuse population, implying that localized lethal control may be ineffective. Non-lethal management strategies, such as habitat modification, providing alternative roosts, and deterrence measures, should be considered to mitigate conflicts. Further research on age structure and breeding ecology is essential for identifying the causes of conflict and developing sustainable management solutions.\u003c/p\u003e","manuscriptTitle":"Weak migration connectivity and high genetic admixture of urban-adapted Eastern Rooks (Corvus frugilegus pastinator) wintering in Korea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 17:54:19","doi":"10.21203/rs.3.rs-7921474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T08:25:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T18:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81663906564850386873224107067265934611","date":"2026-01-08T21:19:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T21:31:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144540234078782085081630999570281952286","date":"2025-11-20T13:23:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-18T06:49:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-12T08:47:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T06:38:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-10T13:19:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-10T13:16:09+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":"87bc28ef-7e83-4db9-b6b9-0c00b4bae348","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58463036,"name":"Biological sciences/Ecology"},{"id":58463037,"name":"Earth and environmental sciences/Ecology"},{"id":58463038,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-05-05T06:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 17:54:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7921474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7921474","identity":"rs-7921474","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}