Coexistence of overwintering avian species in Tibet: scale-dependent niche requirements for landscape structure with body size effects

Coexistence of overwintering avian species in Tibet: scale-dependent niche requirements for landscape structure with body size effects | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coexistence of overwintering avian species in Tibet: scale-dependent niche requirements for landscape structure with body size effects Wanyu Wang, Maohua Ma, Wanyu Qi, Cunfeng Zhao, Jinxia Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9167989/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Rapid environmental changes on the Tibetan Plateau pose significant challenges to overwintering avian assemblages in the resource-limited environments. However, traditional conservation strategies often focus solely on establishing reserve boundaries, failing to recognize that effective protection depends on maintaining complex internal landscape structures tailored to species-specific scale requirements. This study investigated how landscape composition and configuration affect the habitat selection and coexistence of overwintering avian assemblages in a reserve, with a specific focus on the modulating role of body size. Three sympatric species with distinct body sizes, including Black-necked Crane ( Grus nigricollis ), Bar-headed Goose ( Anser indicus ), and Ruddy Shelduck ( Tadorna ferruginea ), were investigated in the Black-necked Crane National Nature Reserve along the Yarlung Zangbo River valley in China’s Tibet. Using a multi-scale analysis ranging from 500 m to 6000 m and a pairwise coexistence index, this study quantified the relationships between species abundance, coexistence patterns, and landscape metrics derived from remote sensing data. The results demonstrated that landscape effects were strongly scale-dependent and modulated by body size. The larger-bodied G. nigricollis exhibited heightened sensitivity to landscape metrics at broader spatial scales, showing a distinct preference for contiguous agricultural lands as critical food subsidies. Conversely, smaller species responded significantly to fine-scale landscape configurations. Furthermore, the scale-dependent niche requirements for landscape structures were found to facilitate niche segregation and mitigate interspecific competition. Our findings underscore that merely delineating reserve boundaries is insufficient. Instead, conservation planning must adopt a multi-scale framework grounded in trait-based ecology. Priority should be given to safeguarding internal habitat heterogeneity, while concurrently fostering synergistic land-use practices across the reserve landscape. Specifically, maintaining the availability of post-harvest croplands and ensuring wetland connectivity are critical to bridging the gap between the fine-scale needs of smaller species and the broad-scale foraging ranges of larger species. Grus nigricollis Landscape structure Body size Scale effect Coexistence Tibetan Plateau Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction Landscape structure serves as a key element for biodiversity conservation, providing the regional context for understanding ecological processes within habitats and supporting species preservation (Tscharntke et al. 2012b ). Consequently, the niche concept, originally conceptualized at the local scale (Hutchinson 1957 ; Chase and Leibold 2003 ; Soberón 2007 ), must be extended to the regional level to enhance the protection of biodiversity within habitats embedded in a surrounding landscape (Margules and Pressey 2000 ; Ricketts 2001 ). Over the past decades, land-use changes have been triggering transformations in landscape structure representing the principal drivers affecting keystone species within protected areas. Primarily, the impact mechanisms of the landscape changes encompass two components: landscape composition (the types and proportions of land covers) and landscape configuration (the spatial arrangement and fragmentation of landscape elements) (Li et al. 2023 ). These two components are intimately linked to processes of habitat selection, connectivity, and spatial patterns, thereby yielding critical insights for systematic conservation planning (Atauri and de Lucio 2001 ; Amici et al. 2015 ). In particular, the influence of the landscape change patterns on avian species is profound and scale-dependent, varying with species traits, biological responses (e.g., abundance and coexistence), and specific landscape metrics (Miguet et al. 2016 ; Martin 2018 ; Zhang et al. 2025 ). These changing patterns drive species persistence, particularly through habitat resource availability, distribution, population density, and interspecific coexistence (Dominik et al. 2012 ; Fahrig 2013 ; Suarez-Castro et al. 2024 ). Among the driving effects, a key aspect is the landscape effect, where the surrounding landscape influences habitat patches, connectivity, and species movement. In addition, selecting an inappropriate scale can lead to misinterpretations of landscape effects on ecological processes (Guo et al. 2023 ). Therefore, multi-scale analyses, such as establishing scaled buffer zones around habitats, are essential to identify the strongest associations and avoid single-scale limitations in design of protected areas amid ongoing global environmental transformations (Boscutti et al. 2019 ; Moraga et al. 2019 ; Adler and Jedicke 2022 ). The Tibetan Plateau, a region highly sensitive to environmental changes, features extreme high-altitude conditions which present distinctive ecological challenges, such as hypoxia, cold temperatures, and scarce resources. Despite these harsh conditions, this region is remarkably biodiversity-rich, hosting numerous protected and endemic taxa that have adapted to its unique environment. However, the rapid environmental changes during recent decades, particularly human-driven land-use alterations, pose significant threats to this biodiversity-rich but less studied region. These alterations include landscape transformations due to agricultural expansion, urbanization, overgrazing, and infrastructure development, which fragment wetlands and disrupt natural ecosystems (Wang et al. 2025 ; Xu et al. 2025 ). Yet, our understanding remains limited regarding how landscape effects influence habitat selection of the taxa in this understudied high-elevation zone. Furthermore, climate change exacerbates this uncertainty, especially for avian species in this region, by altering vegetation, water availability, and seasonal cycles, which degrades natural habitats. During the past decades, the Tibetan Plateau's landscape transformations directly threaten the protected endemic avian species, including the Black-necked Crane ( Grus nigricollis ), an endangered flagship species that migrates to the river valleys along the Yarlung Zangbo River during winter season. In response, the Black-necked Crane National Nature Reserve was established in the middle reaches of the river in China’s Tibet to protect the species. Even though human activities are strictly forbidden in the reserve's core zone, rapid land-use changes surrounding this zone may undermine its effectiveness, emphasizing the necessity for broader conservation strategies to counter persistent risks (Shen et al. 2020 ). However, the impact of the surrounding landscape on these protected avian species remains poorly understood. Furthermore, coexisting species shape the landscape effect more specifically. Landscape structure can influence population survival via interspecific interactions, especially through competitive dynamics (Hansson et al. 1995 ). Typically, coexisting species mitigate excessive competition through niche differentiation (Wood et al. 2021 ). Moreover, body size serves as an informative indicator of such niche differentiation (Macarthur and Levins, 1967 ; MacArthur, 1958 ). Different avian species often exhibit niche differentiation influenced by body size, which mediates foraging behavior, habitat selection, and adaptive responses to environmental changes (Laube et al. 2013 ). Body size differentiation is thus often associated with environmental climate stability and resource abundance (Read et al. 2018 ). For instance, avian species with larger body size may gain a greater advantage in competition, allowing them to occupy higher-quality food resources. In addition, body size affects metabolic rates and energy needs (Makarieva et al. 2003 ; Glazier 2008 ), leading to species-specific vulnerabilities to threats such as habitat fragmentation. Moreover, this niche-based landscape effect tied to body size can exhibit scale dependence. Body size may affect how avian species can respond to the landscape across different spatial scales. Larger avian species might be more sensitive to landscape alterations at larger scales, owing to their expansive home ranges and higher energy demands, whereas smaller species could better adapt to more localized niches. Empirical studies support that body size can be a critical factor in avian habitat selection and responses to disturbances at varied spatial scales (Wu et al. 2024 ), emphasizing its importance in evaluating conservation impacts and tacking niche-specific variations in vulnerability to human-disturbance threats. Building on these insights, the present study is motivated to addresses three primary research questions: 1) what are the niche-specific landscape requirements for the overwintering avian species and their coexistence in a Tibetan reserve? 2) which landscape component, landscape composition or configuration, exerts a stronger influence on the landscape requirements? 3) does difference in body size among the species lead to variation in the landscape requirements? To address the questions, we focus on three sympatric overwintering avian species with differing body sizes in the Black-necked Crane National Nature Reserve along the Yarlung Zangbo River in China’s Tibet: Ruddy Shelduck ( Tadorna ferruginea ), Bar-headed Goose ( Anser indicus ), and Black-necked Crane ( G. nigricollis ). Among the species, G. nigricollis stands out as the only crane species adapted to high-altitude environments, classified as Vulnerable by the IUCN and a national first-class protected animal in China (Li 1997 ). And T. ferruginea and A. indicus are migratory species rated as the Least Concern, often found alongside G. nigricollis (Yang and Zhang 2014 ; Zhao et al. 2014 ). This research intends to provide a comprehensive assessment of reserve effectiveness beyond a single-species focus, thereby informing integrative conservation strategies to protect diverse avian communities amid the plateau’s environmental changes. Our multi-scale, landscape-centered approach offers a generalizable framework to diagnose conservation gaps and align biodiversity goals with broader landscape contexts. It also provides evidence-based levers to buffer high-altitude habitats against compounded stressors, from climate warming to land-use intensification, thereby safeguarding both biodiversity and ecosystem services. 2 Methods 2.1 The avian species examined T. ferruginea , a species of the genus Tadorna in the family Anatidae and order Anseriformes , measures approximately 63 cm in body length (Fig. 1 a). Its breeding range extends from southern Europe to central Asia, as well as northwestern Africa and Ethiopia. Its wintering grounds encompass the Indian subcontinent, parts of Southeast Asia, North Africa, and the Far East (Fig. 1 b). A. indicus , a species of the genus Anser in the family Anatidae and order Anseriformes , measures approximately 70 cm in height (Fig. 1 a). It breeds in high-altitude regions of central Asia, such as Mongolia and China, and migrates in winter to South Asia, as well as central China and southern Tibet (Fig. 1 c). G. nigricollis , belonging to the genus Grus in the family Gruidae , order Gruiformes , is a large wading bird and the only extant crane species that inhabits high-altitude plateaus among the 15 crane species worldwide. Adults can measure approximately 150 cm in height (Fig. 1 a). The species breeds in central-western China within the Eurasian region, including Tibet, and migrates in winter to the southeastern Qinghai-Tibet Plateau, the Yunnan-Guizhou Plateau, and the China-India and China-Pakistan border areas (Fig. 1 d). Biological traits of the three avian species investigated demonstrate these three species are distinctly stratified by size and diet (Table 1 ), based on the data from the AVONET database (Tobias et al., 2022 ) and field records from MacKinnon and Phillipps ( 2000 ). Moreover, these three species exhibit extensive sympatry across their overwintering ranges on the Tibetan Plateau (Fig. 1 b, 1 c, 1 d). Table 1 Basic biological traits of the three avian species Types of traits Tadorna ferruginea Anser indicus Grus nigricollis Morphological traits Beak length (mm) 47 49.9 124.4 Beak width (mm) 18.6 19.8 11.7 Tarsus length (mm) 54.5 70.8 239.8 Wing length (mm) 355.5 437.8 579.2 Height (cm) 63 70 150 Mass (g) 1235.03 2212.55 5999.99 Behavior traits Foraging Plants, aquatic plants, fish, shrimp, and insects Plant leaves, roots, stems, tubers, seeds and algae Agricultural crops, aquatic plants, fish, shrimp, and insects Breeding period April to June April to June May to July Breeding strategy Monogamous Monogamous Monogamous 2.2 Study area The study area is located in the Black-necked Crane National Nature Reserve in the middle reaches of the Yarlung Zangbo River Valley (87°30′E–89°40′E, 29°0′N–29°30′N), which belongs to the Shigatse City of the Tibet Autonomous Region in western China, covering nearly 2600 km² with a narrow and elongated shape along the river valley (Fig. 2 ). Topographically, the area is characterized by rugged terrain with an average elevation of 3700 m. The river valley is broad, with bottom widths ranging from 1 to 7 km, featuring complex braided river patterns, extensive sand bars, and wind-sand deposits. The region exhibits a plateau temperate monsoon semi-humid climate, with annual temperatures ranging from 0–8℃ and the warmest month averaging ≥ 15℃. The area experiences abundant solar radiation, with over 3000 hours of annual sunshine (70–80% sunshine rate), relatively low humidity of around 40%, and annual precipitation between 300–500 mm, predominantly concentrated in the summer months (Latif et al. 2019 ; Hu et al. 2023 ; Luo et al. 2024 ). Over the past few decades, climate warming and economic development have driven rapid shifts in land use patterns across the Tibetan Plateau. From 2000 to 2021, surrounding the reserve, a large area of flood land and grassland were reclaimed as crop land for agricultural cultivation, but a minor proportion of crop land is abandoned and converted into flood land and grassland. In addition, the rapid increase of construction land area has also led to an increase of human disturbance causing the deterioration of ecological environment (Figure S1 ). 2.3 Data sources Following a comprehensive preliminary study of the entire study area, encompassing cropland, grassland, and water habitats in the midstream valley of the Yarlung Zangbo River (Fig. 2 ), a total of 58 sampling sites were established where at least one of the three focal overwintering species ( A. indicus, T. ferruginea, or G. nigricollis ) was detected (Fig. 3 ). Meanwhile, our investigation team, in collaboration with ornithology experts, conducted a comprehensive population survey of the three overwintering avian species along the selected sampling sites in December 2021. At each sampling site, bird abundance was estimated using the fixed-radius point count method with a 250 m radius (Ehlers Smith et al. 2017 ). To minimize inter-observer variability, all counts were conducted by a single trained observer using 10×42 binoculars. At each site, counting lasted for 30 minutes and was performed within the first three hours after sunrise to coincide with peak activity. To ensure optimal conditions for avian detectability, these surveys were conducted only on the days with favorable weather conditions (i.e., no precipitation and wind speed < Beaufort scale 3) (Bibby et al. 2000 ). To investigate the relationship between landscape matrices at multiple scales and the coexistence of overwintering avian species, as well as to reflect land-use changes within the reserve, we acquired high-quality satellite imagery from the Geospatial Data Cloud platform ( https://www.gscloud.cn/ ). Specifically, we downloaded Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images in the year 2000 and 2021, both with a spatial resolution of 30 m. Image selection prioritized scenes with cloud cover < 10% and high imaging quality, specifically acquired during the overwintering period to ensure temporal consistency with bird surveys. All images were preprocessed using ENVI 5.3 (Harris Geospatial Solutions, Broomfield, CO, USA), including radiometric calibration, mosaicking, and clipping to the study area boundary. Referencing the Land Use Classification System of the Chinese Academy of Sciences (Xiang 2023 ) and local characteristics, we employed a supervised classification method to categorize the study area into seven land cover types: cropland, forest land, grassland, water, flood land, bare land, and construction land. To validate classification accuracy, we integrated high-resolution imagery from Google Earth and field survey records for post-classification refinement. The overall accuracy was robust, with Kappa coefficients exceeding 0.90 for both periods. Based on these classified maps, we quantified land-use dynamics between the year 2000 and 2021 (Figure S1 ) and generated a comprehensive overview of the study area. Furthermore, the specific land cover distribution for 2021 is illustrated in Fig. 2 . 2.4 Data analysis 2.4.1 Multiple-scale analysis In this study, we conducted a multi-scale analysis to examine relationships between key landscape metrics of the reserve and the habitat selection preferences of the three overwintering avian species vary across spatial extents. By calculating landscape metrics within concentric buffers of increasing radii, we quantified the influence of the reserve landscape features on avian site selection for foraging during the harsh winter on the Tibetan Plateau. Crucially, this framework allowed us to detect scale effects (Bloom et al. 2012 ; Boscutti et al. 2019 ), determining whether avian habitat preferences are more strongly associated with local conditions or broader regional landscape patterns. Identifying these scale-dependent is critical for informing targeted conservation strategies. Building on this framework, we employed a systematic buffering technique centered on each of the 58 sampling sites where avian populations were observed in the midstream valley. For every sampling site, we created a series of concentric circular buffers at precise 500 m intervals, resulting in a total of 12 buffer zones surrounding a sampling site, with radii progressively expanding from 500 m to 6000 m (Fig. 3 ). This multi-scale buffering strategy enabled the examination of landscape influences across gradients, ranging from local immediate surroundings to broader landscape contexts (Fearer et al. 2007 ; Gao et al. 2020 ). These buffer zones were generated using ArcGIS 10.8 (ESRI, Redlands, CA, USA) based on the 2021 land cover distribution. Subsequently, landscape metrics were calculated for each buffer zone to quantify spatial patterns at every scale. 2.4.2 Landscape Metric we selected nine key landscape metrics based on the previous study (Zhang et al. 2023 ),, balancing the need to avoid redundancy with the requirement to capture ecological processes critical for migratory avian species in fragile ecosystems (Li et al. 2021 ; Adler and Jedicke 2022 ). All metrics were computed using FRAGSTATS v4 (McGarigal et al. 2023 ). Following the framework of Forman and Godron ( 1986 ) and Turner et al. ( 2003 ), these metrics characterize landscape patterns across two orthogonal dimensions: composition (the variety and relative abundance of patch types) and configuration (their spatial arrangement, shape, and connectivity). As previous studies have demonstrated that these two dimensions often exert distinct effects on ecological processes (Wu 2004 ). Specifically, six metrics were calculated at the landscape level: Largest Patch Index (LPI), Edge Density (ED), Landscape Shape Index (LSI), Contagion Index (CONTAG), Splitting Index (SPLIT), and Shannon's Evenness Index (SHEI). Collectively, these metrics characterize the overall landscape composition and configuration of the study area. In contrast, three metrics were computed at the class level: Percentage of Landscape (PLAND), Patch Density (PD), and Landscape Division Index (DIVISION). These class-level metrics quantify the composition and configuration of specific land cover types that are ecologically relevant to avian habitat selection (Fahrig 2003 ) (Table 2 ). Table 2 Metrics selection of landscape level and class level Level Metric Abbreviation Type Landscape level Largest Patch Index LPI Landscape composition Edge Density ED Landscape configuration Landscape Shape Index LSI Landscape configuration Contagion Index CONTAG Landscape configuration Splitting Index SPLIT Landscape configuration Shannon's Evenness Index SHEI Landscape composition Class level Percentage of Landscape PLAND Landscape composition Patch Density PD Landscape configuration Landscape Division Index DIVISION Landscape configuration 2.4.3 Coexistence index In this study, we employed a quantitative approach to evaluate the local coexistence potential of dyads of overwintering avian species. By comparing relative population abundances at each of the 58 sampling sites, we assessed the degree of symmetry in species presence, which serves as a proxy for stable co-occurrence amidst potential interspecific competition for foraging areas and water resources. A balanced abundance between species (high symmetry) may indicate mutual tolerance or effective niche partitioning allowing sympatry, whereas highly skewed ratios (low symmetry) might suggest competitive exclusion or dominant-subordinate dynamics. To quantify this, we calculated a coexistence index, adapted from the mathematical form of Schoener’s similarity metric but reinterpreted here to measure relative abundance evenness between two species at a specific site (Schoener 1970 ). The index is calculated as: $$\:\text{c}\text{o}\text{e}\text{x}\text{i}\text{s}\text{t}\text{e}\text{n}\text{c}\text{e}\:\text{i}\text{n}\text{d}\text{e}\text{x}=1-\frac{1}{2}\left|\frac{{n}_{i}-{n}_{j}}{N}\right|$$ where \(\:{n}_{i}\) denotes the population size of species i , \(\:{n}_{j}\) denotes the population size of species j , N is the total population size of all species in the sampling site. This index yields values ranging from 0 to 1. A value of 0 indicates complete asymmetry (e.g., one species is present while the other is absent or negligible), suggesting potential competitive exclusion (Tilman 1982 ) or habitat specialization preventing co-occurrence (Chesson 2000 ). Conversely, a value approaching 1 signifies high symmetry in relative abundances (i.e., both species are present in nearly equal proportions), implying a balanced cohabitation and a lack of dominant exclusion in that specific local habitat, a pattern often associated with niche partitioning or neutral dynamics (Hubbell 2001 ). For instance, if two species exhibit nearly equal numbers at a given site, the index approaches 1, highlighting a state of effective local coexistence. We applied this metric pairwise for all relevant species dyads across the sampling sites and integrated the results with multi-scale landscape metrics to explore how factors like wetland fragmentation influence these patterns of abundance symmetry (Haddad et al. 2015 ). 2.4.4 Statistical analyses Given that avian abundance data and the selected landscape metrics deviated from normal distribution, as confirmed by the Shapiro-Wilk normality test ( p < 0.05), we opted for non-parametric methods to ensure robust statistical inference. Specifically, we employed Spearman’s rank correlation coefficients to examine the associations between: (1) the observed abundances of the three overwintering avian species and nine landscape metrics at various buffer scales; and (2) the calculated species coexistence index and the same landscape metrics across the 12 concentric buffer zones ranging from 500 m to 6000 m. This approach allowed us to detect monotonic relationships without assuming linearity or normality, providing insights into how landscape features like wetland fragmentation or diversity influence avian populations and coexistence in the study area (Zar 2010 ). Subsequently, we conducted hierarchical partitioning based on generalized linear model (GLM) to quantify independent relative contributions of each metric, This analysis was performed using the glmm.hp package in R (Lai et al. 2022 ). All analyses were conducted using R v4.0.3 (R Core Team 2020), with statistical significance determined at the p < 0.05 threshold to identify significance level of correlations. 3 Results 3.1 Spatial variation in landscape metrics across scales Landscape metrics exhibited distinct responses to changes in spatial scale ranging from 500 m to 6000 m (Fig. 4 ). Specifically, LSI and SPLIT metrics displayed a robust positive correlation with scale, increasing markedly from 2.12 to 12.61 and from 2.18 to 5.82, respectively, indicating increased landscape shape complexity and fragmentation at coarser scales. Conversely, the LPI followed a consistent downward trajectory, dropping significantly from 70.14 to 40.12, which reflects a reduction in the dominance of the largest patch as the observation window expands. The ED metric demonstrated a unimodal pattern, initially rising to a peak of 42.15 at the 2500 m scale before gradually declining to 37.71 at the 6000 m scale. CONTAG showed a rapid increase from 54.85 at 500 m to 62.51 at 2000 m, after which it stabilized, maintaining values around 61.5–62.2 across the remaining scales. The SHEI metric manifested only a marginal upward shift (0.53 to 0.61) across the entire gradient, suggesting that the evenness of landscape components remained relatively stable despite scale variations. For the class level metrics, from the standpoint of the PLAND metric, the cropland constitutes 58.91% of the landscape at the 500 m scale, diminishing to 33.14% when the scale increases to 6000 m, while nonetheless retaining its status as the dominant land use type within the region. In contrast, grassland demonstrated an inverse trajectory, gradually escalating from 20.64% at 500 m scale to 43.64% at the 6000 m scale. Other land use types exhibited relatively minor proportions, with flood land maintaining a consistent range of 15.00%–16.50% at 2000–6000 m scale, construction land declining from 5.74% to 1.75%, water maintaining a consistent range of 2.85%–4.54%, forest decreasing from 2.97% to 1.00%, and bare land maintaining about 2% at 1000–6000 m scale. This pattern indicates that the habitats utilized by the three avian species are primarily characterized by agricultural ecosystems, with the prominence of grassland ecosystems becoming increasingly evident at larger scales (Fig. 5 ). Regarding the PD metric, a consistent decline in patch densities was observed for most land use types as scale expanded, indicative of scale-specific aggregation tendencies in landscape elements (Fig. 5 ). At the 500 m scale, grassland (PD = 2.97), cropland (PD = 1.95) and flood land (PD = 1.88) demonstrated markedly higher patch densities compared to other land use types, indicating substantial spatial heterogeneity in finer-scale landscape. Upon expansion to the 6000 m scale, patch densities for land use types converged, with PD values of grassland reducing to 1.43, flood land declining to 0.86, and cropland declining to 0.86. Such dynamics elucidate the underlying spatial structuring of landscape elements, suggesting progressive homogenization at coarser scales. The DIVISION metric exhibited a consistent upward trend across all land use types as the scale expanded from 500 m to 6000 m, indicating a general decline in spatial connectivity (Fig. 5 ). Bare land and water displayed the highest connectivity (lowest DIVISION values of 0.15 and 0.27, respectively) at the fine scale (500 m), surpassing that of cropland (0.49). For the cropland landscape, the DIVISION metric increased from 0.49 at 500 m to 0.93 at 6000 m, reflecting a gradual diminution in continuity. Notably, while flood land, water, and forest reached near-complete separation (> 0.99) at coarser scales, grassland maintained the lowest separation level among all types at the 6000 m scale (approximately 0.85), suggesting it retains relatively better patch integrity than other landscape elements at large scales. 3.2 Effects of multi-scale landscape metrics on the three avian species The multi-scale Spearman correlation analysis revealed species-specific responses to landscape composition and configuration among the three avian species, with the spatial scope of these associations appearing to correspond to body size. In the case of the smaller-bodied T. ferruginea and the medium-sized A. indicus , avian abundance was primarily associated with landscape fragmentation metrics (DIVISION) rather than composition metrics (PLAND), with significant correlations confined to finer and intermediate scales (500–3500 m) (Fig. 6 and Fig. 7 ). More precisely, T. ferruginea abundance showed consistent negative correlations with the fragmentation of various land cover types. Significant negative associations were detected in the DIVISION of bare land at 500 m ( r = -0.353, p = 0.040) and 1500 m ( r = -0.342, p = 0.048), the DIVISION of construction land at 1500 m ( r = -0.355, p = 0.040), and the DIVISION of water at 2500 m ( r = -0.355, p = 0.041) and 3000 m ( r = -0.361, p = 0.036) (Fig. 7 ). Hierarchical partitioning further confirmed the dominant influence of fragmentation metrics, with the largest contributions arising from bare land DIVISION at 500 m (30.28%) and 1000 m (29.17%), followed by construction land DIVISION at 1500 m (19.51%), and water DIVISION at 2500 m and 3000 m (13.44% and 7.60%, respectively) (Fig. 8 ). Likewise, A. indicus abundance showed negative correlations with the DIVISION of bare land at 2000 m ( r = -0.323, p = 0.042), the DIVISION of construction land at 3500 m ( r = -0.344, p = 0.030), and the DIVISION of water at 2500 m and 3000 m ( r = -0.324 to -0.340, p = 0.030). In contrast to T. ferruginea , however, A. indicus exhibited a positive correlation with the DIVISION of cropland at 1500 m ( r = 0.371, p = 0.019), suggesting a degree of tolerance for specific agricultural configuration patterns at this scale (Fig. 6 and Fig. 7 ). Consistently, hierarchical partitioning analysis demonstrated relatively balanced contributions across these metrics, with values ranging from 19.87% to 20.39%: cropland DIVISION at 1500 m (20.39%), bare land DIVISION at 2000 m (19.93%), construction land DIVISION at 3500 m (19.87%), and water DIVISION at 2500 m and 3000 m (19.88% and 19.93%, respectively) (Fig. 8 ). In contrast, the large-bodied G. nigricollis demonstrated the most extensive and multifaceted relationships with landscape metrics, spanning a wide array of scales (500–6000 m). Its abundance was markedly influenced by both the composition (PLAND) and configuration (PD, DIVISION) metrics of landscape surrounding the habitats of the avian species (Fig. 6 and Fig. 7 ). G. nigricollis showed a pronounced affinity for construction land across all examined scales. Abundance was positively correlated with the PLAND of construction land ( r = 0.302 to 0.484, p < 0.05) and the PD of construction land ( r = 0.296 to 0.423, p < 0.05) consistently from 500 m to 6000 m, with peak associations observed around 2500–3000 m. Moreover, the DIVISION of construction land showed a positive correlation at the finest scale of 500 m ( r = 0.346, p = 0.017) (Fig. 6 and Fig. 7 ). Regarding cropland, G. nigricollis demonstrated a preference for abundant and aggregated patches. Abundance showed positive correlation with the PLAND of cropland over broader scales (1000–6000 m; r = 0.296 to 0.355, p < 0.05) but negative correlation with the DIVISION of cropland across the same scales (1000–6000 m; r = -0.289 to -0.391, p < 0.05), indicating a selective bias toward large, unbroken agricultural expanses (Fig. 6 and Fig. 7 ). Conversely, G. nigricollis manifested negative associations with grassland extent but positive associations with its fragmentation. The PLAND of grassland was negatively correlated with abundance from 500 m to 5500 m ( r = -0.295 to -0.337, p < 0.05), whereas the DIVISION of grassland was positively correlated from 2000 m to 6000 m ( r = 0.289 to 0.343, p < 0.05). Furthermore, regarding bare land, abundance was negatively correlated with the DIVISION of bare land exclusively at the coarsest scales of 5500 m ( r = -0.307, p = 0.036) and 6000 m ( r = -0.292, p = 0.046). Finally, the CONTAG metric showed a positive correlation at 500 m ( r = 0.333, p = 0.022) (Fig. 6 and Fig. 7 ). Hierarchical partitioning by spatial scale further revealed that, for G. nigricollis , landscape index contributions varied substantially across buffer radii. At the finest scale (500 m), four metrics exhibited the highest contributions: CONTAG (25.75%), grassland PLAND (21.14%), construction land DIVISION (21.18%), and construction land PD (20.44%). As the focal scale increased, the dominant metrics shifted, with the PLAND and PD of construction land, the PLAND and DIVISION of cropland, and the PLAND and DIVISION of grassland alternating as the most influential factors, with contributions generally ranging from 10% to 28%. These scale-dependent shifts underscore the spatially structured and multi-dimensional nature of landscape influences on the abundance of this large-bodied species (Fig. 8 ). 3.3 The impact of landscape structure on the coexistence of the avian species The Spearman correlation analysis revealed that coexistence patterns among the three avian species were substantially influenced by landscape composition and configuration, exhibiting considerably variations across spatial scales and species dyads. The coexistence between the medium-sized A. indicus and the smaller-bodied T. ferruginea was negatively associated with the extent of anthropogenic land use intensity at coarser scales. Significant negative correlations were observed between their coexistence index and the PD of construction land at 5500 m ( r = -0.273, p = 0.038) and 6000 m ( r = -0.339, p = 0.009), as well as the PLAND of construction land at 6000 m ( r = -0.275, p = 0.037). Comparable negative associations were observed with the PLAND of cropland at 5500 m ( r = -0.268, p = 0.042) and 6000 m ( r = -0.291, p = 0.027) (Fig. 9 and Fig. 10 ). Hierarchical partitioning analysis revealed that the configuration metrics of construction land were the dominant predictors of their coexistence, with the PD of construction land at 6000 m contributing the largest proportion (46.77%), followed by the PD of construction land at 5500 m (30.65%), and the PLAND of construction land at 6000 m (9.68%), while the PLAND of cropland at both 5500 m and 6000 m showed relatively modest contributions (6.45% each) (Fig. 11 ). In contrast, coexistence patterns involving the large-bodied G. nigricollis exhibited more intricate, scale-dependent responses to landscape heterogeneity. For A. indicus and G. nigricollis dyads, coexistence was positively correlated with landscape fragmentation and density of construction land at intermediate scales, yet negatively correlated with the fragmentation of natural and agricultural habitats. Specifically, positive associations were detected for the DIVISION of construction land from 2500 m to 3500 m ( r = 0.281 to 0.358, p <0.05) and the PD of construction land at 2500 m ( r = 0.262, p = 0.047) and 3000 m ( r = 0.266, p = 0.043). In contrast, negative correlations were identified with the DIVISION of water at 1500 m ( r = -0.287, p = 0.029), the DIVISION of flood land at 5500 m ( r = -0.281, p = 0.032) and 6000 m ( r = -0.299, p = 0.023), and the DIVISION of cropland at 6000 m ( r = -0.261, p = 0.048) (Fig. 9 and Fig. 10 ). Hierarchical partitioning indicated that the DIVISION of construction land at 3500 m was the most influential factor (30.88%), followed by the DIVISION of water at 1500 m (20.59%), DIVISION of flood land at 5500 m (10.29%) and 6000 m (8.82%), DIVISION of construction land at 3000 m (8.82%), and the DIVISION cropland at 6000 m (7.35%), while the PD of construction land at intermediate scales showed relatively minor contributions (2.94–4.41%) (Fig. 11 ). For T. ferruginea and G. nigricollis dyads, coexistence appeared to be promoted by the prevalence of open habitats and the contiguity of agricultural land. Their coexistence index showed positive correlations with the PLAND of bare land across broader scales ranging from 5000 m to 6000 m ( r = 0.267 to 0.274, p <0.05), alongside a negative correlation with the DIVISION of bare land at 2500 m ( r = -0.280, p = 0.034). Furthermore, a significant negative correlation was observed with the DIVISION of cropland at the finer scale of 1000 m ( r = -0.311, p = 0.018) (Fig. 9 and Fig. 10 ). Notably, hierarchical partitioning revealed that the DIVISION of cropland at 1000 m overwhelmingly dominated the prediction of their coexistence (62.00%), with the DIVISION of bare land at 2500 m contributing 14.00%, and the PLAND of bare land at broader scales (5000–6000 m) each contributing equally modest proportions (8.00%) (Fig. 11 ). 4 Discussion Our multi-scale analysis reveals that landscape structure profoundly shapes niche-specific habitat selection and interspecific coexistence among the three overwintering avian species within the study reserve (Tscharntke et al. 2012b ). We found that body size is a primary driver of these scale-dependent responses. The larger species respond to landscape patterns at broader spatial extents, while smaller species are driven by finer-scale features. Such body-size-dependent scaling effect suggests that the reserve does not as a function as a uniform sanctuary. Instead, its conservation value relies on maintaining a diverse range of spatial heterogeneity to match the distinct perceptual ranges of species with varying allometric traits. By examining these sympatric species, we evidenced that multi-scale landscape heterogeneity jointly regulates resource accessibility, foraging efficiency, and competitive dynamics, thereby promoting niche segregation along dietary and spatial axes. Further, our results indicate that neither landscape composition nor configuration exerts a universal effect across all species. Instead, these components act synergistically, with their relative importance shifting according to species body size, spatial scale, and specific metrics (Anjos et al. 2025 ). This strong trait dependency implies that effective conservation within protected areas requires more than simple habitat protection, and it demands multi-scale landscape management strategies tailored to the allometric constraints of target species (Vandewalle et al. 2010 ). Indeed, prioritizing one landscape attribute over another without considering body size and diet could compromise efforts to support the full avian community. These findings challenge single-scale conservation paradigms and advocate for integrated designs that preserve multi-scale structural complexity amidst land-use intensification and climatic shifts on the Tibetan Plateau. 4.1 Niche-specific landscape requirements for the avian species The structural attributes of landscape structure play a pivotal role in shaping habitat selection among the overwintering avian species, primarily through their modulation of resource availability and accessibility. This landscape, encompassing the dominant surrounding land covers, acts as a critical determinant of niche fulfillment by dictating the spatial distribution of foraging opportunities, shelter, and safety. However, our empirical findings highlight that avian response to landscape structure exhibit considerable uniform, driven by species-specific niche requirements. For instance, landscape characterized by high cropland dominance provide essential scattered grain resources that preferentially support larger-bodied avian such as G. nigricollis , evidenced by its robust positive correlation with cropland proportion (PLAND). This alignment highlights a niche specialization wherein G. nigricollis exploits anthropogenically modified landscapes for energy-intensive foraging needs during resource-scarce winters. In contrast, other sympatric avian species exhibit more intricate and multifaceted niche requirements on landscape configuration. Notably, T. ferruginea showed a negative correlation with the DIVISION metric of water bodies, indicating a preference for aggregated wetland habitats, while exhibiting a positive association with the DIVISION of cropland, suggesting an affinity for fragmented cropland mosaics. This pattern implies a niche strategy that leverages edge effects, enabling T. ferruginea to optimize foraging efficiency in patchy croplands while accessing the protective and roosting benefits of interspersed natural habitats (Quan et al. 2001 ). Such a landscape-dependent niche likely a balance between resource exploitation and predation avoidance. Similarly, A. indicus showed a negative correlation with the water DIVISION but minimal sensitive to cropland metrics, suggesting a niche less reliant on the cropland and more oriented toward stable aquatic resources (Hamza et al. 2024 ). These observations collectively illustrate that landscape structure governs habitat selection, yet its efficacy is contingent upon each avian unique resource-access strategies, which are shaped by evolutionary adaptations to high-altitude Tibetan environments. For overwintering avian communities, both population size and interspecific coexistence are jointly governed by the interplay of landscape composition and configuration. Composition metrics directly regulate the abundance of vital resources like food and cover, whereas configuration influences resource quality through factors such as patch connectivity and edge density. Although prior studies suggest that overwintering avian species may be less constrained by landscape configuration due to their capacity for long-distance daily movements (Tinoco et al. 2019 ; Landázuri et al. 2024 ), our multi-scale analysis at radii up to 6000 m radius, encompassing typical foraging ranges (Liu et al. 2020 ), reveals significant configuration effects. This suggests that niche requirements extend beyond mere resource quantity to include high-quality spatial arrangements that promote energy-efficient foraging under hypothermic conditions. In our dataset, both landscape composition and configuration metrics emerged as pivotal predictors of population sizes and coexistence indices, highlighting their roles in mediating behavioral decisions and ecological interactions among the coexisting species. In winter, the avian species confront elevated energetic exigencies amid cold temperatures and limited daylight, rendering food resources niche paramount in habitat selection (Chatterjee and Basu 2018 ). Landscape composition orchestrates the spatiotemporal distribution of these resources by prioritizing land-use types conducive to foraging, such as croplands or aquatic zones. For instance, landscape dominated by cropland provide residual grains that align with granivorous niche of G. nigricollis , fostering higher population densities (Bishop and Li 2002 ). Similarly, water bodies in the landscape enhance algal and vegetative foraging niches for A. indicus and T. ferruginea (Li et al. 1998 ; Quan et al. 2001 ). Concurrently, heightened food demand attenuate sensitivity to anthropogenic disturbances, as seen in G. nigricollis strong positive correlations with construction land PLAND and PD of at broad scales. When the landscape composition favors these food-rich elements, it effectively subsidizes core habitats, enhancing niche suitability and persistence. Conversely, landscape dominated by bare or unproductive land reduce overall niche quality, eliciting compensatory behaviors like mixed-species flocking to partition resources and alleviate competition (Zhu et al. 2020 ; Zhao et al. 2023 ). This dynamic elucidates why composition metrics often supersede density-dependent crowding in suboptimal habitats, as avian species prioritize landscape that maximize foraging niches over spatial segregation. From an evolutionary vantage, the landscape's formative influence on niche embodies adaptive strategies refined for fluctuating winter environments. Mixed-species flocks, often intensified in resource-poor landscape, functions as a niche-differentiation mechanism to partition limited resources and mitigate interspecific competition, aligning with theories of ecological differentiation (Mammides et al. 2015 ). This not only accounts for observed habitat selection patterns but also bears significant conservation implications. Targeted landscape modifications, such as augmenting cropland buffers or wetland connectivity, could fortify overwintering niches in vulnerable Tibetan regions. Nevertheless, However, climate change may precipitate landscape alterations, potentially eroding specialized niches and conferring advantages to generalist species over niche-restricted specialists. 4.2 Body-size effects mediate landscape effect among coexisting species Extending from the fundamental role of landscape structure in modulating habitat selection, body size emerges as a pivotal allometric factor that differentially shapes avian responses to landscape effects among the overwintering avian assemblages. This body size-mediated variation underscores the heterogeneity in niche-specific landscape requirements, whereby larger-bodied species perceive and exploit the surrounding landscape differently from their smaller counterparts, ultimately influencing habitat preferences, population dynamics, and interspecific coexistence. Larger avian species, characterized by greater energetic demands and broader foraging radii, often necessitate expansive landscape resources to fulfill their niche, whereas smaller species may satisfy their requirements through more localized, high-density resource patches. Such disparities amplify the landscape's overarching impact, revealing body size as a key driver of ecological partitioning in resource-constrained Tibetan winter habitats. Furthermore, body size governs fundamental physiological and behavioral traits, including metabolic scaling, locomotor capabilities, foraging ranges, and competitive hierarchies, that engender divergent niche interactions with landscape composition and configuration (Lindstedt et al. 1986 ; Haskell et al. 2002 ; Jetz et al. 2004 ). According to allometric scaling theory, larger organisms often show lower mass-specific metabolic rates, enabling sustained energy allocation over larger spatial scales but imposing higher absolute resource thresholds (Nagy 2005 ; Shankar et al. 2020 ). Consequently, larger avian species may evaluate available resources across broad extents, while smaller species tend to be more sensitive to local resource aggregation. For example, the large-bodied G. nigricollis demonstrates pronounced positive correlations with cropland PLAND and negative associations with cropland DIVISION across 1000–6000 m scales, reflecting a niche strategy optimized for contiguous agricultural landscape that provide abundant grain subsidies to meet its substantial caloric needs. In contrast, smaller avian species such as T. ferruginea and A. indicus exhibit niches attuned to mid-scale, high-quality resource aggregations, with foraging efforts concentrated in fragmented mosaics that balance aquatic and vegetative patches, thereby minimizing energy expenditure within confined ranges (Fig. 12 ). These body size-driven divergences are vividly illustrated in the dietary and competitive niches of the focal species, where landscape effects are exacerbated by interspecific interactions. Larger avian species often dominate resource-rich landscape patches, forcing smaller species toward alternative foraging tactics to mitigate competition (Malpica et al. 2017 ; Bribiesca et al. 2019 ). As the largest body size among the studied assemblage, G. nigricollis leverages its size advantage to prioritize expansive croplands, securing prime foraging position in the mixed–species flocks and thereby alleviating intraspecific competition in food-limited environments (López-Segoviano et al. 2018 ). This competitive preeminence allows G. nigricollis to exploit grain-heavy niches, while displacing smaller congeners to peripheral resources. Consequently, T. ferruginea and A. indicus adopt avoidance strategies, diversifying their niches toward roots, algae, and aquatic subsidies rather than direct competition for cropland. Our analyses reveal that fragmented croplands (high DIVISION) impede coexistence among A. indicus, T. ferruginea , and G. nigricollis , as smaller species evade overlap in reserves. Notably, despite a study indicating A. indicus ’s winter reliance on croplands (Li et al. 2025 ), our findings show no significant correlation between its abundance and cropland PLAND, suggesting that competitive exclusion by larger species reshapes its niche-specific landscape requirements toward less contested aquatic habitats. Therefore, body size acts as a critical modulator of niche-specific landscape requirements, fostering ecological differentiation that enhances coexistence in heterogeneous landscapes. By integrating allometric constraints with landscape metrics like PLAND and DIVISION, this framework elucidates how larger species capitalize on broad-scale resource distributions, while smaller ones exploit fine-scale patchiness, with implications for conservation strategies in dynamic overwintering ecosystems. 4.3 Integrating landscape management for avian niches Our findings underscore the critical role of stable winter food provisioning in safeguarding avian populations, with expansive cropland areas conferring enduring benefits for their survival and resource in the limited high-altitude environments. Historically, biodiversity conservation and agricultural intensification have been framed as antithetical pursuits, often resulting in trade-offs that prioritize one at the expense of the other (Pereira et al. 2010 ; Tscharntke et al. 2012a ). Yet, the pronounced reliance of overwintering avian species on cropland derived subsidies (such as residual grains and stubble) demonstrates a potential synergy in overwintering landscapes, where strategic agricultural allocation can complement rather than contravene conservation imperatives. This reconciliation is especially pertinent when extending protection beyond core habitats (e.g., wetlands and reserves) to encompass the surrounding landscape, which provides critical niche elements including food augmentation, extended foraging arena, and connectivity corridors. By integrating landscape-oriented conservation strategies within broader conservation frameworks, practitioners can holistically address species’ multifaceted niche requirements, bridging core habitat integrity with landscape-mediated resource dynamics to enhance population viability and ecological resilience. Furthermore, habitat restoration initiatives that repurpose cropland into forest or grassland may inadvertently exacerbate food scarcity for overwintering avian species, precipitating demographic declines in vulnerable assemblages (Zhang 2019 ; Jiang et al. 2025 ). Such conversions, when myopically centered on core zones, frequently undervalue the landscape's pivotal function in supporting niche differentiation and dietary specialization, e.g., crop-centric landscape underpin the granivorous niche of G. nigricollis , while landscape of waterbodies support the algal and vegetative foraging needs of A. indicus and T. ferruginea . Accordingly, restoration paradigms in protected landscapes must be judiciously tailored to species-specific objectives, favoring landscape augmentations that amplify rather than diminish these landscape-scale dependencies, thereby preserving the ecological scaffolding essential for winter survival. Equally imperative is an appraisal of how contemporary agronomic practices curtail food accessibility for overwintering avian species across core and landscape domains alike. Mechanized harvesting techniques, for instance, minimize post-harvest residue, thereby curtailing avian access to indispensable grain and stubble resources (Jia et al. 2019 ). Concurrently, the proliferation of greenhouse agriculture supplants conventional arable lands, eroding habitat suitability for overwintering species (Wu et al. 2021 ). These transformations not only compromise core foraging ground but also degrade landscape heterogeneity, impeding niche-dependent processes such as mixed-species flocking that mitigate competition and optimize resource partitioning in austere winter conditions. To counteract these pressures, conservation policies should advocate for agroecological interventions (such as reduced-tillage farming or wildlife-friendly crop rotations) that sustain landscape productivity while fostering avian niches, ensuring the long-term persistence of these migratory avian species in rapidly evolving landscapes. 5 Conclusion In facing the rapid environmental changes, protecting the isolated habitat “island” is no longer enough. Our study emphasizes the imperative for conservation strategies that prioritize landscape structure to sustain the coexistence of the overwintering avian species within a reserve, particularly in high-altitude habitats facing rapid land-use transformations in Tibet. By integrating multi-scale analyses, we demonstrate that composition and configuration of landscape jointly drive niche-specific habitat selection among the sympatric avian species, with these effects exhibiting significant scale-dependency modulated by avian body size. Notably, the large-bodied G. nigricollis displayed heightened sensitivity to landscape metrics at broader spatial extents (up to 6000 m), reflecting its expansive foraging requirements and competitive dominance in resource-limited winter environments, where smaller avian species like T. ferruginea and A. indicus respond more strongly to localized configurations. Through this framework, we identify critical landscape elements, such as cropland proportion, water body aggregation, and patch connectivity, as pivotal for avian persistence, providing the reserves like the Black-necked Crane National Nature Reserve. These findings advocate for management approaches that harmonize agricultural productivity with ecological integrity, emphasizing the landscape's role in subsidizing core habitats. Extending our analyses, we reveal that food-centric landscape components (e.g. post-harvest cropland and flood-associated patches) more robustly avian abundance and pairwise coexistence than non-food metrics across the 500–6000 m scales examined. Body-size-mediated responses, intertwined with dietary niches, facilitate niche segregation and mixed-species flocking, thereby promoting coexistence amid winter resource scarcity. Future investigations should examine interactions between landscape dynamics, climatic stressors, and anthropogenic disturbances to further elucidate these mechanisms, potentially refining predictive models for avian resilience. From a practical perspective, securing reliable winter food subsidies, such as maintaining suitable areas of post-harvest cropland and safeguarding key flood/water patches, can enhance interspecific coexistence while minimizing trade-offs with agricultural practices. Ultimately, embedding these insights into adaptive, multi-species zoning frameworks is essential for preserving the functional ecology of the Tibetan Plateau wetland-farmland mosaics, offering a robust scientific foundation for holistic ecosystem protection in this biodiversity hotspot. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Clinical Trial Number not applicable. Compliance with Ethical Standards not applicable. Author Contribution W.W. conceived the study, developed the methodology, performed the investigation, formal analysis, and data curation, validated the results, managed resources, created visualizations, and wrote the original draft as well as reviewed and edited the manuscript. M.M. acquired funding, administered the project, participated in the investigation, validated the results, and reviewed and edited the manuscript. W.Q. contributed to visualization. C.Z. and J.H. participated in the investigation, with J.H. also handling project administration. All authors reviewed and approved the final manuscript. Acknowledgement We greatly acknowledge the support from the office of the Black-necked Crane National Nature Reserve of the Forestry and Grassland Administration of Shigatse City, Tibet, China. We also extend our thanks to Zhaochun Hong, Research Librarian at the Chongqing Natural History Museum, for her valuable assistance with the avian survey in this study. Specially, we are grateful to the editors and anonymous reviewers for their helpful comments. This study was financially supported by Geological Disaster Patterns and Mitigation Strategies Under River–Reservoir Hydrodynamics in the Three Gorges Reservoir Fluctuation Zone, Chongqing Municipal Bureau of Water Resources (Grant No. 5000002024CC20004), and the Chongqing Municipality Key Project for Technological Innovation and Application Development (Grant No. CSTB2023TIAD-KPX0077). 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Avian Res 16:100295. https://doi.org/10.1016/j.avrs.2025.100295 Zhang J, Hong Z, Cui J et al (2023) Effects of landscape matrix on winter foraging-habitat selection of black-necked crane in the middle reach of the yarlung zangbo river, tibet, china. Acta Ecol Sin 43:7701–7714. https://doi.org/10.20103/j.stxb.202205201429 Zhang Y (2019) How Wintering Habitat Structure Affects Number and Distribution of Black-Necked Cranes (Grus nigricollis): A Case in Xundian Mountains. Yunnan University. (in Chinese) Zhao F, Zhou L, Xu W (2023) Habitat utilization and resource partitioning of wintering Hooded Cranes and three goose species at Shengjin Lake. Avian Res 4:281–290. https://doi.org/10.5122/cbirds.2013.0032 Zhao ZJ, Cai R, Peng C et al (2014) Number and distribution of cranes and waterbirds at Dashanbao Black-necked Cranes National Nature Reserve, China during the 2013 wintering period. Zool Res 35:215–218. https://doi.org/10.13918/j.issn.2095-8137.2014.s1.0215 Zhu Z, Zhou L, Yu C et al (2020) Do Geese Facilitate or Compete with Wintering Hooded Cranes (Grus monacha) for Forage Resources? Diversity-Basel 12:105. https://doi.org/10.3390/d12030105 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 28 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 19 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9167989","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615301351,"identity":"fe89515a-b6b2-42af-87df-a3cf505b3303","order_by":0,"name":"Wanyu Wang","email":"","orcid":"","institution":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wanyu","middleName":"","lastName":"Wang","suffix":""},{"id":615301352,"identity":"435a9f09-7a01-4a4b-83c2-7436c0c19379","order_by":1,"name":"Maohua Ma","email":"","orcid":"","institution":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maohua","middleName":"","lastName":"Ma","suffix":""},{"id":615301353,"identity":"f2a8e4a3-ac01-4e5d-acd9-4d187b9bbce9","order_by":2,"name":"Wanyu Qi","email":"","orcid":"","institution":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wanyu","middleName":"","lastName":"Qi","suffix":""},{"id":615301354,"identity":"5a03a1f5-44f7-44bd-884e-79e4bf82c6d3","order_by":3,"name":"Cunfeng Zhao","email":"","orcid":"","institution":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Cunfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":615301356,"identity":"40008908-5ef0-4357-92f0-5ae43d89e599","order_by":4,"name":"Jinxia Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACAzDJI8HAwMzA+ADI5SFJC7MBSAtBPQZIbDYJsHZCWswlkp89/CJjkWdwnPlZxY+CWhl7BuaHHxhq7uDUYjkjzdxYhkeiWLKZzexmj8FxoMPYjCUYjj3D7bAbCWbSEjwSif3MDGa3GQyOgfxixsDYcBiPlvRvYC1tzOzfiiFa2L8R0JJjJvkBbAuPGTODQQ1QCw8BW868KZMGBnLizGaeYskegwM8PId5iiUSjuHRcjx9m+TPnrrEDeePb/zw40+dPXt7+8YPH2pwawEBZt4eOPswKE4ZGBLwamBgYPzxA86uI6B2FIyCUTAKRiIAAAaxSTgPPPTsAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Jinxia","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-03-19 09:55:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9167989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9167989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106045798,"identity":"b51ac930-69cb-49d3-b2ed-2ff94743f69b","added_by":"auto","created_at":"2026-04-02 19:32:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15011544,"visible":true,"origin":"","legend":"\u003cp\u003eThe body size and distribution of the three avian species studied. (a): Body size of three avian species. (b)(c)(d): The distribution of \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eT. ferruginea\u003c/em\u003e, and \u003cem\u003eG. nigricollis\u003c/em\u003e, the global distribution data of the three species are derived from the Global Biodiversity Information Facility (GBIF) (https: //www.gbif.org). (e): A photo of mixed–species flock of \u003cem\u003eA. indicus\u003c/em\u003e, \u003cem\u003eT. ferruginea\u003c/em\u003e, and \u003cem\u003eG. nigricollis\u003c/em\u003e, photographed in the study area by the authors\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/954a5fe61de694607ba514f1.png"},{"id":106093949,"identity":"d37e189d-2d53-48f4-8654-97d51a8f20f8","added_by":"auto","created_at":"2026-04-03 11:40:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4840143,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area, the Black-necked Crane National Nature Reserve along the Yarlung Zangbo River in China’s Tibet\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/9320b63a1c6dd4c5fb48d1ab.png"},{"id":106045800,"identity":"d26b9854-b933-412c-b8ab-8c7a8e456723","added_by":"auto","created_at":"2026-04-02 19:32:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10837055,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the 58 sampling sites of this study with one example of the multi-scale landscape partitioning surrounding each sampling site\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/f277031b06a073dca5d5220d.png"},{"id":106094574,"identity":"3d8e703a-cbe7-43e3-8213-ba7b11c95563","added_by":"auto","created_at":"2026-04-03 11:42:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":771464,"visible":true,"origin":"","legend":"\u003cp\u003eVariations of landscape metrics (landscape level) at different scales\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/628f668100a9d8bf2e300ee9.png"},{"id":106095118,"identity":"6066902c-2c94-489f-a37d-570fb0300c22","added_by":"auto","created_at":"2026-04-03 11:44:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2617125,"visible":true,"origin":"","legend":"\u003cp\u003eVariations of landscape metrics (class level) at different scales\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/c0997ff4913e43be3a5a174a.png"},{"id":106093894,"identity":"301c4839-8471-4f71-9697-09787a74db16","added_by":"auto","created_at":"2026-04-03 11:39:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":18614664,"visible":true,"origin":"","legend":"\u003cp\u003eScale-dependent correlations between abundance of \u003cem\u003eT. ferruginea\u003c/em\u003e, \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eG. nigricollis\u003c/em\u003e and landscape composition metrics. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; ***: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/82f8c570b7e439367787cb7f.png"},{"id":106045802,"identity":"8b4ff0e6-43ba-49dc-9334-3552e0c2215c","added_by":"auto","created_at":"2026-04-02 19:32:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":16342469,"visible":true,"origin":"","legend":"\u003cp\u003eScale-dependent correlations between abundance of \u003cem\u003eT. ferruginea\u003c/em\u003e, \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eG. nigricollis\u003c/em\u003e and landscape configuration metrics. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/d6c794753a7d9e5eac76f9f9.png"},{"id":106094242,"identity":"9b60babf-7f70-4e34-8543-1f3ad2f34a0f","added_by":"auto","created_at":"2026-04-03 11:41:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4255955,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of landscape metrics to variations of abundance of \u003cem\u003eT. ferruginea\u003c/em\u003e, \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eG. nigricollis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/dc363eeff711de139527fb3a.png"},{"id":106094916,"identity":"9856594a-9640-4609-ae42-5c3021344476","added_by":"auto","created_at":"2026-04-03 11:43:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":5618572,"visible":true,"origin":"","legend":"\u003cp\u003eScale-dependent correlations between landscape composition metrics and coexistence index of three avian species. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/eb41b92c89fd96ada5e90b4d.png"},{"id":106045806,"identity":"ee3918da-f055-4e5c-8404-09320da1d8d8","added_by":"auto","created_at":"2026-04-02 19:32:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":12125840,"visible":true,"origin":"","legend":"\u003cp\u003eScale-dependent correlations between landscape configuration metrics and coexistence index of three avian species. *: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/2a92577f11109a98a0ffd756.png"},{"id":106093932,"identity":"ea078d28-a8a5-428c-b062-a0553d365af0","added_by":"auto","created_at":"2026-04-03 11:40:09","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":8981824,"visible":true,"origin":"","legend":"\u003cp\u003eContributions of landscape metrics to variations of coexistence index among three avian species\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/c41ea9f96b85c85384394294.png"},{"id":106045808,"identity":"8a5aeea7-352c-48d3-8b19-beffd16f407e","added_by":"auto","created_at":"2026-04-02 19:32:05","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":3921197,"visible":true,"origin":"","legend":"\u003cp\u003eA generalized diagram illustrating scale-dependent niche requirements for landscape with different body size of the three avian species\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/8ac9f31256f453afc1fc1a81.png"},{"id":109295989,"identity":"9ae649de-2b2e-42ce-90c8-2aa611359c8a","added_by":"auto","created_at":"2026-05-15 08:43:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":94885989,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/0a91017f-2bac-47a2-80a5-afe49394772a.pdf"},{"id":106045809,"identity":"19a1b442-c5bc-4273-b50f-db33ebc17e8e","added_by":"auto","created_at":"2026-04-02 19:32:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17847598,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9167989/v1/4688234a0ebd351449c16aa7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coexistence of overwintering avian species in Tibet: scale-dependent niche requirements for landscape structure with body size effects","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLandscape structure serves as a key element for biodiversity conservation, providing the regional context for understanding ecological processes within habitats and supporting species preservation (Tscharntke et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). Consequently, the niche concept, originally conceptualized at the local scale (Hutchinson \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Chase and Leibold \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sober\u0026oacute;n \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), must be extended to the regional level to enhance the protection of biodiversity within habitats embedded in a surrounding landscape (Margules and Pressey \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ricketts \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past decades, land-use changes have been triggering transformations in landscape structure representing the principal drivers affecting keystone species within protected areas. Primarily, the impact mechanisms of the landscape changes encompass two components: landscape composition (the types and proportions of land covers) and landscape configuration (the spatial arrangement and fragmentation of landscape elements) (Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These two components are intimately linked to processes of habitat selection, connectivity, and spatial patterns, thereby yielding critical insights for systematic conservation planning (Atauri and de Lucio \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Amici et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn particular, the influence of the landscape change patterns on avian species is profound and scale-dependent, varying with species traits, biological responses (e.g., abundance and coexistence), and specific landscape metrics (Miguet et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martin \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These changing patterns drive species persistence, particularly through habitat resource availability, distribution, population density, and interspecific coexistence (Dominik et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fahrig \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Suarez-Castro et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among the driving effects, a key aspect is the landscape effect, where the surrounding landscape influences habitat patches, connectivity, and species movement. In addition, selecting an inappropriate scale can lead to misinterpretations of landscape effects on ecological processes (Guo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, multi-scale analyses, such as establishing scaled buffer zones around habitats, are essential to identify the strongest associations and avoid single-scale limitations in design of protected areas amid ongoing global environmental transformations (Boscutti et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Moraga et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Adler and Jedicke \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Tibetan Plateau, a region highly sensitive to environmental changes, features extreme high-altitude conditions which present distinctive ecological challenges, such as hypoxia, cold temperatures, and scarce resources. Despite these harsh conditions, this region is remarkably biodiversity-rich, hosting numerous protected and endemic taxa that have adapted to its unique environment. However, the rapid environmental changes during recent decades, particularly human-driven land-use alterations, pose significant threats to this biodiversity-rich but less studied region. These alterations include landscape transformations due to agricultural expansion, urbanization, overgrazing, and infrastructure development, which fragment wetlands and disrupt natural ecosystems (Wang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yet, our understanding remains limited regarding how landscape effects influence habitat selection of the taxa in this understudied high-elevation zone. Furthermore, climate change exacerbates this uncertainty, especially for avian species in this region, by altering vegetation, water availability, and seasonal cycles, which degrades natural habitats.\u003c/p\u003e \u003cp\u003eDuring the past decades, the Tibetan Plateau's landscape transformations directly threaten the protected endemic avian species, including the Black-necked Crane (\u003cem\u003eGrus nigricollis\u003c/em\u003e), an endangered flagship species that migrates to the river valleys along the Yarlung Zangbo River during winter season. In response, the Black-necked Crane National Nature Reserve was established in the middle reaches of the river in China\u0026rsquo;s Tibet to protect the species. Even though human activities are strictly forbidden in the reserve's core zone, rapid land-use changes surrounding this zone may undermine its effectiveness, emphasizing the necessity for broader conservation strategies to counter persistent risks (Shen et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the impact of the surrounding landscape on these protected avian species remains poorly understood.\u003c/p\u003e \u003cp\u003eFurthermore, coexisting species shape the landscape effect more specifically. Landscape structure can influence population survival via interspecific interactions, especially through competitive dynamics (Hansson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Typically, coexisting species mitigate excessive competition through niche differentiation (Wood et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, body size serves as an informative indicator of such niche differentiation (Macarthur and Levins, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; MacArthur, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Different avian species often exhibit niche differentiation influenced by body size, which mediates foraging behavior, habitat selection, and adaptive responses to environmental changes (Laube et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Body size differentiation is thus often associated with environmental climate stability and resource abundance (Read et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, avian species with larger body size may gain a greater advantage in competition, allowing them to occupy higher-quality food resources. In addition, body size affects metabolic rates and energy needs (Makarieva et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Glazier \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), leading to species-specific vulnerabilities to threats such as habitat fragmentation.\u003c/p\u003e \u003cp\u003eMoreover, this niche-based landscape effect tied to body size can exhibit scale dependence. Body size may affect how avian species can respond to the landscape across different spatial scales. Larger avian species might be more sensitive to landscape alterations at larger scales, owing to their expansive home ranges and higher energy demands, whereas smaller species could better adapt to more localized niches. Empirical studies support that body size can be a critical factor in avian habitat selection and responses to disturbances at varied spatial scales (Wu et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), emphasizing its importance in evaluating conservation impacts and tacking niche-specific variations in vulnerability to human-disturbance threats.\u003c/p\u003e \u003cp\u003eBuilding on these insights, the present study is motivated to addresses three primary research questions: 1) what are the niche-specific landscape requirements for the overwintering avian species and their coexistence in a Tibetan reserve? 2) which landscape component, landscape composition or configuration, exerts a stronger influence on the landscape requirements? 3) does difference in body size among the species lead to variation in the landscape requirements? To address the questions, we focus on three sympatric overwintering avian species with differing body sizes in the Black-necked Crane National Nature Reserve along the Yarlung Zangbo River in China\u0026rsquo;s Tibet: Ruddy Shelduck (\u003cem\u003eTadorna ferruginea\u003c/em\u003e), Bar-headed Goose (\u003cem\u003eAnser indicus\u003c/em\u003e), and Black-necked Crane (\u003cem\u003eG. nigricollis\u003c/em\u003e). Among the species, \u003cem\u003eG. nigricollis\u003c/em\u003e stands out as the only crane species adapted to high-altitude environments, classified as Vulnerable by the IUCN and a national first-class protected animal in China (Li \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). And \u003cem\u003eT. ferruginea\u003c/em\u003e and \u003cem\u003eA. indicus\u003c/em\u003e are migratory species rated as the Least Concern, often found alongside \u003cem\u003eG. nigricollis\u003c/em\u003e (Yang and Zhang \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis research intends to provide a comprehensive assessment of reserve effectiveness beyond a single-species focus, thereby informing integrative conservation strategies to protect diverse avian communities amid the plateau\u0026rsquo;s environmental changes. Our multi-scale, landscape-centered approach offers a generalizable framework to diagnose conservation gaps and align biodiversity goals with broader landscape contexts. It also provides evidence-based levers to buffer high-altitude habitats against compounded stressors, from climate warming to land-use intensification, thereby safeguarding both biodiversity and ecosystem services.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The avian species examined\u003c/h2\u003e \u003cp\u003e \u003cem\u003eT. ferruginea\u003c/em\u003e, a species of the genus \u003cem\u003eTadorna\u003c/em\u003e in the family \u003cem\u003eAnatidae\u003c/em\u003e and order \u003cem\u003eAnseriformes\u003c/em\u003e, measures approximately 63 cm in body length (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Its breeding range extends from southern Europe to central Asia, as well as northwestern Africa and Ethiopia. Its wintering grounds encompass the Indian subcontinent, parts of Southeast Asia, North Africa, and the Far East (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. indicus\u003c/em\u003e, a species of the genus \u003cem\u003eAnser\u003c/em\u003e in the family \u003cem\u003eAnatidae\u003c/em\u003e and order \u003cem\u003eAnseriformes\u003c/em\u003e, measures approximately 70 cm in height (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). It breeds in high-altitude regions of central Asia, such as Mongolia and China, and migrates in winter to South Asia, as well as central China and southern Tibet (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003cem\u003eG. nigricollis\u003c/em\u003e, belonging to the genus \u003cem\u003eGrus\u003c/em\u003e in the family \u003cem\u003eGruidae\u003c/em\u003e, order \u003cem\u003eGruiformes\u003c/em\u003e, is a large wading bird and the only extant crane species that inhabits high-altitude plateaus among the 15 crane species worldwide. Adults can measure approximately 150 cm in height (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The species breeds in central-western China within the Eurasian region, including Tibet, and migrates in winter to the southeastern Qinghai-Tibet Plateau, the Yunnan-Guizhou Plateau, and the China-India and China-Pakistan border areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eBiological traits of the three avian species investigated demonstrate these three species are distinctly stratified by size and diet (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), based on the data from the AVONET database (Tobias et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and field records from MacKinnon and Phillipps (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Moreover, these three species exhibit extensive sympatry across their overwintering ranges on the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\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\u003eBasic biological traits of the three avian species\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTypes of traits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTadorna ferruginea\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAnser indicus\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGrus nigricollis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMorphological traits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeak length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeak width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarsus length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e239.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWing length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e579.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1235.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2212.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5999.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior traits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlants, aquatic plants, fish, shrimp, and insects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlant leaves, roots, stems, tubers, seeds and algae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgricultural crops, aquatic plants, fish, shrimp, and insects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApril to June\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApril to June\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMay to July\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonogamous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonogamous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMonogamous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study area\u003c/h2\u003e \u003cp\u003eThe study area is located in the Black-necked Crane National Nature Reserve in the middle reaches of the Yarlung Zangbo River Valley (87\u0026deg;30\u0026prime;E\u0026ndash;89\u0026deg;40\u0026prime;E, 29\u0026deg;0\u0026prime;N\u0026ndash;29\u0026deg;30\u0026prime;N), which belongs to the Shigatse City of the Tibet Autonomous Region in western China, covering nearly 2600 km\u0026sup2; with a narrow and elongated shape along the river valley (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTopographically, the area is characterized by rugged terrain with an average elevation of 3700 m. The river valley is broad, with bottom widths ranging from 1 to 7 km, featuring complex braided river patterns, extensive sand bars, and wind-sand deposits. The region exhibits a plateau temperate monsoon semi-humid climate, with annual temperatures ranging from 0\u0026ndash;8℃ and the warmest month averaging\u0026thinsp;\u0026ge;\u0026thinsp;15℃. The area experiences abundant solar radiation, with over 3000 hours of annual sunshine (70\u0026ndash;80% sunshine rate), relatively low humidity of around 40%, and annual precipitation between 300\u0026ndash;500 mm, predominantly concentrated in the summer months (Latif et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past few decades, climate warming and economic development have driven rapid shifts in land use patterns across the Tibetan Plateau. From 2000 to 2021, surrounding the reserve, a large area of flood land and grassland were reclaimed as crop land for agricultural cultivation, but a minor proportion of crop land is abandoned and converted into flood land and grassland. In addition, the rapid increase of construction land area has also led to an increase of human disturbance causing the deterioration of ecological environment (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data sources\u003c/h2\u003e \u003cp\u003eFollowing a comprehensive preliminary study of the entire study area, encompassing cropland, grassland, and water habitats in the midstream valley of the Yarlung Zangbo River (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a total of 58 sampling sites were established where at least one of the three focal overwintering species (\u003cem\u003eA. indicus, T. ferruginea, or G. nigricollis\u003c/em\u003e) was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Meanwhile, our investigation team, in collaboration with ornithology experts, conducted a comprehensive population survey of the three overwintering avian species along the selected sampling sites in December 2021.\u003c/p\u003e \u003cp\u003eAt each sampling site, bird abundance was estimated using the fixed-radius point count method with a 250 m radius (Ehlers Smith et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To minimize inter-observer variability, all counts were conducted by a single trained observer using 10\u0026times;42 binoculars. At each site, counting lasted for 30 minutes and was performed within the first three hours after sunrise to coincide with peak activity. To ensure optimal conditions for avian detectability, these surveys were conducted only on the days with favorable weather conditions (i.e., no precipitation and wind speed\u0026thinsp;\u0026lt;\u0026thinsp;Beaufort scale 3) (Bibby et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo investigate the relationship between landscape matrices at multiple scales and the coexistence of overwintering avian species, as well as to reflect land-use changes within the reserve, we acquired high-quality satellite imagery from the Geospatial Data Cloud platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gscloud.cn/\u003c/span\u003e\u003cspan address=\"https://www.gscloud.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, we downloaded Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images in the year 2000 and 2021, both with a spatial resolution of 30 m. Image selection prioritized scenes with cloud cover\u0026thinsp;\u0026lt;\u0026thinsp;10% and high imaging quality, specifically acquired during the overwintering period to ensure temporal consistency with bird surveys. All images were preprocessed using ENVI 5.3 (Harris Geospatial Solutions, Broomfield, CO, USA), including radiometric calibration, mosaicking, and clipping to the study area boundary. Referencing the Land Use Classification System of the Chinese Academy of Sciences (Xiang \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and local characteristics, we employed a supervised classification method to categorize the study area into seven land cover types: cropland, forest land, grassland, water, flood land, bare land, and construction land. To validate classification accuracy, we integrated high-resolution imagery from Google Earth and field survey records for post-classification refinement. The overall accuracy was robust, with Kappa coefficients exceeding 0.90 for both periods. Based on these classified maps, we quantified land-use dynamics between the year 2000 and 2021 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and generated a comprehensive overview of the study area. Furthermore, the specific land cover distribution for 2021 is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Multiple-scale analysis\u003c/h2\u003e \u003cp\u003eIn this study, we conducted a multi-scale analysis to examine relationships between key landscape metrics of the reserve and the habitat selection preferences of the three overwintering avian species vary across spatial extents. By calculating landscape metrics within concentric buffers of increasing radii, we quantified the influence of the reserve landscape features on avian site selection for foraging during the harsh winter on the Tibetan Plateau. Crucially, this framework allowed us to detect scale effects (Bloom et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Boscutti et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), determining whether avian habitat preferences are more strongly associated with local conditions or broader regional landscape patterns. Identifying these scale-dependent is critical for informing targeted conservation strategies.\u003c/p\u003e \u003cp\u003eBuilding on this framework, we employed a systematic buffering technique centered on each of the 58 sampling sites where avian populations were observed in the midstream valley. For every sampling site, we created a series of concentric circular buffers at precise 500 m intervals, resulting in a total of 12 buffer zones surrounding a sampling site, with radii progressively expanding from 500 m to 6000 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This multi-scale buffering strategy enabled the examination of landscape influences across gradients, ranging from local immediate surroundings to broader landscape contexts (Fearer et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These buffer zones were generated using ArcGIS 10.8 (ESRI, Redlands, CA, USA) based on the 2021 land cover distribution. Subsequently, landscape metrics were calculated for each buffer zone to quantify spatial patterns at every scale.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Landscape Metric\u003c/h2\u003e \u003cp\u003ewe selected nine key landscape metrics based on the previous study (Zhang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e),, balancing the need to avoid redundancy with the requirement to capture ecological processes critical for migratory avian species in fragile ecosystems (Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adler and Jedicke \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). All metrics were computed using FRAGSTATS v4 (McGarigal et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Following the framework of Forman and Godron (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and Turner et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), these metrics characterize landscape patterns across two orthogonal dimensions: composition (the variety and relative abundance of patch types) and configuration (their spatial arrangement, shape, and connectivity). As previous studies have demonstrated that these two dimensions often exert distinct effects on ecological processes (Wu \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Specifically, six metrics were calculated at the landscape level: Largest Patch Index (LPI), Edge Density (ED), Landscape Shape Index (LSI), Contagion Index (CONTAG), Splitting Index (SPLIT), and Shannon's Evenness Index (SHEI). Collectively, these metrics characterize the overall landscape composition and configuration of the study area. In contrast, three metrics were computed at the class level: Percentage of Landscape (PLAND), Patch Density (PD), and Landscape Division Index (DIVISION). These class-level metrics quantify the composition and configuration of specific land cover types that are ecologically relevant to avian habitat selection (Fahrig \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eMetrics selection of landscape level and class level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLargest Patch Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape composition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdge Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape configuration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandscape Shape Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape configuration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContagion Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCONTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape configuration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSplitting Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPLIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape configuration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShannon's Evenness Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSHEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape composition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of Landscape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLAND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape composition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatch Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape configuration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandscape Division Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDIVISION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandscape configuration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Coexistence index\u003c/h2\u003e \u003cp\u003eIn this study, we employed a quantitative approach to evaluate the local coexistence potential of dyads of overwintering avian species. By comparing relative population abundances at each of the 58 sampling sites, we assessed the degree of symmetry in species presence, which serves as a proxy for stable co-occurrence amidst potential interspecific competition for foraging areas and water resources. A balanced abundance between species (high symmetry) may indicate mutual tolerance or effective niche partitioning allowing sympatry, whereas highly skewed ratios (low symmetry) might suggest competitive exclusion or dominant-subordinate dynamics.\u003c/p\u003e \u003cp\u003eTo quantify this, we calculated a coexistence index, adapted from the mathematical form of Schoener\u0026rsquo;s similarity metric but reinterpreted here to measure relative abundance evenness between two species at a specific site (Schoener \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). The index is calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{c}\\text{o}\\text{e}\\text{x}\\text{i}\\text{s}\\text{t}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{i}\\text{n}\\text{d}\\text{e}\\text{x}=1-\\frac{1}{2}\\left|\\frac{{n}_{i}-{n}_{j}}{N}\\right|$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{i}\\)\u003c/span\u003e\u003c/span\u003e denotes the population size of species \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{j}\\)\u003c/span\u003e\u003c/span\u003e denotes the population size of species \u003cem\u003ej\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e is the total population size of all species in the sampling site.\u003c/p\u003e \u003cp\u003eThis index yields values ranging from 0 to 1. A value of 0 indicates complete asymmetry (e.g., one species is present while the other is absent or negligible), suggesting potential competitive exclusion (Tilman \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) or habitat specialization preventing co-occurrence (Chesson \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Conversely, a value approaching 1 signifies high symmetry in relative abundances (i.e., both species are present in nearly equal proportions), implying a balanced cohabitation and a lack of dominant exclusion in that specific local habitat, a pattern often associated with niche partitioning or neutral dynamics (Hubbell \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For instance, if two species exhibit nearly equal numbers at a given site, the index approaches 1, highlighting a state of effective local coexistence. We applied this metric pairwise for all relevant species dyads across the sampling sites and integrated the results with multi-scale landscape metrics to explore how factors like wetland fragmentation influence these patterns of abundance symmetry (Haddad et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Statistical analyses\u003c/h2\u003e \u003cp\u003eGiven that avian abundance data and the selected landscape metrics deviated from normal distribution, as confirmed by the Shapiro-Wilk normality test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we opted for non-parametric methods to ensure robust statistical inference. Specifically, we employed Spearman\u0026rsquo;s rank correlation coefficients to examine the associations between: (1) the observed abundances of the three overwintering avian species and nine landscape metrics at various buffer scales; and (2) the calculated species coexistence index and the same landscape metrics across the 12 concentric buffer zones ranging from 500 m to 6000 m. This approach allowed us to detect monotonic relationships without assuming linearity or normality, providing insights into how landscape features like wetland fragmentation or diversity influence avian populations and coexistence in the study area (Zar \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Subsequently, we conducted hierarchical partitioning based on generalized linear model (GLM) to quantify independent relative contributions of each metric, This analysis was performed using the \u003cem\u003eglmm.hp\u003c/em\u003e package in R (Lai et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). All analyses were conducted using R v4.0.3 (R Core Team 2020), with statistical significance determined at the \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 threshold to identify significance level of correlations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial variation in landscape metrics across scales\u003c/h2\u003e \u003cp\u003eLandscape metrics exhibited distinct responses to changes in spatial scale ranging from 500 m to 6000 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, LSI and SPLIT metrics displayed a robust positive correlation with scale, increasing markedly from 2.12 to 12.61 and from 2.18 to 5.82, respectively, indicating increased landscape shape complexity and fragmentation at coarser scales. Conversely, the LPI followed a consistent downward trajectory, dropping significantly from 70.14 to 40.12, which reflects a reduction in the dominance of the largest patch as the observation window expands. The ED metric demonstrated a unimodal pattern, initially rising to a peak of 42.15 at the 2500 m scale before gradually declining to 37.71 at the 6000 m scale. CONTAG showed a rapid increase from 54.85 at 500 m to 62.51 at 2000 m, after which it stabilized, maintaining values around 61.5\u0026ndash;62.2 across the remaining scales. The SHEI metric manifested only a marginal upward shift (0.53 to 0.61) across the entire gradient, suggesting that the evenness of landscape components remained relatively stable despite scale variations.\u003c/p\u003e \u003cp\u003eFor the class level metrics, from the standpoint of the PLAND metric, the cropland constitutes 58.91% of the landscape at the 500 m scale, diminishing to 33.14% when the scale increases to 6000 m, while nonetheless retaining its status as the dominant land use type within the region. In contrast, grassland demonstrated an inverse trajectory, gradually escalating from 20.64% at 500 m scale to 43.64% at the 6000 m scale. Other land use types exhibited relatively minor proportions, with flood land maintaining a consistent range of 15.00%\u0026ndash;16.50% at 2000\u0026ndash;6000 m scale, construction land declining from 5.74% to 1.75%, water maintaining a consistent range of 2.85%\u0026ndash;4.54%, forest decreasing from 2.97% to 1.00%, and bare land maintaining about 2% at 1000\u0026ndash;6000 m scale. This pattern indicates that the habitats utilized by the three avian species are primarily characterized by agricultural ecosystems, with the prominence of grassland ecosystems becoming increasingly evident at larger scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the PD metric, a consistent decline in patch densities was observed for most land use types as scale expanded, indicative of scale-specific aggregation tendencies in landscape elements (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). At the 500 m scale, grassland (PD\u0026thinsp;=\u0026thinsp;2.97), cropland (PD\u0026thinsp;=\u0026thinsp;1.95) and flood land (PD\u0026thinsp;=\u0026thinsp;1.88) demonstrated markedly higher patch densities compared to other land use types, indicating substantial spatial heterogeneity in finer-scale landscape. Upon expansion to the 6000 m scale, patch densities for land use types converged, with PD values of grassland reducing to 1.43, flood land declining to 0.86, and cropland declining to 0.86. Such dynamics elucidate the underlying spatial structuring of landscape elements, suggesting progressive homogenization at coarser scales.\u003c/p\u003e \u003cp\u003eThe DIVISION metric exhibited a consistent upward trend across all land use types as the scale expanded from 500 m to 6000 m, indicating a general decline in spatial connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Bare land and water displayed the highest connectivity (lowest DIVISION values of 0.15 and 0.27, respectively) at the fine scale (500 m), surpassing that of cropland (0.49). For the cropland landscape, the DIVISION metric increased from 0.49 at 500 m to 0.93 at 6000 m, reflecting a gradual diminution in continuity. Notably, while flood land, water, and forest reached near-complete separation (\u0026gt;\u0026thinsp;0.99) at coarser scales, grassland maintained the lowest separation level among all types at the 6000 m scale (approximately 0.85), suggesting it retains relatively better patch integrity than other landscape elements at large scales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effects of multi-scale landscape metrics on the three avian species\u003c/h2\u003e \u003cp\u003eThe multi-scale Spearman correlation analysis revealed species-specific responses to landscape composition and configuration among the three avian species, with the spatial scope of these associations appearing to correspond to body size.\u003c/p\u003e \u003cp\u003eIn the case of the smaller-bodied \u003cem\u003eT. ferruginea\u003c/em\u003e and the medium-sized \u003cem\u003eA. indicus\u003c/em\u003e, avian abundance was primarily associated with landscape fragmentation metrics (DIVISION) rather than composition metrics (PLAND), with significant correlations confined to finer and intermediate scales (500\u0026ndash;3500 m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore precisely, \u003cem\u003eT. ferruginea\u003c/em\u003e abundance showed consistent negative correlations with the fragmentation of various land cover types. Significant negative associations were detected in the DIVISION of bare land at 500 m (\u003cem\u003er\u003c/em\u003e = -0.353, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) and 1500 m (\u003cem\u003er\u003c/em\u003e = -0.342, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), the DIVISION of construction land at 1500 m (\u003cem\u003er\u003c/em\u003e = -0.355, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), and the DIVISION of water at 2500 m (\u003cem\u003er\u003c/em\u003e = -0.355, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) and 3000 m (\u003cem\u003er\u003c/em\u003e = -0.361, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Hierarchical partitioning further confirmed the dominant influence of fragmentation metrics, with the largest contributions arising from bare land DIVISION at 500 m (30.28%) and 1000 m (29.17%), followed by construction land DIVISION at 1500 m (19.51%), and water DIVISION at 2500 m and 3000 m (13.44% and 7.60%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLikewise, \u003cem\u003eA. indicus\u003c/em\u003e abundance showed negative correlations with the DIVISION of bare land at 2000 m (\u003cem\u003er\u003c/em\u003e = -0.323, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), the DIVISION of construction land at 3500 m (\u003cem\u003er\u003c/em\u003e = -0.344, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), and the DIVISION of water at 2500 m and 3000 m (\u003cem\u003er\u003c/em\u003e = -0.324 to -0.340, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). In contrast to \u003cem\u003eT. ferruginea\u003c/em\u003e, however, \u003cem\u003eA. indicus\u003c/em\u003e exhibited a positive correlation with the DIVISION of cropland at 1500 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.371, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), suggesting a degree of tolerance for specific agricultural configuration patterns at this scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Consistently, hierarchical partitioning analysis demonstrated relatively balanced contributions across these metrics, with values ranging from 19.87% to 20.39%: cropland DIVISION at 1500 m (20.39%), bare land DIVISION at 2000 m (19.93%), construction land DIVISION at 3500 m (19.87%), and water DIVISION at 2500 m and 3000 m (19.88% and 19.93%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the large-bodied \u003cem\u003eG. nigricollis\u003c/em\u003e demonstrated the most extensive and multifaceted relationships with landscape metrics, spanning a wide array of scales (500\u0026ndash;6000 m). Its abundance was markedly influenced by both the composition (PLAND) and configuration (PD, DIVISION) metrics of landscape surrounding the habitats of the avian species (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eG. nigricollis\u003c/em\u003e showed a pronounced affinity for construction land across all examined scales. Abundance was positively correlated with the PLAND of construction land (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.302 to 0.484, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the PD of construction land (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.296 to 0.423, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) consistently from 500 m to 6000 m, with peak associations observed around 2500\u0026ndash;3000 m. Moreover, the DIVISION of construction land showed a positive correlation at the finest scale of 500 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.346, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding cropland, \u003cem\u003eG. nigricollis\u003c/em\u003e demonstrated a preference for abundant and aggregated patches. Abundance showed positive correlation with the PLAND of cropland over broader scales (1000\u0026ndash;6000 m; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.296 to 0.355, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but negative correlation with the DIVISION of cropland across the same scales (1000\u0026ndash;6000 m; \u003cem\u003er\u003c/em\u003e = -0.289 to -0.391, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a selective bias toward large, unbroken agricultural expanses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, \u003cem\u003eG. nigricollis\u003c/em\u003e manifested negative associations with grassland extent but positive associations with its fragmentation. The PLAND of grassland was negatively correlated with abundance from 500 m to 5500 m (\u003cem\u003er\u003c/em\u003e = -0.295 to -0.337, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the DIVISION of grassland was positively correlated from 2000 m to 6000 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.289 to 0.343, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, regarding bare land, abundance was negatively correlated with the DIVISION of bare land exclusively at the coarsest scales of 5500 m (\u003cem\u003er\u003c/em\u003e = -0.307, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) and 6000 m (\u003cem\u003er\u003c/em\u003e = -0.292, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). Finally, the CONTAG metric showed a positive correlation at 500 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.333, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHierarchical partitioning by spatial scale further revealed that, for \u003cem\u003eG. nigricollis\u003c/em\u003e, landscape index contributions varied substantially across buffer radii. At the finest scale (500 m), four metrics exhibited the highest contributions: CONTAG (25.75%), grassland PLAND (21.14%), construction land DIVISION (21.18%), and construction land PD (20.44%). As the focal scale increased, the dominant metrics shifted, with the PLAND and PD of construction land, the PLAND and DIVISION of cropland, and the PLAND and DIVISION of grassland alternating as the most influential factors, with contributions generally ranging from 10% to 28%. These scale-dependent shifts underscore the spatially structured and multi-dimensional nature of landscape influences on the abundance of this large-bodied species (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The impact of landscape structure on the coexistence of the avian species\u003c/h2\u003e \u003cp\u003eThe Spearman correlation analysis revealed that coexistence patterns among the three avian species were substantially influenced by landscape composition and configuration, exhibiting considerably variations across spatial scales and species dyads.\u003c/p\u003e \u003cp\u003eThe coexistence between the medium-sized \u003cem\u003eA. indicus\u003c/em\u003e and the smaller-bodied \u003cem\u003eT. ferruginea\u003c/em\u003e was negatively associated with the extent of anthropogenic land use intensity at coarser scales. Significant negative correlations were observed between their coexistence index and the PD of construction land at 5500 m (\u003cem\u003er\u003c/em\u003e = -0.273, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and 6000 m (\u003cem\u003er\u003c/em\u003e = -0.339, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), as well as the PLAND of construction land at 6000 m (\u003cem\u003er\u003c/em\u003e = -0.275, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). Comparable negative associations were observed with the PLAND of cropland at 5500 m (\u003cem\u003er\u003c/em\u003e = -0.268, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and 6000 m (\u003cem\u003er\u003c/em\u003e = -0.291, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Hierarchical partitioning analysis revealed that the configuration metrics of construction land were the dominant predictors of their coexistence, with the PD of construction land at 6000 m contributing the largest proportion (46.77%), followed by the PD of construction land at 5500 m (30.65%), and the PLAND of construction land at 6000 m (9.68%), while the PLAND of cropland at both 5500 m and 6000 m showed relatively modest contributions (6.45% each) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, coexistence patterns involving the large-bodied \u003cem\u003eG. nigricollis\u003c/em\u003e exhibited more intricate, scale-dependent responses to landscape heterogeneity. For \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eG. nigricollis\u003c/em\u003e dyads, coexistence was positively correlated with landscape fragmentation and density of construction land at intermediate scales, yet negatively correlated with the fragmentation of natural and agricultural habitats. Specifically, positive associations were detected for the DIVISION of construction land from 2500 m to 3500 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.281 to 0.358, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) and the PD of construction land at 2500 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.262, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) and 3000 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.266, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043). In contrast, negative correlations were identified with the DIVISION of water at 1500 m (\u003cem\u003er\u003c/em\u003e = -0.287, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), the DIVISION of flood land at 5500 m (\u003cem\u003er\u003c/em\u003e = -0.281, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and 6000 m (\u003cem\u003er\u003c/em\u003e = -0.299, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), and the DIVISION of cropland at 6000 m (\u003cem\u003er\u003c/em\u003e = -0.261, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Hierarchical partitioning indicated that the DIVISION of construction land at 3500 m was the most influential factor (30.88%), followed by the DIVISION of water at 1500 m (20.59%), DIVISION of flood land at 5500 m (10.29%) and 6000 m (8.82%), DIVISION of construction land at 3000 m (8.82%), and the DIVISION cropland at 6000 m (7.35%), while the PD of construction land at intermediate scales showed relatively minor contributions (2.94\u0026ndash;4.41%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eT. ferruginea\u003c/em\u003e and \u003cem\u003eG. nigricollis\u003c/em\u003e dyads, coexistence appeared to be promoted by the prevalence of open habitats and the contiguity of agricultural land. Their coexistence index showed positive correlations with the PLAND of bare land across broader scales ranging from 5000 m to 6000 m (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.267 to 0.274, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), alongside a negative correlation with the DIVISION of bare land at 2500 m (\u003cem\u003er\u003c/em\u003e = -0.280, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Furthermore, a significant negative correlation was observed with the DIVISION of cropland at the finer scale of 1000 m (\u003cem\u003er\u003c/em\u003e = -0.311, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Notably, hierarchical partitioning revealed that the DIVISION of cropland at 1000 m overwhelmingly dominated the prediction of their coexistence (62.00%), with the DIVISION of bare land at 2500 m contributing 14.00%, and the PLAND of bare land at broader scales (5000\u0026ndash;6000 m) each contributing equally modest proportions (8.00%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur multi-scale analysis reveals that landscape structure profoundly shapes niche-specific habitat selection and interspecific coexistence among the three overwintering avian species within the study reserve (Tscharntke et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e). We found that body size is a primary driver of these scale-dependent responses. The larger species respond to landscape patterns at broader spatial extents, while smaller species are driven by finer-scale features. Such body-size-dependent scaling effect suggests that the reserve does not as a function as a uniform sanctuary. Instead, its conservation value relies on maintaining a diverse range of spatial heterogeneity to match the distinct perceptual ranges of species with varying allometric traits. By examining these sympatric species, we evidenced that multi-scale landscape heterogeneity jointly regulates resource accessibility, foraging efficiency, and competitive dynamics, thereby promoting niche segregation along dietary and spatial axes. Further, our results indicate that neither landscape composition nor configuration exerts a universal effect across all species. Instead, these components act synergistically, with their relative importance shifting according to species body size, spatial scale, and specific metrics (Anjos et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This strong trait dependency implies that effective conservation within protected areas requires more than simple habitat protection, and it demands multi-scale landscape management strategies tailored to the allometric constraints of target species (Vandewalle et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Indeed, prioritizing one landscape attribute over another without considering body size and diet could compromise efforts to support the full avian community. These findings challenge single-scale conservation paradigms and advocate for integrated designs that preserve multi-scale structural complexity amidst land-use intensification and climatic shifts on the Tibetan Plateau.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Niche-specific landscape requirements for the avian species\u003c/h2\u003e \u003cp\u003eThe structural attributes of landscape structure play a pivotal role in shaping habitat selection among the overwintering avian species, primarily through their modulation of resource availability and accessibility. This landscape, encompassing the dominant surrounding land covers, acts as a critical determinant of niche fulfillment by dictating the spatial distribution of foraging opportunities, shelter, and safety. However, our empirical findings highlight that avian response to landscape structure exhibit considerable uniform, driven by species-specific niche requirements. For instance, landscape characterized by high cropland dominance provide essential scattered grain resources that preferentially support larger-bodied avian such as \u003cem\u003eG. nigricollis\u003c/em\u003e, evidenced by its robust positive correlation with cropland proportion (PLAND). This alignment highlights a niche specialization wherein \u003cem\u003eG. nigricollis\u003c/em\u003e exploits anthropogenically modified landscapes for energy-intensive foraging needs during resource-scarce winters.\u003c/p\u003e \u003cp\u003eIn contrast, other sympatric avian species exhibit more intricate and multifaceted niche requirements on landscape configuration. Notably, \u003cem\u003eT. ferruginea\u003c/em\u003e showed a negative correlation with the DIVISION metric of water bodies, indicating a preference for aggregated wetland habitats, while exhibiting a positive association with the DIVISION of cropland, suggesting an affinity for fragmented cropland mosaics. This pattern implies a niche strategy that leverages edge effects, enabling \u003cem\u003eT. ferruginea\u003c/em\u003e to optimize foraging efficiency in patchy croplands while accessing the protective and roosting benefits of interspersed natural habitats (Quan et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Such a landscape-dependent niche likely a balance between resource exploitation and predation avoidance. Similarly, \u003cem\u003eA. indicus\u003c/em\u003e showed a negative correlation with the water DIVISION but minimal sensitive to cropland metrics, suggesting a niche less reliant on the cropland and more oriented toward stable aquatic resources (Hamza et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These observations collectively illustrate that landscape structure governs habitat selection, yet its efficacy is contingent upon each avian unique resource-access strategies, which are shaped by evolutionary adaptations to high-altitude Tibetan environments.\u003c/p\u003e \u003cp\u003eFor overwintering avian communities, both population size and interspecific coexistence are jointly governed by the interplay of landscape composition and configuration. Composition metrics directly regulate the abundance of vital resources like food and cover, whereas configuration influences resource quality through factors such as patch connectivity and edge density. Although prior studies suggest that overwintering avian species may be less constrained by landscape configuration due to their capacity for long-distance daily movements (Tinoco et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Land\u0026aacute;zuri et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), our multi-scale analysis at radii up to 6000 m radius, encompassing typical foraging ranges (Liu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reveals significant configuration effects. This suggests that niche requirements extend beyond mere resource quantity to include high-quality spatial arrangements that promote energy-efficient foraging under hypothermic conditions. In our dataset, both landscape composition and configuration metrics emerged as pivotal predictors of population sizes and coexistence indices, highlighting their roles in mediating behavioral decisions and ecological interactions among the coexisting species.\u003c/p\u003e \u003cp\u003eIn winter, the avian species confront elevated energetic exigencies amid cold temperatures and limited daylight, rendering food resources niche paramount in habitat selection (Chatterjee and Basu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Landscape composition orchestrates the spatiotemporal distribution of these resources by prioritizing land-use types conducive to foraging, such as croplands or aquatic zones. For instance, landscape dominated by cropland provide residual grains that align with granivorous niche of \u003cem\u003eG. nigricollis\u003c/em\u003e, fostering higher population densities (Bishop and Li \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similarly, water bodies in the landscape enhance algal and vegetative foraging niches for \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eT. ferruginea\u003c/em\u003e (Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Quan et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Concurrently, heightened food demand attenuate sensitivity to anthropogenic disturbances, as seen in \u003cem\u003eG. nigricollis\u003c/em\u003e strong positive correlations with construction land PLAND and PD of at broad scales. When the landscape composition favors these food-rich elements, it effectively subsidizes core habitats, enhancing niche suitability and persistence. Conversely, landscape dominated by bare or unproductive land reduce overall niche quality, eliciting compensatory behaviors like mixed-species flocking to partition resources and alleviate competition (Zhu et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This dynamic elucidates why composition metrics often supersede density-dependent crowding in suboptimal habitats, as avian species prioritize landscape that maximize foraging niches over spatial segregation.\u003c/p\u003e \u003cp\u003eFrom an evolutionary vantage, the landscape's formative influence on niche embodies adaptive strategies refined for fluctuating winter environments. Mixed-species flocks, often intensified in resource-poor landscape, functions as a niche-differentiation mechanism to partition limited resources and mitigate interspecific competition, aligning with theories of ecological differentiation (Mammides et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This not only accounts for observed habitat selection patterns but also bears significant conservation implications. Targeted landscape modifications, such as augmenting cropland buffers or wetland connectivity, could fortify overwintering niches in vulnerable Tibetan regions. Nevertheless, However, climate change may precipitate landscape alterations, potentially eroding specialized niches and conferring advantages to generalist species over niche-restricted specialists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Body-size effects mediate landscape effect among coexisting species\u003c/h2\u003e \u003cp\u003eExtending from the fundamental role of landscape structure in modulating habitat selection, body size emerges as a pivotal allometric factor that differentially shapes avian responses to landscape effects among the overwintering avian assemblages. This body size-mediated variation underscores the heterogeneity in niche-specific landscape requirements, whereby larger-bodied species perceive and exploit the surrounding landscape differently from their smaller counterparts, ultimately influencing habitat preferences, population dynamics, and interspecific coexistence. Larger avian species, characterized by greater energetic demands and broader foraging radii, often necessitate expansive landscape resources to fulfill their niche, whereas smaller species may satisfy their requirements through more localized, high-density resource patches. Such disparities amplify the landscape's overarching impact, revealing body size as a key driver of ecological partitioning in resource-constrained Tibetan winter habitats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, body size governs fundamental physiological and behavioral traits, including metabolic scaling, locomotor capabilities, foraging ranges, and competitive hierarchies, that engender divergent niche interactions with landscape composition and configuration (Lindstedt et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Haskell et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Jetz et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). According to allometric scaling theory, larger organisms often show lower mass-specific metabolic rates, enabling sustained energy allocation over larger spatial scales but imposing higher absolute resource thresholds (Nagy \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Shankar et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, larger avian species may evaluate available resources across broad extents, while smaller species tend to be more sensitive to local resource aggregation. For example, the large-bodied \u003cem\u003eG. nigricollis\u003c/em\u003e demonstrates pronounced positive correlations with cropland PLAND and negative associations with cropland DIVISION across 1000\u0026ndash;6000 m scales, reflecting a niche strategy optimized for contiguous agricultural landscape that provide abundant grain subsidies to meet its substantial caloric needs. In contrast, smaller avian species such as \u003cem\u003eT. ferruginea\u003c/em\u003e and \u003cem\u003eA. indicus\u003c/em\u003e exhibit niches attuned to mid-scale, high-quality resource aggregations, with foraging efforts concentrated in fragmented mosaics that balance aquatic and vegetative patches, thereby minimizing energy expenditure within confined ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese body size-driven divergences are vividly illustrated in the dietary and competitive niches of the focal species, where landscape effects are exacerbated by interspecific interactions. Larger avian species often dominate resource-rich landscape patches, forcing smaller species toward alternative foraging tactics to mitigate competition (Malpica et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bribiesca et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As the largest body size among the studied assemblage, \u003cem\u003eG. nigricollis\u003c/em\u003e leverages its size advantage to prioritize expansive croplands, securing prime foraging position in the mixed\u0026ndash;species flocks and thereby alleviating intraspecific competition in food-limited environments (L\u0026oacute;pez-Segoviano et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This competitive preeminence allows \u003cem\u003eG. nigricollis\u003c/em\u003e to exploit grain-heavy niches, while displacing smaller congeners to peripheral resources. Consequently, \u003cem\u003eT. ferruginea\u003c/em\u003e and \u003cem\u003eA. indicus\u003c/em\u003e adopt avoidance strategies, diversifying their niches toward roots, algae, and aquatic subsidies rather than direct competition for cropland. Our analyses reveal that fragmented croplands (high DIVISION) impede coexistence among \u003cem\u003eA. indicus, T. ferruginea\u003c/em\u003e, and \u003cem\u003eG. nigricollis\u003c/em\u003e, as smaller species evade overlap in reserves. Notably, despite a study indicating \u003cem\u003eA. indicus\u003c/em\u003e\u0026rsquo;s winter reliance on croplands (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), our findings show no significant correlation between its abundance and cropland PLAND, suggesting that competitive exclusion by larger species reshapes its niche-specific landscape requirements toward less contested aquatic habitats.\u003c/p\u003e \u003cp\u003eTherefore, body size acts as a critical modulator of niche-specific landscape requirements, fostering ecological differentiation that enhances coexistence in heterogeneous landscapes. By integrating allometric constraints with landscape metrics like PLAND and DIVISION, this framework elucidates how larger species capitalize on broad-scale resource distributions, while smaller ones exploit fine-scale patchiness, with implications for conservation strategies in dynamic overwintering ecosystems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Integrating landscape management for avian niches\u003c/h2\u003e \u003cp\u003eOur findings underscore the critical role of stable winter food provisioning in safeguarding avian populations, with expansive cropland areas conferring enduring benefits for their survival and resource in the limited high-altitude environments. Historically, biodiversity conservation and agricultural intensification have been framed as antithetical pursuits, often resulting in trade-offs that prioritize one at the expense of the other (Pereira et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tscharntke et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e). Yet, the pronounced reliance of overwintering avian species on cropland derived subsidies (such as residual grains and stubble) demonstrates a potential synergy in overwintering landscapes, where strategic agricultural allocation can complement rather than contravene conservation imperatives. This reconciliation is especially pertinent when extending protection beyond core habitats (e.g., wetlands and reserves) to encompass the surrounding landscape, which provides critical niche elements including food augmentation, extended foraging arena, and connectivity corridors. By integrating landscape-oriented conservation strategies within broader conservation frameworks, practitioners can holistically address species\u0026rsquo; multifaceted niche requirements, bridging core habitat integrity with landscape-mediated resource dynamics to enhance population viability and ecological resilience.\u003c/p\u003e \u003cp\u003eFurthermore, habitat restoration initiatives that repurpose cropland into forest or grassland may inadvertently exacerbate food scarcity for overwintering avian species, precipitating demographic declines in vulnerable assemblages (Zhang \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such conversions, when myopically centered on core zones, frequently undervalue the landscape's pivotal function in supporting niche differentiation and dietary specialization, e.g., crop-centric landscape underpin the granivorous niche of \u003cem\u003eG. nigricollis\u003c/em\u003e, while landscape of waterbodies support the algal and vegetative foraging needs of \u003cem\u003eA. indicus\u003c/em\u003e and \u003cem\u003eT. ferruginea\u003c/em\u003e. Accordingly, restoration paradigms in protected landscapes must be judiciously tailored to species-specific objectives, favoring landscape augmentations that amplify rather than diminish these landscape-scale dependencies, thereby preserving the ecological scaffolding essential for winter survival.\u003c/p\u003e \u003cp\u003eEqually imperative is an appraisal of how contemporary agronomic practices curtail food accessibility for overwintering avian species across core and landscape domains alike. Mechanized harvesting techniques, for instance, minimize post-harvest residue, thereby curtailing avian access to indispensable grain and stubble resources (Jia et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Concurrently, the proliferation of greenhouse agriculture supplants conventional arable lands, eroding habitat suitability for overwintering species (Wu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These transformations not only compromise core foraging ground but also degrade landscape heterogeneity, impeding niche-dependent processes such as mixed-species flocking that mitigate competition and optimize resource partitioning in austere winter conditions. To counteract these pressures, conservation policies should advocate for agroecological interventions (such as reduced-tillage farming or wildlife-friendly crop rotations) that sustain landscape productivity while fostering avian niches, ensuring the long-term persistence of these migratory avian species in rapidly evolving landscapes.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn facing the rapid environmental changes, protecting the isolated habitat \u0026ldquo;island\u0026rdquo; is no longer enough. Our study emphasizes the imperative for conservation strategies that prioritize landscape structure to sustain the coexistence of the overwintering avian species within a reserve, particularly in high-altitude habitats facing rapid land-use transformations in Tibet. By integrating multi-scale analyses, we demonstrate that composition and configuration of landscape jointly drive niche-specific habitat selection among the sympatric avian species, with these effects exhibiting significant scale-dependency modulated by avian body size. Notably, the large-bodied \u003cem\u003eG. nigricollis\u003c/em\u003e displayed heightened sensitivity to landscape metrics at broader spatial extents (up to 6000 m), reflecting its expansive foraging requirements and competitive dominance in resource-limited winter environments, where smaller avian species like \u003cem\u003eT. ferruginea\u003c/em\u003e and \u003cem\u003eA. indicus\u003c/em\u003e respond more strongly to localized configurations.\u003c/p\u003e \u003cp\u003eThrough this framework, we identify critical landscape elements, such as cropland proportion, water body aggregation, and patch connectivity, as pivotal for avian persistence, providing the reserves like the Black-necked Crane National Nature Reserve. These findings advocate for management approaches that harmonize agricultural productivity with ecological integrity, emphasizing the landscape's role in subsidizing core habitats. Extending our analyses, we reveal that food-centric landscape components (e.g. post-harvest cropland and flood-associated patches) more robustly avian abundance and pairwise coexistence than non-food metrics across the 500\u0026ndash;6000 m scales examined. Body-size-mediated responses, intertwined with dietary niches, facilitate niche segregation and mixed-species flocking, thereby promoting coexistence amid winter resource scarcity.\u003c/p\u003e \u003cp\u003eFuture investigations should examine interactions between landscape dynamics, climatic stressors, and anthropogenic disturbances to further elucidate these mechanisms, potentially refining predictive models for avian resilience. From a practical perspective, securing reliable winter food subsidies, such as maintaining suitable areas of post-harvest cropland and safeguarding key flood/water patches, can enhance interspecific coexistence while minimizing trade-offs with agricultural practices. Ultimately, embedding these insights into adaptive, multi-species zoning frameworks is essential for preserving the functional ecology of the Tibetan Plateau wetland-farmland mosaics, offering a robust scientific foundation for holistic ecosystem protection in this biodiversity hotspot.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical Trial Number\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompliance with Ethical Standards\u003c/h2\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.W. conceived the study, developed the methodology, performed the investigation, formal analysis, and data curation, validated the results, managed resources, created visualizations, and wrote the original draft as well as reviewed and edited the manuscript. M.M. acquired funding, administered the project, participated in the investigation, validated the results, and reviewed and edited the manuscript. W.Q. contributed to visualization. C.Z. and J.H. participated in the investigation, with J.H. also handling project administration. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe greatly acknowledge the support from the office of the Black-necked Crane National Nature Reserve of the Forestry and Grassland Administration of Shigatse City, Tibet, China. We also extend our thanks to Zhaochun Hong, Research Librarian at the Chongqing Natural History Museum, for her valuable assistance with the avian survey in this study. Specially, we are grateful to the editors and anonymous reviewers for their helpful comments. This study was financially supported by Geological Disaster Patterns and Mitigation Strategies Under River\u0026ndash;Reservoir Hydrodynamics in the Three Gorges Reservoir Fluctuation Zone, Chongqing Municipal Bureau of Water Resources (Grant No. 5000002024CC20004), and the Chongqing Municipality Key Project for Technological Innovation and Application Development (Grant No. CSTB2023TIAD-KPX0077).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdler K, Jedicke E (2022) Landscape metrics as indicators of avian community structures \u0026ndash; A state of the art review. 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Diversity-Basel 12:105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/d12030105\u003c/span\u003e\u003cspan address=\"10.3390/d12030105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Grus nigricollis, Landscape structure, Body size, Scale effect, Coexistence, Tibetan Plateau","lastPublishedDoi":"10.21203/rs.3.rs-9167989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9167989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid environmental changes on the Tibetan Plateau pose significant challenges to overwintering avian assemblages in the resource-limited environments. However, traditional conservation strategies often focus solely on establishing reserve boundaries, failing to recognize that effective protection depends on maintaining complex internal landscape structures tailored to species-specific scale requirements. This study investigated how landscape composition and configuration affect the habitat selection and coexistence of overwintering avian assemblages in a reserve, with a specific focus on the modulating role of body size. Three sympatric species with distinct body sizes, including Black-necked Crane (\u003cem\u003eGrus nigricollis\u003c/em\u003e), Bar-headed Goose (\u003cem\u003eAnser indicus\u003c/em\u003e), and Ruddy Shelduck (\u003cem\u003eTadorna ferruginea\u003c/em\u003e), were investigated in the Black-necked Crane National Nature Reserve along the Yarlung Zangbo River valley in China\u0026rsquo;s Tibet. Using a multi-scale analysis ranging from 500 m to 6000 m and a pairwise coexistence index, this study quantified the relationships between species abundance, coexistence patterns, and landscape metrics derived from remote sensing data. The results demonstrated that landscape effects were strongly scale-dependent and modulated by body size. The larger-bodied \u003cem\u003eG. nigricollis\u003c/em\u003e exhibited heightened sensitivity to landscape metrics at broader spatial scales, showing a distinct preference for contiguous agricultural lands as critical food subsidies. Conversely, smaller species responded significantly to fine-scale landscape configurations. Furthermore, the scale-dependent niche requirements for landscape structures were found to facilitate niche segregation and mitigate interspecific competition. Our findings underscore that merely delineating reserve boundaries is insufficient. Instead, conservation planning must adopt a multi-scale framework grounded in trait-based ecology. Priority should be given to safeguarding internal habitat heterogeneity, while concurrently fostering synergistic land-use practices across the reserve landscape. Specifically, maintaining the availability of post-harvest croplands and ensuring wetland connectivity are critical to bridging the gap between the fine-scale needs of smaller species and the broad-scale foraging ranges of larger species.\u003c/p\u003e","manuscriptTitle":"Coexistence of overwintering avian species in Tibet: scale-dependent niche requirements for landscape structure with body size effects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 19:31:58","doi":"10.21203/rs.3.rs-9167989/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T13:04:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133335362048273443307204792921571648198","date":"2026-05-09T12:01:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114444949563627833319674329685099252245","date":"2026-04-20T07:21:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-28T21:46:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T01:52:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T16:24:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2026-03-19T09:49:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"616593f2-8529-4232-a243-4c1e7c076015","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T13:04:20+00:00","index":44,"fulltext":""},{"type":"reviewerAgreed","content":"133335362048273443307204792921571648198","date":"2026-05-09T12:01:38+00:00","index":42,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T19:31:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 19:31:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9167989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9167989","identity":"rs-9167989","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}