Multi-Scale Environmental Drivers of Heron and Egret Colony Assemblages in Korea Using Self-Organizing Map Clustering

Multi-Scale Environmental Drivers of Heron and Egret Colony Assemblages in Korea Using Self-Organizing Map Clustering | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multi-Scale Environmental Drivers of Heron and Egret Colony Assemblages in Korea Using Self-Organizing Map Clustering SeonYong Lee, Han-I Choi, Jae-Woong Hwang, Ji-Yeon Lee, Dong-Won Kim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8476247/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Context Colonial herons and egrets breed within highly heterogeneous landscapes where environmental conditions vary across multiple spatial scales. Although landscape structure is expected to shape colony-level species assemblages, explicit multi-scale empirical assessments remain scarce, particularly at national extents. Objectives We sought to (1) identify emergent species-assemblage types among heron and egret colonies across South Korea, (2) determine how these assemblages correspond to landscape heterogeneity across nested spatial scales, and (3) quantify the scale-dependent importance of environmental predictors influencing colony identity. Methods We compiled species-specific nest counts from 176 breeding colonies and applied Self-Organizing Maps (SOM) to classify colony assemblages. Landscape variables were extracted from ESA CCI Plant Functional Type (PFT) datasets within five buffer radii (500 m–10 km). Random Forest models evaluated the relative importance of land-cover predictors at each spatial scale. Results SOM analysis revealed six ecologically distinct colony groups differing in species dominance, richness, and geographic distribution. Large-bodied species predominated in inland forested regions, whereas medium- and small-bodied species clustered in coastal and urban–rural mosaics. Environmental predictors were strongly scale-dependent: BUILT areas, managed grasslands, and water-related cover dominated at fine scales; natural grasslands, bare land, and tree-related PFTs—hallmarks of riparian corridors—emerged at intermediate scales; and regional land-use intensity remained the strongest predictor at 10 km. Conclusions Colony assemblages arise from hierarchical interactions among fine-scale habitat features, river-corridor landscapes, and broader-scale land-use regimes. This hierarchical multi-scale perspective provides a predictive framework for anticipating how land-use change will reorganize ecological networks supporting colonial waterbirds. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Landscape heterogeneity Multi-scale analysis Species assemblage Self-organizing map (SOM) Wading birds Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Colonial waterbirds such as herons and egrets depend on spatially distributed networks of foraging and breeding habitats embedded within heterogeneous landscapes (Fasola and Brichetti 2023 ; Mashiko 2018 ). Their mixed-species colonies—often comprising species with differing ecological requirements—emerge from interactions among local habitat conditions, regional resource availability, and broader-scale landscape structure (Si Bachir and Benyacoub 2008 ; Mashiko 2018 ). Understanding how these colonies assemble across space therefore requires an integrative framework that links ecological processes operating across multiple, nested spatial scales, a core principle of landscape ecology (Cho et al. 2024 ; Mattsson et al. 2020 ). Because landscape structure constrains movement, regulates access to food resources, ensure shapes exposure to disturbance, and mediates interspecific interactions, colonies can be conceptualized as ecological “nodes” whose species composition reflects the surrounding landscape mosaic and its hierarchical context (Mashiko 2018 ; Byer et al. 2025 ). Across East Asia, rapid land-use change has substantially altered both the distribution and spatial configuration of habitats used by Ardeidae species (Xu et al. 2019 ; Liu et al. 2023 ). Key foraging environments—including rice paddies, coastal lowlands, river corridors, and forest–agriculture ecotones—have undergone pronounced modification driven by urban expansion, agricultural intensification, and river engineering (Kidson et al. 2020 ; Wymore et al. 2023 ). These changes affect not only the quantity of foraging habitat but also the spatial connectivity among habitat patches, thereby restructuring the ecological template upon which colonial breeding sites are established (Xu et al. 2019 ; Schweizer et al. 2020). Despite the pronounced spatial heterogeneity of these landscapes, however, little is known about how colony-level species assemblages respond to landscape structure across spatial scales. Most previous studies have focused on habitat selection at individual breeding sites or on single-species responses, providing limited insight into the multi-scale processes underlying the formation and persistence of mixed-species colonies. The Korean Peninsula offers an ideal setting for addressing these questions. The region encompasses strong environmental gradients from coastal to inland areas, extensive river systems forming distinct riparian corridors, and mosaic agricultural landscapes that support the primary foraging activities of herons and egrets (NGII 2013a, 2013b). Colonial breeding sites occur across this diverse landscape, ranging from estuarine rice fields and coastal plains to inland forested valleys, providing a unique opportunity to examine how landscape heterogeneity shapes colony-level species assemblages (Choi et al. 2016 ; Park and Jang 2020 ). Nationwide surveys further reveal substantial variation among colonies in nest numbers, species richness, and dominant species combinations (NIER 2012), suggesting that spatial configurations of landscape elements may function as ecological “filters” influencing colony identity (Park and Jang 2020 ). Nevertheless, no study to date has evaluated these patterns at a national scale using spatially explicit, multi-scale landscape metrics. A landscape-ecological perspective provides several key insights into colony assembly. First, it recognizes that ecological patterns arise from hierarchical spatial processes, allowing colony structure to be interpreted within nested spatial contexts, ranging from immediate foraging environments within hundreds of meters to broader-scale land-use regimes spanning several kilometers (Jedlikowski et al. 2016 ; Lipsey et al. 2017 ). Second, it accounts for the possibility that species assemblages are shaped by nonlinear interactions among water availability, agricultural matrices, forest cover, and other land-cover components (Musseau et al. 2022 ; García-Callejas et al. 2025 ). Third, it emphasizes the importance of spatial configuration and connectivity—factors that are particularly critical for highly mobile foragers whose reproductive success depends on repeated movements between breeding sites and multiple foraging habitats (Evens et al. 2018 ; Teitelbaum et al. 2021). In this study, we integrate nationwide, multi-year breeding colony surveys with Self-Organizing Map (SOM) analyses to identify species-assemblage patterns and emergent colony types across 176 heron and egret breeding sites in South Korea. We then assess how these colony types correspond to landscape gradients and structural features measured across five nested spatial scales (500 m to 10 km) (Ferraz et al. 2010 ; Lipsey et al. 2017 ). Using ESA CCI Plant Functional Type (PFT) layers, we quantify key land-cover components—including water bodies, managed and natural herbaceous cover, forest structure, bare land, and urban areas—to evaluate colony responses not only to local habitat conditions but also to landscape mosaics at broader spatial scales (ESA CCI 2021 ). By combining unsupervised species-assemblage modeling with multi-scale landscape analysis, this study provides the first nationwide assessment of how hierarchical landscape structure shapes colony-level species composition in herons and egrets. Our findings demonstrate that colonial breeding sites should not be viewed as isolated locations but as emergent outcomes of spatial processes operating across nested spatial scales. This multi-scale landscape perspective offers critical insights into the mechanisms governing colony formation and persistence and establishes a foundation for predicting how ongoing and future land-use change may restructure the ecological networks supporting heron and egret colonies across the Korean Peninsula. 2. Methods 2.1. Breeding colony dataset This study examined the breeding distribution of egret and heron species in the Republic of Korea using nationwide survey data collected in 2011–2012 (NIER 2012). These baseline data were supplemented with additional colony records identified through researcher field observations, news reports, and online sources such as blogs, which occasionally document newly established or previously unrecorded colonies. Based on this integrated dataset, all candidate locations were visited twice during the 2018–2019 breeding seasons to verify the presence of active breeding. For each confirmed colony, geographic coordinates (longitude and latitude) and species-specific nest counts were recorded. 2.2. Self-organizing map (SOM) analysis A Self-Organizing Map (SOM) was used to identify latent ecological structure in the species composition of breeding colonies. SOM projects high-dimensional ecological data onto a two-dimensional grid while preserving topological relationships, allowing the simultaneous detection of continuous ecological gradients and discrete assemblage patterns. Breeding colony data are typically right-skewed, with a small number of large colonies exerting disproportionate influence on the distribution. To reduce this bias and improve numerical stability in the SOM analysis, all nest counts were log-transformed using log(n + 1). This transformation limited the influence of extremely large colonies while retaining variability among smaller colonies. To determine the optimal SOM grid configuration, multiple candidate grid sizes were evaluated, with each configuration trained for 100 iterations. Model performance was assessed using two diagnostic metrics: Quantization Error (QE), defined as the mean distance between each observation and its best-matching unit (BMU), where lower values indicate better representation of the input space; and Topographic Error (TE), defined as the proportion of observations for which the two closest BMUs were not adjacent, where lower values indicate stronger preservation of topological structure. The grid exhibiting low QE and acceptably low TE was selected, and a hexagonal topology was adopted to facilitate smoother neighborhood interactions than a rectangular lattice. Using the selected grid, the final SOM was trained for 200 iterations with a learning rate decreasing linearly from 0.05 to 0.01. Similarity among neurons in standardized species-composition space was quantified using Euclidean distance. After training, QE and TE were recalculated to confirm adequate representation of the input structure. Codebook vectors for all neurons were extracted for subsequent cluster analysis. Because SOM does not inherently define discrete clusters, hierarchical clustering was applied to the final codebook vectors. Euclidean distance matrices were computed, and Ward’s D2 linkage method was used to minimize within-cluster variance. The number of clusters was determined based on statistical separability, dendrogram stability, and ecological interpretability, resulting in six clusters (k = 6). Each colony was assigned to a cluster according to the cluster membership of its BMU. Cluster boundaries and neuron indices were visualized on the SOM grid to evaluate cluster separation and topological continuity. 2.3. Kernel density estimation of breeding colony distributions To quantify spatial concentration patterns of breeding colonies within each SOM-derived cluster, kernel density estimation (KDE) was applied separately to each cluster. To account for differences in colony importance, KDE was weighted by the number of nests at each breeding site, such that colonies with larger nest numbers contributed more strongly to the estimated density surface. Weighted KDE was implemented using a bivariate Gaussian kernel, with bandwidth matrices automatically selected for each cluster using the plug-in estimator (Hpi). To ensure numerical stability and comparability among clusters, kernel weights were rescaled prior to density estimation so that their sum equaled the sample size within each cluster. 2.4. Environmental variable extraction and landscape analysis To assess whether SOM-derived clusters reflected meaningful differences in landscape structure rather than statistical artifacts, environmental variables were quantified within multiple spatial buffers surrounding each colony. Circular buffers with radii of 500 m, 1,000 m, 3,000 m, 5,000 m, and 10,000 m were generated, and land-cover composition within each buffer was calculated. Land-cover data were obtained from the ESA CCI Plant Functional Type (PFT) dataset, and environmental variables were classified into 16 land-cover categories (Harper et al. 2023 ; https://maps.elie.ucl.ac.be/CCI/viewer/ , accessed 21 November 2025). The area of each land-cover type was calculated for each buffer radius. To evaluate the environmental predictors of the SOM-derived colony groups, Random Forest classification models were applied to account for potential multicollinearity among spatial predictors. Separate models were fitted for each buffer radius, using SOM cluster membership as the response variable and the areas of the 16 PFT categories as predictors. Each model was built using 1,000 trees, and variable importance was assessed based on the mean decrease in accuracy derived from out-of-bag samples. For cross-scale comparison, the top five predictors at each radius were rescaled to a 0–100% range relative to the most influential variable within that scale. 2.5. Software environment All analyses were conducted in R version 4.5.1. Key packages included kohonen for SOM analysis (Wehrens and Buydens 2007 ), ks for weighted kernel density estimation (Duong 2007 ), sf (Pebesma 2018 ) and geodata (Hijmans et al. 2022 ) for spatial data processing, dplyr (Wickham et al. 2023 ) and tidyr (Wickham and Girlich 2022 ) for data preprocessing, and rstatix for non-parametric statistical analyses (Kassambara 2023 ). 3. Results 3.1. Species composition across 176 heron and egret colonies Analysis of 176 breeding colonies revealed that Grey Heron and Great Egret were the predominant species nationwide, accounting for the largest numbers of nests in most colonies (Fig. 1 ). These large-bodied species were consistently present across southern, central, and eastern regions and were particularly dominant at inland sites. In contrast, medium- and small-bodied species—including Little Egret, Cattle Egret, Intermediate Egret, and Black-crowned Night Heron—showed more spatially restricted distributions. These species were concentrated primarily in southern provinces and lowland plains along the west coast, where several colonies formed regionally distinct mixed-species assemblages (Fig. 1 ). 3.2. Regional variation in colony structure Clear geographic differentiation in colony structure was evident across regions (Fig. 1 ). Colonies in southern inland and southeastern areas were dominated by Grey Heron and Great Egret, forming large mixed aggregations characterized by high nest abundance. In contrast, colonies in the metropolitan region and western coastal plains frequently supported co-occurrence of Little Egret, Cattle Egret, and Black-crowned Night Heron, resulting in more diverse multi-species assemblages. Colonies in central inland areas and parts of the east coast tended to be smaller and simpler, often comprising one or two species and frequently dominated by either Grey Heron or Great Egret. 3.3. SOM-based colony grouping Self-Organizing Map (SOM) analysis identified six distinct clusters (k = 6) based on colony-level species composition. Model performance metrics indicated reliable representation of the high-dimensional species data (quantization error = 0.371; topographic error = 0.158). The resulting clusters differed consistently in both species composition and spatial distribution (Fig. 2 ). Cluster I was concentrated in mid-western coastal and adjacent inland regions and consisted of mixed-species colonies in which Grey Heron, Great Egret, and smaller species co-occurred (Figs. 3 , 4 ). Cluster II occurred primarily in inland agricultural landscapes and was dominated by Grey Heron and Great Egret, forming relatively simplified assemblages. Cluster III was distributed mainly in forested and riverine areas of Gangwon-do and Gyeongsangbuk-do provinces and exhibited strong dominance of large-bodied species. Cluster IV occurred in mosaic landscapes of forest and farmland and showed relatively high proportions of medium- and small-bodied species, particularly Cattle Egret and Little Egret, with Grey Heron and Great Egret present at moderate levels. Cluster V was associated with urban–rural transitional zones and was characterized by a high contribution of medium- and small-bodied species, resulting in the most compositionally diverse assemblages. Cluster VI was found in forested and wetland-dominated landscapes with high naturalness and consisted largely of Grey Heron, forming near single-species assemblages. 3.4. Differences among clusters in species composition Comparison of species composition among clusters revealed marked differences in relative nest abundance despite the consistent presence of Grey Heron and Great Egret across all groups (Fig. 4 ). Clusters III and VI were strongly dominated by large-bodied species, which accounted for more than 90% of total nests. In contrast, Cluster V showed the highest contribution of medium- and small-bodied species, comprising over 80% of total nests. Clusters I and IV exhibited intermediate, mixed-species structures with variable proportional contributions of multiple species. 3.5. Scale-dependent importance of land-cover variables in Random Forest models Random Forest models indicated that SOM-derived colony groups were explained by land-cover variables across all spatial scales, with predictor importance varying by buffer radius (Fig. 5 ). At the 500 m scale, BUILT, WATER_OCEAN, GRASS_MAN, WATER, and SHRUBS_BD were the most influential variables. At 1,000 m, BUILT, GRASS_MAN, TREES_BD, GRASS_NAT, and TREES_NE ranked highest. At 3,000 m, BARE, GRASS_NAT, TREES_BD, GRASS_MAN, and BUILT emerged as key predictors. At 5,000 m, BUILT, BARE, WATER_OCEAN, GRASS_MAN, and GRASS_NAT showed the greatest importance. At the broadest 10,000 m scale, BUILT, BARE, GRASS_MAN, WATER_OCEAN, and TREES_NE were the most influential variables distinguishing SOM clusters. 4. Discussion Self-organizing map (SOM) analysis revealed that heron and egret breeding colonies in Korea form distinct ecological clusters that differ in species composition, dominance structure, and co-occurrence patterns. These clusters closely corresponded to broad geographic gradients, including coastal–inland transitions, contrasts between major riverine lowlands and forested uplands, and variation in urban–rural land-use intensity. Colonies located in coastal plains and estuarine lowlands tended to form large mixed-species aggregations with high nest densities, whereas inland mountainous or hilly regions generally supported smaller colonies dominated by one or two species. This spatial organization indicates that colony-level assemblages are shaped not only by local habitat conditions but also by broader biogeographic context and multi-scale landscape heterogeneity across the Korean Peninsula. Random Forest analysis identified land-cover variables as the primary environmental predictors of SOM-derived colony clusters, although their relative importance varied markedly across nested spatial scales. At fine spatial scales (500–1,000 m), BUILT areas, managed grasslands (GRASS_MAN), and both oceanic and freshwater habitats consistently ranked among the most influential predictors. These variables reflect immediate environmental conditions related to disturbance intensity (Frederick & Collopy 1989 ; Pang et al. 2019 ), access to shallow-water foraging habitats (Fasola & Ruiz 1996 ; Lane & Fujioka 1998 ), and proximity to agricultural feeding grounds (Choi et al. 2007 , 2010 , 2016 ; Kelly et al. 2008 ). Such patterns align with the well-established reliance of many Ardeidae species on rice paddies, wetlands, and other local foraging habitats during the breeding season (Fujioka et al. 2010 ; Yoo et al. 2022 ). In addition, colony placement itself appears shaped by human-associated landscape features: Shin et al. (2018) documented that approximately 12.46% of potential breeding habitat for herons and egrets occurs within ~ 50 m of residential areas in Korea, suggesting that many colonies are established adjacent to villages or settlement edges where agricultural landscapes, shallow-water habitats, and disturbance-mediated foraging opportunities co-occur. The prominence of BUILT at this spatial scale therefore likely reflects not only disturbance pressures but also the long-recognized tendency of some Ardeidae colonies to form near human settlements, leveraging the proximity to rice-paddy foraging grounds and aquatic feeding habitats. At intermediate spatial scales (3–5 km), the most influential predictors shifted toward variables representing landscape mosaics at broader spatial extents, including bare land (BARE), natural grasslands (GRASS_NAT), and tree-related PFTs (TREES_BD/NE). These land-cover types are closely associated with the structural components of river corridors in Korea (Lim et al. 2021 ). Natural grasslands and riparian tree cover typically occur along river embankments, floodplains, and buffer zones, forming linear landscape elements composed of semi-natural vegetation and sparsely vegetated open areas (Kim et al. 2011 ; Lim and Lee 2022). The prominence of these variables therefore suggests that many breeding colonies are embedded within the ecological influence zones of river corridors, and that these linear landscape structures play a key role in differentiating SOM clusters. Colonies associated with major river systems or extensive alluvial lowlands tended to coincide with higher proportions of open or semi-natural land cover, whereas colonies in interior mountainous regions corresponded more closely with forest-dominated landscapes (Kim et al. 2013 ; Lee et al. 2022 ; Mashiko 2018 ). These findings highlight river corridors as key landscape axes shaping foraging opportunities, movement connectivity, and interspecific interactions at intermediate spatial scales. At the broadest spatial extent (10 km), BUILT again emerged as the dominant predictor, underscoring the strong influence of broader-scale land-use regimes—particularly urbanization intensity, landscape fragmentation, and the spatial configuration of agricultural and coastal systems—on colony structure (Park and Jang 2020 ; Chang 2025). The continued importance of BARE and GRASS_MAN further emphasizes the role of extensive open-land systems, while WATER_OCEAN and tree-related variables capture coastal influences and large-scale geomorphological or elevational gradients (Cho et al. 2024 ; Koo et al. 2015 ). Taken together, the combined SOM and scale-dependent Random Forest analyses demonstrate that the species composition of heron and egret colonies in Korea does not arise from a single spatial scale but instead emerges from hierarchical interactions among environmental predictors operating across fine, intermediate, and broader spatial scales. Fine-scale environments capture immediate foraging access and disturbance pressures; intermediate scales reflect landscape mosaics structured around river corridors that regulate ecological connectivity and habitat availability (Bentrup 2012 ; González del Tánago and García de Jalón 2006 ; Lee et al. 2022 ); and broader scales represent land-use regimes that define the environmental context in which colonies are embedded (Kim et al. 2024 ; Cho et al. 2025). The consistent correspondence between SOM-derived clusters and land-cover predictors reinforces the view that colonial breeding systems emerge from nested ecological structures shaped jointly by habitat selection and anthropogenic landscape modification across multiple spatial scales. By integrating species composition, geographic gradients, and land-cover characteristics—including the functional role of river corridors—this study provides a comprehensive framework for understanding the ecological organization of heron and egret breeding colonies in Korea. This hierarchical multi-scale perspective not only clarifies the mechanisms underlying spatial variation in colony assemblages but also offers a foundation for monitoring and anticipating future changes under ongoing land-use transformation. Conservation and management implications From a conservation and management perspective, our results emphasize that heron and egret breeding colonies should be managed as components of hierarchical multi-scale landscape systems rather than as isolated sites. Fine-scale protection of breeding stands and nearby foraging habitats is necessary but insufficient if broader landscape contexts are degraded. In particular, the strong influence of river corridors and agricultural mosaics at intermediate spatial scales underscores the importance of maintaining riparian buffers, floodplain connectivity, and semi-natural open habitats along major river systems. At broader spatial scales, regional land-use planning that limits excessive urban expansion and preserves networks of wetlands, rice paddies, and open grasslands will be critical for sustaining diverse colony assemblages. By identifying landscape features that consistently differentiate colony types across nested spatial scales, this study provides a spatially explicit framework for prioritizing conservation actions and for integrating waterbird conservation into river management, agricultural policy, and regional land-use planning across the Korean Peninsula. Declarations Conflict of interest : The authors declare no competing interests. Ethical approval /permits : Not applicable (observational species occurrence data). Funding: This research was supported by grants from the National Institute of Biological Resource (NIBR), funded by the Ministry of Environment, Republic of Korea (NIBR202516101). Author Contribution JL, JH, DK, YC, and HN conceived and designed the study. Data was curated by JL, JH, and DK. HN and SL carried out the analysis, and the original draft of the manuscript were written by SL and HC. YC and HN reviewed and edited the final draft. 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Water . 5 , 1147561. https://doi.org/10.3389/frwa.2023.1147561 (2023). Xu, Y. et al. Loss of functional connectivity in migration networks induces population decline in migratory birds. Ecol. Appl. 29 (7), e01960. https://doi.org/10.1002/eap.1960 (2019). Yoo, S. Y., Kim, H. N. & Hong, S. Foraging strategy of black-faced spoonbill during breeding season in rice fields. Zool. Stud. 61 , e35. https://doi.org/10.6620/ZS.2022.61-35 (2022). Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers invited by journal 15 Jan, 2026 Editor invited by journal 08 Jan, 2026 Editor assigned by journal 02 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 29 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":4902043,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of nests for the six heron and egret species breeding in Korea during 2018–2019. Maps show the geographic locations of breeding colonies for Grey Heron (N = 166), Great Egret (N = 134), Intermediate Egret (N = 54), Cattle Egret (N = 40), Little Egret (N = 46), and Black-crowned Night Heron (N = 45). Circle size is proportional to the number of nests at each colony, illustrating spatial variation in colony size and regional differences in breeding distribution across the Korean Peninsula.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/2f49fef799d0d5ee521669ee.png"},{"id":100687049,"identity":"4655a313-c665-485f-8b41-a76223718b1b","added_by":"auto","created_at":"2026-01-20 13:13:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":719310,"visible":true,"origin":"","legend":"\u003cp\u003eSOM-derived clusters and codebook dendrogram. (a) SOM lattice displaying six clusters (k = 6) identified from species-composition patterns of breeding colonies. (b) hierarchical clustering (Ward’s D2) of SOM codebook vectors showing the corresponding six-group structure.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/c240a8187f37da9b54e8ae19.png"},{"id":100687063,"identity":"8260acb8-fb4c-4065-a8ba-97d28b062600","added_by":"auto","created_at":"2026-01-20 13:13:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2772265,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial organization of breeding colony clusters across South Korea. Weighted kernel density estimation (KDE) maps illustrate the spatial concentration of colonial breeding sites for each self-organizing map (SOM) cluster (Clusters I–VI). KDE was performed using a bivariate Gaussian kernel, with colony locations weighted by nest numbers; bandwidth matrices (Hpi) were individually selected for each cluster, and weights were rescaled to allow numerical comparability among clusters. Warmer colors indicate higher relative colony density. For geographic context, additional reference panels include (upper left) provincial administrative boundaries labeled using abbreviations (GW: Gangwon-do, GG: Gyeonggi-do, CB: Chungcheongbuk-do, CN: Chungcheongnam-do, JB: Jeonbuk Special Self-Governing Province, JN: Jeollanam-do, GB: Gyeongsangbuk-do, GN: Gyeongsangnam-do) and (lower left) a relief map with major rivers (Han, Geum, Nakdong, Seomjin) overlaid.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/21d121f432c42ba5ed49abd4.jpg"},{"id":100687052,"identity":"7b29b58e-9483-4725-986d-db8b4dc4d4f4","added_by":"auto","created_at":"2026-01-20 13:13:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106664,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies composition of heron and egret colonies across the six SOM-derived clusters. Relative contributions of six species (Grey Heron, Great Egret, Intermediate Egret, Cattle Egret, Little Egret, and Black-crowned Night Heron) are shown for each cluster, illustrating distinct assemblage structures associated with the SOM-defined groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/9ddafd783416da31118c04b4.png"},{"id":100687405,"identity":"3d995e14-5d37-4318-b129-a10b3d633a21","added_by":"auto","created_at":"2026-01-20 13:17:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1040681,"visible":true,"origin":"","legend":"\u003cp\u003eRandom Forest variable importance for the top five land-cover predictors explaining SOM-based heron and egret colony groups at five buffer radii (500 m, 1 km, 3 km, 5 km, and 10 km). Importance values were derived from the mean decrease in classification accuracy based on out-of-bag samples and rescaled to 0–100% within each radius to allow comparison across spatial scales.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/32d145e129411af90bd3a341.png"},{"id":100694054,"identity":"356dc6d0-c1b2-423d-a8ca-2348049a49b3","added_by":"auto","created_at":"2026-01-20 14:39:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9715351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/a3673edb-fad3-4daa-a6dc-24b9aa8cbed3.pdf"},{"id":100687001,"identity":"2a64bed9-b9c8-40c6-a164-c6cbd7889ca9","added_by":"auto","created_at":"2026-01-20 13:12:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23123,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8476247/v1/0d8699b4eebaa4fa84dd70fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Scale Environmental Drivers of Heron and Egret Colony Assemblages in Korea Using Self-Organizing Map Clustering","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColonial waterbirds such as herons and egrets depend on spatially distributed networks of foraging and breeding habitats embedded within heterogeneous landscapes (Fasola and Brichetti \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mashiko \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Their mixed-species colonies\u0026mdash;often comprising species with differing ecological requirements\u0026mdash;emerge from interactions among local habitat conditions, regional resource availability, and broader-scale landscape structure (Si Bachir and Benyacoub \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mashiko \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Understanding how these colonies assemble across space therefore requires an integrative framework that links ecological processes operating across multiple, nested spatial scales, a core principle of landscape ecology (Cho et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mattsson et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because landscape structure constrains movement, regulates access to food resources, ensure shapes exposure to disturbance, and mediates interspecific interactions, colonies can be conceptualized as ecological \u0026ldquo;nodes\u0026rdquo; whose species composition reflects the surrounding landscape mosaic and its hierarchical context (Mashiko \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Byer et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross East Asia, rapid land-use change has substantially altered both the distribution and spatial configuration of habitats used by Ardeidae species (Xu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Key foraging environments\u0026mdash;including rice paddies, coastal lowlands, river corridors, and forest\u0026ndash;agriculture ecotones\u0026mdash;have undergone pronounced modification driven by urban expansion, agricultural intensification, and river engineering (Kidson et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wymore et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These changes affect not only the quantity of foraging habitat but also the spatial connectivity among habitat patches, thereby restructuring the ecological template upon which colonial breeding sites are established (Xu et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schweizer et al. 2020). Despite the pronounced spatial heterogeneity of these landscapes, however, little is known about how colony-level species assemblages respond to landscape structure across spatial scales. Most previous studies have focused on habitat selection at individual breeding sites or on single-species responses, providing limited insight into the multi-scale processes underlying the formation and persistence of mixed-species colonies.\u003c/p\u003e \u003cp\u003eThe Korean Peninsula offers an ideal setting for addressing these questions. The region encompasses strong environmental gradients from coastal to inland areas, extensive river systems forming distinct riparian corridors, and mosaic agricultural landscapes that support the primary foraging activities of herons and egrets (NGII 2013a, 2013b). Colonial breeding sites occur across this diverse landscape, ranging from estuarine rice fields and coastal plains to inland forested valleys, providing a unique opportunity to examine how landscape heterogeneity shapes colony-level species assemblages (Choi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Park and Jang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nationwide surveys further reveal substantial variation among colonies in nest numbers, species richness, and dominant species combinations (NIER 2012), suggesting that spatial configurations of landscape elements may function as ecological \u0026ldquo;filters\u0026rdquo; influencing colony identity (Park and Jang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, no study to date has evaluated these patterns at a national scale using spatially explicit, multi-scale landscape metrics.\u003c/p\u003e \u003cp\u003eA landscape-ecological perspective provides several key insights into colony assembly. First, it recognizes that ecological patterns arise from hierarchical spatial processes, allowing colony structure to be interpreted within nested spatial contexts, ranging from immediate foraging environments within hundreds of meters to broader-scale land-use regimes spanning several kilometers (Jedlikowski et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lipsey et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, it accounts for the possibility that species assemblages are shaped by nonlinear interactions among water availability, agricultural matrices, forest cover, and other land-cover components (Musseau et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garc\u0026iacute;a-Callejas et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Third, it emphasizes the importance of spatial configuration and connectivity\u0026mdash;factors that are particularly critical for highly mobile foragers whose reproductive success depends on repeated movements between breeding sites and multiple foraging habitats (Evens et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Teitelbaum et al. 2021).\u003c/p\u003e \u003cp\u003eIn this study, we integrate nationwide, multi-year breeding colony surveys with Self-Organizing Map (SOM) analyses to identify species-assemblage patterns and emergent colony types across 176 heron and egret breeding sites in South Korea. We then assess how these colony types correspond to landscape gradients and structural features measured across five nested spatial scales (500 m to 10 km) (Ferraz et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lipsey et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Using ESA CCI Plant Functional Type (PFT) layers, we quantify key land-cover components\u0026mdash;including water bodies, managed and natural herbaceous cover, forest structure, bare land, and urban areas\u0026mdash;to evaluate colony responses not only to local habitat conditions but also to landscape mosaics at broader spatial scales (ESA CCI \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By combining unsupervised species-assemblage modeling with multi-scale landscape analysis, this study provides the first nationwide assessment of how hierarchical landscape structure shapes colony-level species composition in herons and egrets.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that colonial breeding sites should not be viewed as isolated locations but as emergent outcomes of spatial processes operating across nested spatial scales. This multi-scale landscape perspective offers critical insights into the mechanisms governing colony formation and persistence and establishes a foundation for predicting how ongoing and future land-use change may restructure the ecological networks supporting heron and egret colonies across the Korean Peninsula.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Breeding colony dataset\u003c/h2\u003e \u003cp\u003eThis study examined the breeding distribution of egret and heron species in the Republic of Korea using nationwide survey data collected in 2011\u0026ndash;2012 (NIER 2012). These baseline data were supplemented with additional colony records identified through researcher field observations, news reports, and online sources such as blogs, which occasionally document newly established or previously unrecorded colonies. Based on this integrated dataset, all candidate locations were visited twice during the 2018\u0026ndash;2019 breeding seasons to verify the presence of active breeding. For each confirmed colony, geographic coordinates (longitude and latitude) and species-specific nest counts were recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Self-organizing map (SOM) analysis\u003c/h2\u003e \u003cp\u003eA Self-Organizing Map (SOM) was used to identify latent ecological structure in the species composition of breeding colonies. SOM projects high-dimensional ecological data onto a two-dimensional grid while preserving topological relationships, allowing the simultaneous detection of continuous ecological gradients and discrete assemblage patterns.\u003c/p\u003e \u003cp\u003eBreeding colony data are typically right-skewed, with a small number of large colonies exerting disproportionate influence on the distribution. To reduce this bias and improve numerical stability in the SOM analysis, all nest counts were log-transformed using log(n\u0026thinsp;+\u0026thinsp;1). This transformation limited the influence of extremely large colonies while retaining variability among smaller colonies.\u003c/p\u003e \u003cp\u003eTo determine the optimal SOM grid configuration, multiple candidate grid sizes were evaluated, with each configuration trained for 100 iterations. Model performance was assessed using two diagnostic metrics: Quantization Error (QE), defined as the mean distance between each observation and its best-matching unit (BMU), where lower values indicate better representation of the input space; and Topographic Error (TE), defined as the proportion of observations for which the two closest BMUs were not adjacent, where lower values indicate stronger preservation of topological structure. The grid exhibiting low QE and acceptably low TE was selected, and a hexagonal topology was adopted to facilitate smoother neighborhood interactions than a rectangular lattice.\u003c/p\u003e \u003cp\u003eUsing the selected grid, the final SOM was trained for 200 iterations with a learning rate decreasing linearly from 0.05 to 0.01. Similarity among neurons in standardized species-composition space was quantified using Euclidean distance. After training, QE and TE were recalculated to confirm adequate representation of the input structure. Codebook vectors for all neurons were extracted for subsequent cluster analysis.\u003c/p\u003e \u003cp\u003eBecause SOM does not inherently define discrete clusters, hierarchical clustering was applied to the final codebook vectors. Euclidean distance matrices were computed, and Ward\u0026rsquo;s D2 linkage method was used to minimize within-cluster variance. The number of clusters was determined based on statistical separability, dendrogram stability, and ecological interpretability, resulting in six clusters (k\u0026thinsp;=\u0026thinsp;6). Each colony was assigned to a cluster according to the cluster membership of its BMU. Cluster boundaries and neuron indices were visualized on the SOM grid to evaluate cluster separation and topological continuity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Kernel density estimation of breeding colony distributions\u003c/h2\u003e \u003cp\u003eTo quantify spatial concentration patterns of breeding colonies within each SOM-derived cluster, kernel density estimation (KDE) was applied separately to each cluster. To account for differences in colony importance, KDE was weighted by the number of nests at each breeding site, such that colonies with larger nest numbers contributed more strongly to the estimated density surface. Weighted KDE was implemented using a bivariate Gaussian kernel, with bandwidth matrices automatically selected for each cluster using the plug-in estimator (Hpi). To ensure numerical stability and comparability among clusters, kernel weights were rescaled prior to density estimation so that their sum equaled the sample size within each cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Environmental variable extraction and landscape analysis\u003c/h2\u003e \u003cp\u003eTo assess whether SOM-derived clusters reflected meaningful differences in landscape structure rather than statistical artifacts, environmental variables were quantified within multiple spatial buffers surrounding each colony. Circular buffers with radii of 500 m, 1,000 m, 3,000 m, 5,000 m, and 10,000 m were generated, and land-cover composition within each buffer was calculated. Land-cover data were obtained from the ESA CCI Plant Functional Type (PFT) dataset, and environmental variables were classified into 16 land-cover categories (Harper et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maps.elie.ucl.ac.be/CCI/viewer/\u003c/span\u003e\u003cspan address=\"https://maps.elie.ucl.ac.be/CCI/viewer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed 21 November 2025). The area of each land-cover type was calculated for each buffer radius.\u003c/p\u003e \u003cp\u003eTo evaluate the environmental predictors of the SOM-derived colony groups, Random Forest classification models were applied to account for potential multicollinearity among spatial predictors. Separate models were fitted for each buffer radius, using SOM cluster membership as the response variable and the areas of the 16 PFT categories as predictors. Each model was built using 1,000 trees, and variable importance was assessed based on the mean decrease in accuracy derived from out-of-bag samples. For cross-scale comparison, the top five predictors at each radius were rescaled to a 0\u0026ndash;100% range relative to the most influential variable within that scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Software environment\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R version 4.5.1. Key packages included kohonen for SOM analysis (Wehrens and Buydens \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), ks for weighted kernel density estimation (Duong \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), sf (Pebesma \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and geodata (Hijmans et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for spatial data processing, dplyr (Wickham et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and tidyr (Wickham and Girlich \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for data preprocessing, and rstatix for non-parametric statistical analyses (Kassambara \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Species composition across 176 heron and egret colonies\u003c/h2\u003e \u003cp\u003eAnalysis of 176 breeding colonies revealed that Grey Heron and Great Egret were the predominant species nationwide, accounting for the largest numbers of nests in most colonies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These large-bodied species were consistently present across southern, central, and eastern regions and were particularly dominant at inland sites. In contrast, medium- and small-bodied species\u0026mdash;including Little Egret, Cattle Egret, Intermediate Egret, and Black-crowned Night Heron\u0026mdash;showed more spatially restricted distributions. These species were concentrated primarily in southern provinces and lowland plains along the west coast, where several colonies formed regionally distinct mixed-species assemblages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Regional variation in colony structure\u003c/h2\u003e \u003cp\u003eClear geographic differentiation in colony structure was evident across regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Colonies in southern inland and southeastern areas were dominated by Grey Heron and Great Egret, forming large mixed aggregations characterized by high nest abundance. In contrast, colonies in the metropolitan region and western coastal plains frequently supported co-occurrence of Little Egret, Cattle Egret, and Black-crowned Night Heron, resulting in more diverse multi-species assemblages. Colonies in central inland areas and parts of the east coast tended to be smaller and simpler, often comprising one or two species and frequently dominated by either Grey Heron or Great Egret.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. SOM-based colony grouping\u003c/h2\u003e \u003cp\u003eSelf-Organizing Map (SOM) analysis identified six distinct clusters (k\u0026thinsp;=\u0026thinsp;6) based on colony-level species composition. Model performance metrics indicated reliable representation of the high-dimensional species data (quantization error\u0026thinsp;=\u0026thinsp;0.371; topographic error\u0026thinsp;=\u0026thinsp;0.158). The resulting clusters differed consistently in both species composition and spatial distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster I was concentrated in mid-western coastal and adjacent inland regions and consisted of mixed-species colonies in which Grey Heron, Great Egret, and smaller species co-occurred (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Cluster II occurred primarily in inland agricultural landscapes and was dominated by Grey Heron and Great Egret, forming relatively simplified assemblages. Cluster III was distributed mainly in forested and riverine areas of Gangwon-do and Gyeongsangbuk-do provinces and exhibited strong dominance of large-bodied species. Cluster IV occurred in mosaic landscapes of forest and farmland and showed relatively high proportions of medium- and small-bodied species, particularly Cattle Egret and Little Egret, with Grey Heron and Great Egret present at moderate levels. Cluster V was associated with urban\u0026ndash;rural transitional zones and was characterized by a high contribution of medium- and small-bodied species, resulting in the most compositionally diverse assemblages. Cluster VI was found in forested and wetland-dominated landscapes with high naturalness and consisted largely of Grey Heron, forming near single-species assemblages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Differences among clusters in species composition\u003c/h2\u003e \u003cp\u003eComparison of species composition among clusters revealed marked differences in relative nest abundance despite the consistent presence of Grey Heron and Great Egret across all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Clusters III and VI were strongly dominated by large-bodied species, which accounted for more than 90% of total nests. In contrast, Cluster V showed the highest contribution of medium- and small-bodied species, comprising over 80% of total nests. Clusters I and IV exhibited intermediate, mixed-species structures with variable proportional contributions of multiple species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Scale-dependent importance of land-cover variables in Random Forest models\u003c/h2\u003e \u003cp\u003eRandom Forest models indicated that SOM-derived colony groups were explained by land-cover variables across all spatial scales, with predictor importance varying by buffer radius (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). At the 500 m scale, BUILT, WATER_OCEAN, GRASS_MAN, WATER, and SHRUBS_BD were the most influential variables. At 1,000 m, BUILT, GRASS_MAN, TREES_BD, GRASS_NAT, and TREES_NE ranked highest. At 3,000 m, BARE, GRASS_NAT, TREES_BD, GRASS_MAN, and BUILT emerged as key predictors. At 5,000 m, BUILT, BARE, WATER_OCEAN, GRASS_MAN, and GRASS_NAT showed the greatest importance. At the broadest 10,000 m scale, BUILT, BARE, GRASS_MAN, WATER_OCEAN, and TREES_NE were the most influential variables distinguishing SOM clusters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSelf-organizing map (SOM) analysis revealed that heron and egret breeding colonies in Korea form distinct ecological clusters that differ in species composition, dominance structure, and co-occurrence patterns. These clusters closely corresponded to broad geographic gradients, including coastal\u0026ndash;inland transitions, contrasts between major riverine lowlands and forested uplands, and variation in urban\u0026ndash;rural land-use intensity. Colonies located in coastal plains and estuarine lowlands tended to form large mixed-species aggregations with high nest densities, whereas inland mountainous or hilly regions generally supported smaller colonies dominated by one or two species. This spatial organization indicates that colony-level assemblages are shaped not only by local habitat conditions but also by broader biogeographic context and multi-scale landscape heterogeneity across the Korean Peninsula.\u003c/p\u003e \u003cp\u003eRandom Forest analysis identified land-cover variables as the primary environmental predictors of SOM-derived colony clusters, although their relative importance varied markedly across nested spatial scales. At fine spatial scales (500\u0026ndash;1,000 m), BUILT areas, managed grasslands (GRASS_MAN), and both oceanic and freshwater habitats consistently ranked among the most influential predictors. These variables reflect immediate environmental conditions related to disturbance intensity (Frederick \u0026amp; Collopy \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), access to shallow-water foraging habitats (Fasola \u0026amp; Ruiz \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lane \u0026amp; Fujioka \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and proximity to agricultural feeding grounds (Choi et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kelly et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Such patterns align with the well-established reliance of many Ardeidae species on rice paddies, wetlands, and other local foraging habitats during the breeding season (Fujioka et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yoo et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, colony placement itself appears shaped by human-associated landscape features: Shin et al. (2018) documented that approximately 12.46% of potential breeding habitat for herons and egrets occurs within ~\u0026thinsp;50 m of residential areas in Korea, suggesting that many colonies are established adjacent to villages or settlement edges where agricultural landscapes, shallow-water habitats, and disturbance-mediated foraging opportunities co-occur. The prominence of BUILT at this spatial scale therefore likely reflects not only disturbance pressures but also the long-recognized tendency of some Ardeidae colonies to form near human settlements, leveraging the proximity to rice-paddy foraging grounds and aquatic feeding habitats.\u003c/p\u003e \u003cp\u003eAt intermediate spatial scales (3\u0026ndash;5 km), the most influential predictors shifted toward variables representing landscape mosaics at broader spatial extents, including bare land (BARE), natural grasslands (GRASS_NAT), and tree-related PFTs (TREES_BD/NE). These land-cover types are closely associated with the structural components of river corridors in Korea (Lim et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Natural grasslands and riparian tree cover typically occur along river embankments, floodplains, and buffer zones, forming linear landscape elements composed of semi-natural vegetation and sparsely vegetated open areas (Kim et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lim and Lee 2022). The prominence of these variables therefore suggests that many breeding colonies are embedded within the ecological influence zones of river corridors, and that these linear landscape structures play a key role in differentiating SOM clusters. Colonies associated with major river systems or extensive alluvial lowlands tended to coincide with higher proportions of open or semi-natural land cover, whereas colonies in interior mountainous regions corresponded more closely with forest-dominated landscapes (Kim et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mashiko \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings highlight river corridors as key landscape axes shaping foraging opportunities, movement connectivity, and interspecific interactions at intermediate spatial scales.\u003c/p\u003e \u003cp\u003eAt the broadest spatial extent (10 km), BUILT again emerged as the dominant predictor, underscoring the strong influence of broader-scale land-use regimes\u0026mdash;particularly urbanization intensity, landscape fragmentation, and the spatial configuration of agricultural and coastal systems\u0026mdash;on colony structure (Park and Jang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chang 2025). The continued importance of BARE and GRASS_MAN further emphasizes the role of extensive open-land systems, while WATER_OCEAN and tree-related variables capture coastal influences and large-scale geomorphological or elevational gradients (Cho et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Koo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, the combined SOM and scale-dependent Random Forest analyses demonstrate that the species composition of heron and egret colonies in Korea does not arise from a single spatial scale but instead emerges from hierarchical interactions among environmental predictors operating across fine, intermediate, and broader spatial scales. Fine-scale environments capture immediate foraging access and disturbance pressures; intermediate scales reflect landscape mosaics structured around river corridors that regulate ecological connectivity and habitat availability (Bentrup \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gonz\u0026aacute;lez del T\u0026aacute;nago and Garc\u0026iacute;a de Jal\u0026oacute;n \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and broader scales represent land-use regimes that define the environmental context in which colonies are embedded (Kim et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cho et al. 2025). The consistent correspondence between SOM-derived clusters and land-cover predictors reinforces the view that colonial breeding systems emerge from nested ecological structures shaped jointly by habitat selection and anthropogenic landscape modification across multiple spatial scales.\u003c/p\u003e \u003cp\u003eBy integrating species composition, geographic gradients, and land-cover characteristics\u0026mdash;including the functional role of river corridors\u0026mdash;this study provides a comprehensive framework for understanding the ecological organization of heron and egret breeding colonies in Korea. This hierarchical multi-scale perspective not only clarifies the mechanisms underlying spatial variation in colony assemblages but also offers a foundation for monitoring and anticipating future changes under ongoing land-use transformation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConservation and management implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFrom a conservation and management perspective, our results emphasize that heron and egret breeding colonies should be managed as components of hierarchical multi-scale landscape systems rather than as isolated sites. Fine-scale protection of breeding stands and nearby foraging habitats is necessary but insufficient if broader landscape contexts are degraded. In particular, the strong influence of river corridors and agricultural mosaics at intermediate spatial scales underscores the importance of maintaining riparian buffers, floodplain connectivity, and semi-natural open habitats along major river systems.\u003c/p\u003e \u003cp\u003eAt broader spatial scales, regional land-use planning that limits excessive urban expansion and preserves networks of wetlands, rice paddies, and open grasslands will be critical for sustaining diverse colony assemblages. By identifying landscape features that consistently differentiate colony types across nested spatial scales, this study provides a spatially explicit framework for prioritizing conservation actions and for integrating waterbird conservation into river management, agricultural policy, and regional land-use planning across the Korean Peninsula.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflict of interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e \u003cb\u003e/permits\u003c/b\u003e: Not applicable (observational species occurrence data).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was supported by grants from the National Institute of Biological Resource (NIBR), funded by the Ministry of Environment, Republic of Korea (NIBR202516101).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJL, JH, DK, YC, and HN conceived and designed the study. Data was curated by JL, JH, and DK. HN and SL carried out the analysis, and the original draft of the manuscript were written by SL and HC. YC and HN reviewed and edited the final draft. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBentrup, G. Connecting landscape fragments through riparian zones. In: (eds Stanturf, J. A., Lamb, D. \u0026amp; Madsen, P.) Forest landscape restoration integrating human and natural systems. CABI International, Wallingford, UK, 190\u0026ndash;220 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByer, N. W. et al. 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Stud.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, e35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6620/ZS.2022.61-35\u003c/span\u003e\u003cspan address=\"10.6620/ZS.2022.61-35\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Landscape heterogeneity, Multi-scale analysis, Species assemblage, Self-organizing map (SOM), Wading birds","lastPublishedDoi":"10.21203/rs.3.rs-8476247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8476247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e \u003cp\u003eColonial herons and egrets breed within highly heterogeneous landscapes where environmental conditions vary across multiple spatial scales. Although landscape structure is expected to shape colony-level species assemblages, explicit multi-scale empirical assessments remain scarce, particularly at national extents.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eWe sought to (1) identify emergent species-assemblage types among heron and egret colonies across South Korea, (2) determine how these assemblages correspond to landscape heterogeneity across nested spatial scales, and (3) quantify the scale-dependent importance of environmental predictors influencing colony identity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe compiled species-specific nest counts from 176 breeding colonies and applied Self-Organizing Maps (SOM) to classify colony assemblages. Landscape variables were extracted from ESA CCI Plant Functional Type (PFT) datasets within five buffer radii (500 m\u0026ndash;10 km). Random Forest models evaluated the relative importance of land-cover predictors at each spatial scale.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSOM analysis revealed six ecologically distinct colony groups differing in species dominance, richness, and geographic distribution. Large-bodied species predominated in inland forested regions, whereas medium- and small-bodied species clustered in coastal and urban\u0026ndash;rural mosaics. Environmental predictors were strongly scale-dependent: BUILT areas, managed grasslands, and water-related cover dominated at fine scales; natural grasslands, bare land, and tree-related PFTs\u0026mdash;hallmarks of riparian corridors\u0026mdash;emerged at intermediate scales; and regional land-use intensity remained the strongest predictor at 10 km.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eColony assemblages arise from hierarchical interactions among fine-scale habitat features, river-corridor landscapes, and broader-scale land-use regimes. This hierarchical multi-scale perspective provides a predictive framework for anticipating how land-use change will reorganize ecological networks supporting colonial waterbirds.\u003c/p\u003e","manuscriptTitle":"Multi-Scale Environmental Drivers of Heron and Egret Colony Assemblages in Korea Using Self-Organizing Map Clustering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:10:30","doi":"10.21203/rs.3.rs-8476247/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T08:20:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T13:22:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T12:40:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166114860566184426799892370721075980633","date":"2026-04-08T06:51:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2930079159679296805139977133546992486","date":"2026-04-07T01:55:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T11:54:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161691386583185890757582195297687263581","date":"2026-02-22T13:03:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234225366920613624397422734031184400008","date":"2026-02-17T11:40:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185038232396167140561709324768436631084","date":"2026-01-20T11:28:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T11:12:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T15:30:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T11:58:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T11:58:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-30T00:25:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aba2db3e-f5c1-43d1-a705-a92dcedafecf","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61385356,"name":"Biological sciences/Ecology"},{"id":61385357,"name":"Earth and environmental sciences/Ecology"},{"id":61385358,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-05-08T09:08:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 11:10:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8476247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8476247","identity":"rs-8476247","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}