Climate-induced decline of habitat and connectivity imperils the Endangered Black-bellied Tern (Sterna acuticauda) in the Ganges-Brahmaputra-Mahanadi River Basin

Climate-induced decline of habitat and connectivity imperils the Endangered Black-bellied Tern (Sterna acuticauda) in the Ganges-Brahmaputra-Mahanadi River Basin | 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 Climate-induced decline of habitat and connectivity imperils the Endangered Black-bellied Tern (Sterna acuticauda) in the Ganges-Brahmaputra-Mahanadi River Basin Imon Abedin, Hyun-Woo Kim, Hilloljyoti Singha, Shantanu Kundu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7469139/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Rivers and wetlands are one of the most biologically diverse ecosystems facilitating vital habitats for a wide range of species. However, the species within these ecosystems are increasingly threatened by climate change and anthropogenic pressures. The Black-bellied Tern ( Sterna acuticauda ), an endangered riverine bird endemic to the Indian subcontinent, is highly reliant on wetland environments. Once widespread in South and Southeast Asia, the species is now locally extinct in much of Southeast Asia. This study evaluates the potential impacts of climate change on habitat suitability and corridor connectivity of S. acuticauda across the Ganges–Brahmaputra–Mahanadi (GBM) River Basin, which remains a key refuge, harboring over 90% of the global population. Current estimates suggest that only 6.10% (143,273 km²) of the GBM Basin provides suitable habitat for this species. In projected future climate scenarios, this suitable area is expected to decline drastically by 88.765% to 93.068%. Moreover, these declines are expected to lead to severe fragmentation of suitable habitats in the future. These spatial transformations diminish both structural and functional landscape connectivity. Circuit-based modeling identified five key ecological corridors, all of which are projected to experience connectivity loss. These findings emphasize the acute vulnerability of water-dependent species under climate change and highlight the urgency of targeted conservation interventions. Therefore, preserving critical riparian habitats, strengthening ecological corridor connectivity, and integrating climate resilience into basin-wide management strategies are vital for safeguarding the future of this imperiled indicator species and ensuring the ecological integrity of riverine wetland systems in Asia. Charadriiformes Shorebirds Threatened species Climate Change Landscape Fragmentation Corridor Connectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Global biodiversity is experiencing unprecedented declines driven by the intensifying impacts of climatic changes (Johnson et al. 2024 ). This shifting climatic conditions are driving biogeographical changes in many species, often resulting in reduced overall distributions that are strongly linked to elevated extinction risk (Chen et al. 2011 ; Tingley et al. 2012 ; Lenoir and Svenning 2015 ; Urban 2015 ). Amongst vertebrates, birds are particularly vulnerable, with numerous species exhibiting range shifts driven by rising climatic pressure and increasing anthropogenic disturbances (Li et al. 2023 ). Simultaneously, the global freshwater crisis is unfolding at an alarming pace that has resulted in the rapid degradation and disappearance of freshwater ecosystems worldwide (Reid et al. 2019 ; Sayer et al. 2025 ). Further, the disruptions in the global hydrological cycle are undergoing significant changes in spatial and temporal availability of freshwater resources, impacting ecosystem structure and function in inland waters (Huntington 2006 ). These changes have profound implications for waterbirds as they are highly sensitive to habitat alterations (Litvinenko and Shibaev 2000 ; Li et al. 2024 ). Waterbirds are a diverse group encompassing over 30 families that are ecologically dependent on riverine and wetland habitats for survival and reproduction (BirdLife International 2023). As key indicators of wetland health and ecological integrity, waterbirds reflect the broader state of freshwater biodiversity. However, current trends are concerning, as nearly 56% of waterbird species are experiencing significant population declines, and approximately 17% are classified as globally threatened (BirdLife International 2023). This situation is particularly alarming for Asia, which is witnessing the steepest declines, with around 60% of its waterbird species affected, underscoring the urgent need for targeted conservation plans in the region (BirdLife International 2023). Owing to their high sensitivity to environmental changes, it is imperative to understand the spatial distribution and habitat quality of waterbirds to identify the ecological factors that underpin their survival and to inform effective conservation strategies (Bai et al. 2018; Urbani et al. 2017 ). This urgency is especially pronounced in riverine systems originating from the Himalayan region, where wetland biodiversity is increasingly vulnerable to environmental stressors such as climate variability, land-use change, and hydrological disruption (Uereyen et al. 2022 ; Chauhan et al. 2023 ). These ecosystems not only support a rich array of species but also provide essential ecological services that sustain human welfare across the Indian subcontinent (Wijngaard et al. 2018 ; Biemans et al. 2019 ). In light of escalating global and regional pressures, research into biodiversity patterns, habitat selection, and targeted conservation strategies of waterbirds has become increasingly vital. Yet, despite growing concern over the vulnerability of waterbirds and their habitats, there is significant gap in species-specific ecological research and wetland conservation efforts across South Asia (Khan and Pant 2017 ). The Black-bellied Tern ( Sterna acuticauda ) (Order: Charadriiformes) is an endangered riverine bird species native to the freshwater systems of South and Southeast Asia (BirdLife International 2022). It exhibits the characteristic morphology of terns, including a slender body, long pointed wings, and a deeply forked tail (Gochfeld et al. 2020 ). The adults in breeding plumage display a distinctive black cap, grey back, and long, pointed grey wings. The white throat and pale grey breast contrast sharply with the strikingly dark belly and undertail-coverts, while the underwings are predominantly white with a darker band across the secondaries. The species also features a yellow-orange bill and reddish-orange legs (Grimmett et al. 2011 ). The global population of the S. acuticauda has experienced a severe decline in recent decades, with the most recent IUCN assessment estimating fewer than 2,000 individuals remaining (BirdLife International 2022). Once widespread across South and Southeast Asia, the species has now become locally extinct in much of Southeast Asia (BirdLife International 2022; Zöckler et al. 2020 ). Currently, over 90% of the remaining population is restricted to the Indian subcontinent, particularly within the Ganges-Brahmaputra-Mahanadi (GBM) River Basins. This significant range contraction also reflects the ongoing population decline, further underscored by the species disappearance from numerous historical breeding sites in India within the past decade. The species faces widespread and escalating threats, primarily due to the degradation and loss of its preferred habitats on riverine sandbars (Inskipp et al. 2016 ). Moreover, major anthropogenic pressures include agricultural expansion, river damming, sand mining, and fluctuations in water levels, all of which significantly alter the breeding environment of this species. In addition, the population decline of this avifaunal species is further exacerbated by egg collection, illegal and excessive fishing, predation by feral dogs and crows, nest flooding, and direct human disturbances such as livestock trampling and unregulated ecotourism activities (Kabir et al. 2016 ; Kar et al. 2018 ; Goes et al. 2010 ). Owing to the continued population decline and severe range contraction of S. acuticauda , the IUCN has strongly recommended comprehensive ecological research to support targeted conservation efforts. However, to date, the species has not been adequately assessed in terms of its habitat preferences or its vulnerability to ongoing climate change impacts. Such assessments are critical for guiding habitat selection and prioritization, which are essential components of effective and evidence-based conservation strategies (He et al. 2018 ). In this context, Species Distribution Models (SDMs) have emerged as valuable tools for assessing habitat suitability. These models help predict the potential spatial distribution of species and quantitatively assess the relative influence of different environmental variables (Elith and Leathwick 2009 ). The model integrates species occurrence data with ecological and climatic variables across spatial and temporal scales and offers insights into habitat dynamics and species–environment relationships (Guisan and Zimmermann 2000 ). Such research has become increasingly important in the fields of biodiversity conservation, habitat management, and climate adaptation planning. Hence, the present study aims to (i) identify suitable habitats in current climatic conditions within the GBM river basin, (ii) estimate potential climate refugia under future climate scenarios, (iii) evaluate changes in the geometry of suitable landscapes, and (iv) identify potential habitat corridors in both present and future climatic projections. This study represents the first comprehensive effort to assess habitat suitability for this imperiled Black Bellied Tern. The findings will contribute to effectively identifying and prioritizing climate refugia, thereby supporting long-term conservation planning of S. acuticauda for persistence in the face of ongoing climate change. 2 Materials and Methods 2.1 Study area and occurrences The vast majority (> 90%) of the global population of S. acuticauda is now confined to India, where the population is estimated to comprise no more than 1,000 mature individuals, with a few critical strongholds (eBird 2021 ; eBird 2025 ). Within India, these individuals are primarily restricted to fragmented habitats within the GBM River Basin, spanning north-central, eastern, and northeastern regions of the country. Given this distribution, the GBM River Basin was selected as the focal area for subsequent analysis in the present study (Fig. 1). The primary field surveys were conducted along the Dibru, Siang, and Brahmaputra Rivers during 2024 and 2025, primarily using boat survey. Following approval (WL/FG.31/RS/38th T.C./2025-Pt, dated 11 April 2025) from the Chief Wildlife Warden, Department of Environment, Forest and Climate Change, Government of Assam, surveys were also extended into the Dibru-Saikhowa National Park. During early 2024, single individuals of S. acuticauda were recorded on seven occasions along the Brahmaputra and Siang Rivers. Notably, in October 2024, a significant sighting occurred along the Aisung stretch of the Brahmaputra River, where a group of approximately 20 individuals was observed, which represented one of the largest recent congregations reported. Additionally, only two sightings of single individuals were recorded in May 2025 within the Dighaltarang area of Dibru-Saikhowa National Park. Further, to enhance the spatial representation of the species presence across the study extent, additional occurrence records were compiled from secondary data sources. The occurrence data were aggregated from the IUCN Geospatial Conservation Assessment Tool (GeoCAT) and eBird citizen science records (eBird 2025 ; Bachman et al. 2011 ). However, records associated with captive individuals or preserved specimens were removed, and only direct human observations were retained to ensure an accurate representation of the ecological niche in the wild. Additionally, all occurrence locations were spatially rarefied at a resolution of 1 km² using the spatial rarefaction function in SDM Toolbox v2.4 to minimize spatial autocorrelation and reduce the overrepresentation of clustered records, thereby limiting model overfitting (Brown et al. 2017 ). The rarefaction scale was selected to match the resolution of the environmental raster layers used in subsequent analyses. After filtering and rarefaction, a total of 160 unique occurrence points were retained for use in the final habitat suitability modeling. 2.2 Predictors for distribution modelling The study utilized a specific set of variables, including climatic parameters (bioclimatic variables), habitat characteristics, and topography to identify the habitat suitability of the species (Peterson and Soberón 2012 ). A total of 19 bioclimatic variables were retrieved from the WorldClim database at a spatial resolution of 30 arcseconds (~ 1 km²) to represent climatic conditions used in SDM studies (Fick and Hijmans 2017 ; https://www.worldclim.org/ ). In addition to climate, a habitat predictor, i.e., Euclidean distance to water, was included to account for the species strong association with wetlands/water bodies. This variable quantified proximity to water sources and was derived from the ESRI Sentinel − 2 10-Meter Land Use/Land Cover (LULC) dataset available on the Living Atlas platform (Karra et al. 2021 ; https://livingatlas.arcgis.com/landcover/ ). The categorical LULC raster was converted into a continuous format using the Euclidean Distance tool in ArcGIS 10.6, allowing for a more nuanced representation of habitat accessibility (Abedin et al. 2025 ). Further, the topography included elevation, slope, and aspect, which were obtained from Shuttle Radar Topography Mission (SRTM) data at a 90-meter resolution ( http://srtm.csi.cgiar.org/srtmdata/ ). All spatial predictor layers were standardized to a common resolution of 30 arcseconds (~ 1 km²) using the Spatial Analyst extension in ArcGIS 10.6 to ensure consistency in scale for model input. The research analyzed projected future conditions using two Shared Socioeconomic Pathways, SSP245 and SSP585, across the mid-century (2041–2060) and late-century (2061–2080) timeframes. The climatic data were derived from the HadGEM3-GC31 LL model, which is part of the CMIP6 framework (Li et al. 2023 ; Gautam and Shany 2024 ). Furthermore, all predictor variables were tested for multicollinearity to minimize correlation and reduce the risk of model overfitting. The variables with a pairwise correlation coefficient (|r|) greater than 0.7 were excluded from the analysis (Online Resource: Fig. S1 ). The three-correlation metrics, i.e., Pearson, Spearman, and Kendall, were calculated using the SAHM (Software for Assisted Habitat Modeling) package within the VisTrails platform (Morisette et al. 2013 ). Any variable exceeding the threshold in any of these correlation tests was removed from the final set of predictors. Following this variable screening process, a total of 9 uncorrelated variables were retained for use in the habitat suitability modeling. 2.3 Ensemble distribution model An ensemble modeling approach was employed to integrate multiple algorithms, thereby constructing a comprehensive and robust model for the target species. This method combines the distinct strengths of individual algorithms, which effectively captures the wide range of ecological and statistical relationships that influence species distributions. This leverages the complementary capabilities of different models and further enhances predictive accuracy and reliability (Hao et al. 2020 ). In this study, five algorithms were incorporated into the ensemble framework: Boosted Regression Trees (BRT), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Models (GLM), Maximum Entropy (MaxEnt), and Random Forests (RF) (Elith and Leathwick 2009 ; Guisan et al. 2007 ; Miller 2010 ). The ensemble model was conducted using the Software for Assisted Habitat Modeling (SAHM) integrated within the VisTrails workflow system (Morisette et al. 2013 ; Talbert and Talbert 2012 ). The resulting models produced continuous habitat suitability maps, with predicted values ranging from 0 (unsuitable) to 1 (highly suitable). These continuous outputs were converted into presence–absence maps using the sensitivity-equals-specificity (SES) threshold method. Only models with an area under the Receiver Operating Characteristic Curve (AUC) greater than 0.75 were retained for further analysis (Lavazza et al. 2023 ). An ensemble agreement map was then generated, with pixel values ranging from 0 to 5 to indicate the number of algorithms predicting each location as suitable habitat. A maximum value of 5 reflected complete consensus among all five algorithms. The model performance was assessed using a suite of evaluation metrics, viz., AUC, True Skill Statistic (TSS), Cohen’s Kappa, Proportion Correctly Classified (PCC), sensitivity, and specificity. These metrics were calculated for both the training datasets and across 10-fold cross-validation replicates to ensure model robustness and reliability (Cohen 1968 ; Allouche et al. 2006 ; Phillips and Elith 2010 ; Jiménez-Valverde et al. 2013 ). 2.4 Spatial geometry assessment A set of class-level landscape metrics was used to evaluate the structural and spatial qualities of suitable habitat patches under current and future projected conditions. These metrics were calculated using FRAGSTATS version 4.2.1, a widely used tool in landscape ecology for quantifying spatial patterns and analyzing landscape composition and configuration (McGarigal and Marks 1995 ; Hesselbarth et al. 2019 ). The class descriptor tables were generated based on habitat suitability outputs for current and future climate scenarios. The analysis adopted the eight-cell neighborhood rule and incorporated user-defined tiles and a uniform sampling strategy to ensure consistency. The key landscape metrics were Number of Patches (NP), Patch Density (PD), Total Edge (TE), Largest Patch Index (LPI), Aggregation Index (AI), and Landscape Shape Index (LSI). The metrics, such as NP, PD, TE, and LPI, capture information on patch quantity, spatial distribution, and edge complexity. Meanwhile, LSI measured the irregularity of patch shapes, and AI quantified the degree of spatial cohesion and aggregation among habitat patches. 2.5 Assessment of corridor connectivity Circuit theory, a commonly used method for evaluating ecological connectivity, was applied to identify potential habitat corridors within the study area. The Corridor simulations were conducted using the Circuitscape toolbox integrated with ArcGIS 10.6 (Wang et al. 2014 ; McRae et al. 2008 ). The analysis was performed in pairwise mode, where resistance surfaces were represented by conductance rasters derived from habitat suitability probability maps. The focal nodes were defined using specific occurrence points of the species, enabling the model to simulate current flow patterns between node pairs (Dickson et al. 2018 ). The resulting current flow maps provided spatially explicit insights into potential movement pathways and the strength of landscape connectivity. This modeling approach was applied under both current and projected future climate scenarios, providing a comparative framework to assess changes in habitat connectivity and identify emerging barriers to species movement in response to shifting environmental conditions. 3 Results 3.1 Ensemble model assessment The ensemble model demonstrated good performance, with all individual models surpassing the AUC threshold of 0.75 in both the training and cross-validation phases (Fig. 2, Table 1 ). Specifically, during training, AUC values ranged from 0.875 to 0.963, while in cross-validation, they ranged from 0.772 to 0.873 across the five models included in the ensemble. The highest AUC in the training phase was achieved by the BRT model, whereas the RF model recorded the lowest. Conversely, in the cross-validation phase, the RF model attained the highest AUC, while the Maxent model generated the lowest. Regarding the difference between training and cross-validation performance (ΔAUC), the RF model exhibited the smallest gap, while the Maxent model showed the largest. In addition, other evaluation metrics also indicated high performance across both training and cross-validation datasets, further supporting the robustness of the ensemble approach (Online Resource: Fig S2). Among the bioclimatic variables, precipitation seasonality (bio_15) contributed the most to the ensemble model, accounting for 14.072% of the total variable importance (µ), followed by temperature mean diurnal range (bio_2), which contributed 11.285% (Online Resource: Fig. S3, Table 2 ). In addition, the euclidean distance to water (euc_river) emerged as a significant predictor, contributing 23.716% to the model. Furthermore, the topography variable elevation was identified as the most influential overall, with a contribution of 27.214% to the predicted distribution of S. acuticauda. Table 1 Model evaluation metrics for each individual modeling method used in the final ensemble model to estimate habitat suitability for S. acuticauda . The participating models include Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF). Evaluation metrics include Area Under the Curve (AUC), difference in AUC between training and testing (ΔAUC), Proportion Correctly Classified (PCC), True Skill Statistic (TSS), Cohen’s Kappa, Specificity, and Sensitivity. Model Dataset AUC ΔAUC PCC TSS Kappa Specificity Sensitivity BRT Train 0.963 0.112 88.2 0.763 0.761 0.876 0.887 CV 0.851 77.1 0.539 0.535 0.753 0.785 GLM Train 0.88 0.077 77.9 0.557 0.552 0.777 0.78 CV 0.803 73.2 0.454 0.454 0.694 0.76 MARS Train 0.891 0.058 79.3 0.586 0.581 0.793 0.792 CV 0.833 75.4 0.496 0.494 0.711 0.785 MAXENT Train 0.898 0.126 86.4 0.725 0.723 0.851 0.873 CV 0.772 73.1 0.457 0.454 0.711 0.746 RF Train 0.875 0.002 78.6 0.569 0.566 0.777 0.792 CV 0.873 77.1 0.534 0.534 0.735 0.798 Table 2 Mean (µ) contribution of environmental predictors to the ensemble model for estimating the habitat suitability of S. acuticauda . Predictors include Temperature Mean Diurnal Range (bio_2), Isothermality (bio_3), Precipitation of Wettest Month (bio_13), Precipitation of Driest Month (bio_14), Precipitation Seasonality (bio_15), Euclidean Distance to Water (euc_river), Elevation (elevation), Aspect (aspect), and Slope (slope). Variable BRT GLM MARS MAXENT RF MEAN (µ) MEAN (µ) (%) aspect 0.000 0.000 0.000 0.014 0.000 0.003 0.524 bio_13 0.024 0.000 0.041 0.065 0.001 0.026 4.852 bio_14 0.000 0.000 0.053 0.094 0.000 0.029 5.495 bio_15 0.044 0.000 0.183 0.150 0.001 0.075 14.072 bio_2 0.043 0.169 0.079 0.011 0.000 0.060 11.285 bio_3 0.000 0.136 0.000 0.035 0.000 0.034 6.366 elevation 0.078 0.256 0.151 0.237 0.008 0.146 27.214 euc_river 0.161 0.266 0.150 0.016 0.042 0.127 23.716 slope 0.000 0.114 0.000 0.060 0.000 0.035 6.476 3.2 Habitat suitability in present and future The model recognized approximately 143,273 km² within the study extent of the GBM River Basin (2,348,805 km²) as currently suitable for S. acuticauda (Fig. 3 , Online Resource: Table S1 ). This suitable area represents only 6.10% of the entire GBM basin. However, projections under future climate scenarios indicate a troubling trend, showing a significant reduction in suitable habitat areas as a result of climate change. Specifically, the reduction in suitable areas is projected to range from 88.765–93.068% compared to the present. Under the SSP245 scenario, the suitable habitat is expected to decrease by 88.765% during the 2041–2060 period, rising to 91.007% by 2061–2080 (Fig. 4 ). In the more extreme SSP585 scenario, habitat loss is projected to be even more severe, with reductions of 91.663% in 2041–2060 and reaching a peak of 93.068% in 2061–2080 (Fig. 4 ). Notably, the 2061–2080 period marks the greatest loss of suitable habitat under both climate projections, underscoring the significant impact of future climate change on the species potential distribution. 3.3 Geometry assessment of Suitable Landscape The spatial geometry of suitable patches for S. acuticauda was also assessed under current and future climatic scenarios. The results raise serious concerns about habitat fragmentation and geometric alterations driven by climate change (Table 3 ). Specifically, the projected loss of suitable areas is accompanied by the complete disappearance of several patches. This is evidenced by a reduction in the number of patches (NP), which declines by 40.262–60.029% under future scenarios. This loss contributes to a marked reduction in patch density (PD), which decreases by 40.156–59.957%, indicating a sparser distribution of suitable areas across the landscape. Furthermore, the remnant patches are notably smaller in size, as reflected in the LPI, which shows a dramatic decline of up to 96.461%. Correspondingly, the TE also decreases by 74.887%, suggesting a loss of edge complexity and overall patch extent. In addition, the geometry of the patches becomes increasingly simplified, with the LSI declining by up to 39.820%, while the AI drops by 25.311%, indicating increased isolation of patches. Together, these spatial changes reveal a significant fragmentation of suitable habitats, with the remaining patches becoming smaller, more isolated, and geometrically simpler in the future time periods due to climate change. Table 3 Landscape geometry metrics of suitable habitat patches for S. acuticauda under present and future climate scenarios. Metrics include Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Total Edge (TE), Landscape Shape Index (LSI), and Aggregation Index (AI). Future scenarios are based on Shared Socioeconomic Pathways (SSPs). Scenario NP PD LPI TE LSI AI Present 2044 13597340 3.004 647.632 53.399 86.102 SSP245 (2041–2060) 1221 8137160 0.149 162.640 40.019 68.995 SSP245 (2061–2080) 1129 7524041 0.108 136.272 37.355 67.548 SSP585 (2041–2060) 1042 6944243 0.131 123.280 35.183 68.372 SSP585 (2061–2080) 817 5444766 0.106 117.232 32.136 64.308 3.4 Corridor connectivity in present and future The circuit analysis identified five major biological corridors supporting the connectivity of S. acuticauda within the study area (Fig. 5 ). These corridors comprise the Gandak–Bagmati–Kosi, Ganges–Sakri, Chambal–Banas, Siang–Upper Brahmaputra, and Lower Brahmaputra within the GBM river basins. The mean connectivity values of these corridors under the present climatic scenario were estimated as follows: 0.380 for Gandak–Bagmati–Kosi, 0.356 for Ganges–Sakri, 0.322 for Lower Brahmaputra, 0.223 for Siang–Upper Brahmaputra, and 0.255 for Chambal–Banas (Table 4 ). Among these, the highest corridor connectivity was observed in the Gandak–Bagmati–Kosi corridor, which is in the transboundary region along the India–Nepal border. Meanwhile, the lowest connectivity was recorded in the Chambal–Banas corridor in the present scenario. However, the projected loss of suitable habitat under future climate scenarios is expected to diminish corridor connectivity across the region. Specifically, the Gandak–Bagmati–Kosi corridor is expected to experience a decline in mean connectivity ranging from 19.015–21.183% across future time periods. The Ganges–Sakri corridor showed the highest decline in mean connectivity, with reductions ranging from 8.800–33.081%, indicating a significant impact of climate change on this corridor. The Lower Brahmaputra corridor also exhibited a notable decrease, with mean connectivity declining by up to 18.518%. In contrast, the Siang–Upper Brahmaputra and Chambal–Banas corridors experienced the least reduction in mean connectivity. Specifically, Siang–Upper Brahmaputra showed a decline ranging from 3.371–8.542%, while Chambal–Banas declined by 2.723–9.523% under future climate scenarios. Overall, corridor connectivity within the GBM basin is already limited and is projected to further deteriorate due to climate-induced habitat loss and fragmentation in the future. Table 4 Mean biological corridor connectivity of the five identified corridors for S. acuticauda within the GBM River basin under present and future climatic scenarios. Future projections are based on Shared Socioeconomic Pathways (SSP245 and SSP585) for the periods 2041–2060 and 2061–2080. Scenario Gandak–Bagmati–Kosi Ganges–Sakri Lower Brahmaputra Siang–Upper Brahmaputra Chambal–Banas Present 0.380 0.356 0.322 0.223 0.255 SSP245 (2041–2060) 0.307 0.324 0.296 0.215 0.248 SSP245 (2061–2080) 0.301 0.312 0.282 0.205 0.238 SSP585 (2041–2060) 0.306 0.297 0.278 0.210 0.249 SSP585 (2061–2080) 0.299 0.238 0.262 0.204 0.231 4 Discussion Rivers and wetlands are vital ecosystems that contribute significantly to biodiversity at local, regional, and global scales, providing critical habitats for a wide range of wildlife species (Dudgeon et al. 2006 ; Junk et al. 2006 ; Londe et al. 2023 ). These ecosystems are highly vulnerable to the impacts of climate change, which is projected to substantially reduce their extent and function in time, thereby threatening bird populations that rely on them (Gill et al. 2019 ). Furthermore, these ecosystems and associated avifauna is largely influenced by local hydrological conditions and regional climate variability (Anteau et al. 2016 ; Fay et al. 2016 ). Specifically, members of the order Charadriiformes are particularly susceptible to the adverse effects of ongoing anthropogenic pressures and habitat exploitation (Galbraith et al. 2014 ; Cruz et al. 2013 ). Given these concerns, it is essential to examine the habitat and connectivity of avian species at regional scales to inform targeted conservation actions. Accordingly, this study examines the impact of climate change on the habitat suitability and corridor connectivity of the S. acuticauda within its key distribution area with the aim of supporting prioritization and direction of future conservation efforts. The present study estimates approximately 143,273 km² suitable habitat of S. acuticauda within the GBM River Basin, representing only 6.10% of the total basin area. This further reinforces the observation that the actual suitable habitat of species is often substantially smaller than their estimated overall distribution range (Gavrutenko et al. 2021 ; Fan et al. 2022 ). This narrow extent is further exacerbated by projected declines in suitable habitat due to climatic shifts. Specifically, the future scenarios indicate a reduction in suitable areas ranging from 88.765–93.068% relative to the present. This alarming trend is consistent with patterns observed in other wetland or water-dependent avian taxa, which also face severe habitat contractions under climate change (Duan et al. 2021 ; De la Cruz and Numa 2024 ). Furthermore, the projected habitat loss far exceeds the thresholds of short and long-term reductions, with estimates surpassing 50% in all future scenarios (Abedin et al. 2025 ; Fan et al. 2022 ; De la Cruz and Numa 2024 ). The projected distributional changes observed across various taxa are primarily driven by shifts in climatic parameters such as temperature and precipitation, highlighting the significant role of climate variables in shaping species distributions (Mukul et al. 2019 ; Patrício et al. 2021 ; Schuerch et al. 2018 ). This pattern is also evident for S. acuticauda , as key climate-related variables, particularly precipitation seasonality (bio_15) and temperature mean diurnal range (bio_2), emerged as major contributors to the ensemble model, accounting for 14.072% and 11.285% of total variable importance, respectively. The temperature-related variables can influence species thermal tolerance or metabolic rates, which directly affects survival and breeding success (Huey et al. 2012 ; Sunday et al. 2014 ). The prominence of precipitation seasonality (bio_15) indicates that changes in rainfall patterns could significantly impact habitat availability (Bhuyan et al. 2025 ). These precipitation patterns play a critical role in maintaining nesting conditions, particularly for species like S. acuticauda , which relies on riparian and sandy zones adjacent to riverine systems and wetlands. In line with this, Euclidean distance to water (euc_river) was identified as another significant predictor, contributing 23.716% to the model. The ensemble model revealed that species occurrence declines with increasing distance from water channels, emphasizing the importance of prioritizing conservation efforts along riverbanks and wetland margins (Online Resource: Fig. S3). Remarkably, elevation was found to be the most influential variable overall, contributing 27.214% to the species predicted distribution. The model showed that the species probability of occurrence decreases with increasing elevation, further supporting its preference for lowland habitats along the Himalayan foothills (Online Resource: Fig. S3). This aligns with broader ecological observations, as elevation is a known determinant for the distribution of wetland-dependent bird species (De la Cruz and Numa 2024 ). These findings underscore the potential for drastic habitat shifts in response to projected changes in hydrology and flooding patterns in the coming decades (Sayol and Marcos 2018 ). Furthermore, the projected decline in suitable habitat areas due to climate change is accompanied by significant fragmentation of these patches (Opdam and Wascher 2004 ). This is particularly evident from the reduction in the Number of Patches (NP), indicating that many previously suitable areas are completely lost in future scenarios. The remaining patches are smaller in size, possess reduced edge complexity, and exhibit simpler geometric forms. Additionally, these fragments become increasingly isolated from one another, further diminishing landscape connectivity. Such fragmentation impairs the functional connectivity between critical habitat zones, ultimately reducing the species adaptive potential in the face of environmental change (Finch et al. 2017 ). This is further supported by the circuit-based connectivity analysis, which identified only five major corridors facilitating species movement across the vast GBM river basin. Overall connectivity is already limited under current conditions, and future projections indicate further declines in mean corridor connectivity due to climate change, reinforcing the severe impacts of habitat loss. The most pronounced losses are projected within corridors located in the Himalayan foothills, including the Gandak–Bagmati–Kosi, Ganges–Sakri, and Lower Brahmaputra corridors. These findings highlight the vulnerability of Himalayan-origin riverine systems, which are increasingly threatened by both climate change and altered hydrological regimes (Uereyen et al. 2022 ). These high-altitude areas are also hotspots for flash flooding and serve as critical drainage systems for mountainous regions, increasing their susceptibility to climate-induced disruptions. Although the Siang–Upper Brahmaputra and Chambal–Banas corridors show relatively smaller declines in connectivity, their mean connectivity values remain the lowest among all identified corridors, raising concern over their long-term viability. Consequently, the persistence of sandy bars, riparian habitats, and wetland complexes, especially those embedded within river systems, becomes increasingly precarious yet vital for the conservation of this imperiled species. 5 Conservation Implications and Recommendations Hence, a definitive and immediate conservation strategy is strongly recommended across the entire distribution range of this imperiled species to prevent its extinction. With fewer than 2,000 individuals estimated in recent assessments, urgent intervention is necessary. The conservation efforts can be strategically aligned with existing initiatives such as the Ganges River conservation program, thereby facilitating more efficient resource allocation and promoting integrated, ecosystem-based management (Hussain et al. 2020 ). The priority should be given to the regions delineated by this study under both present and future climate scenarios. Specifically, attention must be paid to protecting sandy bars and riverine plains, which serve as critical habitat and connectivity pathways for S. acuticauda . These identified areas could be brought under legal protection, as also emphasized by the IUCN assessment, and targeted efforts should be undertaken to ensure adequate water flow within these riparian islands by restoring channel connectivity and managing habitats to maintain their ecological functionality. A comprehensive population survey is also warranted along the identified riverine corridors to locate potential breeding, basking, or roosting sites. While a recent population assessment was conducted in 2022, additional data, including citizen science platforms such as eBird, can enhance monitoring and species occurrence records. Further, human disturbances in these sensitive zones must be minimized, particularly the impacts of stray or feral dogs and cattle. During field surveys of this study, a sighting of 20 individuals coincided with the presence of stray dogs and livestock on inhabited sandbars, raising serious concerns about nest predation and direct threats to the species, which is a pattern already documented in other birds. Moreover, sedimentation, riverbank erosion, and a range of human-induced pressures are key factors driving land use and land cover changes within the GBM river systems (Cheema and Bastiaanssen 2010 ; Debnath et al. 2023 ). Moreover, the conversion of riparian habitats into agricultural land, or their exploitation for recreational or industrial purposes such as the construction of dams, hydropower projects, and related infrastructure poses a significant threat to ecological integrity and necessitates strict regulatory oversight. Therefore, any developmental activity in these ecologically sensitive areas must be subjected to comprehensive Environmental Impact Assessments to evaluate and mitigate ecological risks. Additionally, community-based conservation initiatives involving local residents must be prioritized, promoting alternative livelihoods and awareness programs. From a research perspective, it is equally important to generate genetic data for this species through non-invasive approach to understand its population structure and inform long-term conservation planning. Hence, future studies should adopt an integrated approach combining ecological and genetic data to develop a holistic conservation framework that ensures the long-term survival of this endangered indicator species. 6 Conclusion This study presents the first inclusive assessment of habitat suitability and connectivity for the endangered S. acuticauda across GBM River Basin under both current and future climatic scenarios. The findings underscore a critical contraction of suitable habitat, with less than 6.1% of the basin currently viable for the species and a projected loss exceeding 88% under future climate projections. The alarming fragmentation and isolation of remaining habitat patches further highlight the vulnerability to ongoing and future environmental changes. The identification of five major functional corridors, most of which exhibit declining connectivity, reflects a precarious conservation outlook. These disruptions jeopardize not only the species persistence but also the broader ecological integrity of riverine systems. Given the critical conservation status and its reliance on increasingly threatened habitats, urgent and targeted conservation action is imperative. Thus, protecting key habitats and maintaining river flow regimes, alongside integrating climate-resilient strategies and local community engagement, will be essential for its long-term survival. This study provides an essential spatial framework to guide future ecological monitoring, policy formulation, and management interventions, and highlights the broader need for climate-informed conservation planning for waterbirds across the Indian subcontinent. Declarations Authors contribution I.A: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft. H.-W.K: Funding acquisition, Project administration, Resources, Validation, Visualization. H.S: Conceptualization, Data curation, Investigation, Supervision, Validation, Writing – review and editing. S.K: Conceptualization, Formal analysis, Project administration, Supervision, Visualization, Writing – original draft. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data availability statement The data generated and analyzed during this study are included in this article and its supplementary information files. Additional datasets are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Conflicts of interest The authors declare no conflict of interest. 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Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 03 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 27 Aug, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 27 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7469139","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506353546,"identity":"14ffa154-9d21-45b1-bc63-ed4be60a6396","order_by":0,"name":"Imon Abedin","email":"","orcid":"","institution":"Bodoland University","correspondingAuthor":false,"prefix":"","firstName":"Imon","middleName":"","lastName":"Abedin","suffix":""},{"id":506353547,"identity":"cad75ff6-166d-4dd8-9732-591e3fa0bce5","order_by":1,"name":"Hyun-Woo Kim","email":"","orcid":"","institution":"Pukyong National University","correspondingAuthor":false,"prefix":"","firstName":"Hyun-Woo","middleName":"","lastName":"Kim","suffix":""},{"id":506353548,"identity":"95286115-6ff0-4d18-b61b-4081afaac8bd","order_by":2,"name":"Hilloljyoti Singha","email":"","orcid":"","institution":"Bodoland University","correspondingAuthor":false,"prefix":"","firstName":"Hilloljyoti","middleName":"","lastName":"Singha","suffix":""},{"id":506353549,"identity":"19ed1aa7-5c5c-4c04-94f3-14a4aac48b94","order_by":3,"name":"Shantanu Kundu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCSjNxt58wOADmEGMlgNAzMdzLKFwBkgLM7Fa5CRyDD7zgEQIaZGf3fzs8YeaO4ltEmmJm21+bZPnY2Zg/PAxB7cWgzvHzA0OHHuW2Mbz+LBxbt9twzZmBmbJmdvwaJFIMJM4wHY4sY09Lc04t+c2I1ALGzMvHi3yM9K/SRz4B9TCkGP+27Lntj1BLQw3cswkDrYBtXDkGBgz/LidSFCLwY2cMomzfYeN24CBbNjbcDu5jZmxGa9fgA7bJlHx7bDs/HZgVP74c9sWyDj44SM+h6EAxjYw2UCsehD4Q4riUTAKRsEoGCkAAGW2Vsjw3ajfAAAAAElFTkSuQmCC","orcid":"","institution":"Pukyong National University","correspondingAuthor":true,"prefix":"","firstName":"Shantanu","middleName":"","lastName":"Kundu","suffix":""}],"badges":[],"createdAt":"2025-08-27 07:53:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7469139/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7469139/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90089840,"identity":"424f7901-6cab-4ca7-a3b9-5cd4cea1dea9","added_by":"auto","created_at":"2025-08-28 10:58:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":413111,"visible":true,"origin":"","legend":"\u003cp\u003eOccurrence records of \u003cem\u003eS. acuticauda\u003c/em\u003e within the GBM River basins, compiled from primary field surveys and secondary sources. The map also depicts the IUCN-assessed extent of occurrence and major river systems across the region. Inset representative photograph of \u003cem\u003eS. acuticauda\u003c/em\u003e taken by the first author (I.A.) during the field survey.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7469139/v1/ecebade8233d8d12c93b677f.png"},{"id":90090141,"identity":"c0893706-14d3-4a26-a0d2-5caf649915e7","added_by":"auto","created_at":"2025-08-28 11:06:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":290875,"visible":true,"origin":"","legend":"\u003cp\u003eModel evaluation plots for the ensemble model predicting \u003cem\u003eS. acuticauda\u003c/em\u003edistribution. The plots display the average training ROC curve and AUC values for both training and cross-validation (CV) runs. Also shown are the relative importance scores of the selected predictor variables by each model. (a) Boosted Regression Trees (BRT), (b) Generalized Linear Models (GLM), (c) Multivariate Adaptive Regression Splines (MARS), (d) Maximum Entropy (MaxEnt), and (e) Random Forests (RF).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7469139/v1/d56e906ce3bb89595b179046.png"},{"id":90089845,"identity":"dbfcb52f-2173-4f11-88f6-0e326ebae0e1","added_by":"auto","created_at":"2025-08-28 10:58:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":374329,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted suitable habitat for \u003cem\u003eS. acuticauda\u003c/em\u003e across the GBM River basin under current environmental conditions. The map illustrates model agreement levels from the ensemble approach, where values range from 0 (no model agreement) to 5 (full agreement among all five models). Areas with a value of 5 are considered highly suitable habitats. Representative photograph of \u003cem\u003eS. acuticauda\u003c/em\u003etaken by the first author (I.A.) during the field survey.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7469139/v1/9a4fe3cb29021d57f8067a36.png"},{"id":90090142,"identity":"1681ce49-dc7c-4a5f-a55d-6d0bc80fdc62","added_by":"auto","created_at":"2025-08-28 11:06:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":441725,"visible":true,"origin":"","legend":"\u003cp\u003eProjected suitable habitat for \u003cem\u003eS. acuticauda\u003c/em\u003e in the GBM River basin under future climate scenarios. Suitability is shown for two Shared Socioeconomic Pathways (SSPs): SSP245 and SSP585, across two time periods—2041–2060 and 2061–2080.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7469139/v1/69459672f472ab05f09a678a.png"},{"id":90091038,"identity":"13656aa9-c3ac-4422-b3f7-e1774cddb28a","added_by":"auto","created_at":"2025-08-28 11:14:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":591851,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted biological corridor connectivity for S. acuticauda within the GBM River basin under present and future climatic scenarios. Connectivity is shown for the present and under two Shared Socioeconomic Pathways (SSP245 and SSP585) across two future time periods: 2041–2060 and 2061–2080. The five biological corridors are: (1) Gandak–Bagmati–Kosi, (2) Ganges–Sakri, (3) Chambal–Banas, (4) Siang–Upper Brahmaputra, and (5) Lower Brahmaputra.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7469139/v1/803271240ed43011e56ff5cd.png"},{"id":90089847,"identity":"2a447acd-9ef0-4ced-aeec-06b974e22b2b","added_by":"auto","created_at":"2025-08-28 10:58:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":689129,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7469139/v1/d949ac14c0425e8d249bd60b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate-induced decline of habitat and connectivity imperils the Endangered Black-bellied Tern (Sterna acuticauda) in the Ganges-Brahmaputra-Mahanadi River Basin","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlobal biodiversity is experiencing unprecedented declines driven by the intensifying impacts of climatic changes (Johnson et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This shifting climatic conditions are driving biogeographical changes in many species, often resulting in reduced overall distributions that are strongly linked to elevated extinction risk (Chen et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tingley et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lenoir and Svenning \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Urban \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Amongst vertebrates, birds are particularly vulnerable, with numerous species exhibiting range shifts driven by rising climatic pressure and increasing anthropogenic disturbances (Li et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Simultaneously, the global freshwater crisis is unfolding at an alarming pace that has resulted in the rapid degradation and disappearance of freshwater ecosystems worldwide (Reid et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sayer et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Further, the disruptions in the global hydrological cycle are undergoing significant changes in spatial and temporal availability of freshwater resources, impacting ecosystem structure and function in inland waters (Huntington \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These changes have profound implications for waterbirds as they are highly sensitive to habitat alterations (Litvinenko and Shibaev \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Waterbirds are a diverse group encompassing over 30 families that are ecologically dependent on riverine and wetland habitats for survival and reproduction (BirdLife International 2023). As key indicators of wetland health and ecological integrity, waterbirds reflect the broader state of freshwater biodiversity. However, current trends are concerning, as nearly 56% of waterbird species are experiencing significant population declines, and approximately 17% are classified as globally threatened (BirdLife International 2023). This situation is particularly alarming for Asia, which is witnessing the steepest declines, with around 60% of its waterbird species affected, underscoring the urgent need for targeted conservation plans in the region (BirdLife International 2023).\u003c/p\u003e\u003cp\u003eOwing to their high sensitivity to environmental changes, it is imperative to understand the spatial distribution and habitat quality of waterbirds to identify the ecological factors that underpin their survival and to inform effective conservation strategies (Bai et al. 2018; Urbani et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This urgency is especially pronounced in riverine systems originating from the Himalayan region, where wetland biodiversity is increasingly vulnerable to environmental stressors such as climate variability, land-use change, and hydrological disruption (Uereyen et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chauhan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These ecosystems not only support a rich array of species but also provide essential ecological services that sustain human welfare across the Indian subcontinent (Wijngaard et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Biemans et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In light of escalating global and regional pressures, research into biodiversity patterns, habitat selection, and targeted conservation strategies of waterbirds has become increasingly vital. Yet, despite growing concern over the vulnerability of waterbirds and their habitats, there is significant gap in species-specific ecological research and wetland conservation efforts across South Asia (Khan and Pant \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Black-bellied Tern (\u003cem\u003eSterna acuticauda\u003c/em\u003e) (Order: Charadriiformes) is an endangered riverine bird species native to the freshwater systems of South and Southeast Asia (BirdLife International 2022). It exhibits the characteristic morphology of terns, including a slender body, long pointed wings, and a deeply forked tail (Gochfeld et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The adults in breeding plumage display a distinctive black cap, grey back, and long, pointed grey wings. The white throat and pale grey breast contrast sharply with the strikingly dark belly and undertail-coverts, while the underwings are predominantly white with a darker band across the secondaries. The species also features a yellow-orange bill and reddish-orange legs (Grimmett et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The global population of the \u003cem\u003eS. acuticauda\u003c/em\u003e has experienced a severe decline in recent decades, with the most recent IUCN assessment estimating fewer than 2,000 individuals remaining (BirdLife International 2022). Once widespread across South and Southeast Asia, the species has now become locally extinct in much of Southeast Asia (BirdLife International 2022; Z\u0026ouml;ckler et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Currently, over 90% of the remaining population is restricted to the Indian subcontinent, particularly within the Ganges-Brahmaputra-Mahanadi (GBM) River Basins. This significant range contraction also reflects the ongoing population decline, further underscored by the species disappearance from numerous historical breeding sites in India within the past decade. The species faces widespread and escalating threats, primarily due to the degradation and loss of its preferred habitats on riverine sandbars (Inskipp et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, major anthropogenic pressures include agricultural expansion, river damming, sand mining, and fluctuations in water levels, all of which significantly alter the breeding environment of this species. In addition, the population decline of this avifaunal species is further exacerbated by egg collection, illegal and excessive fishing, predation by feral dogs and crows, nest flooding, and direct human disturbances such as livestock trampling and unregulated ecotourism activities (Kabir et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kar et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Goes et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOwing to the continued population decline and severe range contraction of \u003cem\u003eS. acuticauda\u003c/em\u003e, the IUCN has strongly recommended comprehensive ecological research to support targeted conservation efforts. However, to date, the species has not been adequately assessed in terms of its habitat preferences or its vulnerability to ongoing climate change impacts. Such assessments are critical for guiding habitat selection and prioritization, which are essential components of effective and evidence-based conservation strategies (He et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this context, Species Distribution Models (SDMs) have emerged as valuable tools for assessing habitat suitability. These models help predict the potential spatial distribution of species and quantitatively assess the relative influence of different environmental variables (Elith and Leathwick \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The model integrates species occurrence data with ecological and climatic variables across spatial and temporal scales and offers insights into habitat dynamics and species\u0026ndash;environment relationships (Guisan and Zimmermann \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Such research has become increasingly important in the fields of biodiversity conservation, habitat management, and climate adaptation planning.\u003c/p\u003e\u003cp\u003eHence, the present study aims to (i) identify suitable habitats in current climatic conditions within the GBM river basin, (ii) estimate potential climate refugia under future climate scenarios, (iii) evaluate changes in the geometry of suitable landscapes, and (iv) identify potential habitat corridors in both present and future climatic projections. This study represents the first comprehensive effort to assess habitat suitability for this imperiled Black Bellied Tern. The findings will contribute to effectively identifying and prioritizing climate refugia, thereby supporting long-term conservation planning of \u003cem\u003eS. acuticauda\u003c/em\u003e for persistence in the face of ongoing climate change.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area and occurrences\u003c/h2\u003e\n \u003cp\u003eThe vast majority (\u0026gt;\u0026thinsp;90%) of the global population of \u003cem\u003eS. acuticauda\u003c/em\u003e is now confined to India, where the population is estimated to comprise no more than 1,000 mature individuals, with a few critical strongholds (eBird \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; eBird \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within India, these individuals are primarily restricted to fragmented habitats within the GBM River Basin, spanning north-central, eastern, and northeastern regions of the country. Given this distribution, the GBM River Basin was selected as the focal area for subsequent analysis in the present study (Fig. 1). The primary field surveys were conducted along the Dibru, Siang, and Brahmaputra Rivers during 2024 and 2025, primarily using boat survey. Following approval (WL/FG.31/RS/38th T.C./2025-Pt, dated 11 April 2025) from the Chief Wildlife Warden, Department of Environment, Forest and Climate Change, Government of Assam, surveys were also extended into the Dibru-Saikhowa National Park. During early 2024, single individuals of \u003cem\u003eS. acuticauda\u003c/em\u003e were recorded on seven occasions along the Brahmaputra and Siang Rivers. Notably, in October 2024, a significant sighting occurred along the Aisung stretch of the Brahmaputra River, where a group of approximately 20 individuals was observed, which represented one of the largest recent congregations reported. Additionally, only two sightings of single individuals were recorded in May 2025 within the Dighaltarang area of Dibru-Saikhowa National Park.\u003c/p\u003e\n \u003cp\u003eFurther, to enhance the spatial representation of the species presence across the study extent, additional occurrence records were compiled from secondary data sources. The occurrence data were aggregated from the IUCN Geospatial Conservation Assessment Tool (GeoCAT) and eBird citizen science records (eBird \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bachman et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, records associated with captive individuals or preserved specimens were removed, and only direct human observations were retained to ensure an accurate representation of the ecological niche in the wild. Additionally, all occurrence locations were spatially rarefied at a resolution of 1 km\u0026sup2; using the spatial rarefaction function in SDM Toolbox v2.4 to minimize spatial autocorrelation and reduce the overrepresentation of clustered records, thereby limiting model overfitting (Brown et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The rarefaction scale was selected to match the resolution of the environmental raster layers used in subsequent analyses. After filtering and rarefaction, a total of 160 unique occurrence points were retained for use in the final habitat suitability modeling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Predictors for distribution modelling\u003c/h2\u003e\n \u003cp\u003eThe study utilized a specific set of variables, including climatic parameters (bioclimatic variables), habitat characteristics, and topography to identify the habitat suitability of the species (Peterson and Sober\u0026oacute;n \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). A total of 19 bioclimatic variables were retrieved from the WorldClim database at a spatial resolution of 30 arcseconds (~\u0026thinsp;1 km\u0026sup2;) to represent climatic conditions used in SDM studies (Fick and Hijmans \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003c/span\u003e). In addition to climate, a habitat predictor, i.e., Euclidean distance to water, was included to account for the species strong association with wetlands/water bodies. This variable quantified proximity to water sources and was derived from the ESRI Sentinel\u0026thinsp;\u0026minus;\u0026thinsp;2 10-Meter Land Use/Land Cover (LULC) dataset available on the Living Atlas platform (Karra et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://livingatlas.arcgis.com/landcover/\u003c/span\u003e\u003c/span\u003e). The categorical LULC raster was converted into a continuous format using the Euclidean Distance tool in ArcGIS 10.6, allowing for a more nuanced representation of habitat accessibility (Abedin et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Further, the topography included elevation, slope, and aspect, which were obtained from Shuttle Radar Topography Mission (SRTM) data at a 90-meter resolution (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://srtm.csi.cgiar.org/srtmdata/\u003c/span\u003e\u003c/span\u003e). All spatial predictor layers were standardized to a common resolution of 30 arcseconds (~\u0026thinsp;1 km\u0026sup2;) using the Spatial Analyst extension in ArcGIS 10.6 to ensure consistency in scale for model input. The research analyzed projected future conditions using two Shared Socioeconomic Pathways, SSP245 and SSP585, across the mid-century (2041\u0026ndash;2060) and late-century (2061\u0026ndash;2080) timeframes. The climatic data were derived from the HadGEM3-GC31 LL model, which is part of the CMIP6 framework (Li et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gautam and Shany \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFurthermore, all predictor variables were tested for multicollinearity to minimize correlation and reduce the risk of model overfitting. The variables with a pairwise correlation coefficient (|r|) greater than 0.7 were excluded from the analysis (Online Resource: Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The three-correlation metrics, i.e., Pearson, Spearman, and Kendall, were calculated using the SAHM (Software for Assisted Habitat Modeling) package within the VisTrails platform (Morisette et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). Any variable exceeding the threshold in any of these correlation tests was removed from the final set of predictors. Following this variable screening process, a total of 9 uncorrelated variables were retained for use in the habitat suitability modeling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Ensemble distribution model\u003c/h2\u003e\n \u003cp\u003eAn ensemble modeling approach was employed to integrate multiple algorithms, thereby constructing a comprehensive and robust model for the target species. This method combines the distinct strengths of individual algorithms, which effectively captures the wide range of ecological and statistical relationships that influence species distributions. This leverages the complementary capabilities of different models and further enhances predictive accuracy and reliability (Hao et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, five algorithms were incorporated into the ensemble framework: Boosted Regression Trees (BRT), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Models (GLM), Maximum Entropy (MaxEnt), and Random Forests (RF) (Elith and Leathwick \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Guisan et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Miller \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The ensemble model was conducted using the Software for Assisted Habitat Modeling (SAHM) integrated within the VisTrails workflow system (Morisette et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Talbert and Talbert \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The resulting models produced continuous habitat suitability maps, with predicted values ranging from 0 (unsuitable) to 1 (highly suitable). These continuous outputs were converted into presence\u0026ndash;absence maps using the sensitivity-equals-specificity (SES) threshold method. Only models with an area under the Receiver Operating Characteristic Curve (AUC) greater than 0.75 were retained for further analysis (Lavazza et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). An ensemble agreement map was then generated, with pixel values ranging from 0 to 5 to indicate the number of algorithms predicting each location as suitable habitat. A maximum value of 5 reflected complete consensus among all five algorithms. The model performance was assessed using a suite of evaluation metrics, viz., AUC, True Skill Statistic (TSS), Cohen\u0026rsquo;s Kappa, Proportion Correctly Classified (PCC), sensitivity, and specificity. These metrics were calculated for both the training datasets and across 10-fold cross-validation replicates to ensure model robustness and reliability (Cohen \u003cspan class=\"CitationRef\"\u003e1968\u003c/span\u003e; Allouche et al. \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Phillips and Elith \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jim\u0026eacute;nez-Valverde et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Spatial geometry assessment\u003c/h2\u003e\n \u003cp\u003eA set of class-level landscape metrics was used to evaluate the structural and spatial qualities of suitable habitat patches under current and future projected conditions. These metrics were calculated using FRAGSTATS version 4.2.1, a widely used tool in landscape ecology for quantifying spatial patterns and analyzing landscape composition and configuration (McGarigal and Marks \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e; Hesselbarth et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The class descriptor tables were generated based on habitat suitability outputs for current and future climate scenarios. The analysis adopted the eight-cell neighborhood rule and incorporated user-defined tiles and a uniform sampling strategy to ensure consistency. The key landscape metrics were Number of Patches (NP), Patch Density (PD), Total Edge (TE), Largest Patch Index (LPI), Aggregation Index (AI), and Landscape Shape Index (LSI). The metrics, such as NP, PD, TE, and LPI, capture information on patch quantity, spatial distribution, and edge complexity. Meanwhile, LSI measured the irregularity of patch shapes, and AI quantified the degree of spatial cohesion and aggregation among habitat patches.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Assessment of corridor connectivity\u003c/h2\u003e\n \u003cp\u003eCircuit theory, a commonly used method for evaluating ecological connectivity, was applied to identify potential habitat corridors within the study area. The Corridor simulations were conducted using the Circuitscape toolbox integrated with ArcGIS 10.6 (Wang et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; McRae et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). The analysis was performed in pairwise mode, where resistance surfaces were represented by conductance rasters derived from habitat suitability probability maps. The focal nodes were defined using specific occurrence points of the species, enabling the model to simulate current flow patterns between node pairs (Dickson et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The resulting current flow maps provided spatially explicit insights into potential movement pathways and the strength of landscape connectivity. This modeling approach was applied under both current and projected future climate scenarios, providing a comparative framework to assess changes in habitat connectivity and identify emerging barriers to species movement in response to shifting environmental conditions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Ensemble model assessment\u003c/h2\u003e\n \u003cp\u003eThe ensemble model demonstrated good performance, with all individual models surpassing the AUC threshold of 0.75 in both the training and cross-validation phases (Fig. 2, Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, during training, AUC values ranged from 0.875 to 0.963, while in cross-validation, they ranged from 0.772 to 0.873 across the five models included in the ensemble. The highest AUC in the training phase was achieved by the BRT model, whereas the RF model recorded the lowest. Conversely, in the cross-validation phase, the RF model attained the highest AUC, while the Maxent model generated the lowest. Regarding the difference between training and cross-validation performance (\u0026Delta;AUC), the RF model exhibited the smallest gap, while the Maxent model showed the largest. In addition, other evaluation metrics also indicated high performance across both training and cross-validation datasets, further supporting the robustness of the ensemble approach (Online Resource: Fig S2).\u003c/p\u003e\n \u003cp\u003eAmong the bioclimatic variables, precipitation seasonality (bio_15) contributed the most to the ensemble model, accounting for 14.072% of the total variable importance (\u0026micro;), followed by temperature mean diurnal range (bio_2), which contributed 11.285% (Online Resource: Fig. S3, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, the euclidean distance to water (euc_river) emerged as a significant predictor, contributing 23.716% to the model. Furthermore, the topography variable elevation was identified as the most influential overall, with a contribution of 27.214% to the predicted distribution of \u003cem\u003eS. acuticauda.\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel evaluation metrics for each individual modeling method used in the final ensemble model to estimate habitat suitability for \u003cem\u003eS. acuticauda\u003c/em\u003e. The participating models include Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF). Evaluation metrics include Area Under the Curve (AUC), difference in AUC between training and testing (\u0026Delta;AUC), Proportion Correctly Classified (PCC), True Skill Statistic (TSS), Cohen\u0026rsquo;s Kappa, Specificity, and Sensitivity.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;AUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKappa\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eBRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMARS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMAXENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean (\u0026micro;) contribution of environmental predictors to the ensemble model for estimating the habitat suitability of \u003cem\u003eS. acuticauda\u003c/em\u003e. Predictors include Temperature Mean Diurnal Range (bio_2), Isothermality (bio_3), Precipitation of Wettest Month (bio_13), Precipitation of Driest Month (bio_14), Precipitation Seasonality (bio_15), Euclidean Distance to Water (euc_river), Elevation (elevation), Aspect (aspect), and Slope (slope).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBRT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGLM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMARS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAXENT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMEAN (\u0026micro;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMEAN (\u0026micro;) (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003easpect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eelevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeuc_river\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eslope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Habitat suitability in present and future\u003c/h2\u003e\n \u003cp\u003eThe model recognized approximately 143,273 km\u0026sup2; within the study extent of the GBM River Basin (2,348,805 km\u0026sup2;) as currently suitable for \u003cem\u003eS. acuticauda\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Online Resource: Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). This suitable area represents only 6.10% of the entire GBM basin. However, projections under future climate scenarios indicate a troubling trend, showing a significant reduction in suitable habitat areas as a result of climate change. Specifically, the reduction in suitable areas is projected to range from 88.765\u0026ndash;93.068% compared to the present.\u003c/p\u003e\n \u003cp\u003eUnder the SSP245 scenario, the suitable habitat is expected to decrease by 88.765% during the 2041\u0026ndash;2060 period, rising to 91.007% by 2061\u0026ndash;2080 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the more extreme SSP585 scenario, habitat loss is projected to be even more severe, with reductions of 91.663% in 2041\u0026ndash;2060 and reaching a peak of 93.068% in 2061\u0026ndash;2080 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, the 2061\u0026ndash;2080 period marks the greatest loss of suitable habitat under both climate projections, underscoring the significant impact of future climate change on the species potential distribution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Geometry assessment of Suitable Landscape\u003c/h2\u003e\n \u003cp\u003eThe spatial geometry of suitable patches for \u003cem\u003eS. acuticauda\u003c/em\u003e was also assessed under current and future climatic scenarios. The results raise serious concerns about habitat fragmentation and geometric alterations driven by climate change (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, the projected loss of suitable areas is accompanied by the complete disappearance of several patches. This is evidenced by a reduction in the number of patches (NP), which declines by 40.262\u0026ndash;60.029% under future scenarios. This loss contributes to a marked reduction in patch density (PD), which decreases by 40.156\u0026ndash;59.957%, indicating a sparser distribution of suitable areas across the landscape. Furthermore, the remnant patches are notably smaller in size, as reflected in the LPI, which shows a dramatic decline of up to 96.461%. Correspondingly, the TE also decreases by 74.887%, suggesting a loss of edge complexity and overall patch extent. In addition, the geometry of the patches becomes increasingly simplified, with the LSI declining by up to 39.820%, while the AI drops by 25.311%, indicating increased isolation of patches. Together, these spatial changes reveal a significant fragmentation of suitable habitats, with the remaining patches becoming smaller, more isolated, and geometrically simpler in the future time periods due to climate change.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLandscape geometry metrics of suitable habitat patches for \u003cem\u003eS. acuticauda\u003c/em\u003e under present and future climate scenarios. Metrics include Number of Patches (NP), Patch Density (PD), Largest Patch Index (LPI), Total Edge (TE), Landscape Shape Index (LSI), and Aggregation Index (AI). Future scenarios are based on Shared Socioeconomic Pathways (SSPs).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLPI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLSI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13597340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e647.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP245 (2041\u0026ndash;2060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8137160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP245 (2061\u0026ndash;2080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7524041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP585 (2041\u0026ndash;2060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6944243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP585 (2061\u0026ndash;2080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5444766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Corridor connectivity in present and future\u003c/h2\u003e\n \u003cp\u003eThe circuit analysis identified five major biological corridors supporting the connectivity of \u003cem\u003eS. acuticauda\u003c/em\u003e within the study area (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). These corridors comprise the Gandak\u0026ndash;Bagmati\u0026ndash;Kosi, Ganges\u0026ndash;Sakri, Chambal\u0026ndash;Banas, Siang\u0026ndash;Upper Brahmaputra, and Lower Brahmaputra within the GBM river basins. The mean connectivity values of these corridors under the present climatic scenario were estimated as follows: 0.380 for Gandak\u0026ndash;Bagmati\u0026ndash;Kosi, 0.356 for Ganges\u0026ndash;Sakri, 0.322 for Lower Brahmaputra, 0.223 for Siang\u0026ndash;Upper Brahmaputra, and 0.255 for Chambal\u0026ndash;Banas (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Among these, the highest corridor connectivity was observed in the Gandak\u0026ndash;Bagmati\u0026ndash;Kosi corridor, which is in the transboundary region along the India\u0026ndash;Nepal border. Meanwhile, the lowest connectivity was recorded in the Chambal\u0026ndash;Banas corridor in the present scenario.\u003c/p\u003e\n \u003cp\u003eHowever, the projected loss of suitable habitat under future climate scenarios is expected to diminish corridor connectivity across the region. Specifically, the Gandak\u0026ndash;Bagmati\u0026ndash;Kosi corridor is expected to experience a decline in mean connectivity ranging from 19.015\u0026ndash;21.183% across future time periods. The Ganges\u0026ndash;Sakri corridor showed the highest decline in mean connectivity, with reductions ranging from 8.800\u0026ndash;33.081%, indicating a significant impact of climate change on this corridor. The Lower Brahmaputra corridor also exhibited a notable decrease, with mean connectivity declining by up to 18.518%. In contrast, the Siang\u0026ndash;Upper Brahmaputra and Chambal\u0026ndash;Banas corridors experienced the least reduction in mean connectivity. Specifically, Siang\u0026ndash;Upper Brahmaputra showed a decline ranging from 3.371\u0026ndash;8.542%, while Chambal\u0026ndash;Banas declined by 2.723\u0026ndash;9.523% under future climate scenarios. Overall, corridor connectivity within the GBM basin is already limited and is projected to further deteriorate due to climate-induced habitat loss and fragmentation in the future.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean biological corridor connectivity of the five identified corridors for \u003cem\u003eS. acuticauda\u003c/em\u003e within the GBM River basin under present and future climatic scenarios. Future projections are based on Shared Socioeconomic Pathways (SSP245 and SSP585) for the periods 2041\u0026ndash;2060 and 2061\u0026ndash;2080.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGandak\u0026ndash;Bagmati\u0026ndash;Kosi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGanges\u0026ndash;Sakri\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower Brahmaputra\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSiang\u0026ndash;Upper Brahmaputra\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChambal\u0026ndash;Banas\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP245 (2041\u0026ndash;2060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP245 (2061\u0026ndash;2080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP585 (2041\u0026ndash;2060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSP585 (2061\u0026ndash;2080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eRivers and wetlands are vital ecosystems that contribute significantly to biodiversity at local, regional, and global scales, providing critical habitats for a wide range of wildlife species (Dudgeon et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Junk et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Londe et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These ecosystems are highly vulnerable to the impacts of climate change, which is projected to substantially reduce their extent and function in time, thereby threatening bird populations that rely on them (Gill et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, these ecosystems and associated avifauna is largely influenced by local hydrological conditions and regional climate variability (Anteau et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fay et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Specifically, members of the order Charadriiformes are particularly susceptible to the adverse effects of ongoing anthropogenic pressures and habitat exploitation (Galbraith et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cruz et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Given these concerns, it is essential to examine the habitat and connectivity of avian species at regional scales to inform targeted conservation actions. Accordingly, this study examines the impact of climate change on the habitat suitability and corridor connectivity of the \u003cem\u003eS. acuticauda\u003c/em\u003e within its key distribution area with the aim of supporting prioritization and direction of future conservation efforts.\u003c/p\u003e\u003cp\u003eThe present study estimates approximately 143,273 km\u0026sup2; suitable habitat of \u003cem\u003eS. acuticauda\u003c/em\u003e within the GBM River Basin, representing only 6.10% of the total basin area. This further reinforces the observation that the actual suitable habitat of species is often substantially smaller than their estimated overall distribution range (Gavrutenko et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This narrow extent is further exacerbated by projected declines in suitable habitat due to climatic shifts. Specifically, the future scenarios indicate a reduction in suitable areas ranging from 88.765\u0026ndash;93.068% relative to the present. This alarming trend is consistent with patterns observed in other wetland or water-dependent avian taxa, which also face severe habitat contractions under climate change (Duan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; De la Cruz and Numa \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, the projected habitat loss far exceeds the thresholds of short and long-term reductions, with estimates surpassing 50% in all future scenarios (Abedin et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; De la Cruz and Numa \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The projected distributional changes observed across various taxa are primarily driven by shifts in climatic parameters such as temperature and precipitation, highlighting the significant role of climate variables in shaping species distributions (Mukul et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Patr\u0026iacute;cio et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schuerch et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis pattern is also evident for \u003cem\u003eS. acuticauda\u003c/em\u003e, as key climate-related variables, particularly precipitation seasonality (bio_15) and temperature mean diurnal range (bio_2), emerged as major contributors to the ensemble model, accounting for 14.072% and 11.285% of total variable importance, respectively. The temperature-related variables can influence species thermal tolerance or metabolic rates, which directly affects survival and breeding success (Huey et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sunday et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The prominence of precipitation seasonality (bio_15) indicates that changes in rainfall patterns could significantly impact habitat availability (Bhuyan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These precipitation patterns play a critical role in maintaining nesting conditions, particularly for species like \u003cem\u003eS. acuticauda\u003c/em\u003e, which relies on riparian and sandy zones adjacent to riverine systems and wetlands. In line with this, Euclidean distance to water (euc_river) was identified as another significant predictor, contributing 23.716% to the model. The ensemble model revealed that species occurrence declines with increasing distance from water channels, emphasizing the importance of prioritizing conservation efforts along riverbanks and wetland margins (Online Resource: Fig. S3). Remarkably, elevation was found to be the most influential variable overall, contributing 27.214% to the species predicted distribution. The model showed that the species probability of occurrence decreases with increasing elevation, further supporting its preference for lowland habitats along the Himalayan foothills (Online Resource: Fig. S3). This aligns with broader ecological observations, as elevation is a known determinant for the distribution of wetland-dependent bird species (De la Cruz and Numa \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings underscore the potential for drastic habitat shifts in response to projected changes in hydrology and flooding patterns in the coming decades (Sayol and Marcos \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the projected decline in suitable habitat areas due to climate change is accompanied by significant fragmentation of these patches (Opdam and Wascher \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This is particularly evident from the reduction in the Number of Patches (NP), indicating that many previously suitable areas are completely lost in future scenarios. The remaining patches are smaller in size, possess reduced edge complexity, and exhibit simpler geometric forms. Additionally, these fragments become increasingly isolated from one another, further diminishing landscape connectivity. Such fragmentation impairs the functional connectivity between critical habitat zones, ultimately reducing the species adaptive potential in the face of environmental change (Finch et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is further supported by the circuit-based connectivity analysis, which identified only five major corridors facilitating species movement across the vast GBM river basin. Overall connectivity is already limited under current conditions, and future projections indicate further declines in mean corridor connectivity due to climate change, reinforcing the severe impacts of habitat loss. The most pronounced losses are projected within corridors located in the Himalayan foothills, including the Gandak\u0026ndash;Bagmati\u0026ndash;Kosi, Ganges\u0026ndash;Sakri, and Lower Brahmaputra corridors. These findings highlight the vulnerability of Himalayan-origin riverine systems, which are increasingly threatened by both climate change and altered hydrological regimes (Uereyen et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These high-altitude areas are also hotspots for flash flooding and serve as critical drainage systems for mountainous regions, increasing their susceptibility to climate-induced disruptions. Although the Siang\u0026ndash;Upper Brahmaputra and Chambal\u0026ndash;Banas corridors show relatively smaller declines in connectivity, their mean connectivity values remain the lowest among all identified corridors, raising concern over their long-term viability. Consequently, the persistence of sandy bars, riparian habitats, and wetland complexes, especially those embedded within river systems, becomes increasingly precarious yet vital for the conservation of this imperiled species.\u003c/p\u003e"},{"header":"5 Conservation Implications and Recommendations","content":"\u003cp\u003eHence, a definitive and immediate conservation strategy is strongly recommended across the entire distribution range of this imperiled species to prevent its extinction. With fewer than 2,000 individuals estimated in recent assessments, urgent intervention is necessary. The conservation efforts can be strategically aligned with existing initiatives such as the Ganges River conservation program, thereby facilitating more efficient resource allocation and promoting integrated, ecosystem-based management (Hussain et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The priority should be given to the regions delineated by this study under both present and future climate scenarios. Specifically, attention must be paid to protecting sandy bars and riverine plains, which serve as critical habitat and connectivity pathways for \u003cem\u003eS. acuticauda\u003c/em\u003e. These identified areas could be brought under legal protection, as also emphasized by the IUCN assessment, and targeted efforts should be undertaken to ensure adequate water flow within these riparian islands by restoring channel connectivity and managing habitats to maintain their ecological functionality. A comprehensive population survey is also warranted along the identified riverine corridors to locate potential breeding, basking, or roosting sites. While a recent population assessment was conducted in 2022, additional data, including citizen science platforms such as eBird, can enhance monitoring and species occurrence records. Further, human disturbances in these sensitive zones must be minimized, particularly the impacts of stray or feral dogs and cattle. During field surveys of this study, a sighting of 20 individuals coincided with the presence of stray dogs and livestock on inhabited sandbars, raising serious concerns about nest predation and direct threats to the species, which is a pattern already documented in other birds. Moreover, sedimentation, riverbank erosion, and a range of human-induced pressures are key factors driving land use and land cover changes within the GBM river systems (Cheema and Bastiaanssen \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Debnath et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the conversion of riparian habitats into agricultural land, or their exploitation for recreational or industrial purposes such as the construction of dams, hydropower projects, and related infrastructure poses a significant threat to ecological integrity and necessitates strict regulatory oversight. Therefore, any developmental activity in these ecologically sensitive areas must be subjected to comprehensive Environmental Impact Assessments to evaluate and mitigate ecological risks. Additionally, community-based conservation initiatives involving local residents must be prioritized, promoting alternative livelihoods and awareness programs. From a research perspective, it is equally important to generate genetic data for this species through non-invasive approach to understand its population structure and inform long-term conservation planning. Hence, future studies should adopt an integrated approach combining ecological and genetic data to develop a holistic conservation framework that ensures the long-term survival of this endangered indicator species.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study presents the first inclusive assessment of habitat suitability and connectivity for the endangered \u003cem\u003eS. acuticauda\u003c/em\u003e across GBM River Basin under both current and future climatic scenarios. The findings underscore a critical contraction of suitable habitat, with less than 6.1% of the basin currently viable for the species and a projected loss exceeding 88% under future climate projections. The alarming fragmentation and isolation of remaining habitat patches further highlight the vulnerability to ongoing and future environmental changes. The identification of five major functional corridors, most of which exhibit declining connectivity, reflects a precarious conservation outlook. These disruptions jeopardize not only the species persistence but also the broader ecological integrity of riverine systems. Given the critical conservation status and its reliance on increasingly threatened habitats, urgent and targeted conservation action is imperative. Thus, protecting key habitats and maintaining river flow regimes, alongside integrating climate-resilient strategies and local community engagement, will be essential for its long-term survival. This study provides an essential spatial framework to guide future ecological monitoring, policy formulation, and management interventions, and highlights the broader need for climate-informed conservation planning for waterbirds across the Indian subcontinent.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI.A: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing \u0026ndash; original draft. H.-W.K: Funding acquisition, Project administration, Resources, Validation, Visualization. H.S: Conceptualization, Data curation, Investigation, Supervision, Validation, Writing \u0026ndash; review and editing. S.K: Conceptualization, Formal analysis, Project administration, Supervision, Visualization, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed during this study are included in this article and its supplementary information files. Additional datasets are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Supplementary information related to this article can be found online at:\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbedin I, Mukherjee T, Singha H, Go Y, Kang HE, Kim HW, Kundu S (2025) Predicting climate-driven habitat dynamics of adjutants for implementing strategic conservation measures in South and Southeast Asian landscape. Sci Rep 15:5986\u003c/li\u003e\n\u003cli\u003eAllouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the True Skill Statistic (TSS). J Appl Ecol 43:1223\u0026ndash;1232\u003c/li\u003e\n\u003cli\u003eAnteau MJ, Wiltermuth MT, van der Burg MP, Pearse AT (2016) Prerequisites for understanding climate-change impacts on northern prairie wetlands. Wetlands 36:299\u0026ndash;307\u003c/li\u003e\n\u003cli\u003eBachman S, Moat J, Hill AW, de la Torre J, Scott B (2011) Supporting Red List threat assessments with GeoCAT: Geospatial Conservation Assessment Tool. ZooKeys 150:117\u0026ndash;126\u003c/li\u003e\n\u003cli\u003eBai J, Hou P, Jin D, Zhai J, Ma Y, Zhao J (2022) Habitat suitability assessment of black-necked crane (\u003cem\u003eGrus nigricollis\u003c/em\u003e) in the Zoige grassland wetland ecological function zone on the eastern Tibetan plateau. 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Forktail 36:1\u0026ndash;15\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ornithology-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"orni","sideBox":"Learn more about [Ornithology Research](https://link.springer.com/journal/43388)","snPcode":"43388","submissionUrl":"https://submission.nature.com/new-submission/43388/3","title":"Ornithology Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Charadriiformes, Shorebirds, Threatened species, Climate Change, Landscape Fragmentation, Corridor Connectivity","lastPublishedDoi":"10.21203/rs.3.rs-7469139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7469139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRivers and wetlands are one of the most biologically diverse ecosystems facilitating vital habitats for a wide range of species. However, the species within these ecosystems are increasingly threatened by climate change and anthropogenic pressures. The Black-bellied Tern (\u003cem\u003eSterna\u003c/em\u003e \u003cem\u003eacuticauda\u003c/em\u003e), an endangered riverine bird endemic to the Indian subcontinent, is highly reliant on wetland environments. Once widespread in South and Southeast Asia, the species is now locally extinct in much of Southeast Asia. This study evaluates the potential impacts of climate change on habitat suitability and corridor connectivity of \u003cem\u003eS.\u003c/em\u003e \u003cem\u003eacuticauda\u003c/em\u003e across the Ganges–Brahmaputra–Mahanadi (GBM) River Basin, which remains a key refuge, harboring over 90% of the global population. Current estimates suggest that only 6.10% (143,273 km²) of the GBM Basin provides suitable habitat for this species. In projected future climate scenarios, this suitable area is expected to decline drastically by 88.765% to 93.068%. Moreover, these declines are expected to lead to severe fragmentation of suitable habitats in the future. These spatial transformations diminish both structural and functional landscape connectivity. Circuit-based modeling identified five key ecological corridors, all of which are projected to experience connectivity loss. These findings emphasize the acute vulnerability of water-dependent species under climate change and highlight the urgency of targeted conservation interventions. Therefore, preserving critical riparian habitats, strengthening ecological corridor connectivity, and integrating climate resilience into basin-wide management strategies are vital for safeguarding the future of this imperiled indicator species and ensuring the ecological integrity of riverine wetland systems in Asia.\u003c/p\u003e","manuscriptTitle":"Climate-induced decline of habitat and connectivity imperils the Endangered Black-bellied Tern (Sterna acuticauda) in the Ganges-Brahmaputra-Mahanadi River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 10:58:33","doi":"10.21203/rs.3.rs-7469139/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T18:15:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T16:52:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274340145294734072690976072881044426586","date":"2025-12-05T14:38:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97976238271037389497927894756831678390","date":"2025-11-10T06:33:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253389352190934385126004424058839531749","date":"2025-11-09T15:07:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T05:43:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106143760754720067241252869611418796892","date":"2025-10-31T17:38:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-20T13:01:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-28T03:33:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T03:32:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ornithology Research","date":"2025-08-27T07:51:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ornithology-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"orni","sideBox":"Learn more about [Ornithology Research](https://link.springer.com/journal/43388)","snPcode":"43388","submissionUrl":"https://submission.nature.com/new-submission/43388/3","title":"Ornithology Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"31194f64-5eaf-4c1a-bfc1-4b47737b70f9","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T18:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 10:58:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7469139","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7469139","identity":"rs-7469139","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}