Considering human interference to prioritize spatial conservation in a transboundary river basin using Zonation

Considering human interference to prioritize spatial conservation in a transboundary river basin using Zonation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Considering human interference to prioritize spatial conservation in a transboundary river basin using Zonation Meng Yuan, Lan Li, Hangnan Yu, Jiapeng Xiong, Jiangtao Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5825654/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract Previous studies on priority conservation areas were more focused on ecological elements with less attention to human interference, this study intends to integrate human interference for spatial conservation prioritization (SCP) using Zonation software in the Wusuli River Basin (WRB; China-Russia). Ecosystem services, landscape connectivity, and human interference using the InVEST model, Conefor, and human footprint index along with the human interference index were integrated into Zonation5. The results indicated that the mean ecosystem services of the Wusuli River basin was 0.66, with higher values in Russia (0.75) than in China (0.49). Landscape connectivity was higher in eastern part (Russia), lower in western part (China), and moderate near the boundary. Condition, represented as the inverse of the human interference, averaged 0.49, with Russia achieving a higher value (0.53) than China (0.41). Priority areas were classified into five levels, with all the highest-priority areas located in Russia (31% of its area and 21% of the basin), while over 95% of the lowest-priority areas were in China (55% of its area and 20% of the basin). This study evaluates the conservation priorities of WRBs at the basin-wide scale from a multi-dimensional perspective, providing a basis for transboundary cooperative management. Earth and environmental sciences/Ecology/Ecological modelling Earth and environmental sciences/Ecology/Ecosystem services Wusuli River basin Ecosystem service Landscape connectivity Human interference Spatial conservation prioritization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Watersheds are ecologically, economically, and geopolitically significant, providing ecosystem services (ESs) such as water regulation, biodiversity maintenance, and carbon storage [ 1 ] , while supporting regional economic development, particularly in the agricultural, industrial, and energy sectors [ 2 ] . Globally, there are 286 transboundary watersheds, covering about 47% of the world's land area and inhabited with about 52% of the world's population [ 3 ] . Transboundary watersheds, as key links in the global ecosystem, have irreplaceable value for riparian countries in terms of their ecological functions that are closely linked to human well-being [ 4 ] . These functions rely on landscape connectivity (LC) for their transmission [ 5 ] . However, intensive human activities (agricultural expansion, infrastructure construction, and water overuse) have led to habitat fragmentation, disrupting landscape connectivity and ecosystem services [ 6 ] . For example, the construction of dams along transboundary rivers has fragmented freshwater ecosystems and impeded the migration of fish and freshwater mammals [ 7 ] . Transboundary basin ecosystems are more fragile [ 8 ] . Furthermore, countries along transboundary rivers often lack harmonized water resource management systems [ 9 ] . United Nations reports indicate that only 16% of countries hosting transboundary freshwater rivers, lakes and aquifers currently have effective cooperation mechanisms [ 3 ] . The conservation of watersheds are closely aligned with the United Nations sustainable development goals (SDGs) [ 10 ] .Therefore, for sustainable development of transboundary basins, it is necessary to explore scientific and efficient cooperative conservation planning to balance protection and development. The spatial conservation prioritization (SCP) approach provides efficient decision support for planning and has been widely used in conservation studies [ 11 ] . SCP is a fundamental activity and focus of system conservation planning, it can integrate multiple ecological indicators, prioritize and select areas critical to conservation or development objectives [ 12 , 13 ] . Marxan and Zonation are the most commonly used software for SCP and differ in their algorithms [ 14 ] , with Marxan using the minimum-set method to achieve conservation goals using the lowest cost, while Zonation uses the maximum-cover method to calculate the marginal loss of each image element in the planning area, aiming to maximise conservation benefits [ 15 , 16 ] . In the initial studies, species distribution was used for biodiversity conservation [ 17 ] . This single conservation goal has been effective in protecting threatened species, but has limitations in enhancing human well-being [ 18 ] . ESs are the benefits that humans derive from ecosystems, including provisioning services (such as water and food), regulating services (such as climate regulation), supporting services, and cultural services, all of which are essential for human well-being [ 19 , 20 ] . ES have the potential to contribute to all the SDGs, incorporating ESs into ecological management has become an important trend [ 20 , 21 ] . LC represents the dispersal and movement of species or ecological sources between patches, and is critical for maintaining ecological processes, biodiversity, and the flow of ESs [ 22 , 23 ] . Zeng et al [ 24 ] incorporated landscape connectivity into the selection of priority conservation areas and identified important ecological corridors, achieving sustainable conservation. Recently, multiple ecological factors such as ES and LC have been integrated as key features to prioritize protected area planning [ 24 – 26 ] . For example, Kim and Song [ 27 ] combined ESs and LC to identify priority conservation areas on Jeju Island, South Korea. Similarly, Ma et al [ 28 ] identified areas with species-appropriate habitats, high ecosystem service values, and high landscape diversity by combining SCP, Maxent, InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model, and Fragstats software in Sanjiangyuan National Park, China, and optimized the conservation planning of the area. These studies can efficiently identify prioritized areas for protection, even with limited areas. Although the integration of ESs and LC into conservation planning can effectively select priority protected areas for ecological functions, previous studies have not considered human interference (HI) [ 29 , 30 ] . About 60% of the earth's ecosystem services are degraded by human interference [ 31 ] . Anthropogenic factors, such as agricultural expansion, urbanization, and infrastructure development, have a negative impact on protected area planning [ 32 ] . From 2003 to 2019, approximately 1.14 million km 2 of habitat in 73% of the world's nature reserves were converted from natural habitats to artificial land [ 33 ] . In view of the complex relationship between human activities and the natural environment, it is necessary to optimize the human-land relationship and achieve harmony between human beings and nature. Therefore, effective conservation should not only consider ecological value, but also account for areas with minimal conflicts with human development to ensure sustainability [ 34 ] . Thus, this study incorporated HI into the planning of priority conservation areas in transboundary watersheds. The Wusuli River basin (WRB) is a transboundary basin in Northeast Asia that covers a vast wetland ecosystem and provides vital ESs [ 35 ] . As a key area along the East Asia-Australia migration route, the Wusuli River wetlands are important for the breeding, stopover, and migration of waterbirds [ 36 ] , and contain several Ramsar Convention wetlands. The Sanjiang Plain, where the WRB is located, is an important grain production base in China and is vital for food security. Despite the important ecological and economic roles of the WRB, human activities in the basin have increasingly impacted the ecosystem in recent years [ 36 ] . Agricultural expansion and deforestation have gradually destroyed regional habitats and weakened LC [ 37 , 38 ] . Infrastructure and tourism development, particularly in border areas, further exacerbate habitat fragmentation [ 39 ] . Especially after the end of the COVID-19 pandemic, China and Russia implemented a mutual visa exemption policy, leading to an increase in tourist numbers and more frequent tourism activities between the two countries. In addition, Russia's development plans in the Far East have also exposed the basin to ecological threats [ 40 ] . Regarding the increasing conflict between human activities and nature conservation, environmental assessments and conservation planning in ecologically fragile watersheds have become critical. The aims of this study were to (1) assess the spatial distribution of ESs and LC in the transboundary river basin, (2) evaluate the level of HI in China and Russia, and (3) integrate these three factors to identify ecologically friendly areas with low HI as priority conservation areas. 2. Study area and Materials 2.1. Study area The WRB is a boundary river between China and Russia (Fig. 1 ), with a total length of approximately 890 km and a watershed area of approximately 190,000 km 2 . The terrain of the basin mainly consists of plains and hills. Important nature reserves in the basin include several national nature reserves in China, and Lake Xingkai in both two countries. The WRB is a habitat for globally endangered species, such as Amur tigers and leopards, which depend on the forest and wetland ecosystems in the basin. In addition, wetlands in the watershed are important habitats for rare birds, such as Grus japonensis and Ciconia boyciana . These ecological features emphasize the importance of basins in the conservation of transboundary biodiversity. The study area map was generated by ArcGIS Pro 3.2. [Figure 1 ] 2.2. Data collection The following datasets from the MODIS Data Center were used in this study: normalized difference vegetation index (NDVI), leaf area index (LAI), potential evapotranspiration (PET) and land use and land cover (LULC). In this study, we reclassified LULC into six types (forest, grass, cropland, built-up, water, and bare land); potential evapotranspiration data were obtained from the United States Geological Survey; precipitation data were obtained from the TerraClimate dataset from Climatology Lab; soil datasets, including the percentage of sand, silt, clay particles, and organic carbon in the soil, were obtained from the Food and Agriculture Organization of the United Nations (FAO) and Harmonized World Soil Database (HWSD); elevation data were obtained from the ASTER GDEM product provided by Geospatial Data Cloud at a resolution of 30 m; the human footprint index (HFI) data were the global human footprint data provided by Mu et al [ 41 ] and the road data were obtained from OpenStreetMap (OSM). All raster data were resampled to 1 km using ArcGIS Pro with the WGS_1984_UTM_Zone_52N coordinate system. This study used data in 2020. The details of the data are presented (Table 1 ). The results maps from this study were all finally generated in ArcGIS Pro. Table 1 Description of data layers used in this study. Data Name Data set Data Source Spatial resolution Land use/cover (LULC) MODIS Land Cover Products MCD12Q1.061 MODIS ( https://modis.gsfc.nasa.gov/ ) 500m Leaf area index (LAI) MODIS Leaf Area Index/FPAR Products MCD15A2H.061 500m Normalized difference vegetation index (NDVI) MODIS Vegetation Indices MOD13Q1 250m Potential evapotranspiration (PET) MOD16A3GFv061 500m Precipitation TerraClimate dataset-ppt https://www.climatologylab.org/terraclimate.html 4km Soil Database Harmonized World Soil Database (v2.0) https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/ 1km Digital elevation data (DEM) ASTER GDEM Geospatial Data Cloud ( https://www.gscloud.cn ) 30m Human Footprint Index(HFI) Global record of annual terrestrial Human Footprint dataset China Agricultural University ( https://www.x-mol.com/groups/li_xuecao/news/48145 ) 1km Traffic and road / Open Street Map ( https://www.openstreetmap.org/ ) / [Table 1 ] 3. Methods 3.1. Quantization of ecosystem services Considering the availability of data and the characteristics of the study area, we selected five ESs that reflected the importance of the ecosystem environment: provision services (WY, water yield), regulation services (FR, flood regulation; SC, soil conservation; and CS, carbon storage), and support services (biodiversity support). Areas with higher ecosystem service values were assigned higher priority. Based on a study by Wang et al [42] , WY was calculated by subtracting the actual evapotranspiration from precipitation. The actual evapotranspiration reflects the sum of the water evaporated from the land surface and transpiration by plants. Flood regulation service refers to the capacity of ecosystems to attenuate peak water flows during flood events through natural characteristics, such as vegetation cover and soil structure. This study used the Soil Conservation Service curve number (SCS–CN) model [43, 44] , and the hydrological soil group based on the soil type data provided by the HWSD and the CN value according to Zeng et al [45] . The SCS-CN model was used to evaluate surface runoff. The FR was obtained by subtracting the surface runoff from the precipitation. The revised universal soil loss equation (RUSLE) [46, 47] was used to calculate SC. The RUSLE model quantifies soil erosion and soil conservation by integrating factors including rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C), conservation support practice factor (P). Carbon storage service was assessed using the carbon stock module of the InVEST model. This module spatially quantifies the storage capacity of carbon based on different land use types. The model parameters were obtained from a study by Li et al [48] . Biodiversity support services were assessed using the habitat quality (HQ) module in the InVEST model, which integrates the spatial distribution of threats, extent and intensity of impacts, and sensitivity of habitats to threats for habitat quality assessment. The model parameters were obtained from a study by Wu et al [49] . The formulas used to calculate the ESs are listed in Table 2. After calculating the supply of each ecosystem service, the results were normalized to values ranging from 0 to 1. The study design was illustrated in Fig. 2. [Table 2] & [Figure 2] 3.2. Landscape connectivity assessment In this study, we selected forests, grasslands, and water bodies with a patch area larger than 50 km 2 [50] as the main ecological patches, and calculated the probable connectivity (PC) of the landscape, denoted as LC, using Conefor 2.6 software. PC assesses the overall connectivity of the landscape by quantifying the probability of connectivity between ecological patches. The equation for the PC calculation is as follows: $$\:{I}_{PC}=\frac{{\sum\:}_{i=1}^{n}{\sum\:}_{j=1}^{n}{a}_{i}{a}_{j}{P}_{ij}^{*}}{{A}_{L}^{2}}$$ 1 where I PC is the PC index; a i and a j are the areas of patches i and j , respectively; \(\:{P}_{ij}^{*}\)shows the maximum dispersion probability on all possible paths between patches i and j , and \(\:{A}_{L}^{2}\) is the total landscape area. Further, LC and ESs were assigned different weights according to the entropy weight method, and subsequently, these two datasets were input to Zonation5 software as features using the “weight groups” function of Zonation5. This function allows the assignment of different weights to various datasets entered into Zonation5. The process of calculating weights by entropy weight method is as follows [51] : 1. Calculating the information entropy for each index E j : \(\:{E}_{j}=-k\sum\:_{i=1}^{n}{p}_{ij}\times\:\text{ln}\left({p}_{ij}\right)\) (2) \(\:{p}_{ij}=\frac{{x}_{ij}}{\sum\:_{i=1}^{n}{x}_{ij}}\) (3) \(\:k=\frac{1}{\text{ln}\left(n\right)}\) (4) where x ij means the i-th sample value under the j-th index, and p ij means the proportion of the i-th sample value under the j-th index (i = 1,2,3, ..., n; j = 1,2,3, ..., m). 2. Calculating the entropy weight for each index W j : $$\:{W}_{j}=\frac{{D}_{j}}{\sum\:_{j=1}^{m}{D}_{j}}$$ 5 $$\:{D}_{j}=1-{E}_{j}$$ 6 Where D j represents the coefficient of variation of the j-th index (j = 1, 2, 3, …, m). 3.3. Evaluation of human interference In Zonation, the condition denotes information on localized habitat deterioration and its effects on biodiversity features or feature groups. The condition is represented by 0–1 raster data, where a value of 0 indicates the loss of all habitat values (such as built-up land), and a value of 1 indicates that the grid cell is in an ecological desired state. In this study, the human footprint index (HFI) and human interference index (HII), which represent human interference (HI), were overlaid to characterize the condition. The values were inversely normalized to obtain the condition. The HFI dataset includes eight variables reflecting human pressures, such as the built environment, population density, nighttime lighting, farmland, pastureland, highways, railroads, and navigable waterways [41] . The HII was referenced from Zhang et al [52] and was calculated as follows: $$\:HII=\sum\:_{i,j=0}^{n}\frac{10\sqrt{2}-{D}_{i,j}}{10\sqrt{2}}\times\:\left(I{D}_{i,j}^{min}+\left(1-{LAI}_{i,j}^{std}\right)\right)\times\:\left({ID}_{i,j}^{max}-{ID}_{i,j}^{min}\right)\times\:\left\{\begin{array}{c}{FVC}_{i,j}\:\:\:\:\:\:\:\:Vegetation\:\\\:\left(1-{FVC}_{i.j}\right)\:Non-vegetation\end{array}\right.$$ 7 where D i,j is the distance between the pixel to be evaluated and the neighboring pixel (i,j);\(\:\text{I}{\text{D}}_{\text{i},\text{j}}^{\text{m}\text{i}\text{n}}\) and \(\:{\text{I}\text{D}}_{\text{i},\text{j}}^{\text{m}\text{a}\text{x}}\) are the minimum and maximum values of the interference level of pixel (i,j), respectively. The IDs for each LULC were listed in Table 3 referred to Zhang et al [52] . \(\:{\text{L}\text{A}\text{I}}_{\text{i},\text{j}}^{\text{s}\text{t}\text{d}}\) is the standardized LAI of pixel (i,j), and \(\:{\text{F}\text{V}\text{C}}_{\text{i},\text{j}}\) is the fractional vegetation coverage of pixel (i,j), which is derived from NDVI. When the land use type is vegetation, the coverage is indicated by FVC; otherwise, it is expressed as 1-FVC. Table 3 The minimum and maximum interference degrees of different land cover types. LULC Min Max Built-up 0.8 1.0 Cropland 0.4 0.6 Water 0.2 0.4 Forest, Grass 0.0 0.2 Bare land 0.0 0.0 [Table 3] 3.4. Identification of priority conservation areas This study used the Zonation 5 software, which generates a conservation priority ranking map by gradually removing areas that contribute less to overall conservation, ensuring the maximum conservation of species diversity and ecological functions [53] . Features and condition are important input layers to Zonation. Features contain important ecological factors (objectives that need to be protected), and condition reflects local habitat degradation. Condition ranges from 0–1. In this study, condition is the inverse of human interference (higher HI corresponds to lower condition value). Feature and condition are multiplied in the analysis of Zonation, and if a feature on a grid cell corresponds to a lower condition (meaning more environmental damage), then it has a lower priority value. The main steps were as follows. First, the five ESs were normalized into a “feature” layer, and each service had the same weight [54] . Landscape connectivity was set as another “feature” layer, and the weights of the LC and ESs were set to 0.79 and 0.21 using entropy weight method (Section 3.2). Subsequently, the “condition” layer, generated from the overlap of the HFI and HII, was incorporated to reflect the degree of human interference. Finally, the conservation priority of the WRB was determined based on the principle of marginal loss, where the CAZ2 rule was selected to ensure higher average coverage without significantly affecting the poorer features. 4. Results 4.1. Spatial distribution of features These features included ESs and LC. Overall, the ESs and LC values were higher in Russia than in China, with a decreasing trend from east to west (Fig. 3 ). The basin-wide average ESs are 0.66, 0.49, and 0.75, respectively. WY (Fig. 3 .a) was lower in the Chinese side, while the Russian side showed higher WY. The mean value for WY was 0.45, with values of 0.31 in China and 0.53 in Russia. At the highest WY level, forests accounted for 93.25%. FR (Fig. 3 .b) was worse in the Chinese part, while it was better in the Russian part. FR averaged 0.6 for the entire watershed, with the Chinese and Russian sides showing averages of 0.45 and 0.68, respectively. Croplands constituted 82.12% of areas with the lowest FR. CS (Fig. 3 .c) was higher and continuously distributed in Russia, while the Chinese part showed lower CS near the national boundary. The CS averaged 0.74, with the Chinese part at 0.44, the Russian part at 0.89, and forests covering 96.95% of the highest CS area. Soil conservation (SC) (Fig. 3 .d) was relatively weak in the western and southern parts of the basin, especially in the Chinese section. SC gradually transferred from a low level in China to a higher level in Russia. SC had a basin-wide mean of 0.015, with 0.001 in China and 0.018 in Russia. Habitat quality (Fig. 3 .e) was better in the eastern part of the basin and worse in the western part, especially in the places with road spreads in China, where HQ was the worst. HQ gradually transferred from high quality areas in Russia to low quality areas in China, and this change was obvious near the national boundary. The overall average HQ was 0.88, with 0.68 observed in China and 0.98 in Russia. [Figure 3 ] LC also showed significant differences between the two countries (Fig. 4 ). The eastern basin (Russia) exhibited high connectivity with continuous green areas, whereas the western basin (China) exhibited lower connectivity with more dispersed patchy areas. Areas of medium connectivity were found mainly near the national boundary, reflecting the transitional characteristics of the LC. [Figure 4 ] 4.2. Distribution characteristics of condition The condition (Fig. 5 c) was obtained by overlaying the HFI and HII, and was normalized inversely. Higher values indicated a lower HI. The mean values of the WRB in the Chinese part and 0.53 in the Russian part were 0.49, 0.41, and 0.53, respectively. Areas with a low HFI (Fig. 5 a), mainly concentrated in the east and north, whereas a higher human footprint appeared in the south and west. The average HFI of the WRB were 0.13, 0.25, and 0.066 for China and Russia, respectively. The HII was high in the central and western regions (Fig. 5 b), whereas the eastern side (Russia) had a lower HII. [Figure 5 ] 4.3. Spatial patterns of priority conservation areas Priority values ranged from 0 to 1. The average priority value on the Chinese side was approximately 0.19, whereas that on the Russian side was approximately 0.66. The WRB conservation priority was classified into five levels (Fig. 6 ). Among them, all highest-priority areas were located on the Russian side, accounting for approximately 31% of the basin area in Russia. More than 95% of the lowest-priority areas were located in China, accounting for approximately 55% of the total area in the Chinese section. The area with the highest grade accounted for approximately 21% of the watershed and forests accounted for approximately 99%. The lowest level area accounted for approximately 20% of the basin, of which 84.70% was cropland. At the border, conservation priorities transferred from low-priority areas in China to high priority areas in Russia, indicating notable differences between the countries. [Figure.6] 5. Discussion Areas of high priority, with better ecological features and lower human interference, were distributed on the eastern side of the watershed and getting worse from east to west. The ESs, LC, and HI in the WRB showed significant differences in the spatial distribution between China and Russia. Croplands account for approximately 58% of the Chinese section. Data from the National Bureau of Statistics showed that Heilongjiang (where the Chinese side of the WRB is located) Province's total grain production in 2020 was 75.41 million tons [ 55 ] , nearly 400,000 tons more than in 2019, ranking first in China for the 10th consecutive year. Expansion of agricultural land can lead to severe soil erosion [ 56 ] : a study [ 57 ] showed between 2012 and 2017, the net export of erosion from agricultural land increased by 67% (from 30 to 50 metric tons) in Heilongjiang, one of the provinces with the highest soil loss in China. In this study, the SC of the Chinese side of the WRB was about 117 t/km 2 , which was close to the result of the study [ 58 ] that the Sanjiang Plain (the WRB on the Chinese side is part of it) was about 90 t/km 2 . The area of cultivated land in Sanjiang Plain increased by 20,799km² in 1980–2018, accounting for 53.26% of the cultivated area in 1980. Agriculture consumes huge amounts of freshwater resources, especially as the conversion of drylands to irrigated farmland in the Chinese part has exacerbated water scarcity [ 59 ] , likewise a decrease in habitat quality [ 60 ] . This could explain the low value of WY and HQ in China. Expansion of cropland increases river runoff, while extensive forested areas result in less river runoff [ 61 ] . This illustrates the low value of FR on the Chinese side and the high value of FR on the Russian side (mainly covered with forests). The Sanjiang Plain has experienced extensive loss of natural wetland in the past three decades due to human interference [ 62 ] , and as an important carbon sink, wetland loss is closely associated with declining carbon stocks [ 63 ] . Thus, this area has a relatively low CS value [ 64 ] . It is reasonable to presume that low ESs in China related to anthropogenic factors such as wetland loss and farmland expansion. LC was higher on the Russian side than on the Chinese side, this result is consistent with Mu et al [ 65 ] . In China, deforestation and farmland expansion are the main factors driving the decline in connectivity [ 66 ] . In addition, HFI are closely linked to artificial surfaces, such as croplands and buildings. Thus, the HFI in China was higher than that in Russia, owing to more frequent human activities. In contrast, the Russian side retains large forested areas, covering 99% of its section, which are less exploited and have a more robust ecological situation. A lower population density in the Russian region helps maintain ecosystem connectivity and ecological functions [ 39 ] . Although Russia included all the highest priority protection areas (Fig. 6 ), these areas covered approximately 21% of the watershed. According to Protected Planet data, there is only one protected area on the Russian side of the WRB—Lake Xingkai, and nearly no protected areas exist in the highest-priority regions. More International Union for the Conservation of Nature (IUCN) reserves are located in coastal areas outside the basin ( https://oopt.info/salin/index.html ). In the Russian section, future development policies in the Russian Far East, especially resource exploitation and infrastructure development, may cause irreversible damage to the region's healthy ecosystem [ 67 , 68 ] . Therefore, the establishment of more protected areas in the WRB should be considered. Although several protected areas have been established in China, its ecological situation is relatively poor. The environmental impact of socio-economic development is unavoidable. On the one hand, the rapid development of border tourism and ice tourism in China [ 69 ] , and on the other hand, The importance of agriculture to ensure food security [ 70 ] have accelerated human activities on the Chinese side of the WRB, causing environmental pressures. Although this study provides a relatively comprehensive analysis of the spatial priority conservation areas in the WRB, it has some limitations. This study relied on large-scale data and lacked field surveys, and resampling the data to 1 km may have resulted in some loss of detail, leading to a lack of detailed delineation of certain microenvironmental elements [ 71 ] . Although we compared our findings with studies from neighboring regions, and the results were relatively reasonable. When quantifying ecosystem services (HQ, CS), we referred to the parameters set by previous studies, which need to be corrected locally in further studies through field surveys. In addition, owing to the limited availability of data, priority conservation areas were not analyzed at the administrative scale. Further research should combine field studies with administrative boundaries to provide precise scientific support for regional ecological conservation. Nevertheless, this study provides a new perspective on spatial prioritization conservation in transboundary watersheds and supports future management decisions Under the conflict between conservation and development, China and Russia should explore cooperative management measures. Several cross-border protection measures have been implemented such as water quality monitoring, biodiversity protection, and food security research [ 72 – 74 ] . In the future, China and Russia can develop multi-objective collaborative management and decision-making tools. For example, a triple-objective model of “ecological protection, economic development and geopolitical security” could be used to simulate the distribution of bilateral benefits under different scenarios of cooperation. As for habitat fragmentation, it can be combined with the MCR model and Maxent species distribution simulation to identify important ecological corridors and optimize the ecological network. Considering the possible conflicts of interest in transboundary areas, further research could quantify the supply and demand of ecosystem services and service flows, and design transboundary ecological compensation accounting mechanisms. This study quantified the ESs, LC and HI in the WRB and identified priority conservation areas. These methods and results can provide the basis for further studies, such as ecosystem service flows, multi-objective scenario modeling, and protected area establishment. 6. Conclusions To identify priority conservation areas with better ecological features and less human interference in the WRB, we integrated ESs, LC, and HI into the Zonation. This analysis revealed notable disparities between China and Russia in the basin. The eastern basin (Russia) exhibited higher feature values, with better ESs and LC, whereas the Chinese section was relatively inferior. Similarly, the condition (reversed to HI) on the Russian side were better than those on the Chinese side. The WY, FR, and CS showed distinct spatial transitions that gradually increased from west to east. In addition, the Chinese part experienced higher HI, as reflected by the higher HFI values. The basin was classified into five conservation priority levels, with all the highest-priority regions located in Russia. These areas, predominantly forested, comprise approximately 31% of Russia’s sections and 21% of the entire basin, indicating a robust ecological health with minimal human interference. In contrast, over 95% of the lowest-priority areas were found in China, covering approximately 55% of its land area and primarily consisting of cropland. The WRB showed distinct ecological gradients, with conservation priorities transitioning from low in China to high in Russia. Agricultural activities may affect the Chinese side of the WRB, resulting in lower ESs and LC, and higher HI. Although several protected areas have been established, agricultural development continues to pose a challenge to conservation. On the Russian side, despite its higher ecological quality, development measures may leave the region vulnerable to future ecological degradation, particularly with the trend of resource exploitation in the Russian Far East. Given the importance of the WRB, particularly for waterbird habitats, it is essential to strengthen China-Russia cooperation to seek a relative balance between conservation and development. Declarations Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References LI Z, JIANG W, HOU P, et al. 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Science of the Total Environment, 2022, 847:157491 CHUNDAWAT R S, SHARMA K, GOGATE N, et al. Size matters: Scale mismatch between space use patterns of tigers and protected area size in a Tropical Dry Forest [J]. Biological Conservation, 2016, 197:146-153 SUN X, MIAO L, FENG X, et al. Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang [J]. Remote Sensing, 2024, 16(10):1661 ZHANG M, ZHANG L, HE H, et al. Improvement of ecosystem quality in national key ecological function zones in China during 2000–2015 [J]. Journal of Environmental Management, 2022, 324:116406 MOILANEN A, LEHTINEN P, KOHONEN I, et al. Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance [J]. Methods in Ecology and Evolution, 2022, 13(5):1062-1072 KANG J, LI C, ZHANG B, et al. How do natural and human factors influence ecosystem services changing? A case study in two most developed regions of China [J]. Ecological Indicators, 2023, 146:109891 China Rural Statistical Yearbook 2021 [M]. 2021. DU B, YE S, GAO P, et al. Analyzing spatial patterns and driving factors of cropland change in China's National Protected Areas for sustainable management [J]. Science of the Total Environment, 2024, 912:169102 WO R, FANG D, YE S, et al. Soil erosion drivers in Chinese croplands [J]. Journal of Cleaner Production, 2024, 485:144405 WANG H, WANG W J, LIU Z, et al. Combined effects of multi-land use decisions and climate change on water-related ecosystem services in Northeast China [J]. Journal of Environmental Management, 2022, 315:115131 QI X, FENG K, SUN L, et al. Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water scarce regions [J]. One Earth, 2022, 5(10):1139-1152 JIN S, LIU X, YANG J, et al. Spatial-temporal changes of land use/cover change and habitat quality in Sanjiang plain from 1985 to 2017 [J]. Frontiers in Environmental Science, 2022, 10 LIU W, WU J, XU F, et al. Modeling the effects of land use/land cover changes on river runoff using SWAT models: A case study of the Danjiang River source area, China [J]. Environmental Research, 2024, 242:117810 SHI J, ZHANG P, LIU Y, et al. Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain [J]. Ecological Indicators, 2024, 169:112812 MAO D, HE X, WANG Z, et al. Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China [J]. Journal of Cleaner Production, 2019, 240:117961 WANG S, SHI H, XU X, et al. County zoning and optimization paths for trade-offs and synergies of ecosystem services in Northeast China [J]. Ecological Indicators, 2024, 164:112044 MU J, WU Y, QI P, et al. Spatial and temporal change of hydrological connectivity in the Wusuli River Basin [J]. Journal of Hydrology: Regional Studies, 2024, 53:101814 MU H, GUO S, ZHANG X, et al. Moving in the landscape: Omnidirectional connectivity dynamics in China from 1985 to 2020 [J]. Environmental Impact Assessment Review, 2025, 110:107721 CHU N-C, WU X-L, ZHANG P-Y. Spatiotemporal evolution characteristics of coordinated development of urbanization and ecological environment in eastern Russia—Perspectives from the 3D global trend and 2D plane analysis [J]. PloS One, 2022, 17(7):e0267272 KRUPSKAYA L T, ORLOV A M, GOLUBEV D A, et al. Environmental protection measures in mineral resource development: case study of a gold-bearing deposit in the Russian Far East [J]. Environmental Science and Pollution Research, 2022, 29(44):67135-67158 WANG R, DU B. Development Path of China-Russia Border Tourism Cultural and Creative Industries from the Perspective of Cultural Identity [J]. Chinese Historical Geography, 2024, (11):124-127. CHEN L, ZHAO H, SONG G, et al. Optimization of cultivated land pattern for achieving cultivated land system security: A case study in Heilongjiang Province, China [J]. Land Use Policy, 2021, 108:105589 ZHANG X, WANG J, GAO F, et al. Exploration of scaling effects on coarse resolution land surface phenology [J]. Remote Sensing of Environment, 2017, 190:318-330 WU Y, CHEN D, LIU Y, et al. Water Quality Changes in the Xingkai (Khanka) Lake, Northeast China, Driven by Climate Change and Human Activities: Insights from Published Data (1990–2020) [J]. Water, 2024, 16(21):3080 XING M, WANG Q, LI X, et al. Selection of keystone species based on stable carbon and nitrogen isotopes to construct a typical food web on the shore of Xingkai Lake, China [J]. Ecological Indicators, 2021, 132:108263 YU X, ZHENG S, ZHENG M, et al. Herbicide accumulations in the Xingkai lake area and the use of restored wetland for agricultural drainage treatment [J]. Ecological Engineering, 2018, 120:260-265 Esri Inc. (2023). ArcGIS Pro (Version 3.2). Esri Inc. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview Conefor 2.6 User Manual (April 2012). Universidad Politécnica de Madrid. www.conefor.org. Moilanen, A., Lehtinen, P., Kohonen, I., Kivistö, I. H. J., Jalkanen, J., Virtanen, E. A., & Kujala, H. (2023). Zonation 5 v2rc4 (Release Candidate) software upload (Zonation 5 v2.0rc4 (release candidate)). Zenodo. https://doi.org/10.5281/zenodo.10100555 Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table2.docx Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers invited by journal 31 Mar, 2025 Submission checks completed at journal 28 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5825654","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436580728,"identity":"88276acf-0d3d-4de5-b5c7-1e2b20be5138","order_by":0,"name":"Meng Yuan","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Yuan","suffix":""},{"id":436580729,"identity":"f59ceb9f-b600-44bf-920d-bbd6dcf80827","order_by":1,"name":"Lan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPmbmBgaGCoYEEEeCKC1szIxALWdI0sIA1MLYRpIWdsbGz4XzDucZHGA+eJuHwS6PGIc1S8/cdrjY4ABbsjUPQ3IxMVoapHm3HU7ccIDHTJqH4UBiAzG2/OadA9LC/41oLW3SvA1gW9iI12LNcyy9WPIwm7HlHINkwlr4+Q8fvs1TY53Hd7z54Y03FXaEtUBBMwMDM4g2IFI9ENQRr3QUjIJRMApGHgAA4yo1EMJijKIAAAAASUVORK5CYII=","orcid":"","institution":"Yanbian University","correspondingAuthor":true,"prefix":"","firstName":"Lan","middleName":"","lastName":"Li","suffix":""},{"id":436580730,"identity":"76cc6e75-e5d7-47d1-9168-d112f3010c5c","order_by":2,"name":"Hangnan Yu","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Hangnan","middleName":"","lastName":"Yu","suffix":""},{"id":436580731,"identity":"89c4ac0c-eab2-4522-88e5-17b4f968653d","order_by":3,"name":"Jiapeng Xiong","email":"","orcid":"","institution":"Yanbian University","correspondingAuthor":false,"prefix":"","firstName":"Jiapeng","middleName":"","lastName":"Xiong","suffix":""},{"id":436580732,"identity":"b572ec5a-9100-4ff5-ba3c-abeffed70261","order_by":4,"name":"Jiangtao Yu","email":"","orcid":"","institution":"Harbin Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiangtao","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-01-14 09:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5825654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5825654/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04124-y","type":"published","date":"2025-07-01T15:58:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79783909,"identity":"b933455f-4b64-4d93-8883-dd35b776f38d","added_by":"auto","created_at":"2025-04-02 15:51:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":356144,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of the study area and neighboring countries with major hydro systems and elevations. DPRK: Democratic People's Republic of Korea; ROK: Republic of Korea. Lake is Lake Xingkai shared by China and Russia. PAs-polygons and PAs-points are existing protected areas provided by Protected Planet: The World Database on Protected Areas (WDPA). The map in Figure 1 was generated using ArcGIS Pro (version 3.2).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/b3bd3c84e3c3e109d3c8eba8.png"},{"id":79784490,"identity":"12fdbd46-50d7-4d54-b330-dbd3b6685fd1","added_by":"auto","created_at":"2025-04-02 15:59:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174076,"visible":true,"origin":"","legend":"\u003cp\u003eTechnical framework for identifying priority protected areas.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/73240328ad430b8d11444485.png"},{"id":79783800,"identity":"05182bf4-b158-4b1a-bb56-2a23f447f1d8","added_by":"auto","created_at":"2025-04-02 15:43:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":307726,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of ecosystem services(a-e). Notes: The boundary is the border between China and Russia, the left side is in China and the right side is in Russia. The ecosystem service values were classified into five levels using the natural discontinuity method. Worst represents the lowest level of ecosystem services, and the best represents the highest level of ecosystem services. (g)is the land use and land cover of WRB. The maps in Figure 3 were generated using ArcGIS Pro (version 3.2).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/1574a2d6bd17b7f39ce24cba.png"},{"id":79783906,"identity":"03d11373-4649-4cd8-9493-c2b859633128","added_by":"auto","created_at":"2025-04-02 15:51:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144122,"visible":true,"origin":"","legend":"\u003cp\u003eLandscape connectivity of ecological patches in the Wusuli River Basin. The map in Figure 4 was generated using ArcGIS Pro (version 3.2) in conjunction with the Conefor model (version 2.6).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/a4d79020836302e8eb4db895.png"},{"id":79784489,"identity":"0077d87f-d14e-40ed-89f6-e09fa758f9ee","added_by":"auto","created_at":"2025-04-02 15:59:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":177273,"visible":true,"origin":"","legend":"\u003cp\u003e(a) HFI, Human Footprint Index; (b) HII, Human Interference Index; (c) Condition, obtained by overlaying HFI and HII. Worst indicates the highest intensity of human interference and the worst ecological condition, while best indicates low intensity of human activity and the best ecological condition. The maps in Figure 5 were generated using ArcGIS Pro (version 3.2).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/dadadaad91ebd8e42eb98a53.png"},{"id":79783810,"identity":"d1f54e84-d26e-4780-b29b-980463804143","added_by":"auto","created_at":"2025-04-02 15:43:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4944575,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of priority conservation areas, with highest indicating the highest conservation priority and lowest indicating the lowest level. The map in Figure 6 was generated using ArcGIS Pro (version 3.2) in conjunction with the Zonation5 model (version 2).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/52a46599395a7ee8c5f95cea.png"},{"id":86180123,"identity":"084e0d85-cade-49bb-9699-1e7af3531762","added_by":"auto","created_at":"2025-07-07 16:21:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6839546,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/643f547a-84b7-41d7-9ed5-8cb0dafb2cdc.pdf"},{"id":79783799,"identity":"0bbb2316-bd1f-45dd-be1b-cd39d6ea2d19","added_by":"auto","created_at":"2025-04-02 15:43:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33383,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5825654/v1/69ee98b308a38634758a0721.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Considering human interference to prioritize spatial conservation in a transboundary river basin using Zonation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWatersheds are ecologically, economically, and geopolitically significant, providing ecosystem services (ESs) such as water regulation, biodiversity maintenance, and carbon storage \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, while supporting regional economic development, particularly in the agricultural, industrial, and energy sectors \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Globally, there are 286 transboundary watersheds, covering about 47% of the world's land area and inhabited with about 52% of the world's population \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Transboundary watersheds, as key links in the global ecosystem, have irreplaceable value for riparian countries in terms of their ecological functions that are closely linked to human well-being \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. These functions rely on landscape connectivity (LC) for their transmission\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, intensive human activities (agricultural expansion, infrastructure construction, and water overuse) have led to habitat fragmentation, disrupting landscape connectivity and ecosystem services \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. For example, the construction of dams along transboundary rivers has fragmented freshwater ecosystems and impeded the migration of fish and freshwater mammals \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Transboundary basin ecosystems are more fragile \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Furthermore, countries along transboundary rivers often lack harmonized water resource management systems \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. United Nations reports indicate that only 16% of countries hosting transboundary freshwater rivers, lakes and aquifers currently have effective cooperation mechanisms\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The conservation of watersheds are closely aligned with the United Nations sustainable development goals (SDGs) \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e .Therefore, for sustainable development of transboundary basins, it is necessary to explore scientific and efficient cooperative conservation planning to balance protection and development.\u003c/p\u003e \u003cp\u003eThe spatial conservation prioritization (SCP) approach provides efficient decision support for planning and has been widely used in conservation studies \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. SCP is a fundamental activity and focus of system conservation planning, it can integrate multiple ecological indicators, prioritize and select areas critical to conservation or development objectives \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Marxan and Zonation are the most commonly used software for SCP and differ in their algorithms \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, with Marxan using the minimum-set method to achieve conservation goals using the lowest cost, while Zonation uses the maximum-cover method to calculate the marginal loss of each image element in the planning area, aiming to maximise conservation benefits \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In the initial studies, species distribution was used for biodiversity conservation \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. This single conservation goal has been effective in protecting threatened species, but has limitations in enhancing human well-being \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. ESs are the benefits that humans derive from ecosystems, including provisioning services (such as water and food), regulating services (such as climate regulation), supporting services, and cultural services, all of which are essential for human well-being \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. ES have the potential to contribute to all the SDGs, incorporating ESs into ecological management has become an important trend \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. LC represents the dispersal and movement of species or ecological sources between patches, and is critical for maintaining ecological processes, biodiversity, and the flow of ESs \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Zeng et al\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e incorporated landscape connectivity into the selection of priority conservation areas and identified important ecological corridors, achieving sustainable conservation. Recently, multiple ecological factors such as ES and LC have been integrated as key features to prioritize protected area planning \u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. For example, Kim and Song \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e combined ESs and LC to identify priority conservation areas on Jeju Island, South Korea. Similarly, Ma et al \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e identified areas with species-appropriate habitats, high ecosystem service values, and high landscape diversity by combining SCP, Maxent, InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model, and Fragstats software in Sanjiangyuan National Park, China, and optimized the conservation planning of the area. These studies can efficiently identify prioritized areas for protection, even with limited areas.\u003c/p\u003e \u003cp\u003eAlthough the integration of ESs and LC into conservation planning can effectively select priority protected areas for ecological functions, previous studies have not considered human interference (HI) \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. About 60% of the earth's ecosystem services are degraded by human interference \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Anthropogenic factors, such as agricultural expansion, urbanization, and infrastructure development, have a negative impact on protected area planning \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. From 2003 to 2019, approximately 1.14\u0026nbsp;million km\u003csup\u003e2\u003c/sup\u003e of habitat in 73% of the world's nature reserves were converted from natural habitats to artificial land \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In view of the complex relationship between human activities and the natural environment, it is necessary to optimize the human-land relationship and achieve harmony between human beings and nature. Therefore, effective conservation should not only consider ecological value, but also account for areas with minimal conflicts with human development to ensure sustainability \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Thus, this study incorporated HI into the planning of priority conservation areas in transboundary watersheds.\u003c/p\u003e \u003cp\u003eThe Wusuli River basin (WRB) is a transboundary basin in Northeast Asia that covers a vast wetland ecosystem and provides vital ESs \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. As a key area along the East Asia-Australia migration route, the Wusuli River wetlands are important for the breeding, stopover, and migration of waterbirds \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, and contain several Ramsar Convention wetlands. The Sanjiang Plain, where the WRB is located, is an important grain production base in China and is vital for food security. Despite the important ecological and economic roles of the WRB, human activities in the basin have increasingly impacted the ecosystem in recent years \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Agricultural expansion and deforestation have gradually destroyed regional habitats and weakened LC \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Infrastructure and tourism development, particularly in border areas, further exacerbate habitat fragmentation \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Especially after the end of the COVID-19 pandemic, China and Russia implemented a mutual visa exemption policy, leading to an increase in tourist numbers and more frequent tourism activities between the two countries. In addition, Russia's development plans in the Far East have also exposed the basin to ecological threats \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eRegarding the increasing conflict between human activities and nature conservation, environmental assessments and conservation planning in ecologically fragile watersheds have become critical. The aims of this study were to (1) assess the spatial distribution of ESs and LC in the transboundary river basin, (2) evaluate the level of HI in China and Russia, and (3) integrate these three factors to identify ecologically friendly areas with low HI as priority conservation areas.\u003c/p\u003e"},{"header":"2. Study area and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eThe WRB is a boundary river between China and Russia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a total length of approximately 890 km and a watershed area of approximately 190,000 km\u003csup\u003e2\u003c/sup\u003e. The terrain of the basin mainly consists of plains and hills. Important nature reserves in the basin include several national nature reserves in China, and Lake Xingkai in both two countries. The WRB is a habitat for globally endangered species, such as Amur tigers and leopards, which depend on the forest and wetland ecosystems in the basin. In addition, wetlands in the watershed are important habitats for rare birds, such as \u003cem\u003eGrus japonensis\u003c/em\u003e and \u003cem\u003eCiconia boyciana\u003c/em\u003e. These ecological features emphasize the importance of basins in the conservation of transboundary biodiversity. The study area map was generated by ArcGIS Pro 3.2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection\u003c/h2\u003e \u003cp\u003eThe following datasets from the MODIS Data Center were used in this study: normalized difference vegetation index (NDVI), leaf area index (LAI), potential evapotranspiration (PET) and land use and land cover (LULC). In this study, we reclassified LULC into six types (forest, grass, cropland, built-up, water, and bare land); potential evapotranspiration data were obtained from the United States Geological Survey; precipitation data were obtained from the TerraClimate dataset from Climatology Lab; soil datasets, including the percentage of sand, silt, clay particles, and organic carbon in the soil, were obtained from the Food and Agriculture Organization of the United Nations (FAO) and Harmonized World Soil Database (HWSD); elevation data were obtained from the ASTER GDEM product provided by Geospatial Data Cloud at a resolution of 30 m; the human footprint index (HFI) data were the global human footprint data provided by Mu et al \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e and the road data were obtained from OpenStreetMap (OSM). All raster data were resampled to 1 km using ArcGIS Pro with the WGS_1984_UTM_Zone_52N coordinate system. This study used data in 2020. The details of the data are presented (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results maps from this study were all finally generated in ArcGIS Pro.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of data layers used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use/cover (LULC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS Land Cover Products\u003c/p\u003e \u003cp\u003eMCD12Q1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMODIS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsfc.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://modis.gsfc.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf area index (LAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS Leaf Area Index/FPAR Products\u003c/p\u003e \u003cp\u003eMCD15A2H.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized difference vegetation index (NDVI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS Vegetation Indices\u003c/p\u003e \u003cp\u003eMOD13Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotential evapotranspiration (PET)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOD16A3GFv061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerraClimate dataset-ppt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.climatologylab.org/terraclimate.html\u003c/span\u003e\u003cspan address=\"https://www.climatologylab.org/terraclimate.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarmonized World\u003c/p\u003e \u003cp\u003eSoil Database (v2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/\u003c/span\u003e\u003cspan address=\"https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital elevation data (DEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASTER GDEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeospatial Data Cloud\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gscloud.cn\u003c/span\u003e\u003cspan address=\"https://www.gscloud.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman Footprint Index(HFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal record of annual terrestrial Human Footprint dataset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina Agricultural University\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.x-mol.com/groups/li_xuecao/news/48145\u003c/span\u003e\u003cspan address=\"https://www.x-mol.com/groups/li_xuecao/news/48145\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraffic and road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen Street Map\u003c/p\u003e \u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.openstreetmap.org/\u003c/span\u003e\u003cspan address=\"https://www.openstreetmap.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.1. Quantization of ecosystem services\u003c/h2\u003e\n \u003cp\u003eConsidering the availability of data and the characteristics of the study area, we selected five ESs that reflected the importance of the ecosystem environment: provision services (WY, water yield), regulation services (FR, flood regulation; SC, soil conservation; and CS, carbon storage), and support services (biodiversity support). Areas with higher ecosystem service values were assigned higher priority.\u003c/p\u003e\n \u003cp\u003eBased on a study by Wang et al \u003csup\u003e[42]\u003c/sup\u003e, WY was calculated by subtracting the actual evapotranspiration from precipitation. The actual evapotranspiration reflects the sum of the water evaporated from the land surface and transpiration by plants.\u003c/p\u003e\n \u003cp\u003eFlood regulation service refers to the capacity of ecosystems to attenuate peak water flows during flood events through natural characteristics, such as vegetation cover and soil structure. This study used the Soil Conservation Service curve number (SCS–CN) model \u003csup\u003e[43, 44]\u003c/sup\u003e, and the hydrological soil group based on the soil type data provided by the HWSD and the CN value according to Zeng et al\u003csup\u003e[45]\u003c/sup\u003e. The SCS-CN model was used to evaluate surface runoff. The FR was obtained by subtracting the surface runoff from the precipitation.\u003c/p\u003e\n \u003cp\u003eThe revised universal soil loss equation (RUSLE) \u003csup\u003e[46, 47]\u003c/sup\u003e was used to calculate SC. The RUSLE model quantifies soil erosion and soil conservation by integrating factors including rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C), conservation support practice factor (P).\u003c/p\u003e\n \u003cp\u003eCarbon storage service was assessed using the carbon stock module of the InVEST model. This module spatially quantifies the storage capacity of carbon based on different land use types. The model parameters were obtained from a study by Li et al\u003csup\u003e[48]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eBiodiversity support services were assessed using the habitat quality (HQ) module in the InVEST model, which integrates the spatial distribution of threats, extent and intensity of impacts, and sensitivity of habitats to threats for habitat quality assessment. The model parameters were obtained from a study by Wu et al\u003csup\u003e[49]\u003c/sup\u003e. The formulas used to calculate the ESs are listed in Table 2. After calculating the supply of each ecosystem service, the results were normalized to values ranging from 0 to 1. The study design was illustrated in Fig. 2.\u003c/p\u003e\n \u003cdiv\u003e[Table 2] \u0026amp; [Figure 2]\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.2. Landscape connectivity assessment\u003c/h2\u003e\n \u003cp\u003eIn this study, we selected forests, grasslands, and water bodies with a patch area larger than 50 km\u003csup\u003e2 [50]\u003c/sup\u003e as the main ecological patches, and calculated the probable connectivity (PC) of the landscape, denoted as LC, using Conefor 2.6 software. PC assesses the overall connectivity of the landscape by quantifying the probability of connectivity between ecological patches. The equation for the PC calculation is as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{I}_{PC}=\\frac{{\\sum\\:}_{i=1}^{n}{\\sum\\:}_{j=1}^{n}{a}_{i}{a}_{j}{P}_{ij}^{*}}{{A}_{L}^{2}}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ePC\u003c/em\u003e\u003c/sub\u003e is the PC index; \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e are the areas of patches \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, respectively; \\(\\:{P}_{ij}^{*}\\)shows the maximum dispersion probability on all possible paths between patches \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, and \\(\\:{A}_{L}^{2}\\) is the total landscape area.\u003c/p\u003e\n \u003cp\u003eFurther, LC and ESs were assigned different weights according to the entropy weight method, and subsequently, these two datasets were input to Zonation5 software as features using the “weight groups” function of Zonation5. This function allows the assignment of different weights to various datasets entered into Zonation5.\u003c/p\u003e\n \u003cp\u003eThe process of calculating weights by entropy weight method is as follows \u003csup\u003e[51]\u003c/sup\u003e :\u003c/p\u003e\n \u003cp\u003e1. Calculating the information entropy for each index E\u003csub\u003ej\u003c/sub\u003e:\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\\(\\:{E}_{j}=-k\\sum\\:_{i=1}^{n}{p}_{ij}\\times\\:\\text{ln}\\left({p}_{ij}\\right)\\)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\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\u003e\\(\\:{p}_{ij}=\\frac{{x}_{ij}}{\\sum\\:_{i=1}^{n}{x}_{ij}}\\)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\\(\\:k=\\frac{1}{\\text{ln}\\left(n\\right)}\\)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere x\u003csub\u003eij\u003c/sub\u003e means the i-th sample value under the j-th index, and p\u003csub\u003eij\u003c/sub\u003e means the proportion of the i-th sample value under the j-th index (i = 1,2,3, ..., n; j = 1,2,3, ..., m).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;2. Calculating the entropy weight for each index W\u003csub\u003ej\u003c/sub\u003e:\u003c/p\u003e\n \u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{W}_{j}=\\frac{{D}_{j}}{\\sum\\:_{j=1}^{m}{D}_{j}}$$\u003c/div\u003e\n \u003cdiv\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:{D}_{j}=1-{E}_{j}$$\u003c/div\u003e\n \u003cdiv\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere D\u003csub\u003ej\u003c/sub\u003e represents the coefficient of variation of the j-th index (j = 1, 2, 3, …, m).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.3. Evaluation of human interference\u003c/h2\u003e\n \u003cp\u003eIn Zonation, the condition denotes information on localized habitat deterioration and its effects on biodiversity features or feature groups. The condition is represented by 0–1 raster data, where a value of 0 indicates the loss of all habitat values (such as built-up land), and a value of 1 indicates that the grid cell is in an ecological desired state. In this study, the human footprint index (HFI) and human interference index (HII), which represent human interference (HI), were overlaid to characterize the condition. The values were inversely normalized to obtain the condition. The HFI dataset includes eight variables reflecting human pressures, such as the built environment, population density, nighttime lighting, farmland, pastureland, highways, railroads, and navigable waterways \u003csup\u003e[41]\u003c/sup\u003e. The HII was referenced from Zhang et al \u003csup\u003e[52]\u003c/sup\u003e and was calculated as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ4\"\u003e\n \u003cdiv id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:HII=\\sum\\:_{i,j=0}^{n}\\frac{10\\sqrt{2}-{D}_{i,j}}{10\\sqrt{2}}\\times\\:\\left(I{D}_{i,j}^{min}+\\left(1-{LAI}_{i,j}^{std}\\right)\\right)\\times\\:\\left({ID}_{i,j}^{max}-{ID}_{i,j}^{min}\\right)\\times\\:\\left\\{\\begin{array}{c}{FVC}_{i,j}\\:\\:\\:\\:\\:\\:\\:\\:Vegetation\\:\\\\\\:\\left(1-{FVC}_{i.j}\\right)\\:Non-vegetation\\end{array}\\right.$$\u003c/div\u003e\n \u003cdiv\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere D\u003csub\u003ei,j\u003c/sub\u003e is the distance between the pixel to be evaluated and the neighboring pixel (i,j);\\(\\:\\text{I}{\\text{D}}_{\\text{i},\\text{j}}^{\\text{m}\\text{i}\\text{n}}\\) and \\(\\:{\\text{I}\\text{D}}_{\\text{i},\\text{j}}^{\\text{m}\\text{a}\\text{x}}\\) are the minimum and maximum values of the interference level of pixel (i,j), respectively. The IDs for each LULC were listed in Table 3 referred to Zhang et al\u003csup\u003e[52]\u003c/sup\u003e. \\(\\:{\\text{L}\\text{A}\\text{I}}_{\\text{i},\\text{j}}^{\\text{s}\\text{t}\\text{d}}\\) is the standardized LAI of pixel (i,j), and \\(\\:{\\text{F}\\text{V}\\text{C}}_{\\text{i},\\text{j}}\\) is the fractional vegetation coverage of pixel (i,j), which is derived from NDVI. When the land use type is vegetation, the coverage is indicated by FVC; otherwise, it is expressed as 1-FVC.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe minimum and maximum interference degrees of different land cover types.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\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\u003eBuilt-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest, Grass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e[Table 3]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.4. Identification of priority conservation areas\u003c/h2\u003e\n \u003cp\u003eThis study used the Zonation 5 software, which generates a conservation priority ranking map by gradually removing areas that contribute less to overall conservation, ensuring the maximum conservation of species diversity and ecological functions\u003csup\u003e[53]\u003c/sup\u003e. Features and condition are important input layers to Zonation. Features contain important ecological factors (objectives that need to be protected), and condition reflects local habitat degradation. Condition ranges from 0–1. In this study, condition is the inverse of human interference (higher HI corresponds to lower condition value). Feature and condition are multiplied in the analysis of Zonation, and if a feature on a grid cell corresponds to a lower condition (meaning more environmental damage), then it has a lower priority value. The main steps were as follows. First, the five ESs were normalized into a “feature” layer, and each service had the same weight\u003csup\u003e[54]\u003c/sup\u003e. Landscape connectivity was set as another “feature” layer, and the weights of the LC and ESs were set to 0.79 and 0.21 using entropy weight method (Section 3.2). Subsequently, the “condition” layer, generated from the overlap of the HFI and HII, was incorporated to reflect the degree of human interference. Finally, the conservation priority of the WRB was determined based on the principle of marginal loss, where the CAZ2 rule was selected to ensure higher average coverage without significantly affecting the poorer features.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Spatial distribution of features\u003c/h2\u003e \u003cp\u003eThese features included ESs and LC. Overall, the ESs and LC values were higher in Russia than in China, with a decreasing trend from east to west (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The basin-wide average ESs are 0.66, 0.49, and 0.75, respectively. WY (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.a) was lower in the Chinese side, while the Russian side showed higher WY. The mean value for WY was 0.45, with values of 0.31 in China and 0.53 in Russia. At the highest WY level, forests accounted for 93.25%. FR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.b) was worse in the Chinese part, while it was better in the Russian part. FR averaged 0.6 for the entire watershed, with the Chinese and Russian sides showing averages of 0.45 and 0.68, respectively. Croplands constituted 82.12% of areas with the lowest FR. CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.c) was higher and continuously distributed in Russia, while the Chinese part showed lower CS near the national boundary. The CS averaged 0.74, with the Chinese part at 0.44, the Russian part at 0.89, and forests covering 96.95% of the highest CS area. Soil conservation (SC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.d) was relatively weak in the western and southern parts of the basin, especially in the Chinese section. SC gradually transferred from a low level in China to a higher level in Russia. SC had a basin-wide mean of 0.015, with 0.001 in China and 0.018 in Russia. Habitat quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.e) was better in the eastern part of the basin and worse in the western part, especially in the places with road spreads in China, where HQ was the worst. HQ gradually transferred from high quality areas in Russia to low quality areas in China, and this change was obvious near the national boundary. The overall average HQ was 0.88, with 0.68 observed in China and 0.98 in Russia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eLC also showed significant differences between the two countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The eastern basin (Russia) exhibited high connectivity with continuous green areas, whereas the western basin (China) exhibited lower connectivity with more dispersed patchy areas. Areas of medium connectivity were found mainly near the national boundary, reflecting the transitional characteristics of the LC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Distribution characteristics of condition\u003c/h2\u003e \u003cp\u003eThe condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) was obtained by overlaying the HFI and HII, and was normalized inversely. Higher values indicated a lower HI. The mean values of the WRB in the Chinese part and 0.53 in the Russian part were 0.49, 0.41, and 0.53, respectively. Areas with a low HFI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), mainly concentrated in the east and north, whereas a higher human footprint appeared in the south and west. The average HFI of the WRB were 0.13, 0.25, and 0.066 for China and Russia, respectively. The HII was high in the central and western regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), whereas the eastern side (Russia) had a lower HII.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Spatial patterns of priority conservation areas\u003c/h2\u003e \u003cp\u003ePriority values ranged from 0 to 1. The average priority value on the Chinese side was approximately 0.19, whereas that on the Russian side was approximately 0.66. The WRB conservation priority was classified into five levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among them, all highest-priority areas were located on the Russian side, accounting for approximately 31% of the basin area in Russia. More than 95% of the lowest-priority areas were located in China, accounting for approximately 55% of the total area in the Chinese section. The area with the highest grade accounted for approximately 21% of the watershed and forests accounted for approximately 99%. The lowest level area accounted for approximately 20% of the basin, of which 84.70% was cropland. At the border, conservation priorities transferred from low-priority areas in China to high priority areas in Russia, indicating notable differences between the countries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure.6]\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eAreas of high priority, with better ecological features and lower human interference, were distributed on the eastern side of the watershed and getting worse from east to west. The ESs, LC, and HI in the WRB showed significant differences in the spatial distribution between China and Russia. Croplands account for approximately 58% of the Chinese section. Data from the National Bureau of Statistics showed that Heilongjiang (where the Chinese side of the WRB is located) Province's total grain production in 2020 was 75.41\u0026nbsp;million tons\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e, nearly 400,000 tons more than in 2019, ranking first in China for the 10th consecutive year. Expansion of agricultural land can lead to severe soil erosion \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e: a study \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e showed between 2012 and 2017, the net export of erosion from agricultural land increased by 67% (from 30 to 50 metric tons) in Heilongjiang, one of the provinces with the highest soil loss in China. In this study, the SC of the Chinese side of the WRB was about 117 t/km\u003csup\u003e2\u003c/sup\u003e, which was close to the result of the study \u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e that the Sanjiang Plain (the WRB on the Chinese side is part of it) was about 90 t/km\u003csup\u003e2\u003c/sup\u003e. The area of cultivated land in Sanjiang Plain increased by 20,799km\u0026sup2; in 1980\u0026ndash;2018, accounting for 53.26% of the cultivated area in 1980. Agriculture consumes huge amounts of freshwater resources, especially as the conversion of drylands to irrigated farmland in the Chinese part has exacerbated water scarcity \u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e, likewise a decrease in habitat quality \u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. This could explain the low value of WY and HQ in China. Expansion of cropland increases river runoff, while extensive forested areas result in less river runoff \u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. This illustrates the low value of FR on the Chinese side and the high value of FR on the Russian side (mainly covered with forests). The Sanjiang Plain has experienced extensive loss of natural wetland in the past three decades due to human interference \u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e, and as an important carbon sink, wetland loss is closely associated with declining carbon stocks \u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. Thus, this area has a relatively low CS value \u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. It is reasonable to presume that low ESs in China related to anthropogenic factors such as wetland loss and farmland expansion. LC was higher on the Russian side than on the Chinese side, this result is consistent with Mu et al \u003csup\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. In China, deforestation and farmland expansion are the main factors driving the decline in connectivity \u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e. In addition, HFI are closely linked to artificial surfaces, such as croplands and buildings. Thus, the HFI in China was higher than that in Russia, owing to more frequent human activities. In contrast, the Russian side retains large forested areas, covering 99% of its section, which are less exploited and have a more robust ecological situation. A lower population density in the Russian region helps maintain ecosystem connectivity and ecological functions \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough Russia included all the highest priority protection areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), these areas covered approximately 21% of the watershed. According to Protected Planet data, there is only one protected area on the Russian side of the WRB\u0026mdash;Lake Xingkai, and nearly no protected areas exist in the highest-priority regions. More International Union for the Conservation of Nature (IUCN) reserves are located in coastal areas outside the basin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://oopt.info/salin/index.html\u003c/span\u003e\u003cspan address=\"https://oopt.info/salin/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In the Russian section, future development policies in the Russian Far East, especially resource exploitation and infrastructure development, may cause irreversible damage to the region's healthy ecosystem \u003csup\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e. Therefore, the establishment of more protected areas in the WRB should be considered. Although several protected areas have been established in China, its ecological situation is relatively poor. The environmental impact of socio-economic development is unavoidable. On the one hand, the rapid development of border tourism and ice tourism in China \u003csup\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e, and on the other hand, The importance of agriculture to ensure food security \u003csup\u003e[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e have accelerated human activities on the Chinese side of the WRB, causing environmental pressures.\u003c/p\u003e \u003cp\u003eAlthough this study provides a relatively comprehensive analysis of the spatial priority conservation areas in the WRB, it has some limitations. This study relied on large-scale data and lacked field surveys, and resampling the data to 1 km may have resulted in some loss of detail, leading to a lack of detailed delineation of certain microenvironmental elements\u003csup\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e. Although we compared our findings with studies from neighboring regions, and the results were relatively reasonable. When quantifying ecosystem services (HQ, CS), we referred to the parameters set by previous studies, which need to be corrected locally in further studies through field surveys. In addition, owing to the limited availability of data, priority conservation areas were not analyzed at the administrative scale. Further research should combine field studies with administrative boundaries to provide precise scientific support for regional ecological conservation. Nevertheless, this study provides a new perspective on spatial prioritization conservation in transboundary watersheds and supports future management decisions\u003c/p\u003e \u003cp\u003eUnder the conflict between conservation and development, China and Russia should explore cooperative management measures. Several cross-border protection measures have been implemented such as water quality monitoring, biodiversity protection, and food security research \u003csup\u003e[\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e. In the future, China and Russia can develop multi-objective collaborative management and decision-making tools. For example, a triple-objective model of \u0026ldquo;ecological protection, economic development and geopolitical security\u0026rdquo; could be used to simulate the distribution of bilateral benefits under different scenarios of cooperation. As for habitat fragmentation, it can be combined with the MCR model and Maxent species distribution simulation to identify important ecological corridors and optimize the ecological network. Considering the possible conflicts of interest in transboundary areas, further research could quantify the supply and demand of ecosystem services and service flows, and design transboundary ecological compensation accounting mechanisms. This study quantified the ESs, LC and HI in the WRB and identified priority conservation areas. These methods and results can provide the basis for further studies, such as ecosystem service flows, multi-objective scenario modeling, and protected area establishment.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eTo identify priority conservation areas with better ecological features and less human interference in the WRB, we integrated ESs, LC, and HI into the Zonation. This analysis revealed notable disparities between China and Russia in the basin. The eastern basin (Russia) exhibited higher feature values, with better ESs and LC, whereas the Chinese section was relatively inferior. Similarly, the condition (reversed to HI) on the Russian side were better than those on the Chinese side. The WY, FR, and CS showed distinct spatial transitions that gradually increased from west to east. In addition, the Chinese part experienced higher HI, as reflected by the higher HFI values. The basin was classified into five conservation priority levels, with all the highest-priority regions located in Russia. These areas, predominantly forested, comprise approximately 31% of Russia\u0026rsquo;s sections and 21% of the entire basin, indicating a robust ecological health with minimal human interference. In contrast, over 95% of the lowest-priority areas were found in China, covering approximately 55% of its land area and primarily consisting of cropland. The WRB showed distinct ecological gradients, with conservation priorities transitioning from low in China to high in Russia. Agricultural activities may affect the Chinese side of the WRB, resulting in lower ESs and LC, and higher HI. Although several protected areas have been established, agricultural development continues to pose a challenge to conservation. On the Russian side, despite its higher ecological quality, development measures may leave the region vulnerable to future ecological degradation, particularly with the trend of resource exploitation in the Russian Far East. Given the importance of the WRB, particularly for waterbird habitats, it is essential to strengthen China-Russia cooperation to seek a relative balance between conservation and development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLI Z, JIANG W, HOU P, et al. 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(2023). ArcGIS Pro (Version 3.2). Esri Inc. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview\u003c/li\u003e\n\u003cli\u003eConefor 2.6 User Manual (April 2012). Universidad Polit\u0026eacute;cnica de Madrid. www.conefor.org.\u003c/li\u003e\n\u003cli\u003eMoilanen, A., Lehtinen, P., Kohonen, I., Kivist\u0026ouml;, I. H. J., Jalkanen, J., Virtanen, E. A., \u0026amp; Kujala, H. (2023). Zonation 5 v2rc4 (Release Candidate) software upload (Zonation 5 v2.0rc4 (release candidate)). Zenodo. https://doi.org/10.5281/zenodo.10100555\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Wusuli River basin, Ecosystem service, Landscape connectivity, Human interference, Spatial conservation prioritization","lastPublishedDoi":"10.21203/rs.3.rs-5825654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5825654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies on priority conservation areas were more focused on ecological elements with less attention to human interference, this study intends to integrate human interference for spatial conservation prioritization (SCP) using Zonation software in the Wusuli River Basin (WRB; China-Russia). Ecosystem services, landscape connectivity, and human interference using the InVEST model, Conefor, and human footprint index along with the human interference index were integrated into Zonation5. The results indicated that the mean ecosystem services of the Wusuli River basin was 0.66, with higher values in Russia (0.75) than in China (0.49). Landscape connectivity was higher in eastern part (Russia), lower in western part (China), and moderate near the boundary. Condition, represented as the inverse of the human interference, averaged 0.49, with Russia achieving a higher value (0.53) than China (0.41). Priority areas were classified into five levels, with all the highest-priority areas located in Russia (31% of its area and 21% of the basin), while over 95% of the lowest-priority areas were in China (55% of its area and 20% of the basin). This study evaluates the conservation priorities of WRBs at the basin-wide scale from a multi-dimensional perspective, providing a basis for transboundary cooperative management.\u003c/p\u003e","manuscriptTitle":"Considering human interference to prioritize spatial conservation in a transboundary river basin using Zonation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 15:43:27","doi":"10.21203/rs.3.rs-5825654/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T08:59:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T01:08:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99807093057170364911623643077925387931","date":"2025-04-01T01:03:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T01:00:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-28T06:54:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T08:30:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c6a9c96-62c6-42f4-8e7f-644f97c4d5f0","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46485301,"name":"Earth and environmental sciences/Ecology/Ecological modelling"},{"id":46485302,"name":"Earth and environmental sciences/Ecology/Ecosystem services"}],"tags":[],"updatedAt":"2025-07-07T16:13:53+00:00","versionOfRecord":{"articleIdentity":"rs-5825654","link":"https://doi.org/10.1038/s41598-025-04124-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:58:52","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-02 15:43:27","video":"","vorDoi":"10.1038/s41598-025-04124-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04124-y","workflowStages":[]},"version":"v1","identity":"rs-5825654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5825654","identity":"rs-5825654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}