Maxent modeling for predicting suitable habitats for wild ungulates: a case study of typical canyons in the Sanjiangyuan Nature Reserve of China

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This study aims to understand the habitat preferences of ungulates and predict their spatial distributions within the park, contributing to conservation efforts amid climatic shifts. Due to the scarcity of detailed, scientific data on ungulate distributions, populations, and habitats, this study seeks to fill these critical knowledge gaps. Materials and Methods We deployed 55 infrared cameras along the main stream of the Yellow River and in typical canyons on both sides of the China Railway Military Protection Zone within the Sanjiangyuan Nature Reserve. The cameras recorded 2,948 occurrences of ungulates from April to September 2023. We utilized the MaxEnt model to analyze habitat distribution of horse musk deer and blue sheep, incorporating nine environmental variables. Results The model's predictive accuracy, as indicated by the area under the curve (AUC) for Alpine musk deer (0.980) and Blue sheep (0.976), demonstrates its effectiveness. Our analysis identifies climate as the primary influence on habitat distribution, with key factors being annual mean temperature, daily temperature range, altitude, and annual precipitation-contributing to 39.3%, 25.4%, 18.7%, 10.1% for Alpine musk deer, and 40.5%, 23.5%, 14.1%, 11.8% for Blue sheep, respectively. Habitat suitability analysis reveals that 9.61% and 10.84% of the reserve's terrain are viable for Alpine musk deer and Blue sheep, respectively, based on established model thresholds. Conclusions The findings of this study are crucial for the protection of existing wildlife and the identification of potential conservation zones for ungulates. By delineating areas of high habitat suitability, this research supports targeted conservation planning and management efforts within the Sanjiangyuan Reserve, thereby aiding in the sustainable preservation of these endangered species. Biological sciences/Ecology Biological sciences/Zoology Earth and environmental sciences/Climate sciences Earth and environmental sciences/Ecology Sanjiangyuan National Park Ungulates Suitable habitats MaxEnt model Connectivity analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Global climate change and human activities are exacerbating habitat decline and fragmentation, critically impacting wildlife 1 – 3 . In the past two decades, mountain glaciers have been shrinking in and around Sanjiangyuan Nature Reserve of China (SNRC), directly affecting water supplies to plateau lakes and rivers, and further affecting vegetation structure, biodiversity, water conservation and other functions 4 – 6 . Meanwhile, due to this destructive impact of human activities, such as poaching for meat, hides, and fur, excessive livestock grazing, as well as the construction of roads and railways, the original habitat is often lost or fragmented 7 . Many studies have shown that this degradation is a primary factor behind the decline of wild ungulates, attributed mainly to landscape fragmentation and habitat destruction 8 . Recent studies highlight that habitat fragmentation and climate change threaten ungulate migrations, essential for ecological processes like foraging and reproduction, thereby increasing extinction risks and reducing genetic diversity 9 , 10 . Therefore, elucidating species-habitat relationships becomes imperative in making informed actions for the efective conservation planning. Furthermore, SNRC, situated in the Qinghai-Tibet Plateau's core, serves as a crucial eco-security barrier and houses sensitive ecosystems 11 , 12 . It supports diverse wildlife, including ungulates, which play pivotal roles in alpine forests by influencing floral dynamics and serving as prey for larger carnivores 13 . The region's complex topography results in resource heterogeneity, challenging alpine ungulates' foraging strategies and habitat selection 14 . While research has primarily focused on species like the Tibetan antelope and Przewalski's gazelle 15 , studies on the endangered alpine musk deer and blue sheep are limited. Recent findings suggest alpine musk deer prefer habitats offering concealment, minimal human disturbance, and proximity to water sources in Xinglongshan National Nature Reserve 16 . Habitat use and species distributions are shaped by the interactions between animals and environmental factors. Animals typically exhibit higher survival rates in habitats that closely match their ecological needs compared to less suitable environments 17 . A multitude of environmental factors-including geography, food availability, predation risk, and human disturbances-influence habitat suitability, essentially determining a habitat's quality for a given species. Variations in these factors can affect animal survival, leading to shifts in population sizes and distribution ranges 18 . Thus, assessing habitat suitability is crucial for the conservation of rare and endangered species, providing a scientific foundation for crafting effective conservation strategies 4 . In this study, we applied the MaxEnt model to assess habitat suitability for two predominant ungulates—horse musk deer and blue sheep-within the Sanjiangyuan Nature Reserve's (SNRC) characteristic canyons. Our aims were to: (i) determine ecological determinants of habitat suitability for each species; and (ii) pinpoint their optimal habitats within these canyons and the broader SNRC. This research lays the groundwork for identifying and protecting vital habitats for horse musk deer and blue sheep in the Sanjiangyuan Reserve, thereby facilitating the delineation of crucial conservation areas and the formulation of targeted conservation policies. 2. Results 2.1. Camera monitoring of ungulates In a survey of 60 sites, 5 were excluded due to camera damage, malfunction, or theft, leaving 55 sites for analysis. From April to September 2023, we recorded 1,027 independent detections of four ungulate species from a total of 1,346 detections over 8,339 camera-trap days (mean duration per site: 145 ± 21 days) (Table 1 ).The Photographic rate (PR) per 100 camera trap days differed significantly across species (df = 4, p < 0.001; Table 1 ). The PR values of four ungulate species ranked from the highest to the lowest as follows: alpine musk deer, blue sheep, wapiti and wild boar. Duncan’s multiple range test indicated that the PR values of alpine musk deer and blue sheep showed a significant higher than other two ungulates, but wild alpine musk deer and blue sheep showed no significant difference, and neither did the pairwise of wapiti and wild boar. This study selects two ungulate species, the blue sheep and the musk deer, which have higher capture rates, to investigate their suitability distribution. Table 1 The number of sites, independent detections and photographic rates, CITES appendix, IUCN red list of wild ungulates in typical canyons of the Sanjiangyuan Nature Reserve. Duncan’s multiple range comparison test results of differences among species are shown at their respective columns. Means with varying superscript letters indicate significant differences ( p < 0.05). Species IUCN red list A CITES B Endemism C LKPWA D China red list E No. of Sites Camera Days No. of Independent Detections Photographic Rate(Mean ± SD) Alpine musk deer ( Moschus chrysogaster) EN Ⅱ Yes Ⅰ CR 45 3659 482 13.17 ± 19.39 a Blue sheep ( Pseudois nayaur ) LC Ⅱ No Ⅱ LC 38 2950 455 15.42 ± 25.35 a Cervus elaphus ( Cervus canadensis ) LC Yes Ⅱ EN 21 1197 65 5.43 ± 4.67 b Wild boar ( Sus scrofa ) LC No — LC 12 533 25 4.69 ± 1.20 b A IUCN Red List referred to IUCN Red List Categories and Criteria ( https://www.iucnredlist.org/ ). The date of last assessment for species is different. B Protected species is included in one of three lists in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), called Appendix I, Appendix II and Appendix III, which was valid from 2017. C Endemic species were mainly considered to be endemic to the Qinghai-Tibet Plateau. D List of key protected wild animals in China (2021). E China Red List referred to the Red List of China's Vertebrates 19 . 2.2. Environmental variable screening and model accuracy analysis For the MaxEnt model, environmental variables with minimal correlation were selected. Different parameters were set according to the calculated AICc and BIC scores as well as the smoothness of the fifitted response curve. The optimal model parameters were combinations of linear, quadratic, product features (LQP), and regularization multiplier (Alpine musk deer: 𝛽 = 2; Blue sheep: 𝛽 = 2.5). The ROC curve by the MaxEnt model showed that the average AUC test values of the two ungulates with 10 repeated runs were all over 0.9, with 0.980 and 0.976 respectively. The standard deviations (SD) were all lower than 0.05, with 0.009 and 0.011 respectively (Fig. 1 ). This indicated that the spatial suitability distribution prediction results were very good and had high accuracy. 2.3. Environmental variable contribution rate and single variable analysis Among the nine selected environmental variables, annual mean temperature, mean diurnal range of temperature, Altitude and annual precipitation strongly contributed to the distribution of the alpine musk deer and the blue sheep, at a cumulative total of 93.5% and 89.9% respectively (Table 2 ). These factors, providing the greatest contribution, underscore their critical influence on ungulate habitats. According to the response curve and the frequency distribution map, the response curve was determined to show the impact of each environmental factor in the predicted distribution of wild animals (Fig. 2 ). The response curve for annual mean temperature (Fig. 2 a and e) showed a positive slope with the probability of presence. The association of elevation showed a negative slope (Fig. 2 c and g). For annual precipitation, the curve decreases with the increase in precipitation (Fig. 2 d and h). Table 2 Analysis of environmental variable contributions in the Alpine musk deer and the Blue sheep MaxEnt model. Species Precent contribution(%) Bio_1 Bio_2 Bio_7 Bio_12 Altitude Slope Aspect Land_cover Dis_vill Dis_road Dis_water Alpine musk deer ( Moschus chrysogaster) 39.3 25.4 5.4 10.1 18.7 0.1 — 0.1 0.5 — 0.5 Blue sheep ( Pseudois nayaur ) 40.5 23.5 7.8 11.8 14.1 0.1 — 0.3 0.2 — 1.7 The jackknife test of the habitat model of the nature reserve showed that the environmental factors Bio_1 and Altitude had the highest gain when used alone (Fig. 3), and the Bio_1 environmental factor had the most significant impact on the ungulates habitat. There was a difference between the jackknife test and the variable contribution analysis, which showed an interaction between multi-environmental and single environmental factors. Figyre 3. Jackknife method testing the importance of the influence of main environmental variables on the distribution of suitable ungulates habitat. 2.4. Habitat suitability of the Alpine musk deer and Blue sheep The threshold setting of the suitable growth zone of the Maxent prediction model was obtained from the threshold mean file obtained by running the model 10 times, and the corresponding areas were divided into highly suitable areas, medium suitable areas, poorly suitable and unsuitable areas. According to the model prediction results, the suitable distribution areas for horse musk deer and blue sheep in the typical canyons of the Sanjiangyuan Nature Reserve are mostly distributed along the main stream of the Yellow River and partially on both sides of the canyon of the first-level tributaries (Fig. 4 ). Among them, the areas with high suitability, medium suitability, poor suitability, and unsuitable habitat for horse musk deer were 79.26 km 2 , 182.26 km 2 , 451.58 km 2 , and 6705.27 km 2 , accounting for 1.07%, 2.46%, 6.09%, and 90.39%, respectively. The areas with high suitability, medium suitability, poor suitability, and unsuitable habitat for blue sheep were 87.74 km 2 , 171.06 km 2 , 544.98 km 2 , and 6614.58 km 2 , accounting for 1.18%, 2.31%, 7.35%, and 89.16%, respectively. Table 3 Habitat composition of the Tibetan gazelle in each park of the Sanjiangyuan National Park under both MaxEnt model and HSI model. Rank Alpine musk deer Blue sheep Area (km 2 ) Precentage (%) Area (km 2 ) Precentage (%) Highly suitable 79.26 1.07 87.74 1.18 Medium suitable 182.26 2.46 171.06 2.31 Poorly suitable 451.58 6.09 544.98 7.35 Unsuitable 6705.27 90.39 6614.58 89.16 3. Discussion 3.1. Relationship between environmental variables and spatial distribution of ungulates Among the primary environmental variables, mean annual temperature and mean diurnal temperature range were identified as significant temperature-related factors influencing ungulate distribution. The mean annual temperature in the habitats of the two studied ungulates was below − 1.0°C, indicating a strong correlation with suitable habitats at higher altitudes. Studies within the Sanjiangyuan National Park have shown that the habitats of six ungulate species are typically above 4,300 meters, where the mean annual temperature falls below − 3.0°C 19 . The mean diurnal temperature range, spanning 12.5°C to 15.5°C, suggests that these ungulates inhabit areas with significant daily temperature fluctuations. This is attributed to the high altitude and low air density of the Qinghai-Tibet Plateau, affecting thermal atmospheric conditions, resulting in rapid temperature changes from day to night 20 . During the daytime, the atmosphere exerts a weakening effect on solar radiation. At night, the atmosphere exerts a poor thermal insulation effect on the ground radiation; therefore, the daytime temperature increases quickly and falls equally quickly during the night, which leads to a large mean diurnal range of temperature in this area 21 , 22 . Maxent model analyses revealed that both ungulate species predominantly occupy altitudes between 2,600 and 3,500 meters, aligning with findings by Shen et al. 16 and Tan et al. 23 . The current findings on the distribution is comparable to the results of Zhang et al. 24 and Nandy et al. 25 who concluded that the horse musk deer occupies to coniferous forests and shrubs on high alpine slopes between 3000 and 4000 meters elevation. This altitude range is critical for horse musk deer, which favor coniferous forests and shrublands on high alpine slopes, and for blue sheep, which are adapted to mountainous terrains endemic to the Pan-Himalaya, preferring open vegetation on slopes between 2,500 and 5,500 meters to evade predators like snow leopards 26 . There are also studies showing that most blue sheep in Tibet occur above an altitude of 4000 meters 27 . The study area, situated along the main stream of the Yellow River, offers cliffs for predator avoidance and ample resources, establishing it as an ideal habitat for blue sheep. 3.2. Forecast of potential protected areas for ungulates It is worth noting that the suitable habitats for horse musk deer and blue sheep are more densely distributed and continuous in the main stream of the Yellow River and the canyons on both sides (Fig. 5 ). This is mainly due to the fact that ungulates are mainly distributed on both sides of the river, which has greatly increased Connectivity of suitable habitats in the area. At the same time, the dense forests on both sides of the river provide herbivores with abundant food, habitat and other survival resources, and are also a refuge for ungulates from predation 28 , 29 . The steep cliffs on both sides are natural barriers for blue sheep to avoid natural enemies 30 , 31 . All categories of suitable habitat accounted for only about 10% of the study area, indicating that the area of high-quality habitat (high and medium suitability) for horse musk deer and blue sheep is small, although they account for a larger proportion of all suitability categories (Musk musk deer: 36.7%; Musk musk deer: 32.2%). Therefore, to ensure the long-term survival of ungulates, it is important to improve habitat quality and maintain corridors connecting core habitats. In fact, human activities can have an impact on ungulates 32 . The general pattern of habitat use and selection by horse musk deer is an adaptive strategy to human activities 24 , 33 . Human activities may cause conflicts and disturbances to ungulates, or cut off the habitat, leading to habitat fragmentation or degradation. Fence grazing also has effects on ungulates, such as hindering long-distance migration of ungulates 34 , 35 . This study provides important information on the distribution of ungulates and helps to design effective animal protection and land use planning in the China Railway Military Protection Zone. Judging from the current survey results, the predicted distribution map provided by the MaxEnt model is in line with the current situation, which may be due to the relatively simple data operation process and model parameter optimization process of the MaxEnt model, which helps to achieve better simulation results 36 , 37 . The selection of environmental variables is very important for the prediction accuracy of the Maxent model. Therefore, this study selected relevant natural environmental factors such as altitude, slope, aspect, and distance from water sources, and comprehensively considered human-made environmental factors such as distance from residential areas, distance from roads, and climate change. The environmental variables selected in this study can provide necessary information for the habitat requirements of ungulates at the regional scale and better predict the distribution of suitable habitats for ungulates. In order to further improve the comprehensiveness and accuracy of prediction results, environmental variables such as soil texture and NDVI can also be considered 38 . 3.3. Protection suggestions for ungulates habitat in the reserve The livelihood of local inhabitants in the study area mainly dependent on livestock farming 39 . The estimated heavy cattle density 30.5 head/km² is creates intensive resource competition for food and space with musk deer which can affect the normal seasonal activity of the species 40 . Evidence of grazing and its associated disturbances of different degrees were recorded in all monitored cameras. During the survey, several remains of old snares and poachers camping sites were observed, such as bamboo fencing barricades stretching over the ridge to restrict free animal movement and guide them to a strategic location for traps and snares were found quite often. Therefore, there are potential threats and challenges to the conservation of ungulate wildlife in the region. The creation of ecological corridors to protect ungulates has been widely recommended in the literature 41 – 43 . The study suggests that ecological corridors that improve landscape connectivity can help facilitate the flow of species between suitable areas under climate change and anthropogenic activities 44 . Faced with dramatic habitat shrinkage, animals from high-suitability areas may migrate to mid- and high-suitability areas. Therefore, it may be necessary to arrange assisted migrations of ungulate wildlife to low to moderately suitable habitats. First, human disturbance behaviors such as logging and hunting should be prohibited, and second, the management department should assess and monitor habitat conditions. This study focused on the characteristic canyons within the Sanjiangyuan National Nature Reserve, employing the MaxEnt model to identify potentially suitable habitats for various ungulate species. Our findings pinpoint Bio_1 (annual mean temperature), Bio_2 (mean diurnal range of temperature), Bio_7 (temperature annual range), Bio_12 (annual precipitation), and altitude as pivotal environmental determinants of habitat suitability. Optimal habitats were characterized by favorable annual temperatures, a wide daily temperature range, and moderate precipitation levels. Critically, prime habitats were identified predominantly along the river's main course and adjacent canyon areas. These insights afford a valuable scientific foundation for the effective conservation of ungulates within the Sanjiangyuan Reserve and the strategic development of nature reserves. Moreover, the utilization of the MaxEnt model serves as a methodological guide for habitat analysis and priority area delineation across extensive regions. 4. Materials and methods 4.1. Study area The Sanjiangyuan National Nature Reserve (SNRC) spans from 89˚45´E to 102˚23´E and 31˚39´N to 36˚12´N, located in southern Qinghai Province, China, and on the central-eastern Tibetan Plateau (Fig. 5 ). It forms the source of the Yangtze, Yellow, and Mekong Rivers, earning it the title “Water Tower of Asia”. Encompassing Golmud City and four Tibetan autonomous prefectures-Guoluo, Yushu, Hainan, and Haixi-the SNRC covers an area of 363,000 km², representing 50.4% of Qinghai Province’s total area. The landscape is characterized by a mix of plateaus and mountains, including the Tanggula, Bayan Har, Kunlun, and A’nyêmaqên ranges, with elevations ranging from 1,956 to 6,824 meters and an average elevation of around 4,500 meters 45 . The rate of precipitation variation from 1960 to 2015 was 6.653 mm/10a. The region experiences a plateau continental climate, marked by a cold season dominated by the Tibetan Plateau High Pressure and a warm season influenced by the Indian Ocean monsoon, contributing to its distinct environmental diversity 6 , 46 . The natural environment exhibits considerable diversity, spanning from coniferous forests and shrubs to alpine meadows, grasslands, and sparse vegetation across a southeast to northwest gradient. The area is home to nationally protected wildlife, including the snow leopard ( Panthera uncia ), Przewalski's gazelle ( Procapra przewalskii ), wild yak ( Bos mutus ), Tibetan sand fox ( Vulpes ferrilata ), Tibetan wild ass ( Equus kiang ), and the Tibetan antelope ( Procapra picticaudata ). 4.2. Species distribution data collection For our study, we deployed 55 infrared cameras (Ltl-C180; Beijing Dingxing Technology Co., Ltd., Beijing, China) in seven typical canyons within the SNRC from April to September 2023 (Fig. 5 ). Cameras were strategically placed over 500 m apart to ensure spatial independence and mounted on trees approximately 0.5 m above ground. Cameras were programmed with moderate sensitive sensor setting, to shoot 3 photos and a 20s video when being triggered, and time was set to 24 h per day. Cameras were maintained in the field for 4 to 6 months, and were inspected for SD cards and batteries upon movement of cameras. No bait was used to attract animals, which is important in situations where the aim of the study is to look at animal behaviors in an unbiased way. Photographs and videos were summarized by sites, hour, and date at each camera placement site. To ensure independence of photographic capture events, we defined detection at a sample point as one individual photograph of one species during a 30-min period. The number of effective camera trap days was calculated as the time frame between camera setting, and the date of the last photograph or video was taken if a malfunction occurred (based on date stamp). Photographic Rate (PR) was used to compare independent detections in different wild ungulate species. Photographic Rate for each species in each camera site was calculated as the number of independent detections for every species divided by the total sampling effort for that sample point (number of camera-days), multiplied by 100: PR = (No. of detections/Camera-days) * 100 4.3. Collection and filtering of environment variables Models necessitate two distinct datasets: the geographic coordinates of the target species and a suite of environmental variables encompassing climate, terrain, land cover, and human interference factors (Supplementary Table 1). Climate data, representing averages from 1970–2000, were sourced for 19 bioclimatic factors via GIS 47 . Digital Elevation Model (DEM) information was obtained from NASA’s Alaska Satellite Facility (ASF), with slope and aspect data derived from 12.5m resolution DEMs. Variables indicating human interference were drawn from the 2017 National Catalogue Service for Geographic Information, including road and residential density and proximity to water sources, calculated via Euclidean distance. Land cover data were sourced from the European Space Agency (ESA) 2021 WorldCover dataset, categorized into 11 land use types. In GIS 10.6, through mask extraction and resampling operations, the environmental data of the study area were obtained for subsequent model construction. The autocorrelations and multiple linear duplications among environment variables might affect the prediction results of the model 48 . To reduce the overlap of information between variables, we used the Band Collection Statistics tool in ArcGIS 10.5 to test the spatial correlations of the above environmental variables. The variables with high correlation (|r| ≥ 0.80) were eliminated, and those with low correlation and more biological implications were introduced into the model operation 49 , 50 . Spatial resolution of the data was standardized to 30 m, with coordinates projected to WGS1984 UTM Zone 47 N and converted to ASCII format for model integration. 4.4. Habitat suitability model The MaxEnt model was developed by Steven Phillips as a density estimation and species distribution prediction model based on maximum entropy theory 51 . MaxEnt requires the current geographical distribution of the species and its environmental constraints to explore the possible distribution of maximum entropy under this constraint The probability distribution of the species with the highest entropy is most similar to the current distribution of target 52 . When modeling habitat suitability, the MaxEnt model only requires presence data of the focal species, without the need for its absence data that are difficult to obtain in practice 53 . We used the MaxEnt software (v. 3.4.3) to model the habitat suitability for Alpine musk deer and Blue sheep, respectively 54 . As a popular niche model, the MaxEnt is often used for habitat evaluation due to its high accuracy, high computational efficiency, and ease of use. We used the Jackknife method and the response curve to test the importance and the effect interval of the environmental variables in the model. To ensure model stability, we set the random test percentage to 25% and performed ten repetitions of the model by using the bootstrap method 55 . The regularization multiplier was set to 1, and the maximum number of background points was set to 10,000. After ten repetitions, the average habitat suitability index was used as the final result 56 . The MaxEnt model automatically generates receiver operating characteristic (ROC) curves. This curve uses the false positive rate (1-specificity) as abscissa and the true positive rate (sensitivity) as ordinate. The area under the curve (AUC) values ranges from 0 to 1, with values closer to 1 indicating more accurate results 57 . If the AUC value is 0.5–0.7, the model evaluation reliability is low, if the AUC value is 0.7–0.9, the model evaluation reliability is medium, and if the AUC value exceeds 0.9, the model evaluation reliability is high 58 . We used the area under the receiver operating characteristic curve to verify the accuracy of the MaxEnt model output. MaxEnt computed the contribution percentage of each environmental variable, and variables with relatively high contribution rate were selected to analyze the corresponding response curves. At the same time, a frequency distribution histogram was used to statistically analyze the relatively high environmental variables. The MaxEnt model was operated to obtain the predicted distribution map of the Alpine musk deer and Blue sheep, which was divided into four different suitable habitats. The result map was generated by ArcGIS. Declarations Competing interests The authors declare no competing interests. Author Contribution L.N. designed the study, collected the wild ungulates, analysed the data and drafed the manuscript. P.L., Z.Z.H. conceptualized, designed the study and contributed to manuscript revision. J.Y.M. acquired project funding. All authors have read and agreed to the published version of the manuscript. Data Availability All data generated and analysed throughout the duration of this study can be found within the published manuscript. The raw datasets used as the basis of this research are available from the corresponding author on reasonable request. References Hald-Mortensen, C. The Main Drivers of Biodiversity Loss: A Brief Overview. https://doi.org/10.2139/ssrn.4539049 (2023). Rawat, A., Kumar, D., Khati, B. S. A review on climate change impacts, models, and its consequences on different sectors: a systematic approach. J. Water Clim. Change 15, 104–126. https://doi.org/10.2166/wcc.2023.536 (2023). Pörtner, H. et al. 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The distribution, status and conservation of the Himalayan Musk Deer Moschus chrysogaster in Sakteng Wildlife Sanctuary. Glob. Ecol. Conserv. 17, e00466. https://doi.org/10.1016/j.gecco.2018.e00466 (2019). Wu, X. Y. et al. Predicting the shift of threatened ungulates' habitats with climate change in Altun Mountain National Nature Reserve of the Northwestern Qinghai-Tibetan Plateau. Climatic Change 142, 331–344. https://doi.org/10.1007/s10584-017-1939-7 (2017). Liang, J. C. et al. Climate change, habitat connectivity, and conservation gaps: a case study of four ungulate species endemic to the Tibetan Plateau. Landsc. Ecol. 36, 1071–1087. https://doi.org/10.1007/s10980-021-01202-0 (2021). Hannah, L. et al. Conservation of Biodiversity in a Changing Climate. Conserv. Biol. 16, 264–268. https://doi.org/10.1046/j.1523-1739.2002.00465.x (2002). Salviano, I. R., Gardon, F. R., dos Santos, R. F. Ecological corridors and landscape planning: a model to select priority areas for connectivity maintenance. Landsc. Ecol. 36, 3311–3328. https://doi.org/10.1007/s10980-021-01305-8 (2021). Wei, W., Zhang, K., Zhou, J. Review and prospect of human-land relationship in Three River Headwaters region: Based on the perspective of people, events, time and space. Adv. Earth Sci. 35, 26–37. https://doi.org/10.11867/j.issn.1001-8166.2020.010 (2020). Jin, Z. et al. Changes of climate and climate extremes in the Three-Rivers' Headwaters region over the Tibetan Plateau during the past 60 years. Trans. Atmos. Sci. 43(6), 1042–1055 (2020). Wani, I. A. et al. Ecological analysis and environmental niche modelling of Dactylorhiza hatagirea (D. Don) Soo: A conservation approach for critically endangered medicinal orchid. Saudi J. Biol. Sci. 28(4), 2109–2122. https://doi.org/10.1016/j.sjbs.2021.01.054 (2021). Carlos-Júnior L. A. et al. Occurrence of an invasive coral in the Southwest Atlantic and comparison with a congener suggest potential niche expansion. Ecol. Evol. 5, 2162–2171. https://doi.org/10.1002/ece3.1506 (2015). Jiang, F. et al. Setting priority conservation areas of wild Tibetan gazelle (Procapra picticaudata) in China's first national park. Glob. Ecol. Conserv. 20, e00725. https://doi.org/10.1016/j.gecco.2019.e00725 (2019). Guevara, L., Gerstner, B. E., Kass, J. M., Anderson, R.P. Toward ecologically realistic predictions of species distributions: A cross-time example from tropical montane cloud forests. Glob. Change Biol. 24, 1511–1522. https://doi.org/10.1111/gcb.13992 (2017). Phillips, S. J., Anderson, R. P., Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006). Phillips, S. J., Dudík, M. Modeling of species distributions with MAXENT: new extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x (2008). Phillips, S. J., Anderson, R. P., Schapire, R. E. Maximum entropy modeling of species geographic distribution. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2013). Phillips, S. J. et al. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017). Tanner, E. P. et al. Extreme climatic events constrain space use and survival of a ground-nesting bird. Glob. Change Biol. 23, 1832–1846. https://doi.org/10.1111/gcb.13505 (2017). Polce, C. et al. Climate driven spatial mismatches between British orchards and their pollinators: Increased risks of pollination deficits. Glob. Change Biol. 20, 2815–2828. https://doi.org/10.1111/gcb.12577 (2014). Peng, D. L. et al. Species distribution modelling and seed germination of four threatened snow lotus (Saussurea), and their implication for conservation. Glob. Ecol. Conserv. 17, e00565. https://doi.org/10.1016/j.gecco.2019.e00565 (2019). Araújo, M. B., Peterson, A. T. Uses and misuses of bioclimatic envelope models. Ecology 93, 1527–1539. https://doi.org/10.1890/11-1930.1 (2012). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4323761","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":298743318,"identity":"006a8350-8fbe-420d-8bb2-f22dc73f4ce5","order_by":0,"name":"Le Niu","email":"","orcid":"","institution":"Powerchina Northwest Engineering Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Niu","suffix":""},{"id":298743321,"identity":"6372e39e-5836-40c5-976f-281e06373b91","order_by":1,"name":"Ping Li","email":"","orcid":"","institution":"Powerchina Northwest Engineering Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Li","suffix":""},{"id":298743324,"identity":"72a2891b-2d01-4c6c-bb1b-a37c41c2b01b","order_by":2,"name":"Zhenzhen Hao","email":"","orcid":"","institution":"China Institute of Geo-Environmental Monitoring","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Hao","suffix":""},{"id":298743327,"identity":"cc1c0438-9681-4a23-805b-3e9daa0d7a89","order_by":3,"name":"Junyong Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYFCCAwyH/1Sw8TDOf9j+4wORWhgP8Jzhk2NuSD4gOYNIa5gP8LbJGbM3pCVI8xCjXrfxjMEBCTazxN6GMwbGtjk2DPzt3Ql4tZgdAGox4ElLnNnYY5Ccuy2NQeLM2Q2EtSRIHEvc2MxjcDh322EGA4lcIrQcMPifuP8Yj2Gz5bb/xGk52JDAZszYw5bMzLjtADFajhUcZjjAJsc4g/kYY++2ZB7CfrlxePNnxn/AqJzB2Mbwc5udHH97L34tDBInDFD4REQNf/sDwopGwSgYBaNgZAMAfQBR9QFrL5YAAAAASUVORK5CYII=","orcid":"","institution":"China Institute of Geo-Environmental Monitoring","correspondingAuthor":true,"prefix":"","firstName":"Junyong","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-04-25 11:21:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4323761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4323761/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56029889,"identity":"85178d22-c3e4-4963-90ba-78f11634be0f","added_by":"auto","created_at":"2024-05-07 17:41:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":985630,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves, and average test AUC for the accuracy analysis of predicted habitats using MaxEnt model. (a) Alpine musk deer; (b) Blue sheep.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4323761/v1/2fb1b8c412117b9bef0d8d7a.png"},{"id":56029890,"identity":"a8ddc734-c847-445b-89e7-58d56037d075","added_by":"auto","created_at":"2024-05-07 17:41:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2395147,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between best environmental predictors and the probability of the presence of the alpine musk deer. The response curves show the mean response of the 10 replicated MaxEnt runs (red) and the mean ± one standard deviation (blue). (a) Annual mean temperature, (°C). (b) Mean diurnal range of temperature (°C). (c) Altitude (m). (d) Annual precipitation (mm).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4323761/v1/d8e3eab3e4df801b24a00a96.png"},{"id":56029891,"identity":"0dd0e3f6-2345-4493-8d59-e12e4ceaf793","added_by":"auto","created_at":"2024-05-07 17:41:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1421445,"visible":true,"origin":"","legend":"\u003cp\u003eJackknife method testing the importance of the influence of main environmental variables on the distribution of suitable ungulates habitat.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4323761/v1/ca36b7d22722747379980fce.png"},{"id":56029893,"identity":"ebcdcddb-f067-4fdc-984a-1255b9f6e89b","added_by":"auto","created_at":"2024-05-07 17:41:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8347638,"visible":true,"origin":"","legend":"\u003cp\u003eSuitable habitat for the Two ungulates of typical canyons in the Sanjiangyuan Nature Reserve of China wild herbivores. (a) Alpine musk deer; (b) Blue sheep.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4323761/v1/0ec00a570f85dc478688af38.png"},{"id":56029892,"identity":"3b64ecae-2fdb-48ba-ba4f-c10b4eedbd93","added_by":"auto","created_at":"2024-05-07 17:41:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":10328647,"visible":true,"origin":"","legend":"\u003cp\u003eThe camera trap sites in the study area and land use types.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4323761/v1/3604bb506a49560c1819b21c.png"},{"id":102755139,"identity":"7bb97666-84dc-4857-a060-00330df1cf41","added_by":"auto","created_at":"2026-02-16 09:41:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28751109,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4323761/v1/bb075d4b-0f89-4be0-be74-658ad365a530.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Maxent modeling for predicting suitable habitats for wild ungulates: a case study of typical canyons in the Sanjiangyuan Nature Reserve of China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal climate change and human activities are exacerbating habitat decline and fragmentation, critically impacting wildlife\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In the past two decades, mountain glaciers have been shrinking in and around Sanjiangyuan Nature Reserve of China (SNRC), directly affecting water supplies to plateau lakes and rivers, and further affecting vegetation structure, biodiversity, water conservation and other functions\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Meanwhile, due to this destructive impact of human activities, such as poaching for meat, hides, and fur, excessive livestock grazing, as well as the construction of roads and railways, the original habitat is often lost or fragmented\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Many studies have shown that this degradation is a primary factor behind the decline of wild ungulates, attributed mainly to landscape fragmentation and habitat destruction\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recent studies highlight that habitat fragmentation and climate change threaten ungulate migrations, essential for ecological processes like foraging and reproduction, thereby increasing extinction risks and reducing genetic diversity\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, elucidating species-habitat relationships becomes imperative in making informed actions for the efective conservation planning.\u003c/p\u003e \u003cp\u003eFurthermore, SNRC, situated in the Qinghai-Tibet Plateau's core, serves as a crucial eco-security barrier and houses sensitive ecosystems\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. It supports diverse wildlife, including ungulates, which play pivotal roles in alpine forests by influencing floral dynamics and serving as prey for larger carnivores\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The region's complex topography results in resource heterogeneity, challenging alpine ungulates' foraging strategies and habitat selection\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. While research has primarily focused on species like the Tibetan antelope and Przewalski's gazelle\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, studies on the endangered alpine musk deer and blue sheep are limited. Recent findings suggest alpine musk deer prefer habitats offering concealment, minimal human disturbance, and proximity to water sources in Xinglongshan National Nature Reserve\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHabitat use and species distributions are shaped by the interactions between animals and environmental factors. Animals typically exhibit higher survival rates in habitats that closely match their ecological needs compared to less suitable environments\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. A multitude of environmental factors-including geography, food availability, predation risk, and human disturbances-influence habitat suitability, essentially determining a habitat's quality for a given species. Variations in these factors can affect animal survival, leading to shifts in population sizes and distribution ranges\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Thus, assessing habitat suitability is crucial for the conservation of rare and endangered species, providing a scientific foundation for crafting effective conservation strategies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we applied the MaxEnt model to assess habitat suitability for two predominant ungulates\u0026mdash;horse musk deer and blue sheep-within the Sanjiangyuan Nature Reserve's (SNRC) characteristic canyons. Our aims were to: (i) determine ecological determinants of habitat suitability for each species; and (ii) pinpoint their optimal habitats within these canyons and the broader SNRC. This research lays the groundwork for identifying and protecting vital habitats for horse musk deer and blue sheep in the Sanjiangyuan Reserve, thereby facilitating the delineation of crucial conservation areas and the formulation of targeted conservation policies.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Camera monitoring of ungulates\u003c/h2\u003e \u003cp\u003eIn a survey of 60 sites, 5 were excluded due to camera damage, malfunction, or theft, leaving 55 sites for analysis. From April to September 2023, we recorded 1,027 independent detections of four ungulate species from a total of 1,346 detections over 8,339 camera-trap days (mean duration per site: 145\u0026thinsp;\u0026plusmn;\u0026thinsp;21 days) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).The Photographic rate (PR) per 100 camera trap days differed significantly across species (df\u0026thinsp;=\u0026thinsp;4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The PR values of four ungulate species ranked from the highest to the lowest as follows: alpine musk deer, blue sheep, wapiti and wild boar. Duncan\u0026rsquo;s multiple range test indicated that the PR values of alpine musk deer and blue sheep showed a significant higher than other two ungulates, but wild alpine musk deer and blue sheep showed no significant difference, and neither did the pairwise of wapiti and wild boar. This study selects two ungulate species, the blue sheep and the musk deer, which have higher capture rates, to investigate their suitability distribution.\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\u003eThe number of sites, independent detections and photographic rates, CITES appendix, IUCN red list of wild ungulates in typical canyons of the Sanjiangyuan Nature Reserve. Duncan\u0026rsquo;s multiple range comparison test results of differences among species are shown at their respective columns. Means with varying superscript letters indicate significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIUCN\u003c/p\u003e \u003cp\u003ered list \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCITES \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEndemism \u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLKPWA \u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003cp\u003ered list \u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo. of Sites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCamera Days\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo. of Independent Detections\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePhotographic Rate(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpine musk deer\u003c/p\u003e \u003cp\u003e(\u003cem\u003eMoschus chrysogaster)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.17\u0026thinsp;\u0026plusmn;\u0026thinsp;19.39 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue sheep\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePseudois nayaur\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.42\u0026thinsp;\u0026plusmn;\u0026thinsp;25.35 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervus elaphus\u003c/p\u003e \u003cp\u003e(\u003cem\u003eCervus canadensis\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWild boar\u003c/p\u003e \u003cp\u003e(\u003cem\u003eSus scrofa\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20 \u003csup\u003eb\u003c/sup\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 \u003csup\u003eA\u003c/sup\u003e IUCN Red List referred to IUCN Red List Categories and Criteria (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The date of last assessment for species is different.\u003c/p\u003e \u003cp\u003e \u003csup\u003eB\u003c/sup\u003e Protected species is included in one of three lists in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), called Appendix I, Appendix II and Appendix III, which was valid from 2017.\u003c/p\u003e \u003cp\u003e \u003csup\u003eC\u003c/sup\u003e Endemic species were mainly considered to be endemic to the Qinghai-Tibet Plateau.\u003c/p\u003e \u003cp\u003e \u003csup\u003eD\u003c/sup\u003e List of key protected wild animals in China (2021).\u003c/p\u003e \u003cp\u003e \u003csup\u003eE\u003c/sup\u003e China Red List referred to the Red List of China's Vertebrates\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Environmental variable screening and model accuracy analysis\u003c/h2\u003e \u003cp\u003eFor the MaxEnt model, environmental variables with minimal correlation were selected. Different parameters were set according to the calculated AICc and BIC scores as well as the smoothness of the fifitted response curve. The optimal model parameters were combinations of linear, quadratic, product features (LQP), and regularization multiplier (Alpine musk deer: \u0026#120573; = 2; Blue sheep: \u0026#120573; = 2.5). The ROC curve by the MaxEnt model showed that the average AUC\u003csub\u003etest\u003c/sub\u003e values of the two ungulates with 10 repeated runs were all over 0.9, with 0.980 and 0.976 respectively. The standard deviations (SD) were all lower than 0.05, with 0.009 and 0.011 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This indicated that the spatial suitability distribution prediction results were very good and had high accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Environmental variable contribution rate and single variable analysis\u003c/h2\u003e \u003cp\u003eAmong the nine selected environmental variables, annual mean temperature, mean diurnal range of temperature, Altitude and annual precipitation strongly contributed to the distribution of the alpine musk deer and the blue sheep, at a cumulative total of 93.5% and 89.9% respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These factors, providing the greatest contribution, underscore their critical influence on ungulate habitats. According to the response curve and the frequency distribution map, the response curve was determined to show the impact of each environmental factor in the predicted distribution of wild animals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The response curve for annual mean temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and e) showed a positive slope with the probability of presence. The association of elevation showed a negative slope (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and g). For annual precipitation, the curve decreases with the increase in precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed and h).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of environmental variable contributions in the Alpine musk deer and the Blue sheep MaxEnt model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003ePrecent contribution(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBio_1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBio_2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBio_7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBio_12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLand_cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDis_vill\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDis_road\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDis_water\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpine musk deer\u003c/p\u003e \u003cp\u003e(\u003cem\u003eMoschus chrysogaster)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlue sheep\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePseudois nayaur\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.7\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\u003eThe jackknife test of the habitat model of the nature reserve showed that the environmental factors Bio_1 and Altitude had the highest gain when used alone (Fig.\u0026nbsp;3), and the Bio_1 environmental factor had the most significant impact on the ungulates habitat. There was a difference between the jackknife test and the variable contribution analysis, which showed an interaction between multi-environmental and single environmental factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigyre 3.\u003c/b\u003e Jackknife method testing the importance of the influence of main environmental variables on the distribution of suitable ungulates habitat.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Habitat suitability of the Alpine musk deer and Blue sheep\u003c/h2\u003e \u003cp\u003eThe threshold setting of the suitable growth zone of the Maxent prediction model was obtained from the threshold mean file obtained by running the model 10 times, and the corresponding areas were divided into highly suitable areas, medium suitable areas, poorly suitable and unsuitable areas. According to the model prediction results, the suitable distribution areas for horse musk deer and blue sheep in the typical canyons of the Sanjiangyuan Nature Reserve are mostly distributed along the main stream of the Yellow River and partially on both sides of the canyon of the first-level tributaries (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among them, the areas with high suitability, medium suitability, poor suitability, and unsuitable habitat for horse musk deer were 79.26 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, 182.26 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, 451.58 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and 6705.27 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, accounting for 1.07%, 2.46%, 6.09%, and 90.39%, respectively. The areas with high suitability, medium suitability, poor suitability, and unsuitable habitat for blue sheep were 87.74 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, 171.06 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, 544.98 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and 6614.58 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, accounting for 1.18%, 2.31%, 7.35%, and 89.16%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHabitat composition of the Tibetan gazelle in each park of the Sanjiangyuan National Park under both MaxEnt model and HSI model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAlpine musk deer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBlue sheep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrecentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e171.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly suitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e451.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e544.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6705.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6614.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Relationship between environmental variables and spatial distribution of ungulates\u003c/h2\u003e \u003cp\u003eAmong the primary environmental variables, mean annual temperature and mean diurnal temperature range were identified as significant temperature-related factors influencing ungulate distribution. The mean annual temperature in the habitats of the two studied ungulates was below \u0026minus;\u0026thinsp;1.0\u0026deg;C, indicating a strong correlation with suitable habitats at higher altitudes. Studies within the Sanjiangyuan National Park have shown that the habitats of six ungulate species are typically above 4,300 meters, where the mean annual temperature falls below \u0026minus;\u0026thinsp;3.0\u0026deg;C\u003csup\u003e19\u003c/sup\u003e. The mean diurnal temperature range, spanning 12.5\u0026deg;C to 15.5\u0026deg;C, suggests that these ungulates inhabit areas with significant daily temperature fluctuations. This is attributed to the high altitude and low air density of the Qinghai-Tibet Plateau, affecting thermal atmospheric conditions, resulting in rapid temperature changes from day to night\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. During the daytime, the atmosphere exerts a weakening effect on solar radiation. At night, the atmosphere exerts a poor thermal insulation effect on the ground radiation; therefore, the daytime temperature increases quickly and falls equally quickly during the night, which leads to a large mean diurnal range of temperature in this area\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMaxent model analyses revealed that both ungulate species predominantly occupy altitudes between 2,600 and 3,500 meters, aligning with findings by Shen et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and Tan et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The current findings on the distribution is comparable to the results of Zhang et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and Nandy et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e who concluded that the horse musk deer occupies to coniferous forests and shrubs on high alpine slopes between 3000 and 4000 meters elevation. This altitude range is critical for horse musk deer, which favor coniferous forests and shrublands on high alpine slopes, and for blue sheep, which are adapted to mountainous terrains endemic to the Pan-Himalaya, preferring open vegetation on slopes between 2,500 and 5,500 meters to evade predators like snow leopards\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. There are also studies showing that most blue sheep in Tibet occur above an altitude of 4000 meters\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The study area, situated along the main stream of the Yellow River, offers cliffs for predator avoidance and ample resources, establishing it as an ideal habitat for blue sheep.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Forecast of potential protected areas for ungulates\u003c/h2\u003e \u003cp\u003eIt is worth noting that the suitable habitats for horse musk deer and blue sheep are more densely distributed and continuous in the main stream of the Yellow River and the canyons on both sides (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This is mainly due to the fact that ungulates are mainly distributed on both sides of the river, which has greatly increased Connectivity of suitable habitats in the area. At the same time, the dense forests on both sides of the river provide herbivores with abundant food, habitat and other survival resources, and are also a refuge for ungulates from predation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The steep cliffs on both sides are natural barriers for blue sheep to avoid natural enemies\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. All categories of suitable habitat accounted for only about 10% of the study area, indicating that the area of high-quality habitat (high and medium suitability) for horse musk deer and blue sheep is small, although they account for a larger proportion of all suitability categories (Musk musk deer: 36.7%; Musk musk deer: 32.2%). Therefore, to ensure the long-term survival of ungulates, it is important to improve habitat quality and maintain corridors connecting core habitats.\u003c/p\u003e \u003cp\u003eIn fact, human activities can have an impact on ungulates\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The general pattern of habitat use and selection by horse musk deer is an adaptive strategy to human activities\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Human activities may cause conflicts and disturbances to ungulates, or cut off the habitat, leading to habitat fragmentation or degradation. Fence grazing also has effects on ungulates, such as hindering long-distance migration of ungulates\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This study provides important information on the distribution of ungulates and helps to design effective animal protection and land use planning in the China Railway Military Protection Zone.\u003c/p\u003e \u003cp\u003eJudging from the current survey results, the predicted distribution map provided by the MaxEnt model is in line with the current situation, which may be due to the relatively simple data operation process and model parameter optimization process of the MaxEnt model, which helps to achieve better simulation results\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The selection of environmental variables is very important for the prediction accuracy of the Maxent model. Therefore, this study selected relevant natural environmental factors such as altitude, slope, aspect, and distance from water sources, and comprehensively considered human-made environmental factors such as distance from residential areas, distance from roads, and climate change. The environmental variables selected in this study can provide necessary information for the habitat requirements of ungulates at the regional scale and better predict the distribution of suitable habitats for ungulates. In order to further improve the comprehensiveness and accuracy of prediction results, environmental variables such as soil texture and NDVI can also be considered\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Protection suggestions for ungulates habitat in the reserve\u003c/h2\u003e \u003cp\u003eThe livelihood of local inhabitants in the study area mainly dependent on livestock farming\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The estimated heavy cattle density 30.5 head/km\u0026sup2; is creates intensive resource competition for food and space with musk deer which can affect the normal seasonal activity of the species\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Evidence of grazing and its associated disturbances of different degrees were recorded in all monitored cameras. During the survey, several remains of old snares and poachers camping sites were observed, such as bamboo fencing barricades stretching over the ridge to restrict free animal movement and guide them to a strategic location for traps and snares were found quite often. Therefore, there are potential threats and challenges to the conservation of ungulate wildlife in the region.\u003c/p\u003e \u003cp\u003eThe creation of ecological corridors to protect ungulates has been widely recommended in the literature\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The study suggests that ecological corridors that improve landscape connectivity can help facilitate the flow of species between suitable areas under climate change and anthropogenic activities\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Faced with dramatic habitat shrinkage, animals from high-suitability areas may migrate to mid- and high-suitability areas. Therefore, it may be necessary to arrange assisted migrations of ungulate wildlife to low to moderately suitable habitats. First, human disturbance behaviors such as logging and hunting should be prohibited, and second, the management department should assess and monitor habitat conditions.\u003c/p\u003e \u003cp\u003eThis study focused on the characteristic canyons within the Sanjiangyuan National Nature Reserve, employing the MaxEnt model to identify potentially suitable habitats for various ungulate species. Our findings pinpoint Bio_1 (annual mean temperature), Bio_2 (mean diurnal range of temperature), Bio_7 (temperature annual range), Bio_12 (annual precipitation), and altitude as pivotal environmental determinants of habitat suitability. Optimal habitats were characterized by favorable annual temperatures, a wide daily temperature range, and moderate precipitation levels. Critically, prime habitats were identified predominantly along the river's main course and adjacent canyon areas. These insights afford a valuable scientific foundation for the effective conservation of ungulates within the Sanjiangyuan Reserve and the strategic development of nature reserves. Moreover, the utilization of the MaxEnt model serves as a methodological guide for habitat analysis and priority area delineation across extensive regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Materials and methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Study area\u003c/h2\u003e \u003cp\u003eThe Sanjiangyuan National Nature Reserve (SNRC) spans from 89˚45\u0026acute;E to 102˚23\u0026acute;E and 31˚39\u0026acute;N to 36˚12\u0026acute;N, located in southern Qinghai Province, China, and on the central-eastern Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It forms the source of the Yangtze, Yellow, and Mekong Rivers, earning it the title \u0026ldquo;Water Tower of Asia\u0026rdquo;. Encompassing Golmud City and four Tibetan autonomous prefectures-Guoluo, Yushu, Hainan, and Haixi-the SNRC covers an area of 363,000 km\u0026sup2;, representing 50.4% of Qinghai Province\u0026rsquo;s total area. The landscape is characterized by a mix of plateaus and mountains, including the Tanggula, Bayan Har, Kunlun, and A\u0026rsquo;ny\u0026ecirc;maq\u0026ecirc;n ranges, with elevations ranging from 1,956 to 6,824 meters and an average elevation of around 4,500 meters\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The rate of precipitation variation from 1960 to 2015 was 6.653 mm/10a. The region experiences a plateau continental climate, marked by a cold season dominated by the Tibetan Plateau High Pressure and a warm season influenced by the Indian Ocean monsoon, contributing to its distinct environmental diversity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The natural environment exhibits considerable diversity, spanning from coniferous forests and shrubs to alpine meadows, grasslands, and sparse vegetation across a southeast to northwest gradient. The area is home to nationally protected wildlife, including the snow leopard (\u003cem\u003ePanthera uncia\u003c/em\u003e), Przewalski's gazelle (\u003cem\u003eProcapra przewalskii\u003c/em\u003e), wild yak (\u003cem\u003eBos mutus\u003c/em\u003e), Tibetan sand fox (\u003cem\u003eVulpes ferrilata\u003c/em\u003e), Tibetan wild ass (\u003cem\u003eEquus kiang\u003c/em\u003e), and the Tibetan antelope (\u003cem\u003eProcapra picticaudata\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Species distribution data collection\u003c/h2\u003e \u003cp\u003eFor our study, we deployed 55 infrared cameras (Ltl-C180; Beijing Dingxing Technology Co., Ltd., Beijing, China) in seven typical canyons within the SNRC from April to September 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cameras were strategically placed over 500 m apart to ensure spatial independence and mounted on trees approximately 0.5 m above ground. Cameras were programmed with moderate sensitive sensor setting, to shoot 3 photos and a 20s video when being triggered, and time was set to 24 h per day. Cameras were maintained in the field for 4 to 6 months, and were inspected for SD cards and batteries upon movement of cameras. No bait was used to attract animals, which is important in situations where the aim of the study is to look at animal behaviors in an unbiased way.\u003c/p\u003e \u003cp\u003ePhotographs and videos were summarized by sites, hour, and date at each camera placement site. To ensure independence of photographic capture events, we defined detection at a sample point as one individual photograph of one species during a 30-min period. The number of effective camera trap days was calculated as the time frame between camera setting, and the date of the last photograph or video was taken if a malfunction occurred (based on date stamp). Photographic Rate (PR) was used to compare independent detections in different wild ungulate species. Photographic Rate for each species in each camera site was calculated as the number of independent detections for every species divided by the total sampling effort for that sample point (number of camera-days), multiplied by 100:\u003c/p\u003e \u003cp\u003ePR = (No. of detections/Camera-days) * 100\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Collection and filtering of environment variables\u003c/h2\u003e \u003cp\u003eModels necessitate two distinct datasets: the geographic coordinates of the target species and a suite of environmental variables encompassing climate, terrain, land cover, and human interference factors (Supplementary Table\u0026nbsp;1). Climate data, representing averages from 1970\u0026ndash;2000, were sourced for 19 bioclimatic factors via GIS\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Digital Elevation Model (DEM) information was obtained from NASA\u0026rsquo;s Alaska Satellite Facility (ASF), with slope and aspect data derived from 12.5m resolution DEMs. Variables indicating human interference were drawn from the 2017 National Catalogue Service for Geographic Information, including road and residential density and proximity to water sources, calculated via Euclidean distance. Land cover data were sourced from the European Space Agency (ESA) 2021 WorldCover dataset, categorized into 11 land use types. In GIS 10.6, through mask extraction and resampling operations, the environmental data of the study area were obtained for subsequent model construction.\u003c/p\u003e \u003cp\u003eThe autocorrelations and multiple linear duplications among environment variables might affect the prediction results of the model\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. To reduce the overlap of information between variables, we used the Band Collection Statistics tool in ArcGIS 10.5 to test the spatial correlations of the above environmental variables. The variables with high correlation (|r| \u0026ge; 0.80) were eliminated, and those with low correlation and more biological implications were introduced into the model operation\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Spatial resolution of the data was standardized to 30 m, with coordinates projected to WGS1984 UTM Zone 47 N and converted to ASCII format for model integration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Habitat suitability model\u003c/h2\u003e \u003cp\u003eThe MaxEnt model was developed by Steven Phillips as a density estimation and species distribution prediction model based on maximum entropy theory\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. MaxEnt requires the current geographical distribution of the species and its environmental constraints to explore the possible distribution of maximum entropy under this constraint The probability distribution of the species with the highest entropy is most similar to the current distribution of target\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. When modeling habitat suitability, the MaxEnt model only requires presence data of the focal species, without the need for its absence data that are difficult to obtain in practice\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. We used the MaxEnt software (v. 3.4.3) to model the habitat suitability for Alpine musk deer and Blue sheep, respectively\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. As a popular niche model, the MaxEnt is often used for habitat evaluation due to its high accuracy, high computational efficiency, and ease of use.\u003c/p\u003e \u003cp\u003eWe used the Jackknife method and the response curve to test the importance and the effect interval of the environmental variables in the model. To ensure model stability, we set the random test percentage to 25% and performed ten repetitions of the model by using the bootstrap method\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The regularization multiplier was set to 1, and the maximum number of background points was set to 10,000. After ten repetitions, the average habitat suitability index was used as the final result\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe MaxEnt model automatically generates receiver operating characteristic (ROC) curves. This curve uses the false positive rate (1-specificity) as abscissa and the true positive rate (sensitivity) as ordinate. The area under the curve (AUC) values ranges from 0 to 1, with values closer to 1 indicating more accurate results\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. If the AUC value is 0.5\u0026ndash;0.7, the model evaluation reliability is low, if the AUC value is 0.7\u0026ndash;0.9, the model evaluation reliability is medium, and if the AUC value exceeds 0.9, the model evaluation reliability is high\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. We used the area under the receiver operating characteristic curve to verify the accuracy of the MaxEnt model output.\u003c/p\u003e \u003cp\u003eMaxEnt computed the contribution percentage of each environmental variable, and variables with relatively high contribution rate were selected to analyze the corresponding response curves. At the same time, a frequency distribution histogram was used to statistically analyze the relatively high environmental variables. The MaxEnt model was operated to obtain the predicted distribution map of the Alpine musk deer and Blue sheep, which was divided into four different suitable habitats. The result map was generated by ArcGIS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.N. designed the study, collected the wild ungulates, analysed the data and drafed the manuscript. P.L., Z.Z.H. conceptualized, designed the study and contributed to manuscript revision. J.Y.M. acquired project funding. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated and analysed throughout the duration of this study can be found within the published manuscript. The raw datasets used as the basis of this research are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHald-Mortensen, C. The Main Drivers of Biodiversity Loss: A Brief Overview. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2139/ssrn.4539049\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.4539049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawat, A., Kumar, D., Khati, B. S. A review on climate change impacts, models, and its consequences on different sectors: a systematic approach. J. Water Clim. 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Ecology 93, 1527\u0026ndash;1539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/11-1930.1\u003c/span\u003e\u003cspan address=\"10.1890/11-1930.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sanjiangyuan National Park, Ungulates, Suitable habitats, MaxEnt model, Connectivity analysis","lastPublishedDoi":"10.21203/rs.3.rs-4323761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4323761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIn the fragmented and isolated Zhongtie Military Protection Area of the Sanjiangyuan National Park, endangered ungulate species\u0026mdash;including horse musk deer, blue sheep, and red deer\u0026mdash;face significant risks due to habitat degradation and loss, exacerbated by climate change and human activities. This study aims to understand the habitat preferences of ungulates and predict their spatial distributions within the park, contributing to conservation efforts amid climatic shifts. Due to the scarcity of detailed, scientific data on ungulate distributions, populations, and habitats, this study seeks to fill these critical knowledge gaps.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eWe deployed 55 infrared cameras along the main stream of the Yellow River and in typical canyons on both sides of the China Railway Military Protection Zone within the Sanjiangyuan Nature Reserve. The cameras recorded 2,948 occurrences of ungulates from April to September 2023. We utilized the MaxEnt model to analyze habitat distribution of horse musk deer and blue sheep, incorporating nine environmental variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe model's predictive accuracy, as indicated by the area under the curve (AUC) for Alpine musk deer (0.980) and Blue sheep (0.976), demonstrates its effectiveness. Our analysis identifies climate as the primary influence on habitat distribution, with key factors being annual mean temperature, daily temperature range, altitude, and annual precipitation-contributing to 39.3%, 25.4%, 18.7%, 10.1% for Alpine musk deer, and 40.5%, 23.5%, 14.1%, 11.8% for Blue sheep, respectively. Habitat suitability analysis reveals that 9.61% and 10.84% of the reserve's terrain are viable for Alpine musk deer and Blue sheep, respectively, based on established model thresholds.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings of this study are crucial for the protection of existing wildlife and the identification of potential conservation zones for ungulates. By delineating areas of high habitat suitability, this research supports targeted conservation planning and management efforts within the Sanjiangyuan Reserve, thereby aiding in the sustainable preservation of these endangered species.\u003c/p\u003e","manuscriptTitle":"Maxent modeling for predicting suitable habitats for wild ungulates: a case study of typical canyons in the Sanjiangyuan Nature Reserve of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 17:41:34","doi":"10.21203/rs.3.rs-4323761/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"26b491d0-a8eb-499c-90f4-68ac0bd23b02","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31518580,"name":"Biological sciences/Ecology"},{"id":31518582,"name":"Biological sciences/Zoology"},{"id":31518584,"name":"Earth and environmental sciences/Climate sciences"},{"id":31518586,"name":"Earth and environmental sciences/Ecology"}],"tags":[],"updatedAt":"2026-02-16T09:34:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 17:41:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4323761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4323761","identity":"rs-4323761","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}