基于MAXENT模型的秦岭山系黑熊潜在生境评价

明确物种生境空间分布格局及其与环境因子的关系,对制定合理有效的保护对策十分重要.本文以黑熊(Ursus thibetanus)为研究对象,以其重要柄息地秦岭山系为研究区域,选取68个黑熊分布点数据和34个环境因子,应用MAXENT模型分析其生境空间分布及主要影响因子,以最大化Kappa值的生境适宜性指数为阈值划分适宜生境,结合已建自然保护区进行保护空缺分析,并通过构建阻力面和最小成本路径分析,开展黑熊生境廊道规划.结果表明:人类干扰和土地利用类型是影响黑熊生境选择的主要生态因子,居民点密度、到荒草地距离、到耕地距离3个因子对黑熊生境选择有重要影响,其综合贡献值分别为21.4%、17.5%和15.9%,到阔叶林距离、到水体距离等因子次之.黑熊的适宜生境主要集中分布在秦岭山系主脊的中西部地区,占整个秦岭山系面积的19.23%.空缺分析表明:已建自然保护区群覆盖了23.49%的适宜生境,但尚有8,480 km2处于保护区之外.为更有效保护秦岭黑熊及其生境,建议建设12条生境廊道,同时结合其他物种进行系统保护规划.     

唐家河国家级自然保护区川金丝猴生境适宜性评价

生境适宜性评价是濒危物种保护的重要基础。川金丝猴（Rhinopithecus roxellana）是栖息于温带森林的、中国特有的珍稀灵长类动物。位于岷山山系的四川唐家河国家级自然保护区是川金丝猴的重要分布地之一,但涉及该地区川金丝猴的生境信息却较缺乏。运用最大熵（Maximum entropy,MaxEnt）模型对四川唐家河国家级自然保护区川金丝猴不同季节的生境适宜性进行了研究,发现四个季节的训练集和验证集的受试者工作特征曲线下面积（Area under the receiver operator characteristic curve,AUC）值均超过0.8,说明模型预测结果较好。结果显示：（1）影响不同季节川金丝猴分布的主要因子是海拔、河流和道路。（2）川金丝猴的适宜生境面积存在季节性变化。其中,春季的适宜生境面积最大,为233.94 km²,占全区面积的58.48%;夏季的次之,为192.75 km²,占48.19%;秋冬季的适宜生境面积相对较低,分别为145.54 km²（占36.39%）和142.63 km²（占35.66%）。（3）川金丝猴的适宜生境分布具有明显的季节性垂直变化。研究揭示保护好完整的森林植被带对川金丝猴的生存具有重要意义,尤其要重视对人为干扰较强的低海拔生境的保护。     

卡拉麦里山有蹄类野生动物自然保护区蒙古野驴生境格局动态及其成因分析

人类干扰导致的生境丧失与破碎化严重影响野生动物生存。基于2005年至2019年在新疆卡拉麦里山有蹄类野生动物自然保护区开展野外调查所获数据,选取影响蒙古野驴生境选择的关键环境因子,采用最大熵模型（MaxEnt）模拟矿业开发前（2005年）、矿业开发顶峰时（2011年）以及矿区生态修复和道路修建（2019年）三个时期的蒙古野驴适宜生境空间分布。选取相关的景观格局指数,分析了近15年来保护区内蒙古野驴生境格局的变化趋势。结果显示蒙古野驴的适宜生境主要分布于保护区中部,水源是影响蒙古野驴生境质量的关键自然因素。2005-2019年间该保护区蒙古野驴的生境经历了由较好状态到恶化,随后逐渐恢复的过程。矿业开发前蒙古野驴高适宜生境面积最大（2022.31 km²）,生境破碎化程度低,总体生境质量较好。矿业开发顶峰时蒙古野驴的适宜生境向保护区北部转移,适宜生境大面积丧失,其中高适宜生境面积减少了30.28%,且生境斑块面积小,形状不规则、离散和破碎化程度高,严重威胁蒙古野驴生存。矿区生态修复和道路建设后,大多数已修复的矿区重新成为蒙古野驴的适宜生境,保护区内蒙古野驴适宜生境面积较矿业开发时增加了556.31 km²,但仍处于破碎隔离状态;道路沿线适宜生境丧失严重,蒙古野驴对其明显回避。基于上述研究结果,针对蒙古野驴生境保护提出了具体措施,为卡山保护区蒙古野驴生境恢复及保护提供了科学依据。     

同域分布中华鬣羚与中华斑羚时空生态位特征

为了解同域分布有蹄类在环境复杂的山地森林生境中以何种方式维持种间关系以实现稳定共存,基于物种分布模型与日活动模式分析了四川省岷山、邛崃、大相岭、小相岭和凉山五大山系同域分布中华鬣羚(Capricornis milneedwardsii)与中华斑羚(Naemorhedus griseus)的时空生态位特征。结果显示:(1)在四川省五大山系,中华鬣羚的适宜栖息地面积为28006.07 km²,占研究区总面积的26.18%,其中高适宜栖息地面积为10015.90 km²,中华斑羚的适宜栖息地面积为21073.32 km²,占研究区总面积的19.71%,其中高适宜栖息地面积为8396.22 km²;(2)中华鬣羚与中华斑羚在生境因子选择上相似性高、栖息地重叠面积大,其空间生态位重叠度指数D=0.776,I=0.949,其适宜栖息地的主要重叠区域位于岷山和邛崃山系;(3)中华鬣羚与中华斑羚的日活动节律重叠指数为0.812;(4)中华鬣羚与中华斑羚属于同域分布的资源利用型竞争物种,中华鬣羚的存在会显著影响中华斑羚的日活动节律(P=0.016);二者同域分布时都会增加其昼间活动强度,并增加活动高峰期的强度及持续时间。本研究初步分析了中华鬣羚与中华斑羚的时空生态位特征,揭示了二者在空间、时间生态位上种群共存及种间竞争的耦合关系。研究有利于深入理解同域分布动物时空生态位特征、近缘物种的共存机制及种间竞争关系,为有蹄类等珍稀野生动物种群及栖息地的保护提供科学参考。     

大型野生食草动物生境资源利用竞争关系

生物多样性维持是三江源区的重要生态功能,野生动物保护的科学规划需要掌握区内野生动物的栖息地分布状况与资源竞争关系。利用无人机遥感的新技术,在三江源玛多县开展藏野驴、藏原羚和岩羊的适宜栖息地识别与划定以及生境资源利用竞争关系研究。结果显示,2017年玛多县藏野驴适宜生境面积3559.43 km²,距水源距离是限制适宜生境面积的主要因子;藏原羚的适宜生境面积为283.76 km²,岩羊的适宜生境面积为29.97 km²,坡度、距水源距离以及植被类型是制约适宜栖息地面积的主要因素。由于在栖息地坡度选择中的生态位差异,三种大型野生食草动物的生境重叠面积均较小,生境竞争关系较弱。研究为生物多样性保护提供了全新的思路,可为相似区域的野生动物保护与恢复综合规划提供支撑。     

甘肃兴隆山保护区野生马麝分布、数量特征及影响因素

综合采用样线法、粪堆计数法及重复调查法对甘肃省兴隆山国家级自然保护区的野生马麝（Moschus chrysogaster）进行了种群调查,结合生境分析,确定了其种群分布、数量特征及影响因素。结果表明,兴隆山保护区分布有野生马麝（1159±275）头,平均种群密度为（3.51±0.83）头/km²;各植被类型生境中的野生麝种群数量及密度存在差异,灌丛生境分布有70%的野生马麝种群,达（807±170）头,种群密度为（6.49±1.63）头/km²;针叶林种群密度最大,达（8.85±83.25）头/km²,分布有野生麝（123±45）头;针阔混交林分布最少,仅（41±15）头,种群密度为（5.00±1.84）头/km²;人工林生境无野生马麝分布。保护区各植被类型生境中的野生马麝种群分布差异反映了马麝对适宜生境功能的需求,食物、保温和隐蔽性是制约野生马麝冬季分布和种群数量的关键因素。此外,因生境及人为干扰强度的不同,保护区各区域的野生马麝种群分布存在差异。建议通过减少人为干扰、地表植被管理及人工林管理优化等措施增加生境适宜性,促进兴隆山自然保护区野生马麝种群的快速恢复和增长。     

秦岭南坡两个大熊猫活动密集区的生境特征及大熊猫对生境的选择

对秦岭佛坪和长青2个保护区的大熊猫活动密集区的系列生境因子(包括海拔、坡度、坡向、水系密度、生境类型、竹子种类)进行了研究,并分析了其与大熊猫痕迹点的关系。结果表明：佛坪和长青保护区的生境特征既有相似性又有差异性,故2个保护区的大熊猫对生境的选择也呈相似性和差异性;在长青和佛坪保护区各存在1个大熊猫活动密集区,其具有明显的宏观生境特征;大熊猫活动密集区的生境资源可获得性和大熊猫对生境因子的适应选择对应关系明显;秦岭南坡大熊猫选择的生境特征主要是：海拔1 200～2 600 m,坡度20°～40°,水系密度为2～3条·km^-2,针阔叶混交林,巴山木竹和秦岭箭竹的分布区。     

老爷岭南部狍冬季移动、卧息生境选择及其适宜性评价

在动物生境研究中,移动生境和卧息生境是生境研究的焦点。开展移动生境和卧息生境选择,并在此基础上进行生境评价,有利于深入了解动物对移动和卧息生境条件的需求,制定科学合理的栖息地保护计划。以东北虎（Panthera tigris altaica）的主要猎物物种之一-狍（Capreolus pygargus）为研究对象,于2017-2019年冬季积雪覆盖期在老爷岭南部通过随机布设28个大样方和84条用于足迹链跟踪的样线收集狍的移动点和卧息点信息,再结合近年来收集的东北虎出现点,利用广义可加模型（GAM）和最大熵模型（MaxEnt）进行狍移动、卧息生境选择及评价研究。移动生境选择研究表明,狍在移动的过程中偏好选择坡度小、距农田距离>500 m、远离道路、居民点和低海拔或较高海拔的区域;移动生境评价分析表明,移动适宜和次适宜生境面积之和为1318.16 km²,占研究区域面积的51.28%,当加入虎活动点影响因子后,狍移动适宜和次适宜生境面积之和为901.52 km²,适宜和次适宜生境面积之和减少了31.61%。狍卧息生境选择研究表明,水源、农田、道路和雪深是影响狍卧息的关键因素,其中雪深对狍卧息生境选择的贡献率达到70.13%;卧息生境评价表明,卧息适宜和次适宜生境面积之和为1243.77 km²,占研究区域面积的48.39%,当加入虎出现点因子后,适宜生境和次适宜生境面积之和减少了61.00%,仅为485.02 km²。研究认为,虎的出现对狍移动和卧息生境选择均产生影响,虎的活动及捕食行为可能会减少狍的活动范围和频次,狍远离虎活动区域卧息休息,压缩了狍适宜卧息的空间。     

基于MaxEnt模型的甘肃安西极旱荒漠国家级自然保护区北山羊生境评估

明晰甘肃安西极旱荒漠国家级自然保护区珍稀濒危物种北山羊的分布格局,并阐释气候变化和人类活动对北山羊的影响,对今后北山羊生境管理和物种保护具有重要意义。基于北山羊实测分布点记录和环境变量数据,结合MaxEnt模型和ArcGIS空间分析功能,利用CMIP6的8个气候模式均值预测中度发展路径（SSP2-4.5）下,基准期（1970-2000年）和未来气候（2041-2060年、2081-2100年）变化情景下,甘肃安西极旱荒漠国家级自然保护区北山羊的潜在适生区分布范围及变化,并综合贡献率和置换重要性值对北山羊生境选择关键环境因子进行了分析。研究结果表明：（1） MaxEnt模型的预测精度较高,三种气候条件下ROC曲线下面积（AUC,Area Under Curve）>0.97,且真实技巧统计（TSS,True Skill Statistic）>0.90,模拟结果可靠。（2）影响北山羊生境选择的主要环境因子为气候条件（降水量季节性变异系数和等温性）、海拔和人为干扰（距泉和居名点距离）。水是保护区北山羊生存的最基本要素,气候条件共同控制北山羊生境条件。此外,北山羊习性决定其生境宜选择高海拔和远离人类活动影响地区。（3）基准期保护区北山羊主要分布在北片和南片高海拔山区,面积365.77 km²（占整个保护区的4.31%）,北山羊适生区面积北片>南片、中高等适生区主要位于保护区北片。（4） CMIP6未来气候变化情景下,随着保护区生态环境改善和濒危物种保护措施的实施,北山羊潜在适生区面积呈增加趋势,但是受北山羊近亲繁殖的影响,整体上北山羊数量和适生区面积增加并不显著且有向南部及高海拔地区转移趋势。     

基于MaxEnt模型的四川王朗国家级自然保护区蓝马鸡栖息地适宜性评价

明确野生动物栖息地的空间分布格局和影响因子,是有效的栖息地管理和物种保护的基础。基于王朗国家级自然保护区自2001至2018年间的野外调查记录,首次使用MaxEnt模型对蓝马鸡（Crossoptilon auritum）栖息地适宜性进行了分析和评价。结果显示：（1）地形特征和极端时期的气候是影响蓝马鸡栖息地适宜性的主要环境因子,年最低温高于-14.4℃、海拔约2430-3100 m、坡度小于40°、靠近河流且最干月降水低于4.4 mm的区域属于蓝马鸡的适宜栖息地;（2）保护区内共有蓝马鸡栖息地102.28 km²,约占保护区总面积的三分之一,其中适宜栖息地面积59.41 km²,次适宜栖息地面积42.87 km²,为该物种提供了面积巨大且连通性极好的栖息地;（3）在不适宜蓝马鸡分布的区域,海拔过高和最干月降水量较大是限制其栖息地适宜性的主要环境因子。目前保护区内主要的人为干扰类型是牦牛和马匹的放牧活动,并且放牧强度和区域有扩大趋势,潜在威胁着蓝马鸡种群及其栖息地。因此,基于模型结果和野外调查,为避免保护区内蓝马鸡栖息地的退化和破碎化,提出了控制放牧数量和面积、增强对放牧人员的宣传教育以及定期监测河流水质的保护建议,以期促进蓝马鸡的种群和栖息地保护。     

Application of ecological-niche factor analysis in habitat assessment of giant pandas

Ecological-niche factor analysis (ENFA) is a multivariate approach to study geographic distribution of species on a large scale with only “presence” data. It has been widely applied in many fields including wildlife management, habitat assessment and habitat prediction. In this paper, this approach was applied in habitat suitability assessment for giant pandas in Pingwu County, Sichuan Province, China. With “presence” data of giant pandas and remote sensing data, habitat suitability of pandas in this county was evaluated based on ENFA model, and spatial distribution pattern of nature reserves and conservation gaps were then evaluated. The results show that giant pandas in this county prefer high-elevation zones (> 2128 m) dominated by coniferous forest, and mixed coniferous and deciduous broadleaf forest, and avoid deciduous broadleaf forest and shrubs. Pandas avoid staying at habitats with human disturbances. Farmland is a major factor threatening panda habitat. Panda habitat is mainly distributed in north and west of Pingwu with a total area of 234033 hm², 106345hm² for suitable habitat and 127688 hm² for marginally suitable habitat). 3 nature reserves were located in Pingwu, covering over 49.2% of total suitable habitat and 45.6% of total marginally suitable habitat. Although 47.2% of panda habitat was in reserves under protection, connectivity between reserves was weak and a conservation gap existed in the north part of Pingwu. Thus, a new nature reserve in Baima and Mupi should be established to link the isolated habitats.     

基于ZONATION的岷山山系多物种保护规划

空间布局不尽合理是我国的自然保护区发展面临的问题之一,优化现有的自然保护区体系是在资源有限的情况下实现自然保护区的保护效益最大化的最佳途径。以岷山地区为主要研究区域,以25种珍稀濒危物种为主要研究对象,通过MAXENT与ZONATION模型模拟,鉴别出岷山地区珍稀濒危物种保护优先区,并提出了自然保护区空间选址的优化方案。研究结果表明,(1)在物种出现点记录较少的情况下,MAXENT模型依然能够比较准确地预测出物种的分布状况;(2)目前建立的保护区对25个物种的平均保护比例为51.8%;(3)通过ZONATION鉴定的优先区总面积为19958.7 km~2,岷山地区现有的29个自然保护区只保护了目标优先区的47.1%,九寨沟中部与南部,平武北部,平武与北川的交界处,北川西北部等地都存在保护空缺。建议在保护空缺的6个地方新建或扩建自然保护区,并在规划时考虑这些区域内部的优先次序。新提出的保护体系能将物种栖息地的保护比例提高至77.9%。研究结果对于岷山及全国其他生物多样性保护关键地区保护体系的规划都具有重要的借鉴意义。     

气候变化背景下秦岭地区羚牛生境脆弱性评估及适应性保护对策

脆弱性是指物种受气候变化影响的程度,开展脆弱性评估工作有助于人类认识气候变化对野生动物的影响,为制定野生动物适应气候变化的保护对策提供科学依据。采用最大熵模型评估气候变化背景下秦岭地区羚牛(Budorcas taxicolor bedfordi)生境脆弱性。结果表明：(1)当前秦岭地区羚牛适宜生境总面积为6473 km²,到2050s年,预测秦岭地区羚牛适宜生境总面积为4217 km²,减少34.85%,羚牛适宜生境将向更高海拔地区转移,转移约210 m;(2)已建保护区覆盖49.82%当前羚牛适宜生境,尚有3248 km²的适宜生境处于保护区之外;到2050s年,保护区覆盖了43.87%适宜生境,尚有2367 km²的适宜生境未被保护;(3)到2050s年,当前分布在太白县、佛坪县、洋县和宁陕县等地区的3490 km²羚牛适宜生境将会成为生境脆弱区域,丧失53.92%;(4)分布在秦岭核心区域的2983 km²当前和2050s年保持不变适宜生境,将成为羚...     

基于最大熵模型的野生马麝夏季生境适宜性研究

野生马麝(Moschus chrysogaster)是珍稀濒危资源动物,分布于我国青藏高原及周边地区。甘肃兴隆山国家级自然保护区是野生马麝最重要的分布区之一,深入了解保护区的生境结构、质量及分布是对区域内野生马麝进行成功保护的前提和基础,但迄今缺乏大尺度的马麝生境适宜性研究。利用实地调查得到的兴隆山保护区的野生马麝夏季分布点数据,采用最大熵(MaxEnt)模型,结合地形、归一化植被指数、距河流距离、距道路距离等环境变量数据,进行野生马麝的夏季生境适宜性分析。结果表明：影响野生马麝夏季生境适宜性的主要生态因子是海拔、坡向、植被和河流,其贡献率分别达40.3%,23.4%,18.6%和10.9%;兴隆山保护区野生马麝的夏季潜在适宜生境分布面积为123.34 km²,占整个保护区的41.11%,占保护区林地的61.92%;野生马麝夏季潜在适宜生境主要集中在保护区的中部和西部,其中高适宜性生境约为保护区的4.47%,各适宜区间及适宜区之内均存在一定程度的不连续分布。为加强对兴隆山保护区野生马麝种群及生境的就地保护,建议通过生境保育措施,提高野生马麝潜在分布区的生境适宜性,...     

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China

Climate change might pose an additional threat to the already vulnerable giant panda (Ailuropoda melanoleuca). Effective conservation efforts require projections of vulnerability of the giant panda in facing climate change and proactive strategies to reduce emerging climate‐related threats. We used the maximum entropy model to assess the vulnerability of giant panda to climate change in the Qinling Mountains of China. The results of modeling included the following findings: (1) the area of suitable habitat for giant pandas was projected to decrease by 281 km² from climate change by the 2050s; (2) the mean elevation of suitable habitat of giant panda was predicted to shift 30 m higher due to climate change over this period; (3) the network of nature reserves protect 61.73% of current suitable habitat for the species, and 59.23% of future suitable habitat; (4) current suitable habitat mainly located in Chenggu, Taibai, and Yangxian counties (with a total area of 987 km²) was predicted to be vulnerable. Assessing the vulnerability of giant panda provided adaptive strategies for conservation programs and national park construction. We proposed adaptation strategies to ameliorate the predicted impacts of climate change on giant panda, including establishing and adjusting reserves, establishing habitat corridors, improving adaptive capacity to climate change, and strengthening monitoring of giant panda.     

Impact of road construction on giant panda’s habitat and its carrying capacity in Qinling Mountains

The Qinling giant panda (Ailuropoda melanoleuca) is an endangered endemic species to China. Despite ongoing efforts to ensure its conservation, concerns about maintaining its populations persist. We used GIS fed with data on land use including road network of 2001, third national giant panda survey, and a digital elevation model (DEM) to assess the impact of road construction on giant panda habitat, and estimate the carrying capacity of the Qinling Mountain area. We assessed habitat suitability with a mechanistic model, and conducted correlation analysis to evaluate relationship between the extent of giant panda habitat and amount of sites occupied by pandas within of 5 km × 5 km grid. We also estimated the carrying capacity of the Qinling Mountainous Area.
Our results revealed a significant correlation (R² = 0.447, P < 0.01) between the number of sites with signs left by giant panda and the extent of habitat within of 5 km × 5 km grid. The minimum habitat area that can support one panda was 10 km². Before the road network construction, the area of habitat suitable for the panda amounted about 1561 km² and that of marginally suitable habitat about 1499 km². The corresponding carrying capacity represented about 240 individuals. After the road network construction, the suitable habitat area was reduced by nearly 30% to 1093 km². Marginally suitable habitat and unsuitable habitat have both increased by 17% and 1%, respectively. As a result, the potential population size which the habitat could support was reduced to 217 individuals. The study results also suggested that most impacts on habitat from road construction took place in the high elevation areas above 1500 m. However, regarding the impact on the giant panda habitat, road networks developed much more inside the current nature reserves than outside of them.     

Impact of road construction on giant panda’s habitat and its carrying capacity in Qinling Mountains

The Qinling giant panda (Ailuropoda melanoleuca) is an endangered endemic species to China. Despite ongoing efforts to ensure its conservation, concerns about maintaining its populations persist. We used GIS fed with data on land use including road network of 2001, third national giant panda survey, and a digital elevation model (DEM) to assess the impact of road construction on giant panda habitat, and estimate the carrying capacity of the Qinling Mountain area. We assessed habitat suitability with a mechanistic model, and conducted correlation analysis to evaluate relationship between the extent of giant panda habitat and amount of sites occupied by pandas within of 5 km × 5 km grid. We also estimated the carrying capacity of the Qinling Mountainous Area.
Our results revealed a significant correlation (R² = 0.447, P < 0.01) between the number of sites with signs left by giant panda and the extent of habitat within of 5 km × 5 km grid. The minimum habitat area that can support one panda was 10 km². Before the road network construction, the area of habitat suitable for the panda amounted about 1561 km² and that of marginally suitable habitat about 1499 km². The corresponding carrying capacity represented about 240 individuals. After the road network construction, the suitable habitat area was reduced by nearly 30% to 1093 km². Marginally suitable habitat and unsuitable habitat have both increased by 17% and 1%, respectively. As a result, the potential population size which the habitat could support was reduced to 217 individuals. The study results also suggested that most impacts on habitat from road construction took place in the high elevation areas above 1500 m. However, regarding the impact on the giant panda habitat, road networks developed much more inside the current nature reserves than outside of them.     

基于随机森林模型的国家重点保护陆生脊椎动物物种优先保护区的识别

准确可靠地识别国家重点保护陆生脊椎动物物种的优先保护区,是生物多样性保护的热点问题之一。采用随机森林(random forests)模型,基于12个环境变量,对中国263种国家重点保护陆生脊椎动物建模,并预测各个物种在背景点的适生概率,迭加计算得到国家重点保护陆生脊椎动物物种的生境适宜性指数。此外,基于对生境适宜性指数的空间自相关分析,识别和确定国家重点保护陆生脊椎动物物种优先保护区,并对优先保护区目前的被保护情况进行分析。结果表明,国家重点保护陆生脊椎动物物种的优先保护区的面积为103.16万km~2,约占我国国土面积的10.90%。优先保护区主要分布在我国的西部地区,包括西南地区的秦岭-大巴山山区、云南省与印度及缅甸的交界地区、武陵山山区、喜马拉雅山-横断山脉山区、阿尔泰山脉山区、天山山脉山区、昆仑山山脉山区;东北的大、小兴安岭、东北-华南沿海地区及长江中下游地区有少量分布。优先保护区中被保护的面积为50.40万km~2,占优先保护区总面积的48.86%,保护率偏低,未被充分保护。利用系统聚类分析,将未被保护的优先保护区划分成3种优先保护顺序,以期为相关部门的决策提供科学依据,更好地保护生物多样性。     

Ecological correlates of Himalayan musk deer Moschus leucogaster

Himalayan musk deer (Moschus leucogaster; hereafter musk deer) are endangered as a result of poaching and habitat loss. The species is nocturnal, crepuscular, and elusive, making direct observation of habitat use and behavior difficult. However, musk deer establish and repeatedly use the same latrines for defecation. To quantify musk deer habitat correlates, we used observational spatial data based on presence–absence of musk deer latrines, as well as a range of fine spatial‐scale ecological covariates. To determine presence–absence of musk deer, we exhaustively searched randomly selected forest trails using a 20‐m belt transect in different study sites within the Neshyang Valley in the Annapurna Conservation Area. In a subsequent way, study sites were classified as habitat or nonhabitat for musk deer. A total of 252 plots, 20 × 20 m, were systematically established every 100 m along 51 transects (each ~0.5 km long) laid out at different elevations to record a range of ecological habitat variables. We used mixed‐effect models and principal component analysis to characterize relationships between deer presence–absence data and habitat variables. We confirmed musk deer use latrines in forests located at higher elevations (3,200–4,200 m) throughout multiple seasons and years. Himalayan birch (Betula utilis) dominated forest, mixed Himalayan fir (Abies spectabilis), and birch forest were preferred over pure Himalayan fir and blue pine (Pinus wallichiana) forest. Greater crown cover and shrub diversity were associated with the presence of musk deer whereas tree height, diameter, and diversity were weakly correlated. Topographical attributes including aspect, elevation, distance to water source, and slope were also discriminated by musk deer. Over‐ and understory forest management can be used to protect forests likely to have musk deer as predicted by the models to ensure long‐term conservation of this rare deer.