Ecotone might provide key refugium for sky island mammals in the Southern Appalachian Mountains

Sky islands, ecosystems found on geographically isolated mountain peaks, are among the most biodiverse ecosystems in the world but face a disproportionately high threat from climate change. High‐elevation, montane ecosystems, which are already at their upper altitudinal limits, are predicted to severely contract in response to climate change. The identification and conservation of refugia is an increasingly important approach for protecting biodiversity associated with imperiled ecosystems. We explored the spruce‐fir–northern hardwood ecotone as a possible refugium for mammals in the Southern Appalachian red spruce (Picea rubens)‐Fraser fir (Abies fraseri) sky islands. We conducted livetrapping, camera trapping, and ultrasonic acoustic surveys to characterize mammal diversity across the spruce‐fir–northern hardwood forest gradient on Grandfather Mountain and Roan Mountain Highlands in western North Carolina, USA. We detected four out of the five spruce‐fir‐associated small mammal species in both spruce‐fir and ecotone habitats. Mammal species richness, alpha diversity, and bat activity tended to be higher in the ecotone than in the other forest types on both mountains. Next, the abundance of small mammals associated with spruce‐fir was higher in the spruce‐fir and ecotone forests for one of the three species we were able to estimate. Together, our results suggest that the spruce‐fir–northern hardwood ecotone might serve as refugium for mammal species that are associated with spruce‐fir sky islands in the Southern Appalachian Mountains and mammalian conservation efforts in this biodiversity hotspot should consider focusing on the ecotone in addition to the adjacent spruce‐fir ecosystem.

Methods
Study Area

This study was conducted in the Southern Appalachian Mountains, in western North Carolina, USA. We surveyed mammal communities on two mountains: Grandfather Mountain (36°6′ N and 81°48′ W; 1812 m above sea level [asl]), and Roan Mountain Highlands (36°6′ N and 82°7′ W; 1916 m asl). On each mountain, we conducted surveys at three sites along an elevational gradient, each located within one of the following forest types: spruce‐fir, spruce‐fir–northern hardwood ecotone, and northern hardwood. We surveyed six sites in total.

Mammal Surveys

We surveyed mammals along an elevational gradient at each mountain, from 1324 to 1635 m asl on Grandfather Mountain and 1547 to 1872 m asl in the Roan Mountain Highlands (Table S1 in Appendix S1). The peaks of Grandfather and Roan Mountains were approximately 27 km straight‐line distance from one another. We used three field survey techniques to document volant and non‐volant mammal species within each forest type on each mountain: live traps, remote camera traps, and ultrasonic acoustic recorders (Figure 2). All surveys occurred in June and July 2023 on the southern aspect of both mountains (Table S1 in Appendix S1). We surveyed a total of six sites (three forest types on each mountain) that were spaced by 0.6–1.9 km. Our protocol conformed to the guidelines outlined by the American Society of Mammalogists (Sikes et al. 2016) and was approved by the Appalachian State University Institutional Animal Care and Use Committee (permit #22‐12) with permissions from relevant management authorities.

Livetrapping

We live‐trapped non‐volant small mammal species using Sherman traps (3″ × 3.5″ × 9″; H.B. Sherman Traps, Tallahassee, FL, USA). Each survey site consisted of 36 traps arranged in a 6 × 6 grid with 20 m spacing between traps (Figure 2). We set traps at sunset and checked and closed them at sunrise. Each trapping session consisted of four consecutive trap nights for a total of 144 trap nights per each 100 m2 survey site. We baited traps with sunflower seeds, oats, and mealworms, and placed a cotton ball into each trap to help animals thermoregulate during colder nights. We placed traps at grid intersections in proximity to woody debris, trees, or rocks (Converse et al. 2006).

For each captured rodent, we measured weight, hind foot length, body length, and tail length for species identification (Stephens et al. 2014; Berl et al. 2017), and determined sex, age class (adult or juvenile), and external reproductive status (Steele and Powell 1999; Polyakov et al. 2021). We uniquely marked each adult rodent in the right ear with an ear tag (Style 1005‐1L1, National Band & Tag Company, Newport, KY, USA; McCain 2004) and used surgical scissors to take ear tissue samples from Peromyscus for molecular species assignment (see “Molecular species assignment and discriminant function analysis” section below). Shrews were weighed, sexed, and identified to species. We used a permanent ink pen to mark juvenile rodents and all shrews with a unique symbol on the stomach or tail. All animals were released at their capture location.

Molecular Species Assignment and Discriminant Function Analysis

Morphology‐based identification is often unreliable for the two Peromyscus species in the region, the cloudland deer mouse (P. maniculatus nubiterrae) and the white‐footed mouse (P. leucopus) (Choate 1973; Stephens et al. 2014; Berl et al. 2017). We extracted DNA from the collected Peromyscus ear tissue samples via Qiagen DNeasy blood and tissue kits (Qiagen, Germantown, MD, USA). A random subset of the DNA extracts (n = 48) was analyzed at the North Carolina Museum of Natural Sciences for identification to species using the cytochrome b mitochondrial gene (primers: MTCB‐F and R; Naidu et al. 2012).

For DNA sequencing, polymerase chain reactions (PCR) occurred in 10 μL volumes consisting of 5–10 ng of extracted DNA, 10 nM of forward and reverse primer, 2.0 mM of MgCl2, 5.0 μL of 1:5 mix of Takara Taq Polymerase and Promega GoTaq MasterMix, and 2.9 μL nuclease‐free water. PCRs began with initiation at 95°C for 2 min, then 35 cycles of denaturing at 95°C for 15 s, annealing at 52°C for 30 s, and extension at 72°C for 1 min with a final 10‐min extension at 72°C. We sequenced the forward primer for cytochrome b as it yields sufficient power to discern Peromyscus species (~600 bp of cytochrome b). Sequencing reactions initiated at 96°C for 3 min, then underwent 30 cycles at 96°C for 10 s, 50°C for 5 s, and 60°C for 2.5 min. We used the ethanol precipitation method to clean sequencing reactions (Latch and Rhodes 2005) and then sequenced them on a 3500 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). We aligned all sequences in Geneious Prime (Kearse et al. 2012) and uploaded them to GenBank (Accession Numbers PV487868–PV487919).

Finally, to identify the remaining live‐trapped Peromyscus individuals to species, we used a linear discriminant function analysis model in the “mda” 0.5‐4 package (Hastie et al. 2023) in R 4.3.2 (R Core Team 2023). The model function was built using four body measurements (weight, hind foot length, body length, and tail length). Analysis of variance tests confirmed that each body measurement differed significantly between the two species (p < 0.01). The model correctly assigned species identity for 93.9% of the 48 individuals previously identified by DNA analysis, and we used it to predict the species of the remaining captured individuals (n = 100).

Remote Camera Trapping

We used remote camera traps to detect small to large non‐volant mammal species. We installed four remote cameras (Spec Ops Elite HP5, Browning, Morgan, UT, USA) at each survey site within each forest type so that cameras were at a minimum of 150–200 m apart (Figure 2). We mounted each camera at 30–50 cm on the bole of a tree following standard methodology to survey for small to large mammals (Evans and Mortelliti 2022; Rooney et al. 2025) near animal trails, rock outcrops, logs, and other desirable habitat features to increase the probability of detection (Trolle et al. 2008; Gebert et al. 2019; Hofmeester et al. 2021). We set cameras to take five‐image photo bursts every 5 s when motion was detected. We considered all images taken of the same species at the same camera within an hour as one detection (Hegerl et al. 2017; Gebert et al. 2019). We set the cameras at each site for 31 days, for a total of 372 trap nights per mountain. We uploaded, sorted, and manually identified species in all camera images on Wildlife Insights (https://www.wildlifeinsights.org/; Ahumada et al. 2020).

Ultrasonic Acoustic Monitoring

We used ultrasonic acoustic detectors (SM4, Wildlife Acoustics Inc., Maynard, MA, USA) to record vocalizations and quantify bat activity and species richness, and the activity of American flying squirrels (Carolina northern flying squirrel and southern flying squirrel; Diggins et al. 2016, 2020) across the forest types. We installed two acoustic detectors at each trap grid in the same location as the camera traps (Figure 2). To detect bats and American flying squirrels while minimizing the detection of nontarget species, we set detectors to record sounds with a minimum frequency of 16 kHz and minimum length of 1.5 ms from 30 min before sunset to 30 min after sunrise for 10 days (Gilley et al. 2019; U.S. Fish and Wildlife Service 2024). We attached detectors to tree trunks 1.5 m off the ground, facing the direction with the least amount of clutter to improve recording quality (Diggins et al. 2016, 2020). We conducted acoustic recording for a total of 10 consecutive nights per survey site.

We used SonoBat software (SonoBat 4.4.5, DND Design, Arcata, CA, USA) to sort and identify flying squirrel calls. We confirmed all species identifications using two observers. A total of 845 calls were identified as American flying squirrels. We were able to identify four flying squirrel call types: chirps, trills, upsweeps, and crows (Gilley et al. 2019). We identified bat passes to species using Kaleidoscope Pro Analysis Software (Kaleidoscope 4.4, Wildlife Acoustics Inc., Maynard, MA, USA). Each bat pass had to match a species identification accuracy of at least 60% or the pass was classified as a “no ID” (Li and Kalcounis‐Rueppell 2018; Schimpp et al. 2018; Parker et al. 2019). We estimated bat species activity at each site as the mean number of bat passes recorded per night over the 10‐night survey period. We identified a total of 6436 bat passes to species.

Vegetation Surveys

We conducted vegetation surveys at every survey site to characterize each forest type. We established a circular 400 m2 plot centered at each live‐trap grid. In every plot, all live trees (≥ 1.4 m height) were identified to species and measured at diameter at breast height. In addition, we counted the number of tree snags. Within each 400 m2 plot, we established a 40 m2 subplot where all live saplings (< 1.4 m height) were identified to species and dead saplings counted (Kalies et al. 2012). We used the line interception method (Canfield 1941) along four perpendicular transects within the subplot to identify shrubs and herbaceous plant species. We calculated plant species richness as the total number of species detected at each survey site, including tree, shrub, and herbaceous species. We also calculated the percentage of trees that consisted of Fraser fir, red spruce, and northern hardwood species. Finally, we measured slope and aspect at the center of each plot.

天空群岛（sky islands）是指地理上孤立的山峰上形成的生态系统，是全球生物多样性最丰富的生态系统之一，却承受着与其面积不成比例的高强度气候变化威胁。高海拔山地生态系统本已处于海拔上限，据预测将因气候变化出现严重收缩。识别与保护生物避难所，是保护受威胁生态系统关联生物多样性的日益重要的手段。本研究以美国北卡罗来纳州西部南阿巴拉契亚山脉红皮云杉（*Picea rubens*）-弗雷泽冷杉（*Abies fraseri*）天空群岛中的云杉冷杉-北部硬叶林交错带（spruce-fir–northern hardwood ecotone）为潜在的哺乳动物避难所展开探索。我们通过活捕、红外相机监测与超声波声学调查，对祖父山（Grandfather Mountain）与罗恩山高地（Roan Mountain Highlands）沿线云杉冷杉-北部硬叶林梯度带的哺乳动物多样性进行了表征。我们在云杉冷杉林与交错带生境中，均检测到5种与云杉冷杉林关联的小型哺乳动物中的4种。两座山地的交错带内，哺乳动物物种丰富度、α多样性以及蝙蝠活动水平均高于其他林型。此外，在我们可开展种群数量估算的3个物种中，有1个物种的云杉冷杉林关联小型哺乳动物种群丰度在云杉冷杉林与交错带林中更高。综合来看，本研究结果表明，云杉冷杉-北部硬叶林交错带或可作为南阿巴拉契亚山脉云杉冷杉天空群岛关联哺乳动物物种的避难所，针对这一生物多样性热点区域的哺乳动物保护工作，除相邻的云杉冷杉生态系统外，亦应将交错带纳入保护重点。

## 研究方法
### 研究区域
本研究在美国北卡罗来纳州西部的南阿巴拉契亚山脉开展。我们在两座山地开展哺乳动物群落调查：祖父山（北纬36°6′，西经81°48′，海拔1812米）与罗恩山高地（北纬36°6′，西经82°7′，海拔1916米）。在每座山地，我们沿海拔梯度设置3个调查样地，分别对应3种林型：云杉冷杉林、云杉冷杉-北部硬叶林交错带以及北部硬叶林，总计设置6个调查样地。

### 哺乳动物调查
我们沿每座山地的海拔梯度开展哺乳动物调查，祖父山的调查海拔范围为1324至1635米，罗恩山高地为1547至1872米（详见附录S1中的表S1）。祖父山与罗恩山的山顶直线距离约为27千米。我们采用3种野外调查技术，记录每座山地各林型内的飞行与非飞行哺乳动物物种：活捕笼、红外相机陷阱以及超声波声学记录仪（见图2）。所有调查均于2023年6-7月在两座山地的南坡开展（详见附录S1中的表S1）。我们共设置6个调查样地（每座山地对应3种林型），样地间距为0.6-1.9千米。本实验方案符合美国哺乳动物学会（American Society of Mammalogists）制定的指南（Sikes等，2016），并通过阿巴拉契亚州立大学动物护理与使用委员会审批（审批号：22-12），同时获得了相关管理部门的许可。

### 活捕
我们使用谢尔曼活捕笼（Sherman traps）捕捉非飞行小型哺乳动物。每个调查样地设置36个捕笼，以6×6网格排布，捕笼间距为20米（见图2）。我们于日落时布设捕笼，次日日出时收起并检查捕获物。每次捕笼活动连续开展4个夜晚，即每100平方米调查样地总计开展144个捕笼夜。捕笼内投放葵花籽、燕麦与面包虫作为诱饵，并放置棉球以帮助动物在低温夜晚维持体温。捕笼设置在木质碎屑、树木或岩石附近的网格交点处（Converse等，2006）。

对于每只捕获的啮齿动物，我们测量其体重、后足长、体长与尾长以进行物种鉴定（Stephens等，2014；Berl等，2017），并记录其性别、年龄等级（成体或幼体）以及外部生殖状态（Steele与Powell，1999；Polyakov等，2001）。我们使用耳标（型号1005-1L1，National Band & Tag Company公司，纽波特，肯塔基州，美国；McCain，2004）在成体啮齿动物的右耳进行唯一标记，并使用手术剪采集鹿鼠属（*Peromyscus*）个体的耳组织样本，用于分子物种鉴定（详见下文“分子物种鉴定与判别函数分析”部分）。对鼩鼱，我们测量体重、鉴定性别并确定物种。我们使用永久墨水笔在幼体啮齿动物与所有鼩鼱的腹部或尾部标记唯一符号。所有捕获动物均在捕获地点原地释放。

### 分子物种鉴定与判别函数分析
对于本区域内的两种鹿鼠属物种——云杉鹿鼠（*P. maniculatus nubiterrae*）与白足鼠（*P. leucopus*），基于形态学的鉴定方法往往可靠性不足（Choate，1973；Stephens等，2014；Berl等，2017）。我们使用Qiagen DNeasy血液与组织试剂盒（Qiagen公司，日耳曼敦，马里兰州，美国）从采集的鹿鼠属耳组织样本中提取DNA。我们随机选取48份DNA提取物，送至北卡罗来纳自然科学博物馆，通过细胞色素b线粒体基因（引物：MTCB-F与R；Naidu等，2012）进行物种鉴定。

DNA测序的聚合酶链式反应（PCR）体系体积为10μL，包含5-10ng提取的DNA、10nM的正向与反向引物、2.0mM氯化镁、5.0μL 1:5混合的Takara Taq聚合酶与Promega GoTaq MasterMix，以及2.9μL无核酸酶水。PCR反应程序为：95℃预变性2分钟，随后35个循环：95℃变性15秒，52℃退火30秒，72℃延伸1分钟，最后72℃终延伸10分钟。我们使用正向引物对细胞色素b基因进行测序，因其可提供足够的分辨力以区分鹿鼠属物种（约600bp的细胞色素b序列）。测序反应程序为：96℃初始化3分钟，随后30个循环：96℃变性10秒，50℃退火5秒，60℃延伸2.5分钟。我们使用乙醇沉淀法对测序反应产物进行纯化（Latch与Rhodes，2005），随后在3500基因分析仪（Applied Biosystems公司，福斯特城，加利福尼亚州，美国）上完成测序。我们在Geneious Prime软件中对所有序列进行比对（Kearse等，2012），并将序列上传至GenBank（登录号：PV487868–PV487919）。

最后，为将剩余活捕获得的鹿鼠属个体鉴定至物种水平，我们使用R 4.3.2（R核心团队，2023）中“mda”0.5-4包（Hastie等，2023）构建的线性判别函数分析模型。该模型基于4项身体测量指标（体重、后足长、体长与尾长）构建。方差分析结果证实，两项物种间的各项身体测量指标均存在显著差异（p<0.01）。该模型可正确鉴定93.9%的经DNA分析鉴定的48个个体的物种，随后我们使用该模型预测剩余100个捕获个体的物种。

### 红外相机陷阱监测
我们使用红外相机陷阱检测小型至大型的非飞行哺乳动物物种。我们在每个林型的调查样地内布设4台红外相机（Spec Ops Elite HP5，Browning公司，摩根，犹他州，美国），相机间距至少为150-200米（见图2）。按照标准调查方法，我们将相机安装在距离树干30-50厘米高度处（Evans与Mortelliti，2022；Rooney等，2025），布设于动物步道、岩石露头、倒木及其他适宜生境特征附近，以提升检测概率（Trolle等，2008；Gebert等，2019；Hofmeester等，2021）。我们设置相机在检测到运动时，每5秒拍摄5张连拍照片。我们将同一相机1小时内拍摄的同一物种的所有照片计为1次检测事件（Hegerl等，2017；Gebert等，2019）。每个样地的相机布设时长为31天，每座山地总计开展372个相机陷阱夜。我们将所有相机照片上传至Wildlife Insights平台（https://www.wildlifeinsights.org/; Ahumada等，2020），并进行分类与人工物种鉴定。

### 超声波声学监测
我们使用超声波声学探测器（SM4，Wildlife Acoustics Inc.公司，梅纳德，马萨诸塞州，美国）记录鸣叫声，以量化蝙蝠活动与物种丰富度，以及美洲飞鼠（卡罗莱纳北部飞鼠与南部飞鼠；Diggins等，2016，2020）在各林型中的活动情况。我们在每个捕笼网格的与相机陷阱相同的位置布设2台声学探测器（见图2）。为检测蝙蝠与美洲飞鼠同时尽可能减少非目标物种的检测，我们将探测器设置为记录最低频率16kHz、最短时长1.5ms的声音，记录时长为日落前30分钟至日出后30分钟，持续10天（Gilley等，2019；美国鱼类与野生动物管理局，2024）。我们将探测器固定在距离地面1.5米的树干上，朝向杂物最少的方向以提升录音质量（Diggins等，2016，2020）。每个调查样地的声学记录时长总计为连续10个夜晚。

我们使用SonoBat软件（SonoBat 4.4.5，DND Design公司，阿卡塔，加利福尼亚州，美国）对飞鼠的鸣叫声进行分类与鉴定，并由两名观察员确认所有物种鉴定结果。总计有845个鸣叫声被鉴定为美洲飞鼠，我们可识别出4种飞鼠鸣叫声：啁啾声、颤音、上扫声与乌鸦样叫声（Gilley等，2019）。我们使用Kaleidoscope Pro分析软件（Kaleidoscope 4.4，Wildlife Acoustics Inc.公司，梅纳德，马萨诸塞州，美国）将蝙蝠飞行声鉴定至物种水平。每一次蝙蝠飞行声需达到至少60%的物种鉴定准确率，否则被归类为“无法鉴定”（Li与Kalcounis-Rueppell，2018；Schimpp等，2018；Parker等，2019）。我们将每个样地的蝙蝠物种活动量估算为10天调查期内每晚记录的蝙蝠飞行声平均数量。总计有6436次蝙蝠飞行声被鉴定至物种水平。

### 植被调查
我们在每个调查样地开展植被调查，以表征各林型的特征。我们以每个活捕笼网格为中心，设置一个400平方米的圆形样地。在每个样地内，我们对所有胸径≥1.4米的活立木进行物种鉴定并测量胸径，同时统计枯立木数量。在每个400平方米样地内，我们设置一个40平方米的亚样地，对所有高度<1.4米的活幼树进行物种鉴定，并统计死亡幼树数量（Kalies等，2012）。我们使用样线截距法（Canfield，1941），沿亚样地内的4条垂直样带调查灌木与草本植物物种。我们将每个调查样地内检测到的乔木、灌木与草本植物物种总数计为植物物种丰富度。此外，我们计算了弗雷泽冷杉、红皮云杉与北部硬叶树种在树木中的占比。最后，我们在每个样地中心测量坡度与坡向。