生物多样性 ›› 2025, Vol. 33 ›› Issue (7): 24509. DOI: 10.17520/biods.2024509 cstr: 32101.14.biods.2024509
收稿日期:
2024-11-22
接受日期:
2025-02-06
出版日期:
2025-07-20
发布日期:
2025-08-27
通讯作者:
*E-mail: lijia2530@126.com
基金资助:
Jicai Li1,2, Changliang Shao2, Shuaishuai Gao3, Jia Li3,*()(
)
Received:
2024-11-22
Accepted:
2025-02-06
Online:
2025-07-20
Published:
2025-08-27
Contact:
*E-mail: lijia2530@126.com
Supported by:
摘要: 掌握蒙古野驴(Equus hemionus)活动节律、活动范围和适宜生境分布, 有助于理解蒙古野驴适应环境变化的生存策略。本研究于2021-2023年夏季(6-8月)在卡拉麦里国家公园候选区, 利用红外相机技术开展蒙古野驴夏季水源利用规律研究。于2022-2024年, 对卡拉麦里国家公园候选区野外放归的6匹蒙古野驴佩带GPS颈圈进行跟踪监测。利用GPS颈圈数据开展蒙古野驴50%和95% Kernel夏季活动范围研究。同时, 基于红外相机和GPS颈圈数据, 利用BIOMOD2物种分布模型模拟蒙古野驴夏季适宜生境分布, 并评估国家公园候选区保护空缺。结果表明: (1)安装在卡拉麦里国家公园候选区的红外相机夏季共记录到5,996张蒙古野驴独立照片。其中, 白天2,825张、夜间2,212张、清晨459张、黄昏500张; (2)蒙古野驴在夏季水源利用方面表现出全天活动强度相对稳定的特征, 为昼夜兼性动物, 夜间活动指数略高于其他时间段, 活动高峰出现在23:00至翌日04:00, 清晨和黄昏时分活动强度略低; (3) 6匹蒙古野驴夏季50% K活动范围为4.98-162.18 km2, 平均活动范围为76.34 ± 69.36 km2; 夏季95% K活动范围为27.67-2,117.72 km2, 平均活动范围为892.95 ± 908.91 km2; (4) BIOMOD2模拟结果表明, 卡拉麦里国家公园候选区蒙古野驴夏季适宜生境总面积为1.94 × 104 km2, 76.97%的适宜生境处于国家公园候选区范围内。本研究初步掌握了卡拉麦里国家公园候选区蒙古野驴夏季利用水源规律、活动范围和适宜生境分布, 丰富了行为生态学知识, 可为国家公园候选区制定针对蒙古野驴及其栖息地的保护管理对策提供科学依据。
李基才, 邵长亮, 高帅帅, 李佳 (2025) 新疆卡拉麦里国家公园候选区蒙古野驴夏季水源利用规律、活动范围和适宜生境分布. 生物多样性, 33, 24509. DOI: 10.17520/biods.2024509.
Jicai Li, Changliang Shao, Shuaishuai Gao, Jia Li (2025) Summer water source utilization patterns, activity range and suitable habitat distribution of Equus hemionus in Xinjiang Kalamaili National Park candidate area. Biodiversity Science, 33, 24509. DOI: 10.17520/biods.2024509.
图1 新疆卡拉麦里国家公园候选区布设的红外相机点位和蒙古野驴夏季分布位置
Fig. 1 Camera trapping sites and Equus hemionus summer location in Xinjiang Kalamaili National Park candidate area
编号 ID | 跟踪时间 Tracking time | 工作天数 Working day | 点位数量 No. of point | 50% K活动范围 50% K activity range (km2) | 95% K活动范围 95% K activity range (km2) |
---|---|---|---|---|---|
No. 01 | 2023/05/09-2024/09/18 | 498 | 4,416 | 162.18 | 2,117.73 |
No. 02 | 2022/03/12-2024/08/26 | 898 | 9,523 | 89.56 | 1,476.72 |
No. 03 | 2022/03/12-2022/06/11 | 91 | 122 | 153.37 | 1,489.01 |
No. 04 | 2022/03/12-2022/07/01 | 111 | 242 | 24.28 | 102.03 |
No. 05 | 2022/03/12-2023/12/01 | 629 | 1,642 | 4.98 | 27.67 |
No. 06 | 2022/03/12-2023/10/05 | 572 | 1,637 | 23.67 | 144.51 |
表1 卡拉麦里国家公园候选区蒙古野驴佩戴GPS颈圈点位, 以及95% Kernel和50% Kernel活动范围
Table 1 GPS collar positioning points and activity range (95% K and 50% K) of Equus hemionus in Kalamaili National Park candidate area①
编号 ID | 跟踪时间 Tracking time | 工作天数 Working day | 点位数量 No. of point | 50% K活动范围 50% K activity range (km2) | 95% K活动范围 95% K activity range (km2) |
---|---|---|---|---|---|
No. 01 | 2023/05/09-2024/09/18 | 498 | 4,416 | 162.18 | 2,117.73 |
No. 02 | 2022/03/12-2024/08/26 | 898 | 9,523 | 89.56 | 1,476.72 |
No. 03 | 2022/03/12-2022/06/11 | 91 | 122 | 153.37 | 1,489.01 |
No. 04 | 2022/03/12-2022/07/01 | 111 | 242 | 24.28 | 102.03 |
No. 05 | 2022/03/12-2023/12/01 | 629 | 1,642 | 4.98 | 27.67 |
No. 06 | 2022/03/12-2023/10/05 | 572 | 1,637 | 23.67 | 144.51 |
图3 卡拉麦里国家公园候选区蒙古野驴(编号No. 01-06)夏季活动范围
Fig. 3 Activity range (95% K and 50% K) of Equus hemionus (No. 01-06) in Kalamaili National Park candidate area during summer
图4 卡拉麦里国家公园候选区蒙古野驴夏季环境变量之间的相关性分析(a)、对组合模型的综合贡献率(b)以及模型评估精度(c)。NPP: 净初级生产力; NDVI: 归一化植被指数; Water: 距水源点距离; Ele: 海拔; Way: 距道路距离; Bio5: 最暖月最高温; Bio10: 最暖季平均温; Bio18: 最暖月降水量; ANN: 人工神经网络模型; CTA: 分类与回归树模型; FDA: 柔性判别分析模型; GAM: 广义加法模型; GBM: 广义增强回归模型; GLM: 广义线性模型; MARS: 多元适应回归样条函数; MAXENT: 最大熵模型; RF: 随机森林; SRE: 表面分布区分室模型; EM: 组合模型; AUC: 受试者工作特征曲线下面积; TSS: 真实技巧统计值。
Fig. 4 Correlation analysis among environmental variables (a), comprehensive contribution to combine model (b), and accuracy evaluation of models (c) for Equus hemionus in Kalamaili National Park candidate area during summer. NPP, Net primary productivity; NDVI, Normalized difference vegetation index; Water, Distance to water sites; Ele, Elevation; Way, Distance to ways; Bio5, Max. temperature of warmest month; Bio10, Mean temperature of warmest quarter; Bio18, Precipitation of warmest month; ANN, Artificial neural network; CTA, Classification and regression tree analysis; FDA, Flexible discriminant analysis; GAM, Generalized addition model; GBM, Generalized boosted regression model; GLM, Generalized linear model; MARS, Multivariate adaptive regression splines; MAXENT, Maximum entropy model; RF, Random forest; SRE, Surface range envelope model; EM, Combined model; AUC, Area under the receiver operating characteristic curve; TSS, True skill statistic.
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