中国野生动物红外相机监测网络专题

三江源红外相机社区监测平台概述

  • 贾丁 ,
  • 李沛芸 ,
  • 赵翔 ,
  • 程琛 ,
  • 肖凌云 ,
  • 吕植
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  • 1 北京大学生命科学学院自然保护与社会发展研究中心, 北京 100871
    2 山水自然保护中心, 北京 100871
    3 西交利物浦大学健康与环境科学学院, 江苏苏州 215123

收稿日期: 2020-06-28

  录用日期: 2020-07-31

  网络出版日期: 2020-09-20

Overview of Sanjiangyuan Community-based Camera-trapping Monitoring Platform

  • Ding Jia ,
  • Peiyun Li ,
  • Xiang Zhao ,
  • Chen Cheng ,
  • Lingyun Xiao ,
  • Zhi Lü
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  • 1 Center for Nature and Society, College of Life Sciences, Peking University, Beijing 100871
    2 Shanshui Conservation Center, Beijing 100871
    3 Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123
First author contact: Co-first author

Received date: 2020-06-28

  Accepted date: 2020-07-31

  Online published: 2020-09-20

摘要

三江源红外相机社区监测平台以当地牧民为监测工作的主体, 开展该地区的生物多样性监测、野生动物生态学和行为学研究, 以及基于社区的自然资源管理与保护成效评估。三江源红外相机社区监测平台于2013年10月由北京大学自然保护与社会发展研究中心与山水自然保护中心联合三江源当地社区共同建立与管理。截至2019年6月, 该平台共有有效监测样区9个, 监测覆盖面积7,000多平方公里, 覆盖三江源区域的玉树州全境五县一市和果洛州班玛县, 培养了社区监测队员264名。已处理照片总数252.43万张, 动物独立探测总数12万次, 共识别出30种野生兽类和37种野生鸟类。该平台在调查野生动物多样性本底, 研究雪豹(Panthera uncia)种群密度与动态、雪豹与同域分布的其他食肉动物的关系, 总结社区监测的管理经验等方面取得部分成果。平台未来的工作重点包括总结与发表平台的研究结果、构建云端数据库实现红外相机照片数据的共享与公众参与、打造可互动数据库管理平台和相应监测队员手持客户端以及人工智能辅助下的物种与个体识别。

本文引用格式

贾丁 , 李沛芸 , 赵翔 , 程琛 , 肖凌云 , 吕植 . 三江源红外相机社区监测平台概述[J]. 生物多样性, 2020 , 28(9) : 1104 -1109 . DOI: 10.17520/biods.2019322

Abstract

The Sanjiangyuan Community-based Camera-trapping Monitoring Platform relies on the help of local Tibetan herders with nature conservation efforts. This platform uses infrared-triggered camera trapping, which is important for research on Sanjiangyuan wildlife ecology, animal behavior, biodiversity monitoring, and evaluation of community management for conservation. The platform was established in October 2013 by Center for Nature and Society of Peking University and Shanshui Conservation Center. As of June 2019, the platform has 9 functional monitoring sites with 264 local rangers in Yushu Prefecture and Banma County in Guoluo Prefecture, covering an area of about 7,000 km 2. We obtained 2,524,300 pictures resulting in 120,000 independent detections and documented 30 mammal and 37 bird species. This data has been used in surveying wildlife richness, accessing density and population dynamics of snow leopards (Panthera uncia), studying the inter-species relationship between snow leopards and co-occurring carnivores, and sharing management experience of community-based monitoring. The future directions for this platform will be to further summarize and publish the research results from the database, build a cloud-based database for better data sharing and public participation, establish an interactive management platform for national park administrators with the application of handhold terminals designed for national park rangers, and to identify species and snow leopard individuals via artificial intelligence.

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