生物多样性 ›› 2020, Vol. 28 ›› Issue (9): 0-0.DOI: 10.17520/biods.2019322

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三江源红外相机社区监测平台

贾丁1,李沛芸2,赵翔2,程琛2,肖凌云1,吕植1   

  1. 1. 北京大学生命科学学院自然保护与社会发展研究中心
    2. 山水自然保护中心
  • 收稿日期:2019-10-15 修回日期:2020-07-26 出版日期:2020-09-20 发布日期:2020-09-20
  • 通讯作者: 吕植

Camera Trap Database of Sanjiangyuan Community-based Monitoring Platform

Ding Jia1,Peiyun Li2,Xiang Zhao2,Chen Cheng2,Lingyun Xiao1,Zhi Lü1   

  1. 1. Center for Nature and Society, College of Life Sciences, Peking University
    2. Shanshui Conservation Center
  • Received:2019-10-15 Revised:2020-07-26 Online:2020-09-20 Published:2020-09-20
  • Contact: Zhi Lü

摘要: 基于三江源社区的红外相机监测平台,让当地牧民成为自然保护工作的主体,对该地区生物多样性监测、野生动物生态学研究、动物行为学研究,及基于社区的自然资源管理与保护效应评价都有重要意义。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。截止2019年6月,共有有效监测点九个,监测覆盖面积7000多平方公里,涉及到横跨三江源区域的玉树州全境五县一市和果洛州玛多县,培养了社区监测队员264名。已处理照片总数252.46万张,动物独立探测总数12万次,共识别出30种野生兽类和39种野生鸟类。红外相机平台已经在野生动物多样性本底调查、雪豹 (Panthera uncia) 种群密度与动态、雪豹与同域食肉动物关系、社区监测的管理经验等方向取得一些成果。未来,进一步总结与发表三江源红外相机监测平台研究结果、构建云端数据库实现红外相机照片数据共享与公众参与、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将是平台下一步的工作重心。

关键词: 三江源, 红外相机, 社区监测, 雪豹, 物种名录

Abstract: Community-based monitoring makes local Tibetan herders take the main responsibility of nature conservation by using the technique of infrared-triggered camera trapping, which has important significance on the research of Sanjiangyuan wildlife ecology, animal behavior, biodiversity monitoring, community management and conservation evaluation. Camera trap database of Sanjiangyuan Community-based Monitoring 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 effective monitoring points and 264 community monitoring members in Yushu Prefecture and Maduo County in Guoluo Prefecture, covering an area of about 7000 km2. We obtained 2,524,600 pictures resulting in 120,000 independent detections and recorded 30 wild mammalian and 39 avian species. The data have been used in the survey of wild animal diversity, the density estimation and population dynamics of snow leopard (Panthera uncia), interspecies relationship between snow leopard and the same field carnivores, management experience of community-based monitoring. Summarizing the research results based on the database, building a database network platform for data sharing and public participation, creating an interactive database management platform for data managers and an application for monitoring members, using artificial intelligence to identify species and individual will be the future plan of the platform.

Key words: Sanjiangyuan, camera trapping, community-based monitoring, snow leopard (Panthera uncia), species list