• 首页关于本刊期刊订阅编委会作者指南过刊浏览
何可,杨志松,青菁,何流洋,戴强,齐敦武,毕温磊,Jacob R Owens,古晓东,杨旭煜.2016.大熊猫GPS项圈行为数据的分类阈值.动物学杂志,51(2):169-175.
大熊猫GPS项圈行为数据的分类阈值
The Threshold of Behavior Data from Giant Panda GPS Collar
投稿时间:2015-10-12  修订日期:2016-02-23
DOI:DOI: 10.13859/j.cjz.201602001
中文关键词:  大熊猫(Ailuropoda melanoleuca)  GPS项圈  运动指数  阈值  活动率
英文关键词:Giant panda (Ailuropoda melanoleuca)  GPS collar  Motion index  Threshold  Activity rate
基金项目:大熊猫放归合作基金
作者单位E-mail
何可 西华师范大学生命科学学院
成都大熊猫繁育研究基地四川省濒危野生动物保护生物学省部共建实验室 
heke0611@163.com 
杨志松 西华师范大学生命科学学院 yangzhisong@126.com 
青菁 西华师范大学生命科学学院
中科院成都生物所 
 
何流洋 西华师范大学生命科学学院
四川栗子坪国家级自然保护区 
 
戴强 中科院成都生物所  
齐敦武 成都大熊猫繁育研究基地四川省濒危野生动物保护生物学省部共建实验室  
毕温磊 成都大熊猫繁育研究基地四川省濒危野生动物保护生物学省部共建实验室四川成都  
Jacob R Owens 成都大熊猫繁育研究基地四川省濒危野生动物保护生物学省部共建实验室  
古晓东 四川省野生动物资源调查保护管理站  
杨旭煜 四川省野生动物资源调查保护管理站  
摘要点击次数: 1742
全文下载次数: 2415
中文摘要:
      在大熊猫(Ailuropoda melanoleuca)研究中,利用GPS项圈研究活动节律已经得到广泛应用。但是,由于对GPS项圈行为数据的分类阈值一直缺乏研究,导致了大熊猫体活动节律分析结果出现一定的偏差。本文以半野化过渡训练区内2只佩戴GPS项圈(Lotek_7000 MU)的大熊猫(“倩倩”与“和盛”)为研究对象,通过监控视频观测获得休息与活动行为时间段,对比同期利用GPS项圈行为数据计算得到的运动指数,采用正判率最大化策略,确定大熊猫休息和活动两类行为运动指数的行为分类阈值。结果表明,大熊猫休息状态和活动状态运动指数的行为分类阈值为32,而之前研究中采用0作为阈值。以32为阈值对行为分类,其中,休息行为正判率为98.23%,运动行为正判率94.48%;而以0为阈值,休息行为正判率为100%,运动行为正判率77.34%。利用阈值32和0对两只大熊猫19 d的休息行为和活动行为进行分类识别。以0为阈值时,大熊猫日平均活动率(“倩倩”为59%;“和盛”为70%),高于以32为阈值得到的日平均活动率(“倩倩”为54%;“和盛”为50%),这表明以0为行为分类阈值时,会高估大熊猫活动率。
英文摘要:
      Though GPS collar has been widely used to study the activity rhythm of giant panda (Ailuropoda melanoleuca), the lack of researching on the reclassified value of the collar data leads to a deviation result. To determine the threshold, we carried out the research on the two giant panda (Table 1) in Chengdu Research Base of Giant Panda Breeding Dujiangyan Field Research Center for Giant Pandas. Based on the strategy of maximum positive rate. We compared the behavior data obtained from the video monitor (Table 2) with the motion sensor data of GPS collars in the period of May 23rd, 2015 to June 10th, 2015. The results showed there was no significant difference between rest motion index and active motion index of the two giant panda (Fig. 1). The threshold was determined as 32 while 0 was used as the threshold in prior researches. The percentage of correctly classified rest behavior and active behavior was 98.23% and 94.48%, respectively. Whereas, based on the threshold of 0, the percentage of correctly classified rest behavior and active behavior was 100% and 77.34%, respectively (Fig. 2). We identified the resting and activity behaviors of the two giant pandas in 19 days based on the value of 32 and 0. The result showed that the daily average activity rate of giant pandas based on the threshold of 0 (“Qian Qian” was 59%, “He Sheng” was 70%) was significantly higher than the daily average activity rate based on the threshold of 32 (“Qian Qian” was 54%, “He Sheng” was 50%) (Fig. 3). It suggested that the activity rates of giant pandas could over estimated based on the value of 0.
附件
查看全文  查看/发表评论  下载PDF阅读器