首页> 外文学位 >Individual variation in the echolocation calls of big brown bats (Eptesicus fuscus) and their potential for acoustic identification and censusing.
【24h】

Individual variation in the echolocation calls of big brown bats (Eptesicus fuscus) and their potential for acoustic identification and censusing.

机译:大棕蝙蝠(Eptesicus fuscus)的回声定位调用的个体差异及其在声学识别和人口普查中的潜力。

获取原文
获取原文并翻译 | 示例

摘要

We compared the discriminability of the echolocation calls of big brown bats (Eptesicus fuscus) in three situations: (a) while held in the hand, (b) while perched on a platform, and (c) while flying in an anechoic chamber. Using variables describing each sonar call, we employed discriminant function analysis (DFA) to assign calls to bat across recording situations (which yielded 72% success), and, within a given recording situation (87% success).; Advances in computer equipment made it possible to replace our laboratory recording equipment with a laptop-based system. Using widely available software tools, it is also possible to take recordings analyze them automatically on a computer. This allows a researcher to record and analyze large numbers of calls without investing unreasonable amounts of time.; We recorded sonar calls of bats under laboratory and field conditions and tested the ability of neural networks to estimate the number of bats that produced a given set of recordings. Laboratory tests used calls from both big brown bats (E. fuscus) and little brown bats ( Myotis lucifugus) while field tests used calls from E. fuscus only. The number of animals tested in the lab ranged from three to 24 and in all cases the estimate was within 11 of the correct number. For field recordings, we tested between three and 26 animals and all estimates were within four of the correct number of bats. These results suggest that neural networks might be useful for acoustic censusing of bats in the field.; We tested a series of laboratory recordings of the sonar calls of E. fuscus with DFA and backpropagation neural networks to discriminate sonar calls using recordings made after various intervals. DFA could distinguish animals from recordings made up to five years apart; however, the network was unable to discriminate animals over time spans as short as five months. Using a distance measurement calculated during DFA, we found that bats recorded within five months could be discriminated reliably from novel animals.
机译:我们在以下三种情况下比较了大棕蝙蝠( Eptesicus fuscus )的回声定位的可分辨性:(a)握在手中,(b)栖息在平台上,(c)同​​时在消声室内飞行。使用判别函数分析(DFA)来描述每个声纳调用的变量,以便在记录情况下(成功产生72%)和在给定记录情况下(成功87%)分配对bat的调用。计算机设备的进步使我们可以用基于笔记本电脑的系统代替我们的实验室记录设备。使用广泛使用的软件工具,还可以在计算机上对记录进行自动分析。这使研究人员可以记录和分析大量呼叫,而无需花费不合理的时间。我们在实验室和野外条件下记录了蝙蝠的声纳呼叫,并测试了神经网络估计产生给定记录的蝙蝠数量的能力。实验室测试使用大棕蝙蝠( E。fuscus )和小棕蝙蝠( Myotis lucifugus )的电话,而现场测试则使用 E的电话。仅。在实验室中测试的动物数量在3到24之间不等,在所有情况下,估计数量均在正确数量的11以内。对于野外记录,我们测试了3至26只动物,所有估计值均在正确蝙蝠数量的4个之内。这些结果表明,神经网络对于田间蝙蝠的声普查可能有用。我们使用DFA和反向传播神经网络测试了 E. fuscus 声纳调用的一系列实验室记录,以使用在不同间隔后进行的记录来区分声纳调用。 DFA可以将动物与相隔长达五年的录音区分开来;但是,该网络无法在短短五个月的时间内区分动物。使用DFA期间计算的距离测量结果,我们发现可以可靠地区分五个月内记录的蝙蝠与新动物。

著录项

  • 作者

    Burnett, Stephen Cameron.;

  • 作者单位

    The Ohio State University.;

  • 授予单位 The Ohio State University.;
  • 学科 Biology Zoology.; Physics Acoustics.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 168 p.
  • 总页数 168
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 动物学;声学;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号