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Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance
Methods in Ecology and Evolution ( IF 6.6 ) Pub Date : 2021-02-22 , DOI: 10.1111/2041-210x.13578
Jeffrey W. Doser 1, 2 , Andrew O. Finley 1, 2, 3 , Aaron S. Weed 4 , Elise F. Zipkin 2, 5
Affiliation  

  1. Monitoring wildlife abundance across space and time is an essential task to study their population dynamics and inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations and communities. While current acoustic data models provide information on the presence/absence of individual species, new approaches are needed to monitor population abundance, ideally across large spatio-temporal regions.
  2. We present an integrated modelling framework that combines high-quality but temporally sparse bird point count survey data with acoustic recordings. Our models account for imperfect detection in both data types and false positive errors in the acoustic data. Using simulations, we compare the accuracy and precision of abundance estimates using differing amounts of acoustic vocalizations obtained from a clustering algorithm, point count data, and a subset of manually validated acoustic vocalizations. We also use our modelling framework in a case study to estimate abundance of the Eastern Wood-Pewee (Contopus virens) in Vermont, USA.
  3. The simulation study reveals that combining acoustic and point count data via an integrated model improves accuracy and precision of abundance estimates compared with models informed by either acoustic or point count data alone. Improved estimates are obtained across a wide range of scenarios, with the largest gains occurring when detection probability for the point count data is low. Combining acoustic data with only a small number of point count surveys yields estimates of abundance without the need for validating any of the identified vocalizations from the acoustic data. Within our case study, the integrated models provided moderate support for a decline of the Eastern Wood-Pewee in this region.
  4. Our integrated modelling approach combines dense acoustic data with few point count surveys to deliver reliable estimates of species abundance without the need for manual identification of acoustic vocalizations or a prohibitively expensive large number of repeated point count surveys. Our proposed approach offers an efficient monitoring alternative for large spatio-temporal regions when point count data are difficult to obtain or when monitoring is focused on rare species with low detection probability.


中文翻译:

集成自动声学发声数据和点计数调查以估计鸟类数量

  1. 跨空间和时间监测野生动物的数量是研究其种群动态并为有效管理提供信息的一项基本任务。声学记录装置是一种有效监测鸟类种群和社区的有前途的技术。虽然当前的声学数据模型提供了关于单个物种存在/不存在的信息,但需要新的方法来监测种群丰度,最好是跨越大的时空区域。
  2. 我们提出了一个集成的建模框架,它将高质量但时间上稀疏的鸟点计数调查数据与声学记录相结合。我们的模型考虑了数据类型中的不完美检测和声学数据中的误报。使用模拟,我们使用从聚类算法、点计数数据和手动验证的声学发声的子集获得的不同数量的声学发声来比较丰度估计的准确性和精度。我们还在案例研究中使用我们的建模框架来估计美国佛蒙特州东部 Wood-Pewee ( Contopus virens ) 的丰度。
  3. 模拟研究表明,与仅由声学或点计数数据提供信息的模型相比,通过集成模型将声学和点计数数据相结合可提高丰度估计的准确性和精确度。在广泛的场景中获得了改进的估计,当点计数数据的检测概率较低时,收益最大。将声学数据与仅少量点计数调查相结合,无需验证任何从声学数据中识别出的发声,即可得出丰度估计值。在我们的案例研究中,综合模型为该地区东部 Wood-Pewee 的下降提供了适度的支持。
  4. 我们的集成建模方法将密集的声学数据与很少的点计数调查相结合,以提供对物种丰度的可靠估计,而无需手动识别声学发声或昂贵的大量重复点计数调查。当点计数数据难以获得或监测集中在检测概率低的稀有物种时,我们提出的方法为大时空区域提供了一种有效的监测替代方案。
更新日期:2021-02-22
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