Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance
Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance
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DOI：
10.1111/2041-210x.13578
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发表时间：
2021-03-06
影响因子：
6.6
通讯作者：
Zipkin, Elise F.
中科院分区：
文献类型：
--
作者：
Doser, Jeffrey W.;Finley, Andrew O.;Zipkin, Elise F.
监测野生动物在空间和时间上的丰度是研究其种群动态并为有效管理提供信息的一项重要任务。声学记录装置是一种有效监测鸟类种群和群落的有前途的技术。虽然目前的声学数据模型提供的存在/不存在的个别物种的信息，需要新的方法来监测人口丰度，理想的是在大的时空regions.We提出了一个综合的建模框架，结合高品质，但时间稀疏的鸟类点计数调查数据与声学记录。我们的模型解释了两种数据类型中的不完美检测和声学数据中的假阳性错误。使用模拟，我们比较的准确性和精度的丰度估计使用不同数量的声学发声从聚类算法，点计数数据，和一个子集的手动验证的声学发声。我们还使用我们的建模框架在案例研究中，以估计丰富的东部伍德-Pewee（绿孔雀）在美国佛蒙特州。模拟研究表明，通过一个集成的模型相结合的声学和点计数数据，提高了准确性和精度的丰度估计相比，无论是声学或点计数数据单独通知的模型。在各种情况下都可以获得改进的估计，当点计数数据的检测概率较低时，增益最大。将声学数据与少量的点计数调查相结合，就可以估计丰度，而不需要验证从声学数据中确定的任何发声。在我们的案例研究中，综合模型提供了适度的支持，东部伍德-Pewee在这一地区的下降，我们的综合建模方法结合了密集的声学数据与几个点计数调查，提供可靠的物种丰度的估计，而不需要人工识别的声学发声或昂贵的大量重复点计数调查。我们提出的方法提供了一个有效的监测替代大时空区域时，点计数数据难以获得或监测时，重点是稀有物种的检测概率低。
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.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.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.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.