4.2 Article

Determining temporal sampling schemes for passive acoustic studies in different tropical ecosystems

期刊

TROPICAL CONSERVATION SCIENCE
卷 8, 期 1, 页码 215-234

出版社

SAGE PUBLICATIONS INC
DOI: 10.1177/194008291500800117

关键词

environmental monitoring; animal conservation; tropical environments; soundscape ecology; sampling protocols

资金

  1. FAPEMIG
  2. VALE S. A.
  3. CNPq
  4. FAPEMIG (PPM)

向作者/读者索取更多资源

Among different approaches to exploring and describing the ecological complexity of natural environments, soundscape analyses have recently provided useful proxies for understanding and interpreting dynamic patterns and processes in a landscape. Nevertheless, the study of soundscapes remains a new field with no internationally accepted protocols. This work provides the first guidelines for monitoring soundscapes in three different tropical areas, specifically located in the Atlantic Forest, Rupestrian fields, and the Cerrado (Brazil). Each area was investigated using three autonomous devices recording for six entire days during a period of 15 days in both the wet and dry seasons. The recordings were processed via a specific acoustic index and successively subsampled in different ways to determine the degree of information loss when reducing the number of minutes of recording used in the analyses. We describe for the first time the temporal and spectral soundscape features of three tropical environments. We test diverse programming routines to describe the costs and the benefits of different sampling designs, considering the pressing issue of storing and analyzing extensive data sets generated by passive acoustic monitoring. Schedule 5 (recording one minute of every five) appeared to retain most of the information contained in the continuous recordings from all the study areas. Less dense recording schedules produced a similar level of information only in specific portions of the day. Substantial sampling protocols such as those presented here will be useful to researchers and wildlife managers, as they will reduce time- and resource-consuming analyses, whilst still achieving reliable results.

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