Sampling environmental acoustic recordings to determine species richness : I
Acoustic sensors provide an effective means of monitoring biodiversity at large spatial and temporal scales. They can continuously and passively record large volumes of data over extended periods, however these data must be analysed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced users can produce accurate results, however the time and effort required to process even small volumes of data can make manual analysis prohibitive. Our research examined the use of sampling methods to reduce the cost of analysing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilising five days of manually analysed acoustic sensor data from four sites, we examined a range of sampling rates and methods including random, stratified and biologically informed. Our findings indicate that randomly selecting 120, one-minute samples from the three hours immediately following dawn provided the most effective sampling method. This method detected, on average 62% of total species after 120 one-minute samples were analysed, compared to 34% of total species from traditional point counts. Our results demonstrate that targeted sampling methods can provide an effective means for analysing large volumes of acoustic sensor data efficiently and accurately.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Keywords:||Acoustic Sensor, Biodiversity monitoring, Ecological Informatics|
|Subjects:||Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ECOLOGICAL APPLICATIONS (050100) > Ecological Applications not elsewhere classified (050199)|
Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000) > ECOLOGY (060200) > Terrestrial Ecology (060208)
|Divisions:||Past > Schools > Biogeoscience|
Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute for Future Environments
|Copyright Owner:||Copyright 2012 The Authors|
|Deposited On:||04 Oct 2012 08:17|
|Last Modified:||04 Oct 2012 14:54|
Repository Staff Only: item control page