title: The use of automated bioacoustic recorders to replace human wildlife surveys: an example using nightjars.
abstract: To be able to monitor and protect endangered species, we need accurate information on their numbers and where they live. Survey methods using automated bioacoustic recorders offer significant promise, especially for species whose behaviour or ecology reduces their detectability during traditional surveys, such as the European nightjar. In this study we examined the utility of automated bioacoustic recorders and the associated classification software as a way to survey for wildlife, using the nightjar as an example. We compared traditional human surveys with results obtained from bioacoustic recorders. When we compared these two methods using the recordings made at the same time as the human surveys, we found that recorders were better at detecting nightjars. However, in practice fieldworkers are likely to deploy recorders for extended periods to make best use of them. Our comparison of this practical approach with human surveys revealed that recorders were significantly better at detecting nightjars than human surveyors: recorders detected nightjars during 19 of 22 survey periods, while surveyors detected nightjars on only six of these occasions. In addition, there was no correlation between the amount of vocalisation captured by the acoustic recorders and the abundance of nightjars as recorded by human surveyors. The data obtained from the recorders revealed that nightjars were most active just before dawn and just after dusk, and least active during the middle of the night. As a result, we found that recording at both dusk and dawn or only at dawn would give reasonably high levels of detection while significantly reducing recording time, preserving battery life. Our analyses suggest that automated bioacoustic recorders could increase the detection of other species, particularly those that are known to be difficult to detect using traditional survey methods. The accuracy of detection is especially important when the data are used to inform conservation.
authors.affiliation: Newcastle University, School of Biology, Newcastle upon Tyne, United Kingdom.
authors.forename: Mieke C
authors.initials: MC
authors.lastname: Zwart
authors.affiliation: Baker Consultants Ltd, Matlock, United Kingdom.
authors.forename: Andrew
authors.initials: A
authors.lastname: Baker
authors.affiliation: Newcastle University, School of Biology, Newcastle upon Tyne, United Kingdom.
authors.forename: Philip J K
authors.initials: PJ
authors.lastname: McGowan
authors.affiliation: Newcastle University, School of Biology, Newcastle upon Tyne, United Kingdom.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Whittingham
mesh_terms: D000818:Animals; D001717:Birds / Q000502:physiology*; D003247:Conservation of Natural Resources / Q000379:methods*; D003625:Data Collection / Q000379:methods*; D056727:Endangered Species; D004739:England; D011157:Population Dynamics; D012815:Signal Processing, Computer-Assisted; D014828:Vocalization, Animal