Sasha D. Hafner
Therese Donovan
Jonathan Katz
2016
<p><span>Detecting population-scale reactions to climate change and land-use change may require monitoring many sites for many years, a process that is suited for an automated system. We developed and tested monitoR, an R package for long-term, multi-taxa acoustic monitoring programs. We tested monitoR with two northeastern songbird species: black-throated green warbler (</span><i>Setophaga virens</i><span>) and ovenbird (</span><i>Seiurus aurocapilla</i><span>). We compared detection results from monitoR in 52 10-minute surveys recorded at 10 sites in Vermont and New York, USA to a subset of songs identified by a human that were of a single song type and had visually identifiable spectrograms (e.g. a signal:noise ratio of at least 10 dB: 166 out of 439 total songs for black-throated green warbler, 502 out of 990 total songs for ovenbird). monitoR’s automated detection process uses a ‘score cutoff’, which is the minimum match needed for an unknown event to be considered a detection and results in a true positive, true negative, false positive or false negative detection. At the chosen score cut-offs, monitoR correctly identified presence for black-throated green warbler and ovenbird in 64% and 72% of the 52 surveys using binary point matching, respectively, and 73% and 72% of the 52 surveys using spectrogram cross-correlation, respectively. Of individual songs, 72% of black-throated green warbler songs and 62% of ovenbird songs were identified by binary point matching. Spectrogram cross-correlation identified 83% of black-throated green warbler songs and 66% of ovenbird songs. False positive rates were </span><span class="NLM_inline-graphic"><img src="http://www.tandfonline.com/na101/home/literatum/publisher/tandf/journals/content/tbio20/2016/tbio20.v025.i02/09524622.2015.1133320/20160303/images/medium/tbio_a_1133320_ilm0001.gif" alt="" /></span><span> for song event detection.</span></p>
application/pdf
10.1080/09524622.2015.1133320
en
Taylor & Francis
Assessment of error rates in acoustic monitoring with the R package monitoR
article