Sampling effort for community composition higher by a magnitude compared to species richness in passive acoustic monitoring
Loading...
Date
Publisher
Elsevier
Abstract
1. Comprehensive estimations of relative abundances and changes therein are one of the most important measures of conservation monitoring. The use of passive acoustic monitoring (PAM) has the potential to greatly
enhance information availability, yet has mainly focused on species diversity or coverage, yielding results
comparable to those from expert observers. Comparisons with conventional monitoring data have been made
mainly for point counts, even though area-wide territory mapping is a widely used standard in many monitoring
programs.
2. We compare data derived from the combination of PAM and automated identification via machine learning
with an area-wide conventional breeding bird survey conducted by expert observers across the Marburg Open
Forest in Hesse, Germany. By varying the number of survey intervals, recording duration and locations, we then
determined the sampling effort needed to adequately reflect both species coverage and community composition
of the conventional survey.
3. Only small subsamples of PAM data were required to reach maximum species richness of the conventional
survey; minimum requirements were as low as 1) one survey interval or 2) 30 min duration or 3) three recording
locations if other factors were allowed to vary accordingly. Revealing the community composition of the conventional survey, however, required sampling over three to six survey intervals with recording durations between 10 and 100 h. While communities were similar between methods in terms of species activity and relative
abundance, PAM also partially reflected the composition of territories in the conventional survey.
4. Our study demonstrates the relative importance of greater sampling effort especially for monitoring community composition, requiring more recording locations, duration and intervals in comparison to species richness. We provide insight into the current applicability of PAM in monitoring practice and present best-use
scenarios on how to make the most of high spatio-temporal resolution acoustic monitoring data.
Metadata
Philipps-Universität Marburg
License
Except where otherwise noted, this item's license is described as Attribution 4.0 International