Abstract

Manually annotating audio files for bird species richness estimation or machine learning validation is a time-intensive task. A premium is placed on the subselection of files that will maximize the efficiency of unique additional species identified, to be used for future analyses. Using acoustic data collected in 17 plots, we created 60 subsetting scenarios across three gradients: intensity (minutes in an hour), day phase (dawn, morning, or both), and duration (number of days) for manual annotation. We analyzed the effect of these variables on observed bird species richness and assemblage composition at both the local and entire study area scale. For reference, results were also compared to richness and composition estimated by the traditional point count method. Intensity, day phase, and duration all affected observed richness in decreasing respective order. These variables also significantly affected observed assemblage composition (in the same order of effect size), but only the day phase produced compositional dissimilarity that was due to phenological traits of individual bird species, rather than differences in species richness. All annotation scenarios requiring equal sampling effort to point counts yielded higher species richness than the point count method. Our results show that a great majority of species can be obtained by annotating files at high sampling intensities (every 3 or 6 min) in the morning period (post-dawn) over a duration of two days. Depending on a study's aim, different subsetting parameters will produce different assemblage compositions, potentially omitting rare or crepuscular species, species representing additional functional groups and natural history guilds, or species of higher conservation concern. We do not recommend one particular subsetting regime for all research objectives, but rather present multiple scenarios for researchers to understand how intensity, day phase, and duration interact to identify the best subsetting regime for one's particular research interests.

Keywords

acoustic survey; annotation; bioacoustic; bird richness and composition; forest birds; passive acoustic monitoring; point count; survey design

Published in

Ecology and Evolution
2022, Volume: 12, number: 11, article number: e9491
Publisher: WILEY

SLU Authors

• Mikusinski, Grzegorz

  • School for Forest Management, Swedish University of Agricultural Sciences
    
  • University of Freiburg

UKÄ Subject classification

Evolutionary Biology
Ecology


Publication identifier

DOI: https://doi.org/10.1002/ece3.9491

Permanent link to this page (URI)

https://res.slu.se/id/publ/120176