10.5061/DRYAD.H0QG353
Darras, Kevin
University of Göttingen
Furnas, Brett
California Department of Fish and Wildlife
Fitriawan, Irfan
University of Göttingen
Mulyani, Yeni
Bogor Agricultural University
Tscharntke, Teja
University of Göttingen
Data from: Estimating bird detection distances in sound recordings for
standardising detection ranges and distance sampling
Dryad
dataset
2018
distance estimation
autonomous sound recorders
2019-05-14T00:00:00Z
2019-05-14T00:00:00Z
en
https://doi.org/10.1111/2041-210x.13031
147537 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1) Autonomous sound recorders are increasingly used to survey birds, and
other wildlife taxa. Species richness estimates from sound recordings are
usually compared with estimates obtained from established methods like
point counts, but so far the comparisons were biased: Detection ranges
usually differ between the survey methods, and bird detection distance
data are needed for standardizing data from sound recordings. 2) We
devised and tested a method for estimating bird detection distances from
sound recordings, using a reference recording of test sounds at different
frequencies, emitted from known distances. We used our method to estimate
bird detection distances in sound recordings from tropical forest sites
where point counts were also used. We derived bird abundance and richness
measures and compared them between point counts and sound recordings using
unlimited radius and fixed radius counts, as well as distance sampling
modelling. 3) First we show that it is possible to accurately estimate
bird detection distances in sound recordings. We then demonstrate that
these data can be used to standardize the detection ranges between point
counts and sound recordings with a fixed-radius approach, leading to
higher abundance and richness estimates for sound recordings. Our
distance-sampling approach also revealed that sound recorders sampled
significantly higher bird densities than human point counts. 4) We show
for the first time that it is possible to standardize detection ranges in
sound recordings and that distance sampling can successfully be used too.
We revealed that birds were flushed by human observers and that this
possibly leads to lower density estimates in point counts, although sound
recorders could also have sampled more birds because of their earlier
deployment times. Sound recordings are more amenable to distance-sampling
modelling than point counts as they do not exhibit an observer-induced
avoidance effect, and they can easily collect more replicates for
obtaining more accurate bird density estimates. Quantifying bird detection
distances was so far one important shortcoming that hindered the adoption
of modern autonomous sound recording methods for ecological surveys.
field survey datacontains the bird detection data from point counts and
sound recordings along with species, number of individuals and
distancesdistance estimation datacontains estimated and measured distances
from recorder to bird for several species in different habitatsbird
densities datathis is the result from running the hierarchical distance
sampling R script, which requires a lot of computing power and different
packages that may be difficult to install depending on your operating
system version. This file is needed for the R script "field studies -
analysis.R"hds birds per ha per count.csvhierarchical distance
sampling R scriptthis R script by Dr. Brett Furnas runs the Bayesian
hierarchical distance sampling to generate bird densitieshierarchical
distance sampling B Furnas.Ranalysis of data and figure generation R
scriptthis R script requires the files "field survey data.csv",
"distance estimation data.csv" and "hierarchical distance
sampling B Furnas.R" to reproduce the statistical models, results,
tables and figures of the manuscript.field studies - analysis.R