DOI: 10.5061/dryad.h0qg353
title: Data from: Estimating bird detection distances in sound recordings for standardising detection ranges and distance sampling

description: 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.
description: 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