Comparing the sampling performance of sound recorders versus point counts in bird surveys: A meta‐analysis

By K. Darras, P. Batáry, B. Furnas, Antonio Celis-Murillo, S. V. Van Wilgenburg, Y. Mulyani, T. Tscharntke. Journal of Applied Ecology, 2018

153 citations recorded by the source corpus.

Abstract Autonomous sound recording is a promising sampling method for birds and other vocalizing terrestrial wildlife. However, while there are clear advantages of passive acoustic monitoring methods over classical point counts conducted by humans, it has been difficult to quantitatively assess how they compare in their sampling performance. Quantitative comparisons of species richness between acoustic recorders and human point counts in bird surveys have previously been hampered by the differing and often unknown detection ranges or sound detection spaces among sampling methods. We performed two meta-analyses based on 28 studies where bird point counts were paired with sound recordings at the same sampling sites. We compared alpha and gamma richness estimated by both survey methods after equalizing their effective detection ranges. We further assessed the influence of technical sound recording specifications (microphone signal-to-noise ratio, height and number) on the bird sampling performance of sound recorders compared to unlimited radius point counts. We show that after standardizing detection ranges, alpha and gamma richness from both methods are statistically indistinguishable, while there might be an avoidance effect in point counts. Furthermore, we show that microphone signal-to-noise ratio (a measure of its quality), height and number positively affect performance through increasing the detection range, allowing sound recorders to match the performance of human point counts. Synthesis and applications. We demonstrate that when used properly, high-end sound recording systems can sample terrestrial wildlife just as well as human observers conducting point counts. Correspondingly, we suggest a first standard methodology for sampling birds with autonomous sound recorders to obtain results comparable to point counts and enable practical sampling. We also give recommendations for carrying out effective surveys and making the most out of autonomous sound recorders.

Subfields: Developmental Biology, Ecology

View Paper: https://www.semanticscholar.org/paper/37337971ab0de5064938893d0deeb6f1024cd5fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Developmental Biology

1. C. Pérez‐Granados
2. Holger Klinck
3. J. Traba
4. Maximilian Eibl
5. Connor M. Wood
6. Stefan Kahl
7. Dan Stowell

In Ecology

1. C. Pérez‐Granados
2. Holger Klinck
3. J. Traba
4. Connor M. Wood

Citations in the corpus

• Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research (2021, background): “A potential shortcoming of using ARUs for estimating bird densities (but also bird richness, see meta-analysis in Darras et al .”
• BirdNET: A deep learning solution for avian diversity monitoring (2021, background): “Recent studies have shown that well-designed and executed PAM surveys produce diversity estimates mathcing those derived from traditional point count surveys (Darras et al., 2018).”