Measuring sound detection spaces for acoustic animal sampling and monitoring

Darras, Kevin; Pütz, Peter; Fahrurrozi, -; Rembold, Katja; Tscharntke, Teja

doi:10.1016/j.biocon.2016.06.021

Cited by 112 publications

(134 citation statements)

References 54 publications

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“…Detectability of avian vocalizations can be influenced by the surrounding environment (Darras et al 2016, Yip et al 2017 and by the methods used to record and identify observations (Haselmayer and Quinn 2000). We compared detection distances of different ARUs as well as human observers in the field and Avian Conservation and Ecology 12(1): 11 http://www.ace-eco.org/vol12/iss1/art11/ found differences in detectability depending on which method was used.…”

Section: Discussionmentioning

confidence: 99%

Experimentally derived detection distances from audio recordings and human observers enable integrated analysis of point count data

Yip¹,

Leston²,

Bayne³

et al. 2017

ACE

105

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ABSTRACT. Point counts are one of the most commonly used methods for assessing bird abundance. Autonomous recording units (ARUs) are increasingly being used as a replacement for human-based point counts. Previous studies have compared the relative benefits of human versus ARU-based point count methods, primarily with the goal of understanding differences in species richness and the abundance of individuals over an unlimited distance. What has not been done is an evaluation of how to standardize these two types of data so that they can be compared in the same analysis, especially when there are differences in the area sampled. We compared detection distances between human observers in the field and four commercially available recording devices (Wildlife Acoustics SM2, SM3, RiverForks, and Zoom H1) by simulating vocalizations of various avian species at different distances and amplitudes. We also investigated the relationship between sound amplitude and detection to simplify ARU calibration. We used these data to calculate correction factors that can be used to standardize detection distances of ARUs relative to each other and human observers. In general, humans in the field could detect sounds at greater distances than an ARU although detectability varied depending on species song characteristics. We provide correction factors for four commonly used ARUs and propose methods for calibrating ARUs relative to each other and human observers. Dérivation expérimentale de distances de détection d'enregistrements audio et d'observateurs humains permettant l'analyse intégrée de points d'écouteRÉSUMÉ. Les points d'écoute sont une des méthodes les plus courantes pour évaluer l'abondance d'oiseaux. Les unités d'enregistrement autonomes (ARU, pour autonomus recording units) sont de plus en plus utilisées pour remplacer les points d'écoute réalisés par des observateurs. Les études antérieures ont comparé les avantages relatifs des dénombrements par point d'écoute faits par des observateurs comparativement à ceux réalisés au moyen d'ARU, principalement pour évaluer les différences de richesse spécifique et d'abondance sur une distance illimitée. Ce qui n'a pas été testé toutefois est comment standardiser ces deux types de données de façon à ce qu'elles soient comparables dans une même analyse, particulièrement lorsqu'il y a des différences d'aire échantillonnée. Nous avons comparé la distance de détection entre des observateurs sur le terrain et quatre enregistreurs commerciaux (Wildlife Acoustics SM2, SM3, RiverForks et Zoom H1), en simulant les vocalisations de diverses espèces aviaires à des distances et des amplitudes variées. Nous avons aussi exploré la relation entre l'amplitude du son et la détectabilité dans le but de simplifier la calibration d'ARU. Nous avons utilisé ces données afin de calculer des facteurs de correction servant à standardiser les distances de détection des ARU entre eux et avec les observateurs. En général, les observateurs sur le terrain pouvaient détecter des sons à des distances plus grandes que ...

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Section: Discussionmentioning

confidence: 99%

Experimentally derived detection distances from audio recordings and human observers enable integrated analysis of point count data

Yip¹,

Leston²,

Bayne³

et al. 2017

ACE

105

View full text Add to dashboard Cite

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“…Due to constraints on signal-to-noise ratio (see also Darras et al 2016, Chambert et al 2018, automated methods are most likely to observe species from the foreground, rather than the background of audio recordings. Due to constraints on signal-to-noise ratio (see also Darras et al 2016, Chambert et al 2018, automated methods are most likely to observe species from the foreground, rather than the background of audio recordings.…”

Section: Bioacoustics Offer Useful Descriptors Of Community Structurementioning

confidence: 99%

“…This is because different species are differently detectable, as influenced by their behavior and vocal characteristics (Alldredge et al 2007, Pacifici et al 2008, Luther 2009, Robert et al 2012. the observation process (Darras et al 2016, Chambert et al 2018. We stress that this is a question different from that of physical presence/absence of a potentially Table 1.…”

Section: Bioacoustics Offer Useful Descriptors Of Community Structurementioning

confidence: 99%

Spatio‐temporal scaling of biodiversity in acoustic tropical bird communities

2019

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Automated analysis of acoustic communities is a rapidly emerging approach for the characterization and monitoring of biodiversity. To evaluate its utility, we should verify that such 'bioacoustics' can accurately detect ecological signal in spatiotemporal acoustic data. Targeting the 'Biological Dynamics of Forest Fragments Project' sites in Brazil, we ask: What is the relative contribution of the spatial, temporal and habitat dimension to variation in bird acoustic communities in a previously fragmented tropical rainforest? Does the functional diversity of bird communities scale similarly to space and time as does species diversity, when both are recorded by bioacoustics means? Overall, is the imprint of landscape fragmentation 30 years ago still audible in the present-day soundscape? We sampled forty-four sites in secondary forest and 107 sites in old-growth forest, resulting in 11 000 h of audio recordings. We detected 60 bird species with satisfactory precision and recovered a linear log-log relation between sampling time and species diversity. Sites in primary forest host more species than sites in secondary forest, but the difference decreased with sampling time, as the slope was slightly higher in secondary than primary forests. Functional diversity, as exposed by vocalizing birds, accumulates faster than does species diversity. The similarity among local communities decreases with distance in both time and space, but stability in time is remarkably high: two acoustic samples from the same site one year (or more) apart prove more similar than two samples taken at the same time but from sites situated just a few hundred meters apart. These findings suggest that habitat modification can be heard as a long-lasting imprint on the soundscape of regenerating habitats and identify soundscape-area and soundscape-time relations as a promising tool for biodiversity research, applied biomonitoring and restoration ecology.

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“…Occupancy is likely a good proxy for abundance for point count and automated recorder surveys of breeding birds (Furnas and Callas 2015), and detection/non-detection data provided by these methods could be used to directly model abundance Nichols 2003, Royle 2004). With better information about the effective area of surveys, those estimates of abundance could be transformed into estimates of density and population size (Darras et al 2016, Sollmann et al 2016.…”

Section: Discussionmentioning

confidence: 99%