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

Abstract

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 ...

Potential benefits of augmenting road-based breeding bird surveys with autonomous recordings

“…This might reflect greater microphone sensitivity in the SM3 relative to previous models. Studies have also reported that human-based surveys can detect species at greater distances than ARU surveys (Venier et al 2012, Yip, Leston, et al 2017. Thus, results from ARU-based surveys are not directly comparable to those from human surveys, and correction factors are needed for the different types of units (i.e., SM2, SM3, etc.)…”

“…Studies comparing results from human and ARU (Wildlife Acoustic Inc., i.e., Song Meter units) surveys have found both lower (8%-10% fewer species with the SM1 unit; Venier et al 2012, Rempel et al 2013) and similar ARU performances (SM2 unit; Alquezar and Machado 2015, La and Nudds 2016, but see Klingbeil and Willig 2015, Yip, Leston, et al 2017. We used SM3 units and did not observe a significant difference in species richness (5.7%) or abundance indices (−3.7%) between human BBS and on-road ARU surveys, but nonsignificant species-specific differences ranged from 17.1% lower to 23.6% higher in human BBS surveys.…”

“…Although this source of variance can be problematic, it is probably similar to observer biases in detection of human-based surveys that have not been as well quantified. Using appropriate field methods or using an experimental approach (Yip, Leston, et al 2017) may remove systematic biases in detection associated with ARU data. Nonetheless, it highlights the importance of monitoring detectability of individual microphones and tracking unit-specific recordings.…”

“…Thus, results from ARU-based surveys are not directly comparable to those from human surveys, and correction factors are needed for the different types of units (i.e., SM2, SM3, etc.) to integrate ARU-based data into larger humanbased point count databases (e.g., Sólymos et al 2013, Yip, Leston, et al 2017). …”

Using occupancy modeling to monitor dates of peak vocal activity for passerines in California

“…Based on the findings of Furnas and Callas (2015), the detection component included an intercept representing either one of the 3 recorder survey times (30 min before sunrise, sunrise, 30 min after sunrise) from both projects or either of the 2 point count observers from the PCT for a total of 8 mutually exclusive intercept terms. We also included tree canopy cover to control for variation in the ability of our surveys to detect bird vocalizations in areas of greater vegetation density (Pacifici et al 2008, Yip et al 2017.…”