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 ...
Distance sampling is widely used to estimate animal population densities by accounting for imperfect detection of individuals with increasing distance from an observer. Distance sampling assumes that distances are measured without error; however, it is often applied to human estimated distances, which are known to be inconsistent, inaccurate, and biased. We present an objective technique for estimating distance to vocalizing individuals that relies on the relative sound level (RSL) of the vocalization extracted from autonomous recording unit (ARU) recordings and show the error is less than human estimated error extracted from a literature case study. RSL predicted distances can be obtained by manual measurement in sound viewing software, or automatically with automated signal recognition software. We built calibration datasets of Ovenbirds (Seiurus aurocapilla) and Common Nighthawks (Chordeiles minor) recorded at known distances and used regression of RSL from those recordings to predict distance. There was no error bias of RSL predicted distances when compared to known distances for Common Nighthawk, minimal error bias for Ovenbird, and error from all RSL predicted distances was less than human estimated error extracted from the literature. We then simulated ARU point count surveys with a known density and estimated that density with distance sampling to test whether RSL distance prediction does not violate the assumption that distances are measured without error. There was no difference in density estimates from known distance and density estimates obtained from RSL predicted distance, while density estimates contaminated with human estimated error were significantly lower than density estimates from known distance. We found that a calibration dataset of approximately 300 vocalizations was suitable to minimize error for both species, and so conclude that RSL distance prediction is an accessible method of improving distance estimates relative to human estimation. We provide general recommendations on how to collect calibration recordings for the application of RSL distance prediction to other species and areas.
Yellow Rails (Coturnicops noveboracensis) are among the most secretive bird species in North America. They are poorly sampled by common survey protocols, and as a result their occurrence across much of their range is uncertain. We compiled occurrence records of the species and used resource selection functions to classify habitats as selected, neutral, or avoided using four different land cover maps in the oil sands region of northeastern Alberta. We assessed the accuracy of these maps using 279 previously unsurveyed locations and showed that a consensus-based ensemble classifier predicted occurrence more accurately than any single map. We combined the four maps into one map that rated habitat on a scale from 0 (consensus avoided) to 8 (consensus selected). Occupancy analysis showed increasing occupancy rates in areas with higher habitat suitability classes, with maximum occupancy rates of 0.18 (95% CI: 0.07-0.32) in class 8 habitat. We combined detections of 169 male Yellow Rails at surveyed locations with model predictions for unsurveyed locations to produce two population estimates for our study area, based on two estimates of the detection radius of the species. The estimate assuming a 150-m detection radius was 2747 males (95% CI: 588-5563), and the estimate assuming a 250-m detection radius was 1650 males (95% CI: 416-3266). Although estimates contained substantial uncertainty, our results suggest a larger number of Yellow Rails in the region than previously thought, which alters the current understanding of the distribution of this species. We estimated that about 17% of the population in our study area resides on oil sands leases that cover 14% of the study area, in habitats facing ongoing and future industrial development. The availability of a habitat map based on empirical evidence and detailed analyses for this species of conservation concern will improve targeted monitoring and promote mitigation of potential effects of development. Modélisation de l'occurrence du râle jaune (Coturnicops noveboracensis) dans le contexte du développement continu des ressources dans la région des sables bitumineux de l'Alberta RÉSUMÉ. Le râle jaune (Coturnicops noveboracensis) est l'une des espèces d'oiseau les plus secrètes d'Amérique du Nord. Les protocoles d'enquête courants ne permettant pas de les échantillonner correctement, leur occurrence sur une grande partie de leur territoire demeure incertaine. Nous avons compilé les enregistrements d'occurrence de cette espèce et utilisé des fonctions de sélection des ressources pour classer les habitats comme suit : sélectionnés, neutres ou évités. Nous avons pour ce faire utilisé quatre cartes différentes de couverture du territoire dans la région des sables bitumineux du nord-est de l'Alberta. Nous avons évalué l'efficacité de ces cartes en utilisant 279 sites qui n'avaient encore jamais été étudiés et démontré qu'un classificateur global reposant sur un consensus permet de prévoir l'occurrence avec plus de précision que toute carte individuelle. Nous avons combiné les ...
Acoustic indices combined with clustering and classification approaches have been increasingly used to automate identification of the presence of vocalizing taxa or acoustic events of interest. While most studies using this approach standardize data collection and study design parameters at the project or study level, recent trends in ecological research are to investigate patterns at regional or continental scales. Large‐scale studies often require collaboration between research groups and integration of data from multiple sources to fulfil objectives, which can lead to variation in recording equipment and data collection protocols. Our objectives were to determine how analytical approaches and variation in data collection and processing that is typical of regional acoustic monitoring programmes influences accuracy when identifying vocal activity in breeding birds. We used data from three regional datasets in Northern Alberta, Northern British Columbia, and Southern and Central Yukon, Canada to investigate the effect of analytical framework, sample size, local species richness and data collection variables on classification accuracy. We found supervised classification approaches to be the most effective, with boosted regression trees identifying vocalizations of breeding birds in audio recordings with a 92.0% accuracy and easily able to accommodate variation in data collection and processing parameters. We also provide recommendations on effectively processing large and heterogeneous datasets including sufficient sample size, accommodating potentially confounding variables and selecting suitable model training data. The results presented in this study can help inform decisions in data collection, data processing, and study design and analysis, maximize performance and accuracy during analysis, and efficiently process large, heterogeneous acoustic datasets to answer questions at scales previously difficult to investigate.
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