Paired sampling standardizes point count data from humans and acoustic recorders

Abstract

Acoustic recordings are increasingly used to quantify occupancy and abundance in avian monitoring and research. The recent development of relatively inexpensive programmable autonomous recording units (ARUs) has further increased the utility of acoustic recording technologies. Despite their potential advantages, persistent questions remain as to how comparable data are between ARUs and traditional (human observer) point counts. We suggest that differences in counts obtained from ARUs versus human observers primarily stem from differences in the effective detection radius of humans (EDR H ) versus ARUs (EDR A ). We describe how paired sampling can be used in conjunction with generalized linear (GLM) or generalized linear mixed models (GLMM) to estimate correction factors (δ) to remove biases between ARUs and traditional point counts. Furthermore, if human observers conduct distance estimation, we show that density estimates can be derived from single ARUs by estimating EDR A as a function of EDR H and δ, thus providing alternatives to more complicated and expensive approaches. We demonstrate our approach using data from 363 point count stations in 105 unique boreal study sites at which field staff conducted point count surveys that were simultaneously recorded by an ARU and later transcribed in the lab. Finally, we used repeated random subsampling of the data to split the data into model creation (70%) and validation (30%) subsets to iteratively estimate δ and validate density estimates from ARUs against densities calculated from human observers at the same independent validation locations. We modeled density of 35 species of boreal forest birds and show that incorporating δ in statistical offsets successfully removes systematic biases in estimated avian counts and/or density between human and ARU derived surveys. Our method is therefore easily implemented and will facilitate the integration of ARU and human observer point count data, facilitating expanded monitoring efforts and meta-analyses with historic point count data. Standardisation de données de points d'écoute issus d'échantillons appariés d'observateurs et d'enregistrements acoustiquesRÉSUMÉ. Les enregistrements acoustiques sont de plus en plus utilisés pour quantifier l'occurrence et l'abondance dans le cadre de suivis et de recherches aviaires. Le développement récent d'unités d'enregistrement autonomes (ARU, pour autonomus recording units), programmables et plutôt bon marché a contribué à hausser l'utilité des technologies d'enregistrement acoustique. En dépit de leurs avantages possibles, des questions demeurent quant à la comparabilité des données issues de points d'écoute traditionnels (observateur humain) et d'ARU. Nous supposons que les différences obtenues entre les dénombrements issus d'ARU et ceux issus d'observateurs proviennent de différences dans le rayon de détection effectif (EDR, pour effective dectection radius) des humains (EDRH) comparativement à celui des ARU (EDRA). Nous décrivons de quelle façon un échantillonnage apparié peut être...

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

“…Of these, 23 had usable data for the meta‐analysis, and five additional unpublished datasets (unpublished at that time) were collected (see Table ). In addition, for one study (Van Wilgenburg et al., ) the authors were able to provide additional data added after their initial publication. Most authors were very cooperative and provided the requested data.…”

“…Hutto and Stutzman () used identical microphone capsules in an open habitat; we chose a range of 100 m as farther bird vocalizations were mainly undetected in sound recordings. The distance thresholds we used are approximations for the entire bird community, as it was shown that effective detection distances of birds vary from species to species (Van Wilgenburg et al., ). In the latter study, newer SMX‐II microphones were used, which have a higher signal‐to‐noise ratio (newer element with 68 dB signal‐to‐noise ratio, pers.…”

“…Previous comparisons have thus far failed to find systematic habitat related differences in counts between recorders and humans (Celis‐Murillo et al., ; Van Wilgenburg et al., ) despite there being known differences in sound attenuation (Yip et al., ). It is probable that aural detectability in point counts and sound recordings are similarly affected by habitat and thus introduce no bias in our comparisons owing to the nature of the paired comparisons (Van Wilgenburg et al., ; Vold et al., ). Moreover, our analyses correcting for detection ranges implicitly accounts for the habitat effect on sound detection range.…”

“…A fair comparison is also precluded by the fact that the detection range or sound detection spaces of sound recorders are usually unknown or difficult to measure (Darras, Pütz, Rembold, & Tscharntke, ; Yip, Leston, Bayne, Sólymos, & Grover, ). It has previously been suggested that differences between sound recorders and human point counts come from the differences in effective detection radius (Hutto & Stutzman, ; Van Wilgenburg, Sólymos, Kardynal, & Frey, ). Thus, it is still unclear how to optimize the use of sound recorders, and an unbiased comparison of bird species richness or abundance is impossible as long as their sampling areas are unknown.…”

Estimating bird detection distances in sound recordings for standardizing detection ranges and distance sampling

“…It follows that any comparison of point counts vs. sound recordings with regard to variables like abundance or species richness is biased if the differing detection ranges are not taken into account (Darras et al., ; Yip, Bayne, Sólymos, Campbell, & Proppe, ), as these variables increase with the sampled area. Indeed, it has been suggested recently that the differing effective detection ranges may cause the discrepancy in richness values between point counts and sound recorders (Van Wilgenburg et al., ; Vold, Handel, & Mcnew, ). However, so far bird species richness values were used as measure to compare the sampling efficiency of point counts vs. autonomous sound recorders in several studies (Shonfield & Bayne, ).…”