Increasing popularity in passive acoustic monitoring and the ease with which researchers can accumulate large quantities of acoustic data has resulted in challenges for audio recording storage, archiving, and management. Reductions in file size can be achieved by lowering sample rate and compressing to different formats; however, how these processes affect audio data quality, and the resulting interpretation of wildlife data is not well understood. We investigated the effect of sampling rate and lossy compression of audio recordings to MP3 from their native WAV format on the performance of four commonly applied avian bioacoustic applications: community listening, distance estimation, automated recognition, and acoustic indices. Compression to MP3 decreased the number of detections, including a reduction in total abundance of individuals when transcribing audio files for community listening and lower precision and recall for automated recognisers. Sampling rate reduction introduced systematic bias to acoustic indices and had an influence on precision and recall for recognisers as well. We recommend against the use of MP3 compression to reduce file volume and suggest other lossless forms of audio compression where an exact copy of the original recording can be recovered.

Multiple stressor studies conducted in real-world environments play an important role in discovering how stressor pathways may vary relative to ecological complexity and study scale. We reviewed the evidence for climate and landscape change impacts on freshwater biodiversity in real-world ecosystems at the global scale. Using our compiled database of 150 studies, we asked (1) what are the study characteristics within the available evidence base and (2) what are the main knowledge gaps and recommendations for future research? Most studies employed an observational design and examined climatic and landscape change trends over a broad regional spatial scale (median = 97 sites/study). Ecological complexity was well represented in studies with a median of 11 predictor variables that characterized the relevant climate, landscape condition, and many other environmental attributes. Community-level metrics were common response types across all biota including larger, more mobile organisms such as fish that are challenging to examine in an ecologically-relevant context within controlled laboratory settings. We identified several knowledge gaps including the need for more published time-series data, particularly with respect to understanding climate change impacts. Other opportunities for improved future research included incorporating more stressor and biological interactions, examining potential climate stressors over multiple seasons and streamlining methods for dealing with the pervasive challenges of multicollinearity in real-world systems. We emphasize the unique role of ‘natural experiments’ in validating experimental findings and provide a suite of recommendations for creating more strategic field studies to inform conservation efforts.

Firebrand travel and ignition of spot fires is a major concern in the Wildland-Urban Interface and in wildfire operations overall. Firebrands allow for the efficient breaching across fuel-free barriers such as roads, rivers and constructed fuel breaks. Existing observation-based knowledge on medium-distance firebrand travel is often based on single tree experiments that do not replicate the intensity and convective updraft of a continuous crown fire. Recent advances in acoustic analysis, specifically pattern detection, has enabled the quantification of the rate at which firebrands are observed in the audio recordings of in-fire cameras housed within fire-proof steel boxes that have been deployed on experimental fires. The audio pattern being detected is the sound created by a flying firebrand hitting the steel box of the camera. This technique allows for the number of firebrands per second to be quantified and can be related to the fire's location at that same time interval (using a detailed rate of spread reconstruction) in order to determine the firebrand travel distance. A proof of concept is given for an experimental crown fire that shows the viability of this technique. When related to the fire's location, key areas of medium-distance spotting are observed that correspond to regions of peak fire intensity. Trends on the number of firebrands landing per square metre as the fire approaches are readily quantified using low-cost instrumentation.

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.

Smaller owls and hawks are high-threat predators to small songbirds, like chickadees, in comparison to larger avian predators due to smaller raptors’ agility (Templeton et al. in Proc Natl Acad Sci 104:5479–5482, 2005). The current literature focuses only on high- and low-threat predators. We propose that there may be a continuum in threat perception. In the current study, we conducted an operant go/no-go experiment investigating black-capped chickadees’ acoustic discrimination of predator threat. After obtaining eight hawk and eight owl species’ calls, we assigned each species as: (1) large, low-threat, (2) mid-sized, unknown-threat and (3) small-, high-threat predators, according to wingspan and body size. Black-capped chickadees were either trained to respond (‘go’) to high-threat predator calls or respond to low-threat predator calls. When either low-threat predator calls were not reinforced or high-threat predator calls were not reinforced the birds were to withhold responding (‘no-go’) to those stimuli. We then tested transfer of training with additional small and large predator calls, as well as with the calls of several mid-sized predators. We confirmed that chickadees can discriminate between high- and low-threat predator calls. We further investigated how chickadees categorize mid-sized species’ calls by assessing transfer of training to previously non-differentially reinforced (i.e., pretraining) calls. Specifically, transfer test results suggest that mid-sized broad-winged hawks were perceived to be of high threat whereas mid-sized short-eared owls were perceived to be of low threat. However, mid-sized Cooper’s hawks and northern hawk owls were not significantly differentially responded to, suggesting that they are of medium threat which supports the notion that perception of threat is along a continuum rather than distinct categories of high or low threat.

Avian predators vary in their degree-of-threat to chickadees; for example, smaller owls and hawks are of higher threat to chickadees as they can easily maneuver through the trees, while larger predators cannot. We conducted an operant go/no-go discrimination task to investigate the effect of signal degradation on perceived threat. Chickadees were trained to respond to high-threat northern saw-whet owl (NSWO) or low-threat great horned owl (GHOW) calls that were recorded at short distances, and then tested with high- and low-threat owl calls that were rebroadcast and re-recorded across six distances (25 m, 50 m, 75 m, 100 m, 150 m, and 200 m). Subjects were further tested with high-threat and low-threat synthetic tones produced to mimic the natural calls across the six distances. We predicted that birds would perceive and respond to: (1) high-threat predator calls at longer distances compared to low-threat predator calls, and (2) synthetic tones similarly compared to the stimuli that they were designed to mimic. We believed chickadees would continue to perceive and respond to predators that pose a high threat at further distances; however, only responding to low-threat stimuli was consistent across distance recordings. Synthetic tones were treated similarly to natural stimuli but at lower response levels. Thus, the results of this study provide insights into how chickadees perceive threat.

Bioacoustic recordings are often used to conduct auditory surveys, in which human listeners identify vocalising animals on recordings. In these surveys, animals are typically counted regardless of their distance from the survey point. When these surveys are carried out in patchy habitat or near edges, detected individuals may frequently occur in a different land-cover type than the survey point itself, which introduces uncertainty regarding species-habitat associations. We propose a method to restrict detections from single microphones to within a pre-specified survey radius. The method uses logistic regression to select a sound level threshold corresponding to the desired distance threshold. We applied this method to acoustic data from the centre of 21 1-ha oil wellsites in northern Alberta. To compare our approach with the results from auditory surveys from the same locations, we used sound localisation to locate birds detected via both methods. Just 22.4% of birds detected on auditory surveys fell within the desired survey area. Using our distance-truncation method, 96% of detections were within the desired survey area. We propose that distance truncation via sound level will be useful for surveys of patchily distributed habitat, or when greater certainty about bird locations or habitat associations is desired.

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.

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.

Point counts are one of the most common ways of collecting data to determine the relative abundance of birds. Many studies and monitoring programs, including the North American Breeding Bird Survey, use relative differences in counts of birds to assess changes in abundance over time and space. Many factors influence whether relative differences in counts of birds between various environmental conditions are reflective of actual differences in bird density. A major assumption of relative abundance is that birds with different song frequencies and amplitudes are heard at the same distances in different environmental conditions. We compared sound transmission in forest habitats and along low-use forestry roads, and calculated detection radius for different species to test the assumption that differences in bird counts between forest interior and roadside locations reflect actual differences in bird abundance. A playback-recording experiment was used to broadcast sounds through forest interior, along a forest edge, and down forestry roads in conifer and deciduous forests to determine whether sound propagation differed across environments. Sound attenuated significantly faster in forests than along roads or forest edges. Similarly, the distance at which bird songs could be detected was significantly shorter in forest than along the road or forest edge for 20 of 25 species. We found the area surveyed to be up to twice as large on road compared to within forests, which suggests that roadside surveys might inflate avian density estimates in comparison to off-road counts. Local atmospheric conditions also influenced detection probability, but the magnitude of the effect was weaker than the land-cover effect. Major differences in detection between roads and interior forest suggest that comparisons of surveys conducted along roadsides and in forest areas should be done carefully if the goal is to make direct comparisons of abundance.

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.