Processing methods that maximize species richness from acoustic recordings obtained from regional monitoring programs can increase detections of uncommon, rare, and cryptic species and provide key information on species status and distribution. Using data from regional bird monitoring in Yukon, Canada, we (1) compared the number of bird species detected (species richness) and the cost associated with four acoustic processing methods ( Listening, Visual Scanning, Recognizer, and Recognizer with Validation) and (2) combined Listening and Recognizer with Validation information to increase detections of all bird species at the ecoregion scale. We used comprehensive Visual Scanning to detect all bird species on the recordings. We processed ∼1% of the recordings using Listening and detected 56% of the bird community with 71.5 h of human effort. We used Recognizer (multispecies recognizer BirdNET) with Validation and detected 89% of the bird community with ∼22% of the effort required for Visual Scanning (56 and 257 h, respectively). As an application of our approach, we combined Listening and Recognizer with Validation to process recordings from five northern ecoregions and found a 23%–63% increase in the number of bird species detected with little additional effort. Combining Listening and Recognizer with Validation can maximize species detections from large passive acoustic monitoring data sets.

Yellow Rail (Coturnicops noveboracensis) are a highly specialized wetland obligate bird. They are a species at risk in Canada and very little is known about their abundance in the wetlands of the western boreal forest. Emerging technologies have enabled us to effectively survey for Yellow Rail and other wetland birds in remote areas by using ground-based remote sensors (autonomous recording units; ARUs) to conduct passive acoustic monitoring. We analyzed bird data from the first four years (2013–2016) of an ongoing monitoring program led by the Bioacoustic Unit at the Alberta Biodiversity Monitoring Institute. We developed species abundance models using satellite data from Sentinel-1 and Sentinel-2 processed in Google Earth Engine. We identified covariates from both synthetic aperture radar and optical remote sensing that had strong predictive capacity for this wetland bird (AUC = 0.96). Approximately 1.5% of available wetland habitat in our northeast Alberta study area was predicted to be highly suitable for Yellow Rail.

Earth observation technologies have strong potential to help map and monitor wildlife habitats. Yellow Rail, a rare wetland obligate bird species, is a species of concern in Canada and provides an interesting case study for monitoring wetland habitat with Earth observation data. Yellow Rail has highly specific habitat requirements characterized by shallowly flooded graminoid vegetation, the availability of which varies seasonally and year-to-year. Polarimetric Synthetic Aperture Radar (SAR) in combination with optical data should, in theory, be a great resource for mapping and monitoring these habitats. This study evaluates the use of RADARSAT-2 data and Landsat-8 data to characterize, map, and monitor Yellow Rail habitat in a wetland area within the mineable oil sands region. Specifically, we investigate: (1) The relative importance of polarimetric SAR and Landsat-8 data for predicting Yellow Rail habitat; (2) characterization of wetland habitat with polarimetric SAR data; (3) yearly trends in available habitat; and (4) predictions of potentially suitable habitat across northeastern Alberta. Results show that polarimetric SAR using the Freeman–Durden decomposition and polarization ratios were the most important predictors when modeling the Yellow Rail habitat. These parameters also effectively characterize this habitat based on high congruence with existing descriptions of suitable habitat. Applying the prediction model across all wetland areas showed accurate predictions of occurrence (validated on field occurrence data), and high probability habitats were constrained to very specific wetland areas. Using the RADARSAT-2 data to monitor yearly changes to Yellow Rail habitat was inconclusive, likely due to the different image acquisition times of the 2014 and 2016 images, which may have captured seasonal, rather than inter-annual, wetland dynamics. Polarimetric SAR has proved to be very useful for capturing the specific hydrology and vegetation structure of the Yellow Rail habitat, which could be a powerful technology for monitoring and conserving wetland species habitat.

The Yellow Rail (Coturnicops noveboracensis) is a secretive marsh bird of conservation concern in Canada. However, the status of this species in northern boreal regions remains largely unknown given uncertainty about population abundance and distribution. This knowledge gap is mainly due to limitations of traditional survey methods to detect this species. In this study, avian point count data collected from autonomous recording units and augmented by detections from a machine-learning recognizer were used to generate a species distribution model to provide habitat-specific density estimates and population size estimates for Yellow Rail breeding in the Edehzhie Dehcho Protected Area, Northwest Territories. This protected area is similar to 150 km beyond the currently established northern range limit. A large population estimated at 906 (+/- 146) pairs was discovered. Yellow Rail were found at high densities in marshes (0.063 +/- 0.004 males/ha), but were also observed in fens and bogs, albeit at much lower densities (0.003 +/- 0.002 males/ha and < 0.001 +/- 0.002 males/ha). Our results suggest both the range and the population size of Yellow Rail are much larger than currently reported. Further studies are required to provide better population size and distribution estimates to conserve this species at risk in Canada. Received 18 June 2020, accepted 19 January 2021.

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.

Predicting and mitigating impacts of climate change and development within the boreal biome requires a sound understanding of factors influencing the abundance, distribution, and population dynamics of species inhabiting this vast biome. Unfortunately, the limited accessibility of the boreal biome has resulted in sparse and spatially biased sampling, and thus our understanding of boreal bird population dynamics is limited. To implement effective conservation of boreal birds, a cost-effective approach to sampling the boreal biome will be needed. Our objective was to devise a sampling scheme for monitoring boreal birds that would improve our ability to model species-habitat relationships and monitor changes in population size and distribution. A statistically rigorous design to achieve these objectives would have to be spatially balanced and hierarchically structured with respect to ecozones, ecoregions and political jurisdictions. Therefore, we developed a multi-stage hierarchically structured sampling design known as the Boreal Optimal Sampling Strategy (BOSS) that included cost constraints, habitat stratification, and optimization to provide a cost-effective alternative to other common monitoring designs. Our design provided similar habitat and spatial representation to habitat stratification and equal-probability spatially balanced designs, respectively. Not only was our design able to achieve the desired habitat representation and spatial balance necessary to meet our objectives, it was also significantly less expensive (1.3-2.6 times less) than the alternative designs we considered. To further balance trade-offs between cost and representativeness prior to field implementation, we ran multiple iterations of the BOSS design and selected the one which minimized predicted costs while maximizing a multi-criteria evaluation of representativeness. Field implementation of the design in three vastly different regions over three field seasons showed that the approach can be implemented in a wide variety of logistical scenarios and ecological conditions. We provide worked examples and scripts to allow our approach to be implemented or adapted elsewhere. We also provide recommendations for possible future refinements to our approach, but recommend that our design now be implemented to provide unbiased information to assess the status of boreal birds and inform conservation and management actions.