| Summary: | Degree: Master of Science Abstract: The Yellow Rail (Coturnicops noveboracensis) is a small, secretive, wetland bird, which is apparently rare throughout most of its range. Almost nothing is known about its abundance and density in the wetlands of the western boreal forest. Emerging technologies have enabled us to effectively survey for Yellow Rail in remote wetlands by using ground-based remote sensors (autonomous recording units; ARUs) to conduct passive acoustic monitoring. This technique was employed to survey Yellow Rail populations across two large study areas: one in the taiga plains of the Northwest Territories, and the other in the boreal plains of Alberta, Canada. For the Edéhzhíe Indigenous Protected Area (NWT), a predictive map of Yellow Rail density was developed based on data obtained from a systematic avian survey conducted in 2016, using 205 ARUs. Counts of Yellow Rail were converted to density estimates using habitat specific effective detection radii obtained via call-playback experiments. Generalized linear models and covariates from a detailed landcover classification effort were used to develop the spatial model. Yellow Rail appeared to breed at relatively high densities (0.07 males/ha compared to average densities of 0.04-0.05 males/ha) in Edéhzhíe and they were strongly associated with marsh wetlands. The Mills Lake wetland complex was identified as an important breeding area for Yellow Rail in the Northwest Territories based on a population estimate of ca. 560 breeding pairs. For the Alberta Oilsands Region, a predictive map of Yellow Rail breeding abundance was developed using acoustic data compiled from the first five years (2013-2017) of an ongoing bioacoustic monitoring program. Recent developments in open-access satellite data, cloud computing (Google Earth Engine), and data science were leveraged to secure large-scale, high-resolution (10 m) landcover data. Multiple satellite remote sensors were used to derive fifteen predictor variables: Sentinel-1 synthetic aperture radar, ...
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