Spatial distribution of the Boreal Owl and Northern Saw-whet Owl in the Boreal region of Alberta, Canada

Abstract

Understanding what factors influence the occurrence and distribution across the landscape is necessary for species conservation and management. Distribution data for many owl species are inadequate because of their nocturnal behavior and cryptic nature. We examined the role of climate, land cover, and human disturbance in shaping spatial distribution of the Boreal Owl (Aegolius funereus) and Northern Saw-whet Owl (Aegolius acadicus) in northern Alberta. Using autonomous recording units, we conducted passive acoustic surveys to detect owls of both species throughout Alberta's boreal forest. We compiled data on environmental variables at each sample site corresponding to a local scale and at landscape scale. A boosted regression tree analysis identified average minimum winter temperature as the most important predictor of Boreal Owl distribution. Boreal Owls were more likely to be present in cool environments with cold winters, and a low percentage of grassland cover at the landscape scale. Cropland cover at the local scale was the most influential factor in the final distribution model for the Northern Saw-whet Owl, and they were more likely to be present in areas where cropland was interspersed with deciduous-dominated forests. Furthermore, these areas generally had cool summer temperatures and received less precipitation as snow. Linear features at the landscape scale negatively influenced distribution of Boreal Owls, but edges created by linear features at local scale positively influenced Northern Saw-whet Owl distribution. Our study provides new information about habitat use that can be applied in management and conservation of these two poorly studied species of owls.

Passive acoustic monitoring of the Ferruginous Pygmy-Owl ( Glaucidium brasilianum ) over a complete annual cycle: seasonality and monitoring recommendations

“…Our results suggest that the employment of autonomous sound recorders can be a useful tool for monitoring FPOs and that this technique might be especially well suited for detecting population changes (Buxton et al 2013). This assumption is in agreement with previous studies that found passive acoustic monitoring to be a useful methodology for surveying nocturnal bird species (Frommolt & Tauchert 2014;Pérez-Granados & Schuchmann 2020a;Schroeder & Mcrae 2020), including temperate owls (Shonfield & Bayne 2017;Domahidi et al 2019;Wood et al 2019). We would like to highlight that our study was based on a reduced number of sites (five) and thus some of our generalizations may require further research on a larger number of sites to obtain more robust conclusions about the vocal seasonality of the species in the region or in the Neotropics.…”

“…This technique increases the probability of detecting owls without the need for broadcasting recorded calls, thus not altering owls' vocal behaviors (Wood et al 2019). This technique has proven to be an effective tool for monitoring several nocturnal bird species (Farnsworth & Russell 2007;Goyette et al 2011;Pérez-Granados & Schuchmann 2020a, 2020b, including owls (Shonfield & Bayne 2017;Domahidi et al 2019;Wood et al 2019). However, there is a gap of knowledge in whether passive acoustic monitoring is effective for several species of owls, especially for those inhabiting Neotropical regions.…”

An introduction to predictive distribution modelling for conservation to encourage novel perspectives

“…Boosted regression trees (BRTs) are one of many machine learning techniques well-suited to modelling complex ecological data because they can handle different types of predictor variables, fit complex nonlinear relationships, accommodate missing data and automatically handle interaction effects between predictors (Elith et al, 2008;Graham et al, 2008). BRTs have been used to predict bird distributions using occurrence data for waterfowl (Barker et al, 2014), shorebirds (Dalgarno et al, 2017), seabirds (Oppel et al, 2012), and owls (Domahidi et al, 2019). Hierarchical Bayesian models can integrate various data types (e.g., presence-absence, presence-only, count data) to create reliable spatio-temporal distribution models (Hefley & Hooten, 2015).…”

Wind Farms and Power Lines Have Negative Effects on Territory Occupancy in Eurasian Eagle Owls (Bubo bubo)

“…Land-based wind farms require huge areas, and this effort to reduce global warming might increase biodiversity losses. Investigations have shown the negative effects of landscape disturbance and land use on many bird populations [6][7][8], including boreal owl (Aegolius funereus) and northern saw-whet owl (Aegolius acadicus) in Canada [9]. More specifically, the construction and operation of wind farms negatively impacts birds both by habitat alteration and disturbance [10], as well as direct mortality [11][12][13][14].…”