Multiscale drivers of restoration outcomes for an imperiled songbird

Abstract

Habitat restoration is a cornerstone of conservation, particularly for habitat‐limited species. However, restoration efforts are seldom rigorously monitored at meaningful spatial scales. Poor understanding of how species respond to habitat restoration programs limits conservation efficacy for habitat‐restricted species like the Golden‐winged Warbler (Vermivora chrysoptera, GWWA). We provide one of the first concerted assessments of a national conservation program aimed at restoring songbird habitat across its breeding range. We studied GWWA response to forest habitat restoration across two broad regions with opposing population trajectories and assessed factors driving species use of restored habitats across multiple spatial scales. From 2015 to 2017, we conducted 1,145 (n = 457 locations) and 519 point counts (n = 215 locations) across the Appalachian Mountains and Great Lakes (respectively) within restored habitats. Warbler abundance within restored habitats across the Great Lakes varied with latitude, longitude, elevation, forest type, and number of growing seasons. In the Appalachian Mountains, occupancy (trueψ^) varied with longitude, elevation, forest type, and number of growing seasons. Detections were restricted to areas within close proximity to population centers (usually <24 km) in the Appalachian Mountains, where GWWAs are rare (trueψ^ = 0.22, 95% confidence interval [CI]: 0.20–0.25), but not in the Great Lakes, where GWWAs remain common (trueψ^ = 0.87, 95% CI: 0.84–0.90). Our study suggests that, even when best management practices are carefully implemented, restoration outcomes vary within/across regions and with multiscale habitat attributes. Although assessments of concerted habitat restoration efforts remain uncommon, our study demonstrates the value of monitoring data in the adaptive management process for imperiled species.

Singing in the suburbs: point count surveys efficiently reveal habitat associations for nocturnal Orthoptera across an urban-to-rural gradient

“…wt. ), and Log likelihood (LL) who have leveraged the method for decades (Gooch et al 2006;McNeil et al 2014McNeil et al , 2020. Our analysis, thereby, contributes to a rich body of literature supporting the use of point count surveys to sample animals with conspicuous breeding displays-including night-singing Orthoptera.…”

“…More specifically, we conducted stationary surveys on roadside shoulders and each consisted of a single observer standing at each point location for the duration of a three-minute survey. All aural surveys were conducted by D. McNeil, who has had extensive experience in quantifying wildlife populations using aural sampling techniques (McNeil et al 2014(McNeil et al , 2018(McNeil et al , 2020. During each survey, we recorded all detected Ensifera with the exception of the tree crickets (subfamily: Oecanthinae; see Supporting Table 1).…”

“…The development of more efficient monitoring protocols would help elucidate habitat associations for individual Ensifera species across diverse ecological communities (Kéry and Royle 2015;McNeil et al 2020). Given the community-wide population declines observed in Orthoptera (Dirzo et al 2014;Sánchez-Bayo and Wyckhuys 2019), an understanding of habitat requirements for these sensitive insect populations may be a conservation priority (Brouwers and Newton 2009).…”

“…Indeed, many vertebrate taxa that produce loud calls are studied using aural surveys as a primary sampling technique. For example, aural-based "point counts" are a common method in avian ecology to detect singing male passerines within habitats of interest (e.g., McNeil et al 2018McNeil et al , 2020 or along systematically-placed sampling locations (Ralph et al 1995;Wilson et al 2012;Sauer et al 2017). Likewise, nocturnal birds like nightjars (order: Caprimulgiformes, Knight et al 2016) and shorebirds like Wilson's Snipe (Gallinago delicata) or American Woodcock (Scalopax minor, Kozicky et al 1954) take place at night and require passive listening for singing males.…”

Avian response to plant community composition and structure in regenerating timber harvests protected by ungulate exclusion fencing

“…We used a two-part approach to assess a) density of each focal species between fenced and unfenced harvests and b) which vegetation or habitat variables showed the strongest relationship with the density of our focal bird species while accounting for imperfect detection (Kéry and Royle 2015). In order to use a static modeling framework for distance-based density estimates, we treated each point-year combination as a separate sample in our models (n=110) similar to the approach used by Fogg et al (2014) and McNeil et al (2020). We agree with Fogg et al (2014) that this modeling approach better emphasizes strong associations between habitat conditions and the occupancy or density of avian species.…”