Golden-winged Warblers (Vermivora chrysoptera, Parulidae) are declining migrant songbirds that breed in the Great Lakes and Appalachian regions of North America. Within their breeding range, Golden-winged Warblers are found in early successional habitats adjacent to mature hardwood forest, and previous work has found that Golden-winged Warbler habitat preferences are scale-dependent. Golden-winged Warbler Working Group management recommendations were written to apply to large regions of the breeding range, but there may be localized differences in both habitat availability and preferences. Rapid declines at the southernmost extent of their breeding range in Western North Carolina necessitate investigation into landscape characteristics governing distribution in this subregion. Furthermore, with the increase in availability of community science data from platforms such as eBird, it would be valuable to know if community science data produces similar distribution models as systemic sampling data. In this study, we described patterns of Golden-winged Warbler presence in Western North Carolina by examining habitat variables at multiple spatial scales using data from standardized Audubon North Carolina (NC) playback surveys and community science data from eBird. We compared model performance and predictions between Audubon NC and eBird models and found that Golden-winged Warbler presence is associated with sites which, at a local scale (150m), have less mature forest, more young forest, more herb/shrub cover, and more road cover, and at a landscape scale (2500m), have less herb/shrub cover. Golden-winged Warbler presence is also associated with higher elevations and smaller slopes. eBird and Audubon models had similar variable importance values, response curves, and overall performance. Based on variable importance values, elevation, mature forest at the local scale, and road cover at the local scale are the primary variables driving the difference between Golden-winged Warbler breeding sites and random background sites in Western North Carolina. Additionally, our results validate the use of eBird data, since they produce species distribution modeling results that are similar to results obtained from more standardized survey methods.
Golden-winged Warblers (Vermivora chrysoptera, Parulidae) are declining migrant songbirds that breed in the Great Lakes and Appalachian regions of North America. Within their breeding range, Golden-winged Warblers are found in early successional habitats adjacent to mature hardwood forest, and previous work has found that Golden-winged Warbler habitat preferences are scale-dependent. Golden-winged Warbler Working Group management recommendations were written to apply to large regions of the breeding range, but there may be localized differences in both habitat availability and preferences. Rapid declines at the southernmost extent of their breeding range in Western North Carolina and Georgia necessitate investigation into landscape characteristics governing distribution in this subregion. We describe patterns of Golden-winged Warbler presence in Western North Carolina by examining habitat variables at multiple spatial scales using data from standardized Audubon North Carolina (NC) surveys and unstructured community scientist checklists submitted to eBird. We compared model performance and predictions between Audubon NC and eBird models and found that Golden-winged Warbler presence is associated with sites which, at a local scale (150m), have more young forest, less mature forest, and more road cover, and at a landscape scale (2.5km), have less road cover. eBird and Audubon models had similar parameter estimates for all of the land cover variables and similar overall performance. Based on parameter estimates, road density at the landscape scale (2.5km) is the primary variable driving the difference between Golden-winged Warbler breeding sites and random background sites in Western North Carolina. Our results show that eBird data can produce species distribution modeling results that are similar to results obtained from more standardized survey methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.