Avian Conservation and Ecology (Jun 2020)
Habitat selection and nest survival in two Great Plains shorebirds
Abstract
As breeding populations of many grassland bird species decline, assessments of breeding habitat selection and reproductive success can provide useful insight into breeding ecology to support conservation delivery. Here, we demonstrate the use of nest location and survival data collated from 20 data contributors across the Prairie Pothole Region of the United States and Canada over a half century to examine habitat selection and nest survival of Western Willets (Tringa semipalmata inornata) and Marbled Godwits (Limosa fedoa), hereafter "willets" and "godwits." Both willets and godwits selected territories with less variation in vegetation height and topography relative to available locations. Willets selected nest sites that were flatter, closer to wetlands, and had shorter vegetation than Marbled Godwits, while godwits selected territories with greater wetland cover and shorter vegetation. Despite differences in fine-scale habitat selection, willets and godwits experienced similar daily nest survival rates and ecological drivers of nest survival. Nest success for the entire nest exposure period was estimated to be 0.521 (95% credible interval: 0.39-0.65) for willets and 0.562 (95% credible interval: 0.42-0.70) for godwits. Nest survival for both species increased with nest age and distance from the nest to the nearest wetland edge, while nest survival of godwits declined with conspecific breeding density. These relationships, as well as a weaker positive effect of microtine rodent abundance on nest survival, resembled drivers of upland nesting waterfowl reproductive success in the same region, which we attribute to their shared nest predators. Nest survival analyses of our collaborative dataset required substantial consideration of biases emerging from different data collection methods, ultimately reaffirming the importance of nest aging techniques in proper nest fate assignment. Analysis of compiled datasets using emerging analysis methods will continue to grow our understanding of the ecology of data sparse species.
