(b) Stable hydrogen isotope analyses
We used stable isotope analysis of hydrogen (δ2H) in
feathers and claws to infer breeding and overwintering latitudes,
respectively (Hobson, 1999; Rubenstein & Hobson, 2004; Wunder, 2010;
Hobson, Van Wilgenburg, Wassenaar, & Larson, 2012; Bowen, Liu, Vander
Zanden, Zhao, & Takahashi, 2014). Whole feathers and claws were cleaned
using a 2:1 chloroform: methanol solution to remove external oils and
contaminants. Prior to analysis, we controlled for the effect of
exchangeable hydrogen by forcing isotopic equilibration with a water
vapor of known isotopic composition in a flow-through chamber system at
115°C (Schimmelmann, 1991; Sauer, Schimmelmann, Sessions, & Topalov,
2009). The hydrogen isotopic composition of feathers
(δ2Hf) and claws
(δ2Hc) was then analyzed using a
thermal conversion elemental analyzer coupled with a ThermoFinnigan
Delta Plus XP isotope ratio mass spectrometer at the Indiana University
Stable Isotope Research Facility. The δ2H values are
reported in standard per mil notation (‰) relative to VSMOW (Vienna
Standard Mean Oceanic Water) using two reference materials: USGS77
(polyethylene powder) and hexatriacontane 2 (C36n-alkane 2). Analytical precision was ± 0.6 ‰ for δ2H
values. We then calculated the isotopic composition of the
non-exchangeable hydrogen per sample (Schimmelmann, 1991; Schimmelmann,
Lewan, & Wintsch, 1999).
Based on feather and claw δ2H values, we performed
Bayesian geographic assignments of individual birds during the breeding
and overwintering months, respectively, using the assignR package in R
(Ma & Bowen, 2019; R Core Team 2019). Growing season and monthly
precipitation isoscape rasters from waterisotopes.org (Bowen &
Revenaugh, 2003; Bowen, Wassenaar, & Hobson, 2005), monthly
precipitation amount rasters from CHELSA (Karger et al., 2017a, b), and
resident dark-eyed junco isotopic data (Hobson et al., 2012; Becker et
al., 2019) were utilized to generate geographic assignment probability
maps. Latitudinal estimates were extracted from cells within the top
10% highest posterior probability using the raster package (Hijmans et
al., 2019).