To assess evidence for species-specific responses to snow cover (date of 50% snow cover each year) and temperature (accumulated thaw-degree days from 1 January–10 June each year), we estimated slope parameters of least-squares linear regression models. We first fit models with unique slopes for each species (i.e., models with full interaction between species and environmental variables), with arrival date, duration of the pre-lay period, date of nest initiation, clutch size, and nesting effort (the annual number of nests of each species enumerated on known-area plots) as response variables. If none of the species-specific slopes in these models differed from 0 (i.e., no evidence of a species-specific response to snow cover or temperature), we then fit more parsimonious models with a common slope among species to further assess support for a response to the two environmental variables. Next, we compared these four response variables among the four species using standard anova techniques and conducted Tukey’s HSD tests for post-hoc examination of differences between species. For these assessments, we considered results to be biologically meaningful at α = 0.05.
To assess factors that influenced the growth of brant and snow goose goslings, we fit linear models to estimate body mass as a function of gosling age and sex. Because we weighed goslings only once, we did not estimate a growth curve for the period from hatch until fledge, but instead modeled the growth of both goose species over the span of ages represented in our samples (see Hupp et al., 2017). Although the mass gain of goslings from both species is non-linear overall (Ankneyet al., 1991), gosling growth rates are well approximated by a linear fit for the period shortly prior to fledging over which we recaptured goslings (Cooch et al., 1991). For semipalmated sandpiper and longspur chicks, we log10-transformed both mass and age to control for inherent patterns of unequal variance from hatch until fledging (NB: goslings were captured only during the linear phase of their growth cycle such that variances were equal across ages, making log transformation unnecessary). Because we uniquely marked goslings and semipalmated sandpiper chicks, we modeled the growth of individuals of these species, but used the average brood mass per nest visit as our response variable for longspurs. Finally, to focus on post-hatch factors affecting chick growth, we excluded mass measurements collected on the day of hatch for longspurs. For semipalmated sandpipers, we similarly censored values collected prior to two-days age because chicks of Arctic-breeding shorebirds rely primarily on internal yolk reserves to fuel growth after hatch (Norton, 1973; Schekkerman et al., 1998).
We compared similar environmental metrics across all four chick-growth model sets (Table 2b; see Supporting Information S1.2), and fit linear-mixed effects models using least-squares regression to each species independently using the package ‘lme4’ (Bates et al., 2015), and standardized all four environmental variables to minimize collinearity and facilitate model interpretation. We fit an intercept-only (null) model in all model sets, and otherwise included chick age (semipalmated sandpipers and longspurs) or chick age and sex (brant and snow geese) as unstandardized covariates in all models. We combined these covariates along with additive combinations of the aforementioned environmental variables to create an all-subsets model set of 17 models for each species. We fit mixed-effects models in order to control for multiple observations of individuals from the same nest (all species) and repeat measures of individuals across time (semipalmated sandpipers). We employed multimodel comparisons to rank the support of each model based on Akaike’s information criterion adjusted for small sample size (AICc ) and averaged model results for each species in proportion to Akaike weights wifollowing the approaches of Burnham and Anderson (2002). We calculated the conditional and marginal R 2 of each model using the R package ‘piecewiseSEM’ (Lefcheck, 2016) to assess objective model performance (Nakagawa & Schielzeth, 2013), and performed multimodel comparisons and model averaging using the R package ‘AICcmodavg’ (Mazerolle, 2019). We considered predictor variables with 95% confidence intervals that did not overlap zero to be biologically meaningful, and generated model-averaged predictions for each species using contrasting values of these biologically meaningful predictor variables to visualize chick mass under varying conditions. Specifically, we generated predictions representing growth under what we term optimal (i.e., 75th-quartile values for predictor variables with positive parameter estimates, 25th-quartile values for predictor variables with negative parameter estimates) and sub-optimal (i.e., 25th-quartile values for positive parameters, 75th-quartile values for negative parameters) conditions. For environmental variables whose model-averaged parameter estimates overlapped zero (i.e., uninformative predictors), we set these predictor values at mean levels in the model-averaging process. All analyses were performed in R (R Core Team, 2021), and values represent mean ± SD unless otherwise noted.