Statistical analysis
We used logit survival as the response variable in our models to account
for nonlinearassociations with extrinsic and intrinsic predictors. Prior
to analysis, we log10 transformed body mass and clutch
size due to skewness, and scaled latitude and climate data to z scores
by subtracting their mean and dividing by their standard deviation. Most
variables were weakly correlated, although both measures of temperature
reached Spearman rank correlations >0.75 (Table S1). To
estimate adult survival rates along the latitudinal gradient, we used a
multi-level meta-analytical framework using the R package metafor(Viechtbauer 2010), which fits random and fixed effects models,
weighting effect sizes by the inverse of their squared standard error.
For each model developed, we accounted for sources of non-independence
in our dataset that can arise when multiple survival estimates are
extracted from the same study, are available for the same species, and /
or due to common ancestry, by fitting study identity, species identity,
and phylogeny as random intercepts. To incorporate phylogeny, we used a
majority rules consensus tree derived from a set of 1,000
randomly-selected trees based on the global phylogeny of birds (Jetzet al. 2012), and pruned to our study taxa (Fig. S1) with the R
package phytools (Revell 2012). We used the branch length from
this consensus tree to specify values for the model variance-covariance
matrix.
We first ran a random effects only model on the entire dataset using therma.mv function to estimate a pooled effect size of global avian
survival rates. Given potential differences in selection pressures
experienced by passerines vs. nonpasserines, species from Old World
(Afrotropics, Indomalayan, Palearctic) vs. New World (Neotropics,
Nearctic) biogeographic realms, and mainland vs. island bird
populations, we also evaluated separate meta-analytic models using
effect sizes for these six data subsets. We considered point estimates
to be different from one another if their 95% confidence intervals (CI)
did not overlap. We quantified total heterogeneity of each dataset by
calculating Cochran’s Q and I2statistics (Higgins & Thompson 2002).
To test for publication bias in our global dataset we used three
complimentary methods: (1) We visually assessed asymmetry of funnel
plots (Fig. S2); they appeared close to symmetrical. (2) We removed
studies that reported survival estimates for >10 species,
and which accounted for 64% of effect sizes, and reran the analysis. We
repeated this procedure for studies conducted for <10 years to
examine the effects of study duration on survival estimates. (3) We fit
additional models where study method (i.e., live-recapture, dead
recovery, or both) was used as a moderator or whether package aukwas used to calculate the geographic coordinates. Results of this
sensitivity analysis were all qualitatively similar to the global mean
survival rate based on the entire dataset (Fig. S3).
We conducted meta-regressions (meta-analyses incorporating explanatory
variables, hereafter referred to as “moderators”) whereby we
determined the effects on species-specific adult survival rates of (1)
latitude, (2) extrinsic climatic factors, and (3) intrinsic traits in
accordance with hypotheses described from the primary literature. We
began by comparing fit of a latitude-only model, where regression slopes
varied between hemispheres, to single-predictor linear models testing
the influence of moderators on adult survival rates (Table S2). We next
used AICC values (Burnham & Anderson 2002) to guide
selection of a multi-predictor model of extrinsic climatic factors and
intrinsic traits separately. Starting with the moderator that had the
lowest AICC value, we sequentially added the next
strongest moderator until AICC was no longer improved
(Table S3). We considered the model that minimized AICCthe most appropriate if it had fewer parameters and was at least 2
AICC less than the next most competitive model (Arnold
2010). All of the intrinsic moderators we assessed improved model fit
and were carried forward to the next step of model development.
Temperature seasonality (Temp Seasonality)
provided the best model fit for extrinsic moderators. We then combined
both sets of moderators into a joint extrinsic / intrinsic model and
repeated analysis using the global dataset and each of the six data
subsets.