Results
The genetically distinct bird populations analyzed in this study exhibit a wide variation in seasonal climate overlap. For the two-dimensional climatic niche (temperature + precipitation), overlap varies between 0.086–0.895 (Fig. 1b). Some geographical pattern is apparent in the distribution of seasonal climate overlap: populations with the lowest overlap are distributed in the north-west of North America and populations with the highest niche overlap are distribution in the south-west and south-central parts of North America (Fig. 1b). Similar variation is obtained for seasonal overlaps in temperature and precipitation separately, albeit with a smaller extent of overall variation (Fig. 1c,d). For temperature, seasonal overlap is lower for populations migrating either short or long distances, and it is higher for intermediate migration distances (Fig. 1c). For precipitation, no peak in overlap is observed at intermediate migration distance but overlap tends to decrease with distance (r=-0.52; P=0.013; Fig. 1d). Populations migrating short distances and having relatively high precipitation overlap tend to be distributed in south-west of North America, while populations migrating long distances and having relatively low precipitation overlap tend to be distributed in the north-west of North America (Fig. 1d).
ORSIM – the model simulating migratory connectivity based on energy efficiency – captures the broad migratory connectivity pattern formed by the populations considered in this study (Fig. 1a,e). The model predicts variations in climate overlap (two-dimensional, thermal only, and precipitation only) with migration distance that are matching the observed patterns (Fig. 1). Specifically, it predicts that thermal overlap peaks at intermediate migration distance (Fig. 1g), that precipitation overlap generally decreases with migration distance (Fig. 1h), and that populations from north-west North America tend to have the lowest overlap while populations from south-west and south-central North America tend to have the highest overlap (Fig. 1f). The model also explains the distribution of migration distances across populations with a very high predictive power (r=0.940; P<0.001; Fig. 2a).
ORSIM predicts the variation in thermal overlap with a relatively high correlation between predictions and observations (r=0.469; P=0.028; Fig. 2c). ORSIM predictions for seasonal overlap also show some positive correlation with observations for precipitation (r=0.277; P=0.212; Fig. 2d) and two-dimensional climate (r=0.295; P=0.183; Fig. 2b) although these correlations are not statistically significant. Deviation of empirical seasonal climate overlap from ORSIM predictions is not significantly skewed to the right of a normal distribution centered around 0 and with a standard deviation equal to the observed distribution of errors (one-sample K-S test for two-dimensional climate niche: P=0.150; thermal tracking: P=0.735; precipitation tracking: P=0.136), indicating that populations do not tend to have a higher seasonal climate overlap than predicted by ORSIM.
Contrary to ORSIM, the model simulating migratory connectivity based on tracking two-dimensional climatic conditions does not capture the pattern of variation in climate overlap (two-dimensional, thermal only, and precipitation only) with migration distance (Fig. 1), and it is also the case for the models simulating migratory connectivity based on thermal and precipitation tracking separately (Fig. S7). In addition, the two-dimensional climatic tracking model was not able to predict the empirical variation in population climate overlap (two-dimensional climate: r=-0.173; P=0.44; temperature only: r=0.203; P=0.364; precipitation only: r=-0.009; P=0.969), which was also the case for the models simulating migratory connectivity based on thermal and precipitation tracking separately.
The distribution of the ranks of seasonal climate overlap among simulated values for the null model randomizing wintering destinations around ORSIM expectation is significantly skewed towards low values for the two-dimensional climate niche (one-sample K-S test; P=0.032), marginally significantly skewed towards low values for precipitation (one-sample K-S test; P=0.061) and not significantly different from the null expectation for temperature (one-sample K-S test; P=0.675). For precipitation and two-dimensional climate niche, the variation in seasonal tracking seems to be driven by a set of populations that are tracking precipitation particularly well (i.e. relatively high overlap, underestimated by ORSIM and low rank amongst null values) versus a few populations that appear to be climate switchers for precipitation (i.e. relatively low overlap, overestimated by ORSIM and high rank amongst null values; Fig. 2b,d). Some species appear consistent in their results across their populations. In particular, all populations of Yellow Warbler are apparent precipitation switchers while all populations of American Redstart are apparent precipitation trackers (Fig. 2d).