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).