Discussion
Contrary to a commonly held assumption, our results indicate that
seasonal climate tracking is not a primary driver of intraspecific
migration patterns on a broader scale. Instead, our work supports the
idea that energy efficiency, specifically optimizing the balance between
accessing environmental resources and the cost of movement, is largely
responsible for intraspecific variation in broad-scale patterns of
migratory connectivity (Figs. 1 and 2a). In turn, migratory connectivity
underlies the significant variation in the degree of seasonal climate
overlap observed between populations, with some populations having
relatively high overlap, while others have almost no overlap (Fig. 1).
In addition to energy efficiency at broad scale, our results also
suggest that regional-scale seasonal precipitation tracking affects
migration destinations, thus revealing a potential scale-dependency of
ecological processes driving migration.
Populations with high thermal overlap tended to migrate at intermediate
distances, while the extent of precipitation overlap between populations
decreased with migration distance. Overall, these patterns align more
closely with the predictions of a model based on selecting energetically
optimal wintering destinations rather than climatically similar
wintering destinations (Fig. 1), thus supporting the hypothesis that
seasonal climate tracking is a consequence, rather than a cause, of
migratory connectivity. Additional evidence supporting this hypothesis
is found in the observation that variation in thermal tracking across
populations can be largely explained by the model simulating
energetically optimal wintering destinations (Fig. 2c). Moreover,
populations do not track temperature more than what would be expected by
randomizing wintering destinations in regions around these energetically
optimal sites (Fig. 2c). In contrast to the commonly held belief that
seasonal climate tracking drives migration patterns, our findings
propose that avian populations do not actively follow temperature
patterns, either on a regional or continental scale. Instead, the
variability in thermal tracking among populations is primarily shaped by
migratory connectivity patterns. These connectivity patterns are largely
influenced by the delicate balance between maximizing energy acquisition
from the environment and minimizing the energy costs associated with
migratory movement (Figs. 1 and 2; Somveille et al. 2021).
As energy efficiency drives avian migration patterns at broad scale, our
results also reveal a potential scale-dependency of ecological
processes, with precipitation tracking acting at a more regional scale
to shape intra-specific migration patterns. In contrast with
temperature, we found that the variation in seasonal precipitation
tracking is not well explained by broad-scale migratory connectivity
predicted under energy efficiency, but that populations appear to track
precipitation favorably when compared to the null expectation of
randomizing wintering destinations around the energetically optimal
wintering destinations. These results open up a new avenue for research
as better understanding the scale at which the drivers of migration play
out is crucial for predicting how migratory birds will respond to global
change. While previous high-resolution work focusing on few migratory
bird populations found that population-level seasonal climate tracking
is a bottom-up process that emerges from individual-level
weather-tracking behavior (Fandos et al. 2020), we found that
population-level seasonal climate tracking is also a top-down process
shaped by how energy efficiency drives species’ migratory connectivity.
These two processes are not mutually exclusive as we found that
migratory bird populations tend to track precipitation regionally,
meaning that while energy efficiency structures species’ migratory
connectivity, individuals might additionally be tracking precipitation
conditions throughout the year leading to the emergence of
population-level seasonal precipitation tracking. As precipitation
regimes shape habitat quality and the type of available resources for
birds (Smith et al. 2010; Rockwell et al. 2017),
precipitation likely determines habitat selection (Frishkoff et
al. 2016; Frishkoff & Karp 2019) – potentially more so than
temperature does (Londoño et al. 2016). In turn, precipitation
affects the composition of avian communities (Gomez et al. 2019)
and selects for specific traits, such as beak morphology (Bay et
al. 2021), thus potentially leading natural selection to favor seasonal
precipitation tracking at regional scales.
The extent of seasonal precipitation tracking however varies by species.
In particular, Yellow Warbler seems to consistently not track
precipitation across its populations, with all populations appearing to
be niche switchers for precipitation (i.e relatively low overlap,
overestimated by ORSIM and high rank amongst null values; Fig. 2d). This
could be due to sampling for Yellow Warbler that is not well
representative of the overall realized precipitation niche of the
species for the wintering season, with a bias towards drier areas (Fig.
S9d) as most sampled regions for this species are located on the western
slopes of Central America (Fig. S2) where the dry season is very
pronounced. This bias, combined with a sampling bias on the breeding
grounds towards the western and northern parts of the range (Fig. S2) –
in particular, the south-western and eastern/north-eastern parts of the
range for this species were not included in the analysis (the eastern
population was removed due to insufficient data on the wintering
grounds), might explain the consistent niche switching for the
populations of this species. If Yellow Warbler is removed from the
analysis, then the distribution of ranks compared to the null
expectations of randomizing wintering destinations around the
energetically optimal wintering destinations becomes highly skewed
towards low values for precipitation (one-sample K-S test; P=0.003),
thus reinforcing the finding that populations are tracking a
precipitation regime regionally.
In contrast with Yellow Warbler, American Redstart appears to have
seasonal precipitation overlap consistently higher than the
energetically optimal expectation and higher than most of the null
random expectations for all its populations (Fig. 2d). Such consistent
seasonal precipitation tracking across the populations of a migratory
species could be either due to a direct physiological effect of
precipitation leading to the species not being able to have a broad
precipitation niche, and therefore its populations having to track a
narrow precipitation regime throughout seasons, or due to indirect
effects of precipitation on habitat and resources to which population
are adapted, which in turn affects seasonal precipitation tracking.
Studies on American Redstart have shown that winter precipitation
affects survival and body conditions (Studds & Marra 2007) as well as
spring departure (Studds & Marra 2011), indicating that populations of
this species might be particularly adapted to precipitation regimes. In
addition, range-wide population trends in American Redstart are not
affected by temperature but have an association with winter plant
productivity (Wilson et al. 2011), which in turn is influenced by
precipitation.
Our analysis did not explicitly consider resource availability for the
species investigated (e.g. insect density) due to the lack of available
data at continental scale. Instead, we used seasonal relative abundance
predicted by STEMs, which correlate citizen science occurrence data with
land cover descriptors, assuming that it reflects the distribution of
energy available to the species across their seasonal ranges (Somveilleet al. 2021). As climate is likely shaping the spatio-temporal
availability of resources and the abundance distribution of species, it
could still be an indirect determinant of migratory connectivity. Future
studies using data on the specific distribution of resources for species
could investigate the extent to which climate shape resource
availability and thus indirectly affect migration patterns, which is
important for predicting the impact of climate change on migratory
birds.
The results obtained in this study have implications for local
adaptation and the conservation of migratory species. Our findings
suggest that much of the variation in seasonal climate tracking is a
consequence of how other ecological processes shape migration patterns,
which could lead to populations that happen to track climate to have
evolved traits and behavior to adapt to a narrow set of climate
conditions. This local adaptation could potentially lead to genetic
differentiation, but it could also make seasonal climate tracking
populations potentially more vulnerable to climate change. With changing
climate, these populations are more likely to face new climate
conditions that are outside their relatively narrow climate niche and
since their migration patterns are not driven by seasonal climate
tracking, particularly for temperature, these populations might not be
able to adapt to climate change via change in their migration behavior.
We found that populations migrating intermediate distances tend to have
higher thermal overlap and therefore are potentially at higher risk
under climate change. In addition, as migratory connectivity is largely
driven by energy efficiency, anthropogenic change in the distribution of
resources for migratory birds through land use change is likely to
reshape migratory connectivity patterns, which would drive populations
that are tracking climate seasonally to potentially experience new
climate conditions for which they are not adapted.