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.