A general theory of avian migratory connectivity

Abstract

Birds exhibit a remarkable array of seasonal migrations. Despite much research describing migratory behaviour, the underlying forces driving how a species’ breeding and wintering populations redistribute each year, that is, migratory connectivity, remain largely unknown. Here, we test the hypothesis that birds migrate in a way that minimises energy expenditure while considering intraspecific competition for energy acquisition, by developing a modelling framework that simulates an optimal redistribution of individuals between breeding and wintering areas. Using 25 species across the Americas, we find that the model accurately predicts empirical migration patterns, and thus offers a general explanation for migratory connectivity based on first ecological and energetic principles. Our model provides a strong basis for exploring additional processes underlying the ecology and evolution of migration, but also a framework for predicting how migration impacts local adaptation across seasons and how environmental change may affect population dynamics in migratory species.

BirdFlow: Learning Seasonal Bird Movements from eBird Data

“…Recently, Somveille et al (2021) developed a closely related model for inferring migratory connectivity from breeding and non-breeding distributions and cost-based estimation. Two key differences are that: (1) BirdFlow models the entire track X 1 , .…”

“…Recently, Somveille et al (2021) developed a closely related model for inferring migratory connectivity from breeding and non-breeding distributions and cost-based estimation. Two key differences are that: (1) B ird F low models the entire track X 1 ,…, X T instead of just the starting and ending locations, (2) B ird F low incorporates entropy regularization to combat problems that arise with exact cost minimization, including too little variability in inferred routes (cf.…”

“…Previous studies have approached modeling movement from observational data by first extensively cleaning the data to correct for variability from the observation process, and then investigating specific quantities of interest like centroid movement or estimated movement speed (Supp et al, 2021). Other promising approaches include deterministic models based on the concept of global energy efficiency, in which simulated birds are distributed to optimize both resource acquisition and energy expenditure (Somveille et al, 2021). However, it has proven challenging to accurately infer individual-level behavior across large spatial scales while accounting for the stochasticity inherent in the movement behavior of individuals.…”

Geographic context is a key driver of spatial variation of bird species richness during migration

“…In essence, geographic barriers alter the energetic cost of habitat selection and, thus, the net energy availability of a given location Somveille et al, 2021). In addition, competition from residents may reduce the energy available to migrants, further weakening the expected relationship between what amounts to a subset of the species assemblage and primary productivity.…”

Eco-evolutionary drivers of avian migratory connectivity

“…Although previous studies have identified some geographical and bio-energetic conditions that affect the strength of avian migratory connectivity (Finch et al 2018;Somveille et al 2021), the underlying ecoevolutionary drivers are poorly understood. In particular, no interspecific comparative analysis has been conducted, though such analysis would be essential to disentangle the potential evolutionary and ecological drivers of the extent of migratory connectivity in different species.…”