There is widespread evidence that species distributions are shifting in response to climate change. Warming temperatures and climate niche constraints are hypothesized drivers of northward shifts in temperate migratory bird breeding distributions, but heterogeneity in the direction of distribution shifts suggests that the climate niche hypothesis does not explain all changes in distributions. We propose that: 1) changes in migration costs and benefits related to dampened seasonal differences between breeding and winter areas, 2) sensitivity to supplemental cues that affect duration of migration and onset of reproduction, 3) a latitudinal mismatch‐driven fitness gradient, or a combination of these drivers may explain southward distribution shifts. We examined latitudinal shifts in breeding distribution centroids for 73 species of migratory birds from 1994 to 2017 across eastern, central and western regions of North America using Breeding Bird Survey data and tested if life history characteristics related to the above hypotheses and population status were associated with shift patterns. We found that 44% of regional centroid shifts were southward, 55% were northward, and several species shifted in different directions in different regions. Migratory strategy and protandry predicted breeding distribution centroid shifts, although they tended to be more predictive of northward shifts than southward shifts. There was evidence that supplemental cues explained some southward shifts because herbivorous birds tended to shift southward compared to insectivores, or raptors that shifted northward. Shifts in centroids were not explained by trends in abundance, suggesting that centroid shifts were not attributable to population declines or increases at distribution margins. Our results show the prevalence of heterogeneous breeding distribution shifts, including often overlooked southward shifts, and suggest that more work is needed to develop alternative hypotheses that would explain southward shifts in distributions.

Dispersal is a critical process influencing population dynamics and responses to global change. Long‐distance dispersal (LDD) can be especially important for gene flow and adaptability, although little is known about the factors influencing LDD because studying large‐scale movements is challenging and LDD tends to be observed less frequently than shorter‐distance dispersal (SDD). We sought to understand patterns of natal dispersal at a large scale, specifically aiming to understand the relative frequency of LDD compared to SDD and correlates of dispersal distances. We used bird banding and encounter data for American kestrels (Falco sparverius) to investigate the effects of sex, migration strategy, population density, weather, year and agricultural land cover on LDD frequency, LDD distance and SDD distance in North America from 1961 to 2015. Nearly half of all natal dispersal (48.9%) was LDD (classified as >30 km), and the likelihood of LDD was positively associated with the proportion of agricultural land cover around natal sites. Correlates of distance differed between LDD and SDD movements. LDD distance was positively correlated with latitude, a proxy for migration strategy, suggesting that migratory individuals disperse farther than residents. Distance of LDD in males was positively associated with maximum summer temperature. We did not find sex‐bias or an effect of population density in LDD distance or frequency. Within SDD, females tended to disperse farther than males, and distance was positively correlated with density. Sampling affected all responses, likely because local studies more frequently capture SDD within study areas. Our findings that LDD occurs at a relatively high frequency and is related to different proximate factors from SDD, including a lack of sex‐bias in LDD, suggest that LDD may be more common than previously reported, and LDD and SDD may be distinct processes rather than two outcomes originating from a single dispersal distribution. To our knowledge, this is the first evidence that LDD and SDD may be separate processes in an avian species, and suggests that environmental change may have different outcomes on the two processes.

Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade‐offs between capturing ecological complexity in statistical models and implementing these models. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation‐assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual‐ and population‐level migratory characteristics. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm. For complex ecological statistical models, like those for animal movement, multi‐species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation‐assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.

Aim: Measuring avian migration can prove challenging given the spatial scope and the diversity of species involved. No one monitoring technique provides all the pertinent measures needed to capture this macroscale phenomenon -emphasizing the need for data integration. Migration phenology is a key metric characterizing large-scale migration dynamics and has been successfully quantified using weather surveillance radar (WSR) data and community science observations. Separately, both platforms have their limitations and measure different aspects of bird migration. We sought to make a formal comparison of the migration phenology estimates derived from WSR and eBird data -of which we predict a positive correlation.Location: Contiguous United States.