Migratory connectivity of North American waterfowl across administrative flyways.

Management of waterfowl that migrate seasonally across North America occurs within four flyways that were delineated in the early 1900s to include the annual movements of populations. Movements may have changed over the past century since the administrative flyways were established, and may do so while management plans are in use, so information about transitions among flyways through time can illustrate how management assumptions may change. Today there are more than 12 million records from 60 years of migratory waterfowl band recoveries to assess adaptive management approaches that will be most effective when they account for movements within and between flyways. We examined how much the movement of North American waterfowl occurs between flyways, whether those movements have changed through time, and whether movements of mallards are representative of multiple species, as suggested by current harvest management strategies. We estimated the probability a duck would transition from one flyway to another and the strength of migratory connectivity (MC) for each species within and among flyways. We used capture-mark-recovery models to estimate population-specific movement within and among flyways (transition probabilities) for 15 migratory waterfowl species that were banded during breeding and recovered during winter. We developed new functionality in the R package MigConnectivity to estimate the species-specific strength of MC using transition probability samples from the capture-mark-recovery models. We found the regular movement of duck populations among flyways, overall weak MC, and no consistent change in migratory movements through time. Mallard movements were median among all duck species, but significantly different from many species, particularly diving ducks. Despite the significant movement between flyways, our work suggests flyway management of waterfowl matches many of the seasonal movements of these species when considering mid-continent flyway management. We recommend models accounting for all transition probabilities between populations and regularly estimating harvest derivations, transition probabilities, and MC metrics to verify that the current movements match model assumptions.

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere.

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.