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1. Estimating how much long-distance migrant populations spread out and mix during the non-breeding season (migratory connectivity) is essential for understanding and predicting population dynamics in the face of global change. 2. We quantify variation in population spread and inter-population mixing in long-distance, terrestrial migrant land-bird populations (712 individuals from 98 populations of 45 species, from tagging studies in the Neotropic and Afro-Palearctic flyways). We evaluate the Mantel test as a metric of migratory connectivity, and explore the extent to which variance in population spread can be explained simply by geography. 3. The mean distance between two individuals from the same population during the nonbreeding season was 743 km, covering 10-20% of the maximum width of Africa/South America. Individuals from different breeding populations tended to mix during the non-breeding season, although spatial segregation was maintained in species with relatively large non-breeding ranges (and, to a lesser extent, those with low population-level spread). A substantial amount of between-population variation in population spread was predicted simply by geography, with populations using non-breeding zones with limited land availability (e.g. Central America compared to South America) showing lower population spread. 4. The high levels of population spread suggest that deterministic migration tactics are not generally adaptive; this makes sense in the context of the recent evolution of the systems, and the spatial and temporal unpredictability of non-breeding habitat. 5. The conservation implications of generally low connectivity are that the loss (or protection) of any non-breeding site will have a diffuse but widespread effect on many breeding populations. Although low connectivity should engender population resilience to shifts in habitat (e.g. due to climate change), we suggest it may increase susceptibility to habitat loss. We hypothesize that, because a migrant species cannot adapt to both simultaneously, migrants generally may be more susceptible to population declines in the face of concurrent anthropogenic habitat and climate change.

Determining how migratory animals are spatially connected between breeding and non-breeding periods is essential for predicting the effects of environmental change and for developing optimal conservation strategies. Yet, despite recent advances in tracking technology, we lack comprehensive information on the spatial structure of migratory networks across a species' range, particularly for small-bodied, long-distance migratory animals. We constructed a migratory network for a songbird and used network-based metrics to characterize the spatial structure and prioritize regions for conservation. The network was constructed using year-round movements derived from 133 archival light-level geolocators attached to Tree Swallows (Tachycineta bicolor) originating from 12 breeding sites across their North American breeding range. From these breeding sites, we identified 10 autumn stopover nodes (regions) in North America, 13 non-breeding nodes located around the Gulf of Mexico, Mexico, Florida, and the Caribbean, and 136 unique edges (migratory routes) connecting nodes. We found strong migratory connectivity between breeding and autumn stopover sites and moderate migratory connectivity between the breeding and non-breeding sites. We identified three distinct \"communities\" of nodes that corresponded to western, central, and eastern North American flyways. Several regions were important for maintaining network connectivity, with South Florida and Louisiana as the top ranked non-breeding nodes and the Midwest as the top ranked stopover node. We show that migratory songbird networks can have both a high degree of mixing between seasons yet still show regionally distinct migratory flyways. Such information will be crucial for accurately predicting factors that limit and regulate migratory songbirds throughout the annual cycle. Our study highlights how network-based metrics can be valuable for identifying overall network structure and prioritizing specific regions within a network for conserving a wide variety of migratory animals.

Migratory connectivity is the degree to which populations are linked in space and time across the annual cycle. Low connectivity indicates mixing of populations while high connectivity indicates population separation in space or time. High migratory connectivity makes individual populations susceptible to local environmental conditions; therefore, evaluating migratory connectivity continuously across a species range is important for understanding differential population trends and revealing places and times contributing to these differences. The common nighthawk Chordeiles minor is a widespread, declining, long‐distance migratory bird. Variable population trends across the nighthawk breeding range suggest that knowledge of migratory connectivity is needed to direct conservation. We used GPS tags to track 52 individuals from 12 breeding populations. We estimated migratory connectivity as 0.29 (Mantel coefficient: 0 = no connectivity, 1 = full connectivity) between the breeding and wintering grounds. We then estimated migratory connectivity at every latitude (spatial connectivity) or day (temporal connectivity) of migration and smoothed those migratory connectivity estimates to produce continuous migratory connectivity ‘profiles'. Spatial and temporal connectivity were highest during migration through North America (around 0.3–0.6), with values generally around 0 in Central and South America due to mixing of populations along a common migratory route and similar migration timing across populations. We found local peaks in spatial and temporal connectivity during migration associated with crossing the Gulf of Mexico. We used simulations to estimate the probability that our method missed peaks (spatial: 0.12, temporal: 0.18) or detected false peaks (spatial: 0.11, temporal: 0.37) due to data gaps and showed that our approach remains useful even for sparse and/or sporadic location data. Our study presents a generalizable approach to evaluating migratory connectivity across the full annual cycle that can be used to focus migratory bird conservation towards places and times of the annual cycle where populations are more likely to be limited.

In 2007, the first miniature light-level geolocators were deployed on small landbirds, revolutionizing the study of migration. In this paper, we review studies that have used geolocators to track small landbirds with the goal of summarizing research themes and identifying remaining important gaps in understanding. We also highlight research and opportunities using 2 recently developed tracking technologies: archival GPS tags and automated radio-telemetry systems. In our review, we found that most (54%) geolocator studies focused on quantifying natural history of migration, such as identifying migration routes, nonbreeding range, and migration timing. Studies of behavioral ecology (20%) uncovered proximate drivers of movements, including en route habitat quality; that migration routes, but not timing, may be flexible in some species; and different age and sex classes show significant differences in migration strategy. Studies of the evolution of migration (9%) have illustrated that migration is a potential barrier to hybridizing species or subspecies, and some work has correlated gene polymorphisms and methylation patterns with migration behavior. Studies of migratory connectivity (11%) have shown that a moderate level of connectivity is common, although variability across and within species exists. Studies of seasonal interactions (7%) have found mixed results: in some cases, carryover effects have been identified; in other cases, carryover effects are buffered during intervening stages of the annual cycle. Archival GPS tags provide unprecedented precision in locations of nonbreeding sites and migration routes, and will continue to improve understanding of migration across large spatial scales. Automated radio-telemetry systems are revolutionizing our knowledge of migratory stopover biology, and have led to discoveries of previously unknown stopover behaviors. Together, these tracking technologies will continue to provide insight into small migratory landbird movements and contribute important information for conservation of this rapidly declining group.

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.

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.

Full-annual-cycle (FAC) models integrate seasonal demographic and environmental processes to elucidate the factors that limit and regulate animal populations. Unlike traditional, breeding-season-focused models of migratory populations, FAC population models include the effects on population dynamics of events in both the breeding and the nonbreeding season (i.e. winter and migration). Given that migratory birds can spend most of the year away from the breeding grounds and face seasonally specific threats and limitation, FAC models can provide critical and unique insights about their population dynamics. We review existing FAC population model types, including demographic network models, seasonal matrix models, and individual-based models, with examples of each type. We also suggest some approaches new to FAC population modeling—integrated population models and integral projection models—and make recommendations for the development and implementation of these models. Incorporating model components such as density dependence, migratory connectivity (the demographic linkages between breeding and nonbreeding areas), and seasonal interactions can be critical for model realism but can also increase model complexity and development time. Much of the development of FAC population models has been more theoretical than applied. The main limitation to the application of the developed models is availability of empirical data for all annual stages, particularly knowledge of migratory connectivity and density-dependent seasonal survival. As these data become more available, the models outlined here should find additional uses.

Many migratory species are in decline and understanding these declines is challenging because individuals occupy widely divergent and geographically distant habitats during a single year and therefore populations across the range are interconnected in complex ways. Network modeling has been used to show, theoretically, that shifts in migratory connectivity patterns can occur in response to habitat or climate changes and that habitat loss in one region can affect sub‐populations in regions that are not directly connected. Here, we use a network model, parameterized by integrating long‐term monitoring data with direct tracking of

Migration is common worldwide as species access spatiotemporally varying resources and avoid predators and parasites. However, long-distance migrations are increasingly imperiled due to development and habitat fragmentation. Improved understanding of migratory behavior has implications for conservation and management of migratory species, allowing identification and protection of seasonal ranges and migration corridors. We present a technique that applies circuit theory to predict future effects of development by analyzing season-specific resistance to movement from anthropogenic and natural environmental features across an entire migratory path. We demonstrate the utility of our approach by examining potential effects of a proposed road system on barren ground caribou (Rangifer tarandus granti) and subsistence hunters in northern Alaska. Resource selection functions revealed migratory selection by caribou. We tested five scenarios relating habitat selection to landscape resistance using Circuitscape and GPS telemetry data. To examine the effect of potential roads on connectivity of migrating animals and human hunters, we compared current flow values near communities in the presence of proposed roads. Caribou avoided dense vegetation, rugged terrain, major rivers, and existing roads in both spring and fall. A negative linear relationship between resource selection and landscape resistance was strongly supported for fall migration while spring migration featured a negative logarithmic relationship. Overall patterns of caribou connectivity remained similar in the presence of proposed roads, though reduced current flow was predicted for communities near the center of current migration areas. Such data can inform decisions to allow or disallow projects or to select among alternative development proposals and mitigation measures, though consideration of cumulative effects of development is needed. Our approach is flexible and can easily be adapted to other species, locations and development scenarios to expand understanding of movement behavior and to evaluate proposed developments. Such information is vital to inform policy decisions that balance new development, resource user needs, and preservation of ecosystem function.

Understanding the distribution of breeding populations of migratory animals in the non-breeding period (migratory connectivity) is important for understanding their response to environmental change. High connectivity (low non-breeding population dispersion) may lower resilience to climate change and increase vulnerability to habitat loss within their range. Very high levels of connectivity are reportedly rare, but this conclusion may be limited by methodology. Using multiple tracking methods, we demonstrate extremely high connectivity in a strongly declining, peripheral breeding population of a long-distance migrant, the Common Nightingale in the UK. Non-breeding population dispersion is lower than for previously tracked populations of this and other species and likely lower than can usually be detected by light-level geolocation, the main tracking method for small bodied species. Extremely low levels of population mixing were also detected, so any impacts on this population on the non-breeding grounds are unlikely to be shared with more distant breeding populations, corresponding to the observed patterns of European population change. According to a species distribution model using independent field data, this population’s non-breeding grounds had lower suitability than others and likely declined before the period we were able to assess. These results support hypotheses that climatic and habitat-related deterioration of non-breeding grounds contributes to population declines in peripheral and high-connectivity breeding populations of long-distance migrants, including the one studied here.