Research Paper

INTRODUCTION

Conservation of migratory animals, including birds, is inherently challenging (Albers et al. 2023) due to species’ varied migratory strategies, distributions, and reliance on geographically distant areas during different portions of the annual cycle (Runge et al. 2014). In North America, many species of migratory birds are experiencing population declines (e.g., Rosenberg et al. 2019). Lack of conservation-relevant information about declining populations and other species of conservation interest (i.e., both species of conservation concern [e.g., declining grassland songbirds] and species that are not of concern but are the foci of intensive management [e.g., waterfowl]) has motivated considerable recent research to identify population drivers and inform conservation actions targeting migratory birds across the annual cycle.

As has been summarized elsewhere (e.g., Marra et al. 2015), much of the information relevant to conservation of many bird species has historically come from studies of population ecology, bird–habitat relations, and regulating factors occurring during the breeding period. This bias toward studying migratory birds during the breeding period is a result of several factors, including the importance of assessing reproduction and recruitment as drivers of population dynamics, the difficulty of study birds not tied to a breeding area, and the lack of resources and collaboration devoted to assessing population ecology outside the breeding period (Marra et al. 2015). Consequently, conservation efforts have often lacked relevant information from periods other than the breeding period for many species of North American migratory birds (with the notable exception of waterfowl [Anseriformes]; e.g., Heitmeyer et al. 1996), even though factors during migration and the nonbreeding period can regulate migratory bird populations (e.g., Kramer et al. 2018, Newton 2024a).

There has recently been an explosion of research on migratory birds outside the breeding period (for a general summary, see Newton 2024b) that has the potential to inform conservation of North American migratory birds. Improved tracking technologies (e.g., miniaturization of tracking devices [Bridge et al. 2011, McKinnon and Love 2018]) that allow tracking of individual birds throughout their annual cycle, remote sensing and genetic methods that can be used to assess migratory connectivity and other aspects of migration, and renewed recognition of the importance of having information from the entire annual cycle have the potential to increase effectiveness of migratory bird conservation. To date, however, there has not been a synthesis of how recently derived information about North American bird migration can and is being used to devise more effective conservation strategies for migratory birds (but see Flack et al. 2022 for a general overview). Herein, we (1) as context, assess the prevalence of migration and describe broad patterns of migration strategies and terminology related to migration of North American birds by reviewing recent species accounts, (2) briefly summarize the major technological advances that characterize historical and current eras of bird migration research in North America, (3) illustrate how knowledge derived from migration studies can be used to inform conservation strategies of migratory birds in general, and how these data have shaped ongoing conservation efforts directed at three well-studied species of North American migratory birds, and (4) identify critical knowledge gaps regarding North American bird migration as it relates to conservation, and how current and future approaches might address those gaps. Although we restrict our assessment to migratory birds that breed in North America, we draw more broadly from recent studies of bird migration outside of North America and suggest that our synthesis has relevance beyond North America in the general context of migratory bird conservation.

PREVALENCE AND PATTERNS OF MIGRATION IN NORTH AMERICAN BIRDS

North American birds exhibit a high prevalence of migratory behavior. In a review of accounts of 645 bird species known to breed in North America (Billerman et al. 2022), 89% (577/645) indicated seasonal movements consistent with migration (i.e., seasonal movement with return to or near origin [Table 1, Appendix 1] [Newton 2024b]) (Table 2, Fig. 1). However, species accounts varied in how migration was classified. Some species were reported to be resident despite descriptions of regular, seasonal movements consistent with migration (e.g., Verdin [Auriparus flaviceps] and Canyon Wren [Catherpes mexicanus] exhibit local, two-way movements in response to resource availability and conditions [Webster 2020, Jones et al. 2023], indicative of facultative migration [Table 1]).

Migration patterns are classified in a variety of ways, including by what proportion of the species migrates and by migration distance (Table 1 [Newton 2024b]). In our review, 61% of species were partial migrants; 39% were complete migrants. The characterizations of migration distance in our review were inconsistent, such that some species classified as short- or medium-distance migrants made seasonal movements as far as, or farther than, species classified as long-distance migrants (e.g., Black-and-white Warbler [Mniotilta varia] = long-distance migrant, Prothonotary Warbler [Protonotaria citrea] = medium-distance migrant despite largely overlapping seasonal distributions [Kricher 2020, Petit 2020]).

From a conservation perspective, the extent to which birds cross jurisdictional boundaries as they migrate (as many migratory North American birds do) is important. Conservation of birds that cross jurisdictions requires collaboration among management agencies and organizations (Anderson et al. 2018). Migratory birds that complete their migrations within North America may cross tribal, state, and national boundaries but are jointly managed under the U.S. Migratory Bird Treaty Act and companion treaties with Canada and Mexico (Anderson et al. 2018). Birds that migrate between North and Central and South America (i.e., Nearctic-Neotropical migrants) fall under a variety of other international agreements that are generally less comprehensive, which makes collaborative conservation efforts for these species more complex (McDuffie et al. 2022a).

MAJOR ERAS OF TECHNOLOGICAL ADVANCE IN THE STUDY OF MIGRATORY BIRDS

The opportunity to target conservation actions to benefit migratory birds through the full annual cycle is directly linked to the availability of technology and the types and resolution of data on seasonal bird movements that can be obtained. Based on advances in technology, we propose three eras that are roughly chronological and defined by available tools and technologies to assess aspects of North American bird migration (Fig. 2).

The first era, beginning in the early 20th century (Newton 2010), was dominated by affixing uniquely identifiable markers to birds (e.g., numbered bands, neck collars), combined with subsequent re-observation or recovery (Tautin 2005). This method provides high-precision location and phenology information at the individual scale and coarse information at the population scale (e.g., migration phenology, use of staging and stopover areas). In combination with information from broad-scale surveys initiated during the early- to mid-20th century (e.g., winter waterfowl surveys, North American Breeding Bird Surveys, Christmas Bird Counts), information gleaned during this first era of bird migration research was used to describe and delineate flyways in North America (Lincoln 1935). These flyways were generally defined in the context of waterfowl management and provided coarse-scale descriptions of migration of larger-bodied and/or hunted migratory bird populations (e.g., American Woodcock [Scolopax minor] management regions [Coon et al. 1977]).

The second era of North American bird migration research was characterized by the emergence of remote technologies used to track migrating birds through space and time. The application of remote detection technology (e.g., radar) and the advent of very high frequency (VHF) radio telemetry in the late 1960s (Cochran and Lord 1963, Cochran et al. 1965) led to the identification of migration pathways, stopover sites, and migration habitat use of numerous North American species (e.g., Myatt and Krementz 2007). Size and weight considerations constrained the use of VHF telemetry to larger birds, and radar applications were constrained by the inability to identify species or individuals. Tracking birds marked with VHF transmitters provided the possibility of knowing the locations of individuals during migration. However, VHF telemetry comes with significant logistical constraints, including size and weight limitations and the necessity of being in relatively close proximity to radio-marked individuals.

The third and current era of North American bird migration research was facilitated by the development and rapid miniaturization of several technologies that provide the means of tracking smaller individuals and derive movement data remotely with high temporal and spatial resolution. For example, satellite-based (i.e., Platform Transmitter Terminals [PTTs]) telemetry and Global Positioning System (GPS) tags interfacing with cellular communication networks (i.e., Global System for Mobile Communications [GSM] tags) allow for frequent (PTT) or near-continuous (GPS) tracking of individuals (Berthold 2001, Bridge et al. 2011, Dingle 2014). These technologies ushered forth a flood of new information on annual movements, seasonal ranges, and staging areas, especially for large-bodied migrants that make extensive seasonal movements that could not otherwise be monitored effectively during migration (Berthold 2001). Light-level geolocators (hereafter, “geolocators”), lightweight (< 0.5 g) devices that use variation in seasonal daylight patterns to derive location estimates (Hill 1994), further facilitated tracking of individual birds during migration (reviewed by McKinnon and Love 2018). Geolocators afforded opportunities to address long-standing questions about migratory behaviors of smaller, terrestrial species (e.g., ~10 g warblers; Parulidae; [e.g., Kramer et al. 2017, Delancey et al. 2020, Roberto-Charron et al. 2020]) and have led to advances in understanding migratory patterns (e.g., Delmore et al. 2012, Knight et al. 2021), migratory connectivity (e.g., Kramer et al. 2018, Tonra et al. 2019), and behavior during (e.g., DeLuca et al. 2015, Streby et al. 2015) and outside of migratory periods (e.g., Heckscher et al. 2011, Gow et al. 2015) of both large and small bird species. The ongoing miniaturization of VHF transmitters (tag mass < 0.2 g) and improvements to automated receivers have also facilitated the expansion of passive telemetry arrays that record the passage of individually marked migratory birds as small as hummingbirds (Trochilidae [Zenzal et al. 2018]) at specific points along migratory routes (e.g., Deppe et al. 2015, Schofield et al. 2018) and their movements across continents (e.g., Motus Wildlife Tracking System [Gómez et al. 2017, Taylor et al. 2017]).

Approaches that track individual birds have been supplemented by advances in the analysis of intrinsic markers (e.g., stable isotopes [Rubenstein et al. 2002]), technologies that characterize movements of groups of birds (e.g., radar [Horton et al. 2016, 2019, Stepanian et al. 2016]), and formal databases that collect observations from birders (e.g., citizen-science data [Sullivan et al. 2009]). Applied genetics has also emerged as a tool for identifying migratory divides (Battey et al. 2018) and linking breeding and nonbreeding regions used by populations of migratory birds (e.g., genoscapes [Ruegg et al. 2020, Larison et al. 2021]). Studies using data from a combination of technologies are yielding conservation-relevant insights into North American bird migration at scales ranging from individual genes associated with different migratory behaviors (e.g., Toews et al. 2019) to predictions of daily migration intensity across North America (Van Doren and Horton 2018).

CONTRIBUTION OF TRACKING DATA TO THE CONSERVATION OF NORTH AMERICAN BIRDS

Data describing the migration ecology and movements of populations throughout their annual cycle can be used to tailor strategies that target conservation efforts to address the needs of populations at relevant periods or in specific locations. We provide examples of how data derived from tracking North American migratory birds have shaped conservation actions. We discuss how tracking data inform which groups (e.g., age classes, populations, species, guilds) benefit from conservation efforts, where and when conservation actions are implemented, and how species-specific traits pertaining to how and why birds migrate can be incorporated into effective management strategies.

Migration data provide opportunities to target conservation actions to benefit individuals, populations, species (who)

Efforts to conserve migratory species across the annual cycle require understanding where individuals occur during different periods (Webster et al. 2002, Marra et al. 2015). Identifying relevant conservation units based on shared migration strategies and space use during migration and the nonbreeding period is frequently a primary goal of migration research (McKinnon and Love 2018) and can be accomplished through both indirect (i.e., stable isotope analyses) and direct methods (e.g., tracking individuals). Stable isotope analyses from feather or claw samples provide information that can potentially identify the general breeding or nonbreeding origins of individuals sampled during migration (Hobson 1999, Ulman et al. 2023). Such data can provide insight into population-specific patterns of space use (Studds et al. 2021), variation in the timing of passage through migratory bottlenecks (Cardenas-Ortiz et al. 2020), characteristics of dispersal (Macías-Duarte and Conway 2021), and the geographic origins of individuals killed by anthropogenic factors (e.g., wind energy development [Vander Zanden et al. 2024]). Recently, genetic data (often derived from feather or blood samples) collected from birds during migration or from areas used during the nonbreeding period have proven useful for characterizing linkages among breeding and nonbreeding populations (i.e., building “genoscapes” [Rueda-Hernández et al. 2023]), delineating population boundaries (e.g., migratory divides [Battey et al. 2018]), and defining conservation units of migratory birds (Ruegg et al. 2020). For example, the construction of an American Kestrel (Falco sparverius) genoscape revealed five genetically distinct populations associated with different migratory phenotypes corresponding to the length of migration that likely warrant consideration as distinct conservation units (Ruegg et al. 2021). Although intrinsic markers can be powerful tools for identifying conservation units and delineating populations, they generally do not provide information on finer-scale movements of individuals between breeding and nonbreeding areas.

Alternatively, delineating and linking population segments across the annual cycle can be accomplished by directly tracking migratory individuals using an array of devices and technologies, which often provide finer spatial resolution than intrinsic markers. For example, data collected using a range of tracking technologies have been used in distribution-wide efforts to link breeding populations of numerous migratory bird species to the areas they use during their nonbreeding period and to delineate migration routes (e.g., Fraser et al. 2017, Knight et al. 2018, Kramer et al. 2018, Tonra et al. 2019, Knight et al. 2021, Stanley et al. 2021, McDuffie et al. 2022a [reviewed by Kays et al. 2015, McKinnon and Love 2018]). Data from these efforts have reshaped species-specific conservation strategies by identifying critical areas that are limiting populations (Kramer et al. 2018, Tonra et al. 2019), characterizing space use within and across administrative boundaries (Moore et al. 2021, Lamb et al. 2024), and defining boundaries between conservation units (Wolfson et al. 2017).

Tracking migratory birds also provides information that informs policy decisions with direct consequences for human and animal health in the context of disease ecology. Migratory birds are vectors of disease dispersal over multiple spatial scales (Viana et al. 2016) and have been implicated in spreading highly pathogenic avian influenza (HPAI) between continents (Zhang et al. 2023) and facilitating the invasion of Neotropical ticks and tick-borne pathogens into North America (Cohen et al. 2015), with serious consequences for wildlife populations (Klaassen and Wille 2023), human health (Lycett et al. 2019), and commercial poultry operations (Seeger et al. 2021). Tracking data from migratory bird species that are frequently implicated as vectors for HPAI provide information about movement patterns that can inform monitoring efforts (Ratanakorn et al. 2012), improve predictive epidemiological models (Teitelbaum et al. 2023a, b), and ultimately reduce negative effects on human and animal health.

Delineating space use during migration informs where to direct conservation efforts

Information about the spatial aspects of bird migration can be used to inform targeted conservation efforts. At smaller, localized spatial scales, knowing where individual migratory birds occur outside the breeding period can inform strategies to reduce the negative effects of infrastructure. Near airports, GPS transmitters have been used to characterize the spatial flight patterns of migratory Black Vultures (Coragyps atratus) and Turkey Vultures (Cathartes aura) to inform aircraft traffic decisions to reduce the frequency of collisions (Walter et al. 2012). Similarly, GPS data from soaring raptors have been used to inform renewable energy and infrastructure siting or operating decisions (e.g., wind turbines [Katzner et al. 2012, Miller et al. 2014]; powerlines, [Watts et al. 2015, Mojica et al. 2016]), and GPS data from migrating geese have informed conditions most likely to result in collision with offshore wind farms (Weiser et al. 2024). For small or cryptic species that occur in remote regions during migration or the nonbreeding period, archival GPS tags can provide information about nonbreeding territory size and land cover composition (e.g., Ovenbirds [Seiurus aurocapilla] [Hallworth and Marra 2015]; Eastern Whip-poor-will [Antrostomus vociferus] [Skinner et al. 2023]) that can be used to inform full-annual-cycle conservation.

Migration may constitute a period of the annual cycle with the highest risk of mortality in some species (Sillett and Holmes 2002, Newton 2024a). Therefore, it is plausible that conservation efforts that reduce mortality during these periods may positively influence population ecology. Automated telemetry data collected from migratory birds during stopovers prior to crossing the Gulf of Mexico have provided insights into the interactive effects of weather, timing, and fuel stores (i.e., fat) on individuals' activity during stopover (Schofield et al. 2018), timing of departure (Deppe et al. 2015), and survival (Ward et al. 2018). Although conservation strategies can do little if anything to affect the timing of passage or weather conditions at migratory stopovers, they can be implemented to ensure that functional networks of stopover sites exist to support migratory species with different strategies and energetic requirements (Donnelly et al. 2021). Conservation efforts that include both general habitat restoration (e.g., re-seeding prairie habitat [Stumpf and Muise 2020]) and targeted species-specific management may provide necessary land cover types for migratory birds at the landscape scale. Moreover, management activities at stopover sites can boost food production (e.g., planting native shrubs that produce fruit conferring increased antioxidant and immune capacity [Oguchi et al. 2017, Gallinat et al. 2020]), maintain or improve vegetation structure (Rodewald and Brittingham 2007), and protect important roosting sites (Fournier et al. 2019, Pearse et al. 2017).

At broader spatial scales, tracking data can be used to quantify the exposure of populations to migration risk factors that are associated with reduced survival rate (e.g., human development [Korpach et al. 2022, Kramer et al. 2023]) or protect threatened and endangered species from unintentional harvest during hunting seasons. For example, the opening date and shooting hours of the Sandhill Crane (Antigone canadensis) hunting season were changed to reduce risk to endangered Whooping Cranes (Grus americana) (Sharp et al. 2010). At regional scales, aggregating data from multiple populations or species can identify important bottlenecks and stopover sites (e.g., in migratory shorebirds [McDuffie et al. 2022b]). For example, efforts to characterize the breeding origins of 11 Nearctic-Neotropical songbird species migrating through the Darién region of northwestern Colombia by using stable isotopes revealed extensive population mixing and limited temporal segregation, thereby suggesting that this region acts as a bottleneck during migration and may be an important area for conservation (Cardenas-Ortiz 2020). Conversely, geolocator-marked Swainson’s Thrushes (Catharus ustulatus) from breeding populations separated by ~250 km used different migration pathways and stopped over in geographically distinct regions during post-breeding and pre-breeding migration (Delmore et al. 2012), suggesting that even geographically proximate breeding populations may exhibit dramatic differences in space use during migration and require population-specific conservation actions. Distribution-wide studies characterizing nonbreeding areas and migratory connectivity of populations can provide information on the general regions where full-annual-cycle conservation actions could be targeted to achieve conservation goals (Kramer et al. 2018, Tonra et al. 2019, Moore et al. 2021). Moreover, information on migratory connectivity among species or populations can be useful for delineating critically important areas (e.g., locations used by many individuals [Tonra et al. 2019]), identifying potential drivers of population trends (Kramer et al. 2018, Hallworth et al. 2021), and building interagency and/or international conservation partnerships to achieve conservation goals (Higuchi 2012, Anderson et al. 2018).

For many songbirds where technological limitations preclude direct tracking, radar may be useful for quantifying broad-scale patterns of space use to inform conservation; for example, to identify sites where turbine–bird collisions could be minimized in areas proposed for wind energy development (Cohen et al. 2022). Radar has also been used to characterize the effects of artificial light at night (ALAN) on nocturnally migrating birds and inform continental campaigns aimed at reducing collisions caused by migratory birds being attracted to well-lit urban areas (i.e., “Lights Out” campaigns [Van Doren et al. 2017]). Eliminating or greatly reducing lighting at large buildings in migration corridors during peak migratory periods can reduce fatal bird–window collisions by ~60% (Van Doren et al. 2021).

Temporal aspects of migration inform conservation (when)

Tracking data can provide information on the timing and duration of migratory stopovers (e.g., in Semipalmated Sandpipers [Calidris pusilla] [Holberton et al. 2019]), which can assist in timing conservation efforts to coincide with temporal aspects of migration. For example, at a local scale, knowledge about when large numbers of nocturnally migrating birds will be in an area may inform efforts to reduce negative anthropogenic influences (e.g., “Lights Out” campaigns [Van Doren and Horton 2018, Wells et al. 2022]). In the context of game species management, information about the timing of migratory movements can inform harvest periods to achieve various goals (e.g., adjust hunting seasons to account for changes in migration phenology associated with climate change [Thurber et al. 2020, Pearse et al. 2023]). At broader spatial scales, information about migration timing can provide opportunities to manage critical stopover areas to maximize abundance of resources (e.g., food, water, roosting cover) to coincide with peak migratory periods (Malone et al. 2023). Additionally, individual tracking data from migratory birds can provide context for understanding the flexibility of migratory strategies in response to climate change (Prytula et al. 2022) and help managers assess species’ capacity to respond to phenological shifts (Van Gils et al. 2016, Shipley et al. 2020), which could help steer conservation efforts toward species expected to be most affected.

Understanding how birds migrate can inform conservation (how)

Characterizing species’ migratory strategies can provide relevant data to inform conservation efforts. Migration can require significant energetic resources, depending on flight duration, weather conditions, and migration strategy (e.g., brief versus extended flights, flapping versus soaring flight [Wikelski et al. 2003, Bowlin et al. 2005, Weber 2009, Soriano-Redondo et al. 2023]). These physiological requirements of migration can aid in identifying critical areas for conserving or enhancing stopover sites and inform management to increase the availability of high-quality food and other resources (Seewagen et al. 2011, Smith et al. 2015, Bayly et al. 2016, Gómez et al. 2017).

The importance of specific stopover sites may depend on the migration ecology of focal species. For example, tracking data from many songbirds and shorebirds show that these species often migrate via several extensive movements (i.e., “jumps”) and may benefit from the conservation of strategically located high-quality stopover sites in areas used immediately before initiating and after completing such flights (Piersma 1987, Skagen and Knopf 1994, Warnock 2010). However, focusing conservation efforts on a few stopover sites may be impractical and ineffective for species like American Woodcock that make many, shorter-distance movements to complete migration and rely on a network of sites in close proximity to sustain migration (i.e., “hops” [Piersma 1987, Moore et al. 2021]).

Similarly, whether species migrate primarily diurnally (e.g., soaring raptors, swallows, cranes [Beauchamp 2011]) or nocturnally (most songbirds [Cooper et al. 2023]), and how birds move within those periods may be important for developing targeted conservation strategies that minimize the effect of anthropogenic factors such as wind energy development (potentially a risk for diurnal soaring raptors [Katzner et al. 2012]) or ALAN (primarily an issue for nocturnally migrating songbirds [Evans Ogden 2002, Spoelstra and Visser 2013, Van Doren et al. 2017, McLaren et al. 2018]). Other species-specific characteristics of migration may be critical for informing conservation decisions (e.g., flight altitude to inform the location of wind energy development [Katzner et al. 2012, Cohen et al. 2022]).

Understanding proximate drivers of migration can inform conservation (why)

Whether migratory behavior is innate or socially inherited can affect subsequent conservation decisions. For obligate migrants where migration is presumed to be genetically controlled (e.g., songbirds [Toews et al. 2019, Justen and Delmore 2022]), migration timing appears to be linked to photoperiodic cues that may become disentangled from changing phenological patterns, leading to phenological mismatch (Both et al. 2006, 2010, Youngflesh et al. 2021, Connare and Islam 2022, Robertson et al. 2024). For species that migrate more slowly, arrive earlier on breeding areas, or overwinter farther north, migratory timing may be more flexible and responsive to earlier spring onset resulting from climate change because their migration strategy generally tracks local environmental conditions (Halupka and Halupka 2017, Youngflesh et al. 2021).

Within species, different cohorts may be affected differently by climate change. In many migratory North American bird species, males arrive at breeding sites days to weeks before females (i.e., protandry [Morbey and Ydenberg 2001]). However, decades of banding data suggest that the gap between male and female arrival at breeding grounds has widened because males arrive at breeding sites earlier in response to changes in spring phenology (Neate-Clegg and Tingley 2023). The mechanisms underlying sex-based plasticity in adaptation to climate change are unclear, but the increasing mismatch between arrival of males and females on breeding areas could cause populations to decline (Neate-Clegg and Tingley 2023). In some cases, loss of migratory behavior may be an adaptive response to an erosion of the potential benefits of migration, which could allow for population persistence but eliminate the ecological functions of migration (de Zoeten and Pulido 2020).

The capacity of species to adapt to changing conditions via evolutionary pathways may also affect their persistence under future conditions and may be affected by the rate at which conditions change (with slower rates of change assumed to be more conducive to adaptation [Botero et al. 2015]). Capacity for adaptation is partially dependent on standing genetic variation (Both and Visser 2001, Webster et al. 2002), and individuals that possess traits that are extreme today (e.g., an individual that initiates migration early relative to the rest of the population) may be disproportionately important for population viability under future conditions. Protecting individuals or populations within a species that, for example, initiate their northern migration relatively early or those that migrate relatively far distances (breeding at the northern range margins) could facilitate evolutionary adaptation and promote species persistence in the future (Rehm et al. 2015). Similarly, species that exhibit developmental and other forms of plasticity (e.g., Black-tailed Godwits [Limosa lapponica] [Verhoeven et al. 2022]) that facilitate change in migration patterns over time may fare relatively well in the face of changing climatic conditions.

Migration strategies of three well-studied species

To illustrate how the conservation implications of tracking data play out in single-species conservation plans, we provide more in-depth summaries of migration patterns of three well-studied North American migratory birds with varying life history strategies, sizes, and associated levels of conservation concern (Fig. 2). These case studies ultimately demonstrate that the technology that is available for different species determines the spatio-temporal resolution of the data available to inform conservation, which in turn determines the spatio-temporal resolution of resulting conservation actions.

Golden-winged Warbler

Golden-winged Warblers (Vermivora chrysoptera; IUCN [2022] Near Threatened) are among the most well-studied passerines in North America and are of high conservation concern due to historical declines in eastern portions of their breeding distribution (Rosenberg et al. 2016, Ritterson et al. 2021). They are relatively small songbirds (~10 g) that breed in central and eastern North America and occur in Central and northern South America during the nonbreeding period. Tracking Golden-winged Warblers throughout the annual cycle using geolocators has revealed strong migratory connectivity wherein distinct breeding populations occur in isolation from one another during the nonbreeding period (Fig. 2) (Kramer et al. 2017, 2018). This strong migratory connectivity is associated with variation in historical population trends and suggests that severe declines in abundance of breeding Golden-winged Warblers in the Appalachian Mountains region are associated with deforestation in northern South America (Kramer et al. 2018). Furthermore, whether individuals occur in Central versus northern South America during the nonbreeding period is associated with a single gene (VPS13A), which provides evidence of genetic control of migration in this species (Fig. 2) (Toews et al. 2019). Geolocator-tracking of Golden-winged Warblers has also identified stopover regions in southeastern Mexico and Guatemala (Bennett et al. 2019, Kramer et al. 2023) that could be targeted for conservation by existing international conservation partnerships (e.g., the Golden-winged Warbler Working Group).

American Woodcock

American Woodcock (IUCN [2022] Least Concern) are moderate-sized (~144–230 g), migratory, forest-dwelling shorebirds that can be difficult to survey during most parts of their annual cycle due to their cryptic coloration and preference for dense vegetation. They breed throughout much of the forested region of eastern North America, exhibit a variety of migration strategies, and are hunted during autumn and winter. American Woodcock migration has been assessed using VHF (Myatt and Krementz 2007), and more recently, PTT and GPS transmitters (Moore et al. 2019, 2021), which provide higher resolution location data than can be derived from geolocators. Combining location data, genetic analyses (Rhymer et al. 2005), and updated evaluation of band return data (Moore and Krementz 2017) suggests that woodcock have higher rates of interchange (i.e., relatively weak migratory connectivity) between putative populations than previously suggested based solely on analyses of band-return data. Tracking individual woodcock has revealed that migration occurs at a slower pace during pre-breeding migration than post-breeding migration because pre-breeding migration is characterized by frequent and close-together stopover events (Moore et al. 2021). In portions of their breeding distribution, woodcock are resident year-round, facultative partial migrants, or obligate migrants, and recent evidence suggests that all three migration strategies may exist in the same breeding population (Graham et al. 2022). Data on the pace and facultative nature of American Woodcock migration indicate a migration strategy that allows the species to respond to local environmental conditions and arrive on breeding areas when conditions are conducive for courtship and breeding, including breeding multiple times during migration (Slezak et al. 2024). Information derived from recent advances in tracking technology suggests that the migration strategies used by woodcock likely minimize the potential for phenological mismatch, provides insights into when and where conservation actions during migration are likely to be most effective, and indicates that breeding during migration may be an important aspect of woodcock population ecology and conservation. The relative role of genetic versus social factors in woodcock migration are largely unknown.

Sandhill Crane

Sandhill Cranes (IUCN [2022] Least Concern) are large (~2.7–6.7 kg), long-lived (> 35 yr) birds that breed throughout much of mid-latitude and northern North America and adjacent eastern Siberia, are hunted in portions of their range (Fig. 2) (Krapu et al. 2011), and are composed of both migratory and sedentary populations. Deployment of PTTs and, more recently, GPS-GSM transmitters on Sandhill Cranes (Krapu et al. 2014, Fronczak et al. 2017, Wolfson et al. 2017) has facilitated the monitoring of annual movements and provided insight into shifts in distributions, flyway use, breeding population affiliation, and efficacy of surveys (e.g., Collins et al. 2016, Fronczak et al. 2017, Wolfson et al. 2017, Nowak et al. 2018). Marking Sandhill Cranes with VHF transmitters at migration stopover locations (e.g., Krapu et al. 2014) has provided insight into energy acquisition and food resource depletion during staging (Davis 2003, Krapu et al. 2014, Varner et al. 2020). Location data derived via GPS-GSM transmitters indicate that Sandhill Cranes use a variety of stopover sites during both pre-breeding and post-breeding migration (e.g., Fronczak et al. 2017), with some stopover sites used by large numbers of Sandhill Cranes consistently through time (e.g., Krapu et al. 2014). Increased reliance on crops and the loss of historical wetlands have increased site fidelity at a number of key stopover areas (Donnelly et al. 2021). Providing adequate food resources at key stopover areas and considering strategies to minimize conflicts with agriculture are likely to enhance Sandhill Crane conservation. Migratory pathways used by individual Sandhill Cranes can vary among years (Wolfson et al. 2017), and environmental conditions (e.g., availability of open water for roosting; snow cover) appear to influence their distribution during the nonbreeding period. This plasticity in migration behavior and shifts in space use patterns as Sandhill Cranes age (e.g., Wolfson et al. 2020) is influenced by social learning (Mueller et al. 2013, Whiten 2019) and suggests that Sandhill Cranes have considerable capacity to adapt migration strategies to changing environmental conditions.

FUTURE DIRECTIONS AND INFORMATION NEEDS

The means of quantifying and understanding migration of North American birds have advanced considerably since marking birds with unique identifiers began in the early 20th century, which has greatly expanded understanding of avian migration. Advances in technology and analytical methods are likely to continue, which will provide opportunities for further insight into bird migration that can inform conservation. Some of the topics that can be addressed with new approaches or integrating existing information include (1) understanding how migratory birds respond to climate and other environmental change, and in that context, (2) understanding the mechanistic underpinnings of variation in bird migration within and among species. Increased understanding of these and other topics could be advanced by the following technological and analytical developments: (1) integrating low- and high-throughput tracking technologies (e.g., VHF and satellite telemetry, respectively) to address questions that require fine-scale data at broad spatial scales (e.g., habitat use at stopover sites), (2) developing approaches to assess movements of entire populations or species during migration, (3) combining biologging-derived movement, environmental, and citizen-science data at broad spatial scales to better understand drivers of migration and associated effects on fitness, and perhaps most importantly, (4) evaluating the effects of conservation actions targeting migratory birds outside the breeding period to inform adaptive and effective management strategies.

Understanding how migratory birds respond to climate and other environmental change

Predicting how migratory birds may respond to climate and other environmental change, and understanding their potential for adaptation is increasingly important for conservation. Although general predictions exist as to whether, and to what extent, climate change will affect migratory birds (e.g., relative to migration distance, phenological mismatch, and connectivity [Both et al. 2006, 2010, Gómez et al. 2021, Youngflesh et al. 2021, Connare and Islam 2022]), it remains unclear if and how many species will respond to increasingly extreme climate conditions in the future. In addition, little is known about the nature of factors that regulate populations (i.e., the relative contributions of factors that affect population dynamics during different portions of the annual cycle [e.g., Kramer et al. 2018]), and whether and how regulating factors and population dynamics influence the spatial distribution of populations and species outside the breeding period for most North American migratory bird species. Particularly important for predicting the response of migratory bird populations to environmental change is understanding their capacity for adaptation in terms of the “when” and “where” of migration. Successful migration under different environmental conditions may require individuals to migrate at different times and use different pathways and landscapes. Effective conservation of migratory species in the future will likely require understanding the interactive effects of climate change and anthropogenic effects on habitat while considering the adaptive capacity of individual species.

Variation in bird migration within and among species

Variation in migration pathways and timing within populations is poorly understood but may be critical to the ability of migratory bird species to respond to a changing environment, and to understanding that variation can inform effective conservation. For example, recent work suggests that the transfer of social information between individuals may be particularly important for certain species that rely on memory to optimize migratory behavior as individuals age (Teitelbaum et al. 2016, Flack et al. 2022). The mechanisms that cause intraspecific variation in behavior, timing, and space use during migration are poorly understood, which makes it difficult to predict how much, how quickly, or when birds can adapt their migration strategies to changing conditions (Brown et al. 2021, Verhoeven et al. 2022). Furthermore, individual variation in migration pathways and timing between and among annual cycles is poorly documented for all but a few larger species that have been monitored with current tracking technology (but see Stanley et al. 2012, Fraser et al. 2019). It is also unclear whether migratory bird species currently possess sufficient genetic variation and/or behavioral plasticity to adapt to rapidly changing environmental conditions (Bay et al. 2018). Improved understanding of these issues would help inform conservation strategies for North American migratory birds.

Improving tracking technology

Currently, tracking technology provides the means of collecting information on seasonal movements, and in some instances, location data recorded at high frequency for many North American migratory bird species (Sequeira et al. 2021, Gupte et al. 2022). Especially for smaller birds, collecting high-resolution, high-frequency location data at broad spatial scales remains unfeasible (Nathan et al. 2022); however, further miniaturization of tracking devices coupled with increased flexibility of information transfer from connections such as “Internet of Things” networks (e.g., the ICARUS program) could increase the resolution and frequency of location data over broad spatial scales for many North American migratory bird species. Additionally, whereas most tags used for tracking smaller bird species (e.g., songbirds) are archival due to weight limitations, and therefore require recapture to recover information, advancements in data connection methods may eventually provide the ability to transmit evidence of mortality in near real time. Such gains can provide additional opportunities to refine conservation strategies to include information about space use and habitat needs during migration and identify critical information such as important bottlenecks and stopover sites and exposure to anthropogenic threats (Krondorf et al. 2022, Kays and Wikelski 2023).

Assessing movements of groups of migrating birds

It is logistically difficult and expensive to monitor large numbers of individual migratory birds with tracking devices. As a result, inference about population- or species-specific variation in migration phenology, use of stopover sites, and effects of environmental factors on migration is poorly understood (Aikens et al. 2022). Emerging approaches to assess such population- or species-scale patterns include acoustic monitoring, passive telemetry (e.g., Motus), and advancements in radar technology. Acoustic monitoring can use networks of relatively low-cost autonomous recording units (ARUs) to provide information about species composition of migratory flocks, density of individuals, and timing and intensity of migratory events (Sanders and Mennill 2014, Pérez-Granados and Traba 2021). Although continuous acoustic monitoring by ARUs can quickly amass large volumes of audio data to process, machine-learning methods are quickly maturing to allow automated workflows for species identification (Stowell et al. 2019, Kahl et al. 2021). The Motus network has the potential to assess variation in spatial and temporal aspects of migration within populations and species, and advances in radar (e.g., Next Generation Weather Radar, computational methods that distinguish migrating birds from the overall radar signal [Sheldon et al. 2013, Lin et al. 2019]) have provided information about weather factors that influence bird movements (Van Doren and Horton 2018), and changes in migration phenology at the continental scale (Horton et al. 2020), and now allow near-term forecasting methods that can provide dynamic management of resources that may affect migrating birds, such as offshore wind farms (Bradarić et al. 2024a, b).

Integrating broad-scale environmental, movement, and citizen-science data

Tracking data for North American migratory birds can be stored in global open-access data repositories and annotated with environmental data through the Environmental Data Automated Track Annotation System (e.g., Movebank [Fiedler 2009, Kranstauber et al. 2011], Env-DATA [Dodge et al. 2013]). Further integrating these resources with crowd-sourced databases such as eBird may provide novel means of quantifying migration at a continental scale (e.g., Sullivan et al. 2009, 2014, Laughlin et al. 2013, Supp et al. 2015). In the context of migration, citizen-science data have been beneficial in creating and validating models of migration phenology (Mayer 2010, Kelly et al. 2016, Mondain-Monval et al. 2021). More recently, abundance data from the eBird database have been used to model individual-level migration routes, quantify population-level migratory timing and connectivity, and predict important ecological interactions such as the spread of parasites by migratory birds (Fuentes et al. 2023, Tonelli et al. 2023). However, integrating citizen-science data with tracking data from individuals and populations remains challenging and will likely require the development of new analytical tools to quantify sources of uncertainty arising from different sampling methods.

Evaluating the effects of conservation efforts targeting migratory birds outside the breeding period

Finally, mortality risk in birds during migration is thought to be relatively high (e.g., Newton 2024a), and species may be limited by numerous factors that occur during the stationary nonbreeding period (e.g., habitat loss [Kramer et al. 2018]). Because factors during migration and the nonbreeding period can regulate migratory bird populations, conservation efforts implemented to benefit birds during migration and nonbreeding periods have considerable potential to benefit North American migratory birds. However, evaluating whether conservation efforts derived from recent advances in knowledge about migration in North American birds are effective is largely lacking, primarily because (1) tying population-level responses to specific conservation actions implemented to mitigate regulating factors outside the breeding period is not straightforward, and (2) relatively few resources have been devoted to evaluating the effects of bird conservation efforts compared to the resources devoted to the conservation efforts themselves. Recent advances in describing and understanding bird migration provide the basis for developing and implementing conservation strategies that include considerations of when and where birds migrate, habitat requirements during migration and the nonbreeding period, potential mortality risk outside the breeding period, and how migratory birds may respond to climate change. Without parallel efforts to evaluate the effectiveness of those strategies, however, it will be difficult to discern whether investments in conservation strategies that target migratory birds during migration and the nonbreeding period are worthwhile. At a minimum, we therefore suggest that ongoing and future conservation strategies for North American migratory birds incorporate considerations of how to assess their effectiveness, including identifying metrics (e.g., population trends, habitat use, body condition, subsequent reproduction and survival) that could be used to track the effects of conservation actions intended to benefit birds during migration and the nonbreeding period.

LITERATURE CITED

Aikens, E. O., I. D. Bontekoe, L. Blumenstiel, A. Schlicksupp, and A. Flack. 2022. Viewing animal migration through a social lens. Trends in Ecology & Evolution 37:985-9896. https://doi.org/10.1016/j.tree.2022.06.008

Albers, H. J., K. Kroetz, C. Sims, A. W. Ando, D. Finnof, R. D. Horan, R. Liu, E. Nelson, and J. Merkle. 2023. Where, when, what, and which? Using characteristics of migratory species to inform conservation policy questions. Review of Environmental Economics and Policy 17.

Anderson, M. G., R. T. Alisauskas, B. D. J. Batt, R. J. Blohm, K. F. Higgins, M. C. Perry, J. K. Ringelman, J. S. Sedinger, J. R. Serie, D. E. Sharp, D. L. Trauger, and C. K. Williams. 2018. The Migratory Bird Treaty and a century of waterfowl conservation. Journal of Wildlife Management 82:247-259. https://doi.org/10.1002/jwmg.21326

Battey, C. J., E. B. Linck, K. L. Epperly, C. French, D. L. Slager, P. W. Sykes, Jr., and J. Klicka. 2018. A migratory divide in the Painted Bunting (Passerina ciris). American Naturalist 191:259-268. https://doi.org/10.1086/695439

Bay, R. A., R. J. Harrigan, V. L. Underwood, H. L. Gibbs, T. B. Smith, and K. Ruegg. 2018. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359:83-86. https://doi.org/10.1126/science.aan4380

Bayly, N. J., C. Gómez, K. A. Hobson, and K. V. Rosenberg. 2016. Prioritizing tropical habitats for long-distance migratory songbirds: an assessment of habitat quality at a stopover site in Colombia. Avian Conservation and Ecology 11(2):5. https://doi.org/10.5751/ACE-00873-110205

Beauchamp, G. 2011. Why migrate during the day: a comparative analysis of North American birds. Journal of Evolutionary Biology 24:1969-1974. https://doi.org/10.1111/j.1420-9101.2011.02326.x

Bennett, R. B., A. D. Rodewald, K. V. Rosenberg, R. Chandler, L. Chavarria-Duriaux, J. A. Gerwin, D. I. King, and J. L. Larkin. 2019. Drivers of variation in migration behavior for a linked population of long-distance migratory passerine. Auk 136:ukz051. https://doi.org/10.1093/auk/ukz051

Berthold, P. 2001. Bird migration: a general survey. Oxford University Press on Demand. https://doi.org/10.1093/oso/9780198507864.001.0001

Billerman, S. M., B. K. Keeney, P. G. Rodewald, and T. S. Schulenberg, editors. 2022. Birds of the world. Cornell Laboratory of Ornithology, Ithaca, New York, USA. https://birdsoftheworld.org/bow/home https://doi.org/10.2173/bow

Both, C., J. J. Sanz, A. A. Artemyev, B. Blaauw, R. J. Cowie, A. J. Dekhuijzen, A. Enemar, A. Jarvinen, N. E. I. Nyholm, and J. Potti. 2006. Pied Flycatchers Ficedula hypoleuca travelling from Africa to breed in Europe: differential effects of winter and migration conditions on breeding date. Ardea 94:511.

Both, C., C. A. M. Van Turnhout, R. G. Bijlsma, H. Siepel, A. J. Van Strien, and R. P. B. Foppen. 2010. Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats. Proceedings of the Royal Society B: Biological Sciences 277:1259-1266. https://doi.org/10.1098/rspb.2009.1525

Both, C., and M. E. Visser. 2001. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411:296-298. https://doi.org/10.1038/35077063

Botero, C. A., F. J. Weissing, J. Wright, and D. R. Rubenstein. 2015. Evolutionary tipping points in the capacity to adapt to environmental change. Proceedings of the National Academy of Sciences 112:184-189. https://doi.org/10.1073/pnas.1408589111

Bowlin, M. S., W. W. Cochran, and M .C. Wikelski. 2005. Biotelemetry of New World thrushes during migration: physiology, energetics and orientation in the wild. Integrative & Comparative Biology 45:295-304. https://doi.org/10.1093/icb/45.2.295

Bradarić, M., B. Kranstauber, W. Bouten, and J. Shamoun-Baranes. 2024a. Forecasting nocturnal bird migration for dynamic aeroconservation: the value of short-term datasets. Journal of Applied Ecology 61:1147-1158. https://doi.org/10.1111/1365-2664.14651

Bradarić, M., B. Kranstauber, W. Bouten, H. van Gasteren, and J. Shamoun-Baranes. 2024b. Drivers of flight altitude during nocturnal bird migration over the North Sea and implications for offshore wind energy. Conservation Science and Practice 6:e13114. https://doi.org/10.1111/csp2.13114

Bridge, E. S., K. Thorup, M. S. Bowlin, P. B. Chilson, R. H. Diehl, R. W. Fléron, P. Hartl, R. Kays, J. F. Kelly, W. D. Robinson, and M. Wikelski. 2011. Technology on the move: recent and forthcoming innovations for tracking migratory birds. BioScience 61:689-698. https://doi.org/10.1525/bio.2011.61.9.7

Brown, J. M., E. E. van Loon, W. Bouten, K. C. Camphuysen, L. Lens, W. Müller, C. B. Thaxter, and J. Shamoun-Baranes. 2021. Long-distance migrants vary migratory behaviour as much as short-distance migrants: an individual-level comparison from a seabird species with diverse migration strategies. Journal of Animal Ecology 90(5):1058-1070. https://doi.org/10.1111/1365-2656.13431

Cardenas-Ortiz, L., N. J. Bayly, K. J. Kardynal, and K. A. Hobson. 2020. Defining catchment origins of a geographical bottleneck: implications of population mixing and phenological overlap for the conservation of Neotropical migratory birds. Condor 122:duaa004. https://doi.org/10.1093/condor/duaa004

Cochran, W. W., and R. D. Lord, Jr. 1963. A radio-tracking system for wild animals. Journal of Wildlife Management 27:9-24. https://doi.org/10.2307/3797775

Cochran, W. W., D. W. Warner, J. R. Tester, and V. B. Kuechle. 1965. Automatic radio-tracking system for monitoring animal movements. BioScience 15:98-100. https://doi.org/10.2307/1293346

Cohen, E. B., L. D. Auckland, P. P. Marra, and S. A. Hamer. 2015. Avian migrants facilitate invasions of Neotropical ticks and tick-borne pathogens into the United States. Applied and Environmental Microbiology 81:8366-8378. https://doi.org/10.1128/AEM.02656-15

Cohen, E. B., J. J. Buler, K. G. Horton, S. R. Loss, S. A. Cabrera-Cruz, J. A. Smolinsky, and P. P. Marra. 2022. Using weather radar to help minimize wind energy impact on nocturnally migrating birds. Conservation Letters 15:e12887. https://doi.org/10.1111/conl.12887

Collins, D. P., B. A. Grisham, C. M. Conring, J. M. Knetter, W. C. Conway, S. A. Carleton, and M. A. Boggie. 2016. New summer areas and mixing of two greater Sandhill Crane populations in the Intermountain West. Journal of Fish and Wildlife Management 7:141-152. https://doi.org/10.3996/042015-JFWM-036

Connare, B. M., and K. Islam. 2022. Failure to advance migratory phenology in response to climate change may pose a significant threat to a declining Nearctic-Neotropical songbird. International Journal of Biometeorology 66:803-815. https://doi.org/10.1007/s00484-022-02239-9

Coon, R. A., T. J. Dwyer, and J. W. Artmann. 1977. Identification of harvest units for the American Woodcock. Proceedings of the American Woodcock Symposium 6:147-153.

Cooper, N. W., B. C. Dossman, L. E. Berrigan, J. M. Brown, A. R. Brunner, H. E. Chmura, D. A. Cormier, C. Bégin-Marchand, A. D. Rodewald, P. D. Taylor, C. M. Tonra, J. A. Tremblay, and P. P. Marra. 2023. Songbirds initiate migratory flights synchronously relative to civil dusk. Movement Ecology 11:24. https://doi.org/10.1186/s40462-023-00382-5

Davis, C. A. 2003. Habitat use and migration patterns of Sandhill Cranes along the Platte River, 1998–2001. Great Plains Research 13:199-216.

Delancey, C. D., K. Islam, G. R. Kramer, G. J. MacDonald, A. R. Sharp, and B. M. Connare. 2020. Geolocators reveal migration routes, stopover sites, and nonbreeding dispersion in a population of Cerulean Warblers. Animal Migration 7:19-26. https://doi.org/10.1515/ami-2020-0003

Delmore, K. E., J. W. Fox, and D. E. Irwin. 2012. Dramatic intraspecific differences in migratory routes, stopover sites and wintering areas, revealed using light-level geolocators. Proceedings of the Royal Society B: Biological Sciences 279:4582-4589. https://doi.org/10.1098/rspb.2012.1229

DeLuca, W. V., B. K. Woodworth, C. C. Rimmer, P. P. Marra, P. D. Taylor, K. P. McFarland, S. A. Mackenzie, and D. R. Norris. 2015. Transoceanic migration by a 12 g songbird. Biology Letters 11:20141045. https://doi.org/10.1098/rsbl.2014.1045

Deppe, J. L., M. P. Ward, R. T. Bolus, R. H. Diehl, A. Celis-Murillo, T. J. Zenzal, F. R., Moore, T. J. Benson, J. A. Smolinsky, L. N. Schofield, David A. Enstrom, E. H. Paxton, G. Bohrer, T. A. Beveroth, A. Raim, R. L. Obringer, D. Delaney, and W. W. Cochran. 2015. Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico. Proceedings of the National Academy of Sciences 112:E6331-E6338. https://doi.org/10.1073/pnas.1503381112

de Zoeten, T., and F. Pulido. 2020. How migratory populations become resident. Proceedings of the Royal Society B: Biological Sciences 287:20193011. https://doi.org/10.1098/rspb.2019.3011

Dingle, H. 2014. Migration: the biology of life on the move. Oxford University Press, Oxford, U.K. https://doi.org/10.1093/acprof:oso/9780199640386.001.0001

Dodge, S., G. Bohrer, R. Weinzierl, D. C. Davidson, R. Kays, D. Douglas. S. Cruz, J. Han, D. Brandes, and M. Wikelski. 2013. The environmental-data automated track annotation (Env-DATA) system: linking animal tracks with environmental data. Movement Ecology 1:1-14. https://doi.org/10.1186/2051-3933-1-3

Donnelly, J. P., S. L. King, J. Knetter, J. H. Gammonley, V. J. Dreitz,, B. A. Grisham, M. C. Nowak, and C. P. Collins. 2021. Migration efficiency sustains connectivity across agroecological networks supporting Sandhill Crane migration. Ecosphere 12:e03543. https://doi.org/10.1002/ecs2.3543

Evans Ogden, L. J. 2002. Summary report on the bird friendly building program: effect of light reduction on collision of migratory birds. Fatal Light Awareness Program (FLAP). 5. https://digitalcommons.unl.edu/flap/5

Fiedler, W. 2009. New technologies for monitoring bird migration and behaviour. Ringing & Migration 24:175-179. https://doi.org/10.1080/03078698.2009.9674389

Flack, A., E. O. Aikens, A. Kölzsch, E. Nourani, K. R. S. Snell, W. Fiedler, N. Linek, H.-G. Bauer, K. Thorup, J. Partecke, M. Wikelski, and H. J. Williams. 2022. New frontiers in bird migration research. Current Biology 32:R1187-R1199. https://doi.org/10.1016/j.cub.2022.08.028

Fournier, A. M. V., A. Shave, J. Fischer, J. Siegrist, J. Ray, E. Cheskey, M. MacIntosh, A. Ritchie, M. Pearman, K. Applegate, and K. Fraser. 2019. Precise direct tracking and remote sensing reveal the use of forest islands as roost sites by Purple Martins during migration. Journal of Field Ornithology 90(3):258-65. https://doi.org/10.1111/jofo.12298

Fraser, K. C., A. Shave, E. de Greef, J. Siegrist, and C. J. Garroway. 2019. Individual variability in migration timing can explain long-term, population-level advances in a songbird. Frontiers in Ecology and Evolution 7:324. https://doi.org/10.3389/fevo.2019.00324

Fraser, K. C., A. Shave, A. Savage, A. Ritchie, K. Bell, J. Siegrist, J. D. Ray, K. Applegate, and M. Pearman. 2017. Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology. Journal of Avian Biology 48:339-345. https://doi.org/10.1111/jav.01091

Fronczak, D. L., D. E. Andersen, E. E. Hanna, and T. R. Cooper. 2017. Distribution and migration chronology of Eastern Population Sandhill Cranes. Journal of Wildlife Management 81:1021-1032. https://doi.org/10.1002/jwmg.21272

Fuentes, M., B. M. Van Doren, D. Fink, and D. Sheldon. 2023. BirdFlow: learning seasonal bird movements from eBird data. Methods in Ecology and Evolution 14:923-938. https://doi.org/10.1111/2041-210X.14052

Gallinat, A. S., R. B. Primack, and T. L. Lloyd-Evans. 2020. Can invasive species replace native species as a resource for birds under climate change? A case study on bird–fruit interactions. Biological Conservation 241:108268. https://doi.org/10.1016/j.biocon.2019.108268

Gómez, C., N. J. Bayly, D. R. Norris, S. A. Mackenzie, K. V. Rosenberg, P. D. Taylor, K. A. Hobson, and C. D. Cadena. 2017. Fuel loads acquired at a stopover site influence the pace of intercontinental migration in a boreal songbird. Scientific Reports 7:3405. https://doi.org/10.1038/s41598-017-03503-4

Gómez, C., K. A. Hobson, N. J. Bayly, K. V. Rosenberg, A. Morales-Rozo, P. Cardoza, and C. D. Cadena. 2021. Migratory connectivity then and now: a northward shift in breeding origins of long-distance migratory birds wintering in the tropics. Proceedings of the Royal Society B: Biological Sciences 288:20210188. https://doi.org/10.1098/rspb.2021.0188

Gow, E. A., K. L. Wiebe, and J. W. Fox. 2015. Cavity use throughout the annual cycle of a migratory woodpecker revealed by geolocators. Ibis 157:167-170. https://doi.org/10.1111/ibi.12206

Graham, C. L., T. Steeves, and S. R. McWilliams. 2022. Cross-seasonal effects in the American Woodcock: conditions prior to fall migration relate to migration strategy and implications for conservation. Ornithological Applications 124(2):duac011. https://doi.org/10.1093/ornithapp/duac011

Gupte, P. R., C. E. Beardsworth, O. Spiegel, E. Lourie, S. Toledo, R. Nathan, and A. I. Bijleveld, A. I. 2022. A guide to pre‐processing high‐throughput animal tracking data. Journal of Animal Ecology 91:287-307. https://doi.org/10.1111/1365-2656.13610

Hallworth, M. T., E. Bayne, E. McKinnon, O. Love, J. A. Tremblay, B. Drolet, J. Ibarzabal, S. Van Wilgenburg, and P. P. Marra. 2021. Habitat loss on the breeding grounds is a major contributor to population declines in a long-distance migratory songbird. Proceedings of the Royal Society B: Biological Sciences 288:20203164. https://doi.org/10.1098/rspb.2020.3164

Hallworth, M. T., and P. P. Marra. 2015. Miniaturized GPS tags identify non-breeding territories of a small breeding migratory songbird. Scientific Reports 5:11069. https://doi.org/10.1038/srep11069

Halupka, L., and K. Halupka. 2017. The effect of climate change on the duration of avian breeding seasons: a meta-analysis. Proceedings of the Royal Society B: Biological Sciences 284:20171710. https://doi.org/10.1098/rspb.2017.1710

Heckscher, C. M., S. M. Taylor, J. W. Fox, and V. Afanasyev. 2011. Veery (Catharus fuscescens) wintering locations, migratory connectivity, and a revision of its winter range using geolocator technology. Auk 128:531-542. https://doi.org/10.1525/auk.2011.10280

Heitmeyer, M. E., P. J. Caldwell, B. D. J. Batt, and J. W. Nelson. 1996. Waterfowl conservation and biodiversity in North America. Pages 125-138 in J. T. Ratti, editor. Proceedings of the Seventh International Waterfowl Symposium. Ducks Unlimited, Memphis, Tennessee, USA.

Higuchi, H. 2012. Bird migration and the conservation of the global environment. Journal of Ornithology 153:3-14. https://doi.org/10.1007/s10336-011-0768-0

Hill, R. D. 1994. Theory of geolocation by light levels. In J. R. Sibert and J. L. Nielsen, editors. Elephant seals: population ecology, behavior, and physiology. University of California Press, Berkeley, California, USA. https://doi.org/10.2307/jj.8441712.16

Hobson, K. A. 1999. Tracing origins and migration of wildlife using stable isotopes: a review. Oecologia 120:314-326. https://doi.org/10.1007/s004420050865

Holberton, R. L., P. D. Taylor, L. M. Tudor, K. M. O’Brien, G. H. Mittelhauser, and A. Breit. 2019. Automated VHF radiotelemetry revealed site-specific differences in fall migration strategies of Semipalmated Sandpipers on stopover in the Gulf of Maine. Frontiers in Ecology and Evolution 7:327. https://doi.org/10.3389/fevo.2019.00327

Horton, K. G., F. A. La Sorte, D. Sheldon, T.-Y. Lin, K. Winner, G. Bernstein, S. Maji, W. M. Hochachka, and A. Farnsworth. 2020. Phenology of nocturnal avian migration has shifted at the continental scale. Nature Climate Change 10:63-68. https://doi.org/10.1038/s41558-019-0648-9

Horton, K. G., B. M. Van Doren, F. A. La Sorte, E. B. Cohen, H. L. Clipp, J. J. Buler, D. Fink, J. F. Kelly, and A. Farnsworth. 2019. Holding steady: little change in intensity or timing of bird migration over the Gulf of Mexico. Global Change Biology 25:1106-1118. https://doi.org/10.1111/gcb.14540

Horton, K. G., B. M. Van Doren, P. M. Stepanian, W. M. Hochachka, A. Farnsworth, and J. F. Kelly. 2016. Nocturnally migrating songbirds drift when they can and compensate when they must. Scientific Reports 6:21249. https://doi.org/10.1038/srep21249

International Union for Conservation of Nature (IUCN). 2022. The IUCN red list of threatened species. Version 2022-2.

Jones, S. L., J. S. Dieni, N. B. Warning, D. Leatherman, L. Dargis, and L. Benedict. 2023. Canyon Wren (Catherpes mexicanus), version 2.0. In P. G. Rodewald, editor. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.canwre.02

Justen, H., and K. E. Delmore. 2022. The genetics of bird migration. Current Biology 32:R1144-R1149. https://doi.org/10.1016/j.cub.2022.07.008

Kahl, S., C. M. Wood, M. Eibl, and H. Klinck. 2021. BirdNET: a deep learning solution for avian diversity monitoring. Ecological Informatics 61:101236. https://doi.org/10.1016/j.ecoinf.2021.101236

Katzner, T. E., D. Brandes, T. Miller, M. Lanzone, C. Maisonneuve, J. A. Tremblay, R. Mulvihill, and G. T. Merovich, Jr. 2012. Topography drives migratory flight altitude of Golden Eagles: implications for on-shore wind energy development. Journal of Applied Ecology 49:1178-1186. https://doi.org/10.1111/j.1365-2664.2012.02185.x

Kays, R., M. C. Crofoot, W. Jetz, and M. Wikelski. 2015. Terrestrial animal tracking as an eye on life and planet. Science 348:aaa2478. https://doi.org/10.1126/science.aaa2478

Kays, R., and M. Wikelski. 2023. The internet of animals: what it is, what it could be. Trends in Ecology & Evolution 38:859-869. https://doi.org/10.1016/j.tree.2023.04.007

Kelly, J. F., K. G. Horton, P. M. Stepanian, K. M. de Beurs, T. Fagin, E. S. Bridge, and P. B. Chilson. 2016. Novel measures of continental-scale avian migration phenology related to proximate environmental cues. Ecosphere 7:e01434. https://doi.org/10.1002/ecs2.1434

Klaassen, M., and M. Wille. 2023. The plight and role of wild birds in the current bird flu panzootic. Nature Ecology & Evolution 7:1541-1542. https://doi.org/10.1038/s41559-023-02182-x

Knight, S. M., D. W. Bradley, R. G. Clark, E. A. Gow, M. Bélisle, L. L. Berzins, T. Blake, E. S. Bridge, L. Burke, R. D. Dawson, and P. O. Dunn. 2018. Constructing and evaluating a continent‐wide migratory songbird network across the annual cycle. Ecological Monographs 88:445-460. https://doi.org/10.1002/ecm.1298

Knight, E. C., A. Harrison, A. L. Scarpignato, S. L. Van Wilgenburg, E. M. Bayne, J. W. Ng, E. Angell, R. Bowman, R. M. Brigham, B. Drolet, W. E. Easton, T. R. Forrester, J. T. Foster, S. Haché, K. C. Hannah, K. G. Hick, J. Ibarzabal, T. L. Imlay, S. A. Mackenzie, A. Marsh, L. P. McGuire, G. N. Newberry, D. Newstead, A. Sidler, P. H. Sinclair, J. L. Stephens, D. L. Swanson, J. A. Tremblay, and P. P. Marra. 2021. Comprehensive estimation of spatial and temporal migratory connectivity across the annual cycle to direct conservation efforts. Ecography 44:665-679. https://doi.org/10.1111/ecog.05111

Korpach, A. M., C. J. Garroway, A. M. Mills, V. von Zuben, C. M. Davy, and K. C. Fraser. 2022. Urbanization and artificial light at night reduce the functional connectivity of migratory aerial habitat. Ecography 2022:e05581. https://doi.org/10.1111/ecog.05581

Kramer, G. R., D. E. Andersen, D. A. Buehler, P. B. Wood, S. M. Peterson, J. A. Lehman, K. R. Aldinger, L. P. Bulluck, S. Harding, J. A. Jones, J. P. Loegering, C. Smalling, R. Vallender, and H. M. Streby. 2018. Population trends in Vermivora warblers are linked to strong migratory connectivity. Proceedings of the National Academy of Sciences 115:E3192-E3200. https://doi.org/10.1073/pnas.1718985115

Kramer, G. R., D. E. Andersen, D. A. Buehler, P. B. Wood, S. M. Peterson, J. A. Lehman, K. R. Aldinger, L. P. Bulluck, S. Harding, J. A. Jones, J. P. Loegering, C. Smalling, R. Vallender, and H. M. Streby. 2023. Exposure to risk factors experienced during migration is not associated with recent Vermivora warbler population trends. Landscape Ecology 38:2357-2380. https://doi.org/10.1007/s10980-023-01701-2

Kramer, G. R., H. M. Streby, S. M. Peterson, J. A. Lehman, D. A. Buehler, P. B. Wood, D. J. McNeil, J. L. Larkin, and D. E. Andersen. 2017. Nonbreeding isolation and population specific migration patterns among three populations of Golden-winged Warblers. Condor 119:108-121. https://doi.org/10.1650/CONDOR-16-143.1

Kranstauber, B., A. Cameron, R. Weinzerl, T. Fountain, S. Tilak, M. Wikelski, and R. Kays. 2011. The Movebank data model for animal tracking. Environmental Modelling & Software 26:834-835. https://doi.org/10.1016/j.envsoft.2010.12.005

Krapu, G. L., D. A. Brandt, K. L. Jones, and D. H. Johnson. 2011. Geographic distribution of the mid-continent population of Sandhill Cranes and related management applications. Wildlife Monographs 175:1-38. https://doi.org/10.1002/wmon.1

Krapu, G. L., D. A. Brandt, P. J. Kinzel, and A. T. Pearse. 2014. Spring migration ecology of the mid-continent Sandhill Crane population with an emphasis on use of the Central Platte River Valley, Nebraska. Wildlife Monographs 189:1-41. https://doi.org/10.1002/wmon.1013

Kricher, J. C. 2020. Black-and-white Warbler (Mniotilta varia), version 1.0. In A. F. Poole, editor. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.bawwar.01

Krondorf, M., S. Bittner, D. Plettemeier, A. Knopp, and M. Wikelski. 2022. ICARUS—very low power satellite-based IoT. Sensors 22(17):6329. https://doi.org/10.3390/s22176329

Lamb, J. S., C. Cooper-Mullin, S. G. Gilliland, A. M. Berlin, T. D. Bowman, W. S. Boyd, S. E. W. De La Cruz, D. Esler, J. R. Evenson, P. Flint, C. Lepage, D. E. Meattey, J. E. Osenkowski, P. W. C. Paton, M. C. Perry, D. Rosenberg, J.-P. L. Savard, L. Savoy, J. Schamber, D. H. Ward, J. Y. Takekawa, and S. R. McWilliams. 2024. Evaluating conservation units using network analysis: a sea duck case study. Frontiers in Ecology and the Environment 22:e2648. https://doi.org/10.1002/fee.2648

Larison, B., A. R. Lindsay, C. Bossu, M. D. Sorenson, J. D. Kaplan, D. C. Evers, J. Paruk, J. M. DaCosta, T. B. Smith, and K. Ruegg. 2021. Leveraging genomics to understand threats to migratory birds. Evolutionary Applications 14:1646-1658. https://doi.org/10.1111/eva.13231

Laughlin, A. J., C. M. Taylor, D. W. Bradley, D. Leclair, R. C. Clark, R. D. Dawson, P. O. Dunn, A. Horn, M. Leonard, D. R. Sheldon, D. Shutler, L. A. Whittingham, D. W. Winkler, and D. R. Norris. 2013. Integrating information from geolocators, weather radar, and citizen science to uncover a key stopover area of an aerial insectivore. Auk 130:230-239. https://doi.org/10.1525/auk.2013.12229

Lin, T.-Y., K. Winner, G. Bernstein, A. Mittal, A. M. Dokter, K. G. Horton, C. Nilsson, B. M. Van Doren, A. Farnsworth, F. A. La Sorte, S. Maji, and D. Sheldon. 2019. MistNet: measuring historical bird migration in the US using archived weather radar data and convolutional neural networks. Methods in Ecology and Evolution 10:1908-1922. https://doi.org/10.1111/2041-210X.13280

Lincoln, F. C. 1935. The waterfowl flyways of North America (No. 342). US Department of Agriculture.

Lycett, S. J., F. Duchatel, and P. Digard. 2019. A brief history of bird flu. Philosophical Transactions of the Royal Society B: Biological Sciences 374:20180257. https://doi.org/10.1098/rstb.2018.0257

Macías-Duarte, A., and C. J. Conway. 2021. Geographic variation in dispersal of Western Burrowing Owl (Athene cunicularia hypugaea) populations across North America. Behavioral Ecology 32:1339-1351. https://doi.org/10.1093/beheco/arab100

Malone, K. M., E. B. Webb, D. C. Mengel, L. J. Kearns, A. E. McKellar, S. W. Matteson, and B. R. Williams. 2023. Wetland management practices and secretive marsh bird habitat in the Mississippi Flyway: a review. Journal of Wildlife Management 87:e22451. https://doi.org/10.1002/jwmg.22451

Marra, P. P., E. B. Cohen, S. R. Loss, J. E. Rutter, and C. M. Tonra. 2015. A call for full annual cycle research in animal ecology. Biology Letters 11:20150552. https://doi.org/10.1098/rsbl.2015.0552

Mayer, A. 2010. Phenology and citizen science: volunteers have documented seasonal events for more than a century, and scientific studies are benefiting from the data. BioScience 60:172-175. https://doi.org/10.1525/bio.2010.60.3.3

McDuffie, L. A., K. S. Christie, A. L. Harrison, A. R. Taylor, B. A. Andres, B. Laliberté, and J. A. Johnson. 2022a. Eastern-breeding Lesser Yellowlegs are more likely than western-breeding birds to visit areas with high shorebird hunting during southward migration. Ornithological Applications 124:duab061. https://doi.org/10.1093/ornithapp/duab061

McDuffie, L. A., K. S. Christie, A. R. Taylor, E. Nol, C. Friis, C. M. Harwood, J. Rausch, B. Laliberte, C. Gesmundo, J. R. Wright, and J. A. Johnson. 2022b. Flyway-scale GPS tracking reveals migratory routes and key stopover and non-breeding locations of Lesser Yellowlegs. Ecology and Evolution 12:e9495. https://doi.org/10.1002/ece3.9495

McKinnon, E. A., and O. P. Love. 2018. Ten years tracking the migrations of small landbirds: lessons learned in the golden age of bio-logging. Auk 135:834-856. https://doi.org/10.1642/AUK-17-202.1

McLaren, J. D., J. J. Buler, T. Schreckengost, J. A. Smolinsky, M. Boone, E. Emiel van Loon, D. K. Dawson, and E. L. Walters. 2018. Artificial light at night confounds broad‐scale habitat use by migrating birds. Ecology Letters 21:356-364. https://doi.org/10.1111/ele.12902

Miller, T. A., R. P. Brooks, M. Lanzone, D. Brandes, J. Cooper, K. O’Malley, C. Maisonneuve, J. Tremblay, A. Duerr, and T. Katzner. 2014. Assessing risk to birds from industrial wind energy development via paired resource selection models. Conservation Biology 28:745-755. https://doi.org/10.1111/cobi.12227

Mojica, E. K., B. D. Watts, and C. L. Turrin. 2016. Utilization probability map for migrating Bald Eagles in northeastern North America: a tool for siting wind energy facilities and other flight hazards. PLoS ONE 11:e0157807. https://doi.org/10.1371/journal.pone.0157807

Mondain-Monval, T. O., M. Amos, J.-L. Chapman, A. MacColl, and S. P. Sharp. 2021. Flyway-scale analysis reveals that the timing of migration in wading birds is becoming later. Ecology and Evolution 11:14135-14145. https://doi.org/10.1002/ece3.8130

Moore, J. D., D. E. Andersen, T. Cooper, J. P. Duguay, S. L. Oldenburger, C. A. Stewart, and D. G. Krementz. 2021. Migratory phenology and patterns of American Woodcock in central North America derived using satellite telemetry. Wildlife Biology 2021:wlb.00816. https://doi.org/10.2981/wlb.00816

Moore, J. D., T. R. Cooper, R. Rau, D. E. Andersen, J. P. Duguay, C. A. Stewart, and D. G. Krementz. 2019. Assessment of the American Woodcock singing-ground survey zone timing and coverage. Pages 181-192 in D. G. Krementz, D. E. Andersen, and T. R. Cooper, editors. Proceedings of the Eleventh American Woodcock Symposium, University of Minnesota Libraries Publishing, Minneapolis, Minnesota, USA.

Moore, J. D., and D. G. Krementz. 2017. Migratory connectivity of American Woodcock using band return data. Journal of Wildlife Management 81:1063-1072. https://doi.org/10.1002/jwmg.21269

Morbey, Y. E., and R. C. Ydenberg. 2001. Protandrous arrival timing to breeding areas: a review. Ecology Letters 4(6):663-673. https://doi.org/10.1046/j.1461-0248.2001.00265.x

Mueller, T., R. B. O’Hara, S. J. Converse, R. P. Urbanek, and W. F. Fagan. 2013. Social learning of migratory performance. Science 341:999-1002. https://doi.org/10.1126/science.1237139

Myatt, N. A., and D. G. Krementz. 2007. Fall migration and habitat use of American Woodcock in the central United States. Journal of Wildlife Management 71:1197-1205. https://doi.org/10.2193/2006-154

Nathan, R., C. T. Monk, R. Arlinghaus, T. Adam, J. Alós, M. Assaf, H. Baktoft, C. E. Beardsworth, M. G. Bertram, A. I. Bijeveld, T. Brain, J. L. Brooks, A. Campos-Candela, S. J. Cooke, K. O. Gjelland, P. R. Gupte, R. Harel, G. Hellstrom, F. Jeltsch, S. S. Killen, T. Klefoth, R. Langrock, R. J. Lennox, E. Lourie, J. R. Madden, Y. Orchan, I. S. Pauwels, M. Riha, M. Roeleke, U. E. Schlagel, D. Shohami, J. Signer, S. Toledo, O. Vilk, S. Westrelin, M. W. Witside, and I. Jarić. 2022. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375(6582):eabg1780. https://doi.org/10.1126/science.abg1780

Neate-Clegg, M. H. C., and M. W. Tingley. 2023. Adult male birds advance spring migratory phenology faster than females and juveniles across North America. Global Change Biology 29:341-354. https://doi.org/10.1111/gcb.16492

Newton, I. 2010. The migration ecology of birds. Elsevier, London, U.K.

Newton, I. 2024a. Migration mortality in birds. Ibis. https://doi.org/10.1111/ibi.13316

Newton, I. 2024b. The migration ecology of birds. Second edition. Academic Press, Cambridge, Massachusetts, USA.

Nowak, M. C., K. J. Mougey, D. P. Collins, and B. A. Grisham. 2018. Mixing of two Greater Sandhill Crane populations in northeast Oregon. Proceedings of the North American Crane Workshop 14:110-114.

Oguchi, Y., R. J. Smith, and J. C. Owen. 2017. Fruits and migrant health: consequences of stopping over in exotic- vs. native-dominated shrublands on immune and antioxidant status of Swainson’s Thrushes and Gray Catbirds. Condor 119:800-816. https://doi.org/10.1650/CONDOR-17-28.1

Pearse, A. T., G. L. Krapu, and D. A. Brandt. 2017. Sandhill Crane roost selection, human disturbance, and forage resources. Journal of Wildlife Management 81:477-486. https://doi.org/10.1002/jwmg.21215

Pearse, A. T., M. L. Szymanski, C. A. Anchor, M. J. Anteau, R. M. Murano, D. A. Brandt, and J. D. Stafford. 2023. Factors influencing autumn–winter movements of midcontinent Mallards and consequences for harvest and habitat management. Ecology and Evolution 13:e10605. https://doi.org/10.1002/ece3.10605

Pérez-Granados, C., and J. Traba. 2021. Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research. Ibis 163:765-783. https://doi.org/10.1111/ibi.12944

Petit, L. J. 2020. Prothonotary Warbler (Protonotaria citrea), version 1.0. In A. F. Poole and F. B. Gill, editors. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.prowar.01

Piersma, T. 1987. Hop, skip, or jump? Constraints on migration of arctic waders by feeding, fattening, and flight speed. Limosa 60:185-194.

Prytula, E. D., A. E. McKellar, L. Schwitters, and M. W. Reudink. 2022. Rapid advancement of spring migration and en route adjustment of migration timing in response to weather during fall migration in Vaux’s Swifts (Chaetura vauxi). Canadian Journal of Zoology 100:56-63. https://doi.org/10.1139/cjz-2021-0089

Ratanakorn, P., A. Wiratsudakul, W. Wiriyarat, K. Eiamampai, A. H. Farmer, R. G. Webster, K. Chaichoune, S. Suwanpakdee, D. Pothieng, and P. Puthavathana. 2012. Satellite tracking on the flyways of Brown-headed Gulls and their potential role in the spread of highly pathogenic avian influenza H5N1 virus. PLoS ONE 7:e49939. https://doi.org/10.1371/journal.pone.0049939

Rehm, E. M., P. Olivas, J. Stroud, and K. J. Feeley. 2015. Losing your edge: climate change and the conservation value of range-edge populations. Ecology and Evolution 5:4315-4326. https://doi.org/10.1002/ece3.1645

Rhymer, J. M., D. G. McAuley, and H. L. Ziel. 2005. Phylogeography of the American Woodcock (Scolopax minor): Are management units based on band recovery data reflected in genetically based management units? Auk 122:1149-1160. https://doi.org/10.1093/auk/122.4.1149

Ritterson, J. D., D. I. King, and R. B. Chandler. 2021. Habitat-specific survival of Golden-winged Warblers Vermivora chrysoptera during the non-breeding season in an agricultural landscape. Journal of Avian Biology 52(3):1-9. https://doi.org/10.1111/jav.02442

Roberto-Charron, A., J. Kennedy, L. Reitsma, J. A. Tremblay, R. Krikun, K. A. Hobson, J. Ibarzabal, and K. C. Fraser. 2020. Widely distributed breeding populations of Canada Warbler (Cardellina canadensis) converge on migration through Central America. BMC Zoology 5:10. https://doi.org/10.1186/s40850-020-00056-4

Robertson, E. P., F. A. La Sorte, J. D. Mays, P. J. Taillie, O. J. Robinson, R. J. Ansley, T. J. O’Connell, C. A. Davis, and S. R. Loss. 2024. Decoupling of bird migration from the changing phenology of spring green-up. Proceedings of the National Academy of Sciences 121:e2308433121. https://doi.org/10.1073/pnas.2308433121

Rodewald, P. G., and M. C. Brittingham. 2007. Stopover habitat use by spring migrant landbirds: the roles of habitat structure, leaf development, and food availability. Auk 124:1063-1074. https://doi.org/10.1093/auk/124.3.1063

Rosenberg, K. V., A. M. Dokter, P. J. Blancher, J. R. Sauer, A. C. Smith, P. A. Smith, J. C. Stanton, A. Panjabi, L. Helft, M. Parr, and P. P. Marra. 2019. Decline of the North American avifauna. Science 366:120-124. https://doi.org/10.1126/science.aaw1313

Rosenberg, K. V., T. Will, D. A. Buehler, S. Barker Swarthout, W. E. Thogmartin, R. E. Bennett, and R. B. Chandler. 2016. Dynamic distributions and population declines of Golden-winged Warblers. Pages 3-28 in H. M. Streby, D. E. Andersen, and D. A. Buehler, editors. Golden-winged Warbler ecology, conservation, and habitat management. Studies in Avian Biology 49.

Rubenstein, D. R., C. P. Chamberlain, R. T. Holmes, M. P. Ayres, J. R. Waldbauer, G. R. Graves, and N. C. Tuross. 2002. Linking breeding and wintering ranges of a migratory songbird using stable isotopes. Science 295:1062-1065. https://doi.org/10.1126/science.1067124

Rueda-Hernández, R., C. M. Bossu, T. B. Smith, A. Contina, R. Canales Del Castillo, K. Ruegg, and B. E. Hernández-Baños. 2023. Winter connectivity and leapfrog migration in a migratory passerine. Ecology and Evolution 13:e9769. https://doi.org/10.1002/ece3.9769

Ruegg, K., E. C. Anderson, M. Somveille, R. A. Bay, M. Whitfield, E. H. Paxton, and T. B. Smith. 2021. Linking climate niches across seasons to assess population vulnerability in a migratory bird. Global Change Biology 27:3519-3531. https://doi.org/10.1111/gcb.15639

Ruegg, K. C., R. J. Harrigan, J. F. Saracco, T. B. Smith, and C. M. Taylor. 2020. A genoscape-network model for conservation prioritization in a migratory bird. Conservation Biology 34:1482-1491. https://doi.org/10.1111/cobi.13536

Runge, C. A., T. G. Martin, H. P. Possingham, S. G. Willis, and R. A. Fuller. 2014. Conserving mobile species. Frontiers in Ecology and the Environment 12:395-402. https://doi.org/10.1890/130237

Sanders, C. E., and D. J. Mennill. 2014. Acoustic monitoring of nocturnally migrating birds accurately assesses the timing and magnitude of migration through the Great Lakes. Condor 116:371-383. https://doi.org/10.1650/CONDOR-13-098.1

Schofield, L. N., J. L. Deppe, T. J. Zenzal, Jr., M. P. Ward, R. H. Diehl, R. T. Bolus, and F. R. Moore. 2018. Using automated telemetry to quantify activity patterns of songbirds during stopover. Auk 135:949-963. https://doi.org/10.1642/AUK-17-229.1

Seeger, R. M., A. D. Hagerman, K. K. Johnson, D. L. Pendell, and T. L. Marsh. 2021. When poultry take a sick leave: response costs for the 2014–2015 highly pathogenic avian influenza epidemic in the USA. Food Policy 102:102068. https://doi.org/10.1016/j.foodpol.2021.102068

Seewagen, C. L., C. D. Sheppard, E. J. Slayton, and C. G. Guglielmo. 2011. Plasma metabolites and mass changes of migratory landbirds indicate adequate stopover refueling in a heavily urbanized landscape. Condor 113:284-297. https://doi.org/10.1525/cond.2011.100136

Sequeira, A. M. M., M. O’Toole, T. R. Keates, L. H. McDonnell, C. D. Braun, X, Hoenner, F. R. A. Jaine, I. D. Jonsen, P. Newman, J. Pye, et al. 2021. A standardisation framework for bio-logging data to advance ecological research and conservation. Methods in Ecology and Evolution 12:996-1007. https://doi.org/10.1111/2041-210X.13593

Sharp, D. E., H. M. Hands, J. A. Dubovsky, and J. E. Cornely. 2010. Summary of Sandhill Crane hunting seasons in Kansas 1993–2007. North American Crane Workshop Proceedings 151. http://digitalcommons.unl.edu/nacwgproc/151

Sheldon, D., A. Farnsworth, J. Irvine, B. V. Doren, K. Webb, T. G. Dietterich, and S. Kelling. 2013. Approximate Bayesian inference for reconstructing velocities of migrating birds from weather radar. Pages 1334-1340 in K. L. Wagstaff, N. L. Lanza, D. R. Thompson, T. G. Dietterich, and M.S. Gilmore, editors. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. AAI Press, Palo Alto, California, USA. https://doi.org/10.1609/aaai.v27i1.8486

Shipley, J. R., C. W. Twining, C. C. Taff, M. N. Vitousek, A. Flack, and D. W. Winkler. 2020. Birds advancing lay dates with warming springs face greater risk of chick mortality. Proceedings of the National Academy of Sciences 117:25590-25594. https://doi.org/10.1073/pnas.2009864117

Sillett, T. S., and R. T. Holmes. 2002. Variation in survivorship of a migratory songbird throughout its annual cycle. Journal of Animal Ecology 71:296-308. https://doi.org/10.1046/j.1365-2656.2002.00599.x

Skagen, S. K., and F. L. Knopf. 1994. Residency patterns of migrating sandpipers at a midcontinental stopover. Condor 96:949-958. https://doi.org/10.2307/1369104

Skinner, A. A., S. N. Matthews, M. P. Ward, I. Souza-Cole, J. R. Wright, F. R. Thompson III, T. J. Benson, and C. M. Tonra. 2023. Eastern Whip-poor-wills have larger nonbreeding home ranges in areas with more agriculture and forest fragmentation. Ornithological Applications 125:duac050. https://doi.org/10.1093/ornithapp/duac050

Slezak, C. R., E. J. Blomberg, A. M. Roth, L. Q. Berigan, A. C. Fish, R. Darling, S. J. Clements, G. Balkcom, B. Carpenter, G. Costanzo, J. Duguay, C. L. Graham, W. Harvey, M. Hook, D. L. Howell, S. Maddox, S. W. Meyer, T. C. Nichols, J. B. Pollard, C. Roy, J. E. Kilburn, and S. R. McWilliams. 2024. Unconventional life history in a migratory shorebird: desegregating reproduction and migration. Proceedings of the Royal Society B: Biological Sciences 291:120240021. https://doi.org/10.1098/rspb.2024.0021

Smith, S. B., A. C. Miller, C. R. Merchant, and A. F. Sankoh. 2015. Local site variation in stopover physiology of migrating songbirds near the south shore of Lake Ontario is linked to fruit availability and quality. Conservation Physiology 3:cov036. https://doi.org/10.1093/conphys/cov036

Soriano-Redondo, A., A. M. A. Franco, M. Acácio, A. Payo-Payo, B. Herlando Martins, F. Moreira, and I. Catry. 2023. Fitness, behavioral, and energetic trade-offs of different migratory strategies in a partially migratory species. Ecology 104:e4151. https://doi.org/10.1002/ecy.4151

Spoelstra, K., and M. E. Visser. 2013. The impact of artificial light on avian ecology. Pages 21-28 in D. Gil and J. Brumm, editors. Avian urban ecology. Oxford University Press, Oxford, U.K. https://doi.org/10.1093/acprof:osobl/9780199661572.003.0002

Stanley, C. Q., M. MacPherson, K. C. Fraser, E. A. McKinnon, and B. J. Stutchbury. 2012. Repeat tracking of individual songbirds reveals consistent migration timing but flexibility in route. PLoS ONE 7:e40688. https://doi.org/10.1371/journal.pone.0040688

Stanley, C. Q., M. R. Dudash, T. B. Ryder, W. G. Shriver, K. Serno, S. Adalsteinsson, and P. P. Marra. 2021. Seasonal variation in habitat selection for a Neotropical migratory songbird using high-resolution GPS tracking. Ecosphere 12:e03421. https://doi.org/10.1002/ecs2.3421

Stepanian, P. M., K. G. Horton, V. M. Melnikov, D. S. Zrinić, and S. A. Gauthreaux, Jr. 2016. Dual-polarization radar products for biological applications. Ecosphere 7:e01539. https://doi.org/10.1002/ecs2.1539

Stowell, D., M. D. Wood, H. Pamula, Y. Stylianou, and H. Glotin. 2019. Automatic acoustic detection of birds through deep learning: the first bird audio detection challenge. Methods in Ecology and Evolution 10:368-380. https://doi.org/10.1111/2041-210X.13103

Streby, H. M., G. R. Kramer, S. M. Peterson, J. A. Lehman, D. A. Buehler, and D. E. Andersen. 2015. Tornadic storm avoidance behavior in breeding songbirds. Current Biology 25:98-102. https://doi.org/10.1016/j.cub.2014.10.079

Studds, C. E., J. M. Wunderle, Jr., and P. P. Marra. 2021. Strong differences in migratory connectivity patterns among species of Neotropical-Nearctic migratory birds revealed by combining stable isotopes and abundance in a Bayesian assignment analysis. Journal of Biogeography 48:1746-1757. https://doi.org/10.1111/jbi.14111

Stumpf, K., and C. Muise. 2020. Long-term changes in avian capture rates during twelve years of active grassland and savannah restoration. Georgia Journal of Science 78:2. https://digitalcommons.gaacademy.org/gjs/vol78/iss2/2

Sullivan, B. L., J. L. Aycrigg, J. H. Barry, R. E. Bonney, N. Bruns, C. B. Cooper, T. Damoulas, A. A. Dhondt, T. Dietterich, A. Farnsworth, D. Fink, J. W. Fitzpatrick, T. Fredericks, J. Gerbracht, C. Gomes, W. M. Hockachka, M. J. Iliff, C. Lagoze, F. A. La Sorte, M. Merrifield, W. Morris, T. B. Phillips, M. Reynolds, A. D. Rodewald, K. V. Rosenberg, N. M. Trautmann, A. Wiggins, D. W. Winkler, W.-K. Wong, C. L. Wood, J. Yu, and S. Kelling. 2014. The eBird enterprise: an integrated approach to development and application of citizen science. Biological Conservation 169:31-40. https://doi.org/10.1016/j.biocon.2013.11.003

Sullivan, B. L., C. L. Wood, M. J. Illiff, R. E. Bonney, D. Fink, and S. Kelling. 2009. eBird: a citizen-based bird observation network in the biological sciences. Biological Conservation 142:2282-2292. https://doi.org/10.1016/j.biocon.2009.05.006

Supp, S. R., F. A. La Sorte, T. A. Cormier, M. C. Lim, D. R. Powers, S. M. Wethington, S. Goetz, and C. H. Graham. 2015. Citizen‐science data provides new insight into annual and seasonal variation in migration patterns. Ecosphere 6:1-19. https://doi.org/10.1890/ES14-00290.1

Tautin, J. 2005. One hundred years of bird banding in North America. Pages 815–816 in C. J. Ralph and T. D. Rich, editors. Bird conservation implementation and integration in the Americas. Third International Partners in Flight Conference. U.S. Forest Service General Technical Report GTR-PSW-191.

Taylor, P. D., T. L. Crewe, S. A. Mackenzie, D. Lepage, Y. Aubry, Z. Crysler, G. Finney, C. M. Francis, C. G. Guglielmo, D. J. Hamilton, R. L. Holberton, P. H. Loring, G. W. Mitchell, D. R. Norris, J. Paquet, R. A. Ronconi, J. R. Smetzer, P. A. Smith, L. J. Welch, and B. K. Woodworth. 2017. The Motus Wildlife Tracking System: a collaborative research network to enhance the understanding of wildlife movement. Avian Conservation and Ecology 12(1):8. https://doi.org/10.5751/ACE-00953-120108

Teitelbaum, C. S., M. L. Casazza, F. McDuie, S. E. W. De La Cruz, C. T. Overton, L. A. Hall, E. L Matchett, J. T. Ackerman, J. D. Sullivan, A. M. Ramey, and D. J. Prosser. 2023a. Waterfowl recently infected with low pathogenic avian influenza exhibit reduced local movement and delayed migration. Ecosphere 14:e4432. https://doi.org/10.1002/ecs2.4432

Teitelbaum, C. S., S. J. Converse, W. F. Fagan, K. Böhning-Gaese, R. B. O’Hara, A. E. Lacy, and T. Mueller. 2016. Experience drives innovation of new migration patterns of Whooping Cranes in response to global change. Nature Communications 7:12793. https://doi.org/10.1038/ncomms12793

Teitelbaum, C. S., N. M. Masto, J. D. Sullivan, A. C. Keever, R. L. Poulson, D. L. Carter, A. G. Blake-Bradshaw, C. J. Highway, J. C. Feddersen, H. M. Hagy, R. W. Gerhold, B. S. Cohen, and D. J. Prosser. 2023b. North American wintering Mallards infected with highly pathogenic avian influenza show few signs of altered local or migratory movements. Scientific Reports 13:14473. https://doi.org/10.1038/s41598-023-40921-z

Thurber, B. G., C. Roy, and J. R. Zimmerling. 2020. Long-term changes in the autumn migration phenology of dabbling ducks in southern Ontario and implications for waterfowl management. Wildlife Biology 2020:wlb.00668. https://doi.org/10.2981/wlb.00668

Toews, D. P. L., S. A. Taylor, H. M. Streby, G. R. Kramer, and I. J. Lovette. 2019. Selection on VPS13A linked to migration in a songbird. Proceedings of the National Academy of Sciences 116:18272-18274. https://doi.org/10.1073/pnas.1909186116

Tonelli, B. A., A. E. Zelin, D. C. Dearborn, and M. W. Tingley. 2023. Individual-based models of avian migration for estimating behavioural traits and predicting ecological interactions. Methods in Ecology and Evolution. 14:2464-2481. https://doi.org/10.1111/2041-210X.14189

Tonra, C. M., M. T. Hallworth, T. J. Boves, J. Reese, L. P. Bulluck, M. Johnson, C. Viverette, K. Percy, E. M. Ames, M. C. Slevin, R. R. Wilson, and E. I. Johnson. 2019. Concentration of a widespread breeding population in a few critically important nonbreeding areas: migratory connectivity in the Prothonotary Warbler. Condor 121:duz019. https://doi.org/10.1093/condor/duz019

Ulman, S. E. G., S. L. Van Wilgenburg, J. M. Morton, and C. K. Williams. 2023. Geographic origins of shorebirds using an Alaskan estuary during migration. Waterbirds 46:47-56. https://doi.org/10.1675/063.046.0107

Van Doren, B. M., and K. G. Horton. 2018. A continental system for forecasting bird migration. Science 361:1115-1118. https://doi.org/10.1126/science.aat7526

Van Doren, B. M., K. G. Horton, A. M. Dokter, H. Klinck, S. B. Elbin, and A. Farnsworth. 2017. High-intensity urban light installation dramatically alters nocturnal bird migration. Proceedings of the National Academy of Sciences 114:11175-11180. https://doi.org/10.1073/pnas.1708574114

Van Doren, B. M., D. E. Willard, M. Hennen, K. G. Horton, E. F. Stuber, D. Sheldon, A. H. Sivakumar, J. Wang, A. Farnsworth, and B. M. Winger. 2021. Drivers of fatal bird collisions in an urban center. Proceedings of the National Academy of Sciences 118:e2101666118. https://doi.org/10.1073/pnas.2101666118

van Gils, J. A., S. Lisovski, T. Lok, W. Meissner, A. Ożarowska, J. De Fouw, E. Rakhimberdiev, M. Y. Soloviev, T. Piersma, and M. Klaassen. 2016. Body shrinkage due to Arctic warming reduces Red Knot fitness in tropical wintering range. Science 352:819-821. https://doi.org/10.1126/science.aad6351

Vander Zanden, H. B., D. M. Nelson, T. J. Conkling, T. D. Allison, J. E. Diffendorfer, T. V Dietsch, A. L. Fesnock, S. R. Loss, P. A. Ortiz, R. Paulman, K. H. Rogers, P. M. Sanzenbacher, and T. E. Katzner. 2024. The geographic extent of bird populations affected by renewable-energy development. Conservation Biology 38:e14191. https://doi.org/10.1111/cobi.14191

Varner, D. M., A. T. Pearse, A. A. Bishop, J. I. Davis, J. C. Denton, R. C. Grosse, H. M. Johnson, E. J. Muner, K. D. Schroeder, R. E. Spangler, M. P. Vrtiska, and A. E. Wright. 2020. Roosting habitat use by Sandhill Cranes and waterfowl on the North and South Platte Rivers in Nebraska. Journal of Fish and Wildlife Management 11:56-67. https://doi.org/10.3996/042019-JFWM-030

Verhoeven, M. A., A. H. J. Loonstra, A. D. McBride, W. Kaspersma, J. C. E. W. Hooijmeijer, C. Both, N. R. Senner, and T. Piersma. 2022. Age-dependent timing and routes demonstrate developmental plasticity in a long-distance migratory bird. Journal of Animal Ecology 91:566-579. https://doi.org/10.1111/1365-2656.13641

Viana, D. S., L. Santamaría, and J. Figuerola. 2016. Migratory birds as global dispersal vectors. Trends in Ecology & Evolution 31:763-775. https://doi.org/10.1016/j.tree.2016.07.005

Walter, D. W., J. W. Fischer, J. S. Humphrey, T. S. Daughtery, M. P. Milleson, E. A. Tillman, and M. L. Avery. 2012. Using three-dimensional flight patterns at airfields to identify hotspots for avian–aircraft collisions. Applied Geography 35:53-59. https://doi.org/10.1016/j.apgeog.2012.05.002

Ward, M. P., T. J. Benson, J. Deppe, T. J. Zenzal, R. H. Diehl, A. Celis-Murillo, R. Bolus, and F. R. Moore. 2018. Estimating apparent survival of songbirds crossing the Gulf of Mexico during autumn migration. Proceedings of the Royal Society B: Biological Sciences 285(1889):20181747. https://doi.org/10.1098/rspb.2018.1747

Warnock, N. 2010. Stopping vs. staging: the difference between a hop and a jump. Journal of Avian Biology 41:621-626. https://doi.org/10.1111/j.1600-048X.2010.05155.x

Watts, B. D., E. K. Mojica, and B. J. Paxton. 2015. Using Brownian bridges to assess potential interactions between Bald Eagles and electrical hazards within the upper Chesapeake Bay. Journal of Wildlife Management 79:435-445. https://doi.org/10.1002/jwmg.853

Weber, J.-M. 2009. The physiology of long-distance migration: extending the limits of endurance metabolism. Journal of Experimental Biology 212:593-597. https://doi.org/10.1242/jeb.015024

Webster, M. D. 2020. Verdin (Auriparus flaviceps), version 1.0. In A. F. Poole and F. B. Gill, editors. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.verdin.01

Webster, M. S., P. P. Marra, S. M. Haig, S. Bensch, and R. T. Holmes. 2002. Links between worlds: unraveling migratory connectivity. Trends in Ecology & Evolution 17:76-83. https://doi.org/10.1016/S0169-5347(01)02380-1

Weiser, E. L., C. T. Overton, D. C. Douglas, M. L. Casazza, and P. L. Flint. 2024. Geese migrating over the Pacific Ocean select altitudes coinciding with offshore wind turbine blades. Journal of Applied Ecology 61:951-962. https://doi.org/10.1111/1365-2664.14612

Wells, M. T., E. A. Rigby, K. W. Heist, and N. A. Rathbun. 2022. Migrants employ mixed strategies to route across the Great Lakes basin. Avian Conservation and Ecology 17:46. https://doi.org/10.5751/ACE-2342-170246

Whiten, A. 2019. Cultural evolution in animals. Annual Review of Ecology, Evolution, and Systematics 50:27-48. https://doi.org/10.1146/annurev-ecolsys-110218-025040

Wikelski, M., E. M. Tarlow, A. Raim, R. H. Diehl, R. P. Larkin, and G. H. Visser. 2003. Costs of migration in free-flying songbirds. Nature 423:704-704. https://doi.org/10.1038/423704a

Wolfson, D. W., J. R. Fieberg, and D. E. Andersen. 2020. Juvenile Sandhill Cranes exhibit wider ranging and more exploratory movements than adults during the breeding season. Ibis 162:556-562. https://doi.org/10.1111/ibi.12786

Wolfson, D. W., J. R. Fieberg, J. S. Lawrence, T. R. Cooper, and D. E. Andersen. 2017. Range overlap between mid-continent and Eastern Sandhill Cranes revealed by GPS‐tracking. Wildlife Society Bulletin 41:489-498. https://doi.org/10.1002/wsb.799

Youngflesh, C., J. Socolar, B. R. Amaral, A. Arab, R. P. Guralnick, A. H. Hurlbert, R. LaFrance, S. J. Mayor, D. A. W. Miller, and M. W. Tingley. 2021. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nature Ecology & Evolution 5:987-994. https://doi.org/10.1038/s41559-021-01442-y

Zenzal, T. J., Jr., F. R. Moore, R. H. Diehl, M. P. Ward, and J. L. Deppe. 2018. Migratory hummingbirds make their own rules: the decision to resume migration along a barrier. Animal Behaviour 137:215-224. https://doi.org/10.1016/j.anbehav.2018.01.019

Zhang, G., B. Li, J. Raghwani, B. Vrancken, R. Jia, S. C. Hill, G. Fournié, Y. Cheng, Q. Yang, Y. Wang, Z. Wang, L. Dong, O. G. Pybus, and H. Tian. 2023. Bidirectional movement of emerging H5N8 avian influenza viruses between Europe and Asia via migratory birds since early 2020. Molecular Biology and Evolution 40:msad019. https://doi.org/10.1093/molbev/msad019