Exploring Shifts in Migration Phenology and Breeding Distribution of Declining North American Avian Aerial Insectivores

EXPLORING SHIFTS IN MIGRATION PHENOLOGY AND BREEDING DISTRIBUTION OF DECLINING NORTH AMERICAN AVIAN AERIAL INSECTIVORES

A thesis submitted to the Kent State University Honors College in partial fulfillment of the requirements for University Honors

Nora Honkomp

May, 2021

Thesis written by

Nora Honkomp

Approved by

______, Advisor

______,Chair, Department of Biological Sciences

Accepted by

______, Dean, Honors College

TABLE OF CONTENTS

LIST OF FIGURES…..……………………………………………….………………….iv

LIST OF TABLES………..…………………………………………….………………....v

ACKNOWLEDGMENT…………………….…………….……………………………..vi

CHAPTERS

I. INTRODUCTION……………….………………….…………….………1

II. METHODS……………………….………………………………….…..16

Migration Timing Analysis……………….…………………………..….16

Breeding Distribution Analysis………………………………………..…27

III. RESULTS……………………………………………………………..…30

Migration Timing Analysis………………………………………………30

Breeding Distribution Analysis………………………………………….40

IV. DISCUSSION……………………………………………………………47

LITERATURE CITED…………………………………………………………………..56

APPENDIX...... 61

iii

LIST OF FIGURES

Figure 1. Number of checklists by day of year ………………………...... ………..……....21

Figure 2. Latitude of sighting by day of year .…………………………………………...... 22

Figure 3. Start and end dates for spring and fall migration...... 24

Figure 4. Change in rate of northward movement over time...... 34

Figure 5. Change in rate of southward movement over time...... 35

Figure 6. Day of year of early arrival above the 35th latitude...... 38

Figure 7. Day of year of late departure above the 35th latitude...... 39

Figure 8. Location of BBS routes consistently surveyed...... 40

Figure 9. Change in latitude of center of abundance over time...... 43

Figure 10. Change in longitude of center of abundance over time...... 45

Figure 11. Rate of change in centers of abundance from 1990-2019...... 46

iv LIST OF TABLES

Table 1. Population trends of selected avian aerial insectivores …………….……...…..12

Table 2. Four-letter alpha codes and scientific names...... 19

Table 3. Comparison of linear regression slopes for spring and fall movement...... 33

Table 4. Linear regression results of the latitude of center of abundance by year ...... 42

Table 5. Linear regression results of the longitude of center of abundance by year...... 44

v ACKNOWLEDGMENTS

First and foremost, I would like to thank Dr. Mark Kershner for his vast knowledge, generous guidance, and constant enthusiasm throughout this entire process. I would like to thank Dr. David Singer, Dr. Tim Assal, and Dr. Christie Bahlai for serving as my thesis committee, and for their thoughtful contributions to my work. Next, I would like to thank Ashley Fink and Stephanie Petrycki, as their great efforts in data analysis allowed me to ask a larger research question than I could have handled on my own. A huge thank you to Dr. Shannon Curley who dedicated her time (and code!) to teaching me how to conduct the analysis of breeding distribution and interpret its results. I would also like to show appreciation for all those contributing to the future of avian ecology research including the thousands of volunteers who dedicate their time to completing the

BBS routes on an annual basis, the many citizen scientists that record their sightings, and the organizations and individuals that maintain the BBS and eBird databases. Lastly, thank you to my family, roommates, and friends for their constant support and encouragement throughout this process.

vi 1

Introduction

Recent studies have shown major declines in North American bird populations. In fact, a study by Rosenberg et al. (2019) described the cumulative loss of 3 billion birds over the last five decades, representing a 29% decline in bird abundance since 1970.

Further, it is particularly of concern that 2.5 billion individuals of this loss are migratory species (Rosenberg et al. 2019). There are many, multi-faceted reasons as to why these declines are taking place (Loss et al. 2015), including effects of climate change on migration and subsequent breeding. Given that migratory species make up such a large proportion of missing birds, it is important to look at migration and its challenges in order to understand the major drivers of these losses.

Migration

Many species use large-scale migrations as a life history strategy to increase fitness. Birds are especially notorious for their annual migrations between breeding and wintering grounds, given their conspicuous and charismatic natures. Typically, migratory species will travel to a specific, set breeding location from a specific wintering range

(where they will spend the non-breeding season). Some species, known as ‘partial migrants', only travel a short distance from wintering to breeding grounds, such as up the side of a mountain or up/down a few degrees in latitude at the change in seasons, and often remain in parts of their breeding range year-round.

In contrast, long-distance travelers, known as “neotropical migrants”, spend the breeding season (spring and summer) in North America and the non-breeding season

(winter) in Central and South America, travelling thousands of miles twice a year between breeding and wintering grounds. During their pre-breeding migration (“spring migration”) and post-breeding migration (“fall migration”), they will travel through areas of the continent where they do not typically breed. These areas are considered part of a species’ migratory route and will see neotropical migrants for a short period of time each year during spring and fall (Cornell 2019).

The benefits of these immense, long-distance journeys must be worthwhile considering the amount of time and energy they require, as well as the many possible risks they face during migration (Loss et al. 2015, Cornell 2019). The necessity of migration is likely linked with the pressures for survival and reproduction. For example, breeding grounds need to provide enough food for adult survival, and the provisioning necessary to raise young. Further, there are additive pressures associated with finding ideal climatic conditions, habitat type, space or territories, mates, etc. that affect their choice in breeding grounds. For neotropical migrants, winters in temperate regions of

North America are too cold and do not offer enough food to sustain their populations, as most plants no longer produce seeds or fruits and most invertebrates are dormant. For this reason, they spend these cold months in tropical and subtropical regions where temperature and precipitation conditions are ideal and food is abundant among the many plant and invertebrate species that are active throughout the year.

With plentiful resources and ideal temperatures, the tropical areas may seem like a prime place to stay year-round and even raise young there. However, this is not the case as intense competition exists for the resources and space in these areas from the multitude of tropical species present. Long-distance migrations are thought to have evolved from the advantage of greater food availability in temperate regions which provides the ability for these species to raise more offspring in these areas (Cornell 2019). Additionally, wet and dry seasons change environmental conditions in tropical areas the same way the four seasons shift temperature and precipitation conditions in the temperate regions, leaving periods of unsuitable abiotic conditions for migrant species. To balance this temporal trade-off in a way that maximizes access to resources and survivable environmental conditions, neotropical migrants are adapted to travel between the wintering and breeding environments despite the massive energy cost of prolonged flight and many risks (Cornell

2019).

Given these pressures, migratory birds must determine when to depart so that they arrive on the breeding or wintering grounds at the appropriate time. It is impossible to predict what environmental conditions may be thousands of miles away, so the birds must rely on annually consistent cues to predict when their breeding and wintering grounds may be offering favorable conditions. Though the mechanisms used to determine when to begin migration are not entirely understood for every species, there are multiple hypotheses on which factors may contribute. It is possible that the birds detect changes in temperature, precipitation, and weather patterns and use these abiotic factors as potential cues (Cornell 2019). In conjunction, the birds may be attuned to other species in the area

for determining when the proper time to depart for the northern latitudes may be, particular those related to their food supply (Studds and Marra 2011). Highly variable weather cues are most commonly used by shorter-distance migrants and allow flexibility in timing between years (Hagan et al. 1991). Long-distance migrants, however, have much more consistent departure dates, and are unlikely to use these variable cues

(Schwemmer et al. 2021). Birds may also use changes in photoperiod to predict when they should begin migrating as it will provide annually consistent cues for prime departure date. Interestingly, while photoperiod may be a valuable source of phenological information in temperate regions, changes in day length and position of the sun are much more subtle near the equator where wintering neotropical migrants would be when determining the right time to leave for the breeding grounds. Because of this, it is thought that endogenous cues and genetic triggers are likely to determine departure date for these species (Hagan et al. 1991).

Two final aspects of migration to understand are the route that a given species uses when migrating and at what rate they travel. Across North America, four flyways exist that channel most species from Mexico towards Canada and back; these are the

Pacific, Central, Mississippi, and Atlantic flyways. As the migrants leave Central

America, some populations take the long route in which they fly over land, moving their way up through Mexico, while others take a more direct route, crossing over the Gulf of

Mexico. It is common for the routes a population uses to travel north in the spring to differ from the route they use to travel south in the fall (La Sorte et al. 2013). In terms of rate of migration, several factors may be at play. Bigger species tend to migrate faster (La

Sorte et al. 2013). Some are able to fly very far distances and then stop for long periods of time to rest and refuel before making their next long flight towards their destination.

Others travel in “hops” where they fly short distances, stop for a small amount of time, and then fly another short distance (Klaassen 1996). While not understood as well, species may travel at a different rate during fall in comparison to spring since they may use different routes, face different pressures, and do not have the pressure of needing to get to the breeding grounds.

Challenges of Migration

Along their migration routes, birds face many natural obstacles for survival. The risk of predation is heightened during migration as birds travel for long periods of time through open air and encounter many aerial predators (Cornell 2019). Along with predation, some migrants experience strong competition. In an effort to arrive at the breeding grounds first to obtain the best territories and nesting locations, some species participate in an intraspecific race to more northern latitudes. This however, can result in arrival during a time that does not yet offer favorable weather conditions. A sudden cold snap in the spring can lead to lack of food and a day spent huddled together instead of foraging. These environmental factors faced upon arrival compound difficulties experienced during their stressful journey. During migration, birds deal with many physiological constraints involving energy storage and water retention, as they need to maintain a specific weight for optimal flight, requiring set intervals of refueling at stop-

over sites and optimal food availability that offers the right balance of fats and proteins

(Klaassen 1996).

In addition to the natural challenges associated with migration, humans have added many obstacles to this already incredible feat. The leading anthropogenic mortality factor for birds is predation by domestic cats, which kill billions of birds each year (Loss et al. 2015). Following this, collisions with buildings and automobiles each kill hundreds of millions of birds on an annual basis (Loss et al. 2015). Collisions and electrocution at powerlines, collisions with communication towers and wind turbines, and poisoning from agricultural chemicals all contribute millions of bird deaths each year as well (Loss et al.

2015). These factors are direct and measurable, but there are many indirect mortality factors caused by humans that are harder to quantify. The loss of habitat for nesting and foraging offer challenges to survival and reproduction on the breeding grounds, on the wintering grounds, and on the stopover sites in between. Land transformation for urbanization and agricultural use are generally the driving factors behind habitat loss. The introduction of non-native species can increase predation risk and disease transmission.

Additionally, the altered plant communities created by introduction of exotic species can result in a decrease in food availability (Narango et al. 2018). Each of these factors contributes to large declines in neotropical migrant populations as death rates are higher than birth rates, meaning more birds die without being able to replace themselves.

Ultimately, climate change is one major human impact occurring at a global scale that threatens entire migratory guilds by reducing the environmental and biotic predictability that these animals so greatly rely on for migration and reproduction. The

first impact of climate change relates to the timing of food availability. Changing abiotic factors can lead to new timing for biological processes like spring green-up, insect emergence, and vertebrate breeding seasons (Scranton and Amarasekare 2017). However, various trophic and taxonomic groups have differing levels of sensitivity to climate change (Voigt et al. 2003, Thackeray 2016). This diversity leads to different rates of advancement or delays in timing between biological interactions within food webs and ecosystems. The disparity between the phenology (timing) of organisms that rely on each other is referred to as “phenological mismatch” or “trophic asynchrony” (Renner and

Zohner 2018). For example, birds are known to track vegetation greenness in the spring and fall (La Sorte and Graham 2020), which allows them to synchronize breeding and rearing of young with peak food abundance. When the food source advances its emergence due to warmer springs, the birds may not be able to adapt their timing to meet this change and breeding/reproduction can be negatively impacted (Doiron 2015).

Climate change also poses threats for habitat availability, which impacts bird distribution. Currently, two thirds of North American bird species have moderate or high vulnerability to range loss, range gain, and ineffective dispersal abilities under a 3oC temperature increase (Bateman et al. 2020a). A follow up study found that under unmitigated climate change, 88% of the contiguous United States will be affected by various climate change-related threats (such as sea-level rise, drought, and increased storm severity) and 97% of North American bird species will be affected by at least two of those threats (Bateman et al. 2020b). Further, neotropical migrants are expected to experience decreased rainfall on their wintering grounds and increased temperatures on

their breeding grounds as well as along migration routes (Studds and Marra 2011, La

Sorte et al. 2017). These factors pose threats to the areas in which birds have historically chosen to breed and winter, resulting in large portions of species’ ranges becoming less habitable/uninhabitable for them.

Climate change will impact not only the destinations, but also the journey itself.

Changing wind patterns during spring and fall migration can affect birds’ rate of movement and energy expenditure. Based on potential changes in North American wind patterns, spring migration may become more efficient with respect to energy use while fall migrations may become less efficient (La Sorte et al. 2019a). Further, changing wind patterns may result in faster rates of migration in spring and slower rates of migration in fall (La Sorte et al. 2019a). Finally, birds will likely face conflicting pressures relative to migration timing over the next century as novel climates emerge on differing timelines on wintering grounds relative to breeding grounds (La Sorte et al. 2019b). Ultimately, the ability of Neotropical migrants to travel at the same rate, to the same places, at the same time of year may no longer be an option later this century.

Bird Response to Novel Stressors

There is hope, however, that birds may be able to set a new schedule to keep up with availability of preferred food resources and abiotic conditions. In fact, trophic asynchrony is expected to last for an evolutionarily short amount of time due to strong pressures for adaptation (Renner and Zohner 2018). In fact, some migratory birds are already changing migration timing in response to changing temperatures (Zaifman et al.

2017). When assessing the potential for phenological mismatch, 48 North American bird species have altered their spring arrival dates in the same direction as the advancing green-up date (Mayor et al. 2017). Of those species, 39 were able to keep pace with the advancement or delay of spring green-up (Mayor et al. 2017). Weather radar data has shown that the timing of both spring and fall migration has advanced across the contiguous United States during the past 24 years and these changes can be linked to warmer seasons (Horton et al. 2020). However, while the timing of peak bird passage over the Gulf of Mexico did not change from 1995 through 2015, there was a 3-day advancement of the earliest migrants passing over, and this change was linked to species with larger body size and shorter migration distance (Horton et al. 2019). Shifts in migration timing are becoming well documented, particularly relative to spring arrival on the breeding grounds.

Fall migration is less understood overall as fewer studies have focused on this. It is harder to detect and identify migrating individuals once breeding season has ended as they reduce their singing frequency and may no longer sport bright, distinctive breeding plumage. Further, effects of climate change on the timing of fall migration are less understood and are considered to be more dependent on species-specific life history traits that result in less clear trends (Jenni and Kéry 2003). In fact, species may delay or advance their fall migration based on conditions they will face along their migration route and whether they have the ability to attempt a second brood (Jenni and Kéry 2003).

Despite the difficulties of interpreting differing trends and species-specific variation, fall

migration is important to study as it may complicate birds’ abilities to adapt their life cycle so as to promote a successful breeding season in the following year.

In response to newly unsuitable conditions in portions of migrants’ wintering and breeding grounds, there may also be some flexibility relative to distribution shifts. Many terrestrial species are changing their ranges through latitudinal shifts, with the magnitude and rate of these changes being species-dependent (Chen et al. 2011, Hovick et al. 2016).

Ability to move provides these organisms with the opportunity to take advantage of previously uninhabitable areas by simply shifting the range northward. However, a caveat to this type of range shift includes consideration of ‘range compression’. While resident and partial migrant species in North America have been found to shift their range northward by expanding the northern leading edge and holding the southern edge constant, neotropical migrants may shift their range by holding their northern border constant and shifting their lower border northwards (Rushing et al. 2020). This introduces concern for habitat availability and reproductive success on the breeding grounds of long- distance migrants.

This Study

Based upon my personal interests and the topics covered above, I chose to investigate species-specific shifts in migration timing and breeding distribution for a range of bird species for my Honors thesis. In this study, I used large datasets collected through citizen science efforts and annual surveys. My species of interest are all members of a specific avian foraging guild, the aerial insectivores, that primarily breed in the

eastern United States. By taking a guild-based approach, I can assess variation among individual species and make comparisons based on a shared feeding strategy.

Nineteen species are included in this analysis. These include three nightjar species

(Common Nighthawk, Eastern Whip-poor-will, and Chuck-will’s-widow), one swift species (Chimney Swift), five swallow species (Barn Swallow, Bank Swallow, Cliff

Swallow, Northern Rough-winged Swallow, and Purple Martin), and ten flycatcher species (Great Crested Flycatcher, Eastern Kingbird, Acadian Flycatcher, Alder

Flycatcher, Willow Flycatcher, Least Flycatcher, Olive-sided Flycatcher, Yellow-bellied

Flycatcher, Eastern Phoebe, and Eastern Wood-Pewee). These species share the common trait of foraging for insects while in flight. Unfortunately, they also share the reality of greater declines in the past few decades when compared to other North American bird species. Aerial insectivore populations have declined 32% from 1970 population levels

(Rosenberg et al. 2019). In fact, based upon the North American Breeding Bird Survey

2020 Analysis of Trends (Sauer et al. 2020), 15 of the 19 aerial insectivores included in this study have seen significant population declines from 1966 through 2019 (Table 1).

Table 1. Population trends of selected avian aerial insectivores from the North American Breeding Bird Survey (Sauer et al. 2020). Trend indices are reported for 1966-2019 as percentage change in yearly abundance index. The ‘Adjusted Trend’ accounts for the precision of each estimate and allows for a more accurate comparison among species (than ‘Raw Trend’). These indices are based upon the ‘# of Routes’ that have reported a given species at any point between 1966 and 2019. Finally, declines and increases are considered significant if the confidence interval (CI) does not contain zero. Note: No data were available for Eastern Whip-poor-will.

Adjusted # of Raw Species Trend Routes Trend 2.5%-97.5% CI Bank Swallow -3.3 1920 -3.7 (-5.1, -2.7) Chimney Swift -2.1 2628 -2.1 (-2.2, -1.9) Olive-sided Flycatcher -2 1371 -2.1 (-2.7, -1.6) Chuck-will's-widow -1.6 788 -1.6 (-1.9, -1.3) Common Nighthawk -1.2 2678 -1.2 (-1.6, -0.9) Eastern Wood-Pewee -1.1 2609 -1.1 (-1.2, -0.9) Least Flycatcher -1 1976 -1.1 (-1.4, -0.7) Eastern Kingbird -1 3586 -1 (-1.2, -0.8) Purple Martin -0.5 2443 -0.5 (-0.9, -0.2) Alder & Willow Flycatcher -0.5 2701 -0.5 (-1.0, -0.1) Barn Swallow -0.5 4474 -0.6 (-0.7, -0.4) Northern Rough-winged Swallow -0.2 3324 -0.2 (-0.6, 0.1) Great Crested Flycatcher -0.1 2737 -0.1 (-0.2, 0.0) Acadian Flycatcher -0.1 1352 -0.1 (-0.3, 0.1) Eastern Phoebe 0.4 2681 0.4 (0.1, 0.6) Cliff Swallow 0.6 3248 0.8 (-1.2, 1.4) Yellow-bellied Flycatcher 1.4 561 2.1 (0.3, 3.3) Eastern Whip-poor-will - - - -

For most species, declines are linked to a particular characteristic, such as their specific breeding range. However, when entire guilds see similar declines at the same time, it may indicate that a shared characteristic is in jeopardy or that they are facing similar stressors. This is likely the case for aerial insectivores as their declines became more pronounced in the 1980’s (Nebel et al. 2010, Smith et al. 2015). Many factors have likely contributed to steep population declines for these particular species, including

insect declines from pesticide use and habitat loss, loss of breeding and migration stopover habitat, and phenological changes of climatic conditions. In fact, significant insect declines appear to be occurring globally (Sánchez-Bayo and Wyckhuys 2019,

Wagner 2020, Wagner et al. 2021). Both pesticide- and climate-driven arthropod abundance shifts can be linked to reduced bird abundance (Hallmann et al. 2014, Lister and Garcia 2018). Further, feeding on flying insects means aerial insectivores are more susceptible to cold snaps in early spring as these can temporarily decrease the food supply. Given changes in insect abundance, the pressure to arrive when food is reliable creates greater selection for specific migration timing (English et al. 2017). Other factors driving aerial insectivore declines may include increased agriculture effects on wintering grounds, although there is limited evidence for this (Fraser et al. 2012). Additionally, aerial insectivore declines are not straightforward in terms of geographic coverage as individual species are likely affected differently by various factors depending on where they breed and where they winter. Further complicating this situation, individual stressors may have a greater impact at different times of the year given that certain life stages require different resources and experience different threats. For example, nestling, fledging, and post-fledging success may depend on different food sources and be at risk from different predators (Spiller and Dettmers 2019). Understanding the way these species’ ecological relationships and the habitats they live in are changing can help inform decisions related to their conservation and hopefully improved population trends.

The goal of this study is to assess changes in migration phenology and breeding distribution of the 19 selected aerial insectivores during the past 3 decades. Using eBird

data, I examined the rate of northward movement along the leading edge of spring migration and the rate of southward movement of the trailing edge of fall migration across the United States and Canada for each species in discrete decade chunks: 1988-

1990, 1998-2000, 2008-2010, and 2018-2020. In addition, I assessed the day of year of earliest arrival and latest departure of individuals of each species within this same span of years. Lastly, North American Breeding Bird Survey (BBS) data were used to quantify breeding range shifts across the United States and Canada from 1990-2019. To do this, I examined the significance of directional shifts in the center of abundance of each species in June (breeding period) for every year within this time frame. All of these analyses are focused on addressing the following three hypotheses:

Hypothesis 1: During the past three decades, the rate of species-specific northward movement of avian aerial insectivores to their breeding grounds during spring migration has increased, while the rate of southward movement in the fall has slowed.

Hypothesis 2: During the past three decades, the timing of species-specific spring arrival of migratory avian aerial insectivores in North America has advanced to earlier dates, while fall migration departure dates have gotten later.

Hypothesis 3: The centers of abundance for the breeding ranges of individual avian aerial insectivore species have shifted during the past three decades as a result of changes in climate and habitat.

For the first hypothesis, I predict faster rates of northern movement during spring.

This is because the birds may be leaving the wintering grounds at the same time they always have but as they get closer to their breeding grounds, they begin to pick up on

local weather conditions and advance their speed to match the phenology of the plant and insect species they rely on. This is supported in part by greater flexibility of migration rate in birds with longer migration distances (La Sorte and Fink 2017). In the fall, I expect to see decreased rates of southward movement as the birds may have less pressure to leave quickly before cold weather hits. For the second hypothesis, I predict earlier arrival dates as birds adapt to advancing spring green-ups. During fall, I expect to see later departure dates as this allows migrants to take advantage of the adequate temperatures for longer. Lastly, for the third hypothesis, I predict a northward shift in breeding ranges with no clear trends longitudinally as birds follow the latitudinal gradient of their preferred temperatures for breeding.

Methods

For this study, I focused on 19 avian aerial insectivore species that migrate to/from and breed in eastern and central North America (Table 2). These species share the same foraging approach – capturing and consuming flying insects – and have declined significantly since the 1970s (Rosenberg et al. 2019). With the following analyses, I investigated shifts in their migration timing and breeding ranges in hopes of providing insight into factors that might affect their population status. An additional aerial insectivore, the Tree Swallow (Tachycineta bicolor), was excluded from these analyses given that it has widespread wintering populations in North America, which makes their migration patterns much more challenging to unravel.

In the following sections, I outline methods used in the analysis of shifts in migration timing and breeding range for selected species during the past three decades.

For each analysis (i.e., migration timing or breeding range), I describe the source of the datasets used in these analyses, including information on their format and which data I ultimately used. Following that, I lay out the protocols I followed to analyze each dataset.

Migration Timing Analysis

Data Acquisition and Selection

Datasets containing all validated sightings (reviewed by eBird staff) for each species from 1988-2020 were retrieved from the eBird Basic Dataset (eBird 2021). Of the

19 aerial insectivore species included in this analysis (Table 2), two species required spatial and/or temporal data restrictions based upon their native ranges and frequency of occurrence. Bank and Barn Swallow populations are found on other continents besides

North America. Thus, to avoid any differences in migration timing resulting from variation among continental populations, only data from the United States and Canada were retrieved for these species.

With respect to data used in determining if the timing of spring arrival and fall departure for aerial insectivores, as well as their rates of movement, have shifted in recent years, I initially chose to use a decadal time increment, focusing on the years of 1990,

2000, 2010, and 2020. However, migration can be strongly affected by annual variation in weather patterns [e.g., relative to wind, precipitation (drier vs. wetter years), temperature (warmer vs. colder years)]. To account for this variability to some degree, I aggregated data from each selected year with data from the two preceding years (e.g., for

1990, I combined data from 1988, 1989, and 1990). These three-year time periods will be referred to as “decade chunks” moving forward. Further, grouping data into three-year chunks increases data available for analysis from earlier, pre-eBird years (before 2002) where there are drastically fewer eBird entries due to low levels of retroactive digitization of user sightings into the eBird database. Since that time, eBird users have been able to submit checklists using computers, and more recently, a very effective mobile phone app.

However, this lack of data in earlier years provides an immense obstacle for analysis of

change over time, which I attempt to further account for throughout my methods and address the consequences it creates in my discussion.

Date Conversions and Species Codes

To allow for simplified calculations and presentation, all calendar dates for individual observations were converted from month and day to day of year (DOY).

Numbers were assigned sequentially to each date, such that January 1st serves as DOY=1 and December 31st serves as DOY=366 (as one would have during a leap year). To convert DOY to calendar date, please see Table A1 in the Appendix.

To improve readability of graphs and text, species’ names were often reported using commonly used four-letter alpha codes (on Table 2). Alpha codes were determined by the Institute for Bird Populations using the most recent version of the American

Ornithological Society’s Checklist of North American Birds (Pyle and DeSante 2020).

Table 2. Four-letter Alpha codes and scientific names for the 19 species included in the analyses. Alpha Code English Name Scientific Name ACFL Acadian Flycatcher Empidonax virescens ALFL Alder Flycatcher Empidonax alnorum BANS Bank Swallow Riparia riparia BARS Barn Swallow Hirundo rustica CHSW Chimney Swift Chaetura pelagica CLSW Cliff Swallow Petrochelidon pyrrhonota CONI Common Nighthawk Chordeiles minor CWWI Chuck-will's-widow Antrostomus carolinensis EAKI Eastern Kingbird Tyrannus tyrannus EAPH Eastern Phoebe Sayornis phoebe EAWP Eastern Wood-Pewee Contopus virens EWPW Eastern Whip-poor-will Antrostomus vociferus GCFL Great Crested Flycatcher Myiarchus crinitus LEFL Least Flycatcher Empidonax minimus NRWS Northern Rough-winged Swallow Stelgidopteryx serripennis OSFL Olive-sided Flycatcher Contopus cooperi PUMA Purple Martin Progne subis WIFL Willow Flycatcher Empidonax traillii YBFL Yellow-bellied Flycatcher Empidonax flaviventris

Determining Date Range for Migration

To assess the rate of northward and southward movement in spring and fall using eBird data, I first had to determine when each species was migrating (both when it started and when it was done for both spring and fall). To do this, I created two exploratory graphs for each species to visually assess the abundance of sightings over time as well as the spatial distribution of these sightings.

As a consequence of eBird’s popularity, we were able to estimate when each migratory species starts spring migration into and finishes fall migration out of North

America. The count of lists containing a given species was calculated for each day of the

year and separated by decade chunk. A typical graph contained two peaks, one during spring migration and the other during fall migration, with a dip during the middle of the year when observer effort was low and birds were not singing as well as relatively low sightings during the early and late months when the birds were not present in North

America (example in Figure 1). The day at which the count of lists first began increasing was considered the start date of spring migration. The day at which the lists decreased to constant and relatively low numbers was considered the end of fall migration. This process was repeated for each species.

Figure 1. Number of Checklists by Day of Year for Eastern Wood-Pewee (EAWP) by Decade Chunk. Each point represents the number of checklists recorded containing EAWP on each DOY within each decade chunk. The arrow on the left designates the start of spring migration for EAWP. The arrow on the right designates the end day of fall migration.

To determine when each species finishes spring migration and starts fall migration, I assessed the movement of the most northerly portions of the breeding population. These individuals travel to the highest latitudes and stay for a period of time to breed before eventually leaving. To determine these measurements for each species, the latitude and day of year of each sighting were plotted against each other for all data from 1900 through 2020 (of which a vast majority of the data has come from the last 10 years). As a result of migratory behavior, these graphs all displayed a flattened curve

(example in Figure 2). The flattened top of this curve represents the northern edge of a species’ breeding range for the purpose of this study. The start date of consistent sightings at and below this northernmost latitude was considered the end of spring migration. The end of consistent sightings at and below this latitude was considered the start of fall migration.

Figure 2. Latitude of Sighting by Day of Year for Eastern Wood-Pewee. Each point represents an observation of one or more individuals of the given species. The y-axis displays degrees latitude, with 0 representing the equator. The x-axis displays DOY from 1 to 366. The arrow on the left designates the end of spring migration for birds that breed in the northernmost latitudes of the breeding range. The arrow on the right designates the beginning of fall migration for this same portion of the population.

Multiple species displayed irregular Latitude by DOY sighting graphs. They were members of the genus Empidonax (and two additional species, the Eastern Phoebe and

Olive-sided Flycatcher), which all look very similar to each other. Due to their visual similarity, it is challenging to properly identify these birds unless they are calling. As a

result of the decrease in call frequency later in the summer (following the end of breeding season), species in this group saw a substantive decrease in the number of lists reporting them. To account for this lack of information, the start of fall migration for each of these species was assigned the 240th day of year. This value provides a conservative measure relative to the other aerial insectivores in this study and allows for completion of the analyses with an estimation rather than literally interpreting the data to produce unrealistically early start dates for these species’ fall migrations.

Ultimately, by compiling the information from the two exploratory plots (Figures

1 and 2), date ranges for spring and fall migration were determined for each species to be used in later analysis (Figure 3).

PUMA 25 124 250 299 NRWS 40 129 230 314 CLSW 50 139 235 299 CWWI 60 124 240 299 EAKI 80 149 240 299 GCFL 80 134 275 299 CHSW 80 129 270 304 EAPH* 85 124 240 299 EWPW 85 124 250 274 CONI 95 154 235 289 OSFL* 100 139 240 289 BANS 100 154 220 274 BARS 100 129 245 289 ACFL* 105 149 240 299 EAWP 115 139 265 299 LEFL* 115 144 240 299 WIFL* 125 154 240 274 ALFL* 130 159 240 254 YBFL* 135 154 240 284 1 31 61 91 121 151 181 211 241 271 301 331 361 Day Of Year

Figure 3. Start and end dates for spring and fall migration for each species. Species are listed in order of earliest spring arrival through latest spring arrival. Day of year along the x axis extends from day 1 (January 1st) through day 366 (December 31st). The axis was labelled by units of 30 days as these intervals fall roughly at the beginning of each new month. Members of the genus Empidonax and their allies are indicated with an asterisk to denote the common start date, DOY = 240, for fall migration.

Rate of Northward and Southward Movement

To assess species-specific rates of northward movement during spring and southward movement during fall over time, I used the northernmost sightings across the date range (i.e., beginning and end; Figure 3) of both migration periods by decade chunk.

Within each date range, days were separated into five-day periods. Within these five-day periods for a given decade chunk, the observations with the 10 highest latitudes were recorded. This allowed me to determine how far north each species could be found in a five-day period during migration and to compare these latitudes among decade chunks. When graphing these five-day periods, the median day of each grouping was used regardless of actual day of year of each sighting. One goal of this overall approach was to account for the lack of eBird data available during earlier years and reduce the influence of lower effort during non-peak time periods.

The use of 10 sightings for each five-day period was intended to buffer this analysis against anomalous, unusually early sightings as some individuals may migrate much earlier than the rest of the population. Additionally, some individuals may

“overshoot” their breeding range and migrate farther north than expected. Including a greater number of sightings within the analysis reduces the effect of these possible outliers. Further, when selecting the 10 northernmost sightings for a given five-day period, any sightings reported with the same latitude and longitude (up to 2 decimal places) within the same year were excluded. This is because the sightings were likely made by a group or multiple groups of people recording the same bird, which could introduce the bias of unusually early or late individuals.

Once the northernmost sightings of a given species for each five-day period during the whole date range of spring migration were determined, the latitudes of these sightings were plotted versus the median day for each five-day period. Linear regression was used for each decade chunk to calculate the slope, y-intercept, and R2 value (to determine the strength of the association). I then tested whether the slope of the linear regression of the earliest decade chunk, 1988-1990, was significantly different from the slope of the linear regression for the most recent decade chunk, 2018-2020 for each species during spring and fall.

Early and Late Sightings

Using the eBird datasets, I selected the 10th sighting (early arrival in spring) and

10th to last sighting (late departure in fall) above the 35th latitude for each species in each decade chunk to represent arrival and departure dates for each species. When choosing the 10th sightings, I did not count multiple observations from the same latitude and longitude (up to 2 decimal places) within the same year to avoid re-counting the same bird. The 35th latitude was chosen as a representative latitude that falls above the wintering grounds of the study species, and therefore, should not have individuals present above it between the end of fall migration and start of spring migration. A map is provided in the Appendix to visualize where this 35th latitude falls across North America

(Figure A1).

The 10th earliest (for spring) and 10th to last sightings (for fall) were used to represent arrival and departure of each species rather than the first and last sightings

because the earliest and latest sightings are prone to irregularities in either bird behavior or observer identification ability. Individual birds may come earlier in the spring or stay later in the winter than the majority of the population. Some unusually early or late sightings could also result from misidentifications of similar species. Last, these sightings were chosen as opposed to using smaller (5th) or larger (15th) values in an attempt to decrease the negative effects of less data availability in the eBird datasets in earlier years.

Breeding Distribution Analysis

Data Acquisition

The 2020 release of the North American Breeding Bird survey (hereafter, BBS) was obtained from the BBS ScienceBase Site (Pardieck et al. 2020). These datasets include all bird observations collected using this survey method from 1966-2019, along with the geographic coordinates of each route and the species-specific codes used in the main dataset. BBS data are collected using the following protocol…during peak bird breeding times (i.e., June), trained volunteers drive a set 24.5 mile route, starting at 0.5 hour before sunrise. They stop every 0.5 mile (at a set location; for a total of 50 stops) and conduct a 3-minute point count where they identify, count, and record all birds seen and/or heard within a 0.25 mile radius of that set location. They complete this protocol at all stops until the route is completed.

Filtering Routes and Data Selection

The dataset was filtered to include only the years 1990-2019 so that it coincides with the same period as the Migration Timing Analyses. To ensure that any shifts in breeding ranges were not a result of changes in spatial coverage of the various routes over time, the data was further filtered to exclude routes that were not sampled consecutively each year from 1990-2019, with the exception of routes missing a single year within this timeframe (Curley et al. 2020). This is important because new BBS routes have been frequently added over time, particularly in the western United States and in the far north regions. Lastly, a filter was applied to retrieve only the records of the

19 species included in this analysis.

Estimating Species-specific Breeding Range

To assess if the breeding ranges of these 19 aerial insectivores have shifted from

1990 through 2019, I determined the center of abundance of their breeding distributions for each year. Species-specific centers of abundance (COA) were determined for each year by calculating the weighted mean of the latitudes and longitudes of the locations where a given species was detected. Following Curley et al. (2020), this was done by identifying each BBS route where a species was counted in a given year and multiplying its abundance along this route first by the latitude and then by the longitude of the starting point of the route. For each species, the sums of all weighted latitudes and all weighted longitudes across all routes were each divided by the total abundance of the species found on all routes within that year. This resulted in two values, a weighted latitude and a

weighted longitude, which when paired, provide a measure of the center of each species breeding distribution for a given year. This process was repeated for all years and species.

Analysis of shifts in breeding range for latitude and longitude

Linear regression was used to measure the relationship between weighted latitude and year, as well as weighted longitude and year. The resulting slopes of these graphs were used to assess shifts towards the north or south (based upon weighted latitude) and towards the east or west (based upon weighted longitude). This was repeated for each species. Note: I contacted Shannon Curley (from Curley et al. 2020) who used this type of analysis and she graciously allowed to me to use R code she wrote to filter the dataset, calculate COAs, and analyze these data.

Results

Species Breeding Groups

To improve visual clarity on figures, facilitate description of results, and allow qualitative comparisons among groups facing different environmental pressures, the 19 species were separated into three groups based upon similarity in breeding ranges determined from species-specific range maps on the All About Birds website (Cornell

2019). The first group is labeled “North”, which is made up of six species whose primarily northern breeding ranges include the boreal forest as a major component: Alder

Flycatcher, Bank Swallow, Least Flycatcher, Olive-sided Flycatcher, Willow Flycatcher, and Yellow-bellied Flycatcher. The second group is labeled “Broad”, which includes six species whose breeding range covers a large area across the United States and Canada or does not fit well into the other categories: Barn Swallow, Cliff Swallow, Common

Nighthawk, Eastern Kingbird, Eastern Phoebe, and Northern Rough-winged Swallow.

The last group is labeled “East”, which includes seven species that primarily breed in the eastern portion of the United States: Acadian Flycatcher, Chimney Swift, Chuck-will’s- widow, Eastern Whip-poor-will, Eastern Wood-Pewee, Great Crested Flycatcher, and

Purple Martin.

Migration Timing Analysis

Rate of Northward and Southward Movement

The rate of northward movement of the leading edge of spring migration and the rate of southward movement of the trailing edge of fall migration were estimated for each species using the slope of the linear regression for the ten most northern sightings in each five-day period across the date ranges of both migrations for each decade chunk. For examples of these plots, please see the Appendix (Figures A2 and A3).

In examining rates of northward movement during spring, there was no clear trend of increase or decrease in migration rate (degrees Lat./5 days) across species or breeding groups, with individual species seeing a range of slight or large increases or decreases across decades (Figure 4). Some shifts across decades were consistent (such as the Chimney Swift’s consistent decrease in rate) whereas others fluctuated between increase and decrease across decades (such as the Least Flycatcher). Still other species maintained a consistent rate across decades (e.g., Bank Swallow, Cliff Swallow). Spring migration rates northward showed the greatest variety in magnitude among species in the

North group, with the highest (Olive-sided Flycatcher: 0.84o Lat./5 days, 1988-1990) and lowest (Willow Flycatcher: 0.09o Lat./5 days, 2018-2020) rates of migration recorded in this grouping. The Broad and East species groupings showed similarity in the magnitude and directionality of rate of northward movement across decades. Finally, the East species grouping showed consistently slower migration rates during 2018-2020.

Using linear regression t-tests for differences in slopes, 13/19 species had significantly different slopes when comparing rates of northward movement (during spring) from 1988-1990 with those of 2018-2020 (Table 3). Of those species, eight

species had significantly faster northward rates during 1988-1990, while the remaining five had faster northward rates during 2018-2020 (Table 3).

In examining species-specific rates of southward movement during fall migration, there were no consistent trends across species, breeding groups, or decades (Figure 5).

Through visual inspection, there appears to be a trend towards a decrease (albeit fluctuating) in the rate of fall migration across species, seeing a net decrease in rate from

1988-1990 through 2018-2020 (with some fluctuation). Eastern Wood-Pewee saw the greatest decrease in rate of southward movement declining from 0.78o to 0.14o Lat./5 days from 1988-1990 through 2018-2020. Overall, there were no substantive differences among the different species breeding groups.

Using linear regression t-tests for differences in slopes, 13/19 species had significantly different slopes when comparing rates of southward movement (during fall) from 1988-1990 with those of 2018-2020 (Table 3). Of those species, 11 species had significantly slower southward movement rates during 2018-2020, while the remaining two species had slower southward rates during 1988-1990 (Table 3).

Table 3. Species-specific comparison of linear regression slopes for Spring and Fall from 1988-1990 and 2018-2020. When statistically significant, ‘Fastest Northward’ indicates decade chunk with largest positive slope, while ‘Fasted Southward’ indicates decade chunk with most negative slope. NA = slopes were not significantly different. Significance indicators: * = <0.05-0.01, ** = <0.01-0.0001, *** = <0.0001.

Spring Fastest Fall Fastest Species P-value Northward P-Value Southward Acadian Flycatcher 0.009** 1988-1990 0.121 NA Alder Flycatcher <0.0001*** 2018-2020 0.288 NA Bank Swallow 0.853 NA 0.238 NA Barn Swallow <0.0001*** 2018-2020 0.089 NA Chimney Swift <0.0001*** 1988-1990 0.0003** 1988-1990 Chuck-will's-widow 0.835 NA <0.0001*** 2018-2020 Cliff Swallow 0.14 NA <0.0001*** 1988-1990 Common Nighthawk 0.0005** 1988-1990 0.0058** 1988-1990 Eastern Kingbird <0.0001*** 1988-1990 0.0003** 1988-1990 Eastern Phoebe <0.0001*** 2018-2020 <0.0001*** 2018-2020 Eastern Whip-poor-will 0.051 NA 0.236 NA Eastern Wood-Pewee <0.0001*** 1988-1990 <0.0001*** 1988-1990 Great Crested Flycatcher <0.0001*** 1988-1990 0.588 NA Least Flycatcher <0.0001*** 2018-2020 0.0002** 1988-1990 Northern Rough-winged <0.0001*** 1988-1990 0.0002** 1988-1990 Swallow Olive-sided Flycatcher 0.25 NA <0.0001*** 1988-1990 Purple Martin 0.067 NA 0.032* 1988-1990 Willow Flycatcher <0.0001*** 1988-1990 <0.0001*** 1988-1990 Yellow-bellied Flycatcher 0.001** 2018-2020 0.002** 1988-1990

. Change in rate of northward movement over time separated by general breeding range. The slope of slope The range. overseparatedbreeding by general of northward ratetime . Change in movement Figure 4 Figure was during spring ofplotted eachmigration sightings northernspecies from the most the linearlines regression of northward rates faster indicate x-axis. Larger values decade chunks along the on the y-axis separated by of trends. They are represent directionality visually betweenused to lines Dotted the spring. points in movement betweenchunks. decade or presence of indicate continuity information do not

. Change in rate of southward movement over time separated by general breeding range. The slope of the linear of slope the linear The range. over separatedbreeding by general of time southward rate. Change in movement Figure 5 Figure the y-axis separated species on was ofplotted each during fall migration norther sightings the most regression from lines and CWWI data (NRWS 1998-2000 lack1988- to of excluded due were x-axis. Two points by decade chunk along the visually betweenused are to lines points the fall. Dotted in movement southward faster 1990). Larger values indicate decade between chunks. of orinformation presence continuity trends. Theyindicate do not of represent directionality

Early and Late Sightings

All bird species showed a consistent shift to earlier spring arrival dates (based upon the 10th earliest sighting above the 35th latitude) across decade chunks (Figure 6).

Eastern Phoebe, Barn Swallow, and Northern Rough-winged Swallow, all in the Broad breeding group, had sightings above the 35th latitude in January suggesting that some individuals are present year-round in North America (Figure 6). Excluding these three species, the average advancement of arrival date between 1988-1990 and 2018-2020 was

14 days. Eastern Wood-Pewee, Chuck-will’s-widow, and Purple Martin all saw a decrease of 20 or more days in DOY of spring arrival. The least change occurred in five flycatchers which each had less than 10 days of advancement: Acadian Flycatcher (8 days), Great Crested Flycatcher (8 days), Alder Flycatcher (7 days), Least Flycatcher (7 days), and Yellow-bellied Flycatcher (5 days). Interestingly, Cliff Swallow arrived earlier than Purple Martin, which was unexpected when compared to the date ranges of spring migration (Figure 3).

All bird species showed a consistent shift to later fall departure dates (based upon the 10th to last sighting above the 35th latitude) across decade chunks (Figure 7). Similar to spring, Eastern Phoebe, Barn Swallow, and Northern Rough-winged Swallow again showed values that suggest individuals are present above the 35th latitude during winter months (Figure 7). Excluding these species, the average change in fall departure date was

29 days. The species with the largest change in fall departure date were Least Flycatcher

(53 days), Chuck-will’s-widow (43 days), Eastern Whip-poor-will (42 days), and Cliff

Swallow (42 days). The least amount of change occurred in Chimney Swift (18 days),

Olive-sided Flycatcher (11 days), and Willow Flycatcher (11 days). Chuck-will’s-widow departed from above the 35th latitude before the other species in all decade chunks. Least

Flycatcher stayed later in 2018-2020 than most other species, suggesting it may be approaching year-round status.

Latitude across the 3 years listed. Lower points on the graph represent Lower on the graph listed. points years across Latitude the 3 th . Day of year of early arrival in each decade chunk separated by general breeding range. Points represent Points the range. breeding chunk separated by general decade each early in arrival . Day of of year sighting of each above species the 35 th Figure 6 Figure 10 of trends. They do not are represent directionality visually betweenused to lines points earlier Dotted of arrival. days chunks. decade between or presence ofindicate continuity information

Latitude across the 3 years listed. Higher points on the graph Higher indicate points listed. across Latitude the 3 years th . Day of year of late departure in each decade chunk separated by general breeding range. Points represents the Points range. breeding by general chunk separated decade each late departure in . Day of of year to last sighting of each above to sighting speciesthe 35 last th Figure 7 Figure 10 indicate not of trends. They do represent visually directionality used are to points later between days. lines Dotted betweenchunks. orinformation presence decade ofcontinuity

Breeding Distribution Analysis

Filtering Routes and Data Selection

Initially, the dataset contained records from 5,756 routes. After filtering to include only records from 1990-2019, the number of routes decreased to 5,203. Of these routes, only 1,183 were sampled consecutively throughout the 30-year timespan. Route coverage of the eastern U.S. appears fair, while the western U.S. has many large gaps in coverage, and areas above the contiguous U.S. show very limited coverage (Figure 8). This unevenness disproportionately affects the accuracy of results from species that primarily breed in scarcely surveyed areas.

Figure 8. Location of BBS routes consistently surveyed from 1990-2019. The latitude and longitude of the starting point for each of the 1,183 routes that met the criteria for inclusion is shown.

Latitudinal and Longitudinal Shifts

The weighted means of latitude and longitude of routes containing each breeding species were calculated for each year. Routes that did not contain the species were excluded. When combined, these weighted points show the shift in center of abundance over time relative to breeding (example for one species is in Appendix, Figure A4). In order to assess overall shifts, latitude and longitude must be analyzed separately. To determine the relationships between these variables and time, linear regressions were run for each species (examples for one species are in Appendix, Figures A5, A6).

For changes in the latitude of breeding range from 1990-2019, 14/19 species had significant latitudinal shifts (Table 4). Positive slopes indicate a northward shift and negative slopes indicate a southward shift. To better understand these changes and how general breeding range may be affected, latitudinal slopes were graphed in Figure 9.

Significant northward shifts occurred in 10 species as compared to four significant shifts towards the south during this time. All significant shifts in the North breeding group occurred to the north. The East breeding group saw all northward shifts except for one significant shift south. The remaining southward shifts occurred in the Broad breeding group, which had only one significant northward shift (Figure 9).

Table 4. The results of the linear regression analysis of the latitude of the center of abundance by year for each species. Slopes describe change in latitude over time (1990- 2019) with a positive slope indicating northward movement and a negative slope indicating southward movement. Significance indicators: * = <0.05-0.01, ** = <0.01- 0.0001, *** = <0.0001.

Species Slope (Degree Lat./year) Adjusted R2 P-value Acadian Flycatcher 0.00749 0.200 0.0077** Alder Flycatcher -0.01951 0.070 0.0845 Bank Swallow 0.03499 0.255 0.0026** Barn Swallow -0.02910 0.472 <0.0001*** Chimney Swift 0.03419 0.226 0.0047** Chuck-will's-widow 0.01469 0.170 0.0137* Cliff Swallow -0.08716 0.608 <0.0001*** Common Nighthawk 0.04766 0.395 0.0001** Eastern Kingbird -0.01029 0.064 0.0949 Eastern Phoebe -0.03236 0.610 <0.0001*** Eastern Whip-poor-will 0.01159 0.050 0.1238 Eastern Wood-Pewee 0.01570 0.527 <0.0001*** Great Crested Flycatcher -0.03259 0.532 <0.0001*** Least Flycatcher 0.01544 0.303 0.0010** Northern Rough-winged Swallow 0.00614 0.030 0.7079 Olive-sided Flycatcher -0.01710 0.075 0.0774 Purple Martin 0.03437 0.512 <0.0001*** Willow Flycatcher 0.03287 0.674 <0.0001*** Yellow-bellied Flycatcher 0.03266 0.139 0.0243*

Figure 9. Change in latitude of center of abundance over time. The slope of the linear regression lines for change in latitude of each species are graphed along the y-axis with the unit “degree latitude per year”. Positive changes in latitude represent northward shifts and negative changes represent southward shifts. Significance indicators: * = <0.05-0.01, ** = <0.01-0.0001, *** = <0.0001.

For changes in the longitude of breeding range from 1990-2019, 14/19 species had significant longitudinal shifts (Table 5). Positive slopes indicate an eastward shift and negative slopes indicate a westward shift. To better understand these changes and how general breeding range may be involved, longitudinal slopes were graphed in Figure 10.

Significant westward shifts occurred in nine species, and significant eastward shifts occurred in five species. The North group saw significant westward shifts in all but one species. The Broad group was mixed as three species shifted significantly west but two went to the east. The East group contained the three remaining significant eastward shifts with only one significant shift to the west (Figure 10).

Table 5. The results of the linear regression analysis of the longitude of the center of abundance by year for each species. Slopes describe change in longitude over time with a positive slope indicating eastward movement and a negative slope indicating westward movement. Asterisks denote level of significance.

Species Slope (Degree Lon./year) Adjusted R2 P-value Acadian Flycatcher -0.00597 0.007 0.2836 Alder Flycatcher 0.09075 0.076 0.0764 Bank Swallow -0.22966 0.325 0.0006*** Barn Swallow 0.02216 0.122 0.0333* Chimney Swift 0.06900 0.652 <0.0001*** Chuck-will's-widow -0.03862 0.284 0.0014** Cliff Swallow 0.17318 0.680 <0.0001*** Common Nighthawk -0.07931 0.457 <0.0001*** Eastern Kingbird -0.02264 0.104 0.0455* Eastern Phoebe -0.06125 0.350 0.0003*** Eastern Whip-poor-will 0.07116 0.153 0.0186* Eastern Wood-Pewee -0.00702 0.008 0.2754 Great Crested Flycatcher 0.00497 -0.089 0.3845 Least Flycatcher -0.08935 0.490 <0.0001*** Northern Rough-winged Swallow -0.05232 0.056 0.1104 Olive-sided Flycatcher -0.08972 0.355 0.0003*** Purple Martin 0.02593 0.112 0.0398* Willow Flycatcher -0.18186 0.634 <0.0001*** Yellow-bellied Flycatcher -0.23404 0.339 0.0004***

Figure 10. Change in Longitude of Center of Abundance over time. The slope of the linear regression lines for change in longitude of each species are graphed along the y- axis with the unit degree longitude per year. Positive changes in longitude represent eastern shifts and negative changes represent westward shifts. Asterisks denote significance.

When latitudinal and longitudinal shifts are combined, one can see the overall trends of the shift of species-specific centers of abundance from 1990-2019 (Figure 11).

Within the North group, four of the six species saw significant shifts northwest. The

Broad group showed greater variety among species with significant shifts spread to all quadrants except for the northeast. The East is less clear, with most latitudinal shifts occurring northwards and most longitudinal shifts occurring to the east, but only two species had significant northeast movement. Overall, the northwest quadrant included eight species with some level of significance whereas each of the remaining quadrants contained three species holding significance. The greatest magnitude of shifts occurred in

Bank Swallow and Yellow-bellied Flycatcher which both saw a 0.23 degree western shift per year in longitude. The greatest latitudinal shifts occurred in Common Nighthawk and

Cliff Swallow, the first with a 0.04 degree/year shift north and the latter with a 0.09 degree/year shift south.

0.10

0.08

0.06

0.04

0.02

0.00 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 -0.02

-0.04

-0.06

-0.08 Change in Latitude Change Latitude in (Degree Lat./year) -0.10 Change in Longitude (Degree Long./year)

Figure 11. Rate of change in centers of abundance from 1990-2019. The slopes of the linear regression lines for change in latitude and longitude for each species are plotted on the y and x-axes respectively. Note that the axes are scaled differently. Positive changes in latitude represent northern shifts and negative changes represent southward shifts. Positive changes in longitude represent eastward shifts and negative represent westward shifts. The symbols associated with each alpha code denote significance for latitude and/or longitude: (^) is significant change in latitude, (“) is significant change in longitude, and (*) is significant change in both. Colors indicate general breeding group: Red = North, Blue = Broad, and Green = East.

Discussion

The analyses of migration timing and breeding distribution conducted in this study provide insight into changes that have taken place for 19 aerial insectivore species across the last three decades. In the following sections, I discuss factors affecting my results as well as limitations of the data and potential directions for future research.

Ultimately, my hope is that further study on aerial insectivores will provide information that benefits their populations and helps address the challenges they continue to face.

Before discussing my hypotheses, I wanted to underscore an important assumption underlying these analyses. Clearly, the results can only be as reliable as the quality of data in datasets I analyzed. Below, I address issues associated with differences in effort across time. However, it is also important to note that data quality is ultimately determined by the accuracy of species identification efforts by people entering their observations in eBird or driving BBS routes each June. The aerial insectivores present a wide array of identification challenges that may affect the data. For example, the swallows, martins, and swifts are constantly on the wing and are rapid flyers, which can present challenges for observers. Further, some aerial insectivores in this study look very similar to each other and can be challenging and sometimes impossible to distinguish based upon plumage. These species include five flycatchers from the genus Empidonax

(Acadian, Alder, Least, Willow, and Yellow-bellied flycatchers), Eastern Wood-Pewee,

Eastern Phoebe, and Olive-sided Flycatcher. Due to their visual similarity, it is best to rely on their songs and calls to properly identify these birds. During spring migration and

breeding season, this is realistic. However, call frequency drops following breeding season later in the summer, making fall ids very difficult. Ultimately, we need to rely on the data vetting that eBird and BBS reviewers do to maintain data integrity.

Rate of Northward and Southward Movement

In Hypothesis 1, I predicted the rate of northward movement in the spring would be faster in 2018-2020 than in previous years, and that the rate of southward movement in the fall would be slower in 2018-2020 than in previous years. In this analysis, five species saw significantly faster spring migration rates in 2018-2020 when compared to

1988-1990, but eight had significantly slower rates during this time. This suggests that the factors affecting spring northward movement rates vary between species, representing diverse pressures for timing shifts. Of the species that increased their migration rates, three breed at more northerly latitudes, while two have a broad breeding range across the

United States. It is possible that birds breeding farther north have higher pressure to reach their breeding grounds and begin nesting as the window of adequate temperature conditions may be shorter. As noted above, multiple aerial insectivores saw a slower rate of northward movement during spring, which is contrary to my expectation that pressure exists to speed up spring migration rates in order to combat trophic asynchrony. It is possible that these species move northward more slowly if they experience inadequate conditions for migration with increased frequency of inclement weather during spring in recent years or decreased access to preferred food sources for high-efficiency migration at low-quality stopover sites (Roques et al. 2020).

During fall, the majority of aerial insectivores investigated in this study saw a slower rate of southward movement, as predicted in Hypothesis 1. Eleven species slowed their southerly movement rates from 1988-1990 to 2018-2020 as opposed to only two species which increased their pace during this same time interval. Slower rates may be driven by species taking advantage of increasingly extended periods of warm weather and food supply in temperate regions. However, it is also possible that these species may take longer to migrate if they are experiencing poor wind conditions for efficient southward movement at this time (La Sorte et al. 2019b).

For spring and fall, it is important to note the role that uneven data availability might play in affecting the results. eBird was first launched in 2002, which means that any sightings prior to that time must be retroactively entered from individual user records. While the data used for the 1988-1990 and 1998-2000 decade chunks is still accurate, it represents a much smaller portion of the available data (as seen in Figure 1).

Despite steps taken to account for the difference in volume of sightings, this disparity may have affected my results, particularly at the beginning of spring migration and the end of fall migration. For example, sightings in 1988-1990 were limited and often did not meet the demand of at least ten unique entries. Without more sightings, I was forced to include points in the 1988-1990 selection that were further south than typical in some five-day periods, where the same day range in 2018-2020 provided sightings well within the northern regions. There may well have been individuals of these species present at higher latitudes during these days in 1988-1990, but until those data are retroactively entered by users, there is no way of documenting this. This could have resulted in steeper

slopes during earlier decades, which when compared to less steep slopes resulting from greater coverage in more recent years, led to an apparent decrease of migration rate from

1988-1990 through 2018-2020. Ultimately, an increase in historical records is the best way to solve this problem, but it is possible that altering the date ranges of spring and fall migration that were assessed could also improve the amount of data available for these analyses. By only assessing five-day periods that provide more than ten sightings in both decade chunks, the potential bias towards a slowed rate could be better accounted for.

Early and Late Sightings

For Hypothesis 2, I predicted the spring arrival date of all species above the 35th latitude would advance over time while the fall departure date from above this same latitude would be delayed over time. Though no statistical analysis was completed for this hypothesis (primarily due to the difference in effort across decades), the visual assessment of 10th earliest and 10th latest sightings above the 35th latitude for all species showed consistently earlier arrival and later departure in 2018-2020 than in 1988-1990.

The difference in arrival date of all species that exhibit migratory behavior at this latitude

(excluding Barn Swallow, Eastern Phoebe, and Northern Rough-winged Swallow, as a small portion of their population may now winter above the 35th latitude) ranged from 5-

28 days of advancement. While there is almost certainly advanced arrival among these species, it is unlikely that this large of a difference is due to strictly biological causes.

Inflated changes in early arrival date may also be affected by fewer historical sightings available for the early decade chunks. Horton et al. (2019) found a 1.6 day per decade

advance in the earliest migrants crossing the Gulf of Mexico from 1995-2015, suggesting that realistic advancement values may be smaller in my study species as well.

Latest fall departure qualitatively followed my prediction associated with the second hypothesis as well. All species remained above the 35th latitude longer in more recent years than three decades ago. Excluding the species that showed year-round presence, the delay of fall departure ranged from 11-53 days across species from 1988-

1990 to 2018-2020. It is possible that some species attempt a second brood if conditions remain favorable for longer and this may provide motivation for individuals to remain at higher latitudes later in the year. However, the same issue of data availability remains.

Greater volume of sightings means it is more likely for records of late-staying birds to be provided in more recent years. One potential solution for this issue is provided by

Hurlbert and Liang (2012) who used the proportion of checklists containing the target species during the days of expected spring migration within a given year and fitted a logistic curve to their increase in the spring. Using the inflection point of this curve as the mean date of arrival for the whole population, they were able to see a 0.8 day advancement in arrival for every degree Celsius of warming. Applying a similar analysis to fall departure could provide more robust results documenting timing delays.

Ultimately, the early and late sighting analysis did provide some biologically interesting information. First, Chuck-will’s-widow was the earliest species to depart from the 35th latitude in all decade chunks. This likely reflects the southern nature of their breeding range, as individuals quickly pass the 35th latitude when it is time for them to head south. This raises questions for future projects about the latitudinal aspect of

departure and whether southern breeding birds are able to leave earlier if they arrived and began breeding sooner than other species as a consequence of migration distance. Next,

Barn Swallows, Eastern Phoebe, and Northern Rough-winged Swallows each showed a date of arrival in January and a date of departure in December in 2018-2020. However, most members of these populations did not arrive until at least the end of February and departed before the end of November in 1988-1990. It is possible that species may be establishing resident populations in parts of North America that offer adequate conditions year-round if climatic conditions continue to change. However, this would require individuals to overcome their innate behavior to complete their annual migration.

Latitudinal and Longitudinal Shifts

Following Hypothesis 3, I expected to see northward latitudinal shifts (Chen et al.

2011) and no trends in longitudinal movement when assessing changes in the center of abundance of the breeding range each study species from 1990-2019. Ultimately, the center of abundance of the breeding range for 10 species (of the 14 with significant latitudinal shifts) moved towards the north. These were mainly species breeding in the north and east regions of North America. It is likely that these birds are tracking ideal temperatures for breeding over time. However, four species showed significant shifts towards the south, suggesting temperature gradients may not be the only factors promoting shifts. Other factors including habitat loss and degradation, changes in food availability, and changes in individual breeding populations resulting from changes on

the wintering grounds could provide a better understanding of shifts in breeding range, including compression and expansion.

The longitudinal analysis showed an unexpected westward shift for nine species as well as an eastward shift for five species. This mirrors findings of Curley et al. (2020) who found 37.6% of short-distance migrants in their study shifted their breeding range westward. One potential environmental association the birds may be following in these moves is changes in precipitation. Fei et al. (2017) found tree ranges to be shifting significantly westward and connected these changes to emerging trends in moisture availability. Additionally, changes in land use over time could alter the availability of preferred breeding habitat for different species, driving populations to move away from lower quality and newly disturbed areas. Ultimately, range changes are highly species- dependent (Huang et al. 2017) and an assessment of the many factors a specific population may be facing is required to fully understand the shifts they exhibit.

It is important to note that the data provided by the North American Breeding

Bird Survey does not provide complete coverage across their entire breeding area or incorporate behavioral tendencies of all species. Consecutively sampled routes are not evenly distributed across North America (Figure 8). Higher density of routes in the east compared to western and northern areas could create a bias against species that breed outside of the well-sampled range (Sauer et al. 2017). The northerly breeding species are particularly susceptible to error in their analyses as these populations are represented by very few routes in Canada, particularly lacking in areas associated with the boreal forest, which have very limited roads/accessibility. A second issue to consider with BBS data is

the bias associated with sampling method. The set time and routes may lead to lower detection of species that are nocturnal, have specific preferred off-road habitats, nest away from disturbed areas like roads, or nest in colonies. Eastern Whip-poor-will are a strictly nocturnal and highly camouflaged species (Cornell 2019). It is unlikely that this species would be regularly detected using the BBS protocol, and this may contribute to the lack of population information (as seen in Table 1). Additionally, the routes a surveyor uses follow roads, which may not provide an accurate representation of all habitat types present in the surrounding area, leading to further bias (Veech 2017).

Conclusion

Evidence for alterations to the rate and timing of migrations as well as shifts in the breeding distribution by these declining aerial insectivores provides hope that these species may be able to adapt to changes in environmental conditions driven by climate change. Further research should focus on linking apparent shifts in migration and breeding to the specific factors and risks that each population may be experiencing so as to help focus conservation and remediation efforts. Additionally, studies that provide insight into the rates of movement and arrival/departure dates of individuals breeding at lower latitudes throughout these species’ ranges could provide more thorough understanding of responses within the entire population. The broad spatial and temporal coverage provided by eBird and BBS data can offer insight into species-specific patterns on a never-before-seen scale. However, the information provided by these analyses require careful consideration of the potential biases underlying the data themselves (e.g.,

uneven sampling effort across space and time). Ultimately, the growing number of studies on the impact of climate change on birds as well as the increased emphasis on collaboration among scientists, wildlife managers, policy makers, and conservation organizations gives me a sense of hope for the continued persistence of these amazing organisms and their environments.

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Appendix

Table A1. Convert between day of year and month and day chart. This table lists dates along the left side and months across the top. Values within the center of the chart provide which day of the year each date represents. DOY’s are provided for a leap year, which are accurate for 1988, 2000, and 2020. DOY’s are one value lower (starting after February 28th) in the remaining years analyzed.

Figure A1. Map of North America with key latitudes. This map displays approximate location of latitudes along the U.S. to aid in interpretation of the results of the spatial analyses throughout this study. The 35th Latitude is highlighted as this was the arbitrary value chosen to complete the Early and Late sightings analyses.

CONI Rate of Northward Movement

30 Latitude

0 95 105 115 125 135 145 Median Day 2018-2020 2008-2010 1998-2000 1988-1990 Figure A2. An example of the plots used in rate of northward movement analyses. This graph of Common Nighthawk Sightings shows the 10 most northerly sightings that occurred in each five-day period between the 95th and the 149th day of the year in each decade chunk. “Median Day” refers to the middle day in each five-day period.

CONI Rate of Southward Movement 70

Latitude 30

0 235 245 255 265 275 285 Median Day 2018-2020 2008-2010 1998-2000 1988-1990

Figure A3. An example of the plots used in rate of southward movement analyses. This graph of Common Nighthawk Sightings shows the 10 most northerly sightings that occurred in each five-day period between the 235th and the 289th day of the year in each decade chunk. “Median Day” refers to the middle day in each five-day period.

CONI Center of Abundance 1990-2019 38 2018 37.5 2019 2016 2015 37 2008 2013 2012 1990 20172004 2014 36.5 2006 19971996

Latitude 1999 2010 2009 1991 36 2007 20112005 199219931998 2003 1994 35.5 20021995 2000 2001 35 -103.5 -103 -102.5 -102 -101.5 -101 -100.5 -100 -99.5 -99 Longitude

Figure A4. An example plot of the change in center of abundance for one species. The year each center was calculated from is used as the marker for it’s location.

CONI Mean Latitude 38

37.5

36.5

Latitude 36

35.5

35 1990 1995 2000 2005 2010 2015 2020 Year

Figure A5. An example of the plots used to calculate change in latitude of the center of abundance on a species’ breeding range over time. The weighted latitude calculated in each year was plotted and a linear regression line was applied. The slopes, R2, and P- value for each species are provided in Table A2. A positive slope indicates a northward shift over time.

CONI Mean Longitude -99 -99.5 -100 -100.5 -101 -101.5

Longitude -102 -102.5 -103 -103.5 1990 1995 2000 2005 2010 2015 2020 Year

Figure A6. An example of the plots used to calculate change in longitude of the center of abundance on a species’ breeding range over time. The weighted longitude calculated in each year was plotted and a linear regression line was applied. The slopes, R2, and P- value for each species are provided in Table A3. A negative slope indicates a westward shift.