Deviation Factors in the Mississippi Flyway: Geographic Barriers and Ecological Quality

DEVIATION FACTORS IN THE MISSISSIPPI FLYWAY: GEOGRAPHIC BARRIERS AND ECOLOGICAL QUALITY

Ian A. Anderson

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

December 2020

Committee:

Verner P. Bingman, Advisor

Kevin Neves

Daniel D. Wiegmann

Ian Alfred Anderson

ABSTRACT

Verner P. Bingman, Advisor

Migrating passerines (Passeriformes) can travel thousands of kilometers to reach their summer and winter grounds, and while migrating, they encounter environmental barriers like mountains, deserts, and oceans, which force birds to decide whether to continue in the typical migratory direction or deviate around the barrier. Hundreds of species of migrants utilize the

Mississippi Flyway, which may involve encountering the Great Lakes including the southwestern coast of Lake Erie. Gesicki et al. (2019) found that during Spring migration many migrants deviated westwards along the southern coast of Lake Erie instead of crossing the lake along their same heading. The goal of the current study was to determine whether migrants arriving at the Ohio coast of Lake Erie in the Fall, after crossing the lake, would similarly respond to the coastal features of Lake Erie’s Ohio coastline as they do in Spring. Specifically, would migrants display deviated flight directions with respect to the coastline at three observation sites as well as compared to the broad front direction of migration recorded by

Doppler weather radar in Cleveland? This was determined by comparing individual flight directions recorded from three sites, Cedar Point, Ottawa, and Maumee Bay, as well as the nightly, broad front direction recorded at Cleveland. Across a number of analyses, no meaningful differences in migratory flight directions were observed across the three observation sites nor with respect to the broad front direction recorded by Doppler weather radar. Generally, migrants flew in a south-southwesterly direction irrespective of location. As a separate analysis, no differences were found in the flight directions of migrants when birds observed early in the iv night were compared against birds observed later in the night. In summary, and in contrast to the

Spring (Gesicki et al, 2019), migrant songbirds reaching the southern coast of Lake Erie in Fall do not appear to respond to coastline features nor do they deviate from the broad front migratory direction. Collectively, the Spring and Fall data suggest that migrating birds are active decision makers, choosing to deviate when approaching an obstacle (Ohio-Lake Erie in the Spring), but in the absence of any obvious benefit to deviate (Ohio-Lake Erie in the Fall), they do not respond to the same topographical features so no significant differences from the broad front were found.

This manuscript is dedicated to my partner Maren and my family for their unwavering support.

Additionally, immense thanks to Black Swamp Bird Observatory for putting up with an

overexcited seven-year-old who just wanted to hold birds; thank you for opening me up to the

wonders of the natural world.

ACKNOWLEDGEMENTS

I would like to express my immense thanks to Dr. Verner Bingman for taking in a graduate student switching to a new program and showing immense patience with me to help me expand my limited knowledge of migration sciences. Additionally, I would not have made it this far without the rest of my committee members, Dr. Dan Wiegmann for always being available for help and questions, Dr. Kevin Neves for being a pillar of support, and Dr. Andrew Gregory for his incredibly technical lens on how ecosystems fit together. Thanks for all my fellow graduate students for helping me through these difficult times but most especially to my partner

Maren, and my family for always being there for me.

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TABLE OF CONTENTS

Page

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

METHODS………………………………………………………………………….……… 6

Data Collection…………………………………………………………….……….. 6

Weather Surveillance Radar Data……………………………………………….…... 9

Data Analysis………………………………………………………………………... 9

RESULTS…………………………………………………………………………………… 11

Individual Bearings……….……………………………………………………….… 11

Nightly Mean Directions and Comparison to the Direction

of Broad Front Movement…………………………………………………………… 12

Influence of Time of Night………..…………………………………………………. 15

DISCUSSION……………………………………………………………………...………… 16

REFERENCES………………………………………………………………………………. 25

viii

LIST OF FIGURES

Figure Page

1 Total observations for each site in the observed years………………………………. 6

2 Preferred axis of migration for all observed migrants comparing

the early and late time periods of each year and measures of accuracy

for each dataset………………………………………………………………………. 13

3 Mean preferred axis of migration for each night recorded and measures

of accuracy for each dataset …………………………………………………………. 14

4 Preferred axis of migration for all observed migrants and for the Doppler

broad axis for KCLE………………………………………………………………... 17

INTRODUCTION

Birds migrate based on seasonal availability of resources and their migration paths are

called flyways. The Mississippi Flyway is one of the largest in North America and has 325

migratory species that utilize it (Howard 2019) out of the 993 species of the continent (Gauger

2019). Migrants may travel potentially thousands of kilometers (Battley et al 2000) before

reaching the Mississippi Flyway as they migrate from South America to Canada, and during this journey they have to make decisions on how to approach obstacles they face.

Many obstacles, like oceans or deserts, represent danger to migrants due to their lack of places to stop, rest, or feed (Biebach 1990). These obstacles represent a choice to each individual migrant: do they fly around it or go straight through? Migrants are forced to make a cost-benefit decision as one path is faster but more dangerous while the other is slower but safer. When travelling directly, migrants face higher mortality risk due to not being able to find refuge in

inclement weather and a lack of available food sources (Lowery 1945). However, overcoming

these obstacles allows for migrants to reach breeding grounds earlier than their cautious

counterparts, resulting in fitness benefits for the risk-takers (Velmala et al 2015). Migrating

earlier in the Fall allows for first year migrants to begin their gonadal maturation rate sooner

which allows for easier nutrient acquisition before the Spring migration (Wingfield, 1990).

The cost-benefit challenges described above are observed in the migration paths of many

bird species in the world. Migrating Brent Geese (Branta bernicla) detour from the northern

coast of Russia towards Scandinavia and then other regions of northern Europe, rather than simply flying a straight path over the Kara and Barents Sea which would be a shorter trip

(Alerstam 2001). Red-backed shrikes (Lanius collurio) migrate from southeastern France to

Tanzania, by flying along the eastern Mediterranean coast to avoid crossing the potentially 2

dangerous Sahara (Zink 1987, as cited in Alerstam 2001). Both of these migrant species

seemingly have made the active decision to not take the shortest path to their destination in favor

of their safety. Most migrants do respond to obstacles, but the responses differ based on the type

of barrier (desert, water, etc.), the type of bird flight (soaring or flapping migration), and the

energy reserves of the migrant (Guglielmo 2018).

When migrating over large bodies of water, such as the Gulf of Mexico or the Great

Lakes, birds can drift from their preferred path due to lack of topographical features with which

to reorient themselves (Alerstam and Petersson 1977). When small songbirds drift over water,

there is an increase in the energetic expenditure of migration, which may reduce migrants’ ability

to adapt to future challenges (Nilsson 2019). Passerines often migrate along a coastline and later reorient before flying to suitable stopover areas as a method to compensate for drift; they sometimes even reverse their normal flight path to do so (Alerstam 1978).

Deviations along ocean coastlines are widely observed, like the Mediterranean coasts of

Spain and France in Bruderer and Liecthi (1998), but how inland bodies of water may affect migrants is much less researched. The Great Lakes are near the start of the Mississippi Flyway during Fall migration, and nightly migrant traffic was found to be decreased over Lake Erie in the Spring when compared to the land migration around it (Diehl et al., 2003) suggesting that migrants have a reason to not journey over this barrier. Increased mortality risk due to inclement weather, storms, and flight inefficiency due to drift can all be possible explanations for migrants choosing not to migrate over the Great Lakes (Diehl et al 2014, Newton 2007, Nilsson 2019).

Migrants can take different paths when approaching the obstacle of Lake Erie, and

Gesicki et al. (2019) found that during Spring migration, as many as 62% of birds deviated along the coast of Lake Erie instead of immediately crossing. The findings of the study suggested that 3

migrants chose to cross the narrowest width of the lake and over islands present. While Gesicki et al. (2019) looked at these migration trends in the Spring, using the archived data, similar questions will be examined in the context Fall migration. Migrants usually rest before crossing large barriers (Deppe, 2015) like Lake Erie and Fall birds would have already crossed this barrier. Migrants’ energetic states are fundamentally different when reaching the southern coast of the lake depending on the season as Spring and Fall migrants travel drastically different distances before reaching this obstacle. During Spring migrations, birds have few notable obstacles in their path after they cross the Gulf of Mexico until they reach the Great Lakes.

However, during Fall migration many birds start in Canada not too far from the Great Lakes and can cross the Great Lakes before having to rest.

Passerines (Passeriformes) are a family of songbirds that are charismatic and well- studied, but as these passerines are too small for satellite radio trackers, their migration patterns have to be tracked with Doppler radar data. On clear nights, these weather radar stations pick up the average directions of millions of birds migrating by, and by combining them with FLIR

(Forward Looking Infrared) camera systems, one can compare the average Doppler generated directions against what is directly observed with infrared cameras at chosen study sites. The differences between camera data against broad front data can be called deviations and inferences, and they can be used to inform ecological decisions. Knowing how and where deviations occur along Lake Erie could inform companies where to put wind turbines to avoid harm, as turbines in the US can kill 140,000 birds a year in small areas (Wang, 2015) dependent on its location. The survey site locations are relevant on Lake Erie because there have been plans for turbine development in southeast Lake Erie (McCarty, 2017) by LEEDco, which is not only in the path of the flyway but threatens areas of ecological quality in northwest Ohio as well. 4

Optimal Foraging Theory (OFT) dictates that organisms maximize net energy balance

during foraging (LaSorte 2014). Alerstam (2011) also states that OFT can be applied to

migratory birds as they seek to maximize energy balance of migration as OFT balances behavior

and optimization. In Northwest Ohio, nature reserves would have the best balance of protection

from predators and food availability, or energy for migrants, and the camera observations in this

study were taken in one of three nature reserves, Maumee Bay State Park, Ottawa National

Wildlife Refuge, and Cedar Point Wildlife Refuge. However, these natural areas are surrounded by majority agricultural land, meaning that deviations from the broad front would be choosing for places of ecological quality.

For the current study, the principal null hypothesis to be tested was that birds will not

deviate from the broad-front migratory direction during Fall migration upon reaching the

southern coast of Lake Erie. As crossing this lake barrier represents a significant energy

expenditure, birds who deviate from the broad-front path could need to refuel or rest on the

southern side. This would show up in the FLIR camera observations as deviations from the broad

front. Similarly, not all birds start their migration at the same point (Biebach, 1990), so different

birds will have variable energy reserves after crossing the lake, so even if some birds have the

energy to keep flying past Lake Erie, some could deviate on the southern coast of Lake Erie.

A second hypothesis was tested that time-of-night has no effect on observed flight deviation. Archived data for Fall migration was split into three different periods of night: early, middle, and late. Expectations are that birds would be more likely to rest after their nocturnal migrations (Alerstam and Lindstrom 1990) the later they reach the southern coast of Lake Erie.

Due to the lower state of migrants’ energy from flying later at night, it can result in higher rates 5 of deviation, as migrants’ motivation shifts from advancing migration progress to seeking out suitable stopover habitat.

METHODS

Data Collection

Nocturnal migratory flight directions of relatively low-flying birds along the southern

shore of Lake Erie were recorded at three sites in northwestern Ohio (Figure 1). The three sites on the Lake Erie coast were Maumee Bay at the University of Toledo Lake Erie Center, Oregon,

OH (41°41’18’’N 83°23’55’’W) for one season; Cedar Point National Wildlife Refuge Oregon,

OH (41°42’19’’N 83°20’09’’W) for three seasons; and Ottawa National Wildlife Refuge Oak

Harbor, OH (41°38’18’’N 83°12’54’’W) for two seasons. The three sites were selected to sample from the coastline oriented at different directions as all three have distinct coastlines.

Figure 1: The total observations for each site in the observed years. 2015 and 2016 were pooled due to not being significantly different from each other. 7

To detect nocturnal migrants and record their flight directions, thermal-imaging cameras

(FLIR SR-19, FLIR Systems, Oregon, USA) were used with a focal length of 19 mm, an opening

angle of 36° and a standard resolution array of 320(H) x 240(V) pixels recording at a rate of 20

frames per second. From comparisons with portable radar, it is known that the thermal camera

can detect and resolve characteristic wingbeat patterns of small-sized passerines up to 600 m above ground level under clear skies (see Mirzaei et al. 2015). However, because we could not precisely determine the heights of migrants in our study, we were unable to determine a detection probability function of the camera. An estimated survey volume up to 600 m above ground level of 0.023 km3 was calculated for the cameras. All nights sampled in this study took place under clear conditions to not limit detectability by the camera. Sampling was carried out as follows; in

Fall 2015 and 2016, 24 nights were sampled at Cedar Point and Ottawa using a balanced sampling design across nights, in a crossover sampling design (AB/BA) such that both sites were sampled every night. For example, on August 28th, 2015 sunset occurred at 20:14 and civil twilight ended at 20:44. Recording then began at the first site (A, in this case Cedar Point) at

21:17 and continued for 3 hours 52 minutes before traveling to the second site (B, in this case

Ottawa). Allowing for 30 min travel and prep, recording began at the second site (B) and continued for 3 hours 44 minutes until the start of morning civil twilight at 04:46. Note that on alternate nights the sampling would start at B and then transition to A. On any given night the amount of time sampled at each site was the same. During Fall 2017, 7 nights were sampled at

Maumee Bay and Cedar Point following the above sampling design in addition to a second infrared camera (the same FLIR SR-19), which recorded from civil twilight through the next

morning’s civil twilight on sampled nights at Bowling Green State University. What was

potentially revealing about Maumee Bay is the directional axis of its coastline, east-west, which 8 contrasts with the more northwest/southeast coastline axis at Cedar Point and Ottawa. Each night’s recording began at one site then moved to the other sampling until before dawn.

Nocturnal surveys began at civil twilight and ran through the next morning civil twilight which encompassed the entire period of nocturnal migration. Recording of the flight direction of detected migratory birds took place on nights with no rain and with clear skies to best detect targets by the camera. The camera was fixed on a tripod approximately one meter off the ground, oriented vertically and referenced to magnetic north using a handheld compass to facilitate the determination of target flight directions. Passing targets were observed on a laptop PC using

ISpy Version 6.4 (Developerinabox, 2014) noting the time of detection. Detection time was expressed as a percentage of the night beginning at 0% of the night, referring to the end of evening civil twilight, and ending at 100%, referring to the start of morning civil twilight.

Recorded targets were later reviewed, and flight directions were measured with reference to magnetic north. Targets that behaviorally displayed conspicuous wing-beat modulation, took several seconds to cross the field of view and flew a straight path were identified as migratory birds, which differed with respect to these behavioral indicators when compared to other moving targets such as insects and bats (Zehnder et al. 2001, Gauthreaux and Livingston 2006). We also recognize that there may have been some influence from non-passerine targets. To minimize recording data from non-passerine birds, many of which, e.g. shorebirds, fly too high to be detected by the cameras, and groups of more than one bird were excluded from the analyses to not mistake larger non-passerines for multiple small birds together. All observed flight directions were corrected and referenced to geographic north based on local declination values.

Weather Surveillance Radar Data

As a first-order assessment of the broad front direction of overland migrants approaching

Lake Erie, we used level-II weather surveillance radar (WSR) products from KCLE, Cleveland,

Ohio. WSR profiles were downloaded from the National Centers for Environmental Information

(NCEI) once each hour across the entire period of nocturnal surveys. A program in Matlab was

used (WSRLIB, written by Daniel Sheldon at The University of Massachusetts Amherst) to produce velocity azimuth display (VAD) charts from radar profiles (Sheldon et al. 2013,

Farnsworth et al. 2014). The VAD profile gives an average ground speed and track direction of

flight for migrants at different altitudes around the radar station. Little variation in flight

direction between the lowest six 100 m altitudinal bands was observed on the VAD profiles.

Therefore, the mean track direction corresponding to an altitude range of approximately 500-600

m above ground level was used as an estimate of a night’s broad front direction. As such, the

sampling altitude of the radar overlapped with the higher altitudes sampled by the infrared

camera. We chose to use the 500-600 m window because that is where the peak density of nocturnal migration moving through the Great Lakes has been observed (Archibald et al., 2016).

Further, a recent study utilizing WSR data from Cleveland and Buffalo found a similar seasonal direction of migration at 300 m and 600 m above ground level (Nations and Gordon 2017).

Therefore, it is reasonable to assume that any difference in the sampling height covered by the

WSR and the thermal camera could not explain the observed differences in directional tendencies.

Data Analysis

Standard circular statistics were used in the analysis of the direction data (Batschelet,

1981). Circular distributions capturing the flight directions of individual birds were tested against 10

uniformity using the Rayleigh test and all between-group comparisons were carried out using

Watson U2 and Watson-Williams tests. Only nights with greater than 8 birds detected at each site

were included in comparisons of nightly mean directions. An analysis of nightly mean directions

was carried out in addition to the analysis of individual flight directions to account for any

potential sampling bias caused by a few nights with a disproportionately larger amount of flight

directions recorded. To compare individual flight directions for a time of night effect on flight

direction, nights were divided into three periods in local time, early in the night 0900-0000,

middle 0000-0200, and late in the night 0200-0500, and comparisons were made between the

early and late periods.

Statistical analyses were conducted using program R package circular (R v3.2.1, R Core

Team 2015) and Oriana (v4.02, Kovach Computing Services 2013). Values are provided as

means ± 95% confidence intervals.

In addition to the above circular statistical analyses, an analysis was carried out on the

calculated percent of birds each night/site with flight directions that deviated from the broad

front direction. A flight direction was considered to deviate from the broad front direction if it

fell outside the 90-degree envelope around that night’s broad front mean direction derived from

Doppler radar. The 90-degree envelope was chosen because the largest confidence interval in

nightly mean directions observed at any radar site throughout the time of the study was ± 42

degrees.

RESULTS

Overall, 934 migrant flight directions recorded from 2015, 2016 and 2017 were included

in the analyses. For 2015 and 2016, 392 bearings (233 in 2015 and 159 in 2016) were recorded

from Cedar Point Wildlife Refuge and 350 bearings (231 in 2015 and 119 in 2016) were

recorded from Ottawa National Wildlife Refuge. For 2015 and 2016, the distribution of flight

bearings from Cedar Point and Ottawa were not significantly different from each other

respectively between the two years (Cedar Point F = 1.33, p = 0.27, Ottawa F = 0.71, p = 0.41),

so the bearings from those two years for each site were pooled for all subsequent analyses. For

2017, 92 migrant bearings were recorded from Cedar Point, and 100 bearings from Maumee

Bay.

Individual Bearings

For Cedar Point, the mean direction for the pooled flight directions from 2015-16 was

192 ± 6° (Figure 1, N = 392, mean vector length (r) = 0.53, Rayleigh test: p < 0.001). From

Ottawa, the mean direction was 200 ±13° (Figure 1, N = 350, r =0.59, P < 0.001). Despite the

visually modest difference in the distribution of Flight bearings, a significant difference was

found between Cedar Point and Ottawa (F = 4.05, P = 0.04). Overall, the majority of birds from

both sites moved in a south to south-southwesterly direction, but a more modest peak to

southeast could also be detected (Figure 1).

For 2017, the mean flight direction from Cedar Point was 185±9° (Figure 1, N = 92, r =

0.76, p < 0.001) and the mean flight direction from Maumee Bay was 191±7° (Figure 1, N = 100, r = 0.79, p < 0.001). No difference was found in the distribution of flight bearings between Cedar

Point and Maumee Bay (F = 0.99, p = 0.32). Similar to 2016-17, the majority of birds moved in south to south-southwesterly direction. 12

Nightly Mean Directions and Comparison to the Direction of Broad Front

Movement

The data presented in Figure 1 offer a comprehensive summary of all the recorded

bearings, revealing an overall tendency of migrants to move in a south or south southwesterly

direction from all sites across the three-year sample. The recorded individual bearing means, however, do not necessarily represent nightly mean tendencies as a distribution may be skewed by nights with a larger than normal number of migrants. Therefore, we carried out additional analyses looking at nightly mean directions for those nights when at least 8 migrants were recorded from a site (Figure 3). Overall, the distribution of nightly means directions resembled the directional trends of the individual bearing distributions (Figure 1). For 2015-16, the mean direction of nightly means from Cedar Point was 191±9° (Figure 3, N = 24, r = 0.93, p < 0.001) and from Ottawa 200±13° (Figure 2, N = 24, r = 0.82, p < 0.001). No significant difference was found in the nightly mean directions from Cedar Point and Ottawa (F = 0.93, p = 0.34). For

2017, the mean direction of nightly means from Cedar Point was 184±22° (Figure 3, N= 7, r =

0.92, P = 0.001) and from Maumee Bay 190±10° (Figure 3, N = 7, r = 0.97, p < 0.001). No significant difference was found in the nightly mean directions between Cedar Point and

Maumee Bay (F = 0.36, p = 0.56).

Figure 2: Preferred axis of migration for all observed migrants comparing the early and late time periods of each year and measures of accuracy for each dataset. 14

Figure 3: Mean preferred axis of migration for each night recorded and measures of accuracy for each dataset.

The more important value of the nightly mean direction distributions was as a basis to compare the local flight directions at the observation sites with the direction of the regional, broad front movement recorded by Doppler radar near Cleveland. In the pooled years of 2015-

2016, the mean direction of the distribution of nightly directions recorded at Cleveland near midnight was 211±21° (Figure 3, N = 24, r = 0.56, p < 0.001). Importantly, the distribution of mean directions from Cleveland Doppler did not differ from Cedar Point (F = 0.62, p = 0.43) nor

Ottawa (F = 0.12, p = 0.91) migrants. For 2017, the mean direction of the distribution of nightly directions recorded from Cleveland Doppler was 193±34° (Figure 3, N = 7, r = 0.69, p = 0.05).

Again, the distribution of nightly directions recorded from Cleveland Doppler did not differ from the mean nightly directions recorded from Cedar Point (F = 0.53, p = 0.48) nor Maumee Bay (F

= 0.14, p = 0.71) migrants. In summary, during Fall migration, the different directional alignments of the coastline and other topographic features at the three sample sites at the western 15

basin of Lake Erie seemed to have little influence on the directional orientation of passage

migrants.

Influence of Time of Night

For Cedar Point, and pooling the data over the three seasons, the mean direction of their

early flight bearings was 209±9° (Figure 2, N = 198, r = 0.60, p < 0.001) and for bearings late in

the night 205±15° (Figure 2, N = 134, r = 0.51, p = 0.001). No difference was found in the distributions of flight bearings (F = 1.24, p = 0.27). For Ottawa in 2016-17, the mean direction of their early flight bearings was 198±12° (N = 134, r = 0.51, p < 0.001) and for bearings late in the night 207±13° (N = 79, r = 0.60, p < 0.001). No difference was found in the distributions of flight bearings (F = 0.82, p = 0.37). For Maumee Bay in 2017, the mean direction of their early flight bearings was 182±11° (N = 33, r = 0.77, p < 0.001) and for bearings late in the night

199±23° (N = 14, r = 0.84, p < 0.001). No difference was found in the distributions of flight bearings (F = 1.87, p = 0.18).

DISCUSSION

The purpose of this study was to determine whether nocturnal migrants would react to the

Southwestern coastline upon reaching it for the Fall seasons of 2015, 2016, and 2017. This was determined by comparing the migrants’ tracks at three sites with different coastline orientations

and against the broad front direction of movement recorded from KCLE Cleveland Doppler data.

The Doppler surveys the orientation of thousands of nightly migrants while FLIR camera

systems at the three coastal sites track only what is visible in the camera fields. At all three

recording sites and the Cleveland Doppler observations, the general direction of movement was

to the south and south-southwest. There was little evidence in support of any differences of the

migrants’ directions in relation to the orientation of the coastlines or local topography. Having

said that, when examining the bearings of migrants recorded (Figure 1), there was a modest

difference in migrant tracks between Cedar Point and Ottawa against each other. This modest

difference, if not due to statistical artefact, was possibly due to the different shapes of the

coastline at each site, as birds can and do react to variable coastlines differently (Bruderer and

Liechti 1998).

The current study shows that there was no significant difference between the infrared

data at the sites surveyed and the doppler data for the years studied. This is interesting because

while the migrants show a distinct trend to react to the coastline during Spring migration

(Gesicki et al., 2019), migrants appear unresponsive to the coastline after having crossed the lake

during the Fall. This makes sense because in Spring the birds have a choice of whether to wait

for favorable weather or cross during risky conditions and reach breeding grounds faster. In the

Fall, the individuals have decided whether to cross or not one or two hours prior, and because the

migrants have already crossed the potentially hazardous barrier of Lake Erie, they are unlikely to 17 need to stop again on the southern coast to wait for more favorable conditions again before continuing. This is shown because there is little difference in the sampled sites on the coast and any other stopping place in the region, as the migrants maintained the same direction in

Southwestern Lake Erie as they did in Southeastern Lake Erie where the broad front direction is generated (Figure 4).

Figure 4: Preferred axis of migration for all observed migrants and for the Doppler broad axis for KCLE 18

Variation in migratory direction due to topography is well documented (Bingman et al.

1982, Liechti and Bruderer 1995, Rivera and Bruderer 1998, Fortin et al. 1999, Williams et al.

2001) but that does not appear to be the case for the Fall migration of Lake Erie. Each site sampled had a different topographical coastline shape (Figure 1; Cedar Point is a peninsula, the coastline at Ottawa is northwest -southeast oriented, and Maumee Bay is east-west oriented), but none of their average migratory tracks differed significantly, in a biologically meaningful way, among the three observation sites nor from the broad front movement of KCLE. Migrants display a routine ability to respond to topographical changes of the coastlines underneath them

(Bruderer and Liechti 1998), therefore, this study illustrates that whether migrants react to a coastline is situational and reflects some active decision-making process for costs and benefits.

This is notable because birds have been documented to stop their migration while facing an obstacle, despite having the flight capacity to continue (Alerstam 2001), which demonstrates that the decision-making process is not solely determined on energetic reserve remaining.

Transitioning from water to land is without any apparent cost and contrasts with a transition from land to water; as overwater crossings can result in weather-related mortality (Newton 2007) and loss of flight efficiency due to drifting from lack of geographic details (Alerstam 1990).

Migrants can react to the coastline if they have a reason to, and judging the results of this study, there was little adaptive value for the migrants to react to the different coastal topographies of the surveyed sites.

Overall, little difference was found in the flight directions of the migrants early during the night compared to those recorded later in the night closer to dawn nautical twilight. During the Fall, the flight orientation of the migrant birds reaching the southern shore of the western basin of Lake Erie seemed generally unaffected by the alignment of the coastline or differences 19 in local topography; a result that contrasts strikingly with what happens in the Spring when the birds are moving northward and approaching the coastline (Gesicki et al., 2019). However, we wanted to consider whether a response to the coastline by the Fall migrants might depend on when the birds arrive at the coast. Specifically, we hypothesized that because birds reaching the coastline later in the night were likely more energetically depleted and closer to landing, those late arrivals would be more influenced by local topography as birds may begin to seek out suitable stopover habitat.

Most passerines depart for migration within the first 2 hours after sunset, unless otherwise delayed due to inclement weather (Moore 1987, Åkesson et al. 1996, Sjoberg et al.

2017). Therefore, later periods of night should reflect a more severe state of energy depletion and perhaps a shift in motivation from advancing toward the migratory goal to seeking out appropriate stopover habitat. At night birds may make suboptimal migration choices after crossing a barrier (Moore 1990), and weather events can also force migrants to be grounded after crossing an obstacle; like described in Moore 1987 where birds were forced to stop on the

Northern coast of the Gulf of Mexico post crossing due to intense rainstorms. It was this reasoning that led to the prediction that flight bearings recorded later during the observation periods would differ from directions recorded earlier in the evening against the broad-front direction recorded near midnight. This trend was observed at the same sites during the Spring migration seasons (Gesicki et al., 2019) however the null hypotheses failed to be rejected on all accounts in the Falls as there was no significant difference between early and late arrivals on the coast. The results of this study show that birds travelling later at night do not react to the coastline more than their earlier counterparts, which would indicate that late migrants would have no benefit to stopping. As the late-night migrants are already free of overwater hazards and 20

associated mortalities, stopping here would increase their chance of local mortality by exposing

them to predation before they reach their breeding grounds (Moore, 1990). A migrant’s

likelihood (or adaptive value) to react to a coastline corresponds with overwater distance

remaining (Alerstam and Pettersson 1977; Alerstam 1978), and because this study surveyed at

the end of their overwater flight, it could explain why there may have been little to no adaptive

value to change course.

The present study clearly demonstrates that nocturnal passerine migrants do not show

noticeable responses to the topography of southwestern Lake Erie while travelling during the

Fall. Bruderer and Jenni (1990) found that fuel load of birds captured at high altitude crossing

the Alps was shown to be greater than the fuel load of conspecifics captured detouring a direct mountain crossing in the Swiss lowlands. Generalizing these observations to the current study, birds crossing over Lake Erie (these birds’ Alps) presumably fueled up on the Canadian side or already waited for favorable conditions, removing their need to refuel or react to the southern coastline of Lake Erie.

While birds could be energetically depleted by the time they reach the southern coast of

Lake Erie, this study was unable to find any evidence of this causing them to re-orient their migratory paths. Reasons for the migrants’ lack of changing flight direction despite lower energy reserves could be due the fitness benefit of reaching wintering grounds earlier in order to prepare better for the next breeding cycle. This is because special fat storage, gonad maturation, and portions of the molting progress can only occur in the wintering grounds (Gwinner, 1977) and stopping on the southern shore would minimize that gain by delaying the start. While breeding/summering grounds are arguably the most important part of a bird’s life cycle, birds must travel to their wintering grounds every year in order to ensure the ability to find seasonal 21 food, as their summering grounds will not hold enough food for all of them in the winter (Lack,

1968). All migrants compete for food in their wintering and summering grounds and reaching the wintering grounds sooner helps to set migrants up most optimally for food gathering to aid their wintering developments.

Across many regions, detailed radar and thermal imaging studies show migrants reacting to coastlines including those found along the Hudson River (Bingman et al. 1982) and the coasts of the Netherlands (Dokter et al. 2010), Nova Scotia (Richardson 1978, 1979), Sweden (Åkesson

1993), the Mediterranean (Bruderer and Liechti 1999; Fortin et al., 1999), and the Bay of Biscay

(Weisshaupt et al. 2016). Because this study showed no demonstrable reaction to the coastline, an atypical result, it becomes legitimate to ask whether the finding could be an artefact of something unrelated to navigation and decision making. Possible distractions or causes of this artefact could be the presence of artificial lighting from Detroit and Toledo. This could influence the migration heading of the western portion of their overall south-southwesterly direction pulling them to the lights (Van Doren et al., 2017; Watson et al. 2016). In Van Doren et al

(2017), many birds lost their course due to artificial lighting from New York City’s “Tribute in

Light Monument” as it disproportionately pulled migrants towards the monument. This resulted in an increased bird density around the structure by as much as twenty times of surrounding areas. While cities have bright light sources in both the Fall and Spring, one explanation as to why they could only be prevalent in Fall could be due to the paths of the birds over Lake Erie. In

Spring, the birds detour West through the coastline (Gesicki 2019), but this study did not find any trend of them utilizing the same coastline back. Since migrants do not utilize the reverse course of Gesicki (2019), the migrants could potentially be flying in a completely different path that brings them closer to the artificial lighting of Toledo and Detroit. The migrants still fly 22

through the same coastal sites, but without using the coastline east of the sample sites meaning

that their migratory path would be more westward closer to the lights of Toledo and Detroit.

Along the western coast of Lake Erie, the recording sites in this study were once part of the Great Black Swamp, but today only 5% of the original area of the swamp remains (Bookhout et al 1989) due to agricultural processes like ditching and tiling (Ewert, 2005). These coastal wetlands of Lake Erie, part of the Mississippi Flyway, see high concentrations of stopover migrants during both Spring and Fall migration (Shieldcastle 2004) and have been designated as an area of continental significance for the North American Waterfowl Management Plan

(NAWMP 2004). Because there remain large gaps in our understanding of bird migration

(Robinson et al. 2010), the current study could potentially inform the region regarding how birds utilize the different resources of the region, even if the migrants do not disproportionately stop in the nature reserves during Fall. Estimates are that 3 million waterfowl use these remnant swamps each year (Great Lakes Basin Commission 1975, NAWMP 2004) and while this study focuses on Passerines, they migrate together in the same period. Also, waterfowl, through hunting seasons and duck stamps, determine much of the wealth and conservation efforts that go into maintaining the nature reserves where two of the observation sites of the current study were located.

Acquisition of new technological knowledge has helped to improve understanding of avian migration ecology (Webster et al. 2002). Using programming models, the adaptive aspects of flight behavior and responses to environmental conditions can be used to assess the reactions of migrants. Shannon et al. (2002) and Shamoun-Baranes et al. (2003) applied simulations of atmospheric properties to the study of bird migration at both local and regional scales. These models were limited by the number of processes and the spatial extent of the model (Bowlin et 23

al. 2010). Better models can be developed from more detailed accounts of migratory behavior;

quantitative tools like R and MATLAB used in this study can help the overall comprehension of

migration and decision making. As many passerines are generally still too small for satellite

transmitters, migration studies that use radar instead of satellite trackers are building blocks to

understand the overall picture and future of this field of research until the technology can catch

and create trackers that are small enough for passerines to use without lowering their migration

capacity. Technology will advance in the future to being able to track individual passerine

migrants, but until then even the minor programs, scripts, technologies and packages used in this study, like circular statistics in R, wsrlib in MATLAB, are necessary and vital to better understand the region.

Demonstrating how the flight direction of migrants may respond to changing topography will play a role in predicting how migratory routes could change in response to threats of habitat loss and changing weather patterns. Bird migration must be understood in the context of the paths they do and do not utilize in order to understand what conservation resources need to be allocated where. Even though there were no changes in the direction of migratory movements to the coastal nature reserves in this study, migration ecology has to first be understood in order for other predictions to be tested. Conservationists must know the migration patterns of Passerines

(and all other migrants) in order to inform renewable energy companies, like LEEDCo, to insure smart placement of wind turbine fields in Lake Erie. Wind turbines nationally killed 573,000 birds or 134 to 230 thousand passerines in the US in 2012 (Smallwood 2013, Erickson et al.

2014). The entire field of aeroecology, or how organisms use the aerosphere, is new and still poorly understood (Kunz et al. 2008), making any study that helps fill in the gaps of migration 24

valuable. The migrants surveyed in this study reveal how the techniques and procedures used can be applied to other, understudied migratory pathways across the world.

REFERENCES

Able, K. P. (1974). Environmental influences on the orientation of free-flying nocturnal bird

migrants. Animal Behaviour 22:224–238.

Able, Kenneth P. The orientation of passerine nocturnal migrants following offshore drift. The

Auk, vol. 94, no. 2, 1977, pp. 320–330.

Alerstam, T., and S. Pettersson (1977). Why do migrating birds fly along coastlines? Journal of

Theoretical Biology 65:699–712.

Alerstam, T., and A. Lindstrom (1990). Optimal bird migration: The relative importance of time,

energy, and safety. In Bird Migration (E. Gwinner, Editor). Springer, Heidelberg,

Germany. pp. 331–351.

Alerstam, T. (2001). Detours in bird migration. Journal of Theoretical Biology 209:319–331.

Alerstam Thomas. Optimal bird migration revisited. Journal für Ornithologie Journal of

Ornithology, Springer Verlag, 2011, 152 (S1), pp.5-23. 10.1007/s10336-011-0694-1. hal-

00697238

Archibald, K. M., J. J. Buler, J. A. Smolinsky, and R. J. Smith (2016). Migrating birds reorient

toward land at dawn over the Great Lakes, USA. The Auk: Ornithological Advances 134:

193–201.

Aschwanden, Janine et al. Barrier effects of mountain ranges of broad front directions, Journal of

Ornithology, Deutsche Ornithologen-Gesellschaft 16 August 2019

Battley, P. F., Piersma, T., Dietz, M. W., Tang, S., Dekinga, A. & Hulsman, K. (2000).

Empirical evidence for differential organ reductions during trans-oceanic bird flight.

Proc. R. Soc. ¸ond. B 267, 191}195.

Batschelet, E. (1981). Circular Statistics in Biology. Academic Press, New York, NY, USA. 26

Biebach, H. (1990). Strategies of trans-Sahara migrants. In Bird Migration (E. Gwinner, Editor).

Springer, Heidelberg, Germany. pp. 352–367.

Bingman, Verner, P., Able, Kenneth, P., Kerlinger, Paul. Wind drift, compensation, and the use

of landmarks by nocturnal migrants, Animal Behavior, State University of New York,

Albany, 1982, issue 30, pages 49-53

Bingman, V. P., and P. Moore (2017). Properties of the atmosphere in assisting and hindering

animal navigation. In Aeroecology (P. Chilson, W. Frick, J. Kelly, and F. Liechti,

Editors). Springer, Cham, Switzerland. pp. 119–143.

Bookhout, T.A., K. E. Bednarik, and R. W. Kroll. 1989. The Great Lakes marshes. Pp. 131-156.

In Habitat management for migrating and wintering waterfowl in North America. (L. M.

Smith, R. L. Pederson, and R. M. Kaminski, eds.). Lubbock, TX: Texas Tech University

Press.

Both, Christiaan. Flexibility and timing of avian migration to climate change masked by

environmental constraints en route, Current Biology, Volume 20, Issue 3, 9 February

2010, Pages 243-248

Bruderer, B., & Jenni, L. (1990). Migration across the alps. Bird migration (pp. 60-77).

Heidelberg, Germany: Springer.

Bruderer, B., and F. Liechti (1998). Flight behaviour of nocturnally migrating birds in coastal

areas: crossing or coasting. Journal of Avian Biology 29:499–507.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63:215–244.

Cognitive phenotypes and the evolution of animal decisions. Trends in Ecology & Evolution

31:850–859. 27

Deppe, Jill, L., et al. 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 Nov 2015, 112 (46) E6331-

E6338; DOI: 10.1073/pnas.1503381112

Diehl, R. H., R. P. Larkin, and J. E. Black (2003). Radar observations

of bird migration over the Great Lakes. The Auk 120:278–290.

Diehl, R. H., J. M. Bates, D. E. Willard, and T. P. Gnoske (2014). Bird mortality during

nocturnal migration over Lake Michigan: A case study. The Wilson Journal of

Ornithology 126:19–29.

Dolbeer, Richard, A., Height distribution of birds as recorded by collisions with civil aircrafts,

United States Department of Agriculture, The Wildlife Society 13, December 2010

Ewert, D.N., G.J. Soulliere, R.D. Macleod, M.C. Shieldcastle, P.G. Rodewald, E. Fujimura, J.

Shieldcastle, and R.J. Gates. 2005. Migratory bird stopover site attributes in the western

Lake Erie basin. Final report to The George Gund Foundation.

Eastwood, E. (1967). Radar ornithology. London, UK, Methuen Publishing.

Falsterbo, south Sweden. Journal of Avian Biology 32:239–248.

Farnsworth, A., D. Sheldon, J. Geevarghese, J. Irvine, B. Van Doren, K. Webb, T. G. Dietterich,

and S. Kelling (2014). Reconstructing velocities of migrating birds from weather radar–A

case study in computational sustainability. AI Magazine 35:31–48.

"Flyways: Administrative". US Fish & Wildlife Service. https://www.audubon.org/mississippi-

flyway#

Finch, Deborah M.; Stangel, Peter W. (eds.). Status and management of neotropical migratory

birds: September 21-25, 1992, Estes Park, Colorado. Gen. Tech. Rep. RM-229. Fort 28

Collins, Colo.: Rocky Mountain Forest and Range Experiment Station, U.S. Dept. of

Agriculture, Forest Service: 32-38

Fisher, N. I. (1995). Statistical analysis of circular data. Cambridge University Press, Cambridge,

UK.

Fortin, D., Liechti, F., & Bruderer, B. (1999). Variation in the nocturnal flight behaviour of

migratory birds along the northwest coast of the Mediterranean Sea. Ibis, 141(3), 480-488.

Fronczak, David. Waterfowl harvest and population survey data, US Fish and Wildlife, Division

of Migratory Bird Management, Bloomington, MN, September 30, 2019

Gauger, Erik. How many birds are in North America?, Notes From the Road, September 16 2019

Gauthreaux, S. A. (1978). Importance of the daytime flights of nocturnal migrants: Redetermined

migration following displacement. In Animal Migration, Navigation, and Homing

Schmidt-Koenig, K., Keeton, W. T. Keeton. Springer, Heidelberg, Germany. pp. 219–227.

Gauthreaux, S. A., and J. W. Livingston (2006). Monitoring bird migration with a fixed-beam

radar and a thermal-imaging camera. Journal of Field Ornithology 77:319–328.

Gesicki, David, L., Erica, Cech, L., Bingman, Verner, P., Detoured flight direction responses

along the southwest coast of Lake Erie by night-migrating birds, The Auk, Ornithological

Advances, Volume XX, 2019, pp 1-14

C., and C. Gordon (2017). Assessment of nocturnal bird migration activity from weather radar

data for the proposed Icebreaker Wind Energy Facility, Lake Erie, Ohio. Lake

Energy Development Corporation, Cleveland, OH, USA.

Great Lakes Basin Commission. 1975. Great Lakes Basin framework study. Appendix 17.

Wildlife. U.S. Department Interior, Bureau. Sport Fisheries and Wildlife Ann Arbor, MI

140 pp. 29

Guglielmo, Christopher, G., Obese super athletes: fat fueled migration in birds and bats, Journal

of Experimental Biology, 2018, 221, jeb165753

Gwinner, Eberhard. Circannual rhythms in bird migration, Ann. Rev. Ecol. Syst. 1977, 8:381-

405

Howard, Ron, The Mississippi Flyway, Audobon 2019 https://www.audubon.org/mississippi-

flyway

Johnson, D. H. 1980. The comparison of usage and availability measurements for evaluating

resource preference. Ecology 61:65–71.

Jones, Jason, Habitat selection studies in avian ecology: a critical review, The Auk, Volume 118,

Issue 2, 1 April 2001, Pages 557–562, https://doi.org/10.1642/0004-

8038(2001)118[0557:HSSIAE]2.0.CO;2

Kelling (2013). Approximate Bayesian inference for reconstructing velocities of migrating birds from weather radar. In Proceedings of the Twenty-Seventh AAAI Conference on Artificial

Intelligence, July 2013. pp. 1334–1340.

Kovach Computing Services (2013). Oriana software, version 4.02, Anglesey, Wales.

http://www.kovcomp.co.ik/oriana/oribroc.Html

Lack, David, Bird migration and natural selection, Oikos, Vol 19, No. 1, 1968, pp 1-9

LaSorte, Frank, A., et al, The role of atmospheric conditions in the seasonal dynamics of North

American migration flyways, Journal of Biogeography, Volume 4, Issue 9, September

2014, Pages 1685-1696

McCarty, Larry F., Ohio air national guard drops wind turbine plans for Camp Perry; birding

groups declare victory, cleveland.com, Advance Local, June 29, 2017

McIntyre, N.E. Landscape Ecol (1995) 10: 85. https://doi.org/10.1007/BF00153826 30

Mcwilliams, Scott, R., Guglielmo, Christopher, Pierce, Barbara, Klaasen, Marsel, Flying, fasting,

and feeding in birds during migration: a nutritional and physiological ecology

perspective, August, 2004, Journal of Avian Biology 35(5):377-393

Mirzaei, G., M. M. Jamali, J. Ross, P. V. Gorsevski, and V. P. Bingman (2015). Data fusion of

acoustics, infrared, and marine radar for avian study. IEEE Sensors Journal 15:6625–

6632.

Mendelson, T. C., C. L. Fitzpatrick, M. E. Hauber, C. H. Pence, R. L. Rodriguez, R. J. Safran, C.

A. Stern, and J. R. Stevens (2016).

Mirzaei, G., M. M. Jamali, J. Ross, P. V. Gorsevski, and V. P. Bingman (2015). Data fusion of

acoustics, infrared, and marine radar for avian study. IEEE Sensors Journal 15:6625–

6632.

Moore, Frank, R., Kerlinger, Paul. Stopover and fat deposition by North American wood-

warblers (Parulidae) following Spring migration over the Gulf of Mexico, Oecologia

(Berlin), 1987, 74:47-54

Moore, Frank, R., Kerlinger, Paul, Simons, Ted, R., Stopover on a gulf coast barrier island by

Spring trans-gulf migrants, Wilson Bull, 102(3), 1990, pp 487-500

Journal of Avian Biology, Volume 35, Issue 5, September 2004, Pp 377-393

Newton, I. (2007). Weather‐related mass‐mortality events in migrants. Ibis, 149(3), 453-467.

Newton, I. (2008). The Migration Ecology of Birds. Academic Press, Cambridge, MA, USA

Nilsson, Cecilia et al. Revealing patterns of nocturnal migration using the European weather

radar network, Ecography, 42: 876–886, 2019 31

North American Waterfowl Management Plan. 2004. North American waterfowl management

plan: strengthening the biological foundation (Implementation Framework). U.S. DOI,

Fish and Wildlife Service and Environment Canada, Canadian Wildlife Service. 126 pp.

Pyke, G. H., Pulliam, H. R., Charnov, E. L., Optimal foraging: a selective review of theory and

tests, The Quarterly Review of Biology, Vol 52, No. 2, No. 2. June 1977, pp 137-154

Pyke, Graham, H., Optimal foraging theory: a critical review, Ann. Rev. Ecol. Sys. 1984,

15:523-75

R Core Team (2015). R: a language and environment for statistical computing. R Foundation for

Statistical Computing, Vienna, Austria. https://www.r-project.org/

Reale, D., S. M. Reader, D. Sol, P. T. McDougall, and N. J. Dingemanse (2007). Integrating

animal temperament within ecology and evolution. Biological Reviews 82:291–318.

Shieldcastle, J., Kashmer, T., & Shieldcastle, M. (2004). Passerine habitat use during spring and

fall migration. Black Swamp Bird Observatory, Oak Harbor, Ohio, USA.

Sheldon, D. R., Farnsworth, A., Irvine, J., Van Doren, B., Webb, K. F., Dietterich, T. G., & Kelling,

S. (2013). Approximate Bayesian inference for reconstructing velocities of migrating birds

from weather radar. Paper presented at the Aaai.

Stephens, David, W., Krebs, John, N., Foraging theory, monographs in behavior and ecology,

Princeton University Press, 1986

Van Doren, Benjamin, M. Et al, High intensity urban light installation dramatically alters

nocturnal migration behavior, Cornell Lab of Ornithology, 29 May 2017

Velmala, W., S. Helle, M. P. Ahola, M. Klaassen, E. Lehikoinen, K. Rainio, P. M. Sirkia, and T.

Laaksonen (2015). C. Ecology and Evolution 5:1205–1213. 32

Wang, Shifeng, Wang, Sicong, Smith, Pete. Ecological impacts of wind farms on birds:

questions, hypotheses and research needs, Renewable and Sustainable Energy Review, 4

January 2015, 599-607

Wingfield J.C., Schwabl H., Mattocks P.W. (1990) Endocrine mechanisms of migration. In:

Gwinner E. (eds) Bird Migration. Springer, Berlin, Heidelberg

Zar, J. H. (2010). Biostatistical Analysis, 5th edition. Prentice-Hall/ Pearson, Upper Saddle

River, NJ, USA.

Zehnder, S., S. Akesson, F. Liechti, and B. Bruderer (2001). Nocturnal autumn bird migration at

falsterbo, south sweden. Journal of Avian Biology, 32(3), 239-248

ZINK, G. (1987). Der Zug europäischer Singvögel. Band I. Wiesbaden: AULA-Verlag.