Assessing the Potential Avoidance of Wind Turbines by Migratory Birds Over Bowling Green, Ohio

ASSESSING THE POTENTIAL AVOIDANCE OF WIND TURBINES BY MIGRATORY BIRDS OVER BOWLING GREEN, OHIO

Michelle E. Zdawczyk

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 2012

Committee:

Verner Bingman, Advisor

Sheryl Coombs

Karen Root

ii ABSTRACT

Verner Bingman, Advisor

Migratory songbirds experience numerous physiological and navigational challenges while traveling for the purpose of resources and breeding. Ecological barriers, as well as human- made structures, can play a role in some of the costs associated with migration. Nocturnal flight patterns were observed using thermal imaging cameras at a small-scale wind farm and two nearby control sites. Both number of birds in flight and orientation were recorded. Analyses indicated significantly fewer birds recorded at the turbine site as well as significantly different orientation compared to the control sites. Evaluation of the results indicates that night-migrating birds, mostly songbirds, are capable of re-orienting when encountering turbines, at least under the conditions of the current study, and employ such techniques to avoid approaching turbines. iii ACKNOWLEDGMENTS

I would firstly like to thank my Graduate Advisor, Dr. Vern Bingman, for his patience and encouragement during this entire process. I would also like to thank my committee members, Dr. Karen Root and Dr. Sheryl Coombs, for their additional insight and help along the way. The bulk of the data collection would not have been possible without my research assistant, Adam Heist, and I owe many thanks to his dedication to the field work. I also owe many thanks to Jeremy Ross for his expertise with the cameras and to Andy Wickiser for his assistance with all of the equipment. Cordula Mora was a huge help with the figures, and I am grateful for her expertise. I would like to thank the Department of Energy for their financial support. The Coastal Ohio Wind project was supported by the US DOE Energy Efficiency &

Renewable Energy Award Number DE-FG36-06GO86096.

Lastly, I would like to thank my husband, Austin, and my parents, for their endless support during these last few years. iv

TABLE OF CONTENTS

Page

INTRODUCTION ...... 1

METHODS ...... 5

RESULTS ...... 10

DISCUSSION ...... 17

REFERENCES ...... 20

APPENDIX 1. MIGRATION ...... 30

APPENDIX 2. WIND POWER HISTORY ...... 32

APPENDIX 3. WIND TURBINES ...... 34

APPENDIX 4. AMP WIND FARM ...... 36

APPENDIX 5. SUMMARY OF OBSERVATIONS...... 37

APPENDIX 6. RADAR ...... 39 v

LIST OF FIGURES

Figure Page

1 Site Map ...... 23

2 Histograms Mean Number of Birds/Night ...... 24

3 Seasonal Orientation ...... 25

4 Orientation 27 September 2009 ...... 26

5 Orientation 13 October 2009 ...... 27

6 Orientation 14 May 2010 ...... 28

7 Orientation 25 September 2010 ...... 29

INTRODUCTION Migration occurs with seasonal regularity in many bird species. In particular, many

species migrate within and between the northern and southern hemispheres for the purpose of

breeding (Robinson et al., 2009) and to find the resources necessary for maintenance of energy

and survival. The benefits of migration must outweigh the physical expenditures and risks (such

as heightened predation and stress) that long-distance travel entails. On one hand, data show that

many bird species have the lowest level of survivorship during the migratory phase of their life cycle (Sillett and Holmes, 2002). On the other hand, the process of migration is repeated every spring and fall, year after year, as a clear indication that fitness depends on this movement (see

Appendix 1 for additional information on migration).

Migration for some bird species involves narrow belts of area that are established as traditional routes. These routes, called flyways, often follow natural land features. The

importance of an intact migratory pathway, or flyway, is critical to the success of many

migratory species (Tankersely, Jr. and Orvis, 2003). When possible, routes may be established

that take advantage of updrafts and other wind patterns or to avoid geographical barriers such as

large areas of open water (Newton, 2008). Ecological barriers are a large factor in the evolution

of migratory pathways, and detours that migrants must take to avoid these barriers can affect

pathways.

Ecological barriers (e.g. ocean, mountain range, glacier, desert) are often large in scale, span large distances and have no or few suitable stopover habitats (Henningsson and Alerstam,

2005). Traversing a barrier of this nature entails an extra energy and safety cost due to carrying heavy fuel loads (Henningsson and Alerstam, 2005). Circumventing the barrier has other costs, primarily in time and energy (Alerstam, 2001). Despite the energy costs for traversing a large

barrier, many birds are capable of making lengthy non-stop flights (Alerstam, 1990). However,

there are many cases where birds refrain from crossing barriers which are clearly within their

potential flight range capacity, as there are many factors affecting the costs and benefits of

alternative migration routes (Alerstam, 2001). In fact, in many cases, detours are economical up to a certain distance (Alerstam, 2001). Another component added to the complexity of migration is the use of nocturnal flight by many species. Species which migrate at night attempt to minimize predation and avoid overheating. As with migration in general, there are costs associated specifically with nocturnal migration, including a potential loss of sleep (Fuchs et al.,

2006), as well as limited visibility. The limited visibility at night is important to consider in the case of the potential impact of some barriers on migrating species.

Human-made structures (e.g. communication towers, tall buildings, wind turbines)

present a different type of barrier. In most cases, human-made structures do not span long distances, and are not large on the same scale as, for example, a mountain range. Human-made structures arise much more quickly than ecological barriers; a human-made structure can be constructed in the time between a bird’s going and returning trip on a migratory journey. As such, a human-made structure on the migratory route can require a bird to make unexpected, in- flight avoidance decision. Estimates of fatalities caused by human-made structures put wind turbines, the focus of the current paper, at a relatively low risk (Erickson et al., 2001); however, reliable estimates of avian population mortality are difficult to acquire (Manville, 2002). While the focus of impact on birds by human-made structures has been mortality associated with the structures, Alerstam (2001) has shown that birds are adept at using detours around ecological barriers. There is no reason to assume birds would not also be capable of using detours around barriers presented by human-made structures.

With the current need for sustainable energy, along with both political and environmental pressure, wind turbines are likely the fastest-growing and most well-known of current human- made structures (see Appendices 2 and 3 for additional information on wind power history and wind turbines). The United States is currently the world leader in the installed capacity of wind power (American Wind Energy Association, 2009), with many other countries currently operating and installing wind farms.

One of the earliest large wind farms (and perhaps most notorious) was built along the

California coast east of San Francisco Bay. The Altamont Pass wind farm is composed of over

4,900 small wind turbines installed in the 1970s, and is still the largest concentration of wind turbines in the world. The small type of wind turbine has proven to be very deadly to various raptors in the Altamont Pass area (Howell and Didonato 1991, Orloff and Flannery 1992, Orloff and Flannery 1996). Due to the issues at Altamont Pass, larger turbines were developed.

Raptor mortality has been negligible at all newer generation wind farms in the United

States (Erickson et. al, 2002). Based on these data, proponents of wind energy argue that the larger units, which are higher and move more slowly, have little effect on bird populations. In fact, the argument has been made that the number of birds killed by wind turbines is very minor compared to those that die as a result of other human activities (Sovacool, 2009). However, passerines have been the most common avian order (approximately 80% of all fatalities) killed at new generation wind farms (Erickson et al., 2001). It is estimated that about half of the passerine fatalities involve nocturnal migrants (Erickson et al., 2001).

There have been marked changes in population over the past few decades for many migratory species (Robinson et al., 2009). Proponents of the theory that human-made structures are responsible for increased mortality could argue that this has been brought about, in part, by a

change in human-made structures, such as wind farms, along migratory routes. However, research has shown that birds do appear to generally avoid the newer model wind turbines

(Erickson et al., 2002). The turbines are believed to move slowly enough that the birds have sufficient time to adjust, should they be in the flight path. While the issue of mortality may not be the large-scale problem it was once presumed to be, any avoidance or disruption to flight patterns could be a cause for concern. National bird organizations in various countries (such as the National Audubon Society) have taken the stance that wind power should be broadly supported, but caution36 against situating wind farms in areas especially important to birds and other wildlife.

An important wildlife area in Northwest Ohio is along the shores of Lake Erie

(approximately 48 kilometers from our study site). Coastlines are an important piece of the migratory pathway for birds as they often represent a stopover site. In fact, migrating birds routinely become concentrated at coastlines (Alerstam, 1978). Off-shore and near-shore turbine development is a quickly developing area of turbine installation (Garvin and Kempton, 2008), leading to the largest area of concern for the impact on migratory birds. There are currently no turbines along Lake Erie or at any northwest Ohio stopover site, and there is still a need to learn about the potential response of migratory birds. In this study we chose to study the potential impact of currently operating turbines at a small-scale wind farm on nocturnal flight behavior.

American Municipal Power (AMP) Wind Farm was Ohio’s first utility-scale wind farm (see

Appendix 4 for additional information on the AMP Wind Farm). The AMP Wind Farm has four horizontal-axis turbines, each 119 meters tall (with blades). The proximity of these turbines to

Bowling Green and consequent accessibility for observations were well suited to examine the possible influence of turbines on the behavior of night migrating birds.

METHODS

Field observations were conducted near Bowling Green, OH for three consecutive migratory seasons (Fall 2009, Spring 2010, and Fall 2010). Figure 1 displays the three observation sites used.

Fall 2009

Two SR-19 series infrared cameras (FLIRTM, Wilsonville, Oregon) were used in

conjunction with two operators. The FLIRTM SR-19 employs heat signals to render a thermal

image of the object in flight (i.e., bird, bat, or insect). The FLIRTM SR-19 requires no

supplemental illumination and can detect man-sized objects up to 321 meters away, per

specifications. The SR-19 series has a wide field of view (approximately 36°). In order to

confirm equal capability of the two cameras used, and the limits of detection, the cameras were

set up next to one another (view parallel to the ground) with both operators on a clear night in an

open field. A pigeon in an open cage was carried onto the field directly in front of the cameras

until it could no longer be detected. A surveyor’s wheel was used to measure the distance up to

which the pigeon could still be viewed. It was confirmed that both cameras could similarly

visualize the bird at the same distance of approximately 300 meters.

Utilizing a video capture box, Grab Show 110 (Further Tech. Co., Ltd., Taiwan), the

cameras were each connected to a computer with similar resolution and screen size and the

infrared images were visualized on the screen. In this way, actual recording was not necessary as

data could be collected in real time. Cameras were arranged so that the top of the field of view

on the computer screen was always aligned north. The camera position was verified using both a

compass (for direction) and a level (to confirm vertical positioning directly towards the sky).

One camera was placed underneath a single turbine at the AMP Wind Farm site (41°22’46”N -

83°44’16”W), at the closest proximity that did not capture the turbine blades in the field of

vision (approximately 25 meters away). The second camera was set up at a control site

approximately 1.6 kilometers to the northeast, at Plain Congregational Church, Poe Road (41°23'

12.3"N -83°42'32.0"W). Operators alternated nights at each site to exclude any systematic

observer bias. To assess inter-observer reliability, a one-night pilot study was conducted. Both

observers monitored images side-by-side at the control site for one night. Each observer

documented the number and direction of presumed birds (characteristics defined below) on his or

her respective screen. Each bird noted was described in terms of time and direction of travel. To

record direction, the computer screen image was described as a clock face (e.g. birds are moving

from 12 to 6 when traveling due south). Direction intervals were recorded in half-hour

increments (24 total), where each half hour was equivalent to 15 degrees. The numbers and

directions were compared and evaluated separately for inter-observer reliability. Analyses indicated the inter-observer reliability to be 95% for bird identification and 92% for

directionality (on average, less than a half hour or 15 degree difference, and no clockwise or

counter-clockwise bias was detected). Images were also evaluated for consistency in

distinguishing birds from other objects on the screen. Birds were presumed to be an in-flight

object that maintained a direct path across the field of vision, typically combined with the blur of

flapping wings. The direct path flight eliminated most bats, which typically fly erratically (e.g.

swooping) across the screen. Bats could also be identified by their proximity to the camera,

which made the body shape, including ears, often visible to the observer. Birds moved at a

relatively faster speed than planes on the screen due to the difference in altitude. The relative

speeds were originally confirmed by verifying visual confirmation of a plane in the sky when

observed on the screen. Insects were the most difficult to differentiate. In general, insects

appeared closer to the camera face than either bats or birds, and hence were the largest and

fastest of any in-flight object. During the observation sessions, images considered bats or insects

were not tallied.

Every attempt was made to observe nightly, weather permitting. Site observations occurred starting approximately one hour following sunset. The duration of observation was

determined by the number of birds seen (i.e. if no birds were seen within 30 minutes,

observations ceased). An attempt was made to observe until at least 10 birds were seen at each

site (provided any birds were observed). At the end of the season, the numbers and directions

were tallied. The flight directions recorded as movement across a clock-face were converted to a degree measure with 0 degrees being north (i.e., a bird flying from 12 to 6 would be converted to

180 degrees).

Spring 2010

Due to the possibility that the control site represented an atypical migratory-sampling site

for an unspecified reason, a second control site was added during the spring, 2010 season. The

second control site (Control 2) was chosen approximately 1.6 kilometers to the southwest, near

the Baldwin Woods Preserve (41°21' 30.6"N -84°13'59.4"W). The same method for observing,

data acquisition and alternating observers was utilized. The important difference in comparison

with Fall 2009 was that nightly observations alternated between the Control 1 and Control 2 sites

(i.e., observations occurred at only one control site along with the test site on any given night).

Fall 2010

During the fall 2010 migratory season, only the Control 1 site was utilized to replicate the first season of data collection. However, only one observer was available, and therefore, data were collected using a single operator. The single operator performed nightly observations using an A-B-A/B-A-B model (test-control-test/control-test-control). For example, on night one, observations were initiated at the turbine site one hour post-sunset. After 30 minutes of recording, the observations were moved to the control site. Following 60 minutes at the control site, the observations were again conducted at the turbine site for a final 30 minutes. The next observation night began at the control site and alternated as described. The same standards of observing and recording used during Fall 2009 were followed.

The nightly data summary for all seasons is presented in Appendix 5.

Statistical analyses

Paired t-tests were used to determine any difference in number of birds in flight observed at each site for the three seasons. Separate analyses were carried out for both the nights with at least five observations at one site, and for all nights where at least one bird was observed.

Circular statistics using Oriana software (Kovach Computing Services, Anglesey, Wales) were used to determine mean direction and mean vector length of the recorded birds, and theRayleigh test was used to test for uniformity for distributions of flight directions, for the nights when at least five observations were made at each site as well as the cumulative seasonal data. A

Watson’s U2 test was conducted to compare orientation between test and control sites on the selected nights and total season.

Radar Observations

To place our observations in the broader context of the general movement of migrants in the northwest Ohio/southeast Michigan region, radar images, when available, were collected on nights when observations were attempted. The pattern of the reflected images and the direction of movement can help distinguish migratory birds from other targets (Gauthreaux et al., 2003).

On nights when at least five birds were recorded at each observation site, we attempted to download radar images collected from the DTW (Detroit Metro Airport) radar (42°13’32.5”N -

83°20’52.8”W) via wunderground.com, which is part of the TDWR (Terminal Doppler Weather

Radar) system (see Appendix 6 for additional information on radar). This radar was selected due to the close proximity to the observation sites and its sensitivity to the airspace over western

Lake Erie. Every attempt was made to collect velocity images one hour post-sunset, at the approximate time observations were being conducted. All images were taken at the lowest angle setting of 0.5 degrees. Velocity was visually assessed to determine the average orientation for the overall airborne migration mass. Although only an approximation, regional directional tendencies were determined by evaluating the overall direction of the image captured on radar.

This assessment was performed by approximating the line between positive and negative velocity (objects moving towards and away from the radar, respectively) and then estimating the line perpendicular to it. The approximate direction of travel was the angle of the perpendicular line that corresponded to the transition boundary between negative and positive velocity (see

Figure 5c for a further explanation).

The images collected one hour post-sunset were compared to the direction of the infrared data (i.e., are local birds observed at the turbine and control sites moving in the same prevailing direction as migration in the region?).

RESULTS

Number of birds in flight

1. Fall 2009

The Fall 2009 season was conducted from 24 September 2009 through 29 October 2009.

During that time period there were 19 nights on which observations were attempted. There were observations recorded for 13 of those 19 nights (6 nights where observations were attempted with nothing recorded). Overall, considerably more birds were observed at the control site (127) than at the test site (32) (N (number of observation nights with at least one bird recorded at one site) = 13, t = 149, d.f. = 11, P = 0.0029). To control for statistical skew by including nights with very few birds observed, we also looked at those nights with at least 5 birds at one site.

Comparison indicated there was still a greater number of birds at the control site (mean 14.38,

SEM 3.47) than at the test site (mean 3.88, SEM 1.16) (N = 8, t = 146, d.f. = 6, P = 0.0047;

Figure 2). The data are consistent with the hypothesis that nocturnal migrants were avoiding the turbines during flight.

2. Spring 2010

The Fall 2009 season indicated a greater number of birds at the control site compared to the turbine site. To further assess the potential avoidance of the turbines, observations were conducted in a similar manner again in Spring 2010. However, in order to address the possibility that greater numbers of birds were observed at the Control 1 site for reasons unrelated to turbine avoidance, a second control site was added.

The Spring 2010 season was conducted from 26 April 2010 through 28 May 2010. During

that time period there were 18 nights on which observations were attempted. There were

observations recorded for 15 of those 18 nights. Overall, numbers for the Spring 2010 season

were much lower than Fall 2009. (This was not unexpected, as spring migration is generally

associated with fewer birds), but nonetheless, the Spring 2010 season was similar to Fall 2009

with respect to the relative number of birds observed at the test and control site. More birds were

observed at the two control sites (86; note only one control site was sampled on any given night)

than at the test site (43) (N (number of observation nights with at least one bird recorded at one

site) = 15, t = 129, d.f. = 13, P = 0.0005). For nights with at least five birds at one site, a greater

number was still seen at the control sites (68) as compared to the test site (35) (N = 7, t = 103,

d.f. = 5, P = 0.009). Comparing only the Control 1 site to the test site for nights with at least five

birds at one site showed a similar pattern. A greater number of birds were seen at the Control 1

site (mean 10.00, SEM 2.43) as compared to the test site (mean 4.80, SEM 2.07) (N = 5, t = 74,

d.f. = 3, P = 0.007; Figure 2). We were unable to carry out a statistical comparison between

Control 2 and the turbine site because there were only two nights with enough birds to make

comparisons. Nonetheless, consistent with Control 1, more birds were observed at Control 2

(mean 9.00) as compared to the test site (mean 5.50) on those two nights, and more birds were

seen at the control site on both nights. In conclusion, although the difference was not as large as

for Fall 2009, about twice as many birds were observed at the control sites compared to the

turbine site – a pattern that did not differ at the two control sites, suggesting that the origin of the difference was the turbines.

3. Fall 2010

The Fall 2010 season was conducted from 9 September 2010 through 30 October 2010.

During that time period there were 22 nights on which observations were attempted. There were

observations recorded for 12 of those 22 nights. Observations were conducted at the Control 1

site and the test site in the similar to Spring 2009. Due to the availability of only one observer,

however, data were collected using an A-B-A, B-A-B format. In general, recorded numbers were

very low for the Fall 2010 season due to a large amount of cloudy/rainy weather. However,

nights when observations were obtained showed results consistent with the previous two seasons.

More birds were observed at the control site (55) than the test site (11) (N (number of

observation nights with at least one bird recorded at one site) = 12, t = 66, d.f. = 10, P = 0.035).

For nights with at least 5 birds at one site, there was still a greater number of birds seen at the control site (mean 11.67, SEM 5.70) than the test site (mean 1.67, SEM 0.67); however, the statistical analysis was insignificant because of the small number of nights with 5 birds (N = 3, t

= 42, d.f. = 1, P = 0.22).

4. Summary

Overall, a greater number of birds were consistently seen at the control sites (268 total) as compared to the test site (86). This was true in the fall and spring as well as at the two control sites used. The data are strongly suggestive that nocturnal migrant songbirds are able to detect and avoid turbines during flight, at least on relatively clear nights when most of our observations were carried out.

Orientation

The migrant count data described above offers testimony to the apparent tendency of nocturnal, primarily songbird migrants to avoid the turbines while in flight. Further evidence for avoidance behavior would be that, due to engaging in evasive flight behavior, the orientation of the birds at the turbine site should be different than at the control site, even though they are separated by only a few kilometers.

1. Fall 2009

Consistent with the expectation described above, the seasonal orientation (Figure 3) of the birds recorded at the turbine site (N = 32, α = 166°, r = 0.52, P < 0.005; 141° ≤ µ ≤ 191°) was significantly different than at the Control 1 site (N = 127, α = 208°, r = 0.38, P < 0.005; 190° ≤ µ

≤ 225°; U2 = 0.224, P < 0.005, d.f. 32). Although it is not surprising that the orientation at both sites was generally oriented in a seasonally appropriate southerly direction, the westerly bias at the control site is more in line with the prevailing direction of migration surrounding the Great

Lakes (Diehl et al. 2003).

We examined the orientation behavior in more detail on the few (2) nights where at least 5 birds were seen at both the control site and the turbine site. Migrant orientation on the 27th of

September and the 13th of October are displayed in Figures 4 and 5, respectively. On 27

September (Figure 4), the control birds were well oriented (N = 28, α = 229.6°, r = 0.36, P =

0.02; 191° ≤ µ ≤ 268°) in a seasonally-appropriate southwesterly direction. By contrast, the birds at the turbine site were axially orientated and did not display significant orientation (N = 6,

α = 180°, r = 0, p-value unable to be calculated; confidence interval unable to be calculated; U2 =

0.146, 0.2 < P < 0.1, d.f. 6). More indicative of the overall seasonal pattern was the orientation

behavior observed on the night of 13 October. The control birds flew west of south (N = 30, α =

191.9°, r = 0.92, P < 0.005; 183° ≤ µ ≤ 200°), while the turbine birds flew east of south (N = 10,

α = 154.5°, r = 0.95, P < 0.005; 141° ≤ µ ≤ 168°) and there was a significant difference in their

directional tendencies (U2 = 0.287, P < 0.01, d.f. 10). For the night of 13 October, we also show

the overall directional tendencies of migrants in northwest Ohio/southwest Michigan as recorded

by Doppler radar located at DTW (Figure 5). The velocity image recorded indicates a general

flight direction aligned with the orientation of the individual birds recorded at the control site but

not at the turbine site.

In summary, the orientation behavior of the birds recorded during the fall of 2009 indicates

that birds observed at the turbine site oriented differently from the birds recorded at the control

site, and generally, the orientation at the control site was more in line with the prevailing

orientation of migrants in the region. These data further support the hypothesis that nocturnal

migrants were avoiding the turbines in part by changing their orientation while in flight.

2. Spring 2010

Consistent with the Fall 2009 data, the Spring 2010 orientation data indicated significant

differences between test (N = 43, α = 327°, r = 0.27, P = 0.04; 283° ≤ µ ≤ 10°) and control sites

(N = 86, α = 32°, r = 0.28, P = 0.001; 2° ≤ µ ≤ 62°; Figure 3, U2 = 0.249, P < 0.005, d.f. 43).

(Statistical analyses indicated that there was not a significant difference in orientation between the two control sites, so they were combined as one control for all orientation analyses; U2 =

0.043, P > 0.5). The orientation at both the test and control sites was generally oriented in a

northerly direction, with an easterly bias at the control site and a westerly bias at the test site.

We again examined the orientation behavior in more detail on those nights (3) where at least 5

birds were observed at both the control site and the turbine site. However, on only one of the three nights (14 May) were we able to download a radar image. On 26 April, an episode of reverse migration was observed, with birds oriented in a south-easterly direction at both the test site (N = 8, α = 152.2°, r = 0.34, P = 0.4) and control site (N = 17, α = 132.2°, r = 0.71, P <

0.005; U2 = 0.054, P > 0.5, d.f. 8). On 6 May, an overall westerly orientation was observed at both the test site (N = 7, α = 287.0°, r = 0.77, P = 0.01) and control site (N = 10, α = 267.2°, r =

0.35, P = 0.3; U2 = 0.187, P < 0.05, d.f. 7). The night of 14 May was more typical for spring migration. The orientation behavior the 14th of May is displayed in Figure 6. The control birds flew almost due north (N = 16, α = 356.8°, r = 0.61, P = 0.002; 328° ≤ µ ≤ 26°) and the turbine birds flew slightly west of north (N = 12, α = 342.7°, r = 0.47, P = 0.068; 293° ≤ µ ≤ 92°), and there was not a significant difference in their directional tendencies (U2 = 0.05, P > 0.5, d.f. 12).

The velocity recorded on radar imagery for the night of 14 May indicates a general flight direction east of north, which differed from both our observation sites, but was closer to what was recorded at the control site.

In summary, the orientation behavior in the spring of 2010 was different between the control sites and the test site, and in that sense resembled what was observed in Fall 2009. However, with so few observation nights with enough birds to generate a nightly orientation preference, it was not possible to meaningfully relate the observed orientation with the prevailing direction of migration in northwest Ohio.

3. Fall 2010

Overall, observations during the Fall 2010 (Figure 3C) season indicated that there was not a statistically significant difference in orientation between the test (N = 11, α = 208°, r = 0.31, P =

0.4; 97° ≤ µ ≤ 319°) and control sites (N = 55, α = 180°, r = 0.49, P < 0.005; 160° ≤ µ ≤ 200°;

Figure 3C, U2 = 0.046, P > 0.5, d.f. 10). However, the number of observations for Fall 2010 was very low and as such, it is difficult to draw any reliable conclusions. The general orientation at the test and control sites was in a seasonally-appropriate southerly direction. However, there were no nights in the fall of 2010 with at least five birds observed at each site. As such, the one night on which at least ten birds were seen at one site (25 September) was used for comparison

(Figure 7). On this night, only three birds were observed at the turbine site, which did not provide a large enough n with which to run valid statistical analyses. Visual directional comparison shows a clear difference between the test site (N = 3) and control site (N = 23, α =

196.4°, r=0.55, P < 0.005; 169° ≤ µ ≤ 224°), although Watson statistical analyses could not be conducted. Regional migratory direction on the night of 25 September was determined to be slightly east of south as indicated by the velocity image captured on radar and was much closer to the orientation observed at the control site than at the test site.

4. Summary

Overall, the orientation of birds at the control site was significantly different than the orientation at the test site. This was true for the overall Fall 2009 and Spring 2010 seasons, as well as for the two nights in the Fall 2009 season when at least 5 birds were observed at each site. While the Spring 2010 and Fall 2010 data were less clear, similar tendencies were observed. These data further support the hypothesis that migrant songbirds in flight often exhibit avoidance behavior when approaching the turbines.

DISCUSSION

Little work has been done at small-scale wind farms combining real-time flight observations with radar imagery to determine nocturnal migratory flight patterns around potential barriers. The first achievement of this study was to demonstrate that night migrating birds could be observed on thermal imagery cameras around the wind turbines. In comparing those observations to the control site, our data show that nocturnal flight behavior is impacted by such human-made barriers. Much as migratory birds have the ability to avoid ecological barriers

(Alerstam 2001), our data indicate that birds in flight have the ability to avoid wind turbines.

The Fall 2009 season provided the most robust data for both numbers and orientation. Analyses for the Spring 2010 and Fall 2010 season were fewer, but generally reinforced the impressions drawn from Fall 2009.

One limitation to the observation protocol was that visibility of the cameras was greatly limited on foggy or cloudy nights. Birds were not observed on nights with significant cloud cover, although it is known that migration and flight still occur. The cameras were also not equipped to be utilized in the rain. As such, the number of nights on which observations could be conducted was limited.

One of the greatest challenges to many bird species is the fitness cost of migration, and the expenditures and potential risk that migration entails. The movement of migration is hampered by barriers (Henningsson and Alerstam 2005), which only serve to increase the cost associated with migrating. In addition to ecological barriers, man-made barriers are an area of concern. One of the most well-known man-made structures that could impact migratory birds is wind turbines. The importance of conservation of migratory birds is often thought to be in direct

competition with sustainable wind energy. The assumption is often that expanded wind energy

comes at the price of loss of birds. Others would argue that there is little to no impact on birds

from newer-model turbines (Erickson et al. 2002). Our data show that there is in fact some

impact in terms of avoidance. Birds were consistently seen in larger numbers at the control sites,

indicating a preference for travelling away from the turbines. Birds were also observed to have a

different orientation overall at the control site as compared to at the turbines, again indicative of

avoidance behavior related to the turbines. The use of multiple control sites and more than one

season of data collection serve to reinforce the pattern.

Although the turbines are modest in spatial scale, the apparent avoidance of them by nocturnal, songbird migrants, as reflected by both numbers and orientation, raises an interesting question about how the bids later compensate for the navigational disruption of the turbines.

Useful here would be the actual tracking of birds with some kind of radar or telemetry to capture their approach to the turbines, the distance at which they seem to alter their orientation in response to the turbines, and any subsequent, corrective re-orientation. The advent of increasingly smaller telemetry devices is shifting the focus of navigational studies away from captive birds to free-flying migrants (Guilford et al. 2011), and migrant behavior around turbine farms is one way to assess the dynamics of corrective re-orientation following disruption in flight direction.

It is important to understand that the AMP Wind Farm itself is located in non-critical habitat (no leading lines, flat topography) and is a very small wind farm in comparison to most

(large wind farms may contain several hundred turbines). The AMP Wind Farm operates only four turbines, situated in a corn field and alongside a landfill. These turbines seem as innocuous a site as could be chosen, and therefore not necessarily indicative of what might be observed at a

larger scale farm, or at turbines situated in critical habitat (e.g. along flyways or where larger numbers of birds may congregate, like along the coast of Lake Erie). With that considered, we do think the results of this study demonstrate the ability for migratory birds and wind energy to coexist. The data show that birds are able to avoid turbines. However, turbine placement is crucial. When possible, turbines should be located in areas determined to be non-critical habitat.

This would lessen the possibility of large numbers of migratory birds coming into contact with the turbines, and lessen the energetic costs associated with avoidance. Mortality due to turbines appears not to be the issue it has been assumed to be in the past (Erickson et al. 2002). In fact, a dawn ground survey conducted during the Fall 2009 season at the turbine site revealed no mortality. The implications of wind energy and man-made structures clearly lie in avoidance and added costs to migration, and perhaps to a lesser extent concern of collision and mortality.

A decision regarding the future of sustainable wind energy in this region can be made through an understanding of bird activity surrounding turbines in Northwest Ohio. This evaluation of the impact of the AMP Wind Farm turbines on migratory bird populations, both in number of birds and their orientation, can further serve to assess the impact of wind turbines in other migratory pathways.

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Alerstam, T. 1978. Reoriented bird migration in coastal areas: Dispersal to suitable resting groups? Oikos 30: 405-408. Alerstam, T. and A. Lindstrom. 1990. Optimal bird migration: the relative importance of time, energy and safety. Bird Migration: the physiology and ecophysiology (ed. E. Gwinner): 331-351. Berlin: Springer. Alerstam, T. 2001. Detours in Bird Migration. Journal of Theoretical Biology 209: 319-331. American Wind Energy Association, 2008. U.S. Wind Energy Projects. http://www.awea.org/projects. / Accessed November 27, 2011.

American Wind Energy Association (2009). Annual Wind Industry Report, Year Ending 2008. Bruderer, B. and F. Liechti. 1998. Flight behavior of nocturnally migrating birds in coastal areas: Crossing or coasting. Journal of Avian Biology 29: 499-507. Cox. C. and P. Moore. 2000. Biogeography an ecological and evolutionary approach, 6th edn. Oxford, UK: Blackwell Science Ltd. Diehl, R., R. Larkin, and J. Black. 2003. Radar observations of bird migration over the Great Lakes. The Auk 120(2): 278-290.

Dokter, A., F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman. 2010. Bird migration flight altitudes studied by a network of operational weather radars. Journal of the Royal Society Interface; doi:10.1098/rsif.2010.0116.

Erickson, W., G. Johnson, M. Strickland, D. Young, K. Sernka K, and R. Good. 2001. Avian collisions with wind turbines: A summary of existing studies and comparisons to other sources of avian collision mortality in the United States. National Wind Coordinating Committee Publication. Erickson, W., G. Johnson, D. Young, D. Strickland, R. Good, M. Bourassa, K. Bay, and K. Sernka. 2002. Synthesis of Baseline Avian and Bat Use, Raptor Nesting and Mortality Information from Proposed and Existing Wind Developments. Bonneville Power Administration. http://www.bpa.gov/Power/pwc/wind/Avian_and_Bat_Study_12-2002.pdf / Accessed November 27, 2011.

Fuchs, T., A. Haney, T. Jechura, F. Moore, and V. Bingman. (2006). Daytime naps in night- migrating birds: Behavioural adaptation to seasonal sleep deprivation in the Swainson’s thrush (Catharus ustulatus). Animal Behaviour, 72: 951-958.

Garvine, R. and W. Kempton. 2008. Assessing the wind field over the continental shelf as a resource for electric power. Journal of Marine Research 66 (6): 751-773.

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FIGURES

Figure 1: Location of the three observation sites near Bowling Green in Wood County, OH. A = Turbine site, B = Control 1 site, C = Control 2 site. Scale bar is located to the lower right of the Figure. Location of the county within the state of Ohio is shown in the lower left. Lake Erie is shaded gray in the Ohio map.

Figure 2: Histograms of mean number of birds/observations per night (with standard error bars) at each site for nights with at least 5 birds at one site. A, Fall 2009 season. B, Spring 2010 season. C, Fall 2010 season. D, Mean number of birds per night for all seasons combined.

Figure 3: Seasonal orientation (mean direction, mean vector length and statistical level derived from Rayleigh test) with statistical comparisons between test and control sites. A, Fall 2009 season. B, Spring 2010 season. C, Fall 2010 season. The arrow in each circle represents the mean vector for each season for each class of observation site, and the length of arrow reflects the mean vector length with the radius of the circle corresponding to a mean vector length of 1. Last statistics reflects the comparison Watson U2 score.

Figure 4: Orientation of birds at the turbine and control sites on 27 September 2009. A, Orientation at the turbine site. B, Orientation at the control site. Each dot represents the flight direction of one bird, and the arrow reflects the distribution mean vector as described for Figure 3. Statistics as in Figure 3.

Figure 5: Orientation of birds at the test and control sites on 13 October 2009. A, Orientation at the test site. B, Orientation at the control site. C, Regional radar image from 13 October 2009. See Figures 3 and 4 for an explanation of the remainder of the diagram.

Figure 6: Orientation of birds at the test and control sites on 14 May 2010. A, Orientation at the test site. B, Orientation at the control site. See Figures 3 and 4 for an explanation of the remainder of the diagram.

Figure 7: Orientation of birds at the test and control sites on 25 September 2010. A, Orientation at the test site. B, Orientation at the control site. C, Regional radar image from 25 September 2010. See Figures 3 and 4 for an explanation of the remainder of the diagram.

Appendix 1 – Migration

Conservation of long-distance migratory species is complicated due to factors including the spatial scale that they travel (Yong et al. 1998). Some research into the pattern, altitude, and timing of migration has been done using radar analysis (Dokter et. al 2010). While these studies have provided useful information towards the understanding of migration, much still needs to be done to connect breeding and overwintering sites of certain species (Robinson et al. 2009).

Habitat in extremis: Water represents a poor stopover habitat in extremis (Moore and

Simons 1992). Birds crossing open bodies of water must take into account the energy costs discussed previously. The benefit to crossing rather than avoiding the open water may often be that the distance is shorter if the open water is crossed without detour, also outlined previously.

Based on their location within the continental United States and relative area compared to land mass, the Great Lakes are likely one water source that cannot be completely avoided during flight. Diehl, et al. (2003) found that lake avoidance did occur around the Great Lakes at certain times as observed on NEXRAD (i.e., bird densities over land were significantly greater than bird densities over water) (Diehl et. al 2003); however, in many cases, birds crossed the Great Lakes in large numbers. During two seasons, bird densities over water were significantly or nearly significantly lower than those over land. During the spring 2000 season, this was true around all lakes except the east end of Lake Erie (Diehl et al. 2003). The decision to cross likely shows an

“adjustable compromise” (Bruderer and Liechti 1998) between fitness benefits of early arrival and the risk of assuming hazardous routes to expedite travel. Lake avoidance may also occur as a matter of convenience – as the direction of travel more closely parallels the coast, it may become more favorable to slightly alter course and remain over land (Alerstam and Pettersson

1977). Diehl et al. also discovered that reorientation occurs – birds move back towards land

when near the shore. This was a regular occurrence over the narrower Great Lakes (Lake Erie,

Lake Ontario, western Lake Superior) or near isolated land features (Diehl et al. 2003). Likely

because of this, many sites beside open water serve as major stopover sites for migratory birds.

As millions of land birds confront the Great Lakes as they migrate through North America, the

coasts of the Great Lakes represent first landfall for a bird stranded over water (Diehl et al.

2003). One of the most well known stopover sites around the Great Lakes is Magee Marsh,

along the shore of western Lake Erie (approximately 48 kilometers from AMP Wind Farm).

Magee Marsh has historically been inhabited by a large number of waterfowl, shorebirds and

songbirds (Ohio Department of Natural Resources). The marsh is not only important for the

habitat provided to the migratory birds, but it is also an important area for Northwestern Ohio in

terms of being a location for habitat conservation, with the primary focus being the development

and maintenance of a high-quality wetland (Ohio DNR).

Western Lake Erie differs from the other great lakes in terms of the relative short distance migratory birds need to cover (Diehl, et. al 2003). There are also a fair number of islands in western Lake Erie, providing the option of landing if necessary. Should a bird tire on its migratory path over Lake Erie, it is presumed to be a better circumstance than tiring over Lake

Huron. The open water along the migration pathway remains consistent from year to year; however, the addition of man-made structures is likely to change with an increasing human population.

Appendix 2 – Wind Power History

Wind power has been used by humans in various forms for over 5,000 years (Sathyajith

2006). Wind power is free, relatively continuous, and fairly easily harnessed. There are many ways in which wind can be used, both as its own power source and to fuel other power sources.

Wind power encompasses sails to propel ships, wind pumps for water, windmills for mechanical power, and wind turbines to make electricity. The most prominent form of wind power for energy purposes currently in the United States is the wind turbine, which converts kinetic energy from the wind into mechanical energy.

The history of turbines dates back to 500-900 A.D. (Illustrated History of Wind Power

Development). Early turbines were really windmills, used mainly for pounding grain and drawing water. The first turbine to produce electricity was used as a battery charging machine in

Scotland in 1887 (Price 2005). In the United States, the first windmill for electricity production was built by Charles Brush in 1888 in Cleveland, Ohio (Price 2005).

The early wind turbines in the United States were most common on farms for electricity generation. Larger-scale development led to a megawatt-class wind turbine synchronized to a

utility grid in Vermont in 1941. In the USSR, in 1931, a forerunner of modern turbines was

installed and connected to the local distribution system (Wyatt 1986). This generator was a

prototype of the modern horizontal-axis generators.

The need for sustainable energy has been at the forefront of many discussions, both political and environmental, in the recent past. With both the population of the United States as well as the world population as high as they have ever been and growing, the fossil fuel supply that feeds the world’s energy has likely reached its peak and is now declining. The unlikely

possibility of changing the world’s habits has led to the dire need to develop energy power that does not require the consumption of fossil fuels. One of the forerunners in the field of sustainable energy is wind power. As of 2011, Ohio is home to eleven utility-scale wind power sources installed or under construction. Germany has the second largest number of wind farms in the world after the United States (American Wind Energy Association 2008). Wind farms can also be found in Australia, Brazil, Canada, China, Denmark, India, Japan, Morocco, New

Zealand, the Philippines, South Africa, and the United Kingdom.

Currently, 26 states have new wind turbines under construction. Texas and California lead the way with the largest installed capacity; however, many states have the potential capacity to surpass that (U.S. Wind Energy Projects). Additionally, 70 components manufacturers have begun production, expanded, or announced since January 2007 (The Economic Reach of Wind).

Appendix 3 – Wind Turbines

Wind turbines rotate around either a vertical or horizontal axis (see figure) (American

Wind Energy Association). A common example of a horizontal axis is a typical windmill.

Horizontal-axis turbines must be pointed at the wind. The advantage of a horizontal axis is variable blade pitch, allowing the turbine to collect the maximum amount of wind. Horizontal axis turbines are able to have taller bases, allowing access to stronger winds. They are also very high efficient as the blades always move perpendicular to the wind. Disadvantages of the horizontal axis model include the difficulty in transporting such large machinery. Large components also lead to difficult and expensive installation. In addition, their height makes the turbines quite visible across a large area, which can lead to opposition over disruption of the landscape.

Vertical-axis turbines have an opposite configuration (see figure), with the main rotor shaft arranged vertically. Some advantages to this are that a support structure as large as for a horizontal-axis turbine is typically not needed. Vertical axis turbines also have lower startup wind speeds than horizontal axis turbines. The generator for the turbine can be located near the ground, making it easier to access for maintenance, as opposed to the horizontal axis turbine, where the generator is located at the top of the shaft. Additionally, vertical axis turbines situated lower to the ground have an advantage in areas where the shape of the landscape serves to funnel wind and increase the velocity. Some disadvantages of the vertical-axis design include structural stresses due to wind loading changes and the weight of the structure on the mechanical components.

Figure: Two main types of wind turbines, Horizontal-axis wind turbine (L) and Vertical-axis wind turbine (R).

Wind turbines are located all over the world in areas with consistently high wind speeds.

Turbines are found onshore, nearshore, and offshore (Garvine and Kempton 2008). The modern wind turbine used in wind farms for commercial production of energy are typically three-bladed horizontal axis turbines. Wind farms can vary in size from very few turbines to upwards of 600.

Appendix 4 – AMP Wind Farm

There are four turbines in Bowling Green, Ohio, at the AMP Wind Farm. These four turbines combined produce 7.4 megawatts (MW) of power. The four horizontal-axis turbines are located approximately 10 kilometers from downtown. The first two turbines were installed in

2003, with the third and fourth following the year after. Together they have the capability of powering approximately 1,500 homes (Green Energy Ohio). The turbines are 391 feet tall with the blades, equivalent to a 30-story building. The size of these structures presents a formidable obstacle to a migratory bird encountering them during flight.

Appendix 5 – Summary of Observations

Date Observations Observations Observations Recorded Wind Clouds Started Ended Direction/mph 9-24-09 8:27 p.m. 11:00 p.m. Control (4)/Turbine (0) Calm Clear 9-25-09 8:25 p.m. 9:33 p.m. Control (4)/Turbine (0) E 8 Partly 9-27-09 8:22 p.m. 10:21 p.m. Control (28)/Turbine (6) SW 9 Clear 9-28-09 8:20 p.m. 8:50 p.m. Control (0)/Turbine (0) WSW 20 Overcast 9-29-09 8:18 p.m. 8:48 p.m. Control (0)/Turbine (0) WNW 13 Overcast 9-30-09 8:17 p.m. 10:28 p.m. Control (13)/Turbine (3) NW 3.5 Clear 10-1-09 8:15 p.m. 8:57 p.m. Control (2)/Turbine (0) E 4.6 Clear 10-2-09 8:13 p.m. 9:20 p.m. Control (8)/Turbine (1) WSW 8 Partly 10-3-09 8:11 p.m. 8:41 p.m. Control (0)/Turbine (0) SW 9 Overcast 10-5-09 8:08 p.m. 9:47 p.m. Control (9)/Turbine (6) Calm Clear 10-6-09 8:06 p.m. 8:36 p.m. Control (0)/Turbine (0) SW 19 Overcast 10-7-09 8:05 p.m. 9:28 p.m. Control (14)/Turbine (3) WSW 6.9 Clear 10-13-09 7:55 p.m. 9:48 p.m. Control (31)/Turbine (10) Calm Clear 10-16-09 7:50 p.m. 8:20 p.m. Control (0)/Turbine (0) NE 4.8 Overcast 10-17-09 7:49 p.m. 8:19 p.m. Control (0)/Turbine (0) NNW 5.8 Overcast 10-18-09 7:47 p.m. 8:17 p.m. Control (0)/Turbine (0) WSW 3.5 Clear 10-19-09 7:46 p.m. 8:16 p.m. Control (0)/Turbine (0) S 14 Clear 10-20-09 7:44 p.m. 8:36 p.m. Control (0)/Turbine (1) SSW 5.8 Clear 10-21-09 7:43 p.m. 9:22 p.m. Control (2)/Turbine (0) NA NA 10-25-09 7:37 p.m. 8:07 p.m. Control (0)/Turbine (0) SE 5.8 Overcast 10-26-09 7:36 p.m. 8:41 p.m. Control (7)/Turbine (2) ESE 6.9 Clear 10-28-09 7:33 p.m. 8:23 p.m. Control (5)/Turbine (0) E 3.5 Clear 10-29-09 7:32 p.m. 8:02 p.m. Control (0)/Turbine (0) ENE 4.6 Clear 4-26-10 9:10 p.m. 10:42 p.m. Control 1 (17)/Turbine (8) N 4.6 Clear 4-27-10 9:15 p.m. 9:55 p.m. Control 1 (3)/Turbine (0) ESE 4.6 Clear 4-28-10 9:20 p.m. 10:17 p.m. Control 2 (8)/Turbine (4) WSW 3.5 Clear 4-29-10 9:10 p.m. 10:15 p.m. Control 1 (6)/Turbine (3) SE 9.2 Clear 4-30-10 9:16 p.m. 10:23 p.m. Control 1 (3)/Turbine (3) S 9.2 Clear 5-4-10 9:26 p.m. 10:34 p.m. Control 1 (3)/Turbine (1) S 8.1 Clear 5-6-10 9:18 p.m. 10:41 p.m. Control 2 (10)/Turbine (7) E 4.6 Clear 5-9-10 9:31 p.m. 10:47 p.m. Control 1 (5)/Turbine (0) WNW 3.5 Clear 5-10-10 9:40 p.m. 10:15 p.m. Control 2 (0)/Turbine (1) E 4.6 Clear 5-14-10 9:10 p.m. 10:41 p.m. Control 1(16)/Turbine (12) WNW 9.2 Clear 5-16-10 9:10 p.m. 10:37 p.m. Control 1 (6)/Turbine (1) NE 5.8 Clear 5-19-10 9:15 p.m. 10:32 p.m. Control 2 (2)/Turbine (2) Calm Clear 5-22-10 9:12 p.m. 10:33 p.m. Control 1 (3)/Turbine (0) Calm Partly 5-23-10 9:10 p.m. 10:05 p.m. Control 2 (2)/Turbine (1) E 9.2 Clear 5-24-10 9:35 p.m. 10:05 p.m. Control 1 (0)/Turbine (0) E 5.8 Clear 5-25-10 9:35 p.m. 10:05 p.m. Control 2 (0)/Turbine (0) ENE 5.8 Clear 5-26-10 9:37 p.m. 10:16 p.m. Control 1 (2)/Turbine (0) E 3.5 Clear 5-28-10 9:40 p.m. 10:10 p.m. Control 2 (0)/Turbine (0) ENE 6.9 Clear

Appendix 5 continued – Summary of Observations

Date Observations Observations Observations Recorded Wind Clouds Started Ended Direction/mph 9-9-10 8:53 p.m. 10:41 p.m. Control (5)/Turbine (1) N 4.6 Clear 9-23-10 8:29 p.m. 10:27 p.m. Control (3)/Turbine (0) S 8.1 Clear 9-25-10 8:26 p.m. 10:35 p.m. Control (23)/Turbine (3) NW 8.1 Partly 9-26-10 8:24 p.m. 8:54 p.m. Control (0)/Turbine (0) N 4.6 Overcast 9-28-10 8:20 p.m. 10:13 p.m. Control (4)/Turbine (1) SW 3.5 Partly 9-30-10 8:15 p.m. 10:20 p.m. Control (7)/Turbine (3) NW 8.1 Clear 10-4-10 8:10 p.m. 8:40 p.m. Control (0)/Turbine (0) Calm Overcast 10-5-10 8:07 p.m. 9:20 p.m. Control (3)/Turbine (1) Calm Partly 10-6-10 8:07 p.m. 8:37 p.m. Control (0)/Turbine (0) W 8.1 Clear 10-7-10 8:06 p.m. 8:56 p.m. Control (3)/Turbine (0) Calm Clear 10-8-10 8:04 p.m. 8:47 p.m. Control (2)/Turbine (0) SW 6.9 Clear 10-10-10 8:00 p.m. 9:12 p.m. Control (1)/Turbine (1) W 5.8 Clear 10-12-10 7:57 p.m. 8:27 p.m. Control (0)/Turbine (0) E 5.8 Clear 10-16-10 7:51 p.m. 8:21 p.m. Control (0)/Turbine (0) SSW 6.9 Clear 10-17-10 7:49 p.m. 9:03 p.m. Control (1)/Turbine (0) NW 5.8 Clear 10-19-10 7:46 p.m. 8:16 p.m. Control (0)/Turbine (0) SSW 4.6 Clear 10-20-10 7:45 p.m. 8:15 p.m. Control (0)/Turbine (0) SSW 8.1 Partly 10-21-10 7:43 p.m. 8:50 p.m. Control (2)/Turbine (1) WNW 17 Partly 10-23-10 7:40 p.m. 8:10 p.m. Control (0)/Turbine (0) SW 3.5 Partly 10-24-10 7:39 p.m. 8:09 p.m. Control (0)/Turbine (0) S 6.9 Clear 10-27-10 7:35 p.m. 8:25 p.m. Control (1)/Turbine (0) SW 17.3 Clear 10-30-10 7:31 p.m. 8:01 p.m. Control (0)/Turbine (0) WSW 15 Clear

Appendix 6 – Radar

Radar utilizes the transmission, propagation, scattering, and reception of electromagnetic

waves (Bonter et al., 2008). The energy transmitted by radar is reflected back to the receiver

when it comes into contact with an object in the atmosphere. Thus, the returned energy is called

reflectivity. A greater reflectivity value indicates a greater density of objects in the atmosphere

(Bonter et al., 2008). Radar echoes observed in boundary layer clear air are predominantly

arthropods and flying birds (Dokter et al., 2010).

The TDWR system is used primarily at or near airports, and was created to detect

hazardous wind shear conditions (National Weather Service). TDWR uses the 5 cm wavelength

carrier wave with a range of 90 km and a range gate resolution of 150 m (noaa.gov). The TDWR

completes scans of the atmosphere every minute (noaa.gov).

The DTW radar is a part of TDWR (Terminal Doppler Weather Radar) as opposed to

NEXRAD (Next-Generation Radar). The primary advantage of the TDWR is a range resolution

nearly twice the one of NEXRAD (National Weather Service). The main shortcoming is a signal

that can be strongly attenuated in heavy precipitation, meaning the radar cannot “see” very far

through heavy rain. This was not considered to have impacted this research as nights of severe

weather did not have any field observations conducted. Images from radars that are part of the

NEXRAD system are stored in a database for future access and assessment (i.e. the data can be

processed and displayed in a mosaic map). Images from TDWR radars are not linked to the same system, and thus all images had to be captured in real time. While images from a

NEXRAD radar would have provided a potentially easier means of analysis, the closest

NEXRAD radar is located in Cleveland, and does not capture the area over the AMP Wind Farm site or western Lake Erie.