Great Lakes Avian Radar

Technical Report

Lake Erie Shoreline:

Erie County, Ohio and Erie

County, Pennsylvania

Spring 2012

U.S. Fish & Wildlife Service, Region 3

Funding Provided by the Great Lakes Restoration Initiative

U.S. Fish & Wildlife Service

Great Lakes Avian Radar

Technical Report

Lake Erie Shoreline:

Erie County, Ohio and Erie

County, Pennsylvania

Spring 2012 

U.S. Fish & Wildlife Service, Region 3

Funding Provided by the Great Lakes Restoration Initiative

2016

Principle Investigator: 

Jeff Gosse, Regional Energy Coordinator, USFWS, Region 3 

Authors:

Rebecca Horton, Fish and Wildlife Biologist, USFWS Region 3

Nathan Rathbun, Fish and Wildlife Biologist, USFWS Region 3

Tim Bowden, Fish and Wildlife Biologist, USFWS Region 3

Daniel Nolfi, Fish and Wildlife Biologist, USFWS Region 3

Erik Olson, Fish and Wildlife Biologist (GIS), USFWS Region 3

David Larson, Asst. Reg. Energy Coordinator (Retired), USFWS Region 3

U.S. Fish & Wildlife Service Region 3
Corresponding Author:

Nathan A. Rathbun

U.S. Fish and Wildlife Service

Fish and Wildlife Biologist

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5182

Email: nathan_rathbun@fws.gov

Authors’ Complete Contact Information:

Jeffrey C. Gosse

U.S. Fish and Wildlife Service

Regional Energy Coordinator

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5138

Email: jeff_gosse@fws.gov

Timothy S. Bowden

U.S. Fish and Wildlife Service

Fish and Wildlife Biologist

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5197

Email: timothy_bowden@fws.gov

Rebecca L. Horton

U.S. Fish and Wildlife Service

Fish and Wildlife Biologist

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5196

Email: rebecca_horton@fws.gov

David J. Larson

U.S. Fish and Wildlife Service

Assistant Regional Energy Coordinator

(Retired)

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5336

Daniel C. Nolfi

U.S. Fish and Wildlife Service

Fish and Wildlife Biologist

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5195

Email: daniel_nolfi@fws.gov

Erik C. Olson

U.S. Fish and Wildlife Service

Fish and Wildlife Biologist (GIS)

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5488

Email: erik_olson@fws.gov

Nathan A. Rathbun

U.S. Fish and Wildlife Service

Fish and Wildlife Biologist

Ecological Services

5600 American Blvd. West, Suite 990

Bloomington, MN 55437

Phone: 612/713 5182

Email: nathan_rathbun@fws.gov
Funding for this study was provided by the Great Lakes Restoration Initiative 

Disclaimer: The findings and conclusions in this article are those of the authors and do not 

necessarily represent the views of the U.S. Fish and Wildlife Service. The mention of trade names or 

commercial products in this report does not constitute endorsement or recommendation for use by 

the Federal government.

Key words: Great Lakes, Lake Ontario, migration, avian radar, wind energy, birds, bats 

Recommended citation: 

Horton, R. L., N. A. Rathbun , T. S. Bowden, D. C. Nolfi, E. C. Olson, D. J. Larson, and J. C. Gosse. 2016. 

Great Lakes Avian Radar Technical Report Lake Erie Shoreline: Erie County, Ohio and Erie County, 

Pennsylvania, Spring 2012. U.S. Department of Interior, Fish and Wildlife Service, Biological Technical 

Publication FWS/BTP--R3012-2016
ISSN 2160-9498 Electronic ISSN 2160-9497

FWS/BTP--R3012-2016

Biological Technical Publications online: 

Biological Technical Publications Series
 i

Table of Contents

List of Figures ....................................................................................................................................................... ii

List of Tables ......................................................................................................................................................... iv 

Acknowledgements ............................................................................................................................................... v

Executive Summary ............................................................................................................................................... vi 

Introduction ............................................................................................................................................................ 1

 Objectives ............................................................................................................................................... 2

Methods .................................................................................................................................................................. 3 

 Study Area ............................................................................................................................................. 3

 Equipment ............................................................................................................................................. 4

 Data Collection ...................................................................................................................................... 5

 Data Processing and Quality Control ................................................................................................ 6

 Data Summary and Trends Analysis ................................................................................................. 9

Results .................................................................................................................................................................. 12

 Qualitative Assessments ..................................................................................................................... 12

 Directional Trends ............................................................................................................................... 16

 Temporal Trends ................................................................................................................................. 17 

Altitudinal Trends ............................................................................................................................. 27

Discussion ....... ...................................................................................................................................................... 39 

 Radar Study and Management Considerations .................................................................................. 41

Literature Cited .................................................................................................................................................... 49
ii

List of Figures

Figure 1. Locations at which MERLIN Avian Radar Systems were deployed during the 

spring 2012 migration season ...................................................................................................................................... 3

Figure 2. Land cover types found within a 3.7-km radius of the radar locations in Ohio and Pennsylvania during 

spring 2012 ...................................................................................................................................................................... 4

Figure 3. Computer representation of the potential survey volume scanned by horizontal and vertical radars 

used in Ohio and Pennsylvania during spring 2012. Graphic provided by DeTect, Inc. ...................................... 5

Figure 4. Vertical and horizontal clutter maps from Ohio and Pennsylvania ........................................................ 7

Figure 5. Schematic of the vertical scanning radar and the standard front .......................................................... 8

Figure 6. Graphical representation of the structural form of the vertical scanning radar within the standard 

front used for density estimates ............................................................................................................................... 10

Figure 7. Volume of 50-m altitude bands within the standard front as estimated with a Monte Carlo 

integration .................................................................................................................................................................... 11

Figure 8. Trackplots recorded in 1-hour increments by horizontal and vertical scanning radars during an 

apparent nighttime migration event in Ohio ........................................................................................................... 14

Figure 9. Trackplots recorded in 1-hour increments by horizontal and vertical scanning radars during an 

apparent nighttime migration event in Pennsylvania ............................................................................................ 15

Figure 10. Target direction during four biological periods during spring 2012 at sites in Ohio (left) and 

Pennsylvania (right) .................................................................................................................................................... 16

Figure 11. Hourly counts by horizontal and vertical radars from April 1 – June 11, 2012 in Ohio ................... 18

Figure 12. Hourly counts by horizontal and vertical radars from April 4 –June 11, 2012 in Pennsylvania ..... 19

Figure 13. Box plots showing the variability in target passage rate (targets/km/hour during four biological 

periods in spring 2012 at sites in Ohio and Pennsylvania ...................................................................................... 20

Figure 14. Mean hourly target passage rate in spring 2012 at sites in Ohio and Pennsylvania ........................ 21

Figure 15. Weekly mean nocturnal and diurnal target passage rates (targets/km/hour) in Ohio (top row) and 

Pennsylvania (bottom row) from April 1 –June 11, 2012 ...................................................................................... 21

Figure 16. Comparison of nocturnal and diurnal target passage trends(based on a moving 7-day mean) in 

spring 2012 at sites in Ohio and Pennsylvania ........................................................................................................ 22

Figure 17. Comparison of nocturnal target passage trends (based on a moving 7-day mean) in spring 2012 at 

sites in Ohio and Pennsylvania .................................................................................................................................. 23

Figure 18. Altitude profile of nocturnal and diurnal targets at sites in Ohio and Pennsylvania ....................... 25

Figure 19. Altitude profile of dawn and dusk targets at sites in Ohio and Pennsylvania ................................... 26
Figure 20. A sample of hourly altitude profiles corrected for the shape of the sample volume in Ohio ........... 27

Figure 21. A sample of hourly altitude profiles corrected for the shape of the sample volume in 

Pennsylvania ................................................................................................................................................................ 28

Figure 22. Altitude profile of target density below 400 m at sites in Ohio and Pennsylvania ............................ 29

Figure 23. Percentage of nights when the maximum target density (targets/1,000,000 m3/altitude band) or count 

(targets/altitude band) occurred within an altitude band at sites in Ohio and Pennsylvania ....................................30

Figure 24. Percentage of night hours when the maximum target density (targets/1,000,000 m3/ altitude band) 

or count (targets/altitude band) occurred within an altitude band at sites in Ohio and 

Pennsylvania ................................................................................................................................................................ 31

Figure 25. Variation in flight altitudes based on target density (targets/1,000,000 m3/altitude band) at our site 

in Erie County, Ohio ................................................................................................................................................... 32

Figure 26. Variation in flight altitudes based on target density (targets/1,000,000 m3/altitude band) at our site 

in Erie County, Pennsylvania .................................................................................................................................... 33

Figure 27. Mean hourly target height (m) in spring at sites in Ohio and Pennsylvania ..................................... 34

Figure 28. Example of a sampling schedule in which data were collected once per week (top graphic) vs. a 

continuous sampling schedule (bottom graphic) ..................................................................................................... 36

iii
List of Tables

Table 1. Predominant land cover types found within a 3.7-km radius of the radar locations in Ohio and 

Pennsylvania in spring 2012 ........................................................................................................................................ 3

Table 2. Survey effort (hours) by vertical and horizontal scanning radars in spring 2012 in Ohio and 

Pennsylvania ................................................................................................................................................................ 12

Table 3. Mean direction, angular concentration (r), and percentage of time with strong directionality 

(r ≥ 0.5) of targets by biological time period in Ohio and Pennsylvania .............................................................. 16

Table 4. Mean target passage rate (TPR) with standard deviations during four biological time periods in 

Ohio and Pennsylvania in spring 2012 ...................................................................................................................... 20

Table 5. Comparison of mean target passage rate (TPR) and mean height (m) with standard deviations 

during four biological time periods in Ohio and Pennsylvania in spring 2012 .................................................... 28

iv
v

Acknowledgements

This project would not have been possible 

without the funding provided by the Great Lakes 

Restoration Initiative, for which we are very 

appreciative. We are also grateful for the advice, 

technical assistance, and contributions of our 

collaborators Doug Johnson (U.S. Geological 

Survey), Kevin Heist (University of Minnesota), 

and Anna Peterson (University of Minnesota). The 

statistical and programming expertise of Jake 

Ferguson (University of Florida) provided our model 

of the geometric shape of the radar beam. We also 

thank the landowners that provided space for our 

radar units and other U.S. Fish and Wildlife Service 

programs for their assistance during the season, 

including the Minnesota Valley National Wildlife 

Refuge, the Detroit Lakes Wetland Management 

District, and the Ohio Ecological Services Field 

Office. This manuscript benefited from four external 

reviews, and we thank the contributors: Megan 

Seymour of the U.S. Fish and Wildlife USFWS Ohio 

Field Office, Michael Wellik of the U.S. Geological 

Survey Upper Midwest Environmental Sciences 

Center, David Ewert of the Nature Conservancy, 

and Ryan Zimmerling of the Canadian Wildlife 

Service.
Executive Summary

Global wind patterns help move millions of 

migrating birds and bats through the Great Lakes 

region, where the shorelines provide important 

stopover habitat. Shorelines are thought to 

concentrate migrants because they offer a last 

refuge prior to crossing a geographic obstacle, 

and they are likely used for navigation. However, 

shorelines are also attractive for wind energy 

development, which is a known cause of mortality 

in birds and bats. Due to this this potential for 

conflicting land use interests, more information 

on the aeroecology of the shorelines of the Great 

Lakes region is required. Therefore, we used two 

avian radar systems to identify the activity, temporal 

patterns, and duration of migration in birds and bats 

that occurred along shorelines of the Great Lakes. 

We placed avian radar systems on opposite ends of 

the south shore of Lake Erie, and the automated 

systems continuously tracked and recorded the 

movements of targets (birds and bats) from early 

April to mid-June 2012. We determined the direction 

of movement, target passage rates, and altitude 

profiles for the air space above our study areas, and 

we developed a model of the vertical sample volume 

that allowed us to estimate the target density 

according to the altitude band.

Migration appeared to be strong along the southern 

shoreline of Lake Erie, and at both of our locations, 

the mean target passage rates at night were greater 

than those at dawn, day, and dusk combined. The 

nocturnal movements were typically oriented in 

a northerly direction, although we also recorded 

other behaviors associated with migrants, such as 

reverse migration, dawn ascent, and migrants over 

water returning to land at dawn. After applying 

a correction, the peak density was found to occur 

between 50 – 150 m above ground level, although the 

density at higher and lower altitudes may have been 

underestimated.

The results of our research highlight the potential 

role of radar in implementing the Land-Based Wind 

Energy Guidelines and help to identify areas where 

the impacts to wildlife from wind energy could be 

minimized. We documented migration activity in 

the air space above our study areas, and the results 

indicate that the high target density at low altitudes 

may present conservation concerns. Our data 

revealed the ebb and flow of migration activities 

throughout the sampling period and documented 

the occurrence of nocturnal peaks through late 

May. Given the amount of time during which 

migration occurred in the sampled sites, curtailing 

wind energy operations to minimize bird and bat 

mortality during nocturnal pulses could severely 

limit energy production time along shorelines during 

the migration season. Combining the results of 

radar studies with fatality searches would greatly 

improve risk assessments and assist with the 

interpretation of standardized radar studies.

Avian radar is often used to perform surveys for 

pre-construction risk analyses, and although it is 

an important tool, few regulatory agencies have 

experience implementing avian radar or recognizing 

the strengths and limitations of the technology. This 

report highlights a number of considerations in the 

use of avian radar, and it reviews certain potentially 

confusing metrics and introduces new metrics 

for reporting radar data. In addition to providing 

information relevant for wildlife conservation in the 

Great Lakes region, this report presents concepts 

that are widely relevant for reviews of avian radar 

studies and describes methods for identifying 

critical components of migration, such as the 

following:

n Nocturnal pulses

n Season length 

n Estimated density per altitude band

n Migrant behavior near a geographical obstacle

Given the rapid growth of the wind energy sector, 

our most effective conservation strategy might be 

identifying and avoiding locations where migrants 

concentrate when choosing sites for energy 

development. Our use of commercial-grade avian 

radar to document migration is a broad-scale effort 

toward this end, and to our knowledge, this effort 

is the first of its type by the U.S. Fish and Wildlife 

Service. 

vi
Introduction

Collectively, the Great Lakes are one of the largest 

bodies of freshwater on the planet, and they have a 

surface area of nearly 245,000 km2 and over 17,500 

km of shoreline. Global wind patterns help move 

millions of migrating birds and bats through the 

Great Lakes region (Rich 2004, Liechti 2006, France 

et al. 2012), and globally recognized Important 

Bird Areas are often located on lake shorelines 

(Audubon 2013). Migrants passing through the 

region concentrate near shorelines (Ewert et 

al. 2011, Peterson and Niemi 2011, Buler and 

Dawson 2012, France et al. 2012), which provide 

important stopover habitats, or areas that are used 

temporarily for refueling, rest, and protection while 

en route to their breeding grounds. Compared 

with inland areas, these shorelines offer increased 

foraging opportunities relative to inland areas 

(Smith et al. 2004, 2007, Bonter et al. 2007, 2009) and 

may serve as visual cues for navigation or as refuges 

for migrants before or after they cross open water 

(Buler and Moore 2011). 

Due to their location and size, the Great Lakes 

likely represent a geographic obstacle that migrants 

must cross or avoid based on the environmental 

and physiological conditions at the time of they 

encounter the obstacle (Faaborg et al. 2010, 

Schmaljohann et al. 2011). For migrants that rely on 

powered flight, it is more efficient to make several 

short flights than a single long flight because of the 

cost of carrying high fuel loads (Alerstam 1990), 

and this may be one reason why migrants partially 

circumnavigate the Great Lakes despite being 

physiologically capable of crossing them (Alerstam 

1990, 2001, Ruth 2007). Thus, the decision to cross 

likely represents a trade-off between minimizing 

costs (e.g., energy and time) and exposure to risk 

factors (e.g., predation and fatigue) associated with 

migration (McGuire et al. 2012a). Shorelines offer 

refuge when conditions do not favor flights over 

water. 

When challenged by an obstacle, migrants may 

temporarily reverse or deviate from seasonally 

appropriate flight directions or return to land to 

delay or recover from a crossing (Bruderer and 

Liechti 1998, Akesson 1999, Ewert et al. 2011). 

Schmaljohann and Naef-Daenzer (2011) found that 

birds managing low fuel loads and/or unfavorable 

weather conditions returned to the shoreline habitat 

rather than continue across open water in the 

direction of migration. For bats, the migrants varied 

in their choice to cross over or circumnavigate lakes 

above the shorelines, and a number of long distance 

migrants use torpor to postpone migration in 

unfavorable conditions (McGuire et al. 2012b). These 

behavioral responses as well as the need to use 

stopover habitat during migration likely contribute 

to the increased use of shorelines and demonstrate 

the importance of these areas for conservation. 

Migrants concentrated along shorelines can be 

very mobile. In addition to using the shoreline 

habitats for immediate refueling and rest, migrants 

make broad-scale flights between habitat patches, 

explore wind conditions, and orient for migration. 

For example, radio-tagged bird and bat migrants 

on the north shore of Lake Erie made repeated 

movements between habitat patches, with individual 

birds and bats relocating as far as 18 and 30 

km from their capture site, respectively, prior to 

resuming migration (Taylor et al. 2011). Nocturnal 

migrants, such as warblers and other Neotropical 

species, regularly engage in morning flights along 

shorelines (Wiedner et al. 1992). These flights 

typically occur within 2 hours of sunrise and likely 

represent reorientation along a geographic obstacle 

or movements between stopover habitats (Able 

1977, Moore 1990, Wiedner et al. 1992). Flights of 

this nature often occur above the tree line (Bingman 

1980) but at heights lower than those associated with 

nocturnal migration (Harmata et al. 2000, Mabee 

and Cooper 2004, Newton 2008). Migrants have 

also been observed initiating nightly exploratory 

flights at stopover sites (Schmaljohann et al. 2011), 

and these flights are considered normal activities 

aimed at calibrating internal compasses and testing 

the wind speed and direction aloft. Migrants also 

follow north-south oriented shorelines en route to 

their destination (Buler and Dawson 2012), whereas 

east-west oriented shorelines may be used to 

circumnavigate open water or find narrow points for 

crossing (Alerstam 2001, Diehl et al. 2003, France 

et al. 2012). Cumulatively, these activities define an 

area of use near lake shores and include a variety 

of movements and altitudes for landscape-level, 

exploratory, and migratory flights. However, these 

activities may increase the risk of collision with tall 

structures, such as buildings, communication towers 

or wind turbines. 

Great Lakes Avian Radar - Spring 2012 1
Migrant populations may experience the greatest 

mortality pressure during migration (Newton 2006, 

2007; Sillett and Holmes 2002, Diehl et al. 2014), and 

the negative ramifications of compromised stopover 

habitat are becoming increasingly clear (Sillett and 

Holmes 2002, Mehlman et al. 2005, Faaborg et al. 

2010). Shoreline habitats along the Great Lakes 

are subject to pressures from urban and energy 

development, land conversion, and environmental 

contamination, which may limit habitat availability 

and/or reduce habitat quality (France et al. 2012). 

Of further concern is the devastation of hibernating 

bat populations by white-nose syndrome, which 

has increased the need for identifying conservation 

areas as several of these species face extirpation 

in the Great Lakes region (Turner et al. 2011). The 

increased number of wind energy installations 

within the U.S. is further devastating bat 

populations by causing high numbers of fatalities to 

long-distance migratory tree bats (Kunz et al. 2007a, 

Cryan 2011, Arnett and Bearwald 2013, Hayes 

2013, Smallwood 2013). In response to such factors, 

substantial efforts are being made to identify and 

protect stopover habitat along the shorelines of the 

Great Lakes (Buler and Dawson 2012, Ewert et al. 

2012, France et al. 2012, Johnson, 2013), although 

careful planning is needed to balance the demands 

between increased renewable energy development 

to mitigate climate change and the conservation of 

migratory species. 

There is a national movement towards supplying 

20% of the end-use electricity in the U.S. market 

by wind power by 2030 (US DOE 2008, 2015) and 

35% by 2050 (US DOE 2015). As of 2012, wind 

energy installations were on target to achieve the 

2030 goal (AWEA 2015), which would represent a 

nearly five-fold increase in wind energy capacity 

over the next 15 years (Loss et al. 2013). Coinciding 

with this national effort, wind energy development 

is increasing within the Great Lakes region, where 

windy shorelines are attractive areas for turbine 

placement (Mageau et al. 2008, Great Lakes 

Commission 2011). However, utility-grade wind 

facilities have been associated with mortality events 

in migrating vertebrates (Newton 2007, Arnett 

et al. 2008, Smallwood and Thelander 2008), and 

chronic fatalities across the US, particularly in bats, 

have become a concern (Timm 1989, Johnson 2005, 

Arnett and Bearwald 2013, Hayes 2013, Smallwood 

2013). For example, approximately 75% of all bat 

mortalities occur in three species of long-distance 

migratory bats that are impacted by wind energy 

facilities (Cryan 2011, Kunz et al. 2007a, Arnett 

and Baerwald 2013), and these migrants, the hoary 

bat (Lasiurus cinereus), eastern red bat (Lasiurus 

borealis), and silver-haired bat (Lasionycteris 

noctivagans), typically account for the majority of 

bat fatalities at wind facilities in the Upper Midwest 

(Arnett et al. 2008). Three Wisconsin studies found 

high fatality rates for these same migrant species 

as well as substantial fatalities in the little brown 

bat (Myotis lucifugus) and big brown bat (Eptesicus 

fuscus) (Gruver et al. 2009, BHE Environmental 

2010, Grodsky et al. 2012), although the presence of 

major hibernacula in the vicinity of wind facilities 

may have influenced the results. Additionally, 

low reproductive rates inhibit the ability of bats 

to rebound from population declines (Racey and 

Entwistle 2000), which have already begun in 

several species (Kunz et al. 2007a, Cryan 2011). The 

cumulative impacts on migratory birds and bats are 

a concern that will increase with the growth of wind 

energy if methods to avoid or minimize mortality 

events are not implemented. A number of promising 

conservation measures have been proposed to 

reduce mortality levels, but the greatest benefit to 

the conservation of migrants might lie in our ability 

to identify and avoid future growth in locations 

where migrants concentrate. 

To help meet the needs of both renewable energy 

development and wildlife conservation, we 

established this project to identify the activity, 

temporal patterns, and magnitude of migration 

that occurs along the shorelines of the Great 

Lakes. Since bats and many bird species migrate 

during the night throughout the spring and fall, 

documenting migration is challenging because 

observing sporadic nocturnal movements is difficult. 

We used a combination of techniques to address this 

problem. We primarily used two avian radar units 

that simultaneously scanned the horizontal and 

vertical planes 24 hours per day, and we used nearly 

40 automated ultrasonic/acoustic monitors to record 

bird and bat calls. Finally, we collected incidental 

bird observations in the areas near the monitoring 

equipment. We are reporting only on the avian 

radar portion of the overall study, and our objectives 

include the following:

n Monitor locations along the shorelines of Lake 

Erie using a consistent methodology;

n Maintain an archive of continuously recorded 

radar data during the spring migration season;

n Identify the activity patterns captured by 

radar that are diagnostic of migration;

n Estimate the duration of the migration season.

n Document changes in the behavior of migrants 

under varying conditions and during different 

parts of the season.

2 Great Lakes Avian Radar - Spring 2012 
Methods

Study Area and Site Selection

In spring 2012, we selected two sites along Lake Erie 

for radar placement, with one site located towards 

the western end of the lake in Ohio and the other 

site located towards the eastern end of the lake in 

Pennsylvania (Figure 1). We located sites within 1.5 

km of the Lake Erie shoreline to monitor the airspace 

above inland, shoreline and lake areas. The western 

site, which is located in Erie County, Ohio, at 41.3798° 

N, -82.4376° W, was approximately 1.5 km from the 

shoreline and 177 m above sea level. Here, the radar 

unit was placed in the middle of an agricultural field 

in an area where cultivated crops and deciduous 

forest were the predominant land cover types within 

Figure 1. Locations where MERLIN Avian Radar Systems were deployed during the spring 2012 

migration season.

Table 1. Predominant land cover types found within a 3.7-km radius of the radar locations in Ohio and 

Pennsylvania in spring 2012.

the range of the radar unit according to our analysis 

using Esri ArcGIS software and the 2006 National 

Land Cover Database (Fry et al. 2011) (Table 1, 

Figure 2, Appendix 2). Cultivation in this area of Ohio 

predominately consists of row crops, such as corn 

and soybeans. The eastern site, which is located in 

Erie County, Pennsylvania, at 42.2213° N, -79.8683 W, 

was approximately 1 km from the shoreline and 222 

m above sea level. This radar was also placed in an 

agricultural field although in an area where cultivated 

crops and open water are the primary land cover types 

within range of the radar unit (Table 1, Figure 2, and 

Appendix 2). The crops in this area of Pennsylvania are 

predominately vineyards and fruit orchards.

Great Lakes Avian Radar - Spring 2012 3

National Land Cover Class

Ohio 

% of Land Cover

Pennsylvania 

% of Land Cover

Cultivated Crops, Hay/Pasture 35.83% 36.44%

Developed1 10.96% 16.23%

Forest2 28.52% 5.50%

Open Water 24.40% 35.79%

Other3 0.28% 6.03%

1

Includes low-, medium- and high-intensity development and developed open space.

2

Includes deciduous, evergreen and mixed forests. 3

Includes barren land, herbaceous, shrub/scrub and woody and emergent herbaceous wetlands.
Figure 2. Land cover types (Fry et al. 2011) found within a 3.7-km radius of the radar units in Ohio and 

Pennsylvania in spring 2012.

Esri ArcGIS software was used to model the areas 

of interest to identify suitable locations for radar 

siting. This suitability modeling incorporated 

elevation, land cover, and shoreline datasets for the 

Great Lakes. Additional landscape characteristics 

were derived from these datasets (elevation below 

the local maximum, percent forested, distance to 

forest, distance from shoreline, etc.) and ranked 

to create a continuous raster surface within the 

area of interest with estimated suitability values. 

Contiguous areas with high suitability were 

identified through the GIS modeling process and 

targeted for on-site assessment.

Biologists were dispatched to the areas of 

interest to more thoroughly evaluate the potential 

sites identified by the modeling effort, and this 

assessment included determining the land use, 

line of sight to shorelines, and accessibility for the 

placement of the radar units. Additional suitable 

locations not identified through modeling were 

frequently discovered through this process and 

evaluated. When the field biologists determined that 

a location was highly suitable relative to the other 

locations visited, contact with the property owners 

was initiated to obtain permission to set up the 

radar units.

Equipment

We used two model SS200DE MERLIN Avian 

Radar Systems (DeTect Inc., Panama City, FL) to 

document migration movements, and these systems 

were selected because they are self-contained mobile 

units specifically designed to detect, track, and 

count bird and bat targets. Each system employed 

two marine radars that operated simultaneously, 

with one scanning the horizontal plane and the other 

scanning the vertical plane (Figure 3). Additionally, 

each unit contained four computers for real-time 

automated data processing and a SQL server for 

the storage and review of processed data. The units 

were configured with a wireless router to allow 

remote access to the computers and automated 

status updates.

Description of radar. The solid-state marine radar 

antennas (Kelvin Hughes, London, UK) employed 

by our systems were 3.9 m in length and had a 

peak power of 170 W, wavelength in the S-band (10 

cm), and frequency range of 2.92 – 3.08 GHz, and 

4 Great Lakes Avian Radar - Spring 2012
Figure 3. Computer representation of the potential survey volume scanned by the horizontal and vertical 

radars used by the U.S. Fish and Wildlife Service in spring 2012. Graphic provided by DeTect, Inc.

Great Lakes Avian Radar - Spring 2012 5

they were configured to operate with both short 

and medium pulses (0.1 and 5 μs, respectively). 

The horizontal radar was also equipped with 

Doppler to help filter the stationary targets. The 

radars emanated a fan-shaped beam that had 

an approximately 1° horizontal and 25° vertical 

span when operated in the horizontal plane. The 

S-band radar was selected because it uses longer 

wavelengths that are less sensitive to insect and 

weather contamination compared with the X-band (3 

cm wavelength) antenna (Bruderer 1997), and it is 

also less sensitive to signal attenuation from ground 

clutter, such as vegetation and structures (DeTect 

Inc., unpublished data, 2009). The radars spin 

perpendicular to each other at a rate of 20 RPM and 

were synchronized so that they did not emit over 

one another. The horizontal scanning radar (HSR) 

was affixed to a telescoping base that was raised to 

approximately 7 m above the ground for operation; 

this radar rotated in the x-y plane with a 7° tilt to 

reduce the amount of ground clutter within its view. 

Although the radar was capable of scanning large 

distances, we selected a 3.7-km range setting to 

collect higher resolution data and identify smaller 

targets, such as passerines and bats. The HSR was 

primarily used to provide information on target 

movement direction. The vertical scanning radar 

(VSR) rotated in the x-z plane and scanned a 1° x 25° 

span of the atmosphere. We selected a 2.8 km range 

setting for this radar to collect data with increased 

resolution and used the VSR to provide information 

on the number and height of the targets. 

Weather Station. Each system was equipped 

with a weather station (Davis Vantage Pro 2, 

Hayward, CA, USA) that recorded wind speed and 

direction, humidity, temperature, precipitation, 

and barometric pressure. The weather data were 

summarized and stored every 5 minutes. An 

anemometer was attached to the radar unit, and it 

measured wind speed at a height of approximately 6 

m above the ground.

Radar Set Up and Data Collection

The radar systems were deployed at their 

respective sites during the third week of March; 

however, data collection did not start until the first 

week of April because of the need for adjustments 

at each unit and a malfunction with the vertical 

scanning radar in Ohio. Each radar system was 

maintained into the second week of June to capture 

the anticipated end dates of the migration season.

Establishing a radar system at a site involved 

several steps, including orienting the VSR, microsite selection, and performing adjustments to 

ensure that adequate information was captured.

We anticipated a primarily northbound direction 

of migration along the Lake Erie shoreline during 

spring and oriented the vertical scanning radars 

to an angle that was slightly off-perpendicular to 

the anticipated direction of traffic. This orientation 

was a compromise between a perpendicular angle 

that would intercept the greatest number of targets 

(birds or bats) and a parallel angle that would 

maximize the amount of travel time within the 
radar beam, and it was also influenced by micro-site 

selection, which is important because the position of 

the radar can affect the amount of interference from 

ground clutter or other sources. If large areas were 

obstructed from the radar view or if substantial 

amounts of clutter impeded data collection, then the 

systems were incrementally rotated to improve the 

view and/or reduce interference. 

Once a position was established, clear-air thresholds 

and the radar’s built-in sensitivity time control (STC) 

filters were employed to reduce small non-target 

returns and improve the tracking of distant targets.

These settings are necessary because an object 

reflects more energy at close range than it does when 

it is further from the radar. For example, an object 

within a range of 50 m will return approximately 

16-times more energy compared with an object at 

100 m (Bruderer 1997, Schmaljohann et al. 2008). 

To further improve the data collection, clutter maps 

were generated using 60-scan composite images 

(Figure 4) during time periods with low biological 

activity to identify areas with constant returns (white 

areas) that were not biological targets, such as tree 

lines, fencerows and buildings. These areas were 

assigned a reflectivity threshold that precluded the 

constant returns from being included in the data, thus 

reducing our ability to detect targets in these areas. 

Following this initial set up, MERLIN software 

was customized for the conditions at the sites. The 

MERLIN software provides real-time processing 

of raw radar data to locate and track targets while 

excluding non-targets and rain events, although the 

parameters used by the tracking software require 

adjustments to account for site-specific conditions. 

DeTect personnel trained our biologists to establish 

these settings with the goals of minimizing the 

inclusion of non-targets and maximizing cohesive 

target tracking. The processed data were stored in 

an Access database and then transferred each day to 

a SQL database, where they were stored and later 

queried for data analysis.

Despite the radar system’s ability to be operated 

remotely for extended periods of time, biologists 

remained on site during the data collection period 

to ensure continuous functioning, monitor raw 

(unprocessed analog radar returns) and processed 

radar outputs, provide routine maintenance (such 

as re-fueling and oil changes), and manage data 

storage. In addition to the processed data, we 

maintained all of the raw radar data for potential 

reprocessing. The raw radar data were temporarily 

stored in the field on 2 TB external hard drives and 

regularly transported on ruggedized external drives 

back to the U.S. Fish and Wildlife Service Regional 

Office (Region 3), where the data were transferred 

to long-term tape storage. 

Radar System Outputs

The MERLIN software generates more than 30 

measurements to describe the target size, shape, 

location, speed, and direction of movement. These 

data are of the same type used by biologists when 

identifying biological targets on a radar screen 

(DeTect Inc., unpublished data, 2009), and this 

information was stored in the database for later 

analysis. To reduce potential false tracking, the 

MERLIN tracking algorithm removed tracks with 

less than five observations and an automated filter 

was used to remove sectors of the sample volume 

that were dominated by rain. 

In addition to storing the target attribute data, the 

DeTect software outputs a two-dimensional digital 

display of targets being tracked in real-time and 

static images of tracked targets over a specified 

period of time (Trackplots) for both the vertical and 

horizontal radars. During each site check, we viewed 

the real-time digital display to ensure that it was 

consistent with the raw radar display, and we later 

viewed 15-minute and 1-hour Trackplots to assess 

the target direction and height during the previous 

day’s activity.

Data Processing and Quality Control

Prior to the data analysis, the data processed by 

MERLIN software were further evaluated for 

potential contamination by non-targets. Although an 

automated rain filter was used, it did not remove all 

of the rain from the recorded outputs during certain 

time periods. Additionally, insects and other forms 

of transient clutter may have been recorded during 

data collection. Therefore, biologists reviewed all of 

the data in 15-minute time increments and removed 

the time periods that were dominated by rain, 

although there were no time periods dominated by 

other forms of transient clutter that needed to be 

removed. 

We relied on visual inspections of the track patterns 

to discern contamination events. Rain and insect 

events form diagnostic patterns (Detect Inc., 

personal communication, 2011), and time periods 

with these types of track patterns can be removed. 

Unknown contamination that mimicked the patterns 

of desired targets was not removed from the 

database and contributed to the error associated 

with the indices. In addition, we evaluated initial 

counts by generating a time series of the variation 

in the number of targets per hour throughout the 

season for the HSR and VSR radars. In general, 

the HSR and VSR hourly counts were positively 

correlated with higher HSR counts, and in situations 

where the VSR counts were higher than the HSR 

counts or where the peak counts appeared to be 

outliers, the data were further investigated for 

evidence of contamination or potential issues with 

6 Great Lakes Avian Radar - Spring 2012
Figure 4. Clutter maps from vertical (left) and horizontal (right) scanning radars at study sites in Ohio and 

Pennsylvania during the spring 2012 migration season. Brighter areas represent static returns from stationary 

objects, such as tree lines and fencerows. Target detection may be obscured in these areas because of 

obstructions from the objects.

Great Lakes Avian Radar - Spring 2012 7

Ohio Clutter Maps

Pennsylvania Clutter Maps
Figure 5. Schematic depicting the vertical scanning radar beam from two different views and pictures of 

the radar unit associated with those views. The top left graphic identifies the standard front used for data 

analysis, which extends to 500 m on either side of the radar and up to a height of 2800 m. In this graphic, the 

radar is situated at the bottom center and the red dashed lines represent the lateral limits of the standard 

front. In the bottom graphic, the radar rotation is suspended so that the beam is emitting directly upward; 

this view is an approximation of the beam dispersion as it travels away from the radar unit (schematic not 

drawn to scale).

8 Great Lakes Avian Radar - Spring 2012 
radar performance. On the rare occasions when 

time periods with anomalies appeared to represent 

artifacts that were unrelated to target movement 

(e.g., rain events, insects or data processing errors), 

these periods were removed from further analysis.

Once the contaminated time periods were removed, 

we summarized the data using SQL queries 

provided with the MERLIN radar system. Data 

from the HSR were used to calculate the hourly 

counts and target direction, and all of the targets 

within 3.7 km of the radar unit were included in the 

analysis. Data from the VSR were used to calculate 

the hourly counts and height estimates, and these 

data were truncated to a 1-km or “standard front”. 

We adopted this sampling technique because it is the 

method used by the manufacturer of the MERLIN 

units and has also been used by other researchers 

(Lowery 1951, Liechti et al. 1995, Kunz et al. 2007b). 

The standard front was defined by a volume of 

space that extended 500 m to either side of the 

radar and continued to the maximum data collection 

height (2800 m) (Figure 5). For each site location, 

the time at sunrise and sunset was calculated, and 

target counts were further segregated into four 

biological time periods: dawn, day, dusk, and night. 

Dawn represented 30 minutes before sunrise to 30 

minutes after sunrise; day represented 30 minutes 

after sunrise to 30 minutes before sunset; dusk 

represented 30 minutes before sunset to 30 minutes 

after sunset, and night represented 30 minutes after 

sunset to 30 minutes before sunrise. 

Data Summary and Trends Analysis

We used the processed data to assess the activity 

patterns associated with migration. Horizontal 

Trackplots were viewed to identify changes in 

activity and investigate the behaviors of the 

migrants, such as reverse migration (Akesson 1999) 

and migrants moving toward the shore at dawn, 

and vertical Trackplots were viewed to investigate 

changes in activity, such as dawn ascent (Myres 

1964, Diehl et al. 2003). Target counts represented 

abundance, and we used these indices to identify 

directional, temporal, and altitudinal trends.

Directional Trends. The mean angle and 

concentration (r) of the target movement directions 

were analyzed following the methodology for 

circular statistics (Zar 1999) included in the DeTect 

SQL queries. The angular concentration value has 

a value of 1 when all of the angles are the same 

and a value of 0 when all of the angles cancel each 

other out, indicating that there is no predominant 

direction of travel (e.g., if 50% of the vectors 

are 180° and 50% are 360°, then no direction is 

predominant because there were as many targets 

heading south as there were heading north). We 

anticipated a generally northward direction of 

movement by the nocturnal targets during the 

spring migration season and reported the mean 

direction and the percent of nocturnal targets that 

traveled in a direction between northwest and 

northeast (292.5° – 67.5°). We used radial graphs to 

plot the number of targets per 8 cardinal directions 

(i.e., eight groups centered on N, NE, E, SE, S, SW, 

W, and NW) during the four biological time periods 

(i.e., dawn, day, dusk, and night). 

 

Temporal Trends. We plotted the counts of targets 

per hour processed by the MERLIN software for 

both the HSR and VSR antennas as a time series 

to identify pulses of nocturnal activity, the duration 

of the season, and changes in activity patterns over 

time. The HSR and VSR radars have different 

strengths that complement one another and were 

plotted together. The HSR index tracks low flying 

targets in a 360° span around the radar unit, and 

compared with the VSR, detection is not affected 

by the target’s direction of travel, although the 

HSR index is much more affected by ground clutter, 

which impacts target detection and tracking, and 

errors caused by ground clutter lead to both under‐ 

and over‐counting. Targets blocked by ground 

clutter may not be counted, and targets that fly in 

and out of areas with ground clutter may be counted 

multiple times. Such issues lead to HSR counts that 

are more influenced by site characteristics relative 

to the VSR counts; however, the HSR index better 

captures targets under certain conditions, such 

as when targets are primarily at low elevations 

and/or traveling parallel to the VSR. The HSR 

is also more susceptible to beam-bending from 

dynamic atmospheric conditions relative to the 

VSR, which presents minimal beam refraction 

primarily because of its orientation. The VSR 

index was used to track targets captured within 

the standard front, and it exhibits more consistent 

detection than the HSR because it mostly tracks 

against clear air except in the lowest altitude bands. 

Detection by the VSR index is affected by target 

direction and distance from the radar (Bruderer 

1997, Schmaljohann et al. 2008), and it is impacted 

by ground clutter, particularly at low elevations. 

Plotting these indices together provided a more 

comprehensive understanding of changes in target 

activity over time.

We used the VSR index to calculate the target 

passage rate (TPR), which is the number of targets 

per standard front per hour, using the DeTect 

SQL queries, and hour intervals with less than 

30 minutes of recording time were omitted from 

this calculation. For example, after removing all of 

the hours with less than 30 minutes of clean data, 

the nocturnal TPR for a given night (biological 

time period) was calculated by dividing the target 

Great Lakes Avian Radar - Spring 2012 9
count by the number of nighttime minutes and 

multiplying the value by 60 to yield the number of 

targets per hour during that night. We extended 

this metric to the season and calculated the mean 

TPR for the four biological time periods and hours 

of the season. The mean nocturnal TPR for the 

season is the sum of the night TPRs divided by 

the number of nights sampled. Similarly, the mean 

hourly TPR for the season is the sum of the TPRs 

for an hour-long period divided by the number 

times that hour was sampled. We also calculated 

mean nocturnal (night biological period) and 

diurnal (day biological period) TPR weekly during 

the sampling period using two methods. First, to 

demonstrate the variability among the sampled 

weeks, we divided the sum of the TPRs for a week 

(nocturnal or diurnal) by seven and reported the 

weekly mean TPR and its standard deviation. 

Altitudinal Trends. The DeTect SQL queries were 

used to estimate the height of the targets tracked 

within the standard front from the VSR data.

The height estimates were calculated based on 

the range and bearing of the target location with 

the largest radar echo and reported as the height 

above the ground as measured at the radar unit; 

this measurement does not consider changes in 

topography across the landscape. We used these 

estimates to calculate the mean altitude of the 

targets above the ground according to biological 

time period and hour, and we reported the mean 

and median altitudes for the season. 

Density per Altitude Band. To provide information 

on the density of targets per 50-m altitude band per 

hour within the standard front, we first estimated 

the volume of the radar beam’s approximate 

geometric shape. The width of the radar beam 

expands as it travels from the radar, resulting in 

increased survey volume with distance from the 

origin. The shape of the survey volume represents 

the space in which targets have the potential 

of being detected and is one of several factors 

that define the realized or actual survey volume 

(Bruderer 1997, Schmaljohann et al. 2008). We 

calculated the volume contained by the shape of the 

radar beam and reported the target density (targets 

per 1,000,000 m3

) per 50-m altitude band per hour 

for each biological period. This was calculated by 

dividing the number of targets per volume of an 

altitude band by the number of minutes with clean 

data during the biological time period of interest 

and multiplying the result by 60. 

To estimate the volume of the 50-m altitude bands 

that are constrained by the standard front, we 

used Monte Carlo integration (Press et al. 2007), 

which is described in detail elsewhere (manuscript 

in preparation) and summarized here. The volume 

contained by the shape of the radar beam can be 

calculated using spherical coordinates and multiple 

integration. However, subjecting this volume to 

Cartesian constraints (i.e., the standard front and 

the altitude bands) complicates the calculation, so 

the volume bands are more easily estimated using 

Monte Carlo integration, which is a method used 

to calculate an unknown volume by enclosing it 

in a known volume and saturating the space with 

random points. Monte Carlo integration requires 

rules that determine whether the randomly drawn 

points are inside or outside of the unknown volume, 

and the proportion of points that fall within these 

constraints multiplied by the volume of the known 

space is approximately equal to the unknown 

volume. In Monte Carlo integration, the estimate 

approaches the true value as the number of random 

points approaches infinity. 

We used R software (R Core Team 2012) to describe 

a box of known volume that was large enough to 

enclose the radar beam and saturated this space 

with 10 million random points, and we determined 

two simple rules that defined whether a point was 

in the survey volume. The first rule was that the 

distance of the randomly drawn point from the 

origin had to be less than 2.8 km, and the second 

rule was that the angle between a randomly drawn 

point and the vertical plane (the x-z axis in Figure 

Figure 6. Graphical representation of the structural 

volume of the vertical scanning radar within the 

standard front. In this graphic, the radar unit is 

located at the origin and the radar beam extends to 

500 m on either side of the radar unit (x-axis) up to a 

maximum height of 2800 m (z-axis). The y-axis represents the spread of the radar beam as it extends 

away from the origin. The orange semi-transparent 

points represent the volume contained by the radar 

beam. Dark gray points represent the volume within 

the box but not included in the radar beam.

10 Great Lakes Avian Radar - Spring 2012 
6) had to be less than 12.5o (i.e., half the angle of 

the width of the beam). The volume of a full sweep 

of the radar beam, which was estimated by Monte 

Carlo integration, was within 5% of the analytical 

solution using spherical coordinates; therefore, the 

number of random points that we used provided 

a reasonable approximation of the volume. By 

determining the volume of a full sweep of the radar 

beam, we were able to further constrain the Monte 

Carlo integration to describe the structural volume 

of the radar beam within a standard front (Figure 6) 

and within altitude bands (Figure 7). 

The number of targets per altitude band has often 

been reported by other researchers, although 

volume corrections are seldom reported. We wanted 

to compare our correction to the uncorrected 

method; however, the count data and volume data 

were on different scales. Therefore, we compared 

our density estimate to a density estimate based on 

the number of targets per 50-m altitude band per 

hour while assuming an equal amount of volume 

within each altitude band (the volume of each 

altitude band is equal to the total volume divided 

by the number of altitude bands). An assumption 

implicit to reporting the number of targets per 

altitude band is that comparisons among bands can 

be made directly (i.e., altitude bands are equal); for 

our comparison metric, we made this assumption 

explicit (see Appendix 4). 

Great Lakes Avian Radar - Spring 2012 11

Figure 7. Volume of 50-m altitude bands within the standard front as estimated by Monte Carlo integration. 

Altitude band intervals represent the upper band limit, and the target counts provided by the vertical scanning radar are limited to the structure of the standard front. The red line represents the top of the rotorswept zone at 130 m.
Site Radar

Survey	

Period

Radar	

Downtime

Time	

Radar	

Collected	

Data

Radar	

Data	

w/Rain

Usable	

Radar	

Data %	Usable	Data

OH VSR1 1727 28 1699 107 1592 92%

OH HSR 1727 19 1708 0 1707 99%

PA VSR2 1679 502 1177 76 1101 66%

PA HSR 1679 34 1645 4 1641 98%

1

Vertical and horizontal radars are not equally impacted by rain events or downtime.

2

Vertical radar malfunctioned and did not collect data for approximately 3 weeks of the survey period. 

12 Great Lakes Avian Radar - Spring 2012

Results

Data collection began in the spring 2012 season on 

April 1 and 3 at the Ohio and Pennsylvania sites, 

respectively, and it ended on June 11, 2012 at both 

sites, which produced a survey period of 1,727 hours 

at the Ohio site and 1,679 hours at the Pennsylvania 

site (Table 2). Data were recorded continuously while 

the radar units were operational, although gaps in 

the data occurred during rain events and when the 

radar units were not operational due to maintenance 

or malfunction (radar downtime). The horizontal 

radar at the Ohio site malfunctioned and did not 

collect data from May 7 through mid-day on May 10 

because of water intrusion and a corrosion issue in an 

electrical pin. The vertical radar at the Pennsylvania 

site malfunctioned from April 21 through mid-day on 

May 10 because of the STC filter reverting to a default 

setting, which resulted in a loss of target detection 

during this time. When correcting for radar downtime 

and removing the periods with rain, the vertical and 

horizontal radars collected usable data 92% and 99% 

of the season in Ohio and 66% and 98% of the season in 

Pennsylvania, respectively.

Table 2. Survey effort (hours) by vertical and horizontal scanning radars during spring 2012 at the radar 

sites in Ohio (OH) and Pennsylvania (PA). 

Qualitative Assessments 

Plots of the tracked targets showed images of 

nocturnal migration events at both locations (Figure 

8 and 9). For example, on May 19 at the Ohio site, 

the horizontal radar recorded scattered activity, and 

the vertical radar recorded a low number of targets 

from 12:00 – 18:00 (Eastern Standard Time). During 

the 18:00 hour, directional movement began, and 

biologists observed that these targets were blackbirds 

flying west towards their nighttime roosts. During 

this time, the vertical radar showed low target counts 

because of the low altitude at which these targets 

flew as well as the direction and position of the radar 

unit, which was oriented for optimum data collection 

for targets moving north. During the 20:00 hour, 

directional movement to the north/northwest began, 

and vertical radar detection increased with more 

targets at higher altitudes. At approximately 23:00, 

northern movement continued, although the targets 

began to move northeast as well, and vertical detection 

continued to increase. By 04:00 on May 20, the target 

flight direction shifted from predominantly north/

northeast to northeast/east, and the target counts 

began to decline on both the horizontal and vertical 

radars. By 05:00, the target directions shifted again, 

with certain scattered directional activity as well 

as movement towards the land from out over the 

water. The horizontal and vertical radars detected a 

continued decrease in activity, and the target height 

began to decrease as well. By 12:00, diurnal activity 

appeared to be similar to that of the proceeding day 

at 12:00 (Figure 8). This pattern of target movement 

and changes in altitude were indicative of a pulse of 

migratory activity. 

Site

OH

OH

PA

PA

Radar

VSR1

HSR

VSR2

HSR

Survey 

Period

1727

1727

1679

1679

Radar

Downtime

28

19

502

34

Time

Radar

Collected

Data

1699

1708

1177

1645

Radar

Data

w/Rain

107

0

76

4

Usable

Radar

Data

1592

1707

1101

1641

% Usable Data

92%

99%

66%

98%
Great Lakes Avian Radar - Spring 2012 13

A similar pattern of low target activity and flight 

heights during the day and increased directional 

activity and higher flight heights at night was also 

observed at the Pennsylvania site. Figure 9 shows 

targets moving north towards the shoreline at dusk 

(with certain western movement as well) and a shift in 

the direction of target movement to the northeast at 

approximately 23:00. By 04:00 the next morning, the 

target direction had shifted again, moving south to the 

shore and east along the lakeshore rather than north 

across the lake. This pattern continues until after dawn 

when the activity begins to return to the normal nondirectional diurnal movement. 

Also apparent on the Trackplots from both sites are 

areas that are not well recorded by the radar because 

of beam blockage from ground clutter (i.e., topography, 

vegetation, buildings, etc.) (Figure 4), thus resulting 

in reduced detection in the air space within the data 

collection range (e.g., west of the radar unit at the 

Ohio site and in the center and eastern areas of the 

Pennsylvania radar site, as observed in the horizontal 

Trackplots in Figures 8 and 9). Rings of decreased 

detection near the radar unit and where the radar 

switched from short to medium pulses are also evident 

in both the horizontal (May 19, 20:00) and vertical 

(May 19, 23:00) Trackplots (observed at a range of 

approximately 1,400 – 2,000 m).
14 Great Lakes Avian Radar - Spring 2012

Figure 8. Images of the tracks during 1-hour increments recorded by horizontal and vertical scanning 

radars during a migration event at our radar site in Ohio. Horizontal radar images (columns 1 and 3) show 

the directions of targets as indicated by the color wheel (dark blue indicates travel to the north and red 

indicates travel to the south). Vertical radar images (columns 2 and 4) show the target heights.
Photo Credit: B. Thompson

Great Lakes Avian Radar - Spring 2012 15

Figure 9. Images of tracks during 1-hour increments recorded by horizontal and vertical scanning radars 

during a migration event at our radar site in Pennsylvania. Horizontal radar images (columns 1 and 3) show 

the directions of targets as indicated by the color wheel (dark blue indicates travel to the north and red 

indicates travel to the south). Vertical radar images (columns 2 and 4) show the target heights.
16 Great Lakes Avian Radar - Spring 2012 

Directional Trends

During the spring 2012 season, the nocturnal target 

direction was generally north/northeast at both of 

the sampled locations (Figure 10). At the Ohio site, 

the mean nocturnal direction was 23°, and it had 

an angular concentration (r) of 0.43 (n = 3,078,229 

targets). In addition, on 76% of the nights, the 

mean target direction was between northwest and 

northeast (292.5° – 67.5°). The Pennsylvania site 

had a mean nocturnal direction of 45° (r = 0.56, n 

= 2,874,773) with 68% of nights presenting a mean 

direction between northwest and northeast. Onshore 

movement to the east at dawn was visible at both 

locations (Figure 10), and uniform directionality at 

night was slightly stronger at our Pennsylvania site 

compared with the Ohio site (Table 3).

Figure 10. Target direction per hour during four biological periods in spring 2012 at our sites in Ohio (left) and 

Pennsylvania (right).

Table 3. Mean direction, angular concentration (r), and percent of biological time periods with strong target 

directionality (r ≥ 0.5) at our sites in Ohio and Pennsylvania. 
Great Lakes Avian Radar - Spring 2012 17

Temporal Trends

Time Series Plots. Hourly target counts provided 

by the horizontal and vertical radars showed pulses 

of elevated nocturnal activity at our study sites 

with peaks occurring a few hours before midnight. 

Throughout our sampling period, these events were 

often clustered into groups of several nights, and 

they were first observed on April 3 and 8 at the Ohio 

and Pennsylvania sites, respectively (Figures 11 and 

12). At both sites, the occurrence and magnitude of 

nocturnal pulses decreased substantially after June 1. 

Different activity patterns were apparent at our 

study sites as the season progresses. For example, 

activity patterns were dominated by nocturnal 

pulses, which were observed with the horizontal and 

vertical radars beginning in late April in Ohio. This 

pattern was apparent on the horizontal radar for 

Pennsylvania as well (the vertical radar at our unit 

in Pennsylvania malfunctioned at this time), and it 

continued until late May when the activity levels 

began to decrease overall. 

Differences in the detection capability of the vertical 

and horizontal scanning radars were also apparent. 

As noted earlier, on May 16 at 18:00 at the Ohio 

site, the westward flight path of blackbirds was 

well captured by the horizontal radar, although the 

higher degree of target movement was not captured 

by the vertical radar because of the flight path 

and lower altitude of these targets (note the red 

arrow pointing to this time period in Figure 11). At 

midnight on April 30, many targets passed at high 

elevations above the HSR range of detection, which 

brought the count indices of the two antennas much 

closer together. The record from May 20 provides an 

example of targets above the study area moving in a 

direction and at an altitude distribution that could be 

detected by both radars.
18 Great Lakes Avian Radar - Spring 2012 

Figure 11. Hourly counts by the horizontal and vertical radars from April 1 – June 11, 2012 in Ohio. Light 

gray vertical lines indicate midnight.
Great Lakes Avian Radar - Spring 2012 19

Figure 12. Hourly counts by the horizontal and vertical radars from April 4 – June 11, 2012 in Pennsylvania. Light gray vertical lines indicate midnight, and the gap in the vertical data from April 21 - May 11 was 

because of a radar software malfunction.

Hourly Counts by Horizontal and Vertical Radars: Pennsylvania
20 Great Lakes Avian Radar - Spring 2012 

Target Passage Rate. The pattern of mean TPR 

among the four biological time periods was similar 

between the two study sites (Figure 13), and the 

mean TPR at night was greater than the combined 

means of the other three biological time periods 

(Table 4). The mean nocturnal TPR was 640 ± 601 

SD (n = 68 nights) and 514 ± 518 SD 

(n = 49 nights) at the Ohio and Pennsylvania sites, 

respectively. The mean TPR varied by hour and 

presented peak numbers between 21:00 and 22:00 

hours (approximately dusk through an hour after 

sunset) at both sites. At both locations, the mean 

TPR gradually decreased as the night progressed, 

with the most drastic decline occurring from 

approximately 04:00 to 05:00, which corresponded to 

dawn (Figure 14).

Figure 13. Box plots showing the variability in target passage rate (targets/km/hour) during the four 

biological periods in spring 2012 in Ohio and Pennsylvania. Whiskers represent the 1st and 4th quartiles; 

boxes represent the 2nd and 3rd quartiles (with the line between indicating the median); and blue diamonds 

represent the seasonal mean for the time period.

Table 4. Mean target passage rate (TPR) with standard deviations during the four biological periods in 

Ohio and Pennsylvania in Spring 2012.
Great Lakes Avian Radar - Spring 2012 21

Figure 14. Mean hourly target passage rate (targets/km/hour) in spring 2012 at sites in Ohio 

and Pennsylvania.

Figure 15. . Weekly mean nocturnal and diurnal target passage rates (targets/km/hour) in Ohio (top row) 

and Pennsylvania (bottom row) from April 1 – June 10, 2012. Error bars represent one standard deviation. 

Note the different scales on the nocturnal and diurnal plots.
Weekly Mean of Target Passage Rates. At both 

sites, the weekly mean nocturnal target passage 

rates were relatively high compared with the diurnal 

target passage rates, and both sites showed a general 

increase in the means throughout the season. In 

mid-to-late May, the mean nocturnal TPRs began 

to decrease (Figure 15), and the lower TPR during 

the last week of April was likely due to a change in 

weather conditions bringing lower temperatures and 

a mix of sleet and snow. This shift in weather likely 

resulted in fewer migrants moving through the area 

during this time period in Ohio. The weekly mean 

nocturnal TPR was consistently higher than the 

weekly mean diurnal TPR (Figures 15), and as the 

migration season subsided, less of a difference was 

observed between the nocturnal and diurnal target 

passage rates (Figures 15 and 16). Trends in both the 

nocturnal and diurnal TPRs (7-day moving means) 

were similar at both sites (Figure 17).

22 Great Lakes Avian Radar - Spring 2012 

Figure 16. Comparison of the nocturnal and diurnal target passage trends (based on a 7-day moving mean) 

in spring 2012 in Ohio (top row) and Pennsylvania (bottom row). 
Great Lakes Avian Radar - Spring 2012 23

Altitudinal Trends

Our density estimate accounted for the geometric 

shape of the sampled space and were substantially 

different than the density estimate that assumed 

an equal amount of sample volume per altitude 

band. The dawn and dusk altitude profiles differed 

between our two locations, and the low elevation 

density was greater at our Ohio site (Figures 

18 and 19). The hourly altitude profiles revealed 

considerable variation in the use of altitude bands 

at night (Figures 20 and 21); however, over the 

course of the season, the 50 – 100 m and 100 – 150 

m altitude bands were the most densely used at 

the Ohio and Pennsylvania sites (Figure 22), which 

presented a total of 5.83 targets per 1,000,000 m3 

per night hour and 5.44 targets per 1,000,000 m3

per night hour, respectively. The maximum target 

density was below 150 m for 75.0% and 68.0% of 

the nights at the Ohio and Pennsylvania sites, 

respectively (Figure 23). A similar pattern (although 

with more variation) occurred when the hours from 

20:00 – 04:00 were considered individually and the 

maximum target density that occurred below 150 m 

during 64.9% and 64.7% of these night hours at the 

Ohio and Pennsylvania sites, respectively (Figure 

24).

At both sites, targets were observed within the 

entire range of the sampled altitude bands. The 

mean altitude of nocturnal targets was 587 m ± 424 

m SD and 447 m ± 296 m SD above the ground at 

our Ohio and Pennsylvania sites, respectively, and 

the median altitude at night was 522 m and 405 

m, respectively. The median altitude was greatest 

during the night and dawn biological time periods. 

Although many radar reports include estimates of 

the mean and median target altitudes, we found that 

these estimates were poor indicators of maximum 

density (Table 5) because of differences in the 

volume of air space sampled at various altitude 

bands. Figures 25 and 26 are based on target 

density and show the variation in flight altitudes 

used by birds and bats that were counted by our 

vertical scanning radars throughout our survey 

period (April 1 – June 11, 2012 at the Ohio site and 

April 4 – June 11, 2012 at the Pennsylvania site). 

Figure 17 Comparison of the nocturnal (top row) and diurnal (bottom row) target passage trends (based on 

a 7-day moving mean) in spring 2012 in Ohio and Pennsylvania.
24 Great Lakes Avian Radar - Spring 2012 

Table 5. Comparison of the mean altitude (m) with the standard deviation, median altitude, and altitude 

band (50-m bands) that contained the maximum target density during the four biological periods at our sites 

in Ohio and Pennsylvania in spring 2012. Max Band Density represents the top of the altitude band.

These graphics show the altitude bands of 0 – 1,300 

m where most targets were counted (targets were 

counted up to 2,750 m, which is the extent of our 

sampling range). These graphics show that night 

hours at both sites had the highest density of 

flight activity and that the range of flight altitudes 

increased during the night hours. The graphics also 

show that many targets flew well within a 30 – 130 m 

RSZ and that the mean and median altitudes do not 

reflect peak density altitudes, demonstrating how 

these values can misrepresent flight risk. 

The mean altitude per hour during the season 

showed a similar pattern at the two locations 

(Figure 27), with the mean altitude increasing 

after dusk, tapering from 21:00 to 22:00 hours, and 

decreasing after midnight. A spike in the mean 

altitude occurred at approximately 04:00 in Ohio and 

during 04:00 and 05:00 in Pennsylvania, and these 

time periods occurred during or near dawn in at 

least a portion of the survey period.
Great Lakes Avian Radar - Spring 2012 25

Figure 18. Altitude profile of the targets at our site in Ohio. Corrected lines depict the target density 

(targets/1,000,000 m3

) per 50-m altitude band per hour after adjusting for the structure of the sample 

volume. Uncorrected lines depict the target density per 50-m altitude band per hour with an assumed 

uniform volume distribution (volume of each band is equal to the total volume divided by the number of 

bands). The red band represents the rotor-swept zone (RSZ) between 30 – 130 m, and the y-axis labels 

indicate the top of the altitude band.
26 Great Lakes Avian Radar - Spring 2012 

Figure 19. Altitude profile of the targets at our site in Pennsylvania. Corrected lines depict the target 

density (targets/1,000,000 m3) per 50-m per hour altitude band after adjusting for the structure of the 

sample volume. Uncorrected lines depict the target density per 50-m altitude band per hour with an 

assumed uniform volume distribution (volume of each band is equal to the total volume divided by the 

number of bands). The red band represents the rotor-swept zone (RSZ) between 30 – 130 m, and the y-axis 

labels represent the top of the altitude band.
Great Lakes Avian Radar - Spring 2012 27

Figure 20. Sample of the hourly altitude profiles corrected for the shape of the sample volume at our site in 

Ohio in spring 2012. Hours were selected to portray the variability in density per altitude band of the passing targets. The x-axis represents the target density; the red line represents the top of the rotor-swept zone 

at 130 m; and the y-axis labels represent the top of the altitude band.
Figure 21. Sample of the hourly altitude profiles corrected for the shape of the sample volume at our site in 

Pennsylvania in spring 2012. Hours were selected to portray the variability in density per altitude band of 

the passing targets. The x-axis represents the target density; the red line represents the top of the rotorswept zone at 130 m; and the y-axis labels represent the top of the altitude band.

28 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 29

Figure 22. Altitude profile of the corrected target density below 400 m in Ohio and Pennsylvania. The x-axis 

represents the target density (targets/1,000,000 m3) per 50-m altitude band, and the y-axis labels represent 

the top of the altitude band.
Figure 23 Percent of nights when the maximum density (targets/1,000,000 m3

/ altitude band) or count 

(targets/altitude band) occurred within a given 50-m altitude band in Ohio and Pennsylvania in spring 2012. 

X-axis labels represent the top of the altitude band.

30 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 31

Figure 24. Percent of night hours (20:00 – 04:00) when the maximum density (targets/1,000,000 m3/ altitude 

band) or count (targets/altitude band) occurred within a given 50-m altitude band in Ohio and Pennsylvania 

in spring 2012. X-axis labels represent the top of the altitude band.
Figure 25. Variation in flight altitudes based on target density (targets per million cubic m) at our site in 

Erie County, Ohio throughout the spring study period. Altitude bands are in meters and labels represent 

the max value of each altitude band. Density of targets is in targets per million cubic meters. Colors shown 

in each rectangle indicate the relative density observed for that altitude (key shown on the right) and time. 

The dark blue and light blue lines represent the nocturnal mean and median target heights, respectively. 

The black line at 130 m represents the max height of a turbine with a RSZ of 30 – 130 m. Note the difference 

in density scale used in Figure 26.

32 Great Lakes Avian Radar - Spring 2012
Great Lakes Avian Radar - Spring 2012 33

Figure 26. Variation in flight altitudes based on target density (targets per million cubic m) at our site in 

Erie County, Pennsylvania throughout the spring study period. Altitude bands are in meters and labels 

represent the max value of each altitude band. Density of targets is in targets per million cubic meters. 

Colors shown in each rectangle indicate the relative density observed for that altitude (key shown on the 

right) and time. The dark blue and light blue lines represent the nocturnal mean and median target heights, 

respectively. The black line at 130 m represents the max height of a turbine with a RSZ of 30 – 130 m. Note 

the difference in density scale used in Figure 25
Figure 27. Mean hourly target heights (m) in spring 2012 in Ohio and Pennsylvania. Orange and blue 

markers indicate the hours in which sunrise and sunset occurred during the season, respectively. Error bars 

represent one standard deviation.

34 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 35

Discussion

We undertook this study to document migration 

along the shorelines of the Great Lakes. We found 

that migration movements were common along 

the southern shoreline of Lake Erie, where we 

established our study sites, and we believe that the 

data collected at these two sites are representative of 

migration along the rest of the Lake Erie shoreline. 

Our research contributes to a growing body of 

literature documenting various aspects of migration 

and identifies the Great Lakes shorelines as areas 

important for the conservation of migratory species. 

Our data further provide unique observations on 

the magnitude and timing of nocturnal migration 

that could not be observed without the aid of radar 

technology.

Sampling Regime 

The sampling regime is an important consideration 

for migration studies because migratory movements 

are partially guided by environmental conditions 

and occur in pulses throughout the migratory season 

(Alerstam 1990). Our continuous sampling scheme 

captured the timing of migration events and provided 

a more complete picture of the migratory season than 

a systematic (once per week) or random sampling 

scheme, which could have missed pulses of activity 

(Figure 26). We used diurnal radar observations to 

provide a baseline to evaluate nocturnal activity, and 

including this time period in the sampling scheme 

helped to distinguish the magnitude of the migration 

events (Figure 16). Our sampling regime was also 

useful in indicating when the nocturnal migration 

season for passerines and bats declined in late May, 

although our April start date may not have included 

the onset of migration at our study sites. With 

additional data collection, we will be able to better 

describe the migration season and its variations 

according to location and year, and this information 

will help tailor conservation efforts, such as turbine 

curtailment, to time frames when they will be most 

effective. 

Site Comparison Considerations

Target counts provided by radar are influenced 

by the radar type and calibration, filtering of nonintended targets, count algorithms, frequency band, 

antenna orientation, sampling scheme, and detection 

probability and sample volume variations (Bruderer 

1997, Harmata et al. 1999, Schmaljohann et al. 2008). 

Even when the same equipment and methodology 

are used among sites or studies, comparisons should 

be made cautiously if the probability of detection and 

sampling volume are ignored (Schmaljohann et al. 

2008). Recognizing that our counts represent an index 

of target passage that is site specific, we are cautious in 

performing comparisons among other sites or studies. 

Therefore, rather than relying solely on the magnitude 

of target passage as an indication of migration, we have 

assessed the activity patterns among sites to compare 

the relative strength of migration. For example, a site 

with a nocturnal passage rate that shows peaks that 

are multiple times larger than the lulls for the majority 

of the sampling period would likely experience a 

greater degree of migration than a site with less of a 

discrepancy or a site that had a nocturnal passage rate 

that only occasionally spiked above the baseline value 

of nocturnal passage rates. 

Migration Patterns

The recorded patterns of movement were consistent 

with other migration observations (Newton 2008) 

and indicated that nocturnal migratory flights 

occurred regularly in spring 2012 at both of our 

surveyed locations. This nocturnal activity was 

typically oriented in a north/northeast direction 

(Figure 10) and occurred in seasonal pulses that 

were captured by the horizontal and vertical radars 

(Figures 11 and 12). We also observed targets 

flying over water return to shorelines near dawn, 

and the target passage rate (mean for the season) 

was greatest during the nocturnal biological time 

period at both locations (Table 4, Figure 13). These 

patterns match with what we have observed at 

all other sites surveyed around the Great Lakes 

(Bowden et al. 2015). The mean hourly heights 

exhibited a pattern previously associated with 

migration (Harmata et al. 2000, Mabee and Cooper 

2004), with heights increasing near dusk, peaking 

several hours before midnight, and beginning to 

decrease prior to dawn. The slight increase in mean 

height near dawn at the Pennsylvania site (Figure 

25) was consistent with the migratory behavior 

described as dawn ascent (Myres 1964, Diehl et 

al. 2003), which is attributed to migrants that fly 

at higher altitudes to gain a broader view of the 

surrounding landscape before selecting a stopover 

habitat or returning to the shoreline if flying 

over water. Taken together, we attribute these 
36 Great Lakes Avian Radar - Spring 2012 

Figure 28. Example of a hypothetical sampling schedule in which data are collected once per week (top 

graphic) versus the actual continuous sampling schedule (bottom graphic). Red lines represent the number of 

targets counted per hour by the vertical scanning radar from April 1 – April 30, 2012 in Erie County, Ohio.

nocturnal observations to migrants and suggest 

that the studied shorelines are important for their 

conservation.

At both of our sample locations, nocturnal targets 

appeared to move across the landscape in waves, 

with peaks near April 19 and May 20 at both sites 

(Figure 17) and an additional peak observed at 

the Ohio site near May 7. The vertical radar in 

Pennsylvania was malfunctioning during this 

time period; however, the data collected on the 

horizontal radar (Figure 12) indicated that a peak 

likely occurred during this time as well. These 

fluctuations may be related to broad-scale weather 

fronts, variations in temporal patterns among guilds 

of migrants, or a combination of these and other 

factors (Newton 2009). The trends at locations of 

similar latitude but on opposite ends of Lake Erie 

revealed broad-scale influences and indicated that 

further investigation into their causes would allow 

for the prediction of heavy migration events. 

The weekly mean nocturnal TPR estimates were 

consistently higher than the weekly mean diurnal 

TPR estimates throughout the data collection 

period (Figures 15 and 16); however, the difference 

decreased by early June. This shift from time 

periods with significantly higher nocturnal activity 

to time periods with diminished nocturnal activity 

indicates that migration substantially contributed to 

the aeroecology above our study areas.
Great Lakes Avian Radar - Spring 2012 37

Flight Altitude

The altitude profiles indicated that most of the 

nocturnal targets passed below 800 m with peak 

densities in the 50 – 150 m altitude bands (Figures 

18, 19 and 22). We corrected for the approximate 

shape of the survey volume and included this 

correction in our density estimates, although this 

correction is based on the manufacturer’s estimate 

of beam geometry, which may not be precise. 

Furthermore, beam propagation was not consistent 

over time because it was affected by side lobes, 

target size and distance, and atmospheric conditions. 

Nevertheless, we believe that the correction was 

an improvement over the altitude profiles that 

ignored beam geometry and sampling effort. We 

were not able to correct for the loss of detection 

with distance from the radar (Schmaljohann et al. 

2008), and our vertical scanning radars lost detection 

at a range of approximately 1,400 – 2,000 m, which 

was where the radar transitioned from the short 

to medium pulse. For these reasons, our estimates 

likely under-represented the density as altitude 

increases. However, the densities per altitude band 

were already decreasing (Figure 18) before the 1,400 

m band, and any undercount would be unlikely to 

change the overall picture.

The reported altitude profiles varied considerably 

among the nighttime hours at our sites in Ohio and 

Pennsylvania (Figures 20 and 21). Migrants adjusted 

their flight altitude according to the wind direction 

and speed, visibility, time, and the landscape below 

the flight trajectory (Alerstam 1990, Hueppop et 

al. 2006, Liechti 2006). For example, headwinds 

aloft have resulted in migrants moving en masse to 

lower altitudes that present reduced wind speeds 

(Gauthreaux 1991). In addition, migrants typically 

returned to land at least twice during every 24-hour 

period; therefore, changes in flight altitude occurred 

at various times over the course of the night and 

were associated with targets ascending from and 

descending to stopover sites. Depending on the 

location, these altitude changes may place migrants 

at risk of collision with wind turbines and other tall 

human-made structures. 

Radar Study and Management Considerations

Although radar may be the best tool available 

for gathering large amounts of data on nocturnal 

migration, the interpretation of radar data can be 

challenging. Marine radar is the most common type 

of radar used to track the movements of birds and 

bats (Larkin 2005), and its use in risk assessments 

will likely increase with wind energy development. 

Despite this growing trend, standardized equipment 

and methodologies for establishing radar settings, 

ground-truthing biological targets, and data 

processing have not been adopted, although such 

considerations would substantially improve the 

quality of the data. Standardization presents 

an enormous challenge; however, without it, 

comparisons among studies may be more reflective 

of changes in equipment, methodology, and site 

conditions rather than differences in migratory 

activity among sites. 

Additionally, the metrics reported in radar surveys 

can be misleading to anyone unfamiliar with avian 

radar. For example, the mean altitude of target 

passage is often reported to be above the rotorswept zone and has been interpreted to indicate 

low risk. However, the mean altitude can be well 

above the rotor-swept zone, even when there is a 

high rate of target passage within the zone because 

of the long range over which radars collect altitude 

data, which was up to 3 km above the ground in our 

study; thus, high flying targets can inflate the mean 

altitude. This bias was apparent in our data and 

can be observed by comparing the mean altitude 

of nocturnal targets to the most densely populated 

altitude band (Figures 18, 19, 25, 26, and Table 5). It 

is also misleading to compare the percent of targets 

below and above the height of the rotor-swept zone 

without addressing the inherent difference in radar 

sampling effort at various altitude bands. Within 

our sampling framework, there were three 50-m 

altitude bands below 150 m (an estimate for the 

height of the rotor-swept zone) and 53 altitude bands 

above 150 m. Based on our model, we estimated that 

approximately 1% of the potential survey volume 

was below 150 m. Therefore, we would expect a 

small percentage of targets to be recorded at or 

below the rotor-swept zone, although this does not 

necessarily indicate low risk. Additionally, sampling 

in areas in the lower altitude bands is often poor due 

to the effects of ground clutter (Figure 4).

When examining general migration patterns, high 

nighttime migrant activity was documented at our 

two Lake Erie radar sites as demonstrated by our 

Trackplots (Figures 8 and 9), the time series plots 

from each site (Figures 11 and 12), high target 

passage rates (Figures 13 - 15 and Table 4). Percent 

of targets within a 30 – 130 m rotor-swept zone were 

high during all biological time periods, with target 

density being highest at night (Figures 18, 19, 25 

and 26). Throughout the migration season, nighttime 

targets were recorded flying along the shorelines 

and across the lake (Figure 10); as dawn approaches 

nighttime migrants need a place for refuge, rest, and 

foraging, thus migrants flying over water have been 

recorded returning towards shore around dawn. The 

combination of these behaviors indicates that high 

numbers of nighttime migrants may be at risk of 

collision with wind facilities, communication towers 

or other tall structures located along the shorelines 

of Lake Erie. 
38 Great Lakes Avian Radar - Spring 2012 

Although the target passage rate and target density 

were lower during the dawn, day, and dusk periods, 

the migrants may still be at risk of collision during 

these time periods. Targets were recorded flying 

along the lakeshore during these time periods, 

indicating that the Lake Erie shoreline is used by 

migrants during all times of the day in the migration 

season for both flightpaths and stopover habitat. 

Conclusions

In this report, we provide examples of 

methodologies and analyses that are helpful in 

the interpretation of radar data. We suggest that 

relative changes in the counts at a single site 

indicate the level of migration activity, and these 

data provide a better indicator than comparisons 

between the magnitude of counts recorded in 

different studies. Careful attention should be given 

to how these indices fluctuate over fine temporal 

scales, such as at hourly scales compared with 

monthly or seasonal scales. Our clutter maps 

provided information on our ability to detect 

targets at various altitudes, and we believe that it 

is important for radar operators to address their 

ability to detect targets at low altitudes, particularly 

for collision risk assessments. We provide the basis 

for a method of accounting for the structure of the 

sample volume that offers a partial solution, albeit 

with limitations (Schmaljohann et al. 2008), instead 

of ignoring the biases associated with sampling 

effort. Overall, we found that radar provides insights 

into nocturnal migration that would be otherwise 

unattainable, and we believe that its continued 

development and careful interpretation will result 

in valuable contributions to the management and 

conservation of migrating birds and bats.

The results of our research highlight the potential 

role of radar in implementing recommendations 

from the Land-based Wind Energy Guidelines 

(USFWS 2012) for the identification of areas 

where impacts to wildlife should be minimized. We 

documented clear examples of migrant activity 

along the southern shorelines of Lake Erie at 

our study sites in Ohio and Pennsylvania, and the 

density of targets at lower altitudes is of concern. 

Additionally, increases in turbine heights and blade 

lengths increase the size of the rotor-swept zone, 

thus creating larger areas of flight risk for birds 

and bats. The collected data may be of interest 

to public and private entities involved with wind 

energy development and the potential placement of 

turbines in the Great Lakes region. Coupling avian 

radar systems with other forms of research or using 

radar in conjunction with post-construction fatality 

searches may broaden the utility of their use in risk 

assessments of wind energy developments. 
Great Lakes Avian Radar - Spring 2012 39

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42 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 43

Appendices

Appendix 1: Spring 2012 Report Summary

Appendix 2: Percent Land Cover Associated with Study Sites and the 2006 

 National Land Cover Database Classification

Appendix 3: Corrected Density per Hour by Biological Period

Appendix 4: Comparison of Static and Corrected Density Estimates
Appendix 1

Spring 2012 Report Summary

n Migration occurred on the southern shoreline of Lake Erie at both ends of the lake during spring 2012 

	 • Migration is identified by uniformity of movement of direction (northwards) at night, high 	 	 	

 target passage rate, and nighttime peaks

	 •	Patterns and timing of migration were similar between the sites

	 	 	 •	Waves of migration with highest concentrations near April 19 and May 20 occurred at

 both sites

	 	 	 •	Wave of migration also occurred near May 7 in Ohio. A migration wave likely occurred in

 Pennsylvania at this time; however the vertical scanning radar malfunctioned during 

 this time.

n Date range of pulses that occurred during the migration season

•	April 13 – May 30 in Erie County, Ohio

	 •	April 13 – May 28 in Erie County, Pennsylvania

n Patterns of activity were different between Dawn, Day, Dusk, and Night time periods

•	Movement north, during the night

	 	 	 •	76% of nights surveyed the mean direction of travel was generally northerly at Erie County,

 Ohio

	 	 	 •	68% of nights surveyed the mean direction of travel was generally northerly at Erie County,

 Pennsylvania

	 •	Movement in towards shore at dawn

	 	 	 •	Observed at both sites

	 •	Highest target passage rate at night

	 •	Dawn ascent

	 	 	 •	Increase in height around dawn hours observed at all both sites

n Peak density of targets in volume corrected counts

•	Max density below 150 m 75% of nights and 65% of night hours at Erie County, Ohio

	 •	Max density below 150 m 68% of nights and 65% of night hours at Erie County, Pennsylvania

n Standards for radar studies need to be established and recommendations are included in this report

	 •	Using radar counts as an index of activity and not a population estimate

	 •	Surveying continuously over the whole migration season

	 •	Examining smaller time periods (Dawn/Day/Dusk/Night or Hourly) rather than seasonal metrics

	 •	Using volume corrected counts on the vertical radar to better estimate use of low altitudes and

 the rotor swept zone

	 •	Using 50-m altitude bands to represent height distributions rather than mean or median heights

	 •	Examining the most densely populated altitude bands rather than comparing numbers or

 percentages of targets below, within, and above the rotor swept zone

	 •	Recognizing that migrants change altitude for various reasons over time (due to wind, weather,

 topography, and time of day, for example) and that targets flying several altitude bands above the 

 rotor swept zone may still be at risk.

44 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 45

Appendix 2

Percent Land Cover Associated with Study Sites

and the 2006 National Land Cover Database Classification

Percent landcover found within 3.7 km of radar locations in Ohio and Pennsylvania.

Classification Description

Water

Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.

Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater 

than 25% of total cover.

Developed

Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation 

in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas 

most commonly include large-lot single-family housing units, parks, golf courses, and vegetation 

planted in developed settings for recreation, erosion control, or aesthetic purposes.

Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious 

surfaces account for 20% to 49% percent of total cover. These areas most commonly include singlefamily housing units.

Developed, Medium Intensity – areas with a mixture of constructed materials and vegetation. 

Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include 

single-family housing units.

Developed High Intensity -highly developed areas where people reside or work in high numbers. 

Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces 

account for 80% to 100% of the total cover.

Barren
(Appendix 2 continued)

Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic 

material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen 

material. Generally, vegetation accounts for less than 15% of total cover.

Forest

Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 

20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in 

response to seasonal change.

Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 

20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. 

Canopy is never without green foliage.

Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of 

total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree 

cover.

Shrubland

Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy 

typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, 

herbs, and non-vascular vegetation.

Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater 

than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage 

or trees stunted from environmental conditions.

Herbaceous

Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater 

than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but 

can be utilized for grazing.

Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of 

total vegetation. This type can occur with significant other grasses or other grass like plants, and 

includes sedge tundra, and sedge tussock tundra.

Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of 

total vegetation.

Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.

Planted/Cultivated

Pasture/Hay – areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or 

the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for 

greater than 20% of total vegetation.

Cultivated Crops – areas used for the production of annual crops, such as corn, soybeans, vegetables, 

tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation 

accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.

Wetlands

Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of 

vegetative cover and the soil or substrate is periodically saturated with or covered with water.

Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater 

than 80% of vegetative cover and the soil or substrate is periodically saturated with or covered with 

water.

Barren

46 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 47

Appendix 3

Corrected Density per Hour by Biological Period

Estimated density of targets by altitude band during spring biological time 

periods (dawn, day, dusk, night) in Ohio (targets/1,000,000 m3/time period). 

Altitude	

Band Dawn Day Dusk Night

50 0.8 0.5 0.6 1.0

100 3.1 1.8 2.5 5.8

150 1.5 0.9 0.9 4.1

200 1.5 0.9 0.7 4.4

250 1.3 0.8 0.6 4.3

300 1.1 0.6 0.5 4.0

350 0.8 0.6 0.4 3.5

400 0.7 0.3 0.2 3.0

450 0.6 0.3 0.2 2.7

500 0.9 0.3 0.2 2.8

550 0.8 0.3 0.2 2.4

600 0.7 0.3 0.1 2.2

650 0.6 0.2 0.1 1.9

700 0.6 0.2 0.1 1.8

750 0.5 0.2 0.1 1.5

800 0.4 0.2 0.0 1.4

850 0.3 0.1 0.0 1.2

900 0.3 0.1 0.0 1.0

950 0.3 0.1 0.0 0.8

1000 0.2 0.0 0.0 0.7

Altitude

Band

Dawn Day Dusk Night

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

1000

0.8

3.1

1.5

1.5

1.3

1.1

0.8

0.7

0.6

0.9

0.8

0.7

0.6

0.6

0.5

0.4

0.3

0.3

0.3

0.2

0.5

1.8

0.9

0.9

0.8

0.6

0.6

0.3

0.3

0.3

0.3

0.3

0.2

0.2

0.2

0.2

0.1

0.1

0.1

0.0

0.6

2.5

0.9

0.7

0.6

0.5

0.4

0.2

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.0

0.0

0.0

0.0

0.0

1.0

5.8

4.1

4.4

4.3

4.0

3.5

3.0

2.7

2.8

2.4

2.2

1.9

1.8

1.5

1.4

1.2

1.0

0.8

0.7
Altitude	

Band Dawn Day Dusk Night

50 1.1 0.8 0.5 1.5

100 1.9 1.0 0.6 4.9

150 1.5 0.6 0.4 5.4

200 1.1 0.5 0.2 5.1

250 0.9 0.4 0.2 4.6

300 1.0 0.4 0.3 4.2

350 1.3 0.4 0.4 3.8

400 1.3 0.4 0.3 3.5

450 1.3 0.4 0.2 3.0

500 1.1 0.3 0.2 2.6

550 1.0 0.2 0.2 2.2

600 0.8 0.2 0.1 1.9

650 0.7 0.1 0.1 1.6

700 0.5 0.1 0.1 1.4

750 0.4 0.1 0.1 1.2

800 0.3 0.0 0.0 0.9

850 0.2 0.0 0.0 0.7

900 0.2 0.0 0.0 0.5

950 0.2 0.0 0.0 0.4

1000 0.1 0.0 0.0 0.3

Estimated density of targets by altitude band during spring biological time 

periods (dawn, day, dusk, night) in Pennsylvania (targets/1,000,000 m3/time 

period). 

48 Great Lakes Avian Radar - Spring 2012 

Altitude

Band Dawn Day Dusk Night

50

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

1000

1.1

1.9

1.5

1.1

0.9

1.0

1.3

1.3

1.3

1.1

1.0

0.8

0.7

0.5

0.4

0.3

0.2

0.2

0.2

0.1

1.1

1.9

1.5

1.1

0.9

1.0

1.3

1.3

1.3

1.1

1.0

0.8

0.7

0.5

0.4

0.3

0.2

0.2

0.2

0.1

1.1

1.9

1.5

1.1

0.9

1.0

1.3

1.3

1.3

1.1

1.0

0.8

0.7

0.5

0.4

0.3

0.2

0.2

0.2

0.1

0.8

1.0

0.6

0.5

0.4

0.4

0.4

0.4

0.4

0.3

0.2

0.2

0.1

0.1

0.1

0.0

0.0

0.0

0.0

0.0

0.5

0.6

0.4

0.2

0.2

0.3

0.4

0.3

0.2

0.2

0.2

0.1

0.1

0.1

0.1

0.0

0.0

0.0

0.0

0.0

1.5

4.9

5.4

5.1

4.6

4.2

3.8

3.5

3.0

2.6

2.2

1.9

1.6

1.4

1.2

0.9

0.7

0.5

0.4

0.3
Great Lakes Avian Radar - Spring 2012 49

Appendix 4

Comparison of Static and Corrected Density Estimates

Comparison of methods to estimated target density by altitude band during the dawn biological period in Ohio, 

spring 2012.
(Appendix 4 continued)

50 Great Lakes Avian Radar - Spring 2012 

Comparison of methods to estimated target density by altitude band during the day biological period in Ohio, 

spring 2012.
Great Lakes Avian Radar - Spring 2012 51

(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the dusk biological period in Ohio, 

spring 2012.
(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the night biological period in Ohio, 

spring 2012.

52 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 53

(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the dawn biological period in 

Pennsylvania, spring 2012.
(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the day biological period in 

Pennsylvania, spring 2012.

54 Great Lakes Avian Radar - Spring 2012
Great Lakes Avian Radar - Spring 2012 55

(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the dusk biological period in 

Pennsylvania, spring 2012.
(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the night biological period in 

Pennsylvania, spring 2012.

56 Great Lakes Avian Radar - Spring 2012 
Great Lakes Avian Radar - Spring 2012 57

Notes


Spring 2012