Great Lakes Avian Radar

Technical Report

Lake County, MN

Bayfield County, WI

Keweenaw County, MI

Fall Season 2014

Biological Technical Publication

BTP-R3015-2017

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 County, MN

Bayfield County, WI

Keweenaw County, MI

Fall Season 2014

Biological Technical Publication

BTP-R3015-2017

U.S. Fish & Wildlife Service, Region 3

Funding Provided by the Great Lakes Restoration Initiative

January 2017

Principle Investigator: 

Jeff Gosse, Regional Energy Coordinator, USFWS, Region 3 

Authors:

Nathan Rathbun, Fish and Wildlife Biologist, USFWS Region 3

Tim Bowden, Fish and Wildlife Biologist, USFWS Region 3

Rebecca Horton, 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 Superior, migration, avian radar, wind energy, birds, bats 

Recommended citation: 

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

Gosse. 2016. Great Lakes Avian Radar Technical Report Lake County, MN, Bayfield County, WI, and 

Keweenaw County, MI, Fall 2014. U.S. Department of Interior, Fish and Wildlife Service, Biological 

Technical Publication FWS/BTP-R3015-2017
ISSN 978-1-9389-5607-2 Electronic ISSN 978-1-9389-5607-2

Biological Technical Publications online: 

http://digitalmedia.fws.gov/cdm/search/collection/document/searchterm/Biological%20Technical%20

Publications/field/collec/mode/exact/conn/and/order/nosort
 i

Table of Contents

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

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

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

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

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

 Objectives .............................................................................................................................................. 3

Methods ................................................................................................................................................................. 4

 Study Area .............................................................................................................................................. 4

 Equipment ............................................................................................................................................ 5

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

 Data Processing and Quality Control ................................................................................................. 10

 Data Summary and Trends Analysis ................................................................................................. 10

Results .................................................................................................................................................................. 14

 Qualitative Assessments ...................................................................................................................... 14

 Directional Trends ................................................................................................................................ 19

 Temporal Trends ................................................................................................................................. 23

 Altitudinal Trends ................................................................................................................................ 32

Discussion ....... ...................................................................................................................................................... 46

 Management Considerations ............................................................................................................. 50

Literature Cited ...................................................................................................................................................... 52
ii

List of Figures

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

spring 2014 migration season ...................................................................................................................................... 4

Figure 2. Landcover types found within a 3.7 km radius of the radar locations on Lake Superior during fall 2014. ... 5

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

and vertical radars that were used on Lake Superior during fall 2014.................................................................. 6

Figure 4. Vertical (left) and horizontal (right) clutter maps from Lake County, MN. .......................................... 7 

Figure 5. Vertical (left) and horizontal (right) clutter maps from Bayfield County, WI (top) and Keweenaw 

County, MI (bottom). ................................................................................................................................................... 8

Figure 6. Schematic of the vertical scanning radar beam with photos of the avian radar unit on site. ............. 9

Figure 7. Graphical representation of the structural form of the vertical scanning radar 

within the standard front used for density estimate .............................................................................................. 12

Figure 8. Volume of 50-m altitude bands within the standard front as estimated with 

Monte Carlo integration ............................................................................................................................................. 13

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

during a migration event in Lake County, MN. ...................................................................................................... 16 

Figure 10. Images of tracks during 1 hour increments recorded by horizontal and vertical scanning radars 

during a migration event in Bayfield County, WI. ................................................................................................. 17

Figure 11. Images of tracks during 1 hour increments recorded by horizontal and vertical scanning radars 

during a migration event in Keweenaw County, MI. ............................................................................................. 18

Figure 12. Target direction per hour during four biological periods during fall 2014 in Lake County, MN ... 20

Figure 13. Target direction per hour during four biological periods during fall 2014 in Bayfield County, WI 

(left) and Keweenaw County, MI (right). ................................................................................................................. 21

Figure 14. Predicted Departure Location of Migrants Based on Average Nightly Flight Paths ..................... 22

Figure 15. Hourly counts by horizontal and vertical radars from August 1 – November 11, 2014 at Lake 

County, Minnesota ..................................................................................................................................................... 24

Figure 16. Hourly counts by horizontal and vertical radars from August 1 – September 8, 2014

at Bayfield County, Wisconsin ................................................................................................................................... 25 

Figure 17. Hourly counts by horizontal and vertical radars from September 8 – November 2, 2014

at Keweenaw County, Michigan ................................................................................................................................ 26

Figure 18. Box plots showing variability in target passage rate (targets/km/hr) during four biological periods 

for fall 2014 in three counties along Lake Superior ............................................................................................... 27

Figure 19. Mean hourly target passage rate during fall 2014 in Lake, Bayfield, and Keweenaw Counties .... 28
Figure 20. Weekly mean of nocturnal (left column) and diurnal (right column) target passage rates (targets/

km/hr) at Lake, Bayfield, and Keweenaw counties. ............................................................................................... 29

Figure 21. Comparison of nocturnal (top) and diurnal (bottom) target passage trends (based on a moving 

7-day mean) between sites during fall 2014 in Lake, Bayfield, and Keweenaw counties. ................................. 30

Figure 22. Comparison of nocturnal and diurnal target passage trends (based on a moving 7-day mean) 

within sites during fall 2014 in Lake, Bayfield, and Keweenaw counties ............................................................ 31

Figure 23. Altitude profile of targets in Lake County, MN......................................................................................... 34

Figure 24. Altitude profile of targets in Bayfield County, WI. ............................................................................... 35

Figure 25. Altitude profile of targets in Keweenaw County, MI. ........................................................................... 36

Figure 26. A sample of hourly altitude profiles corrected for the shape of the sample volume in Lake County,

MN during fall 2014. .................................................................................................................................................. 37

Figure 27. A sample of hourly altitude profiles corrected for the shape of the sample volume in Bayfield 

County, WI during fall 2014. ...................................................................................................................................... 38

Figure 28. A sample of hourly altitude profiles corrected for the shape of the sample volume in Keweenaw 

County, MI during fall 2014. ...................................................................................................................................... 39

Figure 29. Altitude profile of target density below 400 meters in Lake, Bayfield, and Keweenaw counties. .. 40

Figure 30. Comparison of the frequency of maximum density or maximum count per night within each 50-m 

altitude bands in Lake, Bayfield, and Keweenaw counties during fall 2014 ...................................................... 41

Figure 31. Comparison of the frequency of maximum density or maximum count per hour within each 50-m 

altitude bands in Lake, Bayfield, and Keweenaw counties during fall 2014 ...................................................... 42

Figure 32. Maximum density per day and day hour at Lake County, MN during fall 2014. ............................. 43

Figure 33. Target Density by Altitude Band Averaged over each of the daily 24 Hours during fall 2014. ..... 44

Figure 34. Mean hourly target height (m) during fall 2014 at Lake, Bayfield, and Keweenaw counties ......... 45

Figure 35. Effect of Sampling Schedule on Data .................................................................................................... 47

iii
List of Tables

Table 1. Predominant landcover types found within a 3.7 km radius of the radar locations on Lake Superior 

during fall 2014 ............................................................................................................................................................. 4

Table 2. Survey effort (hours) by vertical and horizontal scanning radars during fall 2014 in Lake, Bayfield, 

and Keweenaw counties. ........................................................................................................................................... 14

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

directionality (r ≥ 0.5) of targets during four biological time periods in Lake County, MN............................ 19 

Table 4. Mean direction, angular concentration (r), and percent of biological time periods with strong 

directionality (r ≥ 0.5) of targets during four biological time periods in Bayfield County, WI and Keweenaw 

County, MI. .................................................................................................................................................................. 20

Table 5. Mean target passage rate (TPR) with standard deviations during four biological periods in Lake, 

Bayfield, and Keweenaw counties during fall 2014 ................................................................................................. 27

Table 6. Comparison of mean altitude with standard deviations, median altitude, and altitude band (50 m 

bands) that contained the maximum target density during four biological periods in Lake, Bayfield, and 

Keweenaw counties during fall 2014 ......................................................................................................................... 33 

iv
v

Acknowledgements

This project would not have been possible without 

the funding provided through the Great Lakes 

Restoration Initiative for which we are very 

appreciative. We are grateful for the advice, 

technical assistance, and contributions of our 

collaborators Doug Johnson (U.S. Geological 

Service), and Kevin Heist (University of Minnesota). 

Jake Ferguson’s (University of Florida) statistical 

and programming expertise provided our model of 

the geometric shape of the radar beam. We also 

thank the landowners that provided space for our 

radar units: Edwin Hlavka, Fritz Kesanen, and Jeff 

Miller. We also thank the Minnesota Valley National 

Wildlife Refuge, Hawk Ridge Bird Observatory, 

and the EPA’s Mid-Continent Ecology Division in 

Duluth, MN for their assistance during this season. 

Our methods were reviewed in a previous report 

by DeTect, Inc. and other external reviewers. This 

specific manuscript has benefited from reviews by 

Gerald Niemi, Anna Peterson, David Diehl, and Lisa 

Mandell and we would like to thank them for their 

contributions.
Executive Summary

Global wind patterns help move millions of 

migrating birds and bats through the Great Lakes 

region, where shorelines provide important stopover 

habitat. Shorelines are thought to concentrate 

migrants because they offer the last refuge near 

a geographic obstacle and are likely used for 

navigation. Shorelines also offer attractive areas for 

wind energy development. With this potential for 

conflicting interests, more information is needed on 

the aeroecology of the Great Lake shorelines. We 

used two avian radar systems to identify the activity 

patterns, timing, and duration of migration that 

occurred along the shorelines of the Great Lakes, 

with this season focusing on Lake Superior. 

We placed avian radar systems near Lake Superior 

in Michigan, Minnesota, and Wisconsin, where the 

automated systems tracked and recorded target 

(bird and bat) movements continuously from early 

August to mid-November 2014. We calculated 

direction of movement, target passage rates, and 

altitude profiles for the air space above our study 

areas. We also used a model of our vertical sample 

volume that allowed us to report an estimate of 

target density by altitude band.

Migration appeared strong along the shorelines 

we studied on Lake Superior. The mean nocturnal 

passage rates were greater than the mean passage 

rates for dawn, day, and dusk combined at each 

of our three locations. Nocturnal movement was 

typically oriented in a southerly direction. We also 

recorded other behaviors associated with migrants, 

such as slight dawn ascent and migrants over water 

returning to land at dawn. Peak density occurred 

between 100 and 250 m above ground level at each 

of the sites. Patterns indicating diurnal migration 

were also observed along the north shore of Lake 

Superior in Minnesota. The target density may have 

been underestimated at higher altitudes due to loss 

of detection at longer ranges and at lower altitudes 

due to the presence of clutter. 

The results of our research highlight the potential 

role of radar in implementing the USFWS LandBased Wind Energy Guidelines and how radar 

may help identify areas where impacts to wildlife 

could be minimized. We documented migration 

activity in the air space above our study areas, 

and our results show that the density of targets at 

low altitudes may present conservation concerns. 

The data we collected showed the ebb and flow 

of migration across the sampling period and that 

the nocturnal migration of passerines and bats 

continued into November. Given the amount of 

time that migration occurred at the sampled sites, it 

seems that curtailing wind energy operations during 

nocturnal pulses could result in limited operational 

time along shorelines during the migration season. 

Combining the results of radar studies and fatality 

searches could greatly improve risk assessments 

and assist with the interpretation of standardized 

radar studies.

Avian radar is increasingly relied upon to perform 

surveys for pre-construction risk analysis. While 

an important tool, few regulatory agencies 

have experience implementing avian radar or 

otherwise recognize the strengths and limitations 

of the technology. This report highlights some 

considerations about avian radar and reviews some 

potentially confusing metrics. We also introduce 

some new metrics to report radar data. In addition 

to providing information relevant to conservation in 

the Great Lakes region, the concepts we present in 

this report are widely relevant to reviews of avian 

radar studies, and we provide methods that identify 

components of migration, such as:

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 effort may be our 

ability to identify and avoid development in locations 

where migrants concentrate. 

vi
Introduction

The Great Lakes constitute one of the largest 

bodies of fresh water on the planet and collectively 

represent a surface area of nearly 245,000 km2, 

with 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 lake shorelines 

feature widely recognized Important Bird Areas 

(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—areas used temporarily for rest, 

refueling, and protection. These shorelines offer 

increased foraging opportunities relative to inland 

areas (Smith et al. 2004, 2007; Bonter et al. 2007, 

2009) and may be used as a visual cue for navigation 

or for refuge prior to or after crossing open water 

(Buler and Moore 2011). 

Given their location and size, the Great Lakes 

likely represent a geographic obstacle (Diehl et al.

2003) that migrants choose to cross, or not, based 

on environmental and physiological conditions 

at the time of encounter (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 long flight due to the cost of 

carrying high fuel loads (Alerstam 1990). This 

is perhaps one reason why migrants sometimes 

partially circumnavigate the Great Lakes, which 

they have the physiological capability of crossing 

(Alerstam 1990, Alerstam 2001, Ruth 2007). 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) that are associated with migration (McGuire 

et al. 2012a). In this trade-off, shorelines offer 

refuge when conditions do not favor flights over 

water. Lake Superior has the largest surface area 

and has one of the larger spans of open water that 

migrants would have to cross in all of the Great 

Lakes. The size of this lake may make the decision 

to cross it more of a trade-off compared with some of 

the other Great Lakes. 

Migrants challenged by an obstacle 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 with 

low fuel loads and/or facing unfavorable weather 

conditions returned to the shoreline habitat rather 

than continue across open water in a direction 

appropriate for migration. For bats, migrants 

varied their choice to circumnavigate above 

shorelines or cross lakes, and some long-distance 

migrants used torpor to postpone migration during 

periods of unfavorable conditions (McGuire et al. 

2012b). These behavioral responses as well as the 

necessity of using stopover habitat during migration 

likely contribute to the increased use of shorelines 

and emphasize the importance of these areas for 

conservation. A peninsula, such as the Keweenaw 

Peninsula in Lake Superior, may offer a safe area 

for migrants due to its location close to the middle 

of the lake. By extending into the middle of the lake, 

the peninsula can make a shorter crossing for many 

migrants and provide a refuge in case of a change 

in conditions. This refuge area may mitigate some 

decisions by migrants that may have otherwise been 

more costly.

Migrants concentrated along shorelines can be 

very mobile. In addition to immediate refueling 

and rest, migrants make broad-scale flights among 

habitat patches, explore wind conditions, and orient 

themselves for migration. For example, radiotagged bird and bat migrants on the north shore of 

Lake Erie made repeated movements among habitat 

patches. Individuals relocated as far as 18 and 30 

km from their capture site (maximum distances 

tracked for a bat and bird species, respectively) 

prior to resuming migration (Taylor et al. 2011). 

Nocturnal migrants such as warblers and other 

neotropical migrants, regularly engage in morning 

flights along shorelines (Wiedner et al. 1992). These 

flights typically occur within 2 hours of sunrise 

and are thought to represent reorientation along a 

geographic obstacle or movements among stopover 

habitats (Able 1977, Moore 1990, Wiedner et al. 

1992). Flights of this nature often occur above the 

tree line (Bingman 1980), while lower heights are 

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). These flights are thought to represent 

normal activity of migrants as they calibrate 

Great Lakes Avian Radar - Fall 2014 1
their internal compass and test wind speed and 

direction aloft. In addition to these activities while 

in stopover, migration flights follow north-southoriented shorelines en route to their destination 

(Buler and Dawson 2012), while 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). 

Additionally, migrant birds may change their 

altitude many times over the course of a nocturnal 

migratory flight (Bowlin et. al 2015). Cumulatively, 

these types of activities indicate that areas near 

lake shores host a variety of movements at many 

different altitudes for landscape level, exploratory, 

and migratory flights. These activities may increase 

vulnerability to collision risk with tall structures 

such as communication towers or wind turbines. 

Migrant populations may experience the greatest 

mortality pressure during migration (Newton 

2006, 2007; Diehl et al. 2014), and the negative 

ramifications of compromised stopover habitat for 

migratory populations 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 that may limit habitat 

availability and/or reduce habitat quality (France 

et al. 2012). Furthermore, White-nose Syndrome 

is devastating hibernating bat populations and has 

increased the need to identify conservation areas, as 

several of these species face the risk of extirpation 

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

response to factors such as these, substantial efforts 

are being made to identify and protect stopover 

habitat along the Great Lakes shorelines (Buler and 

Dawson 2012, Ewert et al. 2012, France et al. 2012). 

With climate change considerations recommending 

both an increase in renewable energy development 

and conservation of migratory species, careful 

planning is needed to balance these demands. 

There is a national movement towards a 20% wind 

energy sector in the U.S. market by 2030 (U.S. 

DOE 2008). As of 2012, wind energy installation 

is on target towards achieving this goal (AWEA 

2015). If achieved, this would represent a nearly 

five-fold increase in wind energy capacity during the 

next 15 years (Loss et al. 2013). Additionally, the 

U.S. Department of Energy (2015) has conducted 

a study showing that 35% of the energy demands 

of the U.S. could be met by wind energy in 2050. 

Explorations have also been conducted by the 

National Renewable Energy Laboratory that 

conclude that over 400 GW of electricity could be 

produced by wind power in 2050, up from 60 GW in 

2012 (Mai et. al 2012, Loss et al. 2013). Coinciding 

with this national effort, wind energy developments 

are increasing within the Great Lakes region, where 

windy shorelines near population centers offer 

attractive areas for turbine placement (Mageau et 

al. 2008, Great Lakes Commission 2011). 

Utility-grade wind facilities have been associated 

with mortality events for migrating vertebrates 

(Newton 2007, Arnett et al. 2008, Smallwood and 

Thelander 2008), and chronic fatalities across the 

U.S., particularly for bats, are a concern (Timm 

1989, Johnson 2005, Arnett and Baerwald 2013, 

Hayes 2013, Smallwood 2013). For example, three 

species of long-distance migratory bats that are 

impacted by wind energy facilities account for 

approximately 75% of all bat mortalities (Kunz 

et al. 2007a, Cryan 2011, Arnett and Baerwald 

2013). These migrants, the hoary bat (Lasiurus 

cinereus), eastern red bat (Lasiurus borealis), 

and silver-haired bat (Lasionycteris noctivagans) 

typically make up 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 but also 

found that little brown bat (Myotis lucifugus) and 

big brown bat (Eptesicus fuscus) fatalities were 

substantial (Gruver et al. 2009, BHE Environmental 

2010, Grodsky et al. 2012). The presence of major 

hibernacula in the vicinity of these latter three 

studies may have contributed to the difference in 

ratios. Many areas along Lake Superior are home to 

large hibernacula of bats, including the Keweenaw 

Peninsula in the Upper Peninsula of Michigan, 

northern Wisconsin, and the north shore of Lake 

Superior in Minnesota. 

Low reproductive rates inhibit the ability of bats 

to rebound from population decline (Racey and 

Entwistle 2000), and these declines have already 

begun for several species (Kunz et al. 2007a, 

Cryan 2011) and have contributed to the Federal 

listing of the northern long-eared bat (Myotis 

septentrionalis) as a threatened species under the 

Endangered Species Act. Cumulative impacts on 

migrant bird and bat species are a concern, and this 

concern will increase with the growth of wind energy 

if methods to avoid or minimize mortality events 

are not established. Some promising conservation 

measures have been proposed to reduce mortality 

levels, such as reduced cut-in speeds; however, the 

greatest benefit to the conservation of migrants may 

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 

patterns, timing, and magnitude of migration 

occurring along shorelines of the Great Lakes. 

Because bats and many bird species migrate during 

2 Great Lakes Avian Radar - Fall 2014 
the nighttime hours throughout the spring and 

fall, documenting the migration of these animals 

is challenging due to the difficulty of observing 

nocturnal movements that occur sporadically 

throughout the season. To address this challenge, 

we used two avian radar units that operated 

24 hours per day, and each unit simultaneously 

scanned horizontal and vertical planes. We chose 

radar because of these and other benefits and 

to provide an alternative metric to our acoustic 

monitoring program, which has different strengths 

and weaknesses (Horton et al. 2015). Avian radar 

has been shown to reliably track targets that fly 

through its detection area, although the specific 

target counts are not indicative of true population 

counts (Gerringer et al. 2015). Migration traffic on 

radars has been shown to correlate with the density 

of birds in stopover habitat during the day (Horton 

et al. 2016a), indicating that migrants using the 

airspace are also using stopover habitat in the area. 

Our objectives for the portion of the study we are 

reporting on included the following:

n Monitor locations along shorelines and 

peninsulas of Lake Superior using a consistent 

methodology.

n Maintain an archive of continuously recorded 

radar data during the fall migration season;

n Identify activity patterns captured by avian 

radar that are diagnostic of fall migration on 

the north shore of a lake and peninsulas 

extending from the south shore of a lake.

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.

Great Lakes Avian Radar - Fall 2014 3
Methods

Study Area and Site Selection

During fall 2014, we selected three sites along the 

U.S. portion of Lake Superior for radar placement. 

One radar unit was located along the north shore in 

Lake County, Minnesota. We collected data at this site 

throughout the fall migration season (Aug 1 – Nov 11, 

2014). The second radar unit was placed at two sites 

during the migration season, with one site in Bayfield 

County, Wisconsin (July 31 – Sept 8, 2014) and the 

other site in Keweenaw County, Michigan (Sept 8 – 

Nov 2, 2014) (Figure 1). Radar units were placed 1 – 4 

km from the shoreline to monitor the airspace above 

the inland, shoreline, and lake areas. 

The Lake County site was located at 47.435760° N, 

-91.088910° W and was 295 m above mean sea level. 

It was placed near a ridgeline, in a forest opening, 

approximately 1.5 km from the shoreline. Deciduous 

forest and open water were the predominant land 

cover types within the 3.7 km range of the radar unit, 

according to our analysis using Esri ArcGIS software 

and the 2011 National Land Cover Database (Jin et 

al. 2013). The Bayfield County site was located on 

the Bayfield Peninsula at 46.915030° N, -90.871950° W 

and was 234 m above mean sea level. It was placed in 

a fallow field approximately 4 km from the shoreline, 

due to lack of suitable sites closer to the lake shore. 

Deciduous and mixed forest were the predominant 

land cover types present within range of the radar 

Figure 1. Locations where MERLIN Avian 

Radar Systems were deployed during the fall 2014 

migration season. The map image is the intellectual 

property of Esri and is used herein under license. 

Copyright © 2014 Esri and its licensors. All rights 

reserved.

Table 1. Predominant land cover types found within a 3.7 km radius of the radar locations on Lake Superior 

during fall 2014. 

unit. The Keweenaw County site was located at 

47.444820° N, -88.1505680° W and was 205 m above 

mean sea level. It was placed in a forest opening 

where a small sandpit had been created and was 

approximately 2 km from the shoreline. Open water, 

forest (evergreen, deciduous and mixed) and woody 

wetlands were the predominant land cover types 

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

Appendix 2).

4 Great Lakes Avian Radar - Fall 2014
Figure 2. Land cover types found within a 3.7 km radius of the radar locations on Lake Superior during fall 

2014. Map information from Jin et al. 2013.

Equipment

We used two model SS200DE MERLIN Avian 

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

document migration movements. 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 solid-state marine radar antennas 

that operated simultaneously, one that scanned the 

horizontal plane while the other scanned vertically 

(Figure 3). Additionally, each unit contained four 

computers for real-time automated data processing 

and a SQL server for processed data storage and 

review. The units were configured with a wireless 

router to allow remote access to the computers 

and automated status updates. The radar units 

were previously compared against each other and 

were determined to perform similarly (USFWS 

unpublished data).

Description of radars – The solid state marine 

radar antennas (Kelvin Hughes, London, UK) 

employed by our systems were 3.9 m in length, with 

170 W peak power, S-band (10 cm) wavelength, and 

2.92 – 3.08 GHz frequency range, and they were 

configured to operate with both short and medium 

pulses (0.1 and 5 microseconds, respectively). The 

horizontal radar was also equipped with Doppler 

to help filter out stationary targets. The radars 

emitted a fan-shaped beam that had an approximate 

1° horizontal and 25° vertical span when operated 

in the horizontal plane. S-band radar was selected 

because the longer wavelength is less sensitive 

to insect and weather contamination than that of 

X-band (3 cm wavelength) antenna (Bruderer 1997). 

It is also less sensitive to signal attenuation from 

ground clutter such as vegetation and structures 

(DeTect Inc., unpublished data, 2009). The radars 

spun perpendicular to each other at a rate of 20 

revolutions per minute and were synchronized so 

as not to emit over one another. The horizontal 

scanning radar (HSR) was affixed to a telescoping 

base that was raised to approximately 7 m above 

ground for operation. This radar rotated in the 

x-y plane with a 7° tilt to reduce the amount of 

ground clutter included within its view. While the 

HSR had the capability to scan large distances, we 

selected a 3.7 km range setting for data collection 

in order to have higher resolution and to identify 

smaller targets such as passerines and bats. The 

Great Lakes Avian Radar - Fall 2014 5
Figure 3. Computer representation of the potential survey volume scanned by horizontal and vertical radars 

that were used on Lake Superior during fall 2014. Graphic provided by DeTect, Inc.

HSR was primarily used to provide information on 

target 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 for increased resolution and 

used the VSR to provide information on the number 

and height of targets. 

Extended range horizontal antenna – An extended 

range horizontal radar and the accompanying two 

additional computers were installed on the Lake 

County, MN radar. The addition allowed us to 

survey out to 11.1 km with this antenna while still 

continuing our survey efforts at the 3.7 km range. 

The settings for this antenna were developed 

independently of the standard range horizontal 

radar, and all data were analyzed separately for the 

two different ranges.

Weather Station – Each system was equipped 

with a weather station (Davis Vantage Pro 2, 

Hayward, CA) that recorded wind speed and 

direction, humidity, temperature, precipitation, 

and barometric pressure. These weather data 

were summarized and stored every 5 minutes. The 

anemometer was attached to the radar unit and 

measured wind speed at a height of approximately 6 

m above ground level.

Radar Set Up and Data Collection

The radar systems were deployed on July 31st and 

August 1st at their respective sites. The radar unit 

in Lake County was maintained through November 

11th to capture the anticipated end date of the 

nocturnal migration season. The radar unit in 

Bayfield County was moved on September 8th to the 

Keweenaw County site, where it was maintained until 

November 2nd. This radar unit ended data collection 

earlier than the other unit so that it could be used for 

a separate ground-truthing study. 

Establishing radar systems at a selected site 

involved several activities, including orienting the 

VSR, selecting micro-sites, and adjusting to ensure 

adequate information was captured. Based on the 

results from previous studies (Peterson and Niemi 

2011, Evans et. al 2012), we anticipated that migrants 

would fly southwest following the shoreline at the 

Lake County, Minnesota site, and we oriented the 

radar at 90°. We expected migrants to fly southbound 

across Lake Superior at the Bayfield and Keweenaw 

County sites, and the radars were oriented at 75° and 

285° at these sites, respectively. These orientations 

were 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. The orientations were also influenced by 

micro-site selection. Micro-site selection is important 

because the positioning and orientation of the radar 

can affect the amount of interference from ground 

clutter or other sources of noise. In our study, if 

large areas were obstructed from a radar’s view 

or if substantial amounts of clutter impeded data 

collection, the system was rotated incrementally to 

improve the radar’s view and/or reduce interference. 

6 Great Lakes Avian Radar - Fall 2014
Once a position was established, clutter maps were 

generated using 60-scan composite images (Figures 

4-5) at time periods with low biological activity to 

identify areas with constant returns (areas that are 

white) 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 these data 

and, as a result, reduced our ability to detect targets 

in these areas. Transient clutter, such as rain or 

cloud cover, is not included in these clutter maps.

Following this initial set up, MERLIN software was 

adjusted to fit the site conditions. The MERLIN 

software provides real-time processing of raw 

radar data to identify and track targets while 

excluding non-targets and rain events. However, 

the parameters used by the tracking software 

require adjustments to account for site-specific 

conditions. DeTect personnel trained our biologists 

in establishing settings to exclude as many nonbiological tracks as possible while retaining the 

ability to track biological targets. The settings were 

established by varying the parameters on a small 

sample of early season data and rerunning the same 

data repeatedly to determine the optimal settings. 

The settings were established by one person to 

ensure consistency and reduce bias in how they 

are set between units and between seasons. The 

processed data were stored in a Microsoft Access 

database and transferred to a SQL database, where 

they were stored and later queried for data analysis. 

In addition to processed data, we maintained all 

raw radar data for potential reprocessing. The 

raw radar data were temporarily stored in the field 

on 2 TB external hard drives and were regularly 

transported back to the Regional Office, where the 

data were transferred to long-term tape storage. 

Once set up, the radar systems were monitored 

remotely, with biologists visiting the sites every 

few weeks to retrieve the stored data and perform 

maintenance on the radar units. 

Radar System Outputs

The MERLIN software generates more than 

30 measurements to describe the size, shape, 

location, speed, and direction of the movement 

of targets it tracked. These are the same type of 

measurements used by biologists when identifying 

biological targets on a radar screen (DeTect Inc., 

unpublished data, 2009), and this information was 

stored in databases for later analysis. To reduce 

potential false tracking of non-biological targets, 

the MERLIN tracking algorithm removed tracks 

with less than five observations. Additionally, when 

parts of the radar scan were dominated by rain, 

an automated filter would remove data from those 

areas. 

In addition to storing target attribute data, the 

DeTect software outputs included 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 vertical and 

horizontal radars (Figures 9-11). During each site 

check, we verified that the real-time tracking display 

Figure 4. Clutter maps from vertical (left) and horizontal (right) scanning radars from the site in Lake County, 

MN. Brighter areas represent static returns from stationary objects such as tree lines, fencerows, and 

ridgelines. Targets may be lost in these areas due to the high radar returns from these stationary objects.

Lake County

Great Lakes Avian Radar - Fall 2014 7
Figure 5. Clutter maps from vertical (left) and horizontal (right) scanning radars from sites in Bayfield County, 

WI (top) and Keweenaw County, MI (bottom). Brighter areas represent static returns from stationary objects 

such as tree lines, fencerows, and ridgelines. Targets may be lost in these areas due to the high radar returns 

from these stationary objects.

Bayfield County

Keweenaw County

8 Great Lakes Avian Radar - Fall 2014
Figure 6. This schematic depicts the vertical scanning radar beam from two different views as well as 

pictures of the radar unit from those views. The top left graphic identifies the standard front used for data 

analysis. The standard front extends to 500 m on either side of the radar and up to a height of 2,800 m, 

as depicted in the top left graphic. 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 emits directly upward; this view is an approximation of the beam dispersion as 

it travels away from the radar unit (schematic not drawn to scale).

HSR

VSR

Great Lakes Avian Radar - Fall 2014 9
agreed with the raw radar display. We also viewed 

15 minute and 1 hour Trackplots to assess the 

previous day’s activity. 

Data Processing and Quality Control

Prior to data analysis, the data processed by the 

MERLIN software were further evaluated for 

potential contamination by false targets. While 

an automated rain filter was used, during some 

time periods, it did not remove all rain from the 

recorded outputs. In addition, insects and other 

forms of transient clutter may be recorded during 

data collection. Biologists reviewed all data in 15 

minute increments and removed time periods that 

were dominated by rain, insects, or other forms 

of transient clutter. We relied on visual inspection 

of track patterns to discern contamination events. 

Rain and insect events form diagnostic patterns 

(Detect Inc., personal communication, 2011) that 

were readily omitted when present. Additionally, 

if the number of rain targets constituted only a 

small part of the overall targets where the reviewer 

thought that valid conclusions about that time 

period could be made, then that time period was 

retained for analysis. Contamination that mimicked 

the track patterns of the desired targets were not 

removed from the database, and to the extent that 

this occurred, it contributed to the error associated 

with the indices. In addition, we evaluated the 

initial counts by generating a time series to show 

the variation in the number of targets per hour 

across the season for both the HSR and VSR 

antennas. In general, the HSR and VSR hourly 

counts are positively correlated, with the HSR also 

having higher counts. In situations where the VSR 

resulted in higher counts than the HSR or where 

peak counts appeared to be outliers, the data were 

further investigated for evidence of contamination 

or potential issues with radar performance. On 

rare occasions when time periods with anomalies 

appeared to represent artifacts not related to target 

movement (e.g., rain events or data processing 

errors), they 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 hourly counts 

and to target movement direction. All targets 

within 3.7 km of the radar unit were included in the 

analysis. Data from the VSR were used to calculate 

hourly counts and height estimates, and these data 

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

The standard front was defined by a volume of space 

that extended 500 m beyond either side of the radar 

and continued up to the maximum height of data 

collection (2,800 m) (Figure 6). We adopted this 

sampling technique because it is the method used 

by the manufacturer of the MERLIN units, and this 

metric has also been reported by other researchers 

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

Using each site location’s GPS coordinates, sunrise 

and sunset times were calculated and target counts 

were further segregated into four biological time 

periods: dawn, day, dusk, and night. The dawn 

time period was 30 minutes before sunrise to 30 

minutes after sunrise; day, 30 minutes after sunrise 

to 30 minutes before sunset; dusk, 30 minutes 

before sunset to 30 minutes after sunset; and 

night, 30 minutes after sunset to 30 minutes before 

sunrise the following day. The dawn and dusk time 

periods always equaled 60 minutes; however, due 

to changing sunrise and sunset times, the sampling 

periods for the day and night time periods varied 

throughout the season. 

Data Summary and Trends Analysis

We used the processed data to assess activity 

patterns that are associated with migration. 

Trackplots were viewed to identify changes in 

activity and to investigate migrant behaviors such 

as dawn ascent (Myres 1964, Diehl et al. 2003), 

reverse migration (Akesson 1999), and migrants 

moving toward or along the shore or stopover 

habitat at dawn. Target counts represented an 

index of abundance and we used these indices 

to identify directional, temporal, and altitudinal 

trends.

Directional Trends – Mean angle and concentration 

(r) of target directions were analyzed following 

the methodology for circular statistics (Zar 1999) 

provided within DeTect SQL queries. The angular 

concentration value has a value of 1 when all angles 

are the same and a value of 0 when all angles 

cancel each other (e.g., 50% of the vectors are 

180° and 50% are 360°), indicating that there is no 

predominant direction of travel. We reported the 

mean direction of movement of nocturnal targets 

and the percent of nights when targets traveled in a 

direction between south and west (157.5 – 292.5°) for 

Lake County and between southeast and southwest 

(112.5 - 247.5°) for Bayfield and Keweenaw counties. 

These direction bins were chosen because they fit 

the anticipated direction of movement and contain 

the mean direction of movement at night. 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, NW) during four biological 

time periods (i.e., dawn, day, dusk, night). 

Temporal Trends – We plotted count indices of 

targets per hour processed by MERLIN software 

for both the HSR and VSR antennas as a time 

series to identify pulses of nocturnal activity, season 

duration, and changes in patterns of activity over 

time. We plotted both indices together, as the HSR 

and VSR have different strengths that complement 

10 Great Lakes Avian Radar - Fall 2014 
one another. The HSR index tracks lower-flying 

targets in a 360 ̊ span around the radar unit, and 

detection is unaffected by the target’s direction 

of travel, as with the VSR. However, this index 

is much more affected by ground clutter than the 

VSR, which affects target detection and tracking. 

Errors caused by ground clutter can lead to both 

under and over counting. This leads to HSR counts 

that may be more influenced by site conditions 

than VSR counts. However, the HSR index better 

captures targets under certain conditions, such as 

when targets are travelling parallel to the VSR and/

or are primarily at low elevation, which may happen 

more at the beginning and end of the migration 

season. The HSR is also likely more susceptible 

to beam bending from dynamic atmospheric 

conditions than the VSR; beam refraction in the 

VSR is minimal primarily due to its orientation. 

The VSR index tracks target activity captured 

within the standard front and has more-consistent 

detection than the HSR because it mostly tracks 

against clear air, except in the lowest altitude bands, 

where ground clutter and side lobes can affect 

tracking. The VSR’s detection is mainly affected by 

target movement direction and distance from the 

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

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). We calculated TPR as the 

number of targets per standard (1 km) front per 

hour using DeTect SQL queries. Hours with less 

than 30 minutes of recording time were omitted 

from this calculation. For example, after removing 

all 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 

count by the number of nighttime minutes and 

multiplying by 60 to provide the number of targets 

per hour during that night. We extended this 

metric to the season and calculated the mean 

TPR for biological time periods and hours of the 

season. The mean nocturnal TPR for the season 

is the sum of night TPRs divided by the number 

of nights sampled. Similarly, the mean hourly 

TPR for the season is the sum of TPRs for an hour 

period divided by the number times that hour was 

sampled. We also calculated the mean nocturnal 

(night biological time period) and diurnal (day 

biological time period) TPRs for each week during 

the sampling period. These were calculated in 

two ways. To show the variability among 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. To 

better illustrate nocturnal and diurnal trends 

in TPR across the season, we also plotted 7-day 

moving means of TPR as line graphs. 

Altitudinal Trends – SQL queries provided by 

DeTect Inc. calculated height estimates from the 

VSR data of targets tracked within the standard 

front. Height estimates were calculated based on 

the range and bearing of the target from the radar 

unit location using information from the largest 

radar echo (usually the closest to the center of 

the radar beam and, therefore, the actual height 

of the target) and were reported as the height 

above ground level at the radar unit. We used the 

estimates of flight height to calculate the mean 

altitude of targets above ground level by biological 

time period and hour, and we report the mean and 

median altitudes for the season. These height 

estimates were also used to assign each target to 

a 50-m altitude band. Using these 50-m bands, we 

also present graphics showing altitudes with the 

highest frequency of use, a measure that we believe 

better represents risk.

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 vertical 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 radar unit. The shape of the survey 

volume contains the space in which targets have 

the potential of being detected and represents 

one of several considerations that define the 

realized or actual survey volume (Bruderer 1997, 

Schmaljohann et al. 2008). We calculated the 

volume contained by the shape of the vertical 

radar beam and report the density of targets 

(targets/1,000,000 m3) per 50-m altitude band 

per hour for each biological time 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 by 60. 

To estimate the volume of 50-m altitude bands 

that are constrained by the standard front, we 

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

These methods are described in detail elsewhere 

(manuscript in preparation) and are 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 altitude bands) 

complicates the calculation, and the volume bands 

are more easily estimated using Monte Carlo 

integration. Monte Carlo integration is a method 

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 

Great Lakes Avian Radar - Fall 2014 11
Figure 7. A 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) and up to a maximum height of 2,800 

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 structure of the radar 

beam. Dark gray points represent the volume that 

is within the box but are not included in the volume 

of the radar beam.

volume. The proportion of points that fall within 

these constraints multiplied by the volume of 

the known space is approximately equal to the 

unknown volume. As the number of random points 

approaches infinity, the estimation approaches an 

exact calculation of the volume in question. 

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 then saturated this 

space with 10 million random points. For the radar 

beam, 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 was less than 2.8 km. The 

second rule was that the angle between a randomly 

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

Figure 7) was less than 12.5o (i.e., half the angle 

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

of the radar beam, as estimated via Monte Carlo 

integration, was within 5% of the analytical solution 

using spherical coordinates. Thus, the number 

of random points we used provided a reasonable 

approximation of the volume. With the volume 

of a full sweep of the radar beam described, we 

were able to further constrain the Monte Carlo 

integration to describe the structural volume of the 

radar beam within a standard front (Figure 7) and 

within altitude bands (Figure 8). 

The number of targets per altitude band is often 

reported by other researchers, however a volume 

correction is not often reported. We wanted 

to compare our correction to the uncorrected 

method; however, count data and volume data are 

at different scales. For this reason, we compare 

our density estimate to a density estimate based 

on the number of targets per 50-m altitude band 

per hour while assuming that there is 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., that altitude bands 

are equal). For our comparison metric, we made 

this implicit assumption explicit (see Appendix 4). 

Differences in clutter between these 50-m bands is 

not accounted for in our correction method, nor is 

reduction in detection ability due to distance from 

the radar.

12 Great Lakes Avian Radar - Fall 2014
Figure 8. Volume of 50-m altitude bands within the standard front as estimated with Monte Carlo integration. Target counts provided by the vertical scanning radar are limited to the structure of the 1-km standard front. Altitude band labels represent the top of each 50-m band. Red line represents the top of the 

rotor-swept zone. Radar detections occurred up to 2800 m.

Great Lakes Avian Radar - Fall 2014 13
Results

During the fall 2014 season, we began data collection 

on July 31 and Aug 1 at the Bayfield County, WI and 

Lake County, MN sites, respectively. We moved the 

Bayfield County radar unit to Keweenaw County, MI 

for the second part of the season on September 8. We 

ended data collection on November 2 at the Keweenaw 

County site and on November 11 at the Lake County 

site, which resulted in radars being in place for 2,474 

hours at the Lake County site, 937 hours at the 

Bayfield County site, and 1,345 hours at the Keweenaw 

County site (Table 2). We recorded data continuously 

during the survey period while the radar units were 

operational. Radar downtime occurred when the 

radar units were non-operational due to maintenance 

or malfunction. Data with contamination (such as 

rain) were removed from the analysis, resulting in not 

all collected data being useable for analysis. Gaps in 

analyzed data occurred when contaminated data were 

removed or during radar downtime. 

Table 2. Survey effort (hours) by vertical and horizontal scanning radars during fall 2014 in Lake, Bayfield, 

and Keweenaw counties. 

Qualitative Assessments 

Hourly Trackplots showed images of diurnal and 

nocturnal migration events at each of the three 

locations (Figures 9-11). For example, over a 24-

hour period, on Sept 22 at the Lake County site, 

the horizontal radar recorded light activity moving 

southwest along the shoreline at around 12:00, 

indicating that a small amount of diurnal migration 

occurred at this time, which occurred during other 

days surveyed as well. Activity reduced as the day 

progressed, with little activity on the horizontal or 

vertical radars at 17:00. During the 18:00 hour, activity 

increased, with targets occurring in numbers on both 

the horizontal and vertical radars. Activity was moving 

out over the lake and moving south and southeast at 

this time. During the 22:00 hour, directional movement 

had shifted southward, and the vertical radar activity 

increased with more targets at higher altitudes. This 

level of activity continued throughout the night. At 

06:00, when dawn was occurring, the targets shifted 

their activity and began to travel along the lakeshore 

or else return to the shoreline by moving west if they 

were out over the lake. Activity on the vertical radar 

was also reduced as targets flew at lower altitudes and 

began to land from their migration flights. By 12:00, 

diurnal activity had fallen to low levels and no diurnal 

migration occurred. This pattern of target movement 

and changes in altitude were indicative of a pulse of 

nocturnal migratory activity. 

A similar pattern can be seen at the other radar sites 

with lower activity during the day, increasing on both 

antennas near dusk, peaking around midnight, and 

decreasing sharply around dawn. Targets also moved 

14 Great Lakes Avian Radar - Fall 2014
in to the shoreline from over the water and along 

the shoreline at dawn, and activity returned to lower 

diurnal levels the next day. The target movement 

directions often also changed between early in the 

night and times closer to dawn, indicating that the 

migrants changed their behaviors depending on the 

time of night they encountered the obstacle of the lake.

Some areas at each site were not well recorded by 

the radar due to beam blockage from the ground 

clutter (due to topography, vegetation, buildings, etc.) 

(Figures 4-5), which resulted in reduced detection in 

these areas (e.g., the southwest corner of the Lake 

County horizontal radar scan). Artifacts of the radar 

unit setup can also be observed in the Trackplots. 

Examples include rings of decreased detection that 

occur right next to the radar unit and the ring on the 

radar where the radar unit switches pulses (Vertical 

– Sept 22 22:00 Lake County – Figure 9; Horizontal – 

Aug 31 00:00 Bayfield County – Figure 10).

Great Lakes Avian Radar - Fall 2014 15
Figure 9. Images of tracks during 1 hour increments recorded by horizontal and vertical scanning radars 

during a migration event in Lake County, MN. Horizontal radar images (columns 1 and 3) show the 

direction of targets, as indicated by the color wheel. Vertical radar images (columns 2 and 4) show target 

heights.

16 Great Lakes Avian Radar - Fall 2014 
Photo Credit: B. Thompson

Figure 10. Images of tracks during 1 hour increments recorded by horizontal and vertical scanning radars 

during a migration event in Bayfield County, WI. Horizontal radar images (columns 1 and 3) show the 

direction of targets indicated by the color wheel. Vertical radar images (columns 2 and 4) show target 

heights.

Great Lakes Avian Radar - Fall 2014 17
Figure 11. Images of tracks during 1 hour increments recorded by horizontal and vertical scanning radars 

during a migration event in Keweenaw County, MI. Horizontal radar images (columns 1 and 3) show the 

direction of targets indicated by the color wheel. Vertical radar images (columns 2 and 4) show target 

heights.

18 Great Lakes Avian Radar - Fall 2014 
Directional Trends

During the fall 2014 season, the nocturnal target 

movement direction was generally between 

southeast and west (112.5-292.5°) at all sampled 

locations (Figures 12-13, Tables 3-4). At the Lake 

County site, the mean nocturnal direction was 

210°, with an angular concentration (r) of 0.55 (n 

= 3,986,149 targets), and during 85% of nights, 

the mean target direction was between south and 

west (157.5-292.5°), showing movement along the 

lakeshore. The diurnal mean direction was similar 

at 226°, with an angular concentration of 0.57 (n 

= 1,137,367 targets), and during 90% of days, the 

mean target direction was between south and west, 

indicating strong diurnal directionality along the 

lakeshore as well.

The direction at the Bayfield County site was 

slightly more variable during most time periods and 

had a mean nocturnal direction of 170° (r = 0.45, 

n = 1,399,938), with 78% of nights having a mean 

direction between southwest and southeast (112.5-

247.5°). The diurnal movement direction was even 

more variable, with a mean direction of 160° and an 

angular concentration of 0.21 (n = 180,003 targets). 

During 65% of the days, the mean target direction 

was between southeast and southwest.

The Keweenaw County site had a mean nocturnal 

direction of 170° (r=0.55, n=1,341,917), with 70% of 

nights having a mean direction between southwest 

and southeast. The diurnal movement was similar, 

with an average movement direction of 159° with an 

angular concentration of 0.4 (n=111,638 targets). 

During 72% of days, the mean target direction was 

between southeast and southwest. There was some 

evidence for onshore movement at dawn at each of 

the 3 sites (Figures 9-13).

Overall, the general directional trends observed on 

the extended range (11.1 km) horizontal radar at the 

Lake County, MN site matched what we observed 

on the normal (3.7 km) horizontal radar at that site. 

The total counts on the extended range radar were 

higher due to the increased area surveyed, but the 

temporal pattern of when the high and low numbers 

were observed as well as the general direction 

of movement were similar between the normal 

and extended range radars. We do not present 

the extended range data in this report due to its 

similarity to the standard range horizontal radar.

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

(r ≥ 0.5) of targets during these periods in Lake County, MN. This table is meant to give information about the 

direction of movement of the targets, not overall counts, as the amount of time sampled for each biological period is 

different.

Lake County

Biological 

Period

Mean 

Direction r

% Time 

Periods 

(degrees) r ≥ 0.5 n

Dawn 235 0.51 68.3% 243,146

Day 226 0.57 62.6% 1,137,367

Dusk 203 0.52 65.3% 62,695

Night 210 0.55 86.0% 3,986,149

Great Lakes Avian Radar - Fall 2014 19
Target DirectionPer Hour for Four Biological Time Periods

Lake County, MN

Figure 12. Target direction per hour during four biological time periods during fall 2014 in Lake County, MN

Table 4. Mean direction, angular concentration (r), and percent of biological time periods with strong 

directionality (r ≥ 0.5) of targets during biological time periods in Bayfield County, WI and Keweenaw 

County, MI. This table is meant to give information about the direction of movement of the targets, not 

overall counts, as the amount of time sampled for each biological period is different.

20 Great Lakes Avian Radar - Fall 2014 
Figure 13. Target direction per hour during four biological time periods during fall 2014 at Bayfield County, 

WI (left) and Keweenaw County, MI (right). 

Target DirectionPer Hour for Four Biological Time Periods

Bayfield County, WI 

Great Lakes Avian Radar - Fall 2014 21

 Keweenaw County, MI
22 Great Lakes Avian Radar - Fall 2014 

Figure 14. Movement direction of targets for each night over the entire season. Each line represents the 

average movement direction targets were coming from for one night during the season. These directions 

assume that migrants move in a straight line for an entire night, which is not the case, but it helps to show 

where the general direction of migrant movement originates. These directions provide evidence that 

nocturnal migrants are flying across the open water of the Great Lakes and along the shorelines. The color 

of the lines indicates the magnitude of migration on that night. Count categories are derived using natural 

breaks (Jenks) classification. Date labels are missing for some lines that were too closely clumped together.
Temporal Trends

Time Series Plots – Hourly target counts provided 

by horizontal and vertical radars showed pulses 

of elevated nocturnal activity with peaks near or 

slightly after midnight at the three study sites. 

Across the sampling period, these events were often 

clustered into groups of several sequential nights, 

and repeated groups of peaks were observed as soon 

as we deployed the radar units for the season at 

both of the first set of sites (Figures 15-16). These 

clusters of peaks of activity occurred up through 

when we moved one of the radar units to Keweenaw 

County on September 8, reaching their highest 

counts at Bayfield County near the end of August 

and in early September. The peaks occurred from 

near the start of our time at Keweenaw County 

and ended around the middle of October at this site 

(Figure 17). For the Lake County site, nocturnal 

peaks were observed consistently from the start 

of the season through mid-October, with sporadic 

peaks observed after this date up through when 

we ended our surveying season in mid-November. 

Diurnal peaks on the horizontal radar were also 

evident at this site, occurring with regularity from 

mid-September through early November, indicating 

heavy use of the area by diurnal migrants.

Different patterns of activity are apparent as the 

season progresses, with the differences being the 

most obvious at our Lake County site. For example, 

in early August, most of the activity occurs only 

at night. Near the end of August, higher activity 

during the day starts to occur occasionally, but the 

overall numbers are still much less than at night. In 

the middle of September, however, daytime HSR 

numbers begin to rival nocturnal HSR numbers, 

and days with activity occurring in the morning 

hours increase in frequency. In mid to late October, 

nocturnal activity began to drop off, leaving the 

diurnal activity as the main cause of many of the 

peaks in activity (Figure 15). These diurnal peaks 

were almost always moving along the shoreline 

to the southwest, indicating that they were likely 

diurnal migrants and not simply gulls or other 

birds moving back and forth between roosting and 

feeding grounds, as we have observed at some of 

the locations we surveyed during previous seasons. 

These observations match other studies of diurnal 

raptor migration along the coastline of Lake 

Superior in Minnesota (Peterson and Niemi 2011, 

Evans et. al 2012, Seeland et al. 2012, Peterson et al. 

2015). 

Great Lakes Avian Radar - Fall 2014 23
24 Great Lakes Avian Radar - Fall 2014 

Figure 15. Hourly counts by the horizontal radar and the vertical radar’s standard front from August 1 – 

November 11, 2014, at Lake County, Minnesota. Light gray vertical lines represent midnight.
Figure 16. Hourly counts by the horizontal radar and the vertical radar’s standard front from August 1 – 

September 8, 2014, at Bayfield County, Wisconsin. Light gray vertical lines represent midnight.

Great Lakes Avian Radar - Fall 2014 25
26 Great Lakes Avian Radar - Fall 2014 

Figure 17. Hourly counts by the horizontal radar and the vertical radar’s standard front from September 

8 – November 2, 2014, at Keweenaw County, Michigan. Light gray vertical lines represent midnight.
Figure 18. Box plots showing variability in target passage rate (targets/km/hr) during four biological time 

periods for fall 2014 in three counties along Lake Superior: Lake County, MN, Bayfield County, WI, and 

Keweenaw County, MI. Whiskers represent the 1st and 4th quartiles, boxes represent the 2nd and 3rd 

quartiles (with the line between indicating the median), and colored diamonds represent the seasonal mean 

for the time period at that site.

Table 5. Mean target passage rate (targets/km/hour) with standard deviations during four biological time 

periods in Lake, Bayfield, and Keweenaw counties during fall 2014.

Target Passage Rate – The pattern in mean TPR 

among the four biological time periods was similar 

among the four study sites (Figure 17), with the mean 

TPR at night being the greatest (Table 5). The mean 

nocturnal TPR was 398 ± 421 SD (n = 91 nights) at 

Lake County, MN, and at Bayfield and Keweenaw 

counties, it was 450 ± 555 (n= 37 nights) and 266 

± 319 (n=51 nights), respectively. The mean TPR 

varied by the hour, with peak numbers achieved near 

midnight, with a decline afterwards at each of the 

locations (Figure 18). 

Great Lakes Avian Radar - Fall 2014 27
28 Great Lakes Avian Radar - Fall 2014 

Figure 19. Mean hourly target passage rate (targets/km/hr) during fall 2014 at Lake, Bayfield, and Keweenaw counties.

Weekly Mean of Target Passage Rates – The weekly 

means of nocturnal TPR rose quickly during the first 

part of our season at both Lake County and Bayfield 

County (Figure 20). Around the time the Bayfield 

County radar unit was moved to Keweenaw County, 

a drastic drop-off in weekly mean TPR was observed 

at Lake County. This drop-off also occurred at a 

similar time at Keweenaw County, indicating that the 

drop-off may not have been due to the change in site 

(Figure 20). A change in species that were moving 

through (long-distance vs. short-distance migrants) 

may have also been responsible for the differences 

in numbers observed. Weekly means of nocturnal 

TPR were consistently higher than weekly means of 

diurnal TPR (Figure 20). However, as the recorded 

migration season subsided, there was less difference 

between these passage rates (Figures 20-22). 

Patterns in nocturnal TPR (7-day moving means) and 

diurnal TPR were similar among the sites, although 

diurnal and nocturnal movements seemed to be 

less correlated at these sites than at others we have 

surveyed (Bowden et. al 2014, USFWS unpublished 

data). Early in the season, there was high weekly 

mean nocturnal activity. This activity declined near 

the time when we moved the radar unit, at both the 

site we moved to (Keweenaw County) and the site 

that stayed in a constant location (Lake County). 

Diurnal weekly means matched the high early season 

nocturnal activity, but they did not match up as well 

later in the season. For instance, a peak in diurnal 

activity in early October did not seem to match 

up with nightly activity during the same time, and 

increased diurnal activity levels near the end of the 

season did not match the nocturnal activity levels 

(Figures 21-22). 
Figure 20. Weekly mean of nocturnal (left column) and diurnal (right column) target passage rates (targets/

km/hr) at Lake, Bayfield, and Keweenaw counties. Error bars represent one standard deviation. Note 

different scales on nocturnal and diurnal plots. 

Great Lakes Avian Radar - Fall 2014 29

 Weekly Mean Nocturnal Passage Rate Weekly Mean Diurnal Passage Rate
30 Great Lakes Avian Radar - Fall 2014 

Figure 21. Between site comparison (based on a moving 7-day mean) of nocturnal (top graph) and diurnal 

(bottom graph) target passage rate trends (targets/km/hr) during fall 2014 in Lake, Bayfield, and Keweenaw 

counties. Note the different scales on the nocturnal and diurnal graphs.

Moving 7-Day Mean Target Passage Rates – Between Sites
Figure 22. Within site comparison (based on a moving 7-day mean) of nocturnal (solid lines) and diurnal 

(dashed lines) target passage rate trends (targets/km/hr) during fall 2014 in Lake, Bayfield, and Keweenaw 

counties. Note the different scales for each site.

Moving 7-Day Mean Target Passage Rates – Between Sites

Great Lakes Avian Radar - Fall 2014 31
32 Great Lakes Avian Radar - Fall 2014 

Altitudinal Trends

Altitude patterns are important for determining 

the risk wind turbines pose to migrating birds and 

bats, making the use of density estimates critical 

to examining the data while examining wind farm 

locations. Our density estimate that accounted for 

the geometric shape of the sampled space resulted 

in a substantially different density estimate and a 

higher estimate of risk to migrants than assuming 

an equal amount of sample volume per altitude band. 

The rotor-swept zone is the area the rotor blades 

spin through as the turbine turns. To produce more 

power, larger turbine blades are needed, meaning 

the rotor-swept zone is larger and stretches higher 

into the air. An approximate rotor-swept zone for 

a 1.5 megawatt (MW) turbine is 40-120 m and for a 

2.5 MW turbine is 50-150 m. There has also been 

interest in building turbines with rotor-swept zones 

extending up to 200 m in the Great Lakes region. 

Altitude profiles for the dawn biological time period 

were consistent among sites and between the two 

parts of the season, with a high amount of activity 

within the rotor-swept zone at all sites (Figures 

23-25). The altitude profile for the night period had 

much more activity than the other time periods at 

all of the sites and included high concentrations 

within or near the rotor-swept zone. For the day 

and dusk periods, Lake County and Bayfield County 

both had concentrations approximately equal to the 

dawn period, with the highest concentrations within 

or near the rotor-swept zone. Keweenaw County 

however, had much lower activity during the day 

and dusk periods than at dawn, although the highest 

concentrations still remained in or near the rotorswept zone. 

Hourly altitude profiles at night revealed 

considerable variation in use of altitude bands 

(Figures 26-28). However, over the course of the 

season, the lower altitude bands (50-250 m) were the 

most densely used bands at each of the sites during 

each of the biological time periods (Figure 29). In 

Lake County, the 100-150 m altitude band was the 

most densely populated (Figure 29), with a total 

of 4.31 targets/1,000,000 m3/night-hour occurring 

in that band. At Bayfield County, the 100-150 m 

altitude band was the most densely populated, with 

4.02 targets/1,000,000 m3/night-hour. Keweenaw 

County had the 150-200 m altitude band as the most 

densely populated, with 6.88 targets/1,000,000 m3/

night-hour. 

The maximum density of the corrected target 

estimates was below 200 m during 77.6%, 65.8%, 

and 63.0% of the nights for Lake, Bayfield, and 

Keweenaw counties, respectively (Figure 30). A 

similar pattern occurred when the hours from 20:00 

to 04:00 were considered individually, with the 

maximum density of targets occurring at less than 

200 m during 74.2%, 51.7%, and 56.1% of the night 

hours for Lake, Bayfield, and Keweenaw counties, 

respectively (Figure 31). The maximum density 

was below 200 m during 78.0% of days and 68.6% 

of day hours (07:00-17:00) at the Lake County, MN 

site (Figure 32). This analysis was only performed 

for the Lake County site due to the observed high 

numbers of diurnal migrants at this site.

At each of the three sites, targets were observed 

within the entire range of altitude bands sampled. 

The mean altitude of nocturnal targets was 612 m 

± 531 SD above ground level at the Lake County 

site, with a median target altitude at night of 421 

m. At the Bayfield County site, the mean altitude 

of nocturnal targets was 664 m ± 514 SD, and the 

median altitude was 501 m. In Keweenaw County, 

the nocturnal mean altitude of targets was 605 m 

± 451 SD, with a median altitude of 473 m. At two 

of the three sites (Lake and Bayfield counties), the 

mean and median altitudes were greatest during the 

night biological time period with the dawn period 

being the next highest. At the Keweenaw County 

site, however, the mean and median altitudes at 

dawn were much higher than at night. 

Estimates of the mean and median altitudes were 

poor indicators of the altitude band with the highest 

density, and we believe there are metrics that 

better represent the distribution of targets, such as 

volume-corrected measures and 50-m band analysis 

(Table 6). Using mean or median estimates for 

altitude tends to underestimate the risk to migrants. 

This under-representation would have occurred 

at all of the sites during all of the time periods 

sampled. By examining the density of each altitude 

band across the 24-hour cycle, we can see that there 

are particular times where the highest densities 

are within or near the rotor-swept zone for each of 

the sites (Figure 33). The night hours at all three 

sites had the highest density of flight activity, and 

the range of flight altitudes increased during the 

night as well. Many targets fly well within the rotorswept zone during the night, and mean and median 

altitudes do not reflect peak density altitudes. These 

metrics can misrepresent collision risk if not used 

carefully. At the Lake County site, the density within 

the rotor-swept zone was much higher than outside 

it, and this pattern continued even throughout the 

daytime hours. Other studies have also observed 

the greatest amount of movement within 100 m of 

the tree canopy (Peterson et al. 2015). This further 

indicates a high amount of use of the airspace 

above this site by diurnal migrants. In Bayfield 

County, there were also indications of diurnal use by 

migrants (Figure 33). 

The mean altitude per hour during the season 

showed a similar pattern at two of the three 

locations (Lake and Keweenaw counties). At these 
locations, the mean altitude increased following 

dusk, tapered toward midnight, and decreased 

following midnight (Figure 34). A spike in 

mean altitude occurred during the 06:00 hour, 

representing slight dawn ascent. At the Bayfield 

County site, however, there was no increase in 

mean altitude near dawn, but otherwise, the overall 

pattern was similar to those of the other sites. At 

the Keweenaw County site, the dawn mean and 

median altitudes were the highest of all the time 

periods. 

Table 6. Comparison of mean altitude (m) with standard deviations, median altitude, and volume corrected 

altitude band (50-m bands) that contained the maximum target density during four biological time periods 

in Lake, Bayfield, and Keweenaw counties during fall 2014. The max density category is the top of the 50-m 

altitude band. Keweenaw counties. Note the different scales for each site.

Great Lakes Avian Radar - Fall 2014 33
34 Great Lakes Avian Radar - Fall 2014 

Figure 23. Altitude profile of targets in Lake County, MN. Corrected lines (blue) depict 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 (black) depict 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 and 200 m. Y-axis labels represent 

the top of the altitude band. 
Figure 24. Altitude profile of targets in Bayfield County, WI. Corrected lines (pink) depict 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 (black) depict 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 and 200 m. Y-axis labels represent 

the top of the altitude band. 

Great Lakes Avian Radar - Fall 2014 35
36 Great Lakes Avian Radar - Fall 2014 

Figure 25. Altitude profile of targets in Keweenaw County, MI. Corrected lines (purple) depict 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 (black) 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 and 200 m. Y-axis 

labels represent the top of the altitude band.
Figure 26. A sample of nocturnal hourly altitude profiles corrected for the shape of the sample volume in 

Lake County, MN during fall 2014. Hours were selected to portray the variability in density per altitude 

band of passing targets during the night and early morning hours. The x-axis represents the uncorrected 

target density (targets/1,000,000 m3) per 50-m altitude band. The y-axis labels represent the top of each 

altitude band in meters. The red line represents the top of the rotor-swept zone at 200 m. 

Great Lakes Avian Radar - Fall 2014 37
38 Great Lakes Avian Radar - Fall 2014 

Figure 27. A sample of nocturnal hourly altitude profiles corrected for the shape of the sample volume in 

Bayfield County, WI during fall 2014. Hours were selected to portray the variability in density per altitude 

band of passing targets during the night and early morning hours. The x-axis represents the uncorrected 

target density (targets/1,000,000 m3) per 50-m altitude band. The y-axis labels represent the top of each 

altitude band in meters. The red line represents the top of the rotor-swept zone at 200 m.
Figure 28. A sample of nocturnal hourly altitude profiles corrected for the shape of the sample volume in 

Keweenaw County, MI during fall 2014. Hours were selected to portray the variability in density per altitude band of passing targets during the night and early morning hours. The x-axis represents the uncorrected target density (targets/1,000,000 m3

) per 50-m altitude band. The y-axis labels represent the top of 

each altitude band in meters. The red line represents the top of the rotor-swept zone at 200 m.

Great Lakes Avian Radar - Fall 2014 39
40 Great Lakes Avian Radar - Fall 2014 

Figure 29. Altitude profile of target density below 400 meters in Lake, Bayfield, and Keweenaw counties. 

These graphs show the altitude band where the maximum corrected density occurred during fall 2014. The 

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

the top of each altitude bands in meters. The lowest altitude band is often the most affected by ground clutter, greatly reducing the ability to detect targets in this area.

Altitude Profile of Target Density Below 400 m
Great Lakes Avian Radar - Fall 2014 41

Figure 30. These graphs show the percent of nights surveyed when the maximum density (targets/1,000,000 

m3/ altitude band) or count (targets/altitude band) occurred within 50-m altitude bands in Lake, Bayfield, 

and Keweenaw counties during fall 2014. The x-axis labels represent the top of each altitude band.

Comparison of Max Density and Max Counts Per Night 
42 Great Lakes Avian Radar - Fall 2014 

Figure 31. These graphs show the percent of night hours (20:00 – 04:00) surveyed when the maximum 

density (targets/1,000,000 m3/ altitude band) or count (targets/altitude band) occurred within 50-m altitude 

bands in Lake, Bayfield, and Keweenaw counties during fall 2014. The x-axis labels represent the top of 

each altitude band.

Comparison of Max Density and Max Counts Per Hour
Figure 32. These graphs show the percent of days and day hours (07:00 – 17:00) surveyed when the 

maximum density (targets/1,000,000 m3/ altitude band) or count (targets/altitude band) occurred within 50-m 

altitude bands in Lake County, MN during fall 2014. This figure was only completed for this site due to the 

high numbers of diurnal migrants observed. The x-axis labels represent the top of each altitude band. The 

large percentage of Max Count targets above 800 m when only looking at the Day Hours likely comes from 

hours with only a few targets present.

Great Lakes Avian Radar - Fall 2014 43

Maximum Density Per Day and Day Hour
44 Great Lakes Avian Radar - Fall 2014 

Figure 33. These graphs present the density of targets that occurred within each 50-m altitude band in 

Lake, Bayfield, and Keweenaw counties in fall 2014 over each of the 24 hours in a day. Please note the 

different scales for each of the heat maps, especially for Lake County, MN. Deep red areas on one graph 

may be equivalent in density to yellow areas on another heat map. This is at least partially due to differences 

in detection ability due to clutter and target movement direction, which makes comparisons between sites 

difficult. Dark blue lines represent the mean altitude, and light blue lines represent the median altitude 

across all hours. The black lines represent the top of the 200-m rotor-swept zone. The graphs only extend up 

to 1300 m and not to the full height of the radar scan to better show detail of the lower altitude bands.

 Target Density by Altitude Band Averaged Over 

Each of the Daily 24 Hours during Fall 2014
Figure 34. Mean hourly target height (m) during fall 2014 at Lake County, Bayfield County, and Keweenaw 

County. Orange and blue markers indicate the hours in which sunrise and sunset occurred during the 

season, respectively. Due to the different locations and times surveyed, hours where sunrise and sunset 

occurred were not equal for each of the sites across the season. Error bars represent one standard 

deviation.

Hourly Mean Heights of Targets during Fall 2014

Great Lakes Avian Radar - Fall 2014 45
46 Great Lakes Avian Radar - Fall 2014 

Discussion

We undertook this study to document migration 

along the shorelines of the Great Lakes. The 

2014 season was the first time we stationed the 

radar units along Lake Superior and was the first 

time we stationed a radar unit on the northern 

shore of an east-west oriented lake (the Lake 

County site). What we found during this season 

indicates that migration was common along Lake 

Superior’s U.S. shorelines where we established 

our study sites. We also believe that our data can 

be extrapolated to represent much of the rest of the 

shores of the western part of Lake Superior. Our 

research contributes to a growing body of literature 

that documents various aspects of migration and 

identifies the Great Lakes shorelines as areas that 

are important for the conservation of migratory 

species of birds and bats. Our data provide unique 

observations about the magnitude and timing of 

nocturnal migration that could not be observed 

without the aid of radar. 

The sites sampled this season were sampled for 

different periods of time, with one site (Lake 

County) sampled for the entire season and the 

other two sampled for a portion of the season, with 

the first part occurring at Bayfield County and the 

second occurring at Keweenaw County. Due to 

these different time periods and lengths sampled, 

our aggregate summary statistics may not be 

comparable between sites. Additionally, each of 

these sites was affected to varying degrees by 

clutter on the horizontal and vertical radars, further 

emphasizing the need for caution when making 

comparisons between sites and time periods. When 

examining the numbers and patterns between 

sites, it is important to remember that these sitespecific factors can make comparisons between sites 

potentially misleading. 

Patterns of diurnal migration were evident at the 

Lake County, MN site. The migration direction was 

strongly southwest along the lakeshore during the 

day period (Figure 12). Activity often continued 

into the morning hours after a nocturnal pulse as 

well (Figures 15 and 19). These results support 

conclusions made by other researchers that the 

shoreline of Lake Superior in Minnesota is a 

migratory pathway for raptors and other diurnal 

migrants (Peterson and Niemi 2011, Evans et. al 

2012, Seeland et al. 2012, Peterson et al. 2015). 

The Keweenaw County site had some interesting 

patterns that may have arisen due to the site 

location near the tip of the peninsula. At this site, 

it seemed that the peaks of migration occurred 

slightly less often than at other sites; however, 

when they did occur, they seemed to be as large 

or larger in magnitude. This could indicate that 

large numbers of migrants were reluctant to 

cross the large expanse of Lake Superior unless 

conditions were favorable, but when favorable 

conditions did occur, migrants moved across en 

masse and freely and may have been attracted 

to the Keweenaw Peninsula that extends out into 

the lake. There were some migrants that crossed 

over Lake Superior every night. However, we 

have not yet analyzed the weather data in relation 

to radar counts to determine their relationship at 

this site. Qualitatively, however, our biologists in 

the field believe that there appears to be a strong 

relationship between wind direction and migration 

across Lake Superior.

Additionally, many of the timing patterns were 

slightly different at the Keweenaw County site. 

For instance, the migration numbers were slower 

to increase after dusk at this site than the other 

two sites (Figure 19), and the mean altitude did not 

increase until after dusk (Figure 34). This is likely 

due to the length of time it would take migrants 

to cross over the lake and reach the Keweenaw 

Peninsula. For instance, a few migrants would 

start flying from habitat located on the peninsula, 

but once those had passed by the radar location, 

it could be an hour or more before migrants that 

started flying at dusk from the north shore of Lake 

Superior in Canada reached the peninsula. This 

delay would create the difference in the pattern 

of hourly TPR we observed when comparing the 

Keweenaw site to our other sites this season. 

Migrants that were still moving at dawn continued 

longer into the morning at the Keweenaw County 

site than the Bayfield County site, and dawn mean 

altitudes were the highest at the Keweenaw County 

site (Figures 19 and 34). Migrants that were still 

crossing over the lake at dawn may have traveled to 

higher altitudes to scout for a place to land, and the 

Keweenaw Peninsula could have been an attractive 

place for them to take refuge. Due to the varying 

distances the migrants may have been away from 

the peninsula, this would cause a slow trickle of 
Great Lakes Avian Radar - Fall 2014 47

migrants to continue into the post-dawn hours. It 

is difficult to compare the morning hours at the 

Keweenaw County site to the Lake County site 

due to the high numbers of diurnal migrants that 

used the Lake County site, possibly obscuring the 

patterns of the end of nocturnal migration. 

 Sampling Regime 

The sampling regime is an important consideration for 

migration studies. Migratory movements are guided, 

in part, by environmental conditions and occur in 

pulses across the migratory season (Alerstam 1990). 

Our continuous sampling scheme captured the timing 

of migration events and provided a more complete 

picture of migration than a systematic or random 

sampling scheme, which may miss pulses of activity, 

even when sampling as frequently as every four days 

(Figure 35). In this figure, the interpretation could be 

made from the periodic sampling method that there is 

a constant low amount of migration that occurs during 

this time period, which may not actually represent a 

time of risk to migrants. In fact, there were additional 

larger peaks than were observed using the partial 

sampling method, and the peaks that did occur 

happened more regularly than would be concluded 

based on the partial sampling data alone. 

We used diurnal radar observations to provide 

a baseline for comparing nocturnal activity, and 

including this time period in the sampling scheme 

helped us distinguish the magnitude of migration 

events (Figure 15-17). In addition, at the Lake 

County site, we observed high levels of diurnal 

migration that are consistent with data gathered by 

other studies along the shoreline of Lake Superior 

in Minnesota (Peterson and Neimi 2011, Peterson et 

al. 2015), including observations from a hawk watch 

station in Duluth, MN (Evans et. al 2012). This heavy 

diurnal migration may confound the use of daytime 

data to provide a baseline of activity of non-migrants 

to compare with nocturnal migrant activity for this 

site. Our sampling regime was also useful in showing 

that the nocturnal migration season was active even 

when we deployed our radar units in early August and 

continued regularly through the middle of October. 

Figure 35. Example of a hypothetical sampling schedule where data were collected once every four days 

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

number of targets counted per km per hour by the vertical scanning radar from the middle to the end of the 

sampling season (Sept 5 to Nov 11, 2014) at Lake County, Minnesota. Blue circles on the bottom graphic 

represent times that would have been sampled during the top graphic, missing many of the larger migration 

peaks.

Effect of Sampling Schedule on Data
48 Great Lakes Avian Radar - Fall 2014 

After this time, there were still apparent peaks of 

migration, but they occurred with less regularity. 

Although we moved sites with one radar unit in the 

middle of the season to cover more area, we believe 

that our locations were similar enough to represent 

the approximate peak and end dates for fall migration 

in the Lake Superior area. Activity appeared to have 

begun before our start date, so estimating the start 

date of fall migration from our data is more difficult. 

As more data are collected and we examine the general 

patterns of migration at different locations, we will be 

able to better describe the migration season and how 

it varies with location and year. This information will 

help conservation efforts to be tailored to appropriate 

time frames for migratory birds and bats.

Target Counts

Target counts provided by radar are influenced by 

radar type and calibration, filtering of non-intended 

targets, count algorithms, frequency band, antenna 

orientation, sampling scheme, and how researchers 

account for variation in detection probability and 

sample volume (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 (Schmaljohan et al. 2008). Recognizing 

that our counts represent an index of target passage 

and not a true population count that is relative to a 

site, we are cautious in making comparisons among 

sites or studies. Rather than relying solely on the 

magnitude of the target passage as an indication of 

airspace usage, we assess the general patterns of 

activity among sites to compare the relative strength 

of migration. For example, a site with a nocturnal 

passage rate that shows peaks that are many times 

larger than nocturnal lulls for the majority of the 

sampling period would be considered as having 

more migration than a site with less of a discrepancy 

between nocturnal peaks and lulls. A site that had a 

nocturnal passage rate that only occasionally spiked 

above a baseline of low nocturnal passage rates may 

be indicative of an area with low amounts of migration, 

although this may not be the case for all areas. The 

presence of behaviors such as dawn movement to or 

along the shoreline, change in movement direction 

during the course of the night, and other indicators of 

an obstacle to migration may indicate risk to migrants 

in a given area, along with more specific measures such 

as the density of targets in the rotor-swept zone. 

Migration Patterns

The patterns of movement we recorded were 

consistent with other observations of migration 

(Newton 2008), and they indicated that nocturnal 

migratory flights occurred regularly during fall 2014 

at each of our three surveyed locations along Lake 

Superior. The nocturnal activity we observed was 

typically oriented along the shoreline at the Lake 

County site (Figures 12 and 14) and generally towards 

the south at the Bayfield and Keweenaw county sites 

(Figures 13 and 14). There was also evidence that 

targets regularly crossed over large expanses of 

the open water of Lake Superior to the Keweenaw 

Peninsula (Figure 14). The crossing of migrants over 

this large expanse of open water (175 km) suggests 

that they are capable of and willing to fly over any 

expanse of water in the Great Lakes. Care should be 

taken to evaluate migrants that fly over the lakes when 

siting offshore wind facilities in this area. Activity 

occurred in pulses across the season, as captured by 

horizontal and vertical radars (Figures 15-17). We also 

observed targets flying over water return to shorelines 

near dawn (Figure 9-13). Movement towards shore at 

dawn may be due to migrants drifting with the wind as 

they migrate and adjusting their position at dawn to 

find suitable stopover habitat to rest and refuel (Anna 

Peterson, pers. communication, Horton et al. 2016b). 

The target passage rate (mean for the season) was 

greatest during the nocturnal biological time period 

at each location, with the dawn biological time period 

having the next highest mean target passage rate at 

each location (Table 5, Figure 18). 

Mean hourly heights showed a pattern previously 

associated with migration at each of our sites 

(Harmata et al. 2000, Mabee and Cooper 2004, Bowden 

et. al 2015), with heights increasing near dusk, peaking 

around midnight, and decreasing prior to dawn. 

Keweenaw County’s mean hourly height pattern was 

similar to the pattern we observed at other locations; 

however, the highest mean altitudes were recorded 

at dawn and not at midnight (Figure 33). The slight 

increase in mean height near dawn (Figure 33) at 

Lake and Keweenaw counties is consistent with a 

migratory behavior described as dawn ascent (Myres 

1964, Diehl et al. 2003). This behavior is attributed to 

migrants increasing altitude to gain a broader view of 

the surrounding landscape before selecting stopover 

habitat or returning to the shoreline if they were 

flying over water. This phenomenon of dawn ascent 

did not appear to occur at the Bayfield County site, but 

because only the first part of the season was sampled, 

we cannot say with certainty that it does not occur 

there. In previous seasons where we have moved the 

radar units, we have noticed that early season sites 

tend to show less of the pattern of dawn ascent than 

later-season or full-season sites (USFWS unpublished 

data). The Bayfield County site was also further 

inland than the other sites, providing another possible 

explanation for the difference. The dawn ascent was 

also more pronounced at the Keweenaw County site 

than many other sites we have seen. The Keweenaw 

County site was near the tip of a large peninsula that 

reaches into the middle of Lake Superior. Migrants 

crossing over the middle of the lake at dawn may fly to 

even higher altitudes to find a spot to land, likely being 
attracted to the peninsula as a safe refuge.

Taken together, we attribute nocturnal observations 

to migrants and suggest that the areas we studied 

are important for their conservation. High levels 

of diurnal activity with directional movement also 

occurred at the Lake County site, confirming results 

of other studies that show diurnal migration of 

raptors and other birds along the north shore of Lake 

Superior.

At each of our three sample locations, nocturnal 

targets appeared to move across the landscape in 

four waves, with peaks near August 14, September 

4, September 22, and October 15 (Figures 21-22). 

Diurnal targets also appeared to move in 4 waves, with 

the peaks occurring near August 16, September 3, 

October 3, and November 7. These fluctuations may 

be related to broad-scale weather fronts, variation in 

timing among guilds of migrants, or a combination of 

these and other factors (Newton 2009). The similarity 

in the pattern of these waves when compared between 

sites reveals broad-scale influences and could indicate 

that further investigation into their cause would allow 

prediction of high migration events and adjustment of 

wind turbine operations accordingly. 

 

The weekly mean estimates of nocturnal TPR were 

consistently higher than the weekly mean diurnal TPR 

across all weeks of data collection (Figures 20-22). 

Although the nocturnal TPR was always greater, the 

difference between the nocturnal and diurnal time 

periods was the smallest during the beginning and end 

of the migration season. This shift from time periods 

with orders of magnitude more nocturnal activity 

to time periods with much more similar diurnal and 

nocturnal activity indicate that nocturnal migration 

added substantially to the aeroecology above our study 

areas, although this measure may not be appropriate 

for all areas.

Flight Attitude

Altitude profiles indicated that most nocturnal targets 

passed below 800 m with peak density in the 100 - 250 

m altitude bands for each of the sites we surveyed 

this season (Figures 26-31). We believe that analysis 

of altitude bands with the highest densities is a 

better representation of the data than uncorrected 

mean or median flight altitudes. We corrected for 

the approximate shape of the survey volume and 

included this correction in our density estimates. This 

correction is based on the manufacturer’s estimate 

of beam geometry, which may not be precise, and 

beam propagation is not consistent over time. Beam 

propagation is affected by side lobes, target size and 

distance, and atmospheric conditions. Nonetheless, 

we think the correction was an improvement over 

altitude profiles that ignore 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 in the region where the radar 

transitioned from the short to medium pulse, at a 

range of approximately 1,200 m. For these reasons, 

our estimates likely under-represent density as 

altitude increases. 

The altitude profiles we report varied considerably 

among nocturnal hours at our sites along Lake 

Superior (Figures 22-24). Migrants adjust flight 

altitude with wind direction and speed, visibility, time, 

and landscape below flight trajectory (Alerstam 1990, 

Hueppop et al. 2006, Liechti 2006). For example, head 

winds aloft have resulted in migrants moving en masse 

to lower altitudes where wind speeds were reduced 

(Gauthreaux 1991). Also, migrants are typically 

on land at least twice during every 24-hour period. 

Changes in flight altitude can occur at various times 

over the course of the night and are associated with 

targets ascending from and descending to stopover 

sites. Depending on location, these altitude changes 

may place migrants at risk of collision with wind 

turbines and other tall human-made structures. 

Radar Study Considerations

While radar may be the best tool available for 

gathering large amounts of data on nocturnal 

migration, the interpretation of radar data can be 

challenging. Also, the metrics reported in these types 

of surveys can be misleading to someone unfamiliar 

with avian radar. Marine radar is the most common 

type used to track bird and bat movements (Larkin 

2005), and its use to assess risk will likely increase with 

the increase in wind energy development. Despite 

this growing trend, standard methodologies for 

establishing radar settings, ground truthing biological 

targets, and processing data have not been adopted. 

These considerations can substantially affect the 

quality of data and the interpretations that are derived 

from thoese data. This presents a challenge that is 

not easily solved. Yet, without standards, comparisons 

among studies may be more reflective of differences 

in equipment, methodology, and site conditions rather 

than differences in migration activity. 

An example of a potentially misleading metric, mean 

altitude of target passage, is often reported to be above 

the rotor-swept zone and has been interpreted as an 

indication of low risk. However, the mean altitude can 

be well above the rotor-swept zone even when there 

is a high rate of targets passing within it. This is due 

to the long range at which radars collect altitude data, 

up to 3 km above ground level in our study, where high 

flying targets inflate the mean altitude. This bias is 

apparent in our data and can be seen by comparing the 

mean altitude of nocturnal targets to the most densely 

populated altitude band (Table 6 and Figures 23-25 and 

30-32). We do not recommend using mean and median 

altitudes as indicators of risk to migrants.

It is also misleading to compare the percent of targets 

Great Lakes Avian Radar - Fall 2014 49
50 Great Lakes Avian Radar - Fall 2014 

below, within, and above the height of the rotor-swept 

zone without addressing the difference in sampling 

effort between these categories. Within our sampling 

framework, there are four 50-m altitude bands below 

200 m (an estimate for the height of the rotor-swept 

zone) and 52 altitude bands above 200 m. Based on 

our model, we estimated that approximately 2 percent 

of the potential survey volume is below 200 m. Given 

that information, we would expect a small percentage 

of targets to be recorded at or below the rotor-swept 

zone, but this does not necessarily indicate low risk. 

If targets were spread evenly throughout the survey 

volume, we would expect to have a tiny percentage 

of targets within the rotor-swept zone. Uncorrected 

numbers such as 5-10% of targets in the rotor-swept 

zone are often reported and classified as “low risk” 

due to the low percentage of targets in the rotorswept zone, even though this means that this area is 

many times more concentrated with targets than we 

would expect from a random distribution of targets 

throughout the survey volume. When using estimates 

of target counts that are corrected for volume, we 

often see a much higher concentration in the rotorswept zone than if the numbers do not take sampling 

volume into account (Figures 23-25). For these 

reasons, we also do not recommend using percentages 

of targets below, within, or above the rotor-swept zone 

as indicators of risk to migrants.

In this report, we provide examples of methodology 

and analyses that we find helpful in interpreting radar 

data and have used in other seasons. We suggest that 

the patterns of activity and the relative change in 

counts at a site indicate the level of migration activity 

and that this is a better indicator than comparing the 

magnitude of counts among studies. Careful attention 

should be given to how these indices fluctuate over 

fine temporal scales, such as hourly, as opposed to 

monthly or seasonal summaries. These fine-scale 

measures may show more times when risk to migrants 

could be high. The clutter maps we include provided 

information about our ability to detect targets at 

various altitudes, and it is important, particularly for 

risk assessment, that radar operators address their 

ability to detect targets at low altitude. We provide a 

concept for a method to account for the structure of 

the sample volume that, while not without limitations, 

provides a partial solution rather than ignoring the 

biases associated with sampling effort. Overall, we 

found that radar provides insight into nocturnal 

migration that would otherwise be unattainable, while 

continued development and careful interpretation will 

result in valuable contributions to the management 

and conservation of migrants.

Management Considerations

These data provide a larger picture of migration 

that we need to put in perspective. Our radars were 

primarily located along the shoreline, which is the 

area where we can gain the best picture of migrant 

behavior. The general patterns along the shorelines 

of Lake Superior reveal that these areas are used 

heavily by nocturnal and diurnal migrants during fall 

migration. This pattern of migration is evident from 

our sample Trackplots as well as the timelines from 

each of the sites. Beginning in early August, migration 

occurs on many if not most nights. Birds and bats 

likely began migrating prior to this date, as we saw 

large movements almost immediately after we set up 

the radar units. We observed migrants beginning on 

the lake shore and traveling out over the open water 

to cross to the south shore of Lake Superior. At other 

locations, we saw targets arriving at the south shore 

after traveling in from out over the water. From our 

directional analysis, we concluded that migrants 

were crossing over Lake Superior to the Keweenaw 

Peninsula (Figure 14). This expanse of water, at 

nearly 100 miles (175 km), is one of the largest spans 

in the Great Lakes. If migrants are willing and able 

to cross this expanse, they are likely willing and able 

to cross at any area of the Great Lakes, provided 

the weather conditions are appropriate. We often 

observed migrants following along the shoreline. 

They likely used the shoreline for navigation or were 

funneled through the area by geographical features. 

The movement of migrants along the shoreline implies 

that a wind energy facility or communication tower 

constructed in shoreline areas may be encountered by 

more than just migrants moving down from the areas 

directly to the north. 

A close look at the different biological time periods 

also reveals information about the importance of the 

shoreline area. At many of the sites, the high levels 

of activity at dusk may represent birds and bats 

leaving their stopover habitat to continue migrating. 

High activity levels at dawn may be due to new 

migrants moving into land in the stopover habitat 

after migrating into the area at night or coming in 

from flying over the water. These newly arriving birds 

and bats may be in different guilds, be heading to 

different wintering areas, be arriving from different 

breeding grounds, have different physical conditions, 

or may represent different sex or age groups than 

those that previously migrated through the same 

area. Consequently, impacts to the shoreline area from 

development, habitat loss, or other factors may have 

impacts on all parts of the population for a wide variety 

of bird and bat species.

At the survey locations this season, our risk analysis 

revealed that during a large proportion of nocturnal 

hours or nights overall, the numbers and densities of 

birds and bats flying in or near the rotor-swept zone 

were high (Figures 30-33). At the Lake County site, 

there were high densities of targets in the rotor-swept 

zone even throughout the daytime hours (Figures 

32-33). The Lake County site had some of the highest 

daytime migrant activity we have seen around the 

Great Lakes (Bowden et al. 2015, Rathbun et al. 
Great Lakes Avian Radar - Fall 2014 51

2016, USFWS unpublished data). Nocturnal and 

diurnal migrants will change altitudes depending on 

environmental conditions, and thus, targets in altitude 

bands that are near the rotor-swept zone may also 

be at risk. In addition, our analysis only shows the 

rotor-swept zone for turbines that were constructed 

at the time of the study. Wind turbines are already 

being constructed to higher altitudes (Eller 2015), with 

larger rotor-swept zones extending into the altitude 

bands just above where turbine blades currently 

reach, which will likely impact more migrants (Figure 

33).

Our data demonstrate that the shoreline areas of 

Lake Superior are important for migrating birds and 

bats. We have identified behaviors that concentrate 

migrants along the shoreline, demonstrated that these 

behaviors occur regularly throughout the season, and 

established that migrants are flying at altitudes that 

place them at risk of collision with current or future 

wind energy development in the area. The importance 

of shoreline areas, as revealed by our study, highlights 

the need to avoid development in these migration 

corridors as recommended in the wind energy 

guidelines (USFWS 2012). 

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 that these data provide a better indicator 

than comparisons among the magnitude of counts 

recorded in different studies. Careful attention 

should be given to how these indices fluctuate over 

fine temporal scales, such as hourly 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 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, 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 wind energy guidelines (USFWS 2012) to identify 

areas where impacts to wildlife would be minimized. 

We documented clear examples of migrant activity 

along studied shorelines on Lake Superior, and the 

density of targets at lower altitudes is a potential 

concern. The data we collected may be of interest 

to public and private entities that are involved with 

wind energy development and its potential placement 

in the Lake Superior area as well as the entire Great 

Lakes region. Coupling avian radar systems with 

other forms of research or using radar in conjunction 

with acoustic and ultrasonic monitors, as well as post 

construction fatality searches, may broaden the utility 

in making risk assessments and assessing wind energy 

developments.
52 Great Lakes Avian Radar - Fall 2014

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Appendices

Appendix 1: Fall 2014 Report Summary

Appendix 2: Percent Land Cover Associated with Study Sites and the 2011 

 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

Fall 2014 Report Summary

n Migration occurred on both the northwest and south the shorelines of Lake Superior during fall 2014 

	 • Migration was identified by uniformity of movement of direction (south/southwest) at night, high 	 	

 target passage rate, and typically a peaking of numbers near midnight

	 •	General patterns and timing of migration were similar between the sites sampled during the

 same period

	 	 	 •	4 main waves of nocturnal migration with highest concentrations near Aug 14, Sept 4,

 Sept 22, and Oct 15 

	 	 	 •	4 main waves of diurnal migration with the peaks near Aug 16, Sept 3, Oct 3, and Nov 7

n Date range of pulses that occurred during the migration season

•	Began on August 3 in Lake County, MN and Bayfield County, WI

	 •	Ended on November 10 in Lake County, MN and on October 26 in Keweenaw County, MI

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

•	Movement between southeast and west during the night at all locations

	 	 	 • 85% of nights surveyed the mean direction of travel was between S and

 W in Lake County, MN

	 	 	 •	78% of nights surveyed the mean direction of travel was between SE and SW in

 Bayfield County, WI

	 	 	 •	72% of nights surveyed the mean direction of travel was between SE and SW in

 Keweenaw County, MI

	 •	Movement in towards and/or along the shore at dawn

	 	 	 •	Observed at all three sites

	 •	Highest target passage rate at night

	 •	Dawn ascent

	 	 	 •	Slight increase in height around dawn hours observed at two sites

n Peak density of targets in volume corrected counts

•	Max density below 200 m 77.6% of nights and 74.2% of night hours at Lake County, MN

	 •	Max density below 200 m 65.8% of nights and 51.7% of night hours at Bayfield County, WI

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 and that targets flying

 several altitude bands above the rotor swept zone may still be at risk

Great Lakes Avian Radar - Fall 2014 57
58 Great Lakes Avian Radar - Fall 2014 

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 along Lake Superior in fall 2014.

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

Classification Description for the 2011 National Land Cover Database 

(http://www.mrlc.gov/nlcd2011.php).
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

Great Lakes Avian Radar - Fall 2014 59
60 Great Lakes Avian Radar - Fall 2014 

Appendix 3

Corrected Density per Hour by Biological Period

Estimated density of targets by altitude band during all biological periods in Lake, Bayfield, and Keweenaw 

counties during fall 2014 (targets/1,000,000 m3/time period). 
Great Lakes Avian Radar - Fall 2014 61

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 Lake 

County, Minnesota.
(Appendix 4 continued)

62 Great Lakes Avian Radar - Fall 2014 

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

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

County, Minnesota.

Great Lakes Avian Radar - Fall 2104 63

(Appendix 4 continued)
(Appendix 4 continued)

64 Great Lakes Avian Radar - Fall 2014

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

County, Minnesota.
Comparison of methods to estimated target density by altitude band during the dawn biological period in Bayfield 

County, Wisconsin.

Great Lakes Avian Radar - Fall 2014 65

(Appendix 4 continued)
(Appendix 4 continued)

66 Great Lakes Avian Radar - Fall 2014

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

County, Wisconsin.
Comparison of methods to estimated target density by altitude band during the dusk biological period in Bayfield 

County, Wisconsin.

Great Lakes Avian Radar - Fall 2014 67

(Appendix 4 continued)
(Appendix 4 continued)

68 Great Lakes Avian Radar - Fall 2014 

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

County, Wisconsin.
(Appendix 4 continued)

Comparison of methods to estimated target density by altitude band during the dawn biological period in Keweenaw County, Michigan.

Great Lakes Avian Radar - Fall 2014 69
70 Great Lakes Avian Radar - Fall 2014 

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

County, Michigan.

(Appendix 4 continued)
Comparison of methods to estimated target density by altitude band during the dusk biological period in Keweenaw 

County, Michigan

Great Lakes Avian Radar - Fall 2014 71

(Appendix 4 continued)
Comparison of methods to estimated target density by altitude band during the night biological period in 

Keweenaw County, Michigan.

72 Great Lakes Avian Radar - Fall 2014 

(Appendix 4 continued)



Fall 2014