U.S. Fish & Wildlife Service 

Great Lakes Avian Radar 

Technical Report

Huron and Oceana 

Counties, MI

Biological Technical Publication

BTP-R3011-2015

U.S. Fish & Wildlife Service Region 3

Great Lakes Avian Radar 

Technical Report

Huron and Oceana 

Counties, MI

Biological Technical Publication

BTP-R3011-2015

Funded by Great Lakes Restoration Initiative

Principal Investigator:

Jeff Gosse, Regional Energy Coordinator, USFWS Region 3

Authors:

Tim Bowden, Fish and Wildlife Biologist, USFWS Region 3

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

Nathan Rathbun, Fish and Wildlife Biologist, USFWS Region 3

Daniel Nolfi, Fish and Wildlife Biologist, USFWS Region 3

Rebecca Horton, Fish and Wildlife Biologist, USFWS Region 3

David Larson, Assistant Regional Energy Coordinator (Retired)
Corresponding Author:

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

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

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

Email: david_larson@fws.gov

Dan 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, migration, avian radar, wind energy, birds, bats 

Recommended Citation: 

Bowden, T. S., E. C. Olson, N. A. Rathbun, D. C. Nolfi, R. L. Horton, D. J. Larson, and J. C. Gosse. 2015. 

Great Lakes Avian Radar Technical Report Huron and Oceana Counties, Michigan. U.S. Department 

of Interior, Fish and Wildlife Service, Biological Technical Publication FWS/BTP-2015

ISSN 2160-9498 Electronic ISSN 2160-9497

Biological Technical Publications online: Biological Technical Publications Series

Table of Contents

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

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

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

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

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

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

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

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

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

Data Collection ............................................................................................................................... 7

Data Processing and Quality Control.......................................................................................... 7

Data Summary and Analysis of Trends....................................................................................... 7

Results.............................................................................................................................................................11

Qualitative Assessments.............................................................................................................. 11

Directional Trends........................................................................................................................ 14

Temporal Trends........................................................................................................................... 14

Altitudinal Trends ........................................................................................................................ 19

Discussion ......................................................................................................................................................26

Management Considerations ...................................................................................................... 28

Literature Cited ..............................................................................................................................................29

Appendix 1-4...................................................................................................................................................33

Titles .............................................................................................................................................. 33

i
ii

List of Figures

Figure 1. Locations where MERLIN Avian Radar Systems were deployed during the fall 2011 migration 

season.................................................................................................................................................................3

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

fall 2011 ..............................................................................................................................................................4

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

radars used in Michigan during fall 2011. Graphic provided by DeTect, Inc. ..................................................5

Figure 4. Vertical (top row) and horizontal (bottom row) clutter maps from Oceana and Huron Counties, 

Michigan. ...........................................................................................................................................................6

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

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

standard front used for density estimates. .......................................................................................................9

Figure 7. 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 standard front....... 10

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

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

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

target heights with the labels representing 250 m increments ......................................................................12

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

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

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

target heights with the labels representing 250 m increments.......................................................................13

Figure 10. Target direction per hr during four biological periods during the fall of 2011 at Oceana (left) and 

Huron (right) Counties, Michigan. Note the different scales on the plots for Oceana and Huron Counties.... 14 

Figure 11. Hourly counts by horizontal and vertical radars from 15 August – 13 November 2011 in Oceana 

County, Michigan. Light gray vertical lines represent midnight...................................................................15 

Figure 12. Hourly counts by horizontal and vertical radars from 18 August – 13 November 2011 in Huron 

County, Michigan. Light gray vertical lines represent midnight...................................................................16

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

biological periods for fall 2011 in Oceana and Huron Counties, Michigan. Whiskers represent the 1st and 4th 

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

diamonds represent the seasonal mean for the time period. ..........................................................................17 

Figure 14. Mean hourly target passage rate (targets per km per hr) during fall 2011 in Oceana and Huron 

Counties, Michigan ..........................................................................................................................................17

Figure 15. Weekly mean of nocturnal and diurnal target passage rates (targets per km per hr) in Oceana 

(top row) and Huron (bottom row) Counties from 18 August – 10 November 2011. Error bars represent one 

standard deviation. Note the different scales on the nocturnal and diurnal plots.........................................18
iii

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

passage rate (targets per km per hour) during fall 2011 in Oceana and Huron Counties, Michigan.............18

Figure 17. Between site comparison of nocturnal and diurnal trends (based on a moving 7-day mean) in target 

passage rate (targets per km per hour) during fall 2011 in Oceana and Huron Counties, Michigan. ...............19

Figure 18. Altitude profile of targets in Oceana County, MI. Corrected lines depict target density (targets 

per 1,000,000 m3) per 50-m altitude band per hr after adjusting for the structure of the sample volume. 

Uncorrected lines depict target density per 50-m altitude band per hr with an assumed uniform volume 

distribution (the volume of each band is equal to the total volume divided by the number of bands). The 

red band represents the rotor swept zone (RSZ) between 30 – 130 m. Y-axis labels represent the top of the 

altitude band. ...................................................................................................................................................20

Figure 19. Altitude profile of targets in Huron County, MI. Corrected lines depict target density (targets 

per 1,000,000 m3) per 50-m per hr altitude band after adjusting for the structure of the sample volume. 

Uncorrected lines depict target density per 50-m altitude band per hr with an assumed uniform volume 

distribution (the volume of each band is equal to the total volume divided by the number of bands). The 

red band represents the rotor swept zone (RSZ) between 30 – 130 m. Y-axis labels represent the top of the 

altitude band. ...................................................................................................................................................21

Figure 20. A sample of hourly altitude profiles corrected for the shape of the sample volume in Oceana 

County, Michigan during fall 2011. Hours were selected to portray the variability in density per altitude 

band of passing targets. The x-axis represents target density (targets per 1,000,000 m3) per 50-m altitude 

band. The y-axis labels represent the top of altitude bands in meters. The red line represents the top of the 

rotor swept zone at 130 m................................................................................................................................22

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

County, Michigan during fall 2011. Hours were selected to portray the variability in density per altitude 

band of passing targets. The x-axis represents target density (targets per 1,000,000 m3) per 50-m altitude 

band. The y-axis labels represent the top of altitude bands in meters. The red line represents the top of the 

rotor swept zone at 130 m................................................................................................................................23

Figure 22. Altitude profile of target density below 400 meters in Oceana and Huron Counties, Michigan. 

These graphics show the altitude band in which the maximum density occurred during fall 2011. The x-axis 

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

altitude bands in meters ..................................................................................................................................24

Figure 23. Percent of nights when the maximum density (targets per 1,000,000 m3 per altitude band) or 

count (targets per altitude band) occurred within 50-m altitude bands in Oceana and Huron Counties, 

Michigan, during fall 2011 ...............................................................................................................................24

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

altitude band) or count (targets per altitude band) occurred within 50-m altitude bands in Oceana and Huron 

Counties, Michigan, during fall 2011 ...............................................................................................................25

Figure 25. Mean hourly target height (m) during fall in Oceana and Huron Counties, Michigan. Yellow 

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

Error bars represent one standard deviation.................................................................................................25

Figure 26. Example of a hypothetical sampling schedule where data were collected once per week (top 

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

of targets counted km per hr by the vertical scanning radar from 6 October – 5 November 2011 in Huron 

County, MI. ......................................................................................................................................................27
iv

List of Tables

Table 1. Predominant landcover types found within a 3.7-km radius of the radar locations in Michigan 

during fall 2011 ..................................................................................................................................................3

Table 2. Survey effort (hrs) by vertical and horizontal scanning radars during fall 2011 in Oceana and Huron 

Counties, Michigan ..........................................................................................................................................11

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

directionality (r ≥ 0.5) of targets during biological time periods in Oceana and Huron Counties, Michigan.14

Table 4. Mean target passage rate (Targets per kilometer per hour) with standard deviations during four 

biological periods in Oceana and Huron Counties, Michigan during fall 2011. ..............................................17

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

during four biological time periods in Oceana and Huron Counties, Michigan fall 2011.................................. 20
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 (US Geological 

Service), Kevin Heist (University of Minnesota), 

and Anna Peterson (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 Barbra Bull and Chris and Michael Jahn, the 

landowners who provided space for our radar unit. 

We also thank other Service programs for their 

assistance during this season, including Minnesota 

Valley, Shiawassee and Big Stone National Wildlife 

Refuges, Detroit Lakes Wetland Management 

District, and Ludington Biological Station. This 

manuscript benefited from four external reviews, 

and we thank those who contributed, including Ryan 

Zimmerling and Bethany Thurber of the Canadian 

Wildlife Service, David Ewert of The Nature 

Conservancy, the USFWS East Lansing Field 

Office, and DeTect, Inc.
vi

Executive Summary

Global wind patterns help to move millions of 

migrating birds and bats through the Great Lakes 

region, where shorelines provide important stopover 

habitat. Shorelines are thought to concentrate 

migrants, as they offer a last refuge near a 

geographic barrier and are, most 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 Lakes shorelines. We 

used two avian radar systems to identify the activity 

patterns, timing, and duration of migration that 

occurred along shorelines of the Great Lakes. 

We placed avian radar systems on shorelines on 

opposite sides of Michigan, where the automated 

systems tracked and recorded target (bird and 

bat) movements continuously from mid-August to 

mid-November, 2011. We calculated the direction 

of movement, target passage rates, and altitude 

profiles for the air space above our study areas. 

We also developed 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 studied 

shorelines in Michigan. Mean nocturnal passage 

rates were greater than mean passage rates for 

dawn, day, and dusk combined at both of our 

locations. Nocturnal movement was typically 

oriented in a southerly direction, but we also 

recorded other behaviors associated with migrants 

such as reverse migration, dawn ascent, and 

migrants over water returning to land at dawn. 

Peak density occurred between 100 – 150 m above 

radar height; however, density may have been 

underestimated at higher altitudes.

The results of our research highlight the potential 

role of radar in implementing the Land-Based 

Wind Energy Guidelines and in identifying areas 

where impacts to wildlife would be minimized. We 

documented migration activity in the air space 

above our study areas and think the density of 

targets at low altitudes may present conservation 

concerns. The data we collected demonstrated 

the ebb and flow of migration across the sampling 

period and showed that nocturnal peaks continued 

into November. Given the time periods during which 

migration occurred at the sampled sites, it appears 

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 would greatly improve risk assessments 

and assist with 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 in implementing avian radar 

or recognizing the strengths and limitations of 

the technology. This report highlights several 

considerations about avian radar and reviews a 

number of potentially confusing metrics. We also 

introduce new metrics for reporting radar data. 

However, our analysis continues to evolve, and 

changes will be incorporated into our final report. 

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 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 barrier

Given the rapid growth of the wind energy 

sector, the most effective conservation efforts 

might be based on our ability to identify and 

avoid development in locations where migrants 

concentrate. Our use of commercial-grade avian 

radar to document migration and, in subsequent 

reports, to identify concentrations of activity 

is a broad-scale effort toward that end. To our 

knowledge, this effort represents the first of its type 

by the US Fish and Wildlife Service.
1

Introduction

The Great Lakes support one of the largest 

bodies of freshwater 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 to move millions of migrating birds and bats 

through the Great Lakes region (Rich et al. 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 – en route areas used temporarily 

for refueling, rest, 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 

most likely represent a geographic barrier (Diehl 

et al. 2003) that migrants choose to cross 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 

consideration may, perhaps, represent one reason 

why migrants partially circumnavigate the Great 

Lakes, which they have the physiological capability 

to cross (Alerstam 1990, 2001, Ruth 2007). The 

decision to cross most 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. 

igrants challenged by a barrier 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 NaefDaenzer (2011) found that birds with low fuel loads 

and/or facing unfavorable weather conditions 

returned to shoreline habitat rather than continuing 

across open water in a direction appropriate for 

migration. Migrating bats varied their choice to 

circumnavigate above shorelines or cross lakes, 

and certain 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. 

Migrants concentrated along shorelines can be 

highly active. In addition to immediate refueling 

and rest, migrants make broad-scale flights among 

habitat patches, explore wind conditions, and 

orientate for migration. For example, radio-tagged 

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 distance 

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 hrs of sunrise and 

are thought to represent reorientation along a 

geographic barrier or movements among stopover 

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

et al. 1992). Flights of this nature often occur 

above tree line (Bingman 1980) but below heights 

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 

the normal activity of migrants as they calibrate 

their internal compass and test wind speed and 

direction aloft. In addition to these activities while 

in stopover, migrants follow north-south oriented 

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). Cumulatively, these 

types of activities define a use area near lakeshores 

that include a variety of movements and altitudes 

for landscape-level, exploratory, and migration 

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 to 

migratory populations are becoming increasingly 

clear (Sillett and Holmes 2002, Mehlman et al. 2005, 

Faaborg et al. 2010). Shoreline habitats along the 

Great Lakes Avian Radar – Fall 2011
2 Great Lakes Avian Radar – Fall 2011

Great Lakes are subject to pressures from urban 

and energy development, land conversion, and 

environmental contamination that may limit their 

availability and/or reduce habitat quality (France 

et al. 2012). Further, White-nose Syndrome is 

devastating bat populations and has increased the 

need to identify conservation areas, as several 

species face the risk of extirpation in the Great 

Lakes region (Kurta 1995, 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, France et al. 2012). With climate 

change considerations calling for 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 US market by 2030 (U.S. 

Department of Energy 2008). If achieved, this 

would represent nearly a fivefold increase in wind 

energy capacity during the next 15 years (Loss et 

al. 2013). Coinciding with this national effort, wind 

energy developments are increasing within the 

Great Lakes region, where shorelines offer areas 

attractive 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 US, particularly for 

bats, are a concern (Timm 1989, Johnson 2005). For 

example, three species of long-distance migratory 

bats that are impacted by wind energy facilities 

represent approximately 75% of 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 constitute 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. Low reproductive rates inhibit the ability 

of bats to rebound from population declines (Racey 

and Entwistle 2000), and these declines have already 

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

2011). Cumulative impacts to migrant 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. 

Several promising conservation measures have been 

proposed to reduce mortality events; however, the 

greatest benefit to the conservation of migrants 

might lie in our ability to identify and avoid future 

growth in locations where migrants concentrate. 

To help meet the needs of renewable energy 

development and wildlife conservation, we 

established the current project to identify activity 

patterns, timing, and magnitude of migration along 

shorelines of the Great Lakes. Documenting bird 

and bat migration is challenged by the difficulty 

of observing nocturnal movements and because 

migration activity occurs sporadically over the 

course of a season. We used a combination of 

techniques to address this challenge. As the 

primary means of data collection, we used two 

avian radar units that operated 24 hrs per day and 

simultaneously scanned horizontal and vertical 

planes. We used over 30 automated ultrasonic/

acoustic monitors to record bird and bat calls. We 

also collected incidental bird observations in areas 

near monitoring equipment. Our objectives for the 

portion of the study presented in this report were to:

n Monitor locations along shorelines of Lake 

Michigan and Huron using 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 migration

n Estimate the duration of the migration season

n Document changes in the behavior of migrants 

under varying conditions and during different 

parts of the season

The focus of this report is on the radar data collected 

during the fall 2011 migration season. Subsequent 

reports will address the ultrasonic/acoustic 

monitoring data, incidental field observations, 

gradient transects, and patterns associated with the 

multiple seasons of this project.
Great Lakes Avian Radar – Fall 2011 3

Methods

Study Area and Site Selection

During the fall 2011 season, we selected two sites in 

Michigan for radar placement (Figure 1). We placed 

radar units approximately 1.5 km from the shoreline 

to monitor airspace above inland, shoreline, and 

lake areas. One site was located on the east side 

of Lake Michigan in Oceana County. This radar 

unit was located at 43.614095° N, -86.520783° W, 

and 226 m above mean sea level. It was placed 

along the edge of an agricultural field in an area 

where deciduous forest and cultivated crops were 

the predominant land cover types within the range 

of the radar unit, according to our analysis using 

ESRI ArcGIS software and the 2006 National Land 

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

2, Appendix 2). The second site was located on the 

west side of Lake Huron in Huron County. This 

radar unit was located at 43.952649° N, -82.735476° 

W, and 183 m above mean sea level. It was placed 

in a large open agricultural field, which was the 

primary land cover type within range of the radar 

unit (Table 1, Figure 2, Appendix 2).

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

during fall 2011.

National Land Cover Class

Oceana County 

% Land Cover

Huron County 

% Land Cover

Cultivated Crops/Pasture 19.3% 53.5%

Deciduous Forest 32.1% 7.2%

Open water 29.3% 23.7%

Developed* 5.0% 6.7%

Other** 14.3% 8.9%

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

** Includes barren land, evergreen forest, herbaceous, mixed forest, shrub/scrub, and woody or emergent 

herbaceous wetlands.

Figure 1. Locations where MERLIN Avian Radar Systems were deployed 

during the fall 2011 migration season.
4 Great Lakes Avian Radar – Fall 2011

Figure 2. Land cover types found within a 3.7-km radius of the radar locations in Michigan during fall 2011.

Radar monitoring sites were selected through a 

combination of geographic modeling and on-site 

assessments to locate areas near shorelines with 

unimpeded views. First, large sections of the 

Great Lakes shorelines were identified as areas of 

interest for the migration season. ESRI ArcGIS 

software was used to model the areas of interest 

to find locations that could be suitable for radar 

siting. This suitability modeling incorporated 

datasets describing the elevation, land cover, and 

shorelines of the Great Lakes. Additional landscape 

characteristics were derived from these datasets 

(e.g., elevation below local maximum elevation, 

percent forested, distance to forest, distance from 

shoreline) and ranked to create a continuous raster 

surface within the area of interest with estimated 

suitability values. Contiguous areas with high 

suitability identified through the GIS modeling 

process were targeted for on-site assessments.

Biologists were dispatched to the area of interest 

to conduct more thorough assessments of potential 

sites identified by the modeling effort. These 

assessments included evaluating land use, the 

line of sight to shorelines, and accessibility for 

the placement of radar units. Additional locations 

not identified through modeling were frequently 

discovered through this process and evaluated 

as well. When a location was determined by field 

biologists to be highly suitable relative to the other 

locations visited in the field, contact was initiated 

with property owners to obtain permission to set up 

the radar units.
Great Lakes Avian Radar – Fall 2011

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 marine radars that operated simultaneously; 

one scanned the horizontal plane while the other 

scanned vertically (Figure 3). Additionally, the 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. 

Description of radars – solid state marine radar 

antennas (Kelvin Hughes, London, UK) used by 

our systems were 3.9 m in length, with 170 W peak 

power, S-band (10 cm) wavelength, 2.92 – 3.08 

GHz frequency range, and operated with both 

short and medium pulses (0.1 and 5 microseconds, 

respectively). The horizontal radar was also 

equipped with Doppler to help filter stationary 

targets. The radars emanated a fan-shaped beam, 

which had an approximate 1° horizontal and 25° 

vertical span when operated in the horizontal plane. 

The S-band radar was selected because the longer 

wavelength is less sensitive to insects and weather 

contamination than 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 spin perpendicularly to each 

other at a rate of 20 revolutions per minute and were 

synchronized to not 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. Although 

the radar had the capability to scan large distances, 

we selected a 3.7-km range setting for data 

collection to allow higher resolution and identify 

smaller targets, such as passerines and bats. The 

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. 

5

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

radars used in Michigan during fall 2011. Graphic provided by DeTect, Inc.

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. 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 Setup and Data Collection

Radar systems were deployed during the first 

week of August at their respective sites and were 

maintained into the second week of November to 

capture the anticipated start and end dates of the 

migration season.

Establishing radar systems at a selected site 

involved several activities, including orientation of 
6 Great Lakes Avian Radar – Fall 2011

the VSR, micro-site selection, and adjustments to 

ensure that adequate information was captured. 

We anticipated a primarily southbound direction of 

migration along the shorelines of Michigan during 

autumn and oriented vertical scanning radars at an 

angle that was slightly less than perpendicular to 

the anticipated direction of traffic. This orientation 

was a compromise between a perpendicular angle 

that would intercept the greatest number of targets 

(birds or bats) and a parallel angle that would 

maximize the amount of travel time within the radar 

beam. The orientation was also influenced by microsite selection. Micro-site selection is important 

because the positioning of the radar can affect the 

amount of interference from ground clutter or other 

sources of noise. If large areas were obstructed 

from the radar view or if substantial amounts of 

clutter impeded data collection, systems were 

rotated incrementally to improve the radar’s view 

and/or reduce interference.

Once a position was established, clear-air thresholds 

and the radar’s built-in sensitivity time control 

(STC) filters were employed to reduce small nontarget returns and improve the tracking of distant 

targets. These settings are needed because objects 

reflect more energy at close ranges than when 

they are farther from the radar. For example, an 

object at a 50-m range will return approximately 

16 times more energy than when it is at a 100-m 

range (Bruderer 1997, Schmaljohann et al. 2008). To 

further improve data collection, clutter maps were 

generated using 60-scan composite images (Figure 

4). These images were used to identify areas with 

constant returns that were not biological targets. 

These areas were assigned a reflectivity threshold 

that excluded the constant returns from the data 

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

targets in these areas.

Following this initial set up, MERLIN software was 

fitted to site conditions. The MERLIN software 

provides real-time processing of raw radar data 

to identify and track targets while excluding nontargets and rain events. However, parameters used 

by the tracking software require adjustments to 

reflect site-specific conditions. DeTect personnel 

trained our biologists in the establishment of these 

settings, with the goal of minimizing inclusion of 

non-targets while maximizing cohesive tracks of 

targets. Once established, simultaneous visual 

observations of birds in flight and tracked targets 

were used to confirm the settings of the tracking 

algorithm. Processed data were stored in an Access 

database and transferred daily to a SQL database, 

where they were stored and later queried for data 

analysis.

Despite the radar system’s ability to support 

remote operation for extended periods of time, 

biologists remained on site during the data collection 

period to ensure continuous function, monitor 

raw and processed radar outputs, provide routine 

maintenance (such as fueling and oil changes), and 

manage data storage. In addition to processed 

data, we maintained all raw radar data for potential 

reprocessing. Raw radar data were temporarily 

stored in the field on 2 TB external hard drives 

and regularly transported on ruggedized external 

drives back to a Regional Office, where data were 

transferred to long-term tape storage.

Figure 4. Vertical (top row) and horizontal (bottom row) clutter maps from Oceana and Huron Counties, 

Michigan.
Great Lakes Avian Radar – Fall 2011 7

Radar System Outputs

The MERLIN software generates more than 

30 measurements to describe target size, shape, 

location, speed, and direction of movement. These 

data are of the same type used by biologists when 

identifying biological targets on a radar screen 

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

information was stored to the database for later 

analysis. To reduce potential false tracking, the 

MERLIN tracking algorithm removed tracks 

with fewer than five observations. In addition, an 

automated filter was used to remove sectors of the 

sample volume that were dominated by rain. 

In addition to storing target attribute data, 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. These graphics were generated 

to assess target attributes such as reflectivity, 

direction, height, and size class. We viewed 15-min 

and 1-hr Trackplots with direction attributes daily 

to monitor the previous night’s activity and used 

the real-time digital display to ensure that it agreed 

with the raw radar display. 

Data Processing and Quality Control

Prior to data analysis, data processed by MERLIN 

software was further evaluated for potential 

contamination by non-targets. Biologists reviewed 

all data in 15-min time increments and removed 

time periods that were dominated by rain or other 

forms of transient clutter. We relied on the visual 

inspection of track patterns to discern contamination 

events. Rain events form diagnostic patterns 

(Detect Inc., personal communication, 2011) that 

were readily omitted when present. Contamination 

that mimicked track patterns of targets was not 

removed from the database and, to the extent that 

such contamination occurred, contributed to the 

error associated with the indices. In addition, we 

evaluated initial counts by generating a time series 

to show the variation in the number of targets 

per hr across the season for both HSR and VSR 

radars. In general, the HSR and VSR hourly 

counts were positively correlated with the HSR 

having higher counts. In situations in which the 

VSR resulted in higher counts than the HSR or 

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 excluded from further analysis.

During the fall 2011 season, our vertical radar pulse 

heading was oriented to 0 degrees instead of the 

intended 180 degrees. This error had the potential 

to result in double counting or undercounting in 

certain situations. We re-processed a sample of 

our data with the correct pulse heading and found 

a difference in counts that was less than 1% for all 

targets and less than 10% for targets below 200 m. 

For this reason, the data used for analysis were not 

reprocessed. 

After contaminated time periods had been removed, 

we summarized data using SQL queries provided 

with the MERLIN radar system. Data from the 

HSR were used to calculate hourly counts and 

target 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 

heights, and these data were truncated to a 1-km 

front or “standard front.” We adopted this sampling 

technique because it is the method used by the 

manufacturer of the MERLIN units and because it 

is used by other researchers (Lowery 1951, Liechti 

et al. 1995, Kunz et al. 2007b). The standard front 

was defined by a volume of space that extended 

500 m to either side of the radar and continued up 

to the maximum height of data collection (2800 m) 

(Figure 5). Counts were further segregated into 

four biological time periods: Dawn, 30 min prior 

to sunrise to 30 min post sunrise; Day, 30 min post 

sunrise to 30 min prior to sunset; Dusk, 30 min prior 

to sunset to 30 min post sunset; and Night, 30 min 

post sunset to 30 min prior to sunrise. 

Data Summary and Analysis of Trends

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 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 nocturnal targets and the percentage of nights 

that targets traveled in a southerly direction, 

which ranged from 112.5° to 247.5°. We used radial 

graphs to plot the number of targets per 8-cardinal 

directions (i.e., eight groups centered on N, NE, 

E, SE, S, SW, W, NW) during four biological time 

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

Temporal trends – we plotted counts of targets 

per hr processed by MERLIN software for both 

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 radars have different 

strengths that complement one another. The 

HSR index tracks low-flying targets in a 360° span 
8 Great Lakes Avian Radar – Fall 2011

around the radar unit, and detection is not affected 

by the target’s direction of travel as it is 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 lead to both under and overcounting. 

As a result, HSR counts are more influenced by 

site conditions than VSR counts. However, the 

HSR index better captures targets under certain 

conditions, such as cases in which targets are 

primarily at low elevation and/or traveling parallel 

to the VSR. The HSR is also much more susceptible 

than the VSR to beam bending from dynamic 

atmospheric conditions; beam refraction in the 

VSR is minimal, primarily due to its orientation. 

The VSR index was used to track targets captured 

within the standard front, and it provides more 

consistent detection than the HSR because it tracks 

primarily against clear air except in the lowest 

altitude bands. Its detection is affected by target 

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 target passage 

rate (TPR). We calculated TPR as the number of 

targets per standard front per hr using DeTect 

SQL queries. Hours with fewer than 30 min of 

recording time were omitted from this calculation. 

For example, after removing all hours with less 

than 30 min 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 

Figure 5. 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 2800 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, and we view an approximation of the beam dispersion as 

it travels away from the radar unit (schematic not drawn to scale).
Great Lakes Avian Radar – Fall 2011 9

the number of targets per hr during that night. We 

extended this metric to the season and calculated 

mean TPR for biological time periods and hours of 

the season. Mean nocturnal TPR for the season is 

the sum of night TPRs divided by the number of 

nights sampled. Similarly, mean hourly TPR for 

the season is the sum of TPRs for a period of 1 hr 

divided by the number times that hour was sampled. 

We also calculated mean nocturnal (night biological 

period) and diurnal (day biological period) TPR 

for weeks 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 7 and 

reported the weekly mean TPR and its standard 

deviation. To better illustrate nocturnal and diurnal 

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

moving means of TPR as line graphs. 

Altitudinal trends – DeTect SQL queries 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 location with the largest radar echo 

(closest to the center of the radar beam) and 

reported as the height above radar elevation. We 

used these estimates to calculate mean altitude of 

targets above the radar by biological time period 

and hour and reported mean and median altitudes 

for the season. 

Density per altitude band – to provide information 

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

hr within the standard front, we first estimated the 

volume of the radar beam’s approximate geometric 

shape. The width of the radar beam expands as it 

travels from the radar, resulting in increased survey 

volumes with distance from the origin. 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 radar beam 

and reported the density of targets (targets per 

1,000,000 m3) per 50-m altitude band per hr for each 

biological period. This calculation was determined 

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 used to 

calculate an unknown volume by enclosing it in a 

known volume and saturating the space with random 

points. Monte Carlo integration requires rules 

that determine whether the randomly drawn points 

are inside or outside of the unknown volume. 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 estimate approaches the true value (an exact 

calculation). 

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 we 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, 

and the second rule was that the angle between a 

randomly drawn point and the vertical plane (the 

x-z axis in Figure 6) was less than 12.5o (i.e., half 

the angle of 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 that 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 6) and 

within altitude bands (Figure 7). 

x

y

z

Figure 6. 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 2800 

m (z-axis). The y-axis represents the spread of 

the radar beam as it extends away from the origin. 

The orange semi-transparent points represent the 

volume contained by the structure of the radar 

beam. The dark gray points represent the volume 

that is within the box but is not included in the 

volume of the radar beam.

-600

 0

-400

-200

 0

 200

 400

 600

 500

1000

1500

2000

2500

 400 200 0 -200 -400
10 Great Lakes Avian Radar – Fall 2011

Figure 7. 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 

standard front.

The number of targets per altitude band is often 

reported without providing a volume correction. We 

wanted to compare our correction to the uncorrected 

method; however, count data and volume data are 

on different scales. For this reason, we compared 

our density estimate with a density estimate 

based on the number of targets per 50-m altitude 

band per hr 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 in 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). 

0	
   10	
   20	
   30	
   40	
   50	
   60	
   70	
  

50	
  

200	
  

350	
  

500	
  

650	
  

800	
  

950	
  

1100	
  

1250	
  

1400	
  

1550	
  

1700	
  

1850	
  

2000	
  

2150	
  

2300	
  

2450	
  

2600	
  

2750	
  

Volume	
  (1,000,000	
  m3)	
  

50-­‐m	
  Al1tude	
  Bands	
  

Volume	
  per	
  Al1tude	
  Band	
  

Volume	
  per	
  

Al5tude	
  Band	
  
Great Lakes Avian Radar – Fall 2011 11

Results

We began data collection on 14 and 18 August during 

the fall 2011 season at the Oceana and Huron sites, 

respectively. Data were recorded continuously 

while the radar units were operational. Gaps in 

analyzed data occurred during rain events and 

when the radar units were non-operational due 

to maintenance or malfunction. We ended data 

collection on Nov 13, 2011. In all, the radars were in 

place for 2234 and 2138 hrs at the Oceana and Huron 

sites, respectively (Table 2). 

Table 2. Survey effort (hrs) by vertical and horizontal scanning radars during fall 2011 in Oceana and 

Huron Counties, Michigan. 

Site Radar Collected Data Radar Downtime

Radar Data 

with Rain

Usable 

Radar Data

% Season with 

Usable Data

Oceana Vertical* 2172 62 39 2133 96%

Oceana Horizontal 2140 94 24 2116 95%

Huron Vertical 2101 37 211 1890 88%

Huron Horizontal 2111 27 86 2024 95%

Qualitative Assessments 

Plots of tracked targets showed images of nocturnal 

migration events at both locations (Figures 8 and 

9). For example, on 8 September at the Oceana site, 

the horizontal radar recorded scattered activity, and 

the vertical radar recorded few targets from 12:00 

– 18:00. During the 19:00 hr, directional movement 

heading south to southwest began, and the vertical 

radar detections increased, with more targets at 

higher altitude. This pattern grew stronger until 

approximately 02:00, when the target heights 

began to decrease. By 05:00, there was a partial 

direction shift to the southeast, and the vertical 

radar indicated a further decrease in target height. 

During the 06:00 hr, target direction shifted strongly 

to the east (toward land) in a direction not well 

recorded by the vertical radar, and by 12:00 on 9 

September, diurnal activity appeared similar to the 

preceding day at noon (Figure 8). This pattern of 

target movement and the changes in altitude were 

indicative of a pulse of migratory activity. Rings of 

decreased detection near the radar unit and where 

the radar switched between short and medium 

pulses are also apparent on the Trackplots.

A similar pattern can be observed at the Huron 

site, with targets moving to the shoreline at dusk, 

building to peak levels of movement in a southerly 

direction at night, and moving inland at dawn. The 

Huron site was affected to a greater degree than the 

Oceana site by ground clutter, and this resulted in 

reduced detection in the air space that was within 

the range of data collection (e.g., south and west of 

the radar unit).

* Vertical and horizontal radars are not equally impacted by rain events or downtime.
12 Great Lakes Avian Radar – Fall 2011

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

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

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

target heights with the labels representing 250 m increments.

September 8, 12:00

September 8, 19:00

September 9, 02:00

September 9, 06:00

September 8, 18:00

September 8, 23:00

September 9, 05:00

September 9, 12:00
Great Lakes Avian Radar – Fall 2011 13

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

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

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

target heights with the labels representing 250 m increments.

August 27, 12:00

August 27, 19:00

August 28, 02:00

August 28, 06:00

August 27, 18:00

August 27, 23:00

August 28, 05:00

August 28, 12:00
14 Great Lakes Avian Radar – Fall 2011

Directional Trends

During the fall 2011 season, nocturnal target 

direction was generally southerly at both sampled 

locations (Figure 10). At the Oceana site, mean 

nocturnal direction was 179° (r = 0.44, n = 4,064,319 

targets), and during 68% of the nights, the mean 

target direction was between southeast and 

southwest (112.5° – 247.5°). Directions at the Huron 

site were more variable and had a mean nocturnal 

direction of 203° (r = 0.24, n = 1,818,939), with 49% 

of nights having a mean direction between southeast 

and southwest. Onshore movement (east – 

southeast at Oceana, west – south at Huron) at dawn 

was visible at both locations (Figure 10). Uniform 

directionality at night was stronger in Oceana than 

Huron (Table 3).

Oceana Huron

Figure 10. Target direction per hr during four biological periods during the fall of 2011 at Oceana (left) and 

Huron (right) Counties, Michigan. Note the different scales on the plots for Oceana and Huron Counties.

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

directionality (r ≥ 0.5) of targets during biological time periods in Oceana and Huron Counties, Michigan.

Oceana Huron

Biological 

Period

Mean 

Direction 

(degrees) r

% Time 

r ≥ 0.5 n

Mean 

Direction

(degrees) r

% Time r 

≥ 0.5 n

Dawn 123 0.53 57.3% 196,856 239 0.28 1.2% 237,064

Day 147 0.11 23.6% 945,052 4 0.06 0.0% 1,851,502

Dusk 180 0.15 44.4% 82,694 55 0.32 6.9% 172,808

Night 179 0.44 69.2% 4,064,314 203 0.24 21.6% 1,818,939

Temporal Trends

Time series plots – hourly target counts provided 

by horizontal and vertical radars showed pulses 

of elevated nocturnal activity, with peaks near 

midnight at our study sites. Across our sampling 

period, these events were often clustered into 

groups of several nights and were first observed on 

15 and 26 August at Oceana and Huron Counties, 

respectively. At both sites, the occurrence 

and magnitude of nocturnal pulses decreased 

substantially after 1 November (Figures 11 and 12). 

Different patterns of activity were apparent as the 

season progressed at our study sites. For example, 

beginning in late August, activity patterns become 

dominated by nocturnal pulses that were observed 

on both horizontal and vertical radars. This pattern 

continued until about mid-October, when activity 

patterns began to shift. At the Oceana site, there 

was a decrease in activity overall, whereas the 

Huron site shifted to a more pronounced diurnal 

pattern. By November at Huron, a pattern of 

peaks near dawn and dusk was established on the 

horizontal index (visual observations indicated that 

these peaks were caused by low-flying Canada geese 

and several gull species moving between foraging 

and roosting locations). 
Great Lakes Avian Radar – Fall 2011 15

Differences in detection capability of the VSR and 

HSR radars were also apparent. At the Huron 

site in late September, particularly on the 26th, 

nocturnal targets traveled at low elevation and in 

a direction that was parallel to the VSR (Figure 

12). These targets were better represented by 

the horizontal index. On 11 and 22 October, many 

nocturnal targets passed at a high elevation above 

the HSR range of detection. 2 October provided a 

case in which targets passed above the study area 

in a direction and with an altitude distribution that 

allowed detection by both radars.

Figure 11. Hourly counts by horizontal and vertical radars from 15 August – 13 November 2011 in Oceana 

County, Michigan. Light gray vertical lines represent midnight.

0	
  

2,000	
  

4,000	
  

6,000	
  

8,000	
  

10,000	
  

12,000	
  

14,000	
  

16,000	
  

18,000	
  

20,000	
  

8/15/11	
  0:00	
  8/16/11	
  0:00	
  8/17/11	
  0:00	
  8/18/11	
  0:00	
  8/19/11	
  0:00	
  8/20/11	
  0:00	
  8/21/11	
  0:00	
  8/22/11	
  0:00	
  8/23/11	
  0:00	
  8/24/11	
  0:00	
  8/25/11	
  0:00	
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  0:00	
  8/27/11	
  0:00	
  8/28/11	
  0:00	
  8/29/11	
  0:00	
  8/30/11	
  0:00	
  8/31/11	
  0:00	
  

9/1/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

August	
  15	
  -­‐	
  September	
  1,	
  2011	
  Oceana	
  County	
  Michigan	
  

VerIcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

2,000	
  

4,000	
  

6,000	
  

8,000	
  

10,000	
  

12,000	
  

14,000	
  

16,000	
  

18,000	
  

20,000	
  

9/2/11	
  0:00	
  9/3/11	
  0:00	
  9/4/11	
  0:00	
  9/5/11	
  0:00	
  9/6/11	
  0:00	
  9/7/11	
  0:00	
  9/8/11	
  0:00	
  9/9/11	
  0:00	
  

9/10/11	
  0:00	
  9/11/11	
  0:00	
  9/12/11	
  0:00	
  9/13/11	
  0:00	
  9/14/11	
  0:00	
  9/15/11	
  0:00	
  9/16/11	
  0:00	
  9/17/11	
  0:00	
  9/18/11	
  0:00	
  9/19/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

September	
  2	
  -­‐	
  19,	
  2011	
  Oceana	
  County	
  Michigan	
  

VerHcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

2,000	
  

4,000	
  

6,000	
  

8,000	
  

10,000	
  

12,000	
  

14,000	
  

16,000	
  

18,000	
  

20,000	
  

9/20/11	
  0:00	
  9/21/11	
  0:00	
  9/22/11	
  0:00	
  9/23/11	
  0:00	
  9/24/11	
  0:00	
  9/25/11	
  0:00	
  9/26/11	
  0:00	
  9/27/11	
  0:00	
  9/28/11	
  0:00	
  9/29/11	
  0:00	
  9/30/11	
  0:00	
  10/1/11	
  0:00	
  10/2/11	
  0:00	
  10/3/11	
  0:00	
  10/4/11	
  0:00	
  10/5/11	
  0:00	
  10/6/11	
  0:00	
  10/7/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

September	
  20	
  -­‐	
  October	
  7,	
  2011	
  Oceana	
  County	
  Michigan	
  

VerHcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

2,000	
  

4,000	
  

6,000	
  

8,000	
  

10,000	
  

12,000	
  

14,000	
  

16,000	
  

18,000	
  

20,000	
  

10/8/11	
  0:00	
  10/9/11	
  0:00	
  

10/10/11	
  0:00	
  10/11/11	
  0:00	
  10/12/11	
  0:00	
  10/13/11	
  0:00	
  10/14/11	
  0:00	
  10/15/11	
  0:00	
  10/16/11	
  0:00	
  10/17/11	
  0:00	
  10/18/11	
  0:00	
  10/19/11	
  0:00	
  10/20/11	
  0:00	
  10/21/11	
  0:00	
  10/22/11	
  0:00	
  10/23/11	
  0:00	
  10/24/11	
  0:00	
  10/25/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

October	
  8	
  -­‐	
  25,	
  2011	
  Oceana	
  County	
  Michigan	
  

VerFcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

2,000	
  

4,000	
  

6,000	
  

8,000	
  

10,000	
  

12,000	
  

14,000	
  

16,000	
  

18,000	
  

20,000	
  

10/26/11	
  0:00	
  10/27/11	
  0:00	
  10/28/11	
  0:00	
  10/29/11	
  0:00	
  10/30/11	
  0:00	
  10/31/11	
  0:00	
  

11/1/11	
  0:00	
  11/2/11	
  0:00	
  11/3/11	
  0:00	
  11/4/11	
  0:00	
  11/5/11	
  0:00	
  11/6/11	
  0:00	
  11/7/11	
  0:00	
  11/8/11	
  0:00	
  11/9/11	
  0:00	
  

11/10/11	
  0:00	
  11/11/11	
  0:00	
  11/12/11	
  0:00	
  11/13/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

October	
  26	
  -­‐	
  November	
  13,	
  2011	
  Oceana	
  County	
  Michigan	
  

VerIcal	
  

Counts	
  

Horizontal	
  

Counts	
  
16 Great Lakes Avian Radar – Fall 2011

Figure 12. Hourly counts by horizontal and vertical radars from 18 August – 13 November 2011 in Huron 

County, Michigan. Light gray vertical lines represent midnight.

0	
  

1000	
  

2000	
  

3000	
  

4000	
  

5000	
  

6000	
  

7000	
  

8000	
  

9000	
  

10000	
  

8/17/11	
  0:00	
  8/18/11	
  0:00	
  8/19/11	
  0:00	
  8/20/11	
  0:00	
  8/21/11	
  0:00	
  8/22/11	
  0:00	
  8/23/11	
  0:00	
  8/24/11	
  0:00	
  8/25/11	
  0:00	
  8/26/11	
  0:00	
  8/27/11	
  0:00	
  8/28/11	
  0:00	
  8/29/11	
  0:00	
  8/30/11	
  0:00	
  8/31/11	
  0:00	
  

9/1/11	
  0:00	
  9/2/11	
  0:00	
  9/3/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

August	
  17	
  -­‐	
  September	
  3,	
  2011	
  Huron	
  County	
  Michigan	
  

VerHcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

1000	
  

2000	
  

3000	
  

4000	
  

5000	
  

6000	
  

7000	
  

8000	
  

9000	
  

10000	
  

9/4/11	
  0:00	
  9/5/11	
  0:00	
  9/6/11	
  0:00	
  9/7/11	
  0:00	
  9/8/11	
  0:00	
  9/9/11	
  0:00	
  

9/10/11	
  0:00	
  9/11/11	
  0:00	
  9/12/11	
  0:00	
  9/13/11	
  0:00	
  9/14/11	
  0:00	
  9/15/11	
  0:00	
  9/16/11	
  0:00	
  9/17/11	
  0:00	
  9/18/11	
  0:00	
  9/19/11	
  0:00	
  9/20/11	
  0:00	
  9/21/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

September	
  4	
  -­‐	
  21,	
  2011	
  Huron	
  County	
  Michigan	
  

VerGcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

1000	
  

2000	
  

3000	
  

4000	
  

5000	
  

6000	
  

7000	
  

8000	
  

9000	
  

10000	
  

9/22/11	
  0:00	
  9/23/11	
  0:00	
  9/24/11	
  0:00	
  9/25/11	
  0:00	
  9/26/11	
  0:00	
  9/27/11	
  0:00	
  9/28/11	
  0:00	
  9/29/11	
  0:00	
  9/30/11	
  0:00	
  10/1/11	
  0:00	
  10/2/11	
  0:00	
  10/3/11	
  0:00	
  10/4/11	
  0:00	
  10/5/11	
  0:00	
  10/6/11	
  0:00	
  10/7/11	
  0:00	
  10/8/11	
  0:00	
  10/9/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

September	
  22	
  -­‐	
  October	
  9,	
  2011	
  Huron	
  County	
  Michigan	
  

VerHcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

1000	
  

2000	
  

3000	
  

4000	
  

5000	
  

6000	
  

7000	
  

8000	
  

9000	
  

10000	
  

10/10/11	
  0:00	
  10/11/11	
  0:00	
  10/12/11	
  0:00	
  10/13/11	
  0:00	
  10/14/11	
  0:00	
  10/15/11	
  0:00	
  10/16/11	
  0:00	
  10/17/11	
  0:00	
  10/18/11	
  0:00	
  10/19/11	
  0:00	
  10/20/11	
  0:00	
  10/21/11	
  0:00	
  10/22/11	
  0:00	
  10/23/11	
  0:00	
  10/24/11	
  0:00	
  10/25/11	
  0:00	
  10/26/11	
  0:00	
  10/27/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

October	
  10	
  -­‐	
  27,	
  2011	
  Huron	
  County	
  Michigan	
  

VerFcal	
  

Counts	
  

Horizontal	
  

Counts	
  

0	
  

1000	
  

2000	
  

3000	
  

4000	
  

5000	
  

6000	
  

7000	
  

8000	
  

9000	
  

10000	
  

10/28/11	
  0:00	
  10/29/11	
  0:00	
  10/30/11	
  0:00	
  10/31/11	
  0:00	
  

11/1/11	
  0:00	
  11/2/11	
  0:00	
  11/3/11	
  0:00	
  11/4/11	
  0:00	
  11/5/11	
  0:00	
  11/6/11	
  0:00	
  11/7/11	
  0:00	
  11/8/11	
  0:00	
  11/9/11	
  0:00	
  

11/10/11	
  0:00	
  11/11/11	
  0:00	
  11/12/11	
  0:00	
  11/13/11	
  0:00	
  

Targets	
  per	
  Hour	
  

Date	
  

October	
  28	
  -­‐	
  November	
  13,	
  2011	
  Huron	
  County	
  Michigan	
  

VerIcal	
  

Counts	
  

Horizontal	
  

Counts	
  
Great Lakes Avian Radar – Fall 2011 17

Target passage rate – the pattern of mean TPR 

among the four biological time periods was similar 

for the two study sites (Figure 13), with mean 

TPR at night greater than mean TPR during the 

combined means of the other three biological time 

periods (Table 4). Mean nocturnal TPR was 442 ± 

475 SD (n = 82 nights) and 340 ± 328 SD targets 

per km per hr (n = 86 nights) in Oceana and Huron 

Counties, respectively. Mean TPR varied by hour, 

with peak numbers achieved within 1-2 hrs after 

sunset in Oceana and near midnight in Huron. At 

both locations, mean hourly TPR began to decrease 

by 02:00 hrs (Figure 14). 

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

biological periods for fall 2011 in Oceana and Huron Counties, Michigan. Whiskers represent the 1st and 4th 

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

diamonds represent the seasonal mean for the time period.

Figure 14. Mean hourly target passage rate (targets per km per hour) during fall 2011 in Oceana and Huron 

Counties, Michigan.

Table 4. Mean target passage rate (Targets per kilometer per hour) with standard deviations during four 

biological periods in Oceana and Huron Counties, Michigan during fall 2011.

Biological Period

Oceana 

Mean TPR

Huron 

Mean TPR

Dawn 84 ± 98 103 ± 77

Day 17 ± 15 49 ± 26

Dusk 33 ± 41 60 ± 46

Night 442 ± 475 340 ± 328

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

1400	
  

1600	
  

1800	
  

2000	
  

2200	
  

2400	
  

Dawn	
   Day	
   Dusk	
   Night	
   Dawn	
   Day	
   Dusk	
   Night	
  

Target	
  Passage	
  Rate	
  

Biological	
  Period	
  

Oceana	
   Huron	
  

0 

100 

200 

300 

400 

500 

600 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 

Target Passage Rate 

Hour 

Oceana County Huron County 
18 Great Lakes Avian Radar – Fall 2011

Weekly mean of target passage rates – weekly means 

of TPRs at night were relatively high until the last 

two weeks of our sampling period, when a decrease 

was observed at both locations (Figure 15). The 

weekly means of nocturnal TPRs were consistently 

higher than weekly means of diurnal TPRs (Figure 

15). As the recorded migration season subsided, 

however, the difference between these passage rates 

decreased (Figures 15 and 16). Trends in nocturnal 

TPRs (7-day moving means) were similar at our 

sites, but trends in diurnal TPRs differed (Figure 

17). 

Figure 15. Weekly mean of nocturnal and diurnal target passage rates (targets per km per hr) in Oceana 

(top row) and Huron (bottom row) Counties from 18 August – 10 November 2011. Error bars represent one 

standard deviation. Note the different scales on the nocturnal and diurnal plots.

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

passage rate (targets per km per hour) during fall 2011 in Oceana and Huron Counties, Michigan. 

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

17-­‐Aug	
  24-­‐Aug	
  31-­‐Aug	
  

7-­‐Sep	
  

14-­‐Sep	
  21-­‐Sep	
  28-­‐Sep	
  

5-­‐Oct	
  

12-­‐Oct	
  19-­‐Oct	
  26-­‐Oct	
  

2-­‐Nov	
  9-­‐Nov	
  

Target	
  Passage	
  Rate	
  

Date	
  

Huron	
  Nocturnal	
  TPR	
  

Moving	
  Mean	
  

Huron	
  Diurnal	
  TPR	
  Moving	
  

Mean	
  

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

8/17	
  

8/24	
  

8/31	
  

9/7	
  

9/14	
  

9/21	
  9/28	
  10/5	
  

10/12	
  10/19	
  10/26	
  

11/2	
  

11/9	
  

Target	
  Passage	
  Rate	
  

Date	
  

Oceana	
  Nocturnal	
  TPR	
  

Moving	
  Mean	
  

Oceana	
  Diurnal	
  TPR	
  Moving	
  

Mean	
  

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

1400	
  

1600	
  

18-­‐Aug	
  25-­‐Aug	
  1-­‐Sep	
  8-­‐Sep	
  15-­‐Sep	
  22-­‐Sep	
  29-­‐Sep	
  6-­‐Oct	
  13-­‐Oct	
  20-­‐Oct	
  27-­‐Oct	
  3-­‐Nov	
  10-­‐Nov	
  

Targets	
  per	
  Hour	
  

Weeks	
  

Oceana	
  Mean	
  

Nocturnal	
  Passage	
  

Rate	
  

0	
  

15	
  

30	
  

45	
  

60	
  

75	
  

90	
  

105	
  

120	
  

18-­‐Aug	
  25-­‐Aug	
  1-­‐Sep	
  8-­‐Sep	
  15-­‐Sep	
  22-­‐Sep	
  29-­‐Sep	
  6-­‐Oct	
  13-­‐Oct	
  20-­‐Oct	
  27-­‐Oct	
  3-­‐Nov	
  10-­‐Nov	
  

Targets	
  per	
  Hour	
  

Weeks	
  

Oceana	
  Mean	
  

Diurnal	
  Passage	
  Rate	
  

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

1400	
  

1600	
  

18-­‐Aug	
  25-­‐Aug	
  1-­‐Sep	
  8-­‐Sep	
  15-­‐Sep	
  22-­‐Sep	
  29-­‐Sep	
  6-­‐Oct	
  13-­‐Oct	
  20-­‐Oct	
  27-­‐Oct	
  3-­‐Nov	
  10-­‐Nov	
  

Targets	
  per	
  Hour	
  

Weeks	
  

Huron	
  Mean	
  

Nocturnal	
  Passage	
  

Rate	
  

0	
  

15	
  

30	
  

45	
  

60	
  

75	
  

90	
  

105	
  

120	
  

18-­‐Aug	
  25-­‐Aug	
  1-­‐Sep	
  8-­‐Sep	
  15-­‐Sep	
  22-­‐Sep	
  29-­‐Sep	
  6-­‐Oct	
  13-­‐Oct	
  20-­‐Oct	
  27-­‐Oct	
  3-­‐Nov	
  10-­‐Nov	
  

Targets	
  per	
  Hour	
  

Weeks	
  

Huron	
  Mean	
  Diurnal	
  

Passage	
  Rate	
  
Great Lakes Avian Radar – Fall 2011 19

Figure 17. Between site comparison of nocturnal and diurnal trends (based on a moving 7-day mean) in 

target passage rate (targets per km per hour) during fall 2011 in Oceana and Huron Counties, Michigan. 

Altitudinal Trends

The density estimate that incorporated the 

geometric shape of the sampled space resulted 

in a substantially different density value than 

the estimate that assumed an equal amount of 

sample volume per altitude band (Figure 18 and 

19). The altitude profiles for dawn and dusk 

differed between our locations, with a greater 

density at low elevation at Huron (Figures 18 and 

19). The hourly altitude profiles at night revealed 

considerable variations in the use of altitude bands 

(Figure 20 and 21); however, the 100 – 150 m 

altitude band was the most densely populated over 

the course of the season (Figure 22), with a total of 

5.05 targets per 1,000,000 m3 per night-hr and 5.81 

targets per 1,000,000 m3 per night-hr at the Oceana 

and Huron sites, respectively. The maximum 

density of targets occurred at less than 150 m 

during 43.3% and 92% of the nights at Oceana 

and Huron Counties, respectively (Figure 23). A 

similar pattern, although with more variation, 

occurred if the hours from 20:00 – 04:00 were 

considered individually, with the maximum density 

of targets occurring at less than 150 m during 

41% and 71.5% of these night hours at Oceana and 

Huron Counties, respectively (Figure 24).

At both sites, targets were observed within the 

entire range of altitude bands sampled. The mean 

altitude of the nocturnal targets was 500 m ± 398 

m SD and 380 m ± 328 m SD above radar elevation 

at the Oceana and Huron sites, respectively. The 

median altitude at night was 416 m and 305 m 

above radar elevation at the Oceana and Huron 

sites, respectively. The median altitude was 

greatest during the night biological time period, 

with the dawn period the next highest. Estimates 

of mean and median altitude were poor indicators 

of density (Table 5).

Mean altitude per hr during the season showed 

a similar pattern at the two locations (Figure 

25). Mean altitude increased following dusk, 

tapered toward midnight, and decreased following 

midnight. A spike in mean altitude occurred 

during the 07:00 hr in Oceana and during the 06:00 

hr in Huron.

0

200

400

600

800

1000

1200

8/178/248/31

9/7

9/149/219/2810/5

10/1210/1910/26

11/211/9

Target Passage Rate

Date

Oceana Nocturnal TPR

Moving Mean

Huron Nocturnal TPR

Moving Mean
20 Great Lakes Avian Radar – Fall 2011

Table 5. 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 Oceana and Huron 

Counties, Michigan during fall 2011.

Figure 18. Altitude profile of targets in Oceana County, MI. Corrected lines depict target density (targets 

per 1,000,000 m3

) per 50-m altitude band per hr after adjusting for the structure of the sample volume. 

Uncorrected lines depict target density per 50-m altitude band per hr with an assumed uniform volume 

distribution (the volume of each band is equal to the total volume divided by the number of bands). The 

red band represents the rotor swept zone (RSZ) between 30 – 130 m. Y-axis labels represent the top of the 

altitude band.

Biological 

Period Mean Median

Max 

Density Mean Median Density

Dawn 492 ± 461 390 100 375 ± 408 252 100

Day 434 ± 460 351 100 213 ± 290 118 100

Dusk 309 ± 323 242 200 179 ± 197 125 100

Night 500 ± 398 416 150 380 ± 328 304 150

 Oceana Huron

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Dawn Targets in Oceana, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Night Targets in Oceana, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Day Targets in Oceana, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Dusk Targets in Oceana, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 
Great Lakes Avian Radar – Fall 2011 21

Figure 19. Altitude profile of targets in Huron County, MI. Corrected lines depict target density (targets 

per 1,000,000 m3) per 50-m per hr altitude band after adjusting for the structure of the sample volume. 

Uncorrected lines depict target density per 50-m altitude band per hr with an assumed uniform volume 

distribution (the volume of each band is equal to the total volume divided by the number of bands). The 

red band represents the rotor swept zone (RSZ) between 30 – 130 m. Y-axis labels represent the top of the 

altitude band.

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Dawn Targets in Huron, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of DayTargets in Huron, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Dusk Targets in Huron, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 

0 

200 

400 

600 

800 

1,000 

1,200 

1,400 

1,600 

1,800 

2,000 

2,200 

2,400 

2,600 

2,800 

0 1 2 3 4 5 6 

Altitude Band (m) 

Target Density per Altitude Band per Hour 

Altitude of Night Targets in Huron, MI

Uncorrected Density Estimate 

Corrected Density Estimate 

Upper Bound RSZ; 130 m 

RSZ 

Lower Bound RSZ; 30 m 
22 Great Lakes Avian Radar – Fall 2011

Figure 20. A sample of hourly altitude profiles corrected for the shape of the sample volume in Oceana 

County, Michigan during fall 2011. Hours were selected to portray the variability in density per altitude 

band of passing targets. The x-axis represents target density (targets per 1,000,000 m3) per 50-m altitude 

band. The y-axis labels represent the top of altitude bands in meters. The red line represents the top of 

the rotor swept zone at 130 m.

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  11,	
  00:00	
  

0:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  12,	
  21:00	
  

21:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  22,	
  01:00	
  

1:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  1,	
  01:00	
  

1:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  1,	
  02:00	
  

2:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  4,	
  00:00	
  

0:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  12,	
  21:00	
  

21:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  13,	
  00:00	
  

0:00	
  

RSZ	
  

Hourly Variation in Altitude Profiles Oceana County, 2011
Great Lakes Avian Radar – Fall 2011 23

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

County, Michigan during fall 2011. Hours were selected to portray the variability in density per altitude 

band of passing targets. The x-axis represents target density (targets per 1,000,000 m3) per 50-m altitude 

band. The y-axis labels represent the top of altitude bands in meters. The red line represents the top of 

the rotor swept zone at 130 m.

Hourly Variation in Altitude Profiles Huron County, 2011

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  12,	
  21:00	
  

21:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  13,	
  00:00	
  

0:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  13,	
  23:00	
  

23:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  14,	
  01:00	
  

1:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Sep	
  26,	
  02:00	
  

2:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  2,	
  00:00	
  

0:00	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  10,	
  23:00	
  

0.9583

33333	
  

RSZ	
  

0	
   5	
   10	
   15	
   20	
   25	
   30	
   35	
  

2800	
  

2550	
  

2300	
  

2050	
  

1800	
  

1550	
  

1300	
  

1050	
  

800	
  

550	
  

300	
  

50	
  

Oct	
  11,	
  02:00	
  

0.0833

33333	
  

RSZ	
  

23:00 2:00
24 Great Lakes Avian Radar – Fall 2011

Figure 22. Altitude profile of target density below 400 meters in Oceana and Huron Counties, Michigan. 

These graphics show the altitude band in which the maximum density occurred during fall 2011. The x-axis 

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

the top of the altitude bands in meters.

Figure 23. Percent of nights when the maximum density (targets per 1,000,000 m3 per altitude band) or 

count (targets per altitude band) occurred within 50-m altitude bands in Oceana and Huron Counties, 

Michigan, during fall 2011. 

0	
   1	
   2	
   3	
   4	
   5	
   6	
  

50	
  

100	
  

150	
  

200	
  

250	
  

300	
  

350	
  

400	
  

Target	
  Density	
  

Al/tude	
  Band	
  (m)	
  

Night	
  Density	
  Oceana,	
  MI	
  

Night	
  Density	
  

0	
   1	
   2	
   3	
   4	
   5	
   6	
  

50	
  

100	
  

150	
  

200	
  

250	
  

300	
  

350	
  

400	
  

Target	
  Density	
  

Al/tude	
  Band	
  (m)	
  

Day	
  Density	
  Oceana,	
  MI	
  

Day	
  Density	
  

0	
   1	
   2	
   3	
   4	
   5	
   6	
  

50	
  

100	
  

150	
  

200	
  

250	
  

300	
  

350	
  

400	
  

Target	
  Density	
  

Al/tude	
  Band	
  (m)	
  

Night	
  Density	
  Huron,	
  MI	
  

Night	
  Density	
  

0	
   1	
   2	
   3	
   4	
   5	
   6	
  

50	
  

100	
  

150	
  

200	
  

250	
  

300	
  

350	
  

400	
  

Target	
  Density	
  

Al/tude	
  Band	
  (m)	
  

Day	
  Density	
  Huron,	
  MI	
  

Day	
  Density	
  

0%	
  

5%	
  

10%	
  

15%	
  

20%	
  

25%	
  

30%	
  

35%	
  

40%	
  

45%	
  

50%	
  

0	
  

50	
  

100	
  150	
  200	
  250	
  300	
  350	
  400	
  450	
  500	
  550	
  600	
  650	
  700	
  750	
  800	
  

>	
  800	
  

Percent	
  of	
  Nights	
  

Al1tude	
  Band	
  (m)	
  

Percentage	
  of	
  Nights	
  With	
  Maximum	
  Density	
  or	
  Count	
  within	
  an	
  Al1tude	
  Band	
  Huron	
  

County,	
  Michigan	
  

Max	
  Density	
  

Max	
  Count	
  (Uncorrected)	
  

0%	
  

5%	
  

10%	
  

15%	
  

20%	
  

25%	
  

30%	
  

35%	
  

40%	
  

45%	
  

50%	
  

0	
  

50	
  

100	
  150	
  200	
  250	
  300	
  350	
  400	
  450	
  500	
  550	
  600	
  650	
  700	
  750	
  800	
  

>	
  800	
  

Percent	
  of	
  Nights	
  

Al1tude	
  Band	
  (m)	
  

Percentage	
  of	
  Nights	
  With	
  Maximum	
  Density	
  or	
  Count	
  within	
  an	
  Al1tude	
  Band	
  Oceana	
  

County,	
  Michigan	
  

Max	
  Density	
  

Max	
  Count	
  (Uncorrected)	
  

Percent of Nights With Maximum Density or Count within an Altitude Band Oceana County, Michigan

Percent of Nights With Maximum Density or Count within an Altitude Band Huron County, Michigan
Great Lakes Avian Radar – Fall 2011 25

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

per altitude band) or count (targets per altitude band) occurred within 50-m altitude bands in Oceana and 

Huron Counties, Michigan, during fall 2011. 

Figure 25. Mean hourly target height (m) during fall in Oceana and Huron Counties, Michigan. Yellow 

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

Error bars represent one standard deviation. 

0%	
  

5%	
  

10%	
  

15%	
  

20%	
  

25%	
  

30%	
  

35%	
  

40%	
  

45%	
  

50%	
  

0	
  

50	
  

100	
  150	
  200	
  250	
  300	
  350	
  400	
  450	
  500	
  550	
  600	
  650	
  700	
  750	
  800	
  

>	
  800	
  

Percent	
  of	
  Night	
  Hours	
  

Al3tude	
  Band	
  (m)	
  

Percentage	
  of	
  Night	
  Hours	
  With	
  Maximum	
  Density	
  or	
  Count	
  within	
  an	
  Al3tude	
  Band	
  

Oceana	
  County,	
  Michigan	
  

Max	
  Density	
  

Max	
  Count	
  (Uncorrected)	
  

0%	
  

5%	
  

10%	
  

15%	
  

20%	
  

25%	
  

30%	
  

35%	
  

40%	
  

45%	
  

50%	
  

0	
  

50	
  

100	
  150	
  200	
  250	
  300	
  350	
  400	
  450	
  500	
  550	
  600	
  650	
  700	
  750	
  800	
  

>	
  800	
  

Percent	
  of	
  Night	
  Hours	
  

Al3tude	
  Band	
  (m)	
  

Percentage	
  of	
  Night	
  Hours	
  With	
  Maximum	
  Density	
  or	
  Count	
  within	
  an	
  Al3tude	
  Band	
  

Huron	
  County,	
  Michigan	
  

Max	
  Density	
  

Max	
  Count	
  (Uncorrected)	
  

Oceana Huron

Percent of Night Hours With Maximum Density or Count within an Altitude Band Oceana County, Michigan

Percent of Night Hours With Maximum Density or Count within an Altitude Band Huron County, Michigan

0 

200 

400 

600 

800 

1000 

1200 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 

Mean Target Height (m) 

Hour 

0 

200 

400 

600 

800 

1000 

1200 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 

Mean Target Height (m) 

Hour 
26 Great Lakes Avian Radar – Fall 2011

Discussion

We undertook this study to document migration 

along the shorelines of the Great Lakes. Our results 

indicate that migration movements were common 

along Michigan’s east and west shorelines where we 

established study sites. Our research contributes to 

a growing body of literature that documents various 

aspects of migration and identifies Great Lakes 

shorelines as areas important for the conservation 

of migratory species. Our data provide unique 

observations about the magnitude and timing of 

nocturnal migration that could not be observed 

without the aid of radar.

Sampling Regime

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 the migratory 

season than a systematic or random sampling 

scheme, which might have missed pulses of activity 

(Figure 26). We used diurnal radar observations to 

provide a baseline for comparing nocturnal activity. 

Including this time period in the sampling scheme 

helped to distinguish the magnitude of the migration 

events (Figure 16). Our sampling regime was 

also useful in showing when the migration season 

declined in November, but our start date in August 

may not have included the onset of migration at our 

locations. As more data are collected, we will be able 

to better describe the migration season and how it 

varies with location and year. This information will 

help to tailor conservation efforts to appropriate 

time frames.

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 the ways in 

which researchers incorporate variation in detection 

probability and sample volume (Bruderer 1997, 

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

Even if the same equipment and methodology are 

used among sites or studies, comparisons should 

be made cautiously if the probability of detection 

and sampling volume are ignored (Schmaljohann 

et al. 2008). Recognizing that our counts represent 

an index of target passage that is relative to a site, 

we are cautious about making comparisons among 

sites or studies. Rather than relying solely on 

the magnitude of target passage as an indication 

of airspace with or without a concentration of 

migration, we assess the 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 multiple times larger than 

lulls for the majority of the sampling period would 

be considered to have more migration than a site 

with less of a discrepancy between nocturnal peaks 

and lulls or a site that had a nocturnal passage rate 

that only occasionally spiked above a baseline of 

nocturnal passage rates. 

Migration Patterns

The recorded patterns of movement were consistent 

with other observations of migration (Newton 

2008) and indicated that nocturnal migratory 

flights occurred regularly during fall 2011 at both 

of our surveyed locations. The nocturnal activity 

we observed was typically oriented in a southward 

direction (Figure 10) and occurred in pulses across 

the season that were captured by horizontal and 

vertical radars (Figures 11 and 12). We also 

observed targets flying over water change course 

to return to shorelines near dawn. The TPR (mean 

for the season) was greatest during the nocturnal 

biological period at both locations (Table 4, Figure 

13). The mean hourly heights showed a pattern 

previously associated with migration (Harmata 

et al. 2000, Mabee and Cooper 2004), with heights 

that increased near dusk, peaked toward midnight, 

and began to decrease prior to dawn. We also 

documented an abrupt increase in mean height near 

dawn (Figure 25) that represented a migratory 

behavior described as dawn ascent (Myres 1964, 

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

migrants that increase 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. Taken together, we attribute 

these nocturnal observations to migrants and 

suggest that the shorelines we studied are important 

for their conservation.

At both of our sample locations, nocturnal targets 

appeared to move across the landscape in four 

waves, with peaks near 30 August, 10 September, 

2 October, and 18 October (Figure 17). These 

fluctuations may be related to broad-scale weather 

fronts, variations in the timing among guilds of 

migrants, or a combination of these and other 

factors (Newton 2008). The pattern of these trends 

at locations at similar latitudes but on opposite sides 
Great Lakes Avian Radar – Fall 2011 27

of Michigan reveals broad-scale influences and could 

indicate that further investigation into its cause 

would facilitate predictions of high-migration events. 

Weekly mean estimates of nocturnal TPR were 

consistently higher than weekly mean diurnal TPR 

across all weeks of data collection until November 

(Figure 15). In November, this difference decreased 

substantially and, on occasion, diurnal trends 

were greater than nocturnal trends (Figure 

16). This shift from time periods with orders of 

magnitude more nocturnal activity to time periods 

with comparable diurnal and nocturnal activity 

indicates that migration added substantially to the 

aeroecology above our study areas.

Flight Altitude

Altitude profiles indicated that most nocturnal 

targets passed below 800 m, with the peak density 

in the 100 – 150 m altitude band (Figure 18). 

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 does not account for 

the fact that beam propagation is not consistent 

over time. Beam propagation is affected by side 

lobes, target size and distance, and atmospheric 

conditions. Nevertheless, 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 1400 

– 2000 m. For these reasons, our estimates most 

likely under-represent density as altitude increases. 

Altitude profiles varied considerably among 

nocturnal hours at our sites in Michigan (Figures 

20 and 21). Migrants adjust flight altitude with 

wind direction and speed, visibility, time, and the 

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 

Figure 26. Example of a hypothetical sampling schedule where data were collected once per week (top 

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

of targets counted km per hr by the vertical scanning radar from 6 October – 5 November 2011 in Huron 

County, MI. 

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

1400	
  

1600	
  

1800	
  

2000	
  

10/6/11	
  0:00	
  10/7/11	
  0:00	
  10/8/11	
  0:00	
  10/9/11	
  0:00	
  

10/10/11	
  0:00	
  10/11/11	
  0:00	
  10/12/11	
  0:00	
  10/13/11	
  0:00	
  10/14/11	
  0:00	
  10/15/11	
  0:00	
  10/16/11	
  0:00	
  10/17/11	
  0:00	
  10/18/11	
  0:00	
  10/19/11	
  0:00	
  10/20/11	
  0:00	
  10/21/11	
  0:00	
  10/22/11	
  0:00	
  10/23/11	
  0:00	
  10/24/11	
  0:00	
  10/25/11	
  0:00	
  10/26/11	
  0:00	
  10/27/11	
  0:00	
  10/28/11	
  0:00	
  10/29/11	
  0:00	
  10/30/11	
  0:00	
  10/31/11	
  0:00	
  

11/1/11	
  0:00	
  11/2/11	
  0:00	
  11/3/11	
  0:00	
  11/4/11	
  0:00	
  11/5/11	
  0:00	
  

Targets	
  Per	
  Hour	
  Fall	
  2011	
  Huron	
  County,	
  Michigan	
  

Target	
  Passage	
  Rate	
  

0	
  

200	
  

400	
  

600	
  

800	
  

1000	
  

1200	
  

1400	
  

1600	
  

1800	
  

2000	
  

10/6/11	
  0:00	
  10/7/11	
  0:00	
  10/8/11	
  0:00	
  10/9/11	
  0:00	
  

10/10/11	
  0:00	
  10/11/11	
  0:00	
  10/12/11	
  0:00	
  10/13/11	
  0:00	
  10/14/11	
  0:00	
  10/15/11	
  0:00	
  10/16/11	
  0:00	
  10/17/11	
  0:00	
  10/18/11	
  0:00	
  10/19/11	
  0:00	
  10/20/11	
  0:00	
  10/21/11	
  0:00	
  10/22/11	
  0:00	
  10/23/11	
  0:00	
  10/24/11	
  0:00	
  10/25/11	
  0:00	
  10/26/11	
  0:00	
  10/27/11	
  0:00	
  10/28/11	
  0:00	
  10/29/11	
  0:00	
  10/30/11	
  0:00	
  10/31/11	
  0:00	
  

11/1/11	
  0:00	
  11/2/11	
  0:00	
  11/3/11	
  0:00	
  11/4/11	
  0:00	
  11/5/11	
  0:00	
  

Targets	
  Per	
  Hour	
  Fall	
  2011	
  Huron	
  County,	
  Michigan	
  

Target	
  Passage	
  Rate	
  
28 Great Lakes Avian Radar – Fall 2011

reduced (Gauthreaux 1991). In addition, migrants 

are typically on land at least twice during every 

24-hr 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 the 

location, these altitude changes may place migrants 

at risk of collision. 

Management Considerations

Although radar may be the best tool available 

for gathering large amounts of data on nocturnal 

migration, the interpretation of radar data can be 

challenging. Marine radar is the most common type 

used to track bird and bat movements (Larkin 2005), 

and its use to assess risk will likely increase with 

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. This presents a challenge that is not 

easily solved. Yet, without standards, comparisons 

among studies may be more reflective of changes in 

methodology and site conditions than in differences 

in migration activity. Additionally, metrics reported 

in these types of surveys can be misleading to 

someone unfamiliar with avian radar.

For example, the mean altitude of targets is often 

reported to be above the rotor swept zone and 

can be 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 

target passage within the rotor swept zone. This 

difference is due to the long range at which radars 

collect altitude data, often up to 3 km above ground 

level, where high-flying targets inflate the mean 

altitude. This bias is apparent in our data and can 

be observed by comparing the mean altitude of 

nocturnal targets with the most densely populated 

altitude band (Table 5). It is also misleading to 

compare the percentage of targets below and 

above the height of the rotor swept zone without 

addressing the difference in sampling effort. 

Within our sampling framework, there are three 

50-m altitude bands below 150 m (an estimate of 

the height of the rotor swept zone) and 53 altitude 

bands above 150 m. Based on our model of survey 

volume we estimate that approximately 1 percent of 

the potential survey volume is below 150 m. Given 

that calculation, we would expect a small percentage 

of targets to be recorded below the rotor swept 

zone, although this does not necessarily indicate 

low risk. In this report, we provide examples of 

a methodology and analyses that we find helpful 

in interpreting radar data. We suggest that the 

patterns of activity and relative change in counts 

at a site indicate migration and that this is a better 

indicator than comparing the magnitude of counts 

among studies. In addition, careful attention should 

be given to the way in which these indices fluctuate 

over fine temporal scales, e.g., hourly scales, as 

opposed to monthly or seasonal summaries. The 

clutter maps that we include provided information 

about our ability to detect targets at low altitude and 

show that within the 1-km front we had more clutter, 

and thus less detection, at low altitude in Oceana 

than we did at Huron. It is important, particularly 

for risk assessments, that radar operators address 

their ability to detect targets at low altitude. We 

provided a method to account for the structure of 

the sample volume that was not without limitations 

but provided a first-order correction to a difficult 

problem. This represents an improvement over 

ignoring the biases associated with sampling effort. 

Overall, we found that radar provided insights 

into nocturnal migration that would otherwise 

be unattainable, and we think that its continued 

development and careful interpretation will result 

in valuable contributions to the management and 

conservation of migrants.

The results of our research highlight the potential 

role of radar in implementing recommendations 

from the wind energy guidelines (U.S. Fish & 

Wildlife Service 2012) to identify areas where 

impacts to wildlife would be minimized. We 

documented clear examples of migrant activity along 

studied shorelines in Michigan, and the density of 

targets at lower altitudes is a potential concern. 

The data that we collected may be of interest 

to public and private entities that are involved 

with wind energy development and its potential 

placement in the Great Lakes region. Coupling 

avian radar systems with other forms of radar, such 

as NEXRAD, or using them in conjunction with 

post-construction fatality searches may broaden 

their utility in making risk assessments and serving 

wildlife-friendly wind energy developments. 
Great Lakes Avian Radar – Fall 2011 29

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Great Lakes Avian Radar – Fall 2011 33

Appendices

Appendix 1: Fall 2011 Report Summary.............................................................................................34

Appendix 2: Percent Land Cover Associated with Study Sites and the 2006 ...........................35

National Land Cover Database Classification

Appendix 3: Corrected Density per Hr by Biological Period ........................................................37

Appendix 4: Comparison of Static and Corrected Density Estimates .........................................39
34 Great Lakes Avian Radar – Fall 2011

n Migration occurred on the shoreline of both sides of Michigan during fall 2011.

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

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

• Patterns and timing of migration were similar between the sites

 • 4 main waves of migration with highest concentrations near Aug 30, Sept 10, Oct 2, 

and Oct 18

n Date range of pulses that occurred during the migration season

• Aug 15 – Nov 1 in Oceana County, MI

• Aug 26 – Nov 1 in Huron County, MI

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

• Movement south during the night

 • 68% of nights surveyed the mean direction of travel was generally southerly at Oceana County, MI

	 	 • 49% of nights surveyed the mean direction of travel was generally southerly at Huron 

County, MI

• Movement in towards shore at dawn

 • Observed at both sites

• Highest target passage rate at night 

• Dawn ascent

 • Increase in height around dawn hours observed at both sites

n Peak density of targets in volume corrected counts

• Max density below 150m 54% of nights and 46% of night hours at Oceana County, MI

• Max density below 150m 86% of nights and 76% of night hours at Huron County, MI

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.

Appendix 1

Fall 2011 Report Summary
Great Lakes Avian Radar – Fall 2011 35

Percent landcover found within 3.7 km of radar locations in Michigan during fall 2011

National Landcover Class

Oceana County

% Land Cover

Huron County 

% Land Cover

Barren Land 2.20% 0.12%

Cultivated Crops 18.87% 43.63%

Deciduous Forest 32.14% 7.17%

Developed* 5.02% 6.66%

Evergreen Forest 3.36% 0.37%

Hay/Pasture 0.43% 9.89%

Herbaceous 5.80% 0.29%

Mixed Forest 1.13% 0.38%

Open water 29.29% 23.66%

Shrub/Scrub 1.57% 0.03%

Wetlands** 0.20% 7.79%

Appendix 2

Percent Land Cover Associated with Study Sites and the 2006 

National Land Cover Database Classification

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

**Includes woody and emergent herbaceous wetlands.

Classification Description for the 2006 National Land Cover Database (taken from http://www.mrlc.gov/

nlcd06_leg.php; accessed 5/5/2014).

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 single-family 

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.
36 Great Lakes Avian Radar – Fall 2011

(Appendix 2 continued)

Barren

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% 

oftotal 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% oftotal 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.
Great Lakes Avian Radar – Fall 2011 37

Appendix 3

Corrected Density per Hr by Biological Period

Estimated density of targets by altitude band during fall biological periods in Oceana County, Michigan. 

Altitude

Band Dawn Day Dusk Night 

0 0.3 0.1 0.1 0.2

100 1.2 0.4 0.4 2.8

150 1.1 0.2 0.6 5.0

200 0.7 0.2 0.7 4.9

250 0.7 0.1 0.5 4.3

300 0.7 0.1 0.5 3.8

350 0.5 0.1 0.3 2.7

400 0.5 0.1 0.2 2.5

450 0.5 0.1 0.2 2.4

500 0.5 0.1 0.1 2.1

550 0.3 0.1 0.1 1.6

600 0.3 0.1 0.0 1.4

650 0.2 0.0 0.0 1.1

700 0.2 0.0 0.0 1.0

750 0.1 0.0 0.0 0.8

800 0.1 0.0 0.0 0.7

850 0.1 0.0 0.0 0.6

900 0.1 0.0 0.0 0.5

950 0.0 0.0 0.0 0.4

1000 0.0 0.0 0.0 0.3
38 Great Lakes Avian Radar – Fall 2011

(Appendix 3 continued)

Estimated density of targets by altitude band during fall biological periods in Huron County, Michigan. 

Altitude

Band Dawn Day Dusk Night 

0 2.0 1.9 1.7 3.0

100 2.6 1.9 2.3 5.1

150 1.9 1.4 1.9 5.8

200 1.0 0.6 1.0 4.2

250 0.7 0.3 0.5 3.2

300 0.6 0.3 0.5 3.0

350 0.6 0.2 0.3 2.5

400 0.5 0.2 0.2 2.0

450 0.4 0.1 0.1 1.6

500 0.3 0.1 0.1 1.3

550 0.3 0.1 0.1 1.0

600 0.2 0.0 0.0 0.9

650 0.2 0.0 0.0 0.7

700 0.2 0.0 0.0 0.6

750 0.1 0.0 0.0 0.5

800 0.1 0.0 0.0 0.4

850 0.1 0.0 0.0 0.3

900 0.1 0.0 0.0 0.3

950 0.0 0.0 0.0 0.2

1000 0.0 0.0 0.0 0.1
Great Lakes Avian Radar – Fall 2011 39

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 

Oceana County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 131 131 31.3 5.6 0.0 0.3 1.8% 1.8% 3.2%

100 765 634 31.3 5.9 0.2 1.2 8.5% 8.5% 14.4%

150 1394 629 31.3 6.5 0.2 1.1 8.5% 8.5% 13.2%

200 1865 471 31.3 7.1 0.2 0.7 6.3% 6.3% 9.0%

250 2374 509 31.3 7.9 0.2 0.7 6.8% 6.8% 8.7%

300 2883 509 31.3 8.5 0.2 0.7 6.8% 6.8% 8.1%

350 3332 449 31.3 9.5 0.2 0.5 6.0% 6.0% 6.4%

400 3777 445 31.3 10.3 0.2 0.5 6.0% 6.0% 5.8%

450 4307 530 31.3 11.2 0.2 0.5 7.1% 7.1% 6.4%

500 4843 536 31.3 12.2 0.2 0.5 7.2% 7.2% 5.9%

550 5245 402 31.3 13.3 0.1 0.3 5.4% 5.4% 4.1%

600 5566 321 31.3 14.1 0.1 0.3 4.3% 4.3% 3.1%

650 5804 238 31.3 15.3 0.1 0.2 3.2% 3.2% 2.1%

700 6023 219 31.3 16.2 0.1 0.2 2.9% 2.9% 1.8%

750 6187 164 31.3 17.2 0.1 0.1 2.2% 2.2% 1.3%

800 6340 153 31.3 18.2 0.1 0.1 2.1% 2.1% 1.1%

850 6472 132 31.3 19.4 0.0 0.1 1.8% 1.8% 0.9%

900 6582 110 31.3 20.4 0.0 0.1 1.5% 1.5% 0.7%

950 6664 82 31.3 21.4 0.0 0.0 1.1% 1.1% 0.5%

1000 6743 79 31.3 22.4 0.0 0.0 1.1% 1.1% 0.5%
40 Great Lakes Avian Radar – Fall 2011

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 719 719 31.3 5.6 0.0 0.1 4.3% 4.3% 7.1%

100 2961 2242 31.3 5.9 0.1 0.4 13.3% 13.3% 21.0%

150 4449 1488 31.3 6.5 0.0 0.2 8.8% 8.9% 12.8%

200 5484 1035 31.3 7.1 0.0 0.2 6.2% 6.2% 8.1%

250 6423 939 31.3 7.9 0.0 0.1 5.6% 5.6% 6.6%

300 7352 929 31.3 8.5 0.0 0.1 5.5% 5.5% 6.1%

350 8164 812 31.3 9.5 0.0 0.1 4.8% 4.8% 4.8%

400 8990 826 31.3 10.3 0.0 0.1 4.9% 4.9% 4.5%

450 10,168 1178 31.3 11.2 0.0 0.1 7.0% 7.0% 5.9%

500 11,644 1476 31.3 12.2 0.0 0.1 8.8% 8.8% 6.7%

550 12,762 1118 31.3 13.3 0.0 0.1 6.6% 6.7% 4.7%

600 13,573 811 31.3 14.1 0.0 0.1 4.8% 4.8% 3.2%

650 14,203 630 31.3 15.3 0.0 0.0 3.7% 3.7% 2.3%

700 14,605 402 31.3 16.2 0.0 0.0 2.4% 2.4% 1.4%

750 14,814 209 31.3 17.2 0.0 0.0 1.2% 1.2% 0.7%

800 15,033 219 31.3 18.2 0.0 0.0 1.3% 1.3% 0.7%

850 15,230 197 31.3 19.4 0.0 0.0 1.2% 1.2% 0.6%

900 15,377 147 31.3 20.4 0.0 0.0 0.9% 0.9% 0.4%

950 15,500 123 31.3 21.4 0.0 0.0 0.7% 0.7% 0.3%

1000 15,615 115 31.3 22.4 0.0 0.0 0.7% 0.7% 0.3%
Great Lakes Avian Radar – Fall 2011 41

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 69 69 31.3 5.6 0.0 0.1 2.4% 2.4% 3.6%

100 272 203 31.3 5.9 0.1 0.4 7.2% 7.2% 10.1%

150 643 371 31.3 6.5 0.1 0.6 13.1% 13.1% 16.9%

200 1060 417 31.3 7.1 0.1 0.7 14.8% 14.8% 17.3%

250 1448 388 31.3 7.9 0.1 0.5 13.7% 13.7% 14.5%

300 1807 359 31.3 8.5 0.1 0.5 12.7% 12.7% 12.5%

350 2046 239 31.3 9.5 0.1 0.3 8.5% 8.5% 7.4%

400 2245 199 31.3 10.3 0.1 0.2 7.0% 7.0% 5.7%

450 2422 177 31.3 11.2 0.1 0.2 6.3% 6.3% 4.6%

500 2546 124 31.3 12.2 0.0 0.1 4.4% 4.4% 3.0%

550 2608 62 31.3 13.3 0.0 0.1 2.2% 2.2% 1.4%

600 2641 33 31.3 14.1 0.0 0.0 1.2% 1.2% 0.7%

650 2666 25 31.3 15.3 0.0 0.0 0.9% 0.9% 0.5%

700 2683 17 31.3 16.2 0.0 0.0 0.6% 0.6% 0.3%

750 2697 14 31.3 17.2 0.0 0.0 0.5% 0.5% 0.2%

800 2713 16 31.3 18.2 0.0 0.0 0.6% 0.6% 0.3%

850 2719 6 31.3 19.4 0.0 0.0 0.2% 0.2% 0.1%

900 2723 4 31.3 20.4 0.0 0.0 0.1% 0.1% 0.1%

950 2725 2 31.3 21.4 0.0 0.0 0.1% 0.1% 0.0%

1000 2729 4 31.3 22.4 0.0 0.0 0.1% 0.1% 0.1%
42 Great Lakes Avian Radar – Fall 2011

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 971 971 31.3 5.6 0.0 0.2 0.2% 0.2% 0.4%

100 17,280 16,309 31.3 5.9 0.5 2.8 3.9% 3.9% 7.0%

150 49,414 32,134 31.3 6.5 1.0 5.0 7.8% 7.7% 12.6%

200 83,917 34,503 31.3 7.1 1.1 4.9 8.3% 8.3% 12.4%

250 117,097 33,180 31.3 7.9 1.1 4.3 8.0% 8.0% 10.7%

300 149,067 31,970 31.3 8.5 1.0 3.8 7.7% 7.7% 9.6%

350 174,190 25,123 31.3 9.5 0.8 2.7 6.1% 6.1% 6.7%

400 199,199 25,009 31.3 10.3 0.8 2.5 6.0% 6.0% 6.2%

450 226,033 26,834 31.3 11.2 0.9 2.4 6.5% 6.5% 6.1%

500 251,343 25,310 31.3 12.2 0.8 2.1 6.1% 6.1% 5.3%

550 272,954 21,611 31.3 13.3 0.7 1.6 5.2% 5.2% 4.1%

600 292,186 19,232 31.3 14.1 0.6 1.4 4.6% 4.6% 3.5%

650 309,036 16,850 31.3 15.3 0.5 1.1 4.1% 4.1% 2.8%

700 324,800 15,764 31.3 16.2 0.5 1.0 3.8% 3.8% 2.5%

750 337,540 12,740 31.3 17.2 0.4 0.8 3.1% 3.1% 1.9%

800 350,432 12,892 31.3 18.2 0.4 0.7 3.1% 3.1% 1.8%

850 361,321 10,889 31.3 19.4 0.4 0.6 2.6% 2.6% 1.4%

900 370,665 9344 31.3 20.4 0.3 0.5 2.3% 2.3% 1.2%

950 378,583 7918 31.3 21.4 0.3 0.4 1.9% 1.9% 0.9%

1000 384,965 6382 31.3 22.4 0.2 0.3 1.5% 1.5% 0.7%
Great Lakes Avian Radar – Fall 2011 43

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 918 918 31.3 5.6 0.4 2.0 10.9% 10.9% 16.6%

100 2184 1266 31.3 5.9 0.5 2.6 15.1% 15.0% 21.6%

150 3204 1020 31.3 6.5 0.4 1.9 12.1% 12.1% 16.0%

200 3779 575 31.3 7.1 0.2 1.0 6.8% 6.8% 8.2%

250 4199 420 31.3 7.9 0.2 0.7 5.0% 5.0% 5.4%

300 4625 426 31.3 8.5 0.2 0.6 5.1% 5.1% 5.1%

350 5070 445 31.3 9.5 0.2 0.6 5.3% 5.3% 4.8%

400 5474 404 31.3 10.3 0.2 0.5 4.8% 4.8% 4.0%

450 5864 390 31.3 11.2 0.2 0.4 4.6% 4.6% 3.5%

500 6181 317 31.3 12.2 0.1 0.3 3.8% 3.8% 2.6%

550 6477 296 31.3 13.3 0.1 0.3 3.5% 3.5% 2.3%

600 6747 270 31.3 14.1 0.1 0.2 3.2% 3.2% 1.9%

650 6957 210 31.3 15.3 0.1 0.2 2.5% 2.5% 1.4%

700 7192 235 31.3 16.2 0.1 0.2 2.8% 2.8% 1.5%

750 7387 195 31.3 17.2 0.1 0.1 2.3% 2.3% 1.1%

800 7566 179 31.3 18.2 0.1 0.1 2.1% 2.1% 1.0%

850 7682 116 31.3 19.4 0.0 0.1 1.4% 1.4% 0.6%

900 7819 137 31.3 20.4 0.1 0.1 1.6% 1.6% 0.7%

950 7891 72 31.3 21.4 0.0 0.0 0.9% 0.9% 0.3%

1000 7925 34 31.3 22.4 0.0 0.0 0.4% 0.4% 0.2%
44 Great Lakes Avian Radar – Fall 2011

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 8365 8365 31.3 5.6 0.3 1.9 20.5% 20.5% 25.8%

100 17,310 8945 31.3 5.9 0.4 1.9 21.9% 21.9% 26.1%

150 24,473 7163 31.3 6.5 0.3 1.4 17.5% 17.5% 19.2%

200 27,730 3257 31.3 7.1 0.1 0.6 8.0% 8.0% 8.0%

250 29,649 1919 31.3 7.9 0.1 0.3 4.7% 4.7% 4.2%

300 31,554 1905 31.3 8.5 0.1 0.3 4.7% 4.7% 3.9%

350 33,413 1859 31.3 9.5 0.1 0.2 4.6% 4.6% 3.4%

400 34,909 1496 31.3 10.3 0.1 0.2 3.7% 3.7% 2.5%

450 36,107 1198 31.3 11.2 0.0 0.1 2.9% 2.9% 1.8%

500 37,019 912 31.3 12.2 0.0 0.1 2.2% 2.2% 1.3%

550 37,698 679 31.3 13.3 0.0 0.1 1.7% 1.7% 0.9%

600 38,256 558 31.3 14.1 0.0 0.0 1.4% 1.4% 0.7%

650 38,654 398 31.3 15.3 0.0 0.0 1.0% 1.0% 0.5%

700 39,035 381 31.3 16.2 0.0 0.0 0.9% 0.9% 0.4%

750 39,343 308 31.3 17.2 0.0 0.0 0.8% 0.8% 0.3%

800 39,571 228 31.3 18.2 0.0 0.0 0.6% 0.6% 0.2%

850 39,792 221 31.3 19.4 0.0 0.0 0.5% 0.5% 0.2%

900 39,966 174 31.3 20.4 0.0 0.0 0.4% 0.4% 0.1%

950 40,059 93 31.3 21.4 0.0 0.0 0.2% 0.2% 0.1%

1000 40,136 77 31.3 22.4 0.0 0.0 0.2% 0.2% 0.1%
Great Lakes Avian Radar – Fall 2011 45

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 719 719 31.3 5.6 0.3 1.7 15.8% 15.8% 19.5%

100 1763 1044 31.3 5.9 0.4 2.3 22.9% 22.9% 26.7%

150 2693 930 31.3 6.5 0.4 1.9 20.4% 20.4% 21.9%

200 3208 515 31.3 7.1 0.2 1.0 11.3% 11.3% 11.0%

250 3480 272 31.3 7.9 0.1 0.5 6.0% 6.0% 5.2%

300 3779 299 31.3 8.5 0.1 0.5 6.6% 6.6% 5.3%

350 3999 220 31.3 9.5 0.1 0.3 4.8% 4.8% 3.5%

400 4158 159 31.3 10.3 0.1 0.2 3.5% 3.5% 2.3%

450 4280 122 31.3 11.2 0.1 0.1 2.7% 2.7% 1.7%

500 4367 87 31.3 12.2 0.0 0.1 1.9% 1.9% 1.1%

550 4431 64 31.3 13.3 0.0 0.1 1.4% 1.4% 0.7%

600 4477 46 31.3 14.1 0.0 0.0 1.0% 1.0% 0.5%

650 4499 22 31.3 15.3 0.0 0.0 0.5% 0.5% 0.2%

700 4513 14 31.3 16.2 0.0 0.0 0.3% 0.3% 0.1%

750 4520 7 31.3 17.2 0.0 0.0 0.2% 0.2% 0.1%

800 4523 3 31.3 18.2 0.0 0.0 0.1% 0.1% 0.0%

850 4526 3 31.3 19.4 0.0 0.0 0.1% 0.1% 0.0%

900 4526 0 31.3 20.4 0.0 0.0 0.0% 0.0% 0.0%

950 4526 0 31.3 21.4 0.0 0.0 0.0% 0.0% 0.0%

1000 4529 3 31.3 22.4 0.0 0.0 0.1% 0.1% 0.0%
46 Great Lakes Avian Radar – Fall 2011

(Appendix 4 continued)

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

County, Michigan.

Altitude 

Band

Running 

Total

Count per 

Band

Static 

Volume

Corrected 

Volume 

Static 

Density 

per Hr

Corrected 

Density 

per Hr

% Total 

per Band

% Static 

Density

% Corrected

Density

50 14,857 14,857 31.3 5.6 0.5 3.0 5.0% 5.0% 8.0%

100 41,828 26,971 31.3 5.9 1.0 5.1 9.1% 9.1% 13.7%

150 75,414 33,586 31.3 6.5 1.2 5.8 11.3% 11.3% 15.7%

200 102,087 26,673 31.3 7.1 1.0 4.2 9.0% 9.0% 11.4%

250 124,523 22,436 31.3 7.9 0.8 3.2 7.5% 7.5% 8.6%

300 147,454 22,931 31.3 8.5 0.8 3.0 7.7% 7.7% 8.2%

350 168,591 21,137 31.3 9.5 0.8 2.5 7.1% 7.1% 6.8%

400 187,117 18,526 31.3 10.3 0.7 2.0 6.2% 6.2% 5.4%

450 203,415 16,298 31.3 11.2 0.6 1.6 5.5% 5.5% 4.4%

500 217,267 13,852 31.3 12.2 0.5 1.3 4.6% 4.6% 3.4%

550 229,537 12,270 31.3 13.3 0.4 1.0 4.1% 4.1% 2.8%

600 240,480 10,943 31.3 14.1 0.4 0.9 3.7% 3.7% 2.3%

650 250,506 10,026 31.3 15.3 0.4 0.7 3.4% 3.4% 2.0%

700 259,328 8822 31.3 16.2 0.3 0.6 3.0% 3.0% 1.6%

750 267,182 7854 31.3 17.2 0.3 0.5 2.6% 2.6% 1.4%

800 273,924 6742 31.3 18.2 0.2 0.4 2.3% 2.3% 1.1%

850 279,757 5833 31.3 19.4 0.2 0.3 2.0% 2.0% 0.9%

900 284,406 4649 31.3 20.4 0.2 0.3 1.6% 1.6% 0.7%

950 287,362 2956 31.3 21.4 0.1 0.2 1.0% 1.0% 0.4%

1000 288,726 1364 31.3 22.4 0.0 0.1 0.5% 0.5% 0.2%
Great Lakes Avian Radar – Fall 2011 47
Fall 2011