PhD Dissertation - Final-full color

LANDSCAPE ECOLOGY OF THE RED-TAILED HAWK:
WITH APPLICATIONS FOR LAND-USE PLANNING AND EDUCATION
by
William E. Stout
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
(Land Resources)
at the
UNIVERSITY OF WISCONSIN-MADISON
2004

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For the Birds and Other Wildlife Around Us,
That They May Continue to Enrich Our Lives.

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LANDSCAPE ECOLOGY OF THE RED-TAILED HAWK:
WITH APPLICATIONS FOR LAND-USE PLANNING AND EDUCATION
Abstract
I used a multi-scale approach to describe land-cover patterns surrounding focal
points (Red-tailed Hawk nests), and to determine which scale or scales are most appropriate
to describe habitat for the species. Based on variations in land-cover composition
surrounding Red-tailed Hawk nests, one to three scales (a 100m-radius circular plot: nest
area; a 250m-radius circular plot: macrohabitat; and a 1000m-radius circular plot:
landscape) adequately describe landscape-scale habitat features.
Red-tailed Hawk reproductive success for this 14-yr study averaged 80.1% nest
success and 1.36 young per active nest. Productivity for 1994 was significantly greater than
other years. Red-tailed Hawk productivity, an index of habitat quality, varied with habitat
composition surrounding nest sites. Wetland area was significantly greater for low
productivity sites, indicating that wetlands are not beneficial for Red-tailed Hawk
productivity. The area of roads and high-density urban habitat were greater for high
productivity sites, and the landscape consisted of smaller habitat patches, indicating that
urban/suburban locations provide high-quality habitat for Red-tailed Hawks. Higher
productivity in high-density urban areas suggests that urban Red-tailed Hawk populations
may be source, not sink, populations. Increased nesting on human-made structures in urban
locations and enhanced reproductive success for these nests reinforce this hypothesis, and
suggest that Red-tailed Hawks are adapting to urban environments.
The Red-tailed Hawk population in southeast Wisconsin is increasing in density and
expanding its range into developed areas as it adapts to the urban environment. It doesn’t

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appear that the population is approaching limits within the urban study area at this time.
While productivity did not vary significantly with density for this study, the predicted trend
(i.e., reduced productivity at higher densities) exists. Detecting density-dependence may be
difficult because of wide annual variations due to density-independent factors such as
weather. While space, and nest site and prey availability may ultimately be the major
limiting factors for this population, my study suggests that their effects are not yet
detectable in this urban environment.
Suitable Red-tailed Hawk habitat in urban/suburban Milwaukee includes a
significant amount of grassland and other herbaceous cover types (e.g., freeways and
freeway intersections, parks, golf courses, cemeteries). With Red-tailed Hawks nesting on
and hunting from human-made structures in urban areas, the amount of woodland area may
be less important in urban than rural locations. Hunting habitat and wetlands are
consistently present in urban, suburban and rural habitat within 100m of nests, and
therefore, may constitute important habitat components. Consistent Red-tailed Hawk
habitat components (i.e., hunting habitat and wetlands) and nesting habitat (i.e., woodlands)
can be used to measure performance of land-use planning models.

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ACKNOWLEDGMENTS
Stanley Temple (Beers-Bascom Professor in Conservation, Professor of Wildlife
Ecology and Professor of Environmental Studies, University of Wisconsin - Madison), my
graduate advisor, provided continual support and direction for this project. His guidance
and recommendations along the way provided the framework for quality research in all
aspects: design, analysis and final presentations (e.g., this dissertation). I greatly appreciate
his accepting me as a graduate student.
I greatly appreciate the expertise and time given by my graduate committee
members Scott Craven (Chair, Department of Wildlife Ecology, Extension Wildlife
Specialist and Professor of Wildlife Ecology, University of Wisconsin - Madison), Nancy
Mathews (Associate Professor of Wildlife Ecology and Environmental Studies, University
of Wisconsin - Madison), Lisa Naughton (Assistant Professor of Geography, University of
Wisconsin - Madison) and James Stewart (Professor of Education, University of Wisconsin
- Madison). Certainly, any time that they spent with me and my research project was time
that they could have spent working on their own projects. Nancy Mathews offered
numerous additional and constructive suggestions regarding landscape analyses, and Jim
Stewart provided editorial assistance on the educational unit. John Cary (Senior
Information Processing Consultant, Department of Wildlife Ecology, University of
Wisconsin - Madison) provided invaluable assistance with statistical analyses and
modeling.
Numerous individuals provided assistance with fieldwork and the logistics of my
research for a project that has run for over 15 years. In a very special way, I thank Joe
Papp, wildlife field biologist, friend and colleague, for his continued help with fieldwork

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for over 15 years, and for our thought provoking discussions along the way. Sergej
Postupalsky has graciously allowed me to work as a subpermittee under his master banding
permit issued through the U.S. Geological Survey, Bird Banding Laboratory. Several other
individuals, notably Bill Holton and Diane Visty Hebbert, have given countless hours, days
and months over several years of this study to help with the fieldwork. I also greatly
appreciate the cooperation of the many landowners that have graciously allowed access to
their private lands, in my mind, the ultimate treasure: where Red-tailed Hawks soar, hunt
and nest.
This research has been supported in part by a grant from the U.S. Environmental
Protection Agency (EPA). The grant was a part of EPA’s National Center for
Environmental Research and their Science to Achieve Results (STAR) Graduate Fellowship
Program. Although the research described in this dissertation has been funded in part by
the EPA's STAR program through grant U915758, it has not been subjected to any EPA
review and therefore does not necessarily reflect the views of the Agency, and no official
endorsement should be inferred.
The Zoological Society of Milwaukee provided partial funding through the Wildlife
Conservation Grants for Graduate Student Research program. This funding was secured
with the assistance and collaboration of the Wisconsin Society for Ornithology (WSO). In a
very special way, I thank the deceased Alex Kailing, past WSO Treasurer and new, lost
friend, for all his help with grant writing and application processing for this project and
others.
My Wife, Vicki, daughter, Jennifer, and sons, Tim and Matt provided continual
support, patience and assistance in all areas of this project. I sincerely apologize to my

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family for being unavailable for Christmas and other family gatherings throughout this
research project, most notably, for the 2003 holiday season; I was writing this dissertation.

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TABLE OF CONTENTS
DEDICATION......................................................................................................................... i
ABSTRACT............................................................................................................................ ii
ACKNOWLEDGMENTS ..................................................................................................... iv
LIST OF TABLES............................................................................................................... xiii
LIST OF FIGURES ...............................................................................................................xv
LIST OF APPENDICES..................................................................................................... xvii
GENERAL INTRODUCTION................................................................................................1
CHAPTER
I. WHAT IS THE APPROPRIATE SCALE FOR DESCRIBING
HABITAT OF RED-TAILED HAWKS?..............................................................2
Introduction......................................................................................................2
Methods............................................................................................................3
Study Area ...........................................................................................3
Nest Surveys ........................................................................................4
GIS.......................................................................................................4
Statistical Analyses..............................................................................6
Results/Discussion...........................................................................................6
Conclusion .....................................................................................................10
Acknowledgements........................................................................................11
Literature Cited..............................................................................................11

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II. LANDSCAPE CORRELATES OF REPRODUCTIVE SUCCESS
FOR AN URBAN/SUBURBAN RED-TAILED HAWK
POPULATION. ...................................................................................................23
Introduction....................................................................................................23
Methods..........................................................................................................24
Study Area .........................................................................................24
Nest Surveys ......................................................................................25
Breeding Areas...................................................................................25
Productivity Comparisons and GIS ...................................................27
Statistical Analyses............................................................................28
Results............................................................................................................29
Reproductive Success ........................................................................29
High and Low Productivity................................................................29
Discriminant Function Analysis ........................................................30
Human-Made Nest Structures............................................................31
Discussion......................................................................................................31
Reproductive Success ........................................................................31
High and Low Productivity, and Habitat Quality..............................32
Discriminant Function Analysis ........................................................34
Human-Made Nest Structures............................................................34
Conclusion .....................................................................................................35

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III. DYNAMICS OF A RED-TAILED HAWK POPULATION IN
AN URBAN ENVIRONMENT. .......................................................................49
Introduction....................................................................................................49
Methods..........................................................................................................50
Study Area .........................................................................................50
Population Surveys ............................................................................51
GIS.....................................................................................................52
Density Correlations and Dispersion Patterns ...................................52
Habitat Expansion..............................................................................53
Results............................................................................................................54
Density...............................................................................................54
Density and Productivity....................................................................55
Density, Percentage of Sites Active and Breeding
Area Re-Use...........................................................................55
Dispersion Patterns ............................................................................56
Discussion......................................................................................................56
Population Density.............................................................................56
Population Growth.............................................................................57
Density and Productivity....................................................................58
Future Densities .................................................................................59

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Density, Percentage of Sites Active and Breeding
Area Re-Use...........................................................................60
Dispersion Patterns ............................................................................61
Conclusion .....................................................................................................63
IV. HOW LANDSCAPE FEATURES AFFECT RED-TAILED
HAWK HABITAT SELECTION......................................................................81
Introduction....................................................................................................81
Methods..........................................................................................................82
Study Area .........................................................................................82
Nest Surveys ......................................................................................82
Urban/suburban Habitat and GIS.......................................................83
Habitat Model and Hexagon Predictions...........................................84
Results............................................................................................................85
Urban/suburban Habitat.....................................................................85
Habitat: Use and Non-Use Comparisons...........................................85
Habitat Model and Predictions...........................................................86
Discussion......................................................................................................86
Urban/suburban Habitat.....................................................................86
Habitat: Use and Non-Use Comparisons...........................................87

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Habitat Model and Predictions...........................................................88
Conclusion .....................................................................................................88
V. CONSISTENT FEATURES OF RED-TAILED HAWK
HABITAT ACROSS RURAL, SUBURBAN AND URBAN
LANDSCAPES....................................................................................................98
Introduction....................................................................................................98
Methods..........................................................................................................99
Study Area .........................................................................................99
Nest Surveys ......................................................................................99
Urban, Suburban and Rural Comparisons, and GIS ........................100
Statistical Analyses..........................................................................102
Results..........................................................................................................102
Discussion....................................................................................................103
Urban, Suburban and Rural Comparisons .......................................103
An Application for Land-Use Planning...........................................105
Conclusion ...................................................................................................107
Acknowledgements......................................................................................107
Literature Cited............................................................................................108
VI. WHERE IN THE CITY ARE RED-TAILED HAWKS? THE
CONCEPTUAL BASIS FOR A GIS EDUCATION UNIT............................119
Introduction..................................................................................................119

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The GIS Education Unit...............................................................................121
National Science Education Standards ............................................124
Wisconsin Model Academic Standards ...........................................125
ArcView GIS Instructions................................................................126
Acknowledgements......................................................................................133
Literature Cited............................................................................................133

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LIST OF TABLES
CHAPTER I
Table 1. Area frequencies for each of the 12 land-cover classes within the
indicated concentric buffers (50m- to 2000m-radius). ..........................................15
Table 2. Perimeter frequencies for each of the 12 land-cover classes within
the indicated concentric buffers (50m- to 2000m-radius)......................................16
Table 3. Patch count frequencies for each of the 12 land-cover classes within
the indicated concentric buffers (50m- to 2000m-radius)......................................17
CHAPTER II
Table 1. Red-tailed Hawk reproductive success over a 14-year period, 1989
through 2002..........................................................................................................40
Table 2. Matrix of pairwise comparisons using the Tukey Multiple
Comparisons Test...................................................................................................41
Table 3. Comparison of habitat surrounding high productivity Red-tailed
Hawk breeding areas (N=24) and low productivity breeding areas
(N=24). Values for area and perimeter are ha and m, respectively. .....................42
Table 4. Summary of stepwise discriminant function analysis for high
productivity and low productivity breeding areas. ................................................44
Table 5. Classification results for the stepwise discriminant function analysis. ..................45
CHAPTER III
Table 1. Red-tailed Hawk population density (minimum estimates) for
occupied sites and active sites in the MMSA and two townships
within this area from 1988 to 2002........................................................................70
Table 2. Dispersion patterns (uniform, random or clumped) for active Red-
tailed Hawk nest sites in the MMSA and two townships within this
area from 1988 to 2002..........................................................................................71
Table 3. Comparison of Red-tailed Hawk habitat cover types for three 5-yr
periods. MPS (Mean Patch Size), PSSD (Patch Size Standard
Deviation), Minimum and Maximum values are in hectare. .................................72

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CHAPTER IV
Table 1. Red-tailed Hawk use areas were compared to non-use areas at the
landscape scale (1000-m radius). Land-cover type area (ha),
perimeter (m), patch counts and FRAGSTAT metrics are reported......................93
CHAPTER V
Table 1. Comparison of Red-tailed Hawk habitat for urban, suburban and
rural locations at the landscape scale (1000m-radius buffer). Values
are for percent area...............................................................................................111
rural locations at the macrohabitat scale (250m-radius buffer).
Values are for percent area. .................................................................................112
rural locations at the nest area scale (100m-radius buffer). Values
are for percent area...............................................................................................113

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LIST OF FIGURES
CHAPTER I
Figure 1. Southeast Wisconsin Study Area...........................................................................18
Figure 2. Southeast Wisconsin Study Area. The Southeast Wisconsin
Regional Planning Commission (SEWRPC) data set was combined
into the above 12 land-cover classes......................................................................19
Figure 3. Land cover area (%) for 12 classes at varying scales surrounding
Red-tailed Hawk nest sites.....................................................................................20
Figure 4. Land cover perimeter (%) for 12 classes at varying scales
surrounding Red-tailed Hawk nest sites. ...............................................................21
Figure 5. Land cover patch count (%) for 12 classes at varying scales
surrounding Red-tailed Hawk nest sites. ...............................................................22
CHAPTER II
Figure 1. Southeast Wisconsin Study Area showing active (i.e., eggs laid)
Red-tailed Hawk nests from 1989 through 2002...................................................46
Figure 2. Red-tailed Hawk productivity over a 14-year period, 1989 through
2002. ......................................................................................................................47
Figure 3. High and low productivity Red-tailed Hawk breeding areas. ...............................48
CHAPTER III
Figure 1. Metropolitan Milwaukee Study Area. ...................................................................73
Figure 2. Red-tailed Hawk population size for the MMSA..................................................74
Figure 3. Red-tailed Hawk population size for the township of Brookfield.........................75
Figure 4. Red-tailed Hawk population size for the township of Granville...........................76
Figure 5. Red-tailed Hawk breeding density and productivity. ............................................77
Figure 6. Red-tailed Hawk breeding density and percentage of sites active. .......................78
Figure 7. Red-tailed Hawk breeding density and breeding area re-use................................79

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Figure 8. Metropolitan Milwaukee Study Area: Urban Red-Tailed Hawk
habitat expansion. The maps include a slightly larger area than the
MMSA. ..................................................................................................................80
CHAPTER IV
Figure 1. Metropolitan Milwaukee Study Area: Red-tailed Hawk use and non-
use areas.................................................................................................................95
Figure 2. Land-cover composition for Red-tailed Hawk use areas and non-use
areas. ......................................................................................................................96
Figure 3. Predictions of the Red-tailed Hawk habitat model................................................97
CHAPTER V
Figure 1. Southeast Wisconsin Study Area (SWSA). The Southeast
Wisconsin Regional Planning Commission (SEWRPC) data set was
combined into the above 12 land-cover classes...................................................114
Figure 2. Landscape-scale buffers (1000-m radius) around urban, suburban
and rural nests in the Southeast Wisconsin Study Area.......................................115
Figure 3. Landscape (1000m buffer area) composition (%) around urban,
suburban and rural Red-tailed Hawk nests in the Southeast
Wisconsin Study Area. ........................................................................................116
Figure 4. Macrohabitat (250m buffer area) composition (%) around urban,
Figure 5. Nest area (100m buffer area) composition (%) around urban,
CHAPTER VI
Figure 1. Map of Red-tailed Hawk Habitat for Milwaukee County. ..................................136

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LIST OF APPENDICES
Appendix A. Southeast Wisconsin Regional Planning Commission
(SEWRPC) 1995 Land-use (Land-cover) Codes and
Descriptions and the corresponding land-cover classes for this
project (and the legend color used for project maps and
graphs)..........................................................................................................137
Appendix B. Post hoc test for 10 Buffer Scales, Tukey Multiple
Comparisons - Matrix of pairwise comparison probabilities for
each land-cover type. One-way ANOVA indicated that each
land-cover type (area and perimeter frequencies) is
significantly different over the 10 buffer scales (P<0.001 for
each case).....................................................................................................143
Appendix C. FRAGSTATS Metrics (FRAGSTATS for ArcView, version
1.0) were used to compare habitat of high productivity Red-
tailed Hawk breeding areas to low productivity breeding areas
(Chapter 2), and Red-tailed Hawk use areas to non-use areas
(Chapter 4). FRAGSTATS for ArcView was used to calculate
landscape-scale metrics................................................................................155
Appendix D. Definition, Description and Calculations of CLASS and
LANDSCAPE Metrics, FRAGSTATS Metrics (FRAGSTATS
for ArcView, version 1.0)............................................................................156

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General Introduction
The wildlife around us continually enrich our lives. My initial exposure to and
fascination with wildlife began as a child as I was raised on our family dairy farm in
Germantown, and included running a trap-line with my brothers and sister each fall. The
experience of releasing a badger from a fox set is certainly an unforgettable one, and
remains a vivid memory. My interest in wildlife continued through young adulthood, and
has led to my passion for and obsession with wildlife research.
In 1987, I started my research on Red-tailed Hawks in the metropolitan Milwaukee
area because the population appeared to be increasing in urban locations. My initial
question was, “are Red-tailed Hawks adapting to the urban environment, occupying suitable
habitat in urban locations that resembles habitat in rural areas, or both?” To accurately
answer this question, I needed to carefully describe the habitat that Red-tailed Hawks were
using. This study quickly became a part of my obsession. Finally, after more than 15 years
of fieldwork, analyzing habitat in multiple ways (e.g., at the nest site, habitat surrounding
the nest site, nest area, macrohabitat and landscape), documenting nest locations and
productivity, and comparing habitat quality based on productivity, I can finally answer a
part of my original question satisfactorily. With 15 years of data, obviously now a long-
term study, I am able to address additional important questions related to Red-tailed Hawk
population dynamics, density and density-dependence. While many questions are not
addressed, answers are within reach through this 15-year data set. This dissertation
provides a good foundation on which additional research questions can be addressed.

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WHAT IS THE APPROPRIATE SCALE FOR DESCRIBING
HABITAT OF RED-TAILED HAWKS?
Introduction
Habitat has been described at a wide range of scales for different taxa (Wood and
Pullin 2002, Steffan-Dewenter et al. 2002, Mladenoff et al. 1995). Many studies have used
a multi-scale approach to either describe landscape features that characterize habitat
(Griffith et al. 2000, Orth and Kennedy 2001), or explore how species respond to
heterogeneity in the habitats they occupy (Swindle et al. 1999, Kie et al. 2002). Many
recent attempts to standardize raptor habitat descriptions have focused on either 1.0-km or
1.5-km radius circular plots around nest sites or other focal points (B.R. Noon, M.R. Fuller
and J.A. Mosher, unpublished manuscript). Nonetheless, habitats of raptor species have
been described at various landscape scales because of the complex relationships these wide-
ranging predators have with landscape features (Dykstra et al. 2001, Orth and Kennedy
2001). For Red-tailed Hawks (Buteo jamaicensis), the species used for this study, habitat
has been described at several landscape scales ranging from 20ha to 707ha (Howell et al.
1978, Stout et al. 1998).
Although many studies have described habitat at various scales (e.g., Swindle et al.
1999, Fuhlendorf et al. 2002), few have attempted to determine which scales are most
appropriate. Holland et al. (2004) recently developed a method of determining the spatial
scale in which a species responds to habitat. This method may be validated as it is applied
to a wide range of different species. Selection of an appropriate scale is critical, and it
depends on the research question and the taxonomic group or landscape features of interest
(Mitchell et al. 2001, Turner et al. 2001, Mayer and Cameron 2003). Geographic

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Information Systems (GIS) can help researchers select the appropriate scale for describing
landscape features and comparing landscape features at different scales.
I studied a Red-tailed Hawk population in southeast Wisconsin over a 15-yr period.
My objective was to compare the composition of land-cover types at varying scales around
Red-tailed Hawk nests and to determine the appropriate scale (i.e., spatial extent) for
describing Red-tailed Hawk habitat. I used a multi-scale approach with ten concentric
buffer rings to describe land-cover surrounding Red-tailed Hawk nests. This method of
determining appropriate scale can be applied to other species for which habitat can be
described in circular plots centered on a focal point (e.g., den, nest or perch site).
Methods
Study Area
The study area covers approximately 1600 km2
in the metropolitan Milwaukee area
of southeast Wisconsin (43 N, 88 W), and includes Milwaukee County and parts of
Waukesha, Washington and Ozaukee Counties (Figure 1). Milwaukee and Ozaukee
Counties are bordered by Lake Michigan to the east. Milwaukee County covers an area of
626.5 km2
. Human population density in urban locations (i.e., the city of Milwaukee)
within the study area averages 2399.5/km2
; the city of Milwaukee covers an area of 251.0
km2
with a human population of 596,974 (United States Census Bureau 2000). Landscape
composition ranges from high-density urban use to suburban communities and rural areas.
Population density and human land-use intensity decrease radially from urban to rural.
Two interstate highways (Interstate 43 and Interstate 94) transect the study area. Land
cover within the study area includes agricultural, natural, industrial/commercial, and
residential areas.

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Curtis (1959) described vegetation, physiography and soil for the study area.
Remnants of historical vegetation that are marginally impacted by development are sparsely
scattered throughout the study area. The size and abundance of these remnants increase
from urban to rural locations (Matthiae and Stearns 1981).
Nest Surveys
Red-tailed Hawk nests were located annually from a vehicle (Craighead and
Craighead 1956) between 1 February and 30 April and visited at least twice (once at an
early stage of incubation within 10 d of clutch initiation, and again near fledging) during
each nesting season to determine Red-tailed Hawk reproductive success (Postupalsky
1974). Woodlots within an intensive study area that were not entirely visible from the road
early in the season before leaf-out were checked by foot.
GIS
For the purposes of analyzing land-cover at varying scales surrounding nest sites, I
used Red-tailed Hawk nest locations for 1988 through 2002. For land-cover, I used the
Southeast Wisconsin Regional Planning Commission’s (SEWRPC) 1995 land-cover data
set (SEWRPC 1995). Every five years SEWRPC flies aerial surveys and documents land-
cover through aerial photography. These aerial photos are produced at a 1:4800 scale, and
are digitized into ortho photos as well as a vector GIS land-cover database. The grain of
these ortho photos is less than 0.3m. I used the 1995 SEWRPC data set because it
represents land-cover from approximately the mid-point of the study time frame. SEWRPC
classifies land-cover into 104 different categories (see Appendix A). For the purposes of
this study, I combined the 104 different SEWRPC categories into the following 12 land-
cover classes: urban (high-density), urban (low-density), roads, parking, recreational,

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graded, cropland, pasture, grassland, woodland, wetland and water (Figure 2). Appendix A
lists each SEWRPC land-cover code and description, the corresponding land-cover class
that I assigned it, and a legend color used in the land-cover map (Figure 2) and graphs
(Figures 3-5). The SEWRPC data set may contain biases because the regional planning
commission is probably more concerned with urban land-cover and its distribution within
cities and suburbs. From an aerial view, a row of houses in one part of a city block looks
the same as another row of adjacent houses within the same city block. However, they are
classified as two different high-density residential patches. Conversely, two adjacent
agricultural fields in a rural area are separated by a distinct hedgerow, yet they are classified
as a single patch. To minimize these potential biases, I merged all adjacent land-cover
patches for each class. ArcView GIS version 3.3 (ESRI 2002) was used for GIS procedures
and analyses.
I used a multi-scale approach (ten concentric buffer rings) to describe and analyze
land-cover patterns surrounding Red-tailed Hawk nest sites. Nest site locations were
mapped in a GIS (Figure 1). I use sites that were at least 2km from the perimeter of the
four-county area to allow for a complete coverage within the SEWRPC land-cover data set
and subsequent analysis. For 1988 through 2000, locations were digitized “on the fly” in a
GIS from knowledge of the actual locations and with the SEWRPC ortho photos and land-
cover data set displayed. For 2001 and 2002, real-time Global Positioning System (GPS)
locations with accuracy of one to three meters were logged using a Trimble GeoExplorer3
and differentially corrected for greater accuracy. These locations were used to verify the
accuracy of 1988-2000 locations. Eight 250m-radius concentric rings were used to buffer
nest sites within a 2000m-radius (250m- to 2000m-radius areas). Two additional areas

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(50m- and 100m-radius areas) were used for information at smaller scales closer to each
nest site. The boundaries between the buffers were dissolved to maintain independence
(i.e., each land-cover patch is only included once), and the SEWRPC land-cover data were
clipped to fit each buffer. The area, perimeter and patch count for each of the 12 land-cover
classes were determined for each buffer area through GIS procedures. These values were
converted to frequencies (and percentages) for a comparison of the different buffer scales.
Statistical Analyses
A One-way Analysis of Variance (ANOVA) was used to determine whether the area
and perimeter frequencies for each land-cover class were different across buffer scales. For
land-cover area and perimeter frequencies that were different, a post hoc test (Tukey
Multiple Comparisons test) was used to determine which adjacent buffer frequencies were
different.
Results/Discussion
Area, perimeter and patch count frequencies for each of the 12 land-cover classes
within the varying size buffers (50m- to 2000m-radius) are listed in Tables 1-3.
Frequencies were converted to percentages and plotted against the buffer distance from nest
sites (Figures 3 through 5). For each land-cover class, “percent area” is the amount of each
class in relation to the total area for all classes within the buffer area expressed as a percent
(Figure 3). For land-cover area, the percent coverage for each class varies greatly close to
the nest site (e.g., percentages were very different between the 50m- and 100m-radius
buffer areas), and differences decrease as the buffer area increases (e.g., the smallest
differences were between the 1750m- and 2000m-radius buffer areas). The amount of
woodlands and wetlands were the only two classes that increase rapidly at smaller scales,

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and therefore composed a greater percentage area surrounding the nest. For all other land-
cover classes, the percent composition decreases rapidly at smaller scales. The percent
coverage for three classes, cropland, pasture and grasslands, increases slightly between
250m and 1000m from the nest.
“Percent perimeter” describes the amount of perimeter for each land-cover class in
relation to the total combined perimeters for all classes within the buffer area expressed as a
percentage (Figure 4). The percent perimeter for woodlands and wetlands increases rapidly
at smaller scales around the nest. The percent perimeter for cropland and pasture increases
to 100m then decreases rapidly 50m from nests; grassland percent perimeter increases to
250m then decreases rapidly. These data generally are consistent with the slight rise in
percent area surrounding the nest sites for these three classes. The percent perimeter for
other land-cover classes (high-density urban, low-density urban, roads, parking,
recreational, graded and water) decreases rapidly at smaller scales closer to nest sites.
“Percent patch count” is the number of patches for one land-cover class in relation
to the total number of patches for all classes within the buffer area expressed as a
percentage (Figure 5). The percent patch count for woodlands and wetlands increases
rapidly closer to nest sites, as expected relative to the increases in percent area and
perimeter. Conversely, the percent patch count for four land-cover classes (high-density
urban, low-density urban, parking and graded) decreases at smaller scales closer to nest
sites. The percent patch count for grasslands, water and recreational land remains relatively
constant from 2000m to 250m, peak at the 100m-radius scale, followed by a decline at the
50m-radius scale. Percent patch count for cropland and pasture increase rapidly closer to

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the nests and then appear to level off. Percent patch count for the road class increases from
the 2000m-radius scale to the 250m-radius scale, and decreases to the 50m-radius scale.
The increase in the percent composition of woodlands (area, perimeter and patch
count) within the buffer areas closer to nest sites is expected since Red-tailed Hawks
typically nest in trees associated with woodlots, at least in southeast Wisconsin. On the
other hand, an increase in the amount of wetlands surrounding nest sites is not necessarily
expected. When comparing landscape composition at Red-tailed Hawk nest sites with high
and low productivity, wetland area was the only land-cover class that was significantly
greater for low productivity sites, indicating that wetlands are not beneficial for
reproduction (Stout, 2004). However, wetlands may provide a natural type of buffer
between human activity and Red-tailed Hawk nesting activity. Because of the sensitive
nature of wetlands and a number of benefits that they provide humans, the land-use
planning process tends to preserve these areas. The slight rise in percent composition of
cropland, pasture and grasslands near nests (i.e., between 250 and 1000m of nest sites) may
be related to suitable hunting habitat in the surrounding area and within a reasonable
hunting distance of the nests (i.e., within their home range of approximately 150 to 250ha).
Based on these variations in land-cover composition at increasing distances from
nest sites, I suggest that one to three different scales should be adequate to describe
landscape-scale features and to address most research questions. When a multi-scale
approach is required for a specific research question, a preliminary analysis can plot gradual
land-cover changes as the area for analysis increases. Land cover features plotted against
varying buffer areas (i.e., different scales) can be used to determine appropriate scales for
further analysis. Based on Figures 3 through 5, one to three areas are sufficient to describe

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landscape features. For Red-tailed Hawk nest sites, a 100m-radius circular plot (3.1ha) is
an appropriate scale to describe habitat at a “nest area” scale. At this nest area scale, the
variations in landscape composition are greatest for most land-cover classes (e.g.,
approaching vertical asymptote; Figures 3-5). A 250m-radius circular plot (19.6ha) is
appropriate to describe habitat at a “macrohabitat” scale because the variations in
composition for most land-cover classes are shifting at this point (e.g., closest to the
hyperbolic focus). A 1000m-radius circular plot (314.2ha) is appropriate to describe habitat
at a “landscape” scale because the variations in composition for most land-cover classes are
smallest at this point (e.g., approaching horizontal asymptote). These areas can be used in
conjunction with nest site (nest height, tree species, etc.) and habitat (vegetative cover
surrounding the nest, frequently an 11.3m-radius circular plot) data collected at the nest.
Holland et al. (2004) recently presented a method to determine the scale in which species’
respond to habitat. This method may be validated as it is applied to a wide range of
different taxa. However, this paper presents a similar, additional method to determine the
appropriate scale or scales for landscape analysis of habitat. This multi-scale approach used
as a preliminary analysis can identify the important scales or extents for any focal point
(e.g., den, nest or perch site) associated with any taxonomic group. This method can aid in
determining which scale or scales will be useful in addressing the research problem.
Each land-cover class was significantly different for both area and perimeter
frequencies across the ten buffer scales (One-way ANOVA: P<0.001 for every case). For
pairwise comparisons (Tukey Multiple Comparisons test, Appendix B), at smaller buffer
scales around nests (i.e., 50m, 100m, 250m), frequencies for both area and perimeter were
usually significantly different. Infrequently (i.e., 4 out of 72 pairwise comparisons), area

10
frequencies were not significantly different. Consistently for area and perimeter of each
land-cover class, a buffer scale was reached in which all subsequent adjacent frequencies
were not significantly different (Tables 1 and 2). I used this characteristic of adjacent
frequencies to aid in determining an appropriate scale for landscape analysis. The 1000-m
buffer consistently accounts for differences in area and perimeter frequencies, and therefore
is an appropriate scale for Red-tailed Hawk habitat analyses.
Land cover area, perimeter and patch count all indicate that a 1000m-radius area
(314.2ha) surrounding Red-tailed Hawk nest sites is an appropriate scale for landscape
analysis of habitat. While variations and fluctuations exist at smaller scales, land-cover
area, perimeter and patch count metrics (i.e., percent composition) generally level off
1000m from the nest site. Analysis of area and perimeter frequencies for differences across
the varying buffer scales supports this conclusion. I will use this scale (1000m-radius area)
for subsequent Red-tailed Hawk habitat descriptions and comparisons (e.g., nesting habitat
and non-use areas, high and low productivity habitat).
Conclusion
A detailed description of a species’ habitat can help explain relationships between
the species and its environment, and it can be used for management and conservation
purposes. Using the appropriate scale or scales to describe habitat is critical. I used a
multi-scale approach (ten concentric buffer rings) to describe land-cover patterns
surrounding focal points (Red-tailed Hawk nests), and to determine which scale or scales
are most appropriate to describe the habitat for the species.
Based on the variations in land-cover composition at increasing distances from Red-
tailed Hawk nest sites, one to three different scales should be adequate to describe

11
landscape-scale features and to address most research questions. For Red-tailed Hawks, a
100m-radius circular plot is an appropriate scale to describe the nest area, a 250m-radius
circular plot is appropriate for macrohabitat, and a 1000m-radius circular plot is appropriate
for landscape.
This multi-scale approach can be used to determine the most appropriate scale or
scales for describing the habitat associated with any taxonomic group at any focal point
(e.g., den, nest or perch site).
Acknowledgements
I thank S.A. Temple, S.R. Craven, N.E. Mathews, L. Naughton and J.H. Stewart for
providing valuable comments that greatly improved this manuscript. J.R. Cary provided
technical assistance. J.M. Papp and W. Holton provided field assistance. This research has
been supported in part by a grant from the U.S. Environmental Protection Agency's Science
to Achieve Results (STAR) program. Although the research described in this article has
been funded in part by the U.S. Environmental Protection Agency's STAR program through
grant U915758, it has not been subjected to any EPA review and therefore does not
necessarily reflect the views of the Agency, and no official endorsement should be inferred.
Conservation Grants for Graduate Student Research program. My family provided
continual support, patience and assistance in all areas of this project.
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15
Table1.Areafrequenciesforeachofthe12land-coverclasseswithintheindicatedconcentricbuffers(50m-to2000m-
radius).
LandCoverClass50m100m250m500m750m1000m1250m1500m1750m2000m
Urban(highdensity)0.0180.0250.0500.0750.0890.1000.1080.115
a
0.120
ab
0.123
b
Urban(lowdensity)0.0290.0410.0680.1020.1220.1340.1380.136
a
0.134
ab
0.132
b
Roads0.027
a
0.048
a
0.0770.092
b
0.095
bc
0.097
bcd
0.097
cd
0.096
cd
0.095
d
0.095
d
Parking0.0090.0110.0190.0240.0250.026
a
0.026
ab
0.025
bc
0.025
c
0.024
c
Recreational0.0120.015
a
0.023
a
0.022
b
0.021
bc
0.023
cd
0.025
cd
0.025
cd
0.025
d
0.025
d
Graded0.0040.0060.0100.013
a
0.016
ab
0.017
bc
0.017
bc
0.017
bc
0.016
c
0.016
c
Cropland0.051
a
0.070
a
0.0980.1040.1000.095
b
0.093
bc
0.092
bc
0.090
c
0.089
c
Pasture0.112
a
0.157
a
0.2150.2230.220
b
0.215
bc
0.213
cd
0.213
cd
0.214
d
0.214
d
Grassland0.0740.0980.1230.1320.1330.127
a
0.121
ab
0.118
bc
0.115
bc
0.112
c
Woodland0.2860.1990.0900.0520.043
a
0.042
ab
0.042
ab
0.044
ab
0.045
b
0.046
b
Wetland0.3720.3240.2210.1540.127
a
0.114
ab
0.108
bc
0.105
bc
0.103
bc
0.102
c
Water0.0050.0070.007
a
0.008
ab
0.009
b
0.010
b
0.012
b
0.014
b
0.017
b
0.021
b
a-d
ValueswiththesamesuperscriptarenotstatisticallydifferentattheP≤0.05level(TukeyMultipleComparisonstest).
15

16
Table2.Perimeterfrequenciesforeachofthe12land-coverclasseswithintheindicatedconcentricbuffers(50m-to
2000m-radius).
Urban(highdensity)0.0300.0400.0770.1020.1190.1290.1380.1450.1500.155
Urban(lowdensity)0.0430.0620.0980.1280.1400.1440.1430.1390.1360.133
Roads0.0530.0870.1610.211
a
0.232
ab
0.245
ab
0.252
abc
0.255
bc
0.257
bc
0.260
c
Parking0.0150.0210.0400.0510.0540.0560.0560.056
a
0.055
ab
0.054
b
Recreational0.0120.0170.0180.0160.014
a
0.015
ab
0.016
bc
0.016
bc
0.016
bc
0.016
c
Graded0.0050.0080.0110.0140.0140.014
a
0.013
ab
0.013
bc
0.013
bc
0.013
c
Cropland0.0680.0740.0730.0630.0570.053
a
0.050
ab
0.049
bc
0.048
bc
0.047
c
Pasture0.1390.1560.1360.1110.1000.093
a
0.089
ab
0.087
bc
0.086
bc
0.085
c
Grassland0.0950.1220.1370.1360.1290.1220.117
a
0.114
ab
0.112
bc
0.110
c
Woodland0.2320.1600.0810.0500.0420.040
a
0.040
ab
0.041
ab
0.042
b
0.043
b
Wetland0.2950.2330.1490.1020.0850.076
a
0.072
ab
0.069
bc
0.068
bc
0.067
c
Water0.0110.0200.0190.0160.015
a
0.014
ab
0.014
b
0.015
b
0.015
b
0.016
b
a-c
ValueswiththesamesuperscriptarenotstatisticallydifferentattheP≤0.05level(TukeyMultipleComparisonstest).
16

17
Table3.Patchcountfrequenciesforeachofthe12land-coverclasseswithintheindicatedconcentricbuffers(50m-to
2000m-radius).
Urban(highdensity)0.0450.0600.1290.1730.2050.2220.2370.2470.2540.260
Urban(lowdensity)0.0640.0980.1570.1950.2070.2090.2040.1990.1960.194
Roads0.0750.1050.1280.0980.0730.0590.0530.0480.0450.043
Parking0.0280.0380.0770.1050.1190.1270.1320.1360.1380.139
Recreational0.0130.0190.0140.0130.0110.0120.0130.0130.0130.013
Graded0.0060.0140.0220.0310.0350.0370.0360.0360.0360.037
Cropland0.0700.0690.0550.0410.0370.0350.0330.0320.0310.030
Pasture0.1470.1370.0860.0640.0550.0490.0450.0440.0440.042
Grassland0.1190.1450.1420.1360.1280.1270.1280.1250.1240.122
Woodland0.1780.1180.0680.0500.0470.0440.0440.0450.0450.046
Wetland0.2380.1710.1010.0720.0610.0570.0550.0540.0530.052
Water0.0150.0270.0220.0230.0220.0220.0210.0220.0210.021
17

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Lake
Michigan
Milwaukee Co.
Ozaukee Co.
Waukesha Co.
Washington Co.
10 0 10 20 Kilometers
Red-tailed Hawk Nests#S N
Wisconsin
Southeast Wisconsin
Study Area
Figure 1. Southeast Wisconsin Study Area.

19
Milwaukee Co.
Ozaukee Co.
Washington Co.
Waukesha Co.
Lake
Michigan
N
Southeast Wisconsin
Study Area
Urban (high density)
Urban (low density)
Roads
Parking
Recreational
Graded
Cropland
Pasture
Grassland
Woodland
Wetland
Water
Land Cover Classes
Figure 2. Southeast Wisconsin Study Area. The Southeast Wisconsin
Regional Planning Commission (SEWRPC) data set was combined
into the above 12 land-cover classes.

20
LandCoverArea(%)
0%
5%
10%
15%
20%
25%
30%
35%
40%
0250500750100012501500175020002250
BufferRadius(m,distancefromnestsite)
Percentage
Urban(highdensity)
Urban(lowdensity)
Roads
Parking
Recreational
Graded
Cropland
Pasture
Grassland
Woodland
Wetland
Water
Figure3.Landcoverarea(%)for12classesatvaryingscalessurroundingRed-tailedHawknestsites.
20

21
LandCoverPerimeter(%)
0%
5%
10%
15%
20%
25%
30%
35%
0250500750100012501500175020002250
Percentage
Urban(highdensity)
Urban(lowdensity)
Roads
Parking
Recreational
Graded
Cropland
Pasture
Grassland
Woodland
Wetland
Water
Figure4.Landcoverperimeter(%)for12classesatvaryingscalessurroundingRed-tailedHawknestsites.
21

22
LandCoverPatchCount(%)
0%
5%
10%
15%
20%
25%
30%
0250500750100012501500175020002250
Percentage
Urban(highdensity)
Urban(lowdensity)
Roads
Parking
Recreational
Graded
Cropland
Pasture
Grassland
Woodland
Wetland
Water
Figure5.Landcoverpatchcount(%)for12classesatvaryingscalessurroundingRed-tailedHawknestsites.
22

23
LANDSCAPE CORRELATES OF REPRODUCTIVE SUCCESS FOR AN
URBAN/SUBURBAN RED-TAILED HAWK POPULATION
Introduction
Reproductive success can be used as a measure of fitness of individuals and an
index for habitat quality. Changes in reproductive success can indicate changes in
environmental factors such as resource availability, human disturbance, competition,
weather or the presence of chemical contaminants in the environment (Preston and Beane
1993, Newton 1998). Reproductive success for Red-tailed Hawks (Buteo jamaicensis) has
been well studied throughout its range (Preston and Beane 1993). While long-term studies
have documented Red-tailed Hawk reproductive success, including several studies in rural
Wisconsin (Orians and Kuhlman 1956, Gates 1972, Petersen 1979), only a few focus on
urban or suburban populations (Minor et al. 1993, Stout et al. 1998). The paucity of
information on these expanding urban raptor populations warrants continued studies
(Cringan and Horak 1989).
Habitat selection theory predicts that individuals will prefer high-quality habitats
over low-quality habitats (Fretwell and Lucas 1970). Habitat quality can affect population
parameters such as density and reproductive success (Newton 1998). Reproductive success
can be used as an index of habitat quality and has been correlated with several
environmental factors that affect habitat quality. For Red-tailed Hawks, these factors
include availability of prey and perch sites for hunting (e.g., Janes 1984), and composition
of habitat cover (e.g., Howell et al. 1978). While studies have focused on the impacts of
these factors on the habitat quality of rural populations, they may not adequately describe
the effects on urban/suburban populations. A clearer understanding of habitat quality in

24
urban/suburban locations will provide insight into overall habitat quality for Red-tailed
Hawks across all landscape types.
I studied an urban/suburban Red-tailed Hawk population in southeast Wisconsin
over a 14-year period. The objectives of this study were to document long-term
reproductive success for this population, and to determine the characteristics of high-quality
Red-tailed Hawk habitat by comparing habitat structure and composition surrounding nests
exhibiting high and low reproductive success. I also document Red-tailed Hawks nesting
on human-made structures during this study and compare productivity of these nests to
nests built in trees.
Methods
Study Area
The study area is located in southeast Wisconsin, and includes Milwaukee County
(43 N, 88 W) and parts of Waukesha, Washington, Ozaukee and Dodge Counties (Figure
1). Milwaukee and Ozaukee Counties are bordered by Lake Michigan to the east.
Milwaukee County covers an area of 626.5 km2
. Human population density in urban
locations (i.e., the city of Milwaukee) within the study area averages 2399.5/km2
; the city of
Milwaukee covers an area of 251.0 km2
with a human population of 596,974 (United States
Census Bureau 2000). Landscape composition ranges from high-density urban use to
suburban communities and rural areas. Population density and human land-use intensity
decrease radially from urban to rural. Two interstate highways (Interstate 43 and Interstate
94) transect the study area. Land cover within the study area includes agricultural, natural,
industrial/commercial, and residential areas.

25
Nest Surveys
early stage of incubation within 10 d of clutch initiation, and again at or near fledging)
during each nesting season to determine Red-tailed Hawk reproductive success
(Postupalsky 1974). Nest locations found throughout the study area are included in
reproductive success. An active nest is a nest in which eggs were laid and constitutes a
nesting attempt (Postupalsky 1974). Productivity is based on the number of young that are
≥ 15 days old (range: 15-40d). Consistent nest searching efforts were made within a survey
area (Figure 3). Woodlots within an intensive study area that were not entirely visible from
the road early in the season before leaf-out were checked by foot. Nest substrate (i.e., tree
species or structure type) was recorded.
Breeding Areas
Red-tailed Hawk home ranges are relatively large, and nests that are used in
different years by a mated pair can be widely spaced within this area. The home ranges for
adjacent pairs commonly overlap, making if difficult to determine which nest structures are
a part of which individual breeding area. A “breeding area” is an area that contains one or
more nests within the home range of a pair of mated birds (Postupalsky 1974, Steenhof
1987). I used a multi-scale approach in a Geographic Information System (GIS) to

26
determine which active nests belong within a single breeding area over the 14-yr study
(1989-2002). I used the following procedures and guidelines to determine which nests are
included within a breeding area.
1) Ten concentric buffer rings (50m- to 500m-radius buffers in 50m increments) were
used to link individual Red-tailed Hawk nests incrementally. For example, two
nests that are active in different years and within 100m of each other are linked by
the 50m-radius buffer. These two nests are more likely to be in the same breeding
area than two nests that are 500m apart (and active in different years).
2) The 350m-radius buffer area (i.e., nests that were 700m apart or less) was used as
the initial buffer to link the nest locations into “nest clusters” (i.e., nests within the
350m-radius buffer area).
3) Nests within a nest cluster that were active during the same year were separated into
different breeding areas.
4) The nest closest to the nest structure from the previous year was included in the
breeding area. In some cases, one nest cluster included two breeding areas. That is,
two mated pairs of Red-tailed Hawks consistently nested within 700m of each other
over the 14-yr period. Frequently, one nest was used in multiple years (i.e.,
appeared to be a favorite nest).
5) Nests in larger buffer areas (i.e., 400m-radius, then 450m-radius, etc.) were included
in a breeding area if it was not in a different breeding area and was active in a year
that was not already accounted for in that breeding area.
6) A breeding area was not necessarily active every year.

27
7) A minimum breeding area was calculated using the minimum convex polygon
(MCP) method. For breeding areas that included only two nest structures, I used a
1m buffer around a straight line connecting the two nests to calculate breeding area.
Breeding areas rarely overlapped and infrequently a nest structure was used by
different breeding pairs in different years.
Productivity Comparisons and GIS
Only breeding areas that were active for five or more years over the 14-yr study
period were examined for productivity. A nest site was considered to have high
productivity if it averaged ≥ 1.67 young per nesting attempt, and low productivity if it
averaged ≤ 1.00 young per nesting attempt. Nest sites with productivity between 1.00 and
1.67 were not included in the productivity comparison. These values were used to obtain
an appropriate and equal sample size without jeopardizing the validity of the productivity
comparison.
Red-tailed Hawk habitat was compared for 24 high and 24 low productivity
breeding areas within a 1000m-radius buffer area (314.2ha; Stout 2004) around the center
(arithmetic mean of nest site locations) of each breeding area (Figure 3). Overlap of the
buffer areas (i.e., two areas with high productivity, areas with high and low productivity, or
two areas with low productivity) and, therefore, pseudoreplication was allowed for this
comparison since the overlapping areas may contain important habitat components that
affect breeding area productivity.
To describe and compare Red-tailed Hawk habitat within the 1000m-radius buffer
areas, I used the Southeast Wisconsin Regional Planning Commission’s (SEWRPC) 1995
land-cover data set (SEWRPC 1995) and combined 104 different SEWRPC categories into

28
the following 12 land-cover classes: urban (high-density), urban (low-density), roads,
parking, recreational, graded, cropland, pasture, grassland, woodland, wetland and water.
See Stout (2004) for a description of the SEWRPC data set, which SEWRPC categories are
included in each of the above 12 land-cover types, and methods used to enter Red-tailed
Hawk nest locations into a GIS. ArcView GIS version 3.3 (ESRI 2002) was used for GIS
procedures and analyses. Area, perimeter and patch count (FRAGSTSTATS metrics) were
compared for each of the 12 land-cover classes (Table 3). Eighteen additional
FRAGSTATS landscape metrics (Appendix C and D) and breeding area size (MCP for
nests) were compared (Table 3). FRAGSTATS for ArcView version 1.0 (Space Imaging
2000) was used to calculate the additional 18 FRAGSTATS metrics.
For statistical analyses, parametric methods were used for comparing productivity
across years and habitat around high and low productivity nests, and non-parametric
methods were used to compare productivity of nests on human-made structures to nests in
trees. A One-way Analysis of Variance (ANOVA) was used to compare Red-tailed Hawk
productivity across years. A post hoc test (Tukey Multiple Comparisons test) was used to
identify differences in productivity between years. A two-sample t-test (Snedecor and
Cochran 1989) was used to compare habitat surrounding high and low productivity Red-
tailed Hawk breeding areas. A Mann-Whitney U test (Chi-square approximation: Sokal
and Rohlf 1981) was used to compare productivity of nests built on human-made structures
to nests in trees. Non-parametric analysis was used to compare productivity of nests on
structures to those in trees because of the disparity in sample size and small range (0-3).

29
All uni-variate tests were considered significant when P  0.05. SYSTAT (SPSS 2000)
was used for these statistical analyses.
Multi-variate analysis (stepwise discriminant function analysis) was used to
distinguish between high productivity and low productivity nest sites, and thus, to identify
variables that differentiate between high-quality and low-quality habitat. To determine
which habitat variables to include in the discriminant function analysis, a two-sample t-test
was used to identify variable means significantly different at P ≤ 0.10. A Pearson
correlation analysis was used to eliminate highly correlated variables (r ≥ 0.7). Variables
different at P ≤ 0.10 that were not highly correlated were entered into the stepwise
discriminant function analysis. Rao's V was used as the selection criteria for the stepwise
procedure. The Statistical Package for the Social Sciences (SPSS version 12.0, Nie et al.
1975, SPSS 2003) was used for the multi-variate analysis.
Results
Reproductive Success
I observed 1136 Red-tailed Hawk nesting attempts (55 to 101 nesting attempts
annually) from 1989 to 2002. Red-tailed Hawk nest success averaged 80.1%, with 1.36
young per active nest and 1.70 young per successful nest (Table 1). Productivity for active
nests (Figure 2) varied significantly over the 14-yr study (One-way ANOVA: F=2.774,
df=13, P=0.001). A Tukey Multiple Comparisons test showed that productivity for 1994
was significantly higher than all other years except 1992 (Table 2).
High and Low Productivity
Red-tailed Hawk productivity averaged 1.85 young per nesting attempt (range: 1.67-
2.40) for the 24 high productivity breeding areas compared to 0.83 young per nesting

30
attempt (range: 0.14-1.00) for low productivity breeding areas. High productivity breeding
areas were active more often and produced more total young than low productivity breeding
areas (Table 3). Four high productivity areas, active for a combined 52 years (one of which
was active for 14 consecutive years), produced a total of 87 young. Conversely, four low
productivity areas, active for a combined 42 years, only produced 28 young. Although
breeding areas with multiple nests (i.e., > 2 nests) were larger than breeding areas with two
nests, size of breeding area was not different for high and low productivity sites (Table 3).
In a comparison of habitat surrounding the 24 high and 24 low productivity Red-
tailed Hawk breeding areas (1000m-radius buffer area), six of 54 FRAGSTATS metrics for
habitat features were significantly different (Table 3). High-density urban area, perimeter
and patch count, and road area were greater for high productivity sites compared to low
productivity sites. Wetland area was less and mean patch size (FRAGSTATS metric MPS)
was smaller for high productivity sites compared to low productivity sites.
Discriminant Function Analysis
Twelve of 54 habitat variables were significantly different at P ≤ 0.10 (Table 3), and
seven of these 12 variables were not highly correlated (r ≤ 0.7). These seven variables were
entered into a stepwise discriminant function analysis. The discriminant analysis selected
two variables, road area and mean patch fractal dimension (MPFD, FRAGSTATS metric),
for inclusion in one canonical discriminant function (Table 4). Based on these two
variables, the discriminant function correctly re-classified 75.0% of 48 nest sites (Table 5).
The discriminant function was weighted slightly more on road area compared to mean
patch fractal dimension (MPFD).
Human-Made Nest Structures

31
Stout et al. (1996) documented 15 successful Red-tailed Hawk nests on five human-
made structures in five separate breeding areas in southeast Wisconsin over a 4-yr period.
For this study, Red-tailed Hawks continued to nest on these human-made structures, and
they nested on 11 additional structures. Red-tailed Hawks made 65 nesting attempts on 16
different human-made structures in 13 different breeding areas over the 15-yr study
(includes data from Stout et al. 1996). Fifty-eight (90.6%) of 64 nesting attempts were
successful, and 101 young were raised in 61 nests (1.66 young per active nest). I was
unable to determine success for one nest and productivity for four nests because access was
denied by landowners. Nest structures included six different high-voltage transmission
towers (35 nesting attempts), four billboards (15), two civil defense sirens (6), the outfield
lights of a professional baseball team ballpark (3), a building fire-escape platform (3), a 76-
m high cell phone tower (2), and a water tower (1). Productivity was significantly greater
for nests on human-made structures (mean ± SE, range: 1.66 ± 0.11, 0-3, N=61) compared
to nests built in trees (1.33 ± 0.03, 0-3, N=1074; Mann-Whitney U test: χ2
=6.725, P=0.010).
Discussion
Reproductive Success
Measures of Red-tailed Hawk reproductive success for this study are consistent with
other studies throughout North America. Nest success for this study averaged 80.1% over
the 14-yr period compared to an 83% average nest success reported by Mader (1982) for
several combined studies (typical range: 58%, Hagar 1957 to 93%, Mader 1978). For other
studies in Wisconsin, nest success averaged 73.6% (range: 63.6% to 88.9%) for Orians and
Kuhlman (1956) and 64.5% (range: 50.0% to 77.8%) for Gates (1972), each over a 3-yr
period. Productivity for this study averaged 1.36 young per active nest compared to 1.43

32
(range: 1.09 to 1.78) for Orians and Kuhlman (1956) and 1.13 (range: 0.92 to 1.44) for
Gates (1972). In a comparable urban/suburban study in central New York, Minor et al.
(1993) reported an average productivity of 1.10 young per active nest over a 10-yr period.
Red-tailed Hawk productivity varies annually with prey abundance and availability,
and weather. Furthermore, weather is correlated with the abundance of many species
commonly associated with the Red-tailed Hawk prey base (e.g., Microtus spp.).
Productivity for 1994 was significantly higher than all other years over the 14-yr period
except 1992. While weather during 1994 was unremarkable, the lack of adverse weather
conditions may have positively affected prey populations, and consequently, Red-tailed
Hawk productivity. However, in 1996 and 1997, the absence of any Red-tailed Hawk nests
with three young was probably due to inclement weather conditions. I noted unusually cold
spring seasons for both of these years, and leaf-out was unusually late. The cold spring air
temperatures for these two years were probably responsible for minimal leaf growth on
trees into mid-May. Weather records for the Milwaukee area confirm these weather
conditions (i.e., heavy snows during mid-March and record-cold spring temperatures; NWS
2003, SCO 2003).
High and Low Productivity, and Habitat Quality
Red-tailed Hawk productivity is associated with habitat quality surrounding nest
sites. Janes (1984) studied Red-tailed Hawks in Oregon and found that reproductive
success correlated with dispersion and density of perch sites used for hunting, as well as
prey availability, suggesting that prey availability is more important to reproductive success
than abundance; and therefore, an increase in prey availability improves habitat quality.
Howell et al. (1978) studied a rural population in Ohio and correlated reproductive success

33
with habitat features. Productivity was associated with the amount of fallow land, cropland
and woodlots surrounding the nest site. High productivity sites had more than twice as
much fallow land, less than half as much cropland, and less than half the number of
woodlots compared to low productivity sites. Howell et al.’s (1978) study also suggests
that hunting habitat (i.e., fallow land) may be important for habitat quality.
For this study, wetland area is the only habitat type that was significantly greater for
low productivity sites, indicating that wetlands are not beneficial for Red-tailed Hawk
reproductive success and, therefore, may provide low-quality habitat. However, wetlands
may also provide a natural buffer between human activity and Red-tailed Hawk nesting
activity. Because of the sensitive nature of wetlands and a number of benefits that they
provide, they tend to be preserved as other areas are developed.
High-density urban habitat composition (area, perimeter and patch counts) and the
area of roads were greater for high productivity sites, and the landscape consisted of smaller
habitat patches (i.e., mean patch size). This indicates that urban locations provide high-
quality habitat for Red-tailed Hawks. Higher productivity in high-density urban areas
suggests that urban Red-tailed Hawk populations may be source, not sink, populations.
Additional data on local recruitment rates are necessary to support this hypothesis (Pulliam
1988). A positive recruitment rate for this study area would indicate that the urban
population is a source population. Smaller mean patch size, a characteristic of urbanization,
for high productivity sites is further evidence that urban areas are beneficial for Red-tailed
Hawk reproduction.
Discriminant Function Analysis

34
The discriminant function analysis combined one habitat feature, road area, and one
habitat characteristic, mean patch fractal dimension, into a single discriminant function to
explain habitat quality with 75% accuracy. The importance of road area in the discriminant
function combined with the greater area of roads surrounding high productivity sites
reinforces the hypothesis that urban/suburban areas provide high-quality habitat. Roads, in
particular freeways and the grassy areas associated with them, may provide high-quality
hunting habitat. The emergence of mean patch fractal dimension as a useful habitat
characteristic provides a new aspect to high-quality habitat. High-quality habitat (i.e., high
productivity sites) has patches that are, on average, less convoluted than low-quality
habitat. A lower mean patch fractal dimension may be consistent with a smaller mean
patch size (MPS), another characteristic of high-quality habitat and a characteristic of
urbanization.
Human-Made Nest Structures
Stout et al. (1996) documented Red-tailed Hawks nesting on five different human-
made structures, and compared nest site characteristics and habitat for these structures to
nests on natural structures. For this study, Red-tailed Hawks continued to consistently nest
on these human-made structures, and nested on 11 additional structures. Nesting success
and productivity for nests on human-made structures are higher than for nests in trees,
suggesting that nesting on human-made structures is beneficial for reproductive success.
These locations may provide protection from some types of natural nest predators (e.g.,
Great Horned Owls and raccoons; Bubo virginianus, Procyon lotor, respectively) because
they tend to be higher (Stout et al. 1996) and on steel structures that are more difficult for
mammalian predators to climb. Landscape features surrounding these structures may also

35
provide quality habitat and contribute to improved reproductive success and fitness.
Increased use of human-made structures in urban locations during this study suggests that
Red-tailed Hawks are adapting to urban environments.
Conclusion
Red-tailed Hawk reproductive success for this 14-yr study is consistent with other
studies across North America, averaging 80.1% nest success and 1.36 young per active
nest. Productivity for 1994 was significantly greater than other years.
Red-tailed Hawk productivity, an index of habitat quality, varied with habitat
composition surrounding nest sites. Wetland area was the only habitat type that was
significantly greater for low productivity sites, indicating that wetlands are not beneficial
for Red-tailed Hawk productivity. The area of roads and high-density urban habitat were
greater for high productivity sites, and the landscape consisted of smaller habitat patches.
This indicates that urban/suburban locations provide high-quality habitat for Red-tailed
Hawks. Higher productivity in high-density urban areas suggests that urban Red-tailed
Hawk populations may be source, not sink, populations. Increased nesting on human-made
structures in urban locations and enhanced reproductive success for these nests reinforce
this hypothesis, and suggests that Red-tailed Hawks are adapting to urban environments.
Acknowledgements

36
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Space Imaging. 2000. FRAGSTATS for ArcView version 1.0. Space Imaging, Inc.
Thornton, Colorado USA.
SPSS. 2000. SYSTAT 10 for Windows. SPSS Inc. Chicago, Illinois USA.
SPSS. 2003. SPSS version 12.0 for Windows. SPSS Inc. Chicago, Illinois USA.
Steenhof, K. 1987. Assessing raptor reproductive success and productivity. Pages 157-
170 in B.G. Pendleton, B.A. Millsap, K.W. Cline and D.M. Bird, eds. Raptor
management techniques manual. National Wildlife Federation Scientific and
Technical Series No. 10. Washington, D.C. USA.
Wisconsin USA.

40
Table 1. Red-tailed Hawk reproductive success over a 14-year period, 1989 through 2002.
a
eggs were laid.
b
at least one young reached 15 days old.
Nests with Indicated
Active Nesting Number of Young Number Young per Young per
Year Sitesa
Failures Success 1 2 3 of Young Active Sitea
Successful Nestb
1989 60 12 80.0% 20 24 4 80 1.33 1.67
1990 87 22 74.7% 20 39 6 116 1.33 1.78
1991 93 17 81.7% 34 39 3 121 1.30 1.59
1992 84 10 88.1% 24 45 5 129 1.54 1.74
1993 55 18 67.3% 16 18 3 61 1.11 1.65
1994 55 5 90.9% 11 23 16 105 1.91 2.10
1995 68 15 77.9% 23 21 9 92 1.35 1.74
1996 86 19 77.9% 32 35 0 102 1.19 1.52
1997 66 10 84.8% 27 29 0 85 1.29 1.52
1998 101 20 80.2% 37 36 8 133 1.32 1.64
1999 100 21 79.0% 29 40 10 139 1.39 1.76
2000 85 19 77.6% 25 36 5 112 1.32 1.70
2001 95 17 82.1% 37 37 4 123 1.29 1.58
2002 101 21 79.2% 32 44 4 132 1.31 1.65
All Years 1136 226 80.1% 368 468 80 1544 1.36 1.70

41
Table2.MatrixofpairwisecomparisonsusingtheTukeyMultipleComparisonstest.
Year19891990199119921993199419951996199719981999200020012002
19891.000
19901.0001.000
19911.0001.0001.000
19920.9830.9620.8741.000
19930.9830.9660.9900.2001.000
19940.024*0.008*0.003*0.412<0.001*1.000
19951.0001.0001.0000.9910.9570.026*1.000
19960.9990.9981.0000.3141.000<0.001*0.9961.000
19971.0001.0001.0000.8990.9980.006*1.0001.0001.000
19981.0001.0001.0000.9100.9780.003*1.0000.9991.0001.000
19991.0001.0001.0000.9970.8050.023*1.0000.9451.0001.0001.000
20001.0001.0001.0000.9350.9830.006*1.0000.9991.0001.0001.0001.000
20011.0001.0001.0000.8460.9930.002*1.0001.0001.0001.0001.0001.0001.000
20021.0001.0001.0000.9200.9750.004*1.0000.9991.0001.0001.0001.0001.0001.000
*Valuesindicateasignificantdifferenceexistsfortheindicatedpairwisecomparison.
41

42
Table3.ComparisonofhabitatsurroundinghighproductivityRed-tailedHawkbreedingareas(N=24)andlowproductivitybreeding
areas(N=24).Valuesforareaandperimeterarehaandm,respectively.
HighProductivityRed-tailedHawkBreedingAreasLowProductivityRed-tailedHawkBreedingAreas
VariablesMeanSTDMaxMinNMeanSTDMaxMinNtP
Urban(highdensity)Area43.534.1111.21.32421.525.282.50.724-2.5510.014
Urban(highdensity)Perimeter17510.314097.150839.4998.8248509.39393.536070.8350.624-2.6030.012
Urban(highdensity)Count35.726.997.02.02418.717.770.01.024-2.5930.013
Urban(lowdensity)Area36.939.2157.60.02451.344.7169.81.1241.1880.241
Urban(lowdensity)Perimeter12757.410679.745634.70.02417426.113264.750384.2983.1241.3430.186
Urban(lowdensity)Count23.013.553.00.02427.515.363.05.0241.0920.281
RoadArea39.621.084.66.72424.212.759.86.024-3.0660.004
RoadPerimeter26706.310368.445979.88254.72422110.610656.549274.56011.724-1.5140.137
RoadCount10.14.220.04.0249.04.518.01.024-0.8940.376
ParkingArea11.613.751.70.0246.17.229.00.024-1.7520.086
ParkingPerimeter7211.77331.826106.70.0244559.85649.620975.90.024-1.4040.167
ParkingCount18.517.367.00.02412.413.651.00.024-1.3560.182
RecreationalArea7.013.653.90.0247.115.676.40.0240.0200.984
RecreationalPerimeter1452.62282.19818.30.0241414.61955.28914.80.024-0.0620.951
RecreationalCount1.21.56.00.0241.31.34.00.0240.1040.918
GradedArea1.93.114.80.0246.912.540.10.0241.8920.065
GradedPerimeter1045.71045.93026.60.0241683.82099.06527.00.0241.3330.189
GradedCount4.44.313.00.0244.65.923.00.0240.1690.866
CroplandArea36.041.8162.90.02431.530.489.10.024-0.4250.673
CroplandPerimeter6063.75702.719850.40.0245340.24822.514977.80.024-0.4750.637
CroplandCount4.94.114.00.0244.13.411.00.024-0.6870.495
PastureArea39.950.8155.30.02452.762.3203.30.0240.7770.441
PasturePerimeter6781.17209.121277.20.0247687.96703.218018.80.0240.4510.654
PastureCount6.15.517.00.0245.64.513.00.024-0.3180.752
GrasslandArea56.337.2155.611.62446.429.2123.70.024-1.0270.310
GrasslandPerimeter16162.27670.339050.04169.12413840.36896.726806.70.024-1.1030.276
GrasslandCount19.09.039.06.02417.78.334.00.024-0.5510.584
42

43 43
Table3(cont’d).
HighProductivitySitesLowProductivitySites
WoodlandArea9.77.234.01.5249.78.337.80.0240.0220.982
WoodlandPerimeter3292.22120.48001.4646.2243001.41877.96611.30.024-0.5030.617
WoodlandCount5.12.910.01.0244.62.710.00.024-0.6120.543
WetlandArea28.729.4101.40.02451.243.1169.20.5242.1120.040
WetlandPerimeter6671.74980.114626.80.0249297.35786.624879.6464.0241.6850.099
WetlandCount7.25.119.00.0246.83.212.02.024-0.3410.735
WaterArea1.51.97.30.0244.07.432.00.0241.6630.103
WaterPerimeter860.7943.23104.90.0241830.62710.39422.10.0241.6560.105
WaterCount2.42.711.00.0242.92.812.00.0240.6330.530
NP137.5037.70229.0075.0024115.0839.92207.0056.0024-2.0000.051
MPS2.440.664.171.36243.071.125.581.51242.3680.022
MSI1.660.091.951.51241.690.111.951.53241.1970.238
MPFD1.390.031.461.33241.450.152.091.35241.7670.084
PSSD5.962.1910.892.70247.444.1419.382.96241.5490.128
LPI15.867.2034.575.812417.589.8549.437.56240.6900.494
PD43.9912.0673.2724.002436.8212.7766.2317.9224-2.0000.051
PSCV243.2957.59372.79152.6124235.4560.91409.14132.5224-0.4580.649
AWMSI2.300.332.951.74242.230.242.801.8224-0.8340.408
DLFD1.390.021.441.37241.390.011.421.3624-0.0090.993
AWMPFD1.350.021.391.31241.340.021.381.3124-1.1840.243
SHDI1.770.232.161.30241.770.222.081.28240.0100.992
SIDI0.780.060.870.67240.770.080.860.5524-0.5900.558
MSIDI1.540.282.031.11241.500.301.930.8124-0.5430.590
SHEI0.760.080.870.61240.740.080.860.5624-0.7910.433
SIEI0.860.060.950.75240.840.080.930.6224-0.9000.373
MSIEI0.660.110.820.48240.630.120.790.3724-1.1030.276
PR10.331.4012.007.002410.880.9912.009.00241.5440.129
BreedingArea(MCPforNests)11.5912.9446.690.162412.6815.6163.680.0324-0.2640.793
NumberofYearsActive10.382.9215.006.00248.712.4615.006.00242.1410.038
YoungperActiveNest1.850.162.401.67240.830.221.000.142418.264<0.001
TotalYoungProduced14.924.3824.008.00245.381.869.001.00249.817<0.001

44
Table 4. Summary of stepwise discriminant function analysis for high productivity
breeding areas and low productivity breeding areas.
Parameters Value
Eigenvalue 0.315
Percentage of Eigenvalue Associated
with Function
100%
Canonical Correlation 0.489
Chi-square Statistic 12.325
Significance 0.002
Degrees of Freedom 2
Standardized Canonical Discriminant Function Coefficients
Road Area 0.896
Mean Patch Fractal Dimension (MPFD) -0.600
Functions at Group Centroids
Low Productivity -0.549
High Productivity 0.549

45
Table 5. Classification results for the stepwise discriminant function analysis.
Predicted Productivity
a
Measure
Observed
Productivity Low High Total
Count Low 19 5 24
High 7 17 24
Percent Low 79.2% 20.8% 100.0%
High 29.2% 70.8% 100.0%
a
75.0% of original grouped cases correctly classified.

46
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Lake
Michigan
Milwaukee Co.
Ozaukee Co.
Waukesha Co.
Washington Co.
Racine Co.
Dodge Co.
Red-tailed Hawk Nests#S
N
Southeast
Wisconsin
Study Area
Wisconsin
Figure 1. Southeast Wisconsin Study Area showing active (i.e., eggs laid) Red-tailed
Hawk nests from 1989 through 2002.

47
Red-tailedHawkProductivity
0.00
0.50
1.00
1.50
2.00
2.50
3.00
19891990199119921993199419951996199719981999200020012002
Year
Average#ofYoungperActiveNest(+/-SE)
Figure2.Red-tailedHawkproductivityovera14-yearperiod,1989through2002.
47

48
Washington Co.
Ozaukee Co.
Waukesha Co.
Milwaukee Co.
Lake
Michigan
N
Red-tailed Hawk Breeding Areas
High and Low Productivity
Survey Area
Red-tailed Hawk Productivity
High
Low
Key to Features
Figure 3. High and low productivity Red-tailed Hawk breeding areas.

49
DYNAMICS OF A RED-TAILED HAWK POPULATION
IN AN URBAN ENVIRONMENT
Introduction
Red-tailed Hawks (Buteo jamaicensis) nest in urban environments across North
America, yet no comprehensive demographic studies have been published on urban
populations. Urban raptor populations for some species exist at higher densities than rural
populations (Bird et al. 1996). Some studies document reproductive success and density for
Red-tailed Hawks in urban areas (Minor et al. 1993, Stout et al. 1998); however, there is
scant information on the dynamics of urban populations. While Red-tailed Hawk
populations throughout the Midwest are stable or increasing (Castrale 1991, Temple et al.
1997), the lack of long-term studies in urban environments warrants further study.
Population density can affect demographic parameters of populations such as
reproductive success and survival rates. Density is affected by limiting factors, including
resources such as nest-site availability and food supply. Nest-site availability, and prey
abundance and availability are often the external limiting factors that have the greatest
impact on Red-tailed Hawk populations, as well as other raptors (Preston and Beane 1993,
Newton 1998). However, the relationship between density, limiting factors and
reproductive success in urban locations is largely unknown.
Population density can also influence mechanistic parameters, such as breeding area
re-use and territory size fluctuations. Range expansion, dispersion patterns and shifts in
these patterns can provide insight into habitat quality, resource availability, population
trends and potential density limits in urban areas. Population fluctuations, and range
expansions and contractions are natural phenomena (Newton 1998, Smallwood 2002). No

50
studies have examined whether expansions of urban Red-tailed Hawk populations are the
result of birds adapting to novel urban environments or simply finding and occupying
patches of habitat within urban locations that are similar to rural habitat.
I studied an urban/suburban Red-tailed Hawk population in southeast Wisconsin
over a 15-year period. The objectives of this study were to describe changes in Red-tailed
Hawk population density over a 15-year period, to determine the relationship between
breeding density and productivity, to determine the relationship between breeding density
and the percentage of occupied site that are active, to determine the relationship between
breeding density and breeding area re-use (i.e., consistency in breeding area use), to
determine whether the dispersion pattern shifts over time as density changes, and to
determine if the Red-tailed Hawk populations are expanding into urban areas.
Methods
Study Area
The Metropolitan Milwaukee Study Area (MMSA) covers 63,095 ha in southeast
Wisconsin (43 N, 88 W), and includes parts of Milwaukee, Waukesha and Washington
Counties (Figure 1). Milwaukee and Ozaukee Counties are bordered by Lake Michigan to
the east. Human population density in urban locations (i.e., the city of Milwaukee) within
the study area averages 2399.5/km2
; the city of Milwaukee covers an area of 251.0 km2
composition includes a wide range of development patterns. Land cover includes
agricultural, natural, industrial, commercial, and residential areas. Population density and
human land-use intensity decrease radially from the urban center of Milwaukee. Two
interstate highways (Interstate 43 and Interstate 94) transect the MMSA. Curtis (1959)

51
described natural vegetation, physiography and soil for the study area. Remnants of
historical vegetation that are marginally impacted by development are sparsely scattered
throughout the study area. The size and abundance of these remnants increase farther from
the urban center (Matthiae and Stearns 1981). I also report information for two individual
urban townships within the MMSA, Brookfield (9,468ha) and Granville (9,438ha). An area
slightly larger than the MMSA (Figure 8) was used to determine if the Red-tailed Hawk
range is expanding into urban locations.
Population Surveys
1974). The MMSA was surveyed completely for Red-tailed Hawk nests from 1988 through
2002. Woodlots within the MMSA that were not entirely visible from the road early in the
season before leaf-out were checked by foot. I document both active Red-tailed Hawk nest
sites and occupied sites (Postupalsky 1974). An “active site” is a nest site in which eggs
were laid and constitutes a nesting attempt by a breeding pair of birds, and an “occupied
site” is an area with a mated pair of birds associated with a nest (Postupalsky 1974).
Productivity (number of young per active nest) was determined for nesting attempts from
1989 through 2002. A “breeding area” is an area that contains one or more nests within the
home range of a pair of mated birds (Postupalsky 1974, Steenhof 1987).

52
GIS
Locations for active Red-tailed Hawk nests and occupied sites were mapped in a
GIS. For occupied sites, I calculated the center (arithmetic mean) of adult locations within
the breeding area. For land-cover, I used the Southeast Wisconsin Regional Planning
Commission’s (SEWRPC) 1995 land-cover data set (SEWRPC 1995). For the purposes of
this study, SEWRPC categories were combined into the following 12 land-cover classes:
urban (high-density), urban (low-density), roads, parking, recreational, graded, cropland,
pasture, grassland, woodland, wetland and water. See Stout (2004) for a description of the
SEWRPC data set, which SEWRPC categories are included in each of the above 12 land-
cover classes, and methods used to enter Red-tailed Hawk nest locations into a GIS.
ArcView GIS version 3.3 (ESRI 2002) was used for GIS procedures and analyses.
Density Correlations and Dispersion Patterns
Red-tailed Hawk density (for active sites and occupied sites) was documented for
the MMSA. Densities for active sites and occupied sites are minimum values. Breeding
density was examined for correlations with productivity, percentage of active sites and
breeding area re-use for the MMSA, and the townships of Brookfield and Granville.
“Percentage of sites active” is the percentage of occupied sites that are active in a given
year. Breeding area “re-use” (i.e., consistency in breeding area use) is the percentage of
active breeding areas from one year that are active the following year. Dispersion patterns
were calculated for the MMSA, and the townships of Brookfield and Granville for each
year.

53
Habitat Expansion
To determine if the Red-tailed Hawk populations are expanding into urban
locations, I classified active and occupied Red-tailed Hawk sites for 1988 through 2002 into
three 5-yr periods: 1988 to 1992, 1993 to 1997 and 1998 to 2002. I used a 1000m-radius
buffer (Stout 2004) around these sites to describe Red-tailed Hawk habitat for each of the 5-
yr periods. The total area of habitat was different for each 5-yr period (i.e., the area
increased over time). Therefore, percent area (i.e., composition) of each cover type was
used to compare Red-tailed Hawk habitat for the three time periods.
Parametric statistics were used for statistical analyses where applicable. Linear
regression was use to determine if the Red-tailed Hawk population is increasing within the
MMSA and two townships, and to determine if productivity, percentage of active sites and
breeding area re-use are density-dependent (i.e., to determine if the slope is significantly
different than zero, t statistic and the associated probability are reported). The Nearest
Neighbor Analysis Test for Complete Spatial Randomness (Hooge and Eichenlaub 1997)
was used to determine spatial dispersion (clumped, random or uniform) of nests within the
MMSA and two townships for each year. An R value and z statistic are reported (Hooge
and Eichenlaub 1997). An R value (range: 0-2) indicates how clustered or dispersed points
are within a defined study area (i.e., polygon). An R < 1 indicates a tendency towards a
clumped pattern (e.g., R near 0), R = 1 indicates a random dispersion, and R > 1 indicates a
uniform pattern (e.g., R near 2), with results dependent on sample size and dispersion
within the study area. A linear regression (2-tailed t-test) was used to determine if
populations are increasing, and whether productivity or breeding area re-use are density

54
dependant. A One-way Analysis of Variance (ANOVA) was used to compare percent area
of each Red-tailed Hawk habitat cover type across the three 5-yr periods. Two
FRAGSTATS landscape metrics, Mean Patch Size (MPS) and Patch Size Standard
Deviation (PSSD), are reported. FRAGSTATS for ArcView version 1.0 (Space Imaging
2000) was used to calculate values. For habitat cover types that were significantly
different, a post hoc test (Tukey Multiple Comparisons test) was used to identify
differences between the three 5-yr periods. All tests were considered significant when P 
0.05. SYSTAT (SPSS 2000) was used for statistical analyses.
Results
Density
The Red-tailed Hawk population density (minimum estimate) increased from 1988
to 2002 within the MMSA for both active sites and occupied sites (linear reg.: N=15;
t=6.298, P<0.001; t=7.567, P<0.001, respectively; Table 1, Figure 2). The population
increased from 32 occupied sites (18 active sites) in 1988 to 72 occupied sites (48 active
sites) in 2002. The highest breeding density for the MMSA was one breeding pair per
1315ha in 2002.
For the township of Brookfield, the Red-tailed Hawk population density (minimum
estimate) increased for both active sites and occupied sites (linear reg.: N=15; t=3.068,
P=0.009; t=4.301, P=0.001, respectively; Table 1, Figure 3). Over the 15-yr study, the
population increased from 9 occupied sites (6 active sites) in 1988 to 15 occupied sites (10
active sites, one pair per 947ha) in 2002. The highest breeding density for this township
was one pair per 728ha in both 1999 and 2001.

55
For the township of Granville, the Red-tailed Hawk population density (minimum
estimate) increased for both active sites and occupied sites (linear reg.: N=15; t=4.764,
P<0.001; t=7.785, P<0.001, respectively; Table 1, Figure 4). Over the 15-yr study, the
population increased from 5 occupied sites (3 active sites) in 1988 to 17 occupied sites (11
active sites, one pair per 858ha) in 2002. The highest breeding density for this township
was one pair per 674ha in 1998.
Density and Productivity
Productivity (number of young per active site) for this study is described in Stout
(2004), and does not vary over 14 years with changes in density for the MMSA, or the
townships of Brookfield and Granville (linear reg.: N=14; t=1.064, P=0.308; t=1.237,
P=0.240; t=0.301, P=0.769, respectively; Figure 5).
Density, Percentage of Sites Active and Breeding Area Re-Use
The percentage of occupied sites that were active in a year did not vary with density
over 15 years for the MMSA, or the townships of Brookfield and Granville (linear reg.:
N=15; t=-0.092, P=0.928; t=1.094, P=0.294; t=-0.535, P=0.602, respectively; Table 1,
Figure 6). The MMSA averaged 74.5%, and the townships of Brookfield and Granville
averaged 70.2% and 72.5% active sites, respectively.
Breeding area re-use did not vary over 14 years with changes in density for the
MMSA or the township of Granville (linear reg.: N=14; t=1.776, P=0.101; t=0.871,
P=0.401, respectively; Figure 7). For the township of Brookfield, breeding area re-use
increased with density (linear reg.: N=14, t=3.415, P=0.005).

56
Dispersion Patterns
The Red-tailed Hawk nesting dispersion pattern for the MMSA was random
throughout the 15-yr study (1988 through 2002, Table 2). The nesting dispersion pattern
was uniform for the township of Brookfield in 2002, and for the township of Granville in
1994, 1995 and 2002 (Table 2). Nest dispersion for these two townships was random for all
other testable years. Nearest neighbor analysis was unable to determine significance when
the sample size was 7 or less.
Habitat Expansion
Composition of Red-tailed Hawk habitat varied over the three 5-yr time periods
(Table 3), and expanded into urban locations (Figure 8). Mean Patch Size (MPS) was
significantly different for five habitat cover types (Table 3). The percentage of high-density
urban land and parking areas increased within Red-tailed Hawk habitat as more birds used
urban areas. The number of patches for all five habitat cover types that varied (high-density
urban land, low-density urban land, parking, grassland and woodland) increased over the
three 5-yr periods.
Discussion
Population Density
Red-tailed Hawk population density for this study is consistent with the densities
reported throughout North America. The highest breeding density for the MMSA in 2002
was a minimum of one breeding pair per 13.15km2
. However, a large part of the study area
consists of heavily developed regions within the city of Milwaukee in which Red-tailed
Hawks were not present. Red-tailed Hawks are probably unable to utilize these heavily
urbanized areas at this time. Minor et al. (1993) studied an urban/suburban Red-tailed

57
Hawk population in Syracuse, New York, and reported a breeding density of one pair per
12.50km2
. They also note that some of the heavily urbanized areas of the city were devoid
of suitable habitat for hunting and nesting. For rural areas in Wisconsin, Orians and
Kuhlman (1956) and Gates (1972) reported breeding densities of one breeding pair per
8.48km2
and 10.53km2
, respectively. While separated by decades, the 2002 breeding
densities for the two urban/suburban townships in this study, Brookfield and Granville (a
minimum of one breeding pair per 9.47km2
and 8.58km2
, respectively), are similar to rural
densities. Fitch et al. (1946) reported the highest breeding density of Red-tailed Hawks for
North America in Madera County, California, at 1 pair per 1.29km2
. In time, Red-tailed
Hawks may adapt to even the most heavily urbanized areas, and urban breeding densities
may continue to increase.
Population Growth
The Red-tailed Hawk population in southeast Wisconsin is increasing, and the
highest densities reported for this study (the urban/suburban townships of Brookfield and
Granville: a minimum of one occupied site per 5.26km2
in 2000, and one occupied site per
5.55km2
in 2002, respectively) are greater than previously observed (Orians and Kuhlman
1956). In my study area, the Red-tailed Hawk population increased over the 15-year
period, and doesn’t appear to be approaching limits within the urban study area at this time.
Increasing regional population trends were reported by Robbins et al. (1986) for the North
American Breeding Bird Surveys, and by Temple et al. (1997) for the Wisconsin Checklist
Project.

58
Density and Productivity
For this study, productivity does not vary significantly with density and, therefore,
does not appear to be density-dependent within this study area at this time. Productivity is
generally considered to be density-dependent with reproductive output declining with
higher densities (Newton 1994). While studies have demonstrated this trend in some birds
(Newton 1994, Johnson and Geupel 1996, Panek 1997), several studies on raptors have
found that productivity was not density-dependent over the range of densities examined.
Mearns and Newton (1988) studied a Peregrine Falcon (Falco peregrinus) population that
more than doubled over the study period, and they found no density-dependent depression
of productivity. Petty (1989) studied a Tawny Owl (Strix aluco) population with large
variations in productivity and density but found no density-dependence. While productivity
does not vary significantly with density for Red-tailed Hawks in this study, the predicted
trend (i.e., reduced productivity at higher densities) exists. A density-dependent response
by productivity may become more obvious at higher density levels but not at lower and
moderate levels. Density-dependence may not be obvious (i.e., significant) in this study
because density doesn’t appear to be approaching limits. Detecting a density-dependent
response also may be difficult because of wide year-to-year variations due to density-
independent factors such as weather.
Nest-site availability and food supply may not be limiting for the Red-tailed Hawk
population in urban locations, at least in the MMSA, at this time. Consequently, population
density will likely continue to increase. Preston and Beane (1993) and Newton (1998)
suggest that prey abundance and availability, and nest-site availability may be the limiting
factors that have the greatest impact on Red-tailed Hawk and other raptor populations.

59
Horne and Fielding (2002) studied a Peregrine Falcon population and suggest that an
increase in density may have been due to an expanding food supply. Janes (1984)
correlated perch site density and prey abundance with reproductive success, suggesting that
prey availability may be more important than abundance. Stout (2004) documented
relatively high productivity in urban locations around metropolitan Milwaukee, Wisconsin.
This may indicate that Red-tailed Hawks are able to exploit prey populations within urban
habitats and that prey abundance and availability may not be a major limiting factor in
urban locations at this time.
Stout et al. (1996) documented Red-tailed Hawks nesting on five different human-
made structures. Stout (2004) documented the nesting of Red-tailed Hawks on an
increasing number of human-made structures in urban locations, and found that
reproductive success (i.e., nesting success and productivity) for nests on human-made
structures is higher than for nests in trees. These studies suggest that Red-tailed Hawks are
adapting to new nest substrates in the urban environment, and nest-site availability may not
be limiting in urban locations at this time.
Future Densities
As urbanization has increased, raptor populations have adapted well to these heavily
developed environments. Oliphant and Haug (1985) and Oliphant et al. (1993) documented
an expanding Merlin (Falco columbarius) population in Saskatoon, Saskatchewan from
1971 to 1982; Rosenfield et al. (1995, 1996) documented the highest known nesting density
of Cooper's hawks (Accipiter cooperii) in an urban/suburban area of Stevens Point,
Wisconsin. Several other raptor studies document high population densities and survival
rates for several species in urban locations (Bloom and McCrary 1996, Botelho and

60
Arrowood 1996, Gehlbach 1996). The breeding density for this urban Red-tailed Hawk
population may continue to increase and exceed that of rural populations as Gehlbach
(1996) and others suggest.
Density, Percentage of Sites Active and Breeding Area Re-Use
As density increases, mechanistic parameters for populations such as percentage of
sites active and breeding area re-use may be expected to increase. However, at low and
moderate densities, these mechanistic parameters may be affected by density-independent
factors (e.g., weather) more than density. At high densities, when limiting factors such as
prey availability and space have a greater impact on a population through competition, the
percentage of sites active and breeding area re-use may be expected to decrease in response
to density, and density-dependence may be detectable. At higher densities, reduced
productivity may be a more conspicuous response that compensates for high density levels
than mechanistic parameters.
For this study, the percentage of sites active appears to be consistent, on average,
across different densities, and therefore, does not exhibit this trend. Other studies report a
wide range of values for average percentage of occupied sites active in a year by Red-tailed
Hawks (Preston and Beane 1993). Orians and Kuhlman (1956) reported 90% in Wisconsin
and Hagar (1957) reported 74% in New York. The percentage of sites active for this study
(e.g., MMSA: 75%) is similar to that reported by Hagar (1957).
Breeding area re-use is a measure of consistency in breeding activity from one year
to the following year. This measure of breeding performance may be more sensitive to
density-dependence for a population that is increasing in density than the percentage of
sites active. As population density increases, breeding territories occupy more of the

61
available habitat, territory size may compress, and productivity may decrease (Newton
1998). For this study, population density is increasing, suitable habitat is available (i.e.,
space is not limiting), and density-dependence may not be detectable. With density
increasing, an increase in breeding area re-use may be expected, reaching an average upper
limit. For this study, breeding area re-use tends to increase with density, and appears to
reach an average of approximately 80% at higher densities (i.e., the townships of
Brookfield and Granville). For the MMSA and the township of Granville, breeding area re-
use is similar and does not vary statistically across different densities. However, for the
township of Brookfield breeding area re-use increases with density. Nevertheless, an
increasing trend is seen in all three study areas.
Neither mechanistic parameter, percentage of active sites or breeding area re-use,
decrease at the higher densities reported for this study. The absence of a negative density-
dependent response suggests that the Red-tailed Hawk population may not be reaching
limits for this study area at this time.
Dispersion Patterns
Dispersion patterns and changes in these patterns can provide insight into
population trends and potential density limits. Dispersion patterns for species (i.e.,
uniform, random or clumped) can be caused by a relationship between the species and
resources within the environment, and by interactions between individuals (Smallwood
1993, 2002). Deviations from a random dispersion in ecological systems may be due to
changes in key resources such as habitat quality, food abundance and availability, or inter-
and intra-specific competition for these resources (Luttich et al. 1971, Smallwood 2002).
Without resource limitations, species that are not gregarious, such as the Red-tailed Hawk,

62
form a random dispersion. As the breeding density of a population increases and limiting
factors begin to take effect, a shift in the dispersion pattern is expected. Territorial species
like Red-tailed Hawks should exhibit uniform population dispersion patterns as they
approach density limits (i.e., carrying capacity). However, if habitat quality is influenced
by human activity, as it is in urban locations, territorial species may avoid certain areas,
giving the appearance, at a large scale, of a clumped dispersion pattern. The dispersion
pattern for the MMSA was random for 1988 through 2002, suggesting that the Red-tailed
Hawk population is not approaching density limits. Measurable dispersion patterns for the
townships of Brookfield and Granville were, for the most part, random. However, the
dispersion pattern for these townships was uniform for a total of only four years (Brookfield
2002, Granville 1994, 1995 and 2002). The dispersion patterns for Brookfield and
Granville over the next five to ten years may provide insight into potential density limits in
these urban areas.
Habitat Expansion
A change in habitat composition over time may indicate that a population is
adapting to a new environment. For this study, Red-tailed Hawk habitat composition
changed over time. The area of high-density urban land and the number of patches for most
urban habitat variables increased within Red-tailed Hawk habitat over a 15-yr period. This
indicates that the Red-tailed Hawk is expanding into the city of Milwaukee, and suggests
that Red-tailed Hawks are adapting to urbanization.
Habitat expansion and nesting on human-made structures are evidence that Red-
tailed Hawks are adapting to the urban environment in southeast Wisconsin. Based on the
observed habitat expansion, random population dispersion, increasing density, high

63
productivity in urban areas, and lack of density-dependent depression of productivity, it
doesn’t appear that the Red-tailed Hawk population is approaching its natural density limits
(i.e., carrying capacity) in this urban location at this time.
Conclusion
The Red-tailed Hawk population in southeast Wisconsin is increasing in density and
expanding its range into developed areas as it adapts to the urban environment. It doesn’t
appear that the population is approaching limits within the urban study area at this time.
None of the demographic or mechanistic parameters I measured showed responses to
density. While productivity did not vary significantly with density for this study, the
predicted trend (i.e., reduced productivity at higher densities) exists. Detecting density-
dependence may be difficult because of wide annual variations due to density-independent
factors such as weather. While space, and nest site and prey availability may ultimately be
the major limiting factors for this population, my study suggests that their effects are not yet
detectable in this urban environment.
Acknowledgements

64
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69

70
Table 1. Red-tailed Hawk population density (minimum estimates) for occupied sites and
active sites in the MMSA and two townships within this area from 1988 to 2002.
Density - Occupied Sites Density - Active Sites Percentage of
Year N Occupied Sites/Ha Ha/Occupied Site N Active Sites/Ha Ha/Active Sites Sites Active
MMSA (63,095ha)
1988 32 0.00051 1971.7 18 0.00029 3505.3 56.3%
1989 35 0.00055 1802.7 20 0.00032 3154.7 57.1%
1990 46 0.00073 1371.6 34 0.00054 1855.7 73.9%
1991 50 0.00079 1261.9 32 0.00051 1971.7 64.0%
1992 34 0.00054 1855.7 33 0.00052 1912.0 97.1%
1993 47 0.00074 1342.4 39 0.00062 1617.8 83.0%
1994 48 0.00076 1314.5 39 0.00062 1617.8 81.3%
1995 49 0.00078 1287.6 43 0.00068 1467.3 87.8%
1996 46 0.00073 1371.6 35 0.00055 1802.7 76.1%
1997 49 0.00078 1287.6 38 0.00060 1660.4 77.6%
1998 67 0.00106 941.7 53 0.00084 1190.5 79.1%
1999 65 0.00103 970.7 53 0.00084 1190.5 81.5%
2000 64 0.00101 985.9 45 0.00071 1402.1 70.3%
2001 71 0.00113 888.7 47 0.00074 1342.4 66.2%
2002 72 0.00114 876.3 48 0.00076 1314.5 66.7%
Average 74.5%
Brookfield Township (9,468ha)
1988 9 0.00095 1052.0 6 0.00063 1578.0 66.7%
1989 6 0.00063 1578.0 2 0.00021 4734.1 33.3%
1990 13 0.00137 728.3 10 0.00106 946.8 76.9%
1991 10 0.00106 946.8 6 0.00063 1578.0 60.0%
1992 8 0.00084 1183.5 8 0.00084 1183.5 100.0%
1993 14 0.00148 676.3 10 0.00106 946.8 71.4%
1994 10 0.00106 946.8 6 0.00063 1578.0 60.0%
1995 13 0.00137 728.3 11 0.00116 860.7 84.6%
1996 10 0.00106 946.8 8 0.00084 1183.5 80.0%
1997 11 0.00116 860.7 5 0.00053 1893.6 45.5%
1998 14 0.00148 676.3 11 0.00116 860.7 78.6%
1999 15 0.00158 631.2 13 0.00137 728.3 86.7%
2000 18 0.00190 526.0 11 0.00116 860.7 61.1%
2001 16 0.00169 591.8 13 0.00137 728.3 81.3%
2002 15 0.00158 631.2 10 0.00106 946.8 66.7%
Average 70.2%
Granville Township (9438.1ha)
1988 5 0.00053 1887.6 3 0.00032 3146.0 60.0%
1989 9 0.00095 1048.7 3 0.00032 3146.0 33.3%
1990 8 0.00085 1179.8 6 0.00064 1573.0 75.0%
1991 12 0.00127 786.5 6 0.00064 1573.0 50.0%
1992 8 0.00085 1179.8 7 0.00074 1348.3 87.5%
1993 9 0.00095 1048.7 8 0.00085 1179.8 88.9%
1994 13 0.00138 726.0 12 0.00127 786.5 92.3%
1995 12 0.00127 786.5 9 0.00095 1048.7 75.0%
1996 13 0.00138 726.0 9 0.00095 1048.7 69.2%
1997 13 0.00138 726.0 11 0.00117 858.0 84.6%
1998 16 0.00170 589.9 14 0.00148 674.1 87.5%
1999 14 0.00148 674.1 13 0.00138 726.0 92.9%
2000 15 0.00159 629.2 9 0.00095 1048.7 60.0%
2001 15 0.00159 629.2 10 0.00106 943.8 66.7%
2002 17 0.00180 555.2 11 0.00117 858.0 64.7%
Average 72.5%

71
Table2.Dispersionpatterns(uniform,randomorclumped)foractiveRed-tailedHawknestsitesintheMMSA
andtwotownshipswithinthisareafrom1988to2002.
MMSA(63,095ha)BrookfieldTownship(9,468ha)GranvilleTownship(9,438ha)
YearNDispersionzRNDispersionzRNDispersionzR
198818random-1.8530.7726*-0.7580.8383*-0.4890.853
198920random-0.0730.9922*-0.1340.9513*-0.2510.924
199034random0.3001.02710random1.6761.2776*1.5251.326
199132random1.4101.1306*0.5031.1076*0.1341.029
199233random-0.3250.9718random0.4211.0787*1.8751.371
199338random0.2111.01810random1.5541.2578random1.4011.259
199439random0.4181.0355*1.7881.41812uniform2.8021.423
199543random0.0401.00312random0.7291.1109uniform2.1201.369
199634random0.1811.0167*0.2541.0509random1.8851.328
199738random0.0591.0055*1.3171.30811random1.6071.253
199853random-0.2300.98411random0.1311.02114random1.8071.253
200247random0.8271.06310uniform2.2501.3729uniform3.8311.668
*Samplesizetoosmalltodeterminesignificance.
71

72 72
Table3.ComparisonofRed-tailedHawkhabitatcovertypesforthree5-yrperiods.MPS(MeanPatchSize),PSSD(Patch
SizeStandardDeviation),MinimumandMaximumvaluesareinhectare.
1988through19921993through19971998through2002One-wayANOVA
Land-Use%AreaMPSPSSD%AreaMPSPSSD%AreaMPSPSSD
MinMaxNMinMaxNMinMaxNFP
Urban(highdensity)12.071.25
a
2.2612.131.23
a
2.2915.511.31
b
2.2711.881<0.001
<0.0147.643383<0.0156.943564<0.0160.885215
Urban(lowdensity)15.232.00
a
2.9614.191.90
b
2.5813.891.99
c
2.5820.974<0.001
<0.0133.752661<0.0134.222710<0.0126.753078
Roads10.993.988.5511.104.419.7711.914.7211.230.8260.438
<0.0192.36967<0.01115.14914<0.01147.751115
Parking3.510.59
a
1.423.640.60
a
1.463.860.59
b
1.258.539<0.001
<0.0134.982064<0.0140.362191<0.0125.702912
Recreational2.946.2814.193.035.6913.572.994.7711.202.6200.074
<0.01123.67164<0.01123.67193<0.01123.67277
Graded1.781.165.541.511.034.571.581.045.040.7750.461
<0.0189.09535<0.0151.54530<0.0188.28668
Cropland8.898.0713.258.988.1212.357.888.0812.582.4650.085
<0.01112.26386<0.0199.87401<0.0199.64431
Pasture13.329.9521.4213.5610.6022.1012.0010.5022.291.2840.277
<0.01185.97469<0.01190.34464<0.01185.09505
Grassland15.863.07
a
9.0015.943.05
ab
8.9115.873.06
b
8.993.7200.024
<0.01210.231807<0.01210.611892<0.01232.762292
Woodland3.362.58
a
3.213.492.50
ab
3.353.322.64
b
3.303.4930.031
<0.0126.48457<0.0126.48507<0.0125.46557
Wetland11.376.0521.3911.696.1119.2010.395.9516.461.2480.287
<0.01378.59658<0.01295.10694<0.01241.66772
Water0.670.811.840.740.912.130.811.012.250.1750.839
0.0120.44291<0.0125.99295<0.0124.89354
abc
Valuesfollowedbythesamesuperscriptletter
a
,
b
or
c
,arenotsignificantlydifferentattheP≤0.05level(TukeyMultipleComparisonstest).

73
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Lake
Michigan
Ozaukee Co.
Milwaukee Co.
Waukesha Co.
Washington Co.
Wisconsin
Figure 1. Metropolitan Milwaukee Study Area.

74
MetropolitanMilwaukeeStudyArea(MMSA)Red-tailedHawkPopulation
0
10
20
30
40
50
60
70
80
19871988198919901991199219931994199519961997199819992000200120022003
Year
ActiveSitesorOccupiedSites
OccupiedActiveLinear(Occupied)Linear(Active)
Occupied
LinearRegression
N=15
t=7.567
P<0.001
Active
LinearRegression
N=15
t=6.298
P<0.001
Figure2.Red-tailedHawkpopulationsizefortheMMSA.
74

75
BrookfieldRed-tailedHawkPopulation
0
2
4
6
8
10
12
14
16
18
20
19871988198919901991199219931994199519961997199819992000200120022003
Year
Occupied
LinearRegression
N=15
t=4.301
P=0.001
Active
LinearRegression
N=15
t=3.068
P=0.009
Figure3.Red-tailedHawkpopulationsizeforthetownshipofBrookfield.
75

76
GranvilleRed-tailedHawkPopulation
0
2
4
6
8
10
12
14
16
18
19871988198919901991199219931994199519961997199819992000200120022003
Year
Occupied
LinearRegression
N=15
t=7.785
P<0.001
Active
LinearRegression
N=15
t=4.764
P<0.001
Figure4.Red-tailedHawkpopulationsizeforthetownshipofGranville.
76

77
Red-tailedHawkBreedingDensityandProductivity
0.0
0.5
1.0
1.5
2.0
2.5
0.00020.00040.00060.00080.00100.00120.00140.0016
BreedingDensity(activesites/ha)
Productivity(young/activesite)
MMSABrookfieldGranville
Linear(MMSA)Linear(Brookfield)Linear(Granville)
MMSA
LinearRegression
N=14
t=1.064
P=0.308Brookfield
LinearRegression
N=14
t=1.237
P=0.240
Granville
LinearRegression
N=14
t=0.301
P=0.769
Figure5.Red-tailedHawkbreedingdensityandproductivity.
77

78
Red-tailedHawkBreedingDensityandPercentageofSitesActive
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.00040.00060.00080.00100.00120.00140.00160.00180.0020
Density(occupiedsites/ha)
PercentageofSitesActive
MMSA
LinearRegression
N=15
t=-0.092
P=0.928
Brookfield
LinearRegression
N=15
t=1.094
P=0.294
Granville
LinearRegression
N=15
t=0.535
P=0.602
Figure6.Red-tailedHawkbreedingdensityandpercentageofsitesactive.
78

79
Red-tailedHawkBreedingDensityandBreedingAreaRe-Use
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.00020.00040.00050.00070.00080.00100.00110.00130.00140.0016
BreedingDensity(activesites/ha)
Re-Use(%)
MMSA
LinearRegression
N=14
t=1.776
P=0.101
Brookfield
LinearRegression
N=14
t=3.415
P=0.005
Granville
LinearRegression
N=14
t=0.871
P=0.401
Figure7.Red-tailedHawkbreedingdensityandbreedingareare-use.
79

80
1998 to 2002
1993 to 19971988 to 1992
Urban Red-tailed Hawk Habitat Expansion
N
Habitat Expansion Since 1992
Figure 8. Metropolitan Milwaukee Study Area: Urban Red-Tailed Hawk habitat
expansion. The maps include a slightly larger area than the MMSA.

81
HOW LANDSCAPE FEATURES AFFECT RED-TAILED
HAWK HABITAT SELECTION
Introduction
Habitats provide basic resource requirements such as food, cover, and other
resources for wildlife. For raptors, including Red-tailed Hawks(Buteo jamaicensis), nest-
site availability, and prey abundance and availability may be the major habitat components
that influence populations (Preston and Beane 1993, Newton 1998). While Red-tailed
Hawk habitat has been described for rural locations throughout North America (Titus and
Mosher 1981, Bednarz and Dinsmore 1982, Speiser and Bosakowski 1988), the results of
these studies may not be applicable to Red-tailed Hawk habitat in urban locations.
Stout (2004) determined that Red-tailed Hawk populations are expanding into urban
locations, however, the study did not differentiate between suitable and unsuitable habitat in
urban locations. It remains unclear whether landscape features important in habitat
selection in rural areas also play a role in habitat selection in urban areas, or Red-tailed
Hawks avoid particular urban landscape features. A better understanding of suitable habitat
in urban/suburban locations will provide a basis for determining whether suitable habitat
exists in urban areas where Red-tailed Hawks are not present.
I studied an urban/suburban Red-tailed Hawk population in the metropolitan
Milwaukee area over a 15-year period. The objectives of this study were to describe
urban/suburban Red-tailed Hawk habitat, to compare suitable and unsuitable habitat, and to
determine if suitable but unoccupied patches of habitat exist in urban locations for Red-
tailed Hawks to eventually occupy.

82
Methods
Study Area
The Metropolitan Milwaukee Study Area (MMSA) covers 63,095 ha in southeast
Wisconsin (43 N, 88 W), and includes parts of Milwaukee, Waukesha and Washington
Counties (Figure 1). Milwaukee and Ozaukee Counties are bordered by Lake Michigan to
the east. Human population density in urban locations (i.e., the city of Milwaukee) within
the study area averages 2399.5/km2
; the city of Milwaukee covers an area of 251.0 km2
composition includes urban and suburban use. Population density and human land-use
intensity decrease radially from the urban center of Milwaukee. Two interstate highways
(Interstate 43 and Interstate 94) transect the MMSA. Land cover within the study area
includes agricultural, natural, industrial/commercial, and residential areas.
farther from the urban center (Matthiae and Stearns 1981).
Nest Surveys
1974). The MMSA was surveyed completely for Red-tailed Hawk nests from 1988 through

83
2002. Woodlots that were not entirely visible from the road early in the season before leaf-
out were checked by foot.
Urban/suburban Habitat and GIS
Locations for active Red-tailed Hawk nests were mapped in a GIS. A 1000m-radius
buffer (i.e., a 314.2ha circular plot centered on the nest tree) was used to describe Red-
tailed Hawk habitat at the landscape scale; see Stout (2004) for an explanation of this
spatial scale. Thirty nests were selected randomly from 771 nesting attempts that occurred
from 1988 to 2002 within the MMSA such that the 1000m-radius buffers were completely
within the MMSA and did not overlap (to maintain independence of samples). Habitat
within these “use areas” were compared to 30 randomly generated, non-overlapping
1000m-radius circular plots located in areas within the MMSA where Red-tailed Hawks
were not present (i.e., “non-use areas”).
To describe Red-tailed Hawk habitat and compare use areas to non-use areas, I used
the Southeast Wisconsin Regional Planning Commission’s (SEWRPC) 1995 land-cover
data set (SEWRPC 1995). For the purposes of this study, 104 different SEWRPC
categories were combined into the following 12 land-cover classes: urban (high-density),
urban (low-density), roads, parking, recreational, graded, cropland, pasture, grassland,
woodland, wetland and water. See Stout (2004) for a description of the SEWRPC data set,
which SEWRPC categories are included in each of the above 12 land-cover classes, and
methods used to enter Red-tailed Hawk nest locations into a GIS. ArcView GIS version 3.3
(ESRI 2002) was used for GIS procedures and analyses. Area, perimeter and patch count
(FRAGSTSTATS metrics) were compared for each of the 12 land-cover classes (Table 1).
Eighteen additional FRAGSTATS landscape metrics were compared (Appendix C).

84
FRAGSTATS for ArcView version 1.0 (Space Imaging 2000) was used to calculate the
additional 18 FRAGSTATS metrics.
Habitat Model and Hexagon Predictions
To determine if suitable habitat exists in urban locations, I developed a prediction
model to identify locations within the urban study area that contain suitable habitat but are
not currently occupied by a Red-tailed Hawks. A complete, non-overlapping coverage of
234 contiguous 314.1ha hexagons was produced to completely cover the MMSA. The
hexagon grid was used to approximate the 314.2ha areas used for Red-tailed Hawk habitat
analysis (Stout 2004). Hexagons were also used for the following reasons: 1) hexagons
produce a complete coverage that is, for the most part, randomized, 2) hexagons produced
though a random initial base point minimize and may eliminate biases that are present due
to development practices as they relate to township sections (e.g., some roads in urban
location typically follow section lines, etc.), 3) a complete, non-overlapping coverage
produces independence of samples (i.e., no individual land-cover patch is counted more
than once, as would occur with overlapping circular plots). The SEWRPC land-cover data
were merged with the hexagon grid for analyses. Hexagons were classified as Red-tailed
Hawk use or non-use areas based on a 1000-m buffer around 1988 through 2002 nesting
attempts. Each hexagon was classified as a Red-tailed Hawk use area if its center
overlapped a 1000m buffer around a nest. Other hexagons were classified as non-use areas.
Parametric statistics (Two-sample t-test, Snedecor and Cochran 1989) were used to
compare Red-tailed Hawk use areas to non-use areas. When all values for one group of a
variable were equal to zero, a 2 by 2 contingency table and chi-square analysis (Sokal and

85
Rohlf 1981) were used to compare presence and absence between use areas and non-use
areas. All tests were considered significant when P  0.05. SYSTAT (SPSS 2000) was
used for all statistical analyses. Multivariate statistics (Logistic Regression) used the
hexagon grid to develop a model for predicting whether suitable, unoccupied Red-tailed
Hawk habitat exists in urban locations. Area, perimeter and patch count for land-cover
types, and FRAGSTATS metrics that were significantly different for Red-tailed Hawk use
and non-use areas were included in the analysis. One hundred hexagons (54 use areas and
46 non-use areas) were randomly selected from the MMSA for logistic regression analysis.
A Pearson correlation was used to identify and eliminate highly correlated variables (r ≥
0.7). Twenty of 43 variables were entered into a stepwise logistic regression analysis. The
model was applied to 134 hexagons (72 Red-tailed Hawk use areas and 62 non-use areas)
from the MMSA that were not used to develop the logistic regression model.
Results
Urban/suburban Habitat
Urban/suburban Red-tailed Hawk nesting habitat in the MMSA averages 16.9%
high-density and 16.8% low-density urban land, 14.7% roads and 10.3% other developed
land-cover types (parking, recreational and graded). Habitat includes 27.3% herbaceous
cover (18.1% grassland, 6.4% cropland and 2.8% pasture), 1.9% woodland, 11.2% wetland
and 0.9% water (Figure 2).
Habitat: Use and Non-Use Comparisons
Fifty-four variables are used to compare Red-tailed Hawk use areas to non-use areas
(Table 1, Figure 2). Thirty-seven of the 54 variables are significantly different for use areas
and non-use areas. Six variables describing cropland and pasture (area, perimeter and patch

86
count for each) were not present within non-use areas. Cropland was present in 18 of 30
use areas and 0 of 30 non-use areas, and pasture was present in 17 of 30 use areas and 0 of
30 non-use areas. Based on 2 by 2 contingency tables, the presence of both cropland and
pasture were significantly different (Chi-square test: χ2
=25.714, df=3, P<0.001; χ2
=23.721,
df=3, P<0.001, respectively) for use and non-use areas. Land cover types that were
consistently different include high and low-density urban, roads, cropland, pasture,
grassland, woodland and wetland. Sixteen of 18 FRAGSTATS metrics are different for
Red-tailed Hawk use areas compared to non-use areas (Table 1).
Habitat Model and Predictions
Of 234 hexagons across the MMSA, 126 were classified as Red-tailed Hawk use
areas and 108 were non-use areas. Of 100 randomly selected hexagons used for a
multivariate logistic regression analysis, 54 were use areas and 46 were non-use areas.
Twenty variables that were not highly correlated were entered into the analysis. High and
low-density urban area, wetland area, the number of recreational patches, and largest patch
index (FRAGSTATS metric - LPI) were included in the regression model. The regression
model was applied to the 134 hexagon that were not used to predict Red-tailed Hawk
habitat. The model correctly classified 58 of 72 (80.6%) Red-tailed Hawk use areas and 51
of 62 (82.3%) Red-tailed Hawk non-use areas for a combined 81.3% correct classification
(Figure 3).
Discussion
Urban/suburban Habitat
This study reinforces the importance of adequate hunting habitat for nesting Red-
tailed Hawks. Howell et al. (1978) correlated landscape features and productivity for rural

87
Red-tailed Hawk nest sites in Ohio, and report that high productivity sites had more than
twice as much fallow land and less than half as much cropland and woodland than did low
productivity sites. A significant part of suitable habitat includes grassland and other
herbaceous cover types. Some type of roads such as freeways and the large intersections
associated with them provide this type of good hunting habitat. Cemeteries and recreational
areas such as golf courses and parks also may provide suitable hunting and nesting habitat
in urban locations. Janes (1984) correlated hunting perch density and reproductive success;
sites with high reproductive success have a higher perch density than sites with low
reproductive success. However, as Red-tailed Hawks nest on and hunt from human-made
structures in urban areas (Stout 2004, Stout et al. 1996), the amount of woodland area may
be less important than in rural locations.
Habitat: Use and Non-Use Comparisons
Use areas contain fewer land-cover patches with a larger average size, and have
greater land-cover diversity and patch richness compared to non-use areas. Non-use areas
have more than three times as much high-density urban land and twice as much road area,
but less than one-tenth as much low-density urban land. More than three times as much
grassland and woodland areas were present in Red-tailed Hawk use areas compared to non-
use areas. Use areas also frequently contain agricultural land (cropland and pasture) and
wetlands. These characteristics suggest that Red-tailed Hawks are avoiding areas of
heaviest urbanization at this time, probably because of insufficient hunting habitat and
possibly unsuitable nesting locations.

88
Habitat Model and Predictions
The logistic regression model included five variables and correctly classified 81.3%
of 134 hexagons. Thus, five variables (high and low-density urban area, wetland area, the
number of recreational patches, and largest patch index) explain approximately 81% of the
differences between use and non-use areas. While the model may be useful in predicting
Red-tailed Hawk presence and absence with 81% accuracy, it may not be useful in
predicting whether suitable Red-tailed Hawk habitat exists in urban locations. For this
model, approximately the same percentage of use hexagons (19.4%) and non-use hexagons
(17.7%) were incorrectly classified. The likelihood for both types of error, error of
omission (i.e., incorrectly classify use hexagons) and error of commission (i.e., incorrectly
classify non-use hexagons), within any randomly generated model are equal. For this
model to predict that suitable habitat exists in urban locations, the error rates must be
different, with the error of commission being greater than the error of omission. In this
case, some non-use hexagons which the model classifies as use hexagons (error of
commission) may represent suitable but unoccupied Red-tailed Hawk habitat in urban
locations. The model developed in this study has equal error rates and, therefore, does not
suggest (i.e., fails to predict) that suitable habitat exists in the MMSA where Red-tailed
Hawks are not already present. Since the population is increasing in the MMSA,
urban/suburban Red-tailed Hawks may be adapting to new habitat conditions as Stout
(2004) suggests, rather than simply occupying patches that resemble habitat in rural areas.
Conclusion
Suitable Red-tailed Hawk habitat in urban/suburban Milwaukee includes large areas
of grassland and other herbaceous cover types. Freeways and freeway intersections, parks,

89
golf courses and cemeteries may provide this suitable hunting and nesting habitat. With
Red-tailed Hawks nesting on and hunting from human-made structures in urban areas, the
amount of woodland area may be less important in urban than rural locations. Red-tailed
Hawk use areas have more than three times as much grasslands and woodlands compared to
non-use areas. In heavily developed urban areas Red-tailed Hawks may be adapting to
urbanization, rather than simply occupying patches that resemble rural habitat.
Acknowledgements
Literature Cited
Bednarz, J.C. and J.J. Dinsmore. 1982. Nest sites and habitat of Red-shouldered and Red-
tailed Hawks in Iowa. Wilson Bulletin 94:31-45.

90
Curtis, J.T. 1959. The vegetation of Wisconsin: An ordination of plant communities.
Howell, J., B. Smith, J.B. Holt and D.R. Osborne. 1978. Habitat structure and productivity
in the Red-tailed Hawk. Bird Banding 49:162-171.
USA.
Preston, C.R. and R.D. Beane. 1993. Red-tailed Hawk Buteo jamaicensis. In A. Poole and
F. Gill, eds. The birds of North America, No. 52. The Academy of Natural Sciences,
The American Ornithologists' Union, Washington, D.C. USA. 24 pp.

91
Smallwood, K.S. 2002. Habitat models based on numerical comparisons. Pages 83-95 in
J.M. Scott, P.J. Heglund, M. Morrison, M. Raphael. J. Haufler and B. Wall, eds.
Predicting species occurrences: Issues of scale and accuracy. Island Press,
Washington, D.C. USA.
Snedecor, G.W. and W.G. Cochran. 1989. Statistical Methods, Eighth Edition. Iowa State
University Press, Iowa USA.
USA.
Speiser, R. and T. Bosakowski. 1988. Nest site preferences of Red-tailed Hawks in the
highlands of southeastern New York and northern New Jersey. Journal of Field
Wisconsin USA.
Stout, W.E., R.K. Anderson and J.M. Papp. 1996. Red-tailed Hawks nesting on human-
made and natural structures in southeast Wisconsin. Pages 77-86 in D.M. Bird,
D.E. Varland and J.J. Negro, eds. Raptors in human landscapes. Academic Press,
London, England.

92
Titus, K. and J.A. Mosher. 1981. Nest-site habitat selected by woodland hawks in the
central Appalachians. Auk 98:270-281.

93
93
Table1.Red-tailedHawkuseareaswerecomparedtonon-useareasatthelandscapescale(1000-mradius).Land-cover
typearea(ha),perimeter(m),patchcountsandFRAGSTATmetricsarereported.
Red-tailedHawkUseRed-tailedHawkNon-Use
Urban(highdensity)Area52.839.6125.80.930170.331.0212.3112.63012.794<0.001
Urban(highdensity)Perimeter21083.715149.450521.2639.33072856.013876.6102004.640622.13013.803<0.001
Urban(highdensity)Count42.928.2105.02.030158.347.2263.074.03011.493<0.001
Urban(lowdensity)Area52.456.5180.30.0304.918.786.10.030-4.379<0.001
Urban(lowdensity)Perimeter16745.316665.452796.80.0301434.15422.424626.30.030-4.785<0.001
Urban(lowdensity)Count24.721.070.00.0301.97.132.00.030-5.623<0.001
RoadArea46.018.884.616.03078.310.6113.159.1308.209<0.001
RoadPerimeter32797.111881.056660.09051.23071240.611407.993690.855761.43012.784<0.001
RoadCount13.06.828.03.03027.213.563.09.0305.144<0.001
ParkingArea15.212.342.60.23018.111.143.25.1300.9540.344
ParkingPerimeter9729.16957.026939.1191.43015991.37404.237059.95891.4303.3760.001
ParkingCount26.318.972.01.03074.635.2177.019.0306.617<0.001
RecreationalArea13.021.883.30.03013.59.834.60.0300.1170.907
RecreationalPerimeter2693.13826.715100.50.0303841.92871.39355.50.0301.3150.194
RecreationalCount2.33.213.00.0305.24.218.00.0303.0430.004
GradedArea3.98.645.10.0301.52.811.20.030-1.3990.167
GradedPerimeter1350.31558.66435.50.0302645.95241.822787.80.0301.2980.200
GradedCount4.34.518.00.03023.748.3222.00.0302.1940.032
CroplandArea20.026.291.90.0300.00.00.00.030**
CroplandPerimeter3270.44002.112243.70.0300.00.00.00.030**
CroplandCount2.53.012.00.0300.00.00.00.030**
PastureArea8.913.857.20.0300.00.00.00.030**
PasturePerimeter2273.03349.212765.70.0300.00.00.00.030**
PastureCount2.43.814.00.0300.00.00.00.030**
GrasslandArea56.527.8112.915.83020.615.761.22.030-6.173<0.001
GrasslandPerimeter16704.76313.629191.24978.5309153.85539.720488.11902.530-4.924<0.001
GrasslandCount19.87.836.08.03025.622.398.04.0301.3410.185
*Insufficientdatafortest.

94
94
Table1(cont’d).
Red-tailedHawkUseRed-tailedHawkNon-Use
WoodlandArea6.07.129.10.0301.35.027.40.030-2.9970.004
WoodlandPerimeter1849.21724.45910.80.030465.01527.38084.40.030-3.2910.002
WoodlandCount2.82.29.00.0300.61.79.00.030-4.323<0.001
WetlandArea35.146.2195.30.0300.82.08.70.030-4.063<0.001
WetlandPerimeter6472.35408.221006.50.030418.61056.34775.10.030-6.017<0.001
WetlandCount5.83.814.00.0300.51.57.00.030-7.059<0.001
WaterArea2.73.717.40.0303.27.226.80.0300.3340.740
WaterPerimeter1632.72151.69144.20.0301316.02713.110444.10.030-0.5010.618
WaterCount2.52.711.00.0301.93.513.00.030-0.7510.456
NP149.1741.59219.0058.0030319.43136.38645.00164.00306.541<0.001
MPS2.310.865.391.43301.130.401.910.4830-6.803<0.001
MSI1.700.081.841.54301.610.081.791.5030-4.162<0.001
MPFD1.410.071.581.26301.440.041.571.35301.6720.100
PSSD5.242.6514.242.58302.350.703.841.3430-5.763<0.001
LPI13.627.8333.145.15307.912.1014.324.2330-3.856<0.001
PD47.7213.3170.0718.5630102.2043.63206.3652.47306.541<0.001
PSCV225.0162.38375.64125.8030219.2654.61374.38158.5930-0.3800.705
AWMSI2.400.273.211.88303.240.403.872.35309.467<0.001
DLFD1.400.021.441.37301.410.021.461.37302.6510.010
AWMPFD1.360.021.381.32301.400.011.431.38308.879<0.001
SHDI1.740.222.141.37301.210.221.610.8930-9.428<0.001
SIDI0.770.070.870.58300.610.090.770.4830-7.798<0.001
MSIDI1.510.282.020.88300.970.241.470.6630-8.060<0.001
SHEI0.750.080.890.59300.640.100.830.5030-5.006<0.001
SIEI0.860.070.950.65300.720.100.900.5830-6.133<0.001
MSIEI0.650.110.820.38300.510.110.750.3730-4.890<0.001
PR10.201.3212.007.00306.700.928.005.0030-11.913<0.001

95
N
Red-tailed Hawk Use and Non-Use Areas
Lake
Michigan
Milwaukee Co.
Ozaukee Co.
Waukesha Co.
Washington Co.
7 0 7 14 Kilometers
MMSA
Buffers
Red-tailed Hawk Use
Red-tailed Hawk Non-Use
Key to Features
Figure 1. Metropolitan Milwaukee Study Area: Red-tailed Hawk use and non-use
areas.

96
Red-tailedHawkUseAreas
Water,0.9%
Wetland,11.2%
Woodland,1.9%
Grassland,18.1%
Pasture,2.8%
Cropland,6.4%
Graded,1.2%
Recreational,
4.2%
Parking,4.9%
Roads,14.7%
Low-Density
Urban,16.8%
High-Density
Urban,16.9%
Non-UseAreas
Roads,25.1%
Low-Density
Urban,1.6%
High-Density
Urban,54.5%
Woodland,0.4%Wetland,0.3%
Water,1.0%
Grassland,6.6%
Pasture,0.0%
Cropland,0.0%
Graded,0.5%
Recreational,
4.3%
Parking,5.8%
Figure2.Land-covercompositionforRed-tailedHawkuseareasandnon-useareas.
96

97
Red-tailed Hawk
Habitat Model
Ozaukee Co.
Washington Co.
Milwaukee Co.
Waukesha Co.
Lake
Michigan
N
8 0 8 16 Kilometers
Model Application (Incorrect)
RTHA Use (N=14)
Non-Use (N=11)
Model Application (Correct)
RTHA Use (N=58)
Non-Use (N=51)
Model Hexagons
RTHA Use
Non-Use
Key to Features
Figure 3. Predictions of the Red-tailed Hawk habitat model.

98
CONSISTENT FEATURES OF RED-TAILED HAWK HABITAT ACROSS
RURAL, SUBURBAN AND URBAN LANDSCAPES
Introduction
Habitat for Red-tailed Hawks (Buteo jamaicensis) has been described for rural
locations throughout North America, and has been compared to random locations to
identify habitat features that Red-tailed Hawks consistently select (Titus and Mosher 1981,
Bednarz and Dinsmore 1982, Speiser and Bosakowski 1988). However, these studies may
not be applicable to habitat in urban locations. Stout (2004) correlated habitat quality and
reproductive success for an urban/suburban Red-tailed Hawk population, and compared
habitat to non-habitat, but he did not determine consistent habitat features.
Comparing features across a wide variety of landscape types such as urban,
suburban and rural locations, and at different scales may provide additional insight into
which features are consistent habitat components, and at which scale or scales they are
consistent. Consistencies across different landscape types may constitute important habitat
components. Stout et al. (1998) compared Red-tailed Hawk habitat features for urban,
suburban and rural locations over a 6-yr period. This study extends the data to a 15-yr
period, and uses GIS methods and a standardized land-use data set.
I studied a Red-tailed Hawk population in southeast Wisconsin over a 15-year
period. The objectives of this study are to describe and compare habitat in urban, suburban
and rural areas at three different scales, to determine consistent habitat components at each
scale, and to suggest ways to use consistent Red-tailed Hawk habitat components to
measure performance of land-use planning models.

99
Methods
Study Area
The Southeast Wisconsin Study Area (SWSA) covers approximately 1600 km2
located in the metropolitan Milwaukee area of southeast Wisconsin (43 N, 88 W), and
includes Milwaukee County and parts of Waukesha, Washington and Ozaukee Counties
(Figure 1). Milwaukee and Ozaukee Counties are bordered by Lake Michigan to the east.
Milwaukee County covers an area of 626.5 km2
. Human population density in urban
locations (i.e., the city of Milwaukee) within Milwaukee County averages 2399.5/km2
; the
city of Milwaukee covers an area of 251.0 km2
with a human population of 596,974 (United
States Census Bureau 2000). Landscape composition ranges from high-density urban use
to suburban communities and rural areas. Population density and human land-use intensity
decrease radially from urban to rural. Two interstate highways (Interstate 43 and Interstate
94) transect the study area. Land cover within the study area includes agricultural, natural,
industrial/commercial, and residential areas.
Nest Surveys

100
1974). An active nest is a nest in which eggs were laid and constitutes a nesting attempt
(Postupalsky 1974). Consistent nest searching efforts were made within a survey area.
Woodlots within an intensive study area that were not entirely visible from the road early in
the season before leaf-out were checked by foot.
Urban, Suburban and Rural Comparisons, and GIS
Habitats for urban, suburban and rural Red-tailed Hawk nesting locations were
compared at three different scales around active nests: landscape, macrohabitat and nest
area. “Landscape” describes habitat within a 1000m-radius buffer area (314.2ha) around
nests, “macrohabitat” describes habitat within a 250-m radius buffer area (19.6ha) and “nest
area” describes habitat within a 100-m radius buffer area (3.1ha; Stout 2004). A nest was
classified as urban if  70% of the landscape (1000m-radius buffer area) consisted of high-
density urban, low-density urban, roads and parking land cover (i.e., developed), suburban
if > 30% and < 70%, and rural if  30% was developed (Stout et al. 1998). For the habitat
comparisons, 25 of 55 urban nests were selected that covered nearly all habitat that was
classified as urban (Figure 2). Overlap of the 1000-m buffer areas (landscape) was allowed
only for urban habitat, and only to produce an adequate sample size for comparison (i.e.,
N=25). Pseudoreplication was therefore allowed (with reservations and concern) at the
landscape scale for urban habitat only. Minimal overlap (i.e., negligible pseudoreplication)
of the 250-m buffer areas (macrohabitat) and no overlap (i.e., no pseudoreplication) of the
100-m buffer areas (nest area) occurred for urban habitat, such that the analyses for the
habitat comparisons were valid at these scales (i.e., samples maintained independence).
Twenty-five random nests were selected from each suburban and rural area such that the

101
1000-m buffer areas (landscape) did not overlap for independence (i.e., no
pseudoreplication) of samples (Figure 2).
To describe and compare habitat in urban, suburban and rural areas, I used the
Southeast Wisconsin Regional Planning Commission’s (SEWRPC) 1995 land-cover data
set (SEWRPC 1995) and combined 104 different SEWRPC categories into the following 12
land-cover types: urban (high-density), urban (low-density), roads, parking, recreational,
graded, cropland, pasture, grassland, woodland, wetland and water (Figure 1). See Stout
(2004) for a description of the SEWRPC data set, which SEWRPC categories are included
in each of the above 12 land-cover types, and methods used to enter Red-tailed Hawk nest
locations into a GIS. The percent area for each of the 12 land-cover types was used to
describe and compare urban, suburban and rural Red-tailed Hawk habitat. Two additional,
combined categories, hunting habitat and nesting habitat, were compared. Hunting habitat
consists of recreational, graded, cropland, pasture and grassland; and nesting habitat
consists of recreational land and woodlands. Recreational land (e.g., golf courses, county
parks) was included in both hunting and nesting habitat because it probably provides both
suitable hunting and nesting locations. ArcView GIS version 3.3 (ESRI 2002) was used for
GIS procedures and analyses.
Consistencies (i.e., habitat features that are not significantly different) across urban,
suburban and rural areas were identified at the different scales (i.e., landscape, macrohabitat
and nest area). Habitat features that are significantly different across urban, suburban and
rural areas (e.g., the amount of high-density urban land) are probably the result of human
development, not habitat selection by Red-tailed Hawks. Conversely, features that are not
significantly different (i.e., are consistent) across different areas may constitute important

102
habitat features because they are consistently present within the habitat. The appropriate
patch size for each consistent habitat feature was determined by selecting entire patches that
intersected the different buffer scales (i.e., landscape, macrohabitat and nest area).
A One-way Analysis of Variance (ANOVA, Sokal and Rohlf 1981) was used to
compare Red-tailed Hawk habitat in urban, suburban and rural locations. All tests were
considered significant when P  0.05. SYSTAT (SPSS 2000) was used for all statistical
analyses.
Results
At the landscape scale, nine of the 12 habitat cover types and the two combined
categories, hunting and nesting habitat, were significantly different; three habitat cover
types (recreational, graded and water) were not significantly different (Table 2, Figure 3).
At the macrohabitat scale, eight of the 12 habitat cover types, and hunting and nesting
habitat were significantly different; four habitat cover types (recreational, graded, wetland
and water) were not significantly different (Table 3, Figure 4). At the nest area scale, six of
the 12 habitat cover types and the combined category, nesting habitat, were significantly
different; six habitat cover types (low-density urban, recreational, graded, cropland, wetland
and water), and the combined category, hunting habitat, were not significantly different
(Table 4, Figure 5).
Wetland and hunting habitat were not significantly different for urban, suburban and
rural locations, and comprised a large percentage of the nest area. Patch size that
intersected (i.e., overlapped) the nest area averaged 12.4ha (range: 3.4-24.4ha, STD=9.9,
N=5) for wetlands and 7.0ha (range: 0.1-27.6ha, STD=7.3, N=31) for hunting habitat.

103
While significantly different for urban, suburban and rural locations, woodland habitat
comprised 8.5% of urban nest areas (Table 4). No recreational land was present within
urban nest areas; therefore, nesting habitat consisted of woodlands only. Woodland patch
size that intersected the nest area averaged 9.0ha (range: 3.4-12.6ha, STD=4.0, N=4).
Wetland habitat was not significantly different for urban, suburban and rural
locations, and comprised a large percentage of the macrohabitat (i.e., 250m buffer).
Wetland patch size that intersected the macrohabitat averaged 7.7ha (range: 0.2-24.4ha,
STD=8.3, N=14).
Discussion
Urban, Suburban and Rural Comparisons
Habitats in urban, suburban and rural areas are defined by land cover at the
landscape scale (i.e., amount of developed land: high and low-density urban land, roads and
parking area), and therefore, differences between urban, suburban and rural areas are
expected. In the absence of habitat selection, varying scales (i.e., landscape, macrohabitat
and nest area) should not be significantly different. However, Stout (2004) documented
that significant differences exist at varying scales, and therefore, nesting habitat selection
probably occurs at smaller scales. Habitat cover types that are not significantly different at
the landscape scale (i.e., recreational and graded land, and water) are probably due to the
small percent coverage and large variations. These habitat cover types are also not
significantly different at the macrohabitat and nest area scales, and individually, comprise a
small percentage of the areas with large variations. Hunting habitat and wetlands are
consistently present in urban, suburban and rural habitat at the nest area scale (i.e., within

104
100m of nests) and comprise a large proportion of the area, and therefore, may constitute
important habitat components.
Wetlands are not significantly different at either the macrohabitat or nest area scales
and comprise a large percentage of the areas (8 to 29%), and therefore are a consistent
habitat component. In areas with a greater percentage of development (i.e., urban and
suburban locations) they comprise 20 to 30% of the macrohabitat and nest areas. Because
of the sensitive nature of wetlands and a number of benefits that they provide, the land-use
planning process tends to preserve these areas as other areas are developed. Wetlands may
provide a natural type of buffer between human activity and Red-tailed Hawk nesting
activity. However, Stout (2004) reported that low-productivity Red-tailed Hawk nesting
habitat has significantly more wetlands than high-productivity habitat. While wetlands are
consistently present at both the macrohabitat and nest area scales, and are left undeveloped,
they may not provide high-quality habitat.
Hunting habitat is comprised of recreational and graded land, agricultural land (i.e.,
cropland and pasture), and grasslands. Hunting habitat is significantly different for urban,
suburban and rural Red-tailed Hawk nesting locations at both the landscape scale and
macrohabitat scale; however, it is not significantly different within the nest area. Hunting
habitat consistently comprises, on average, about 35% of the nest area (34 to 36%). The
consistency of hunting habitat at this relatively small scale (i.e., nest area) but not at the
macrohabitat scale is not necessarily expected. Stout (2004) noted that, in a multi-scale
analysis of Red-tailed Hawk nesting habitat, the percent composition of pasture, cropland
and grassland increased slightly from 250 to 750m around nests: an area and distance from
nests that may be more consistent with hunting patterns.

105
Nesting habitat is comprised of woodlands and recreational land, and is not
significantly different for urban, suburban and rural locations at any of the three scales:
landscape, macrohabitat or nest area. Stout (2004) documented 65 Red-tailed Hawk nesting
attempts on 16 different human-made structures, and suggests that nest site availability may
not be a major limit factor in urban locations because Red-tailed Hawks are nesting on
human-made structures and may be adapting to the urban environment. The data presented
here supports this hypothesis because nesting habitat is not consistent within urban,
suburban and rural Red-tailed Hawk habitat.
An Application for Land-use Planning
Maintaining biological diversity within developed ecosystems may be the best
attainable goal for landscape planners (Blum 1989). Avian species, top predators, and
species that occupy large home ranges (e.g., Red-tailed Hawks) are commonly used as
flagship, focal or target species for land-use planning purposes (Hildebrandt and Yarchin
1999, Ranta et al. 1999). Many raptors persist and even thrive in urban locations because
they are tolerant of human-altered habitats and benefit from enhanced prey populations.
Urban and regional planners can use consistent Red-tailed Hawk habitat features
and their composition to measure the performance of comprehensive land-use planning
models such as “Smart Growth” (Gibson and Taft 2001, Bernstein 2003) when considering
wildlife and biodiversity in urban locations. Current land-use planning practices focus on
incorporating plant, not animal, communities into urban areas. While the plant-community-
based land-use planning approach has mixed results (Schamberger and O’Neil 1986, Kilgo
et al. 2002), this application using animal-species-based habitat can validate the plant-
community-based approach.

106
Consistent features of Red-tailed Hawk habitat (i.e., across urban, suburban and
rural landscapes) include wetlands and hunting habitat. Hunting habitat in urban locations
consists of grasslands, and graded and recreational land. Freeways, freeway intersections
and cemeteries also may provide suitable hunting habitat. Habitat features described in this
section should be considered minimum habitat composition for urban locations based on the
definition of “urban” presented in this paper (i.e.,  70% of the landscape developed:
consisting of high-density urban, low-density urban, roads and parking land cover).
Within urban locations, patches of Red-tailed Hawk hunting habitat average 7ha,
range in size from 1-30ha, and comprise approximately 17% of the urban landscape (e.g.,
1000m buffers). Wetlands may provide a natural buffer between human activity and Red-
tailed Hawk nesting activity. This characteristic may be important at a larger scale because
wetlands are consistent within both the macrohabitat and nest area. Within urban habitat,
patches of wetlands average 12ha, range in size from 3-25ha, and comprise approximately
4% of the urban landscape.
Because nesting habitat (i.e., woodlands) is not consistent across urban, suburban
and rural habitats, it may not be as important in urban areas as rural areas. However, I
suggest that, because woodlands comprise 8.5% of nest areas, it contributes to overall
habitat suitability for Red-tailed Hawks. Within urban habitat, patches of woodlands
average 9ha, range from 3-13ha, and comprise approximately 3% of the urban landscape.
Red-tailed Hawks may respond to habitat composition at a smaller scale (i.e., 100m
buffer area) because the consistent habitat features were identified within the nest area.
Therefore, patches of hunting and nesting habitat may be clustered around naturally
occurring wetlands to form clusters of Red-tailed Hawk nesting habitat within 3-5ha areas.

107
Additional wetlands within 20ha surrounding these habitat clusters may be beneficial as a
natural buffer.
These consistent Red-tailed Hawk habitat components should be considered
minimum requirements for urban locations. This study provides an additional tool for
urban and regional planners to assess the performance of comprehensive land-use plans that
include wildlife habitat in urban locations to maintain biodiversity.
Conclusion
Hunting habitat and wetlands are consistently present in urban, suburban and rural
habitat at the nest area scale (i.e., within 100m of nests), and therefore, may constitute
important habitat components. Wetlands may provide a buffer between Red-tailed Hawks
and people, but they may not provide high-quality habitat. Because traditional nesting
habitat is not consistently present in urban, suburban and rural locations, and because Red-
tailed Hawks appear to be adapting to urbanization by nesting on human-made structures,
nest-site availability may not be a major limiting factor in urban locations. Consistent Red-
tailed Hawk habitat components (i.e., hunting habitat and wetlands) and nesting habitat
(i.e., woodlands) can be used to measure performance of comprehensive land-use planning
models such as “Smart Growth.”
Acknowledgements

108
Literature Cited
Bednarz, J.C. and J.J. Dinsmore. 1982. Nest sites and habitat of Red-shouldered and Red-
tailed Hawks in Iowa. Wilson Bulletin 94:31-45.
Bernstein, R.A. 2003. A guide to Smart Growth and cultural resource planning.
Wisconsin Historical Society, Madison, Wisconsin USA.
Blum, L.L. 1989. Influencing the land-use planning process to conserve raptor habitat.
Pages 287-297 in B.A. Giron Pendleton, ed. Proceedings of the western raptor
management symposium and workshop. National Wildlife Federation, Washington,
D.C. USA.

109
Gibson, T. and G.A. Taft. 2001. Making the brownfield-transportation link: Smart Growth
options for states and metropolitan areas. ECOStates, located at:
http://www.epa.gov/opei/ecos010611.htm. Last visited 05/01/2004.
Hildebrandt, T. and J. Yarchin. 1999. Urban raptors. Arizona Wildlife Views 42: 8-10.
Kilgo, J.C., D.L. Gartner, B.R. Chapman, J.B. Dunning Jr., K.E. Franzreb, S.A.
Gauthreaux, C.H. Greenberg, D.L. Levey, K.V. Miller and S.F. Pearson. 2002. A
test of an expert-based bird-habitat relationship model in South Carolina. Wildlife
Society Bulletin 30:783-793.
Ranta, P., A. Tanskanen, J. Niemela and A. Kurtto. 1999. Selection of islands for
conservation in the urban archipelago of Helsinki, Finland. Conservation Biology
13:1293-1300.
Schamberger, M.L. and L.J. O’Neil. 1986. Concepts and constraints of habitat-model
testing. Pages 5-10 in J. Verner, M.L. Morrison and C.J. Ralph, eds. Wildlife 2000:
Modeling Habitat Relationships of Terrestrial Vertebrates. Fort Collins, Colorado,
USA.

110
USA.
Speiser, R. and T. Bosakowski. 1988. Nest site preferences of Red-tailed Hawks in the
highlands of southeastern New York and northern New Jersey. Journal of Field
Wisconsin USA.
Titus, K. and J.A. Mosher. 1981. Nest-site habitat selected by woodland hawks in the
central Appalachians. Auk 98:270-281.

111
Table1.ComparisonofRed-tailedHawkhabitatforurban,suburbanandrurallocationsatthelandscapescale(1000m-radius
buffer).Valuesareforpercentarea.
UrbanSuburbanRuralOne-wayANOVA
VariablesMeanSEMaxMinNMeanSEMaxMinNMeanSEMaxMinNFP
Urban(highdensity)23.43.854.01.52513.72.030.30.1252.00.510.10.02518.578<0.001
Urban(lowdensity)29.24.557.40.02516.22.436.00.02510.21.323.91.42510.194<0.001
Roads19.21.027.612.42511.60.819.85.5255.50.512.32.22575.781<0.001
Parking5.80.816.80.3253.30.57.70.0250.40.23.90.02521.956<0.001
Recreational1.40.48.30.0253.41.222.90.0251.70.712.40.0251.5220.225
Graded0.70.23.10.0251.30.411.00.0252.00.920.20.0251.3330.270
Cropland0.70.35.50.02510.21.828.80.02512.62.342.50.02514.047<0.001
Pasture0.60.35.50.02510.42.437.80.02538.44.282.26.62549.431<0.001
Grassland13.51.328.33.62515.71.940.93.1256.20.818.10.52512.459<0.001
Woodland1.30.46.50.0253.40.613.40.2254.60.612.30.0259.155<0.001
Wetland3.91.016.70.0259.31.727.50.22514.82.647.40.3258.4220.001
Water0.30.13.60.0251.60.614.10.0251.51.023.80.0251.1850.312
Hunting16.91.229.64.72540.92.256.819.82560.92.782.836.925109.355<0.001
Nesting2.70.59.00.1256.81.323.10.6256.30.919.90.4255.2280.008
Developed(%)77.61.395.370.32544.81.761.731.32518.11.329.17.825420.915<0.001
111

112
Table2.ComparisonofRed-tailedHawkhabitatforurban,suburbanandrurallocationsatthemacrohabitatscale(250m-
radiusbuffer).Valuesareforpercentarea.
Urban(lowdensity)15.33.964.90.0257.72.748.10.0251.10.49.30.0256.6100.002
Roads18.62.657.71.8256.51.220.90.0253.31.224.30.02519.658<0.001
Parking5.01.221.30.0253.00.916.10.0250.10.01.00.0258.3350.001
Recreational0.50.34.70.0252.41.326.20.0251.91.227.00.0250.8640.426
Graded1.00.921.20.0251.40.917.70.0250.00.00.00.0251.0970.339
Cropland0.30.23.70.02514.74.368.40.02511.84.882.40.0254.1340.020
Pasture1.51.020.70.02511.44.481.20.02537.97.098.20.02515.416<0.001
Grassland25.24.191.10.02513.03.254.30.0256.12.658.90.0258.3350.001
Woodland5.02.439.70.02510.22.132.90.02515.83.250.40.0254.2790.018
Wetland7.52.443.30.02520.25.485.70.02520.95.694.00.0252.6030.081
Water0.30.23.00.0251.00.820.50.0250.60.36.60.0250.5020.607
Hunting28.64.195.80.02542.95.491.61.82557.75.398.20.0258.628<0.001
Nesting5.52.439.70.02512.62.551.00.02517.73.559.80.0254.6650.012
112

113
Table3.ComparisonofRed-tailedHawkhabitatforurban,suburbanandrurallocationsatthenestareascale(100m-radius
buffer).Valuesareforpercentarea.
Urban(lowdensity)11.65.485.70.0255.32.029.90.0250.20.26.00.0252.9240.060
Roads14.83.458.30.0253.91.420.70.0251.10.819.70.02510.981<0.001
Parking4.11.323.40.0251.20.612.20.0250.00.00.00.0256.6340.002
Recreational0.00.00.00.0250.60.615.30.0250.40.48.60.0250.5950.554
Graded1.81.741.60.0250.60.615.20.0250.00.00.00.0250.7640.470
Cropland1.00.923.10.02513.85.487.30.0256.23.559.80.0252.9660.058
Pasture1.41.017.90.02510.64.699.50.02525.76.4100.00.0257.0790.002
Grassland30.16.1100.00.0258.12.945.60.0253.72.560.70.02511.432<0.001
Woodland8.54.884.40.02524.55.789.20.02532.85.780.70.0255.1860.008
Wetland11.15.178.00.02526.97.6100.00.02529.27.5100.00.0252.0690.134
Water0.00.00.00.0250.60.614.50.0250.60.512.30.0250.5930.556
Hunting34.36.0100.00.02533.86.6100.00.02536.06.0100.00.0250.0350.965
Nesting8.54.884.40.02525.16.095.10.02533.25.880.70.0255.1650.008
113

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Milwaukee Co.
Ozaukee Co.
Washington Co.
Waukesha Co.
Lake
Michigan
N
Southeast Wisconsin
Study Area
Wisconsin
Red-tailed Hawk Nests#S
Urban (high density)
Urban (low density)
Roads
Parking
Recreational
Graded
Cropland
Pasture
Grassland
Woodland
Wetland
Water
Key to Features
Figure 1. Southeast Wisconsin Study Area (SWSA). The Southeast Wisconsin
Regional Planning Commission (SEWRPC) data set was combined into
the above 12 land-cover classes.

115
Urban, Suburban and Rural
Southeast Wisconsin Study Area
Lake
Michigan
Milwaukee Co.
Ozaukee Co.
Waukesha Co.
Washington Co.
N
Urban
Suburban
Rural
Key to Features
Figure 2. Landscape-scale buffers (1000-m radius) around urban, suburban and rural
nests in the Southeast Wisconsin Study Area.

116 116
Urban
Pasture,0.6
Cropland,0.7
Graded,0.7
Recreational,1.4
Water,0.3Wetland,3.9
Woodland,1.3
Grassland,13.5
Parking,5.8
Roads,19.2
Urban(low
density),29.2
Urban(high
density),23.4
Suburban
Water,1.6
Wetland,9.3
Woodland,3.4
Parking,3.3
Recreational,3.4
Graded,1.3
Roads,11.6
Urban(low
density),16.2
Urban(high
density),13.7
Cropland,10.2
Pasture,10.4
Grassland,15.7
Figure3.Landscape(1000mbufferarea)composition(%)aroundurban,suburbanandruralRed-tailedHawknestsin
theSoutheastWisconsinStudyArea.
Rural
Graded,2.0
Cropland,12.6
Pasture,38.4
Grassland,6.2
Woodland,4.6
Wetland,14.8
Water,1.5
Recreational,1.7
Parking,0.4
Roads,5.5
Urban(low
density),10.2
Urban(high
density),2.0

117
117
Urban
Water,0.3
Wetland,7.5
Woodland,5.0
Grassland,25.2
Parking,5.0
Recreational,0.5
Graded,1.0
Cropland,0.3
Roads,18.6
Urban(low
density),15.3
Urban(high
density),19.7
Pasture,1.5
Suburban
Pasture,11.4
Cropland,14.7
Graded,1.4
Recreational,2.4
Parking,3.0
Roads,6.5
Urban(low
density),7.7
Urban(high
density),8.4
Water,1.0
Wetland,20.2
Grassland,13.0
Woodland,10.2
Rural
Water,0.6
Urban(high
density),0.4
Recreational,1.9
Graded,0.0
Parking,0.1
Urban(low
density),1.1Roads,3.3
Cropland,11.8
Pasture,37.9
Grassland,6.1
Woodland,15.8
Wetland,20.9
Figure4.Macrohabitat(250mbufferarea)composition(%)aroundurban,suburbanandruralRed-tailedHawknests
intheSoutheastWisconsinStudyArea.

118
118
Urban
Parking,4.1
Pasture,1.4
Recreational,0.0
Graded,1.8
Cropland,1.0
Roads,14.8
Urban(low
density),11.6
Urban(high
density),15.6Wetland,11.1
Woodland,8.5
Grassland,30.1
Water,0.0
Suburban
Recreational,0.6
Parking,1.2
Roads,3.9
Urban(low
density),5.3
Wetland,26.9
Woodland,24.5
Grassland,8.1
Pasture,10.6
Cropland,13.8
Water,0.6
Urban(high
density),3.9
Graded,0.6
Figure5.Nestarea(100mbufferarea)composition(%)aroundurban,suburbanandruralRed-tailedHawknestsin
theSoutheastWisconsinStudyArea.
Rural
Water,0.6
Graded,0.0
Cropland,6.2
Recreational,0.4
Urban(high
density),0.0
Roads,1.1Urban(low
density),0.2
Parking,0.0
Wetland,29.2
Woodland,32.8
Grassland,3.7
Pasture,25.7

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WHERE IN THE CITY ARE RED-TAILED HAWKS?
THE CONCEPTUAL BASIS FOR A GIS EDUCATION UNIT
Introduction
Computer technologies such as Geographic Information Systems (GIS) are teaching
tools that encourage students to use higher level critical thinking skills. Integrating
technology in ways that “foster student-centered learning, promote critical thinking, and
support authentic assessment has been heralded by the federal government, national
professional organizations, and teacher education accreditation agencies for over a decade”
(Cunningham and Stewart 2003). Visual processing skills, including computer-based
learning, are correlated with standardized math and science assessments (Dickey and
Roblyer 1997, Neisser 1997). GIS computer technology can be used to integrate many
different areas into an interdisciplinary unit or project that encourages students to use
higher level thinking skills.
A Geographic Information System (GIS) is a computerized tool designed to answer
geographic questions and is commonly used as a research tool (Lawrence 1997, Worah et al
1989, Harris et al. 1995, Nevo and Garcia 1996), and as a tool in land-use planning
(DeGouvenain 1995, Delorme 1998). A GIS stores multiple types of information about a
particular site or location in several “data sets” or “layers”. These data layers are linked
through geographic coordinate systems and can be overlaid one on top of another to answer
geographic questions.
GIS is used in some classrooms in Wisconsin as well as throughout the U.S., and
will become more common as teacher training becomes available (Ramirez and Althouse
1995, Ramirez 1996). GIS computer technology provides an educational method that

120
engages students in active, hands-on learning and stimulates higher-level critical thinking
skills such as application, analysis, synthesis and evaluation (Bloom 1956, Barron 1995,
Broda and Baxter 2003). The ArcView software package is a user-friendly sub-system or
computer shell for ARC/INFO, and is appropriate for elementary, middle and high school
students.
GIS can be used to study wildlife, including flagship species. Flagship species are
“popular, charismatic species that serve as symbols and rallying points to stimulate
conservation awareness and action” (European Communities 2000). Many wildlife species
have the ability to win the attention of students and to pique their curiosity. Some species
are more captivating than others. Top predators such as snakes, wolves and bears will
always attract interest. Birds, with their envious ability to fly, also fascinate humans.
Hawks and owls, with both of these charismatic characteristics, possess a unique ability to
lure students’ minds. Certainly, the Red-tailed Hawk (Buteo jamaicensis) is one of these
appealing species that will capture the attention of both elementary and secondary students,
and are common throughout North America. In conjunction with computers and computer
technology, certain wildlife are ‘can’t miss’ student attractants.
My objective was to develop the framework for an interdisciplinary educational unit
that integrates wildlife ecology, land-use planning and GIS computer technology. This unit
uses GIS technology and information about urban Red-tailed Hawks to develop a model
that predicts where Red-tailed hawk habitat exists in urban locations. While each GIS
analysis is individualized, the same basic results will be obtained. The model can be
validated by students through field surveys to determine if Red-tailed Hawks are present in
the predicted locations. Land-use planning recommendations can be developed from the

121
habitat information. This educational unit provides a method to engage students in active,
hands-on learning that stimulates higher-level critical thinking skills including application,
analysis, synthesis and evaluation. Teachers throughout Wisconsin and the Midwest can
use this unit to integrate principles of wildlife ecology, land-use planning methods and GIS
computer technology, and to engage students in higher-level thinking skills.
The GIS Education Unit
ArcView GIS Unit
Where in the City Are Red-tailed Hawks?
Title: Where in the City Are Red-tailed Hawks?
Subject: Wildlife Ecology, Conservation Biology, Earth Science, Geography, Geographic
Information Systems (GIS), Computer Technology, Land-Use Planning
Grade: High School
Methods/Skills/Learning Styles: Project-Based Learning; Integrated, Interdisciplinary
Curriculum; Hand-On Learning; Higher-Level Critical Thinking Skills
Goal: Students will understand habitat and resource requirements for wildlife species.
Students will understand the GIS process and the role it can play in wildlife habitat
analyses. Students will be able to problem solve for land-use planning using GIS as
a tool.
Objectives:
Upon completion of this unit students will be able to:
a. Describe habitat requirements for a wildlife species.
b. Explain how habitat resource requirements affect wildlife species.

122
1. Positively.
2. Adversely.
c. Explain how urban wildlife habitat may differ from rural habitat.
d. Apply wildlife ecology principles to urban land-use planning.
e. Explain the usefulness of GIS as a tool for describing and analyzing wildlife
habitat.
f. Use the following procedures in ArcView:
1. Add themes to a new view
2. Select an object from a theme
3. Convert selected to a shapefile
4. Geoprocess using the GeoProcessing Wizard:
a) clip one theme based on another
b) union two themes
5. Edit a theme several ways:
a) select by theme
b) select using the Query Builder
then delete the selected items and ‘save as’
6. Recalculate area and perimeter of areas in a table using the Field
Calculator
7. Design a professional layout to present the recommendations
g. Predict where suitable wildlife (i.e., Red-tailed Hawk) habitat exists within
urban locations.
h. Conduct field surveys for wildlife.

123
i. Apply field survey data to a model that predicts where suitable habitat exists for
a species.
j. Describe the land-use planning process and how it can accommodate wildlife.
Materials:
Computer (PC compatible, Windows operating system, see ArcView GIS 3.x
installation requirements for processor speed, memory and additional
requirements)
ArcView GIS 3.x installed and operating, with one student per computer, and a
basic understanding of ArcView GIS (ESRI 2002).
SEWRPC (Southeast Wisconsin Regional Planning Commission) Land-Use Data
Set
The SEWRPC Data Set can be purchased from SEWRPC, Waukesha, WI.
Each township is individual (SEWRPC 1995).
Wiscland Data Set (optional)
(free to download at:
http://www.dnr.state.wi.us/maps/gis/datalandcover.html)
Themes: Roads, State Highways, Counties, Rivers, Lakes (WDNR 2004).
Procedures: See complete, detailed instructions that follow.
Evaluation: Students will create a layout to display suitable wildlife (i.e., Red-tailed
Hawk) habitat in an urban landscape (i.e., a habitat prediction model). Students will
validate the model by conducting field surveys to confirm the presence of Red-tailed
Hawks in the predicted areas. Students will develop land-use planning
recommendations that incorporate wildlife habitat in urban locations.

124
National Science Education Standards
Science Content Standards
Science as Inquiry
CONTENT STANDARD A: As a result of activities in grades 9-12, all students should
develop
 Abilities necessary to do scientific inquiry (A.1)
 Understandings about scientific inquiry (A.2)
Life Science
CONTENT STANDARD C: As a result of their activities in grades 9-12, all students
should develop understanding of
 Interdependence of organisms (C.3)
 Matter, energy, and organization in living systems (C.4)
 Behavior of organisms (C.5)
Science and Technology
CONTENT STANDARD E: As a result of activities in grades 9-12, all students should
develop
 Abilities of technological design (E.1)
 Understandings about science and technology (E.2)
Science in Personal and Social Perspectives
CONTENT STANDARD F: As a result of activities in grades 9-12, all students should
develop understanding of
 Population growth (F.2)
 Natural resources (F.3)

125
 Environmental quality (F.4)
 Natural and human-induced hazards (F.5)
 Science and technology in local, national, and global challenges (F.6)
History and Nature of Science
CONTENT STANDARD G: As a result of activities in grades 9-12, all students should
develop understanding of
 Nature of scientific knowledge (G.2)
Wisconsin Model Academic Standards
TWELFTH GRADE
Performance Standards
By the end of grade twelve, students will:
A.12.2 Analyze information generated from a computer about a place, including statistical
sources, aerial and satellite images, and three-dimensional models.
A.12.9 Identify and analyze cultural factors, such as human needs, values, ideals, and
public policies, that influence the design of places, such as an urban center, and industrial
park, a public project, or a planned neighborhood.
A.12.11 Describe scientific and technological development in various regions of the world
and analyze the ways in which development affects environment and culture.
A.12.12 Assess the advantages and disadvantages of selected land-use policies in the local
community, Wisconsin, the United States, and the world.
PI 34.02 Teacher Standards. To receive a license to teach in Wisconsin, an applicant shall
complete an approved program and demonstrate proficient performance in the knowledge,
skills and dispositions under all of the following standards:

126
(1) The teacher understands the central concepts, tools of inquiry, and structures of
the disciplines he or she teaches and can create learning experiences that make
these aspects of subject matter meaningful for pupils.
(4) The teacher understands and uses a variety of instructional strategies, including
the use of technology to encourage children's development of critical thinking,
problem solving and performance skills.
(6) The teacher uses effective verbal and nonverbal communication techniques as
well as instructional media and technology to foster active inquiry, collaboration,
and support of interaction in the classroom.
(8) The teacher understands and uses formal and informal assessment strategies to
evaluate and insure the continuous intellectual, social, and physical development
of the pupil.
(10) The teacher fosters relationships with school colleagues, parents, and agencies in
the larger community to support pupil learning and well-being and acts with
integrity, fairness and in an ethical manner.
ArcView GIS Instructions
Wildlife Habitat Analysis and Land-Use Planning
The Problem: Waukesha County, a suburb of the city of Milwaukee, is characterized by
rapid urban sprawl. The Regional Planning Commission in collaboration with the County
Park and Planning Department want to develop a land-use plan that is sensitive to the needs
of wildlife in urban areas. They come to you as a Land-Use Planning Consultant and ask
you to determine ways to allow for humans and wildlife to coexist in an urban environment.
They endorse the flagship species concept and agree that the Red-tailed Hawk fits flagship

127
species criteria for Waukesha County. As an additional objective, the County Park and
Planning Department would like to insure the highest aesthetic value possible.
Background: The following information was obtained from a statewide expert on Red-
tailed Hawk habitat at the University of Wisconsin – Madison.
Red-Tailed Hawks have two basic habitat resource requirements, food or hunting
habitat and nesting habitat. Quality hunting habitat includes large areas (50ha) of
grasslands, agricultural lands, graded land such as gravel pits and landfills, and recreational
lands. Recreational lands such as golf courses and sports fields that are located in urban
areas are particularly good habitat because some also include suitable nesting habitat. Red-
tailed Hawk habitat includes the previous mentioned hunting habitat areas (types and size)
that are within 1.5 km of nesting habitat, and lands within 1km of these areas (i.e., 1.0km
radius buffer). Hunting habitat greater than 1.5 km from nesting habitat may be used by
non-breeding (i.e., non-nesting) birds (also referred to as occupied territories or areas).
Traditional Red-tailed Hawk nesting habitat consists of woodlands at least 2ha in
size. Useable nesting habitat is located not more than 1.5km from hunting habitat. Lands
within 1.0km of these woodlots (i.e., nesting habitat) are considered part of Red-tailed
Hawk habitat. While Red-tailed Hawks will nest on recreational land, sufficient suitable
hunting habitat must be nearby.
Freeways provide good hunting habitat for Red-tailed Hawks, resulting in higher
productivity for nests within 1.0km of freeways than other nests. Sufficient nesting habitat
within 0.5km of freeways provides suitable nesting habitat (1.0km outside buffer) with
adequate hunting habitat nearby (the freeways). Red-tailed Hawks also will utilize
freeways for hunting in the absence of nesting habitat. Their presence represents an

128
occupied area. If alternate suitable nest sites are present along these freeways (i.e., human-
made structures such as billboards, civil defense sirens or cell phone towers), Red-tailed
Hawks may nest on these structures, possibly because of quality hunting habitat by
freeways. Nesting in these locations is very difficult to predict.
Wetlands can provide hunting habitat but may not be of the same quality (i.e., poor)
as other suitable hunting areas. Consequently, while Red-tailed Hawks may utilize these
areas, they stay closer to the wetland area (minimum = 10ha), nest closer and generally
don’t produce as many young. Nesting habitat must be within 0.5km of the wetland. Red-
tailed Hawk habitat includes wetlands that are at least 10ha in size and lands within 0.5km
of these wetlands, and nesting habitat associated with wetland hunting habitat (i.e., within
0.5km of the wetland) and lands within 0.5km of these woodlands.
Nesting Red-tailed Hawks may prefer to utilize nearby resources for their hunting
needs (i.e., nesting and hunting resources in close proximity to each other), even if the
quality is marginal because flying long distances is energetically expensive. This may be
why they utilize wetlands for hunting and human-made structures for nesting.
The Project: Identify Red-tailed Hawk habitat based on both nesting and hunting
requirements. Using GIS procedures, develop a GIS model (i.e., layout) that predicts where
suitable wildlife (i.e., Red-tailed Hawk) habitat exists within urban locations. Validate the
model by conducting field surveys to confirm the presence of Red-tailed Hawks in the
predicted areas. Apply field survey data to a model that predicts where suitable habitat
exists for a species. Develop a comprehensive, flagship-species based land-use plan
utilizing the Red-tailed Hawk as the focal species for urban development.

129
GIS Background: This project is designed to be open-ended and students should have an
adequate background in GIS procedures. An understanding of the following procedures is
beneficial.
1. Open new views in ArcView.
2. Add themes to view.
3. Select an object from a theme.
4. Convert selected features to a shapefile.
5. Geoprocess using the GeoProcessing Wizard and understand what they do:
a. Dissolve themes based on an attribute
b. Merge themes together
c. clip one theme based on another
d. intersect to themes
e. union to themes
f. assign data by location (spatial join)
6. Edit a theme several ways:
a. select by theme
b. select using the Query Builder
c. save features as a new theme
d. delete and/or de-select selected items
7. Edit theme tables.
8. Add new fields and records to a theme table.
9. Create, edit and save a legend for a theme.
10. Recalculate area and perimeter of areas in a table using the Field Calculator.

130
11. Design a professional layout.
Instructor’s Notes (key to producing Red-tailed Hawk habitat theme):
Procedures to determine Red-tailed Hawk habitat. These are procedure that will
produce the required results. However, students should problem-solve to develop their own
set of procedures to identify Red-tailed Hawk habitat.
1. Open a new view in ArcView.
2. Add theme(s) to the view. The SEWRPC Land-Use Data Set is available in State
Plane and WTM projections. If using the WTM projection, the WISCLAND Data
Set is also available in this projection. WISCLAND is an alternate, free database
that provides additional themes such as Counties, Lakes, Rivers and Streams, and
State Highways and Roads, for Wisconsin. These themes may be helpful but are
optional.
a. SEWRPC Land-Use Data Set (add townships of interest, e.g., Milwaukee
County townships).
b. Ctypw91c.shp (Wisconsin Counties, optional)
c. Hydpw91c.shp (Wisconsin Lakes, optional)
d. Sthlw91c.shp (Wisconsin State Highways, optional)
e. Rdslw91c.shp (Wisconsin Roads, optional)
f. Hydlw91c.shp (Wisconsin Rivers and Streams, optional)
3. Merge themes together using the GeoProcessing Wizard.
4. Edit the Theme Table to include Area, Perimeter and Land Cover Type. See
Appendix A for SEWRPC land-use codes and suggested land-cover types for each
code.

131
5. Dissolve themes based on Land Cover Type attribute.
6. Optional: Select each land-cover type and convert to an individual theme. This may
make some of the GIS processing run faster.
7. Create a Red-tailed Hawk Hunting Habitat theme based on the information provided
on Red-tailed Hawks.
8. Create a Red-tailed Hawk Nesting Habitat theme based on the information provided.
9. Select hunting habitat (areas ≥ 50ha) that is ≤ 1.5km from nesting habitat (areas ≥
2ha), buffer with a 1km outside buffer, and create a Red-tailed Hawk Habitat theme
#1.
10. Select nesting habitat (areas ≥ 2ha) that is ≤ 1.5km from hunting habitat (areas ≥
2ha), buffer with a 1km outside buffer, and create a Red-tailed Hawk Habitat theme
#2.
11. Create a Freeways theme.
12. Select nesting habitat (areas ≥ 2ha) that is ≤ 0.5km from freeways, buffer with a
1.0km outside buffer, and create a Red-tailed Hawk Habitat theme #3.
13. Create a Wetlands theme.
14. Select nesting habitat (areas ≥ 2ha) that is ≤ 0.5km from wetlands, buffer with a
0.5km outside buffer, and create a Red-tailed Hawk Habitat theme #4.
15. Buffer Wetlands theme with a 0.5km buffer and create a Red-tailed Hawk Habitat
theme #5.
16. Merge the five Red-tailed Hawk Habitat themes to one final Red-tailed Hawk
Habitat theme.

132
Identify Urban Red-tailed Hawk habitat. A landscape is considered urban if  70% of
the land is used for industrial or residential purposes (developed), rural if  30%, and
suburban if > 30% and < 70% was developed. Select Red-tailed Hawk habitat that fits the
urban criteria.
1. Produce a uniform point theme with points  2.0km apart that covers the Red-tailed
Hawk habitat area.
2. Buffer these points with a 1.0km buffer.
3. Clip land-cover with the 1.0km buffer.
4. Recalculate areas for the land-cover buffer theme using the Field Calculator.
5. Determine which buffers are considered urban ( 70% developed, i.e., residential,
commercial, industrial, roads and parking).
6. Describe land-cover composition for these urban areas.
Incorporate these habitat requirements into a comprehensive, flagship-species based urban
land-use plan.
The comprehensive urban land-use plan should include Red-tailed Hawk habitat
information and land-cover composition information from the urban buffer areas.
1. 2ha woodlands for suitable nesting habitat.
2. A combination of suitable Red-tailed Hawk hunting habitat.
a. Grasslands, agricultural land (if any), recreational (and graded) land possibly
in 50ha areas.
b. Freeways and freeway intersections for additional hunting habitat.

133
3. Other urban land-uses in a composition that is consistent with the composition of
the urban land-use areas.
A map of Red-tailed Hawk habitat for Milwaukee County is produced (Figure 1).
Acknowledgements
Literature Cited
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systems for education. School Library Media Activities Monthly 11:49-50.
Bloom, B. 1956. Taxonomy of educational objectives: Cognitive domain. David McKay
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Broda, H.W. and R.E. Baxter. 2003. Using GIS and GPS technology as an instructional
tool. The Social Studies 94:158-160.

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Cunningham, A.C. and L.M. Stewart. 2003. A systems analysis approach to learning
theory in pre-service teacher education: Using technology to facilitate representation
of complex relationships in educational theory and practice. Action in Teacher
Education 24:18-26.
DeGouvenain, R. 1995. Use of GIS for sensitive plant species conservation in land use
planning. Environmental Professional 17: 27-33.
Delorme, R. 1998. GIS protects our natural resources. GIS-World 11:42-44.
Dickey, E.M. and M.D. Roblyer. 1997. Technology, NAEP, and TIMSS: How does
technology influence our national and international report cards? Learning and
Leading with Technology 25:55-57.
European Communities. 2000. European Community Clearing-House Mechanism.
Located at: http://biodiversity-chm.eea.eu.int/. Last visited April 10, 2004.
Harris, L.K., R.H. Gimblett and W.W. Shaw. 1995. Multiple use management: Using a
GIS model to understand conflicts between recreationists and sensitive wildlife.
Society and Natural Resources 8: 559-572.
Lawrence, R.K. 1997. GIS in wildlife science: Current and future directions.
Environmental Conservation 24: 86-87.
Neisser, E. 1997. Rising scores on intelligence tests. American Scientist 84:440-447.
Nevo, A. and L. Garcia. 1996. Spatial optimization of wildlife habitat. Ecological
Modelling 91: 271-281.

135
Ramirez, M. 1996. A driving force in technology education: Geographic information
systems (GIS). TechTrends 41:34-36.
Ramirez, M. and P. Althouse. 1995. Fresh thinking: GIS in environmental education.
T.H.E. Journal 23:87-90.
WDNR. 2004. Wisconsin Department of Natural Resources (WDNR). Wiscland data.
Located at: http://www.dnr.state.wi.us/maps/gis/datalandcover.html.
Worah, S., E.K. Bharucha and W.A. Rogers. 1989. The use of geographic information
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History Society 86:125-128.

136
Milwaukee Co.
N
Milwaukee County
Red-Tailed Hawk Habitat
7 0 7 14 Kilometers
Freeways
Rtha habitat
Key to Features
Figure 1. Map of Red-tailed Hawk Habitat for Milwaukee County.

137
Appendix A. Southeast Wisconsin Regional Planning Commission (SEWRPC) 1995 Land-
use (Land-cover) Codes and Descriptions and the corresponding land-cover
classes for this project (and the legend color used for project maps and
graphs).
SEWRPC Land Land Cover Class Legend
Use Code Land-use Description for Project Color
Residential
111L Single-Family - Low-Density Residential Urban (low-density) Pink
111M Single-Family - Medium-Density
Residential
Urban (high-density) Magenta
111S Single-Family - Suburban-Density
Residential
Urban (low-density) Pink
111X Single-Family - High-Density Residential Urban (high-density) Magenta
120 Two Family Urban (high-density) Magenta
141 Multi-Family Low Rise Urban (high-density) Magenta
142 Multi-Family High Rise Urban (high-density) Magenta
150 Mobile Homes Urban (high-density) Magenta
199 Residential Land Under Development Graded Gray
Commercial
210 Retail Sales and Service - Intensive Urban (high-density) Magenta
210H Retail Sales and Service - Intensive
Unused Lands
Grasslands Yellow
220 Retail Sales and Service - Nonintensive Urban (high-density) Magenta
220H Retail Sales and Service - Nonintensive
Unused Lands
Grasslands Yellow
299 Retail Sales and Service Land Under
Development
Graded Gray
Industrial
310 Manufacturing Urban (high-density) Magenta
310H Manufacturing - Unused Lands Grasslands Yellow
340 Wholesale and Storage Urban (high-density) Magenta
340H Wholesale and Storage - Unused Lands Grasslands Yellow
360 Extractive Graded Gray
360H Extractive - Unused Lands Grasslands Yellow
399 Industrial Land Under Development Graded Gray

138
Appendix A (cont’d).
Tranportation
Motor Vehicle-Related
411 Freeway Roads Black
411F Freeway - Woodlands Woodlands Forest Green
411G Freeway - Wetlands Wetlands Aqua
414 Standard Arterial Street and
Expressway
Roads Black
414F Standard Arterial Street and
Expressway - Woodlands
Woodlands Forest Green
414G Standard Arterial Street and
Expressway - Wetlands
Wetlands Aqua
418 Local and Collector Streets Roads Black
425 Bus Terminal Urban (high-density) Magenta
425H Bus Terminal - Unused Lands Grasslands Yellow
426 Truck Terminal Urban (high-density) Magenta
426H Truck Terminal - Unused Lands Grasslands Yellow
Off-Street Parking
430 Parking - Multiple Land-use Parking Peach
431 Parking - Residential Parking Peach
432 Parking - Retail Sales and Service Parking Peach
433 Parking -Industrial Parking Peach
434 Parking - Transportation Parking Peach
435 Parking - Communication and
Utilities
Parking Peach
436 Parking - Government and Institution Parking Peach
437 Parking - Recreation Parking Peach
Rail-Related
441 Rail - Track Right-of-Way Grasslands Yellow
441F Rail - Track Right-of-Way -
Woodlands
441G Rail - Track Right-of-Way - Wetlands Wetlands Aqua
443 Rail - Switching Yards Grasslands Yellow
445 Rail - Stations and Depots Urban (high-density) Magenta
Air-Related
463 Air - Air Fields Grasslands Yellow
463H Air - Air Fields - Unused Lands Grasslands Yellow
465 Air - Air Terminals and Hangars Urban (high-density) Magenta

139
485 Ship Terminal Water Blue
499 Transportation Land Under Development Graded Gray
Communication and Utilities
510 Communication and Utilities Grasslands Yellow
510G Communication and Utilities - Wetlands Wetlands Aqua
510H Communication and Utilities - Unused
Lands
Grasslands Yellow
599 Communication and Utility Land Under
Development
Graded Gray
Government and Institutional
Administrative, Safety, and Assembly
611 Government and Institutional - Local Urban (high-density) Magenta
611H Government and Institutional - Local
- Unused Lands
Grasslands Yellow
612 Government and Institutional -
Regional
612F Government and Institutional -
Regional - Woodlands
612H Government and Institutional -
Regional - Unused Lands
Grasslands Yellow
Educational
Educational, Local
Educational, Local - Woodlands
Educational, Local - Unused
Lands
Grasslands Yellow
Educational, Regional
Educational, Regional -
Woodlands
642G Government and Institutional -
Educational, Regional - Wetlands
Wetlands Aqua
Educational, Regional - Unused
Lands
Grasslands Yellow

140
Group Quarters
661 Government and Institutional - Group
Quarters, Local
661F Government and Institutional - Group
Quarters, Local - Woodlands
661H Government and Institutional - Group
Quarters, Local - Unused Lands
Grasslands Yellow
662 Government and Institutional - Group
Quarters, Regional
662F Government and Institutional - Group
Quarters, Regional - Woodlands
662H Government and Institutional - Group
Quarters, Regional - Unused
Lands
Grasslands Yellow
Cemeteries
Cemeteries, Local
Grasslands Yellow
Cemeteries, Local - Woodlands
Cemeteries, Local - Unused Lands
Grasslands Yellow
Cemeteries, Regional
Grasslands Yellow
Cemeteries, Regional - Woodlands
Cemeteries, Regional - Unused
Lands
Grasslands Yellow
699 Government and Institutional Land Under
Development
Graded Gray
Recreational
Cultural/Special Recreation Areas
711 Recreation - Cultural/Special Public Recreational Green
712 Recreation - Cultural/Special
Nonpublic
Recreational Green

141
Land-Related Recreation Areas
731 Recreation - Public (e.g., golf
courses, soccer fields, baseball parks)
Recreational Green
731G Recreation - Public, Wetlands (e.g.,
golf courses, soccer fields,
baseball parks)
Wetlands Aqua
732 Recreation - Nonpublic (e.g., golf
courses, soccer fields, baseball
parks)
Recreational Green
Water-Related Recreation Areas
781 Recreation - Public Water Water Blue
782 Recreation - Nonpublic Water Water Blue
799 Recreation Land Under Development Graded Gray
Agricultural
811 Cropland Cropland Violet
811P Cropland - Preservation Area Pasture Lavender
815 Pasture and Other Agriculture Pasture Lavender
815P Pasture and Other Agriculture -
Preservation Area
Pasture Lavender
816 Lowland Pasture Pasture Lavender
816P Lowland Pasture - Preservation Area Pasture Lavender
820 Orchards and Nurseries Cropland Violet
820P Orchards and Nurseries - Preservation
Area
Pasture Lavender
841 Special Agriculture Cropland Violet
841P Special Agriculture - Preservation Area Pasture Lavender
871 Farm Buildings Urban (low-density) Pink
Open Lands
910 Wetlands Wetlands Aqua
Unused Lands
921 Unused Lands - Urban Grasslands Yellow
922 Unused Lands - Rural Grasslands Yellow
930 Landfills and Dumps Graded Gray
940 Woodlands Woodlands Forest Green
950 Surface Water Water Blue

142
Supplemental Land-use Suffix Codes*
X High-density Residential
M Medium-density Residential
L Low-density Residential
S Suburban-density Residential
F Woodlands
G Wetlands
H Unused Lands
P Agricultural Land Preservation Area
* Supplemental land-use suffix codes F, G and H identify natural resource features and open
space lands which may occur within certain urban uses, and suffix code P identifies those
agricultural lands which may have been included in agricultural land preservation areas.
Residential density codes apply only to single-family residential (111).

143
Appendix B. Post hoc test for 10 Buffer Scales, Tukey Multiple Comparisons - Matrix of
pairwise comparison probabilities for each land-cover type. One-way
ANOVA indicated that each land-cover type (area and perimeter frequencies)
is significantly different over the 10 buffer scales (P<0.001 for each case).
Urban (high-density) 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.035 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.155 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.411 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.012 1.000

144
Appendix B (cont’d).
Urban (low-density) 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.001 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.016 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.061 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.319 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.018 1.000

145
Roads 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m 0.076 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 0.770 1.000
1000m <0.001 <0.001 <0.001 0.292 0.999 1.000
1250m <0.001 <0.001 <0.001 0.024 0.814 0.994 1.000
1500m <0.001 <0.001 <0.001 0.002 0.362 0.829 1.000 1.000
1750m <0.001 <0.001 <0.001 <0.001 0.062 0.329 0.913 0.999 1.000
2000m <0.001 <0.001 <0.001 <0.001 0.005 0.054 0.461 0.889 0.999 1.000
Perimeter
50m 1.000
100m 0.003 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 0.931 1.000
1000m <0.001 <0.001 <0.001 0.704 1.000 1.000
1250m <0.001 <0.001 <0.001 0.141 0.923 0.994 1.000
1500m <0.001 <0.001 <0.001 0.026 0.583 0.855 1.000 1.000
1750m <0.001 <0.001 <0.001 0.001 0.113 0.295 0.897 0.997 1.000
2000m <0.001 <0.001 <0.001 <0.001 0.006 0.026 0.317 0.733 0.996 1.000

146
Parking 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.189 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.611 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.030 0.942 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.277 0.980 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.003 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.122 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.426 1.000

147
Recreational 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 0.177 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 0.325 1.000
1000m <0.001 <0.001 <0.001 0.005 0.927 1.000
1250m <0.001 <0.001 <0.001 <0.001 0.358 0.994 1.000
1500m <0.001 <0.001 <0.001 <0.001 0.083 0.820 1.000 1.000
1750m <0.001 <0.001 <0.001 <0.001 0.018 0.463 0.964 1.000 1.000
2000m <0.001 <0.001 <0.001 <0.001 0.006 0.252 0.847 0.996 1.000 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.301 1.000
1250m <0.001 <0.001 <0.001 <0.001 0.005 0.932 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.359 0.993 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 0.062 0.762 0.999 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 0.011 0.380 0.940 1.000 1.000

148
Graded 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 0.464 1.000
1000m <0.001 <0.001 <0.001 0.015 0.908 1.000
1250m <0.001 <0.001 <0.001 0.001 0.345 0.995 1.000
1500m <0.001 <0.001 <0.001 <0.001 0.070 0.814 0.999 1.000
1750m <0.001 <0.001 <0.001 <0.001 0.010 0.374 0.930 1.000 1.000
2000m <0.001 <0.001 <0.001 <0.001 0.002 0.153 0.715 0.984 1.000 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.002 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.447 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.009 0.909 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.199 0.975 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.017 0.568 0.997 1.000

149
Cropland 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m 0.482 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.007 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.659 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.054 0.969 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 0.002 0.469 0.994 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.063 0.687 0.996 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.330 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.861 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.150 0.974 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.006 0.456 0.992 1.000

150
Pasture 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m 0.998 1.000
250m 0.001 0.008 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.300 1.000
1250m <0.001 <0.001 <0.001 <0.001 0.002 0.828 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.090 0.949 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.359 0.991 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.107 0.858 1.000 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.008 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.321 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.774 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.078 0.959 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.006 0.572 0.999 1.000

151
Grassland 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.147 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.878 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.132 0.958 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.006 0.429 0.994 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.216 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.479 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.006 0.843 1.000

152
Woodland 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.853 1.000
1250m <0.001 <0.001 <0.001 <0.001 0.241 0.994 1.000
1500m <0.001 <0.001 <0.001 <0.001 0.053 0.870 1.000 1.000
1750m <0.001 <0.001 <0.001 <0.001 0.008 0.520 0.979 1.000 1.000
2000m <0.001 <0.001 <0.001 <0.001 0.002 0.282 0.883 0.995 1.000 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.030 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.645 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.053 0.972 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.407 0.987 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.087 0.749 0.999 1.000

153
Wetland 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.243 1.000
1250m <0.001 <0.001 <0.001 <0.001 0.003 0.912 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.289 0.991 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 0.067 0.840 1.000 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 0.012 0.495 0.982 1.000 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.001 1.000
1250m <0.001 <0.001 <0.001 <0.001 <0.001 0.349 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.003 0.852 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.237 0.994 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.022 0.712 0.997 1.000

154
Water 50m 100m 250m 500m 750m 1000m 1250m 1500m 1750m 2000m
Area
50m 1.000
100m 0.001 1.000
250m <0.001 0.005 1.000
500m <0.001 <0.001 0.158 1.000
750m <0.001 <0.001 0.020 0.999 1.000
1000m <0.001 <0.001 0.005 0.968 1.000 1.000
1250m <0.001 <0.001 0.003 0.917 1.000 1.000 1.000
1500m <0.001 <0.001 0.002 0.924 1.000 1.000 1.000 1.000
1750m <0.001 <0.001 0.005 0.981 1.000 1.000 1.000 1.000 1.000
2000m <0.001 <0.001 0.008 0.996 1.000 1.000 0.999 0.999 1.000 1.000
Perimeter
50m 1.000
100m <0.001 1.000
250m <0.001 <0.001 1.000
500m <0.001 <0.001 <0.001 1.000
750m <0.001 <0.001 <0.001 <0.001 1.000
1000m <0.001 <0.001 <0.001 <0.001 0.254 1.000
1250m <0.001 <0.001 <0.001 <0.001 0.004 0.945 1.000
1500m <0.001 <0.001 <0.001 <0.001 <0.001 0.460 0.998 1.000
1750m <0.001 <0.001 <0.001 <0.001 <0.001 0.242 0.972 1.000 1.000
2000m <0.001 <0.001 <0.001 <0.001 <0.001 0.116 0.888 1.000 1.000 1.000

155
Appendix C. FRAGSTATS Metrics (FRAGSTATS for ArcView, version 1.0)
were used to compare habitat of high productivity Red-tailed Hawk
breeding areas to low productivity breeding areas (Chapter 2), and
Red-tailed Hawk use areas to non-use areas (Chapter 4).
FRAGSTATS for ArcView was used to calculate landscape-scale
metrics.
Item / Acronym Metric and Units
Class Scale
MPS Mean patch size (ha)
PSSD Patch size standard deviation (ha)
MAX* Largest patch size (ha)
MIN* Smallest patch size (ha)
PERIMETER Perimeter (in coverage units: m)
PPSD* Patch perimeter standard deviation (m)
PPMAX* Largest patch perimeter (m)
PPMIN* Smallest patch perimeter (m)
NP Number of patches (#)
NPSD* Number of patches (#) standard deviation
NPMAX* Largest number of patches (#)
NPMIN* Smallest number of patches (#)
Landscape Scale
NP Number of patches (#)
MPS Mean patch size (ha)
MSI Mean shape index
MPFD Mean patch fractal dimension
PSSD Patch size standard deviation (ha)
LPI Largest patch index (%)
PD Patch density (#/100 ha)
PSCV Patch size coefficient of variation (%)
AWMSI Area-weighted mean shape index
DLFD Double log fractal dimension
AWMPFD Area-weighted mean patch fractal dimension
SHDI Shannon's diversity index
SIDI Simpson's diversity index
MSIDI Modified Simpson's diversity index
SHEI Shannon's evenness index
SIEI Simpson's evenness index
MSIEI Modified Simpson's evenness index
PR Patch richness (#)
*Not FRAGSTATS Metrics

156
Appendix D. Definition, Description and Calculations of CLASS and LANDSCAPE
Metrics, FRAGSTATS Metrics (FRAGSTATS for ArcView, version 1.0).
Class Area - CA
The total area for each class (in hectares) is calculated.
Units: Hectares
Range: CA > 0, without limit.
CA approaches 0 as the patch type becomes increasing rare in the landscape. CA = TA
when the entire landscape consists of a single patch type; that is, when the entire image is
comprised of a single patch.
Description: CA equals the sum of the areas (m2) of all patches of the corresponding patch
type, divided by 10,000 (to convert to hectares); that is, total class area.
Total Area - TA
Units: Hectares
Range: TA > 0, without limit.
Description: TA equals the total area of the landscape convert to hectares. The above
equation illustrates a the sq. meters conversion (divided by 10,000). TA excludes the area of
any background patches within the landscape.

157
Appendix D (cont’d).
Largest Patch Index - LPI
Units: Percent
Range: 0 < LPI  100
LPI approaches 0 when the largest patch in the landscape is increasingly small. LPI = 100
when the entire landscape consists of a single patch; that is, when the largest patch
comprises 100% of the landscape.
Description: LPI equals the area (m2
) of the largest patch in the landscape divided by total
landscape area (m2
), multiplied by 100 (to convert to a percentage); in other words, LPI
equals the percent of the landscape that the largest patch comprises.
Number of Patches - NP
Units: None
Range: NP  1, without limit.
NP = 1 when the landscape contains only 1 patch.
Description: NP equals the number of patches in the landscape. Note, NP does not include
any background patches within the landscape or patches in the landscape border.
Patch Density - PD
Units: Number per 100 hectares
Range: PD > 0, without limit.
Description: PD equals the number of patches in the landscape divided by total landscape
area, multiplied by 10,000 and 100 (to convert to 100 hectares).

158
Mean Patch Size - MPS
Units: Hectares
Range: MPS > 0, without limit.
The range in MPS is limited by the grain and extent of the image and the minimum patch size
in the same manner as patch area (AREA).
Description: MPS equals the sum of the areas (m2) of all patches of the corresponding patch
type, divided by the number of patches of the same type, divided by 10,000 (to convert to
hectares).
Patch Size Standard Deviation - PSSD
Units: Hectares
Range: PSSD ³ 0, without limit.
PSSD = 0 when all patches in the class are the same size or when there is only 1 patch (i.e.,
no variability in patch size).
Description: PSSD equals the square root of the sum of the squared deviations of each patch
area (m2) from the mean patch size of the corresponding patch type, divided by the number
of patches of the same type, divided by 10,000 (to convert to hectares);

159
Perimeter - PERIMETER
Units: Meters (or units of the coverage)
Range: PERIMETER > 0, without limit.
Description: PERIMETER equals the perimeter (m) of the patch, including any internal holes
in the patch.
Number of Patches - NP
Units: None
Range: NP ³ 1, without limit.
NP = 1 when the landscape contains only 1 patch of the corresponding patch type; that is,
when the class consists of a single patch.
Description: NP equals the number of patches of the corresponding patch type (class).
Mean Shape Index - MSI
Units: None
Range: MSI  1, without limit.
MSI = 1 when all patches in the landscape are circular (vector) or square (raster); MSI
increases without limit as the patch shapes become more irregular.
Description: MSI equals the sum of the patch perimeter (m) divided by the square root of
patch area (m2
) for each patch in the landscape, adjusted by a constant to adjust for a
circular standard (vector) or square standard (raster), divided by the number of patches (NP);
in other words, MSI equals the average shape index (SHAPE) of patches in the landscape.

160
Mean Patch Fractal Dimension - MPFD
Units: None
Range: 1  MPFD  2
A fractal dimension greater than 1 for a 2-dimensional landscape mosaic indicates a
departure from a euclidean geometry (i.e., an increase in patch shape complexity). MPFD
approaches 1 for shapes with very simple perimeters such as circles or squares, and
approaches 2 for shapes with highly convoluted, plane-filling perimeters.
Description: MPFD equals the sum of 2 times the logarithm of patch perimeter (m) divided by
the logarithm of patch area (m2
) for each patch in the landscape, divided by the number of
patches; the raster formula is adjusted to correct for the bias in perimeter (Li 1989).
Patch Size Standard Deviation - PSSD
Units: Hectares
Range: PSSD  0, without limit.
PSSD = 0 when all patches in the landscape are the same size or when there is only 1 patch
(i.e., no variability in patch size).
Description: PSSD equals the square root of the sum of the squared deviations of each patch
area (m2
) from the mean patch size, divided by the total number of patches, divided by
10,000 (to convert to hectares); that is, the root mean squared error (deviation from the
mean) in patch size. Note, this is the population standard deviation, not the sample standard
deviation.

161
Largest Patch Index - LPI
Units: Percent
Range: 0 < LPI  100
LPI approaches 0 when the largest patch in the landscape is increasingly small. LPI = 100
when the entire landscape consists of a single patch; that is, when the largest patch
comprises 100% of the landscape.
Description: LPI equals the area (m2
) of the largest patch in the landscape divided by total
landscape area (m2
), multiplied by 100 (to convert to a percentage); in other words, LPI
equals the percent of the landscape that the largest patch comprises.
Patch Density - PD
Range: PD > 0, without limit.
Description: PD equals the number of patches in the landscape divided by total landscape
area, multiplied by 10,000 and 100 (to convert to 100 hectares).

162
Patch Size Coefficient of Variation - PSCV
Units: Percent
Range: PSCV  0, without limit.
PSCV = 0 when all patches in the landscape are the same size or when there is only 1 patch
(i.e., no variability in patch size).
Description: PSCV equals the standard deviation in patch size (PSSD) divided by the mean
patch size (MPS), multiplied by 100 (to convert to percent); that is, the variability in patch size
relative to the mean patch size. Note, this is the population coefficient of variation, not the
sample coefficient of variation.
Area-Weighted Mean Shape Index - AWMSI
Units: None
Range: AWMSI  1, without limit.
AWMSI = 1 when all patches in the landscape are circular (vector) or square (raster); AWMSI
increases without limit as the patch shapes become more irregular.
Description: AWMSI equals the sum, across all patches, of each patch perimeter (m) divided
by the square root of patch area (m2
), adjusted by a constant to adjust for a circular standard
(vector) or square standard (raster), multiplied by the patch area (m2
) divided by total
landscape area. In other words, AWMSI equals the average shape index (SHAPE) of
patches, weighted by patch area so that larger patches weigh more than smaller ones.

163
Double Log Fractal Dimension - DLFD
Units: None
Range: 1  DLFD  2
departure from a euclidean geometry (i.e., an increase in patch shape complexity). DLFD
approaches 2 for shapes with highly convoluted, plane-filling perimeters. DLFD employs
regression techniques and is subject to small sample problems. Specifically, DLFD may
greatly exceed the theoretical range in values when the number of patches is small (e.g.,
<10), and its use should be avoided in such cases. In addition, DLFD requires patches to
vary in size. Thus, DLFD is undefined and reported as "NA" in the "basename".full file and a
dot "." in the "basename".land file if all patches are the same size or there is only 1 patch.
Description: DLFD equals 2 divided by the slope of the regression line obtained by regressing
the logarithm of patch area (m2
) against the logarithm of patch perimeter (m).

164
Area-Weighted Mean Patch Fractal Dimension - AWMPFD
Units: None
Range: 1  AWMPFD  2
departure from a euclidean geometry (i.e., an increase in patch shape complexity). AWMPFD
approaches 2 for shapes with highly convoluted, plane-filling perimeters.
Description: AWMPFD equals the sum, across all patches, of 2 times the logarithm of patch
perimeter (m) divided by the logarithm of patch area (m2
), multiplied by the patch area (m2
)
divided by total landscape area; the raster formula is adjusted to correct for the bias in
perimeter (Li 1989). In other words, AWMPFD equals the average patch fractal dimension
(FRACT) of patches in the landscape, weighted by patch area.
Shannon's Diversity Index - SHDI
Units: None
Range: SHDI  0, without limit
SHDI = 0 when the landscape contains only 1 patch (i.e., no diversity). SHDI increases as the
number of different patch types (i.e., patch richness, PR) increases and/or the proportional
distribution of area among patch types becomes more equitable.
Description: SHDI equals minus the sum, across all patch types, of the proportional
abundance of each patch type multiplied by that proportion.

165
Simpson's Diversity Index - SIDI
Units: None
Range: 0  SIDI < 1
SIDI = 0 when the landscape contains only 1 patch (i.e., no diversity). SIDI approaches 1 as
the number of different patch types (i.e., patch richness, PR) increases and the proportional
Description: SIDI equals 1 minus the sum, across all patch types, of the proportional
abundance of each patch type squared.
Modified Simpson's Diversity Index - MSIDI
Units: None
Range: MSIDI  0
MSIDI = 0 when the landscape contains only 1 patch (i.e., no diversity). MSIDI increases as
the number of different patch types (i.e., patch richness, PR) increases and the proportional
Description: MSIDI equals minus the logarithm of the sum, across all patch types, of the
proportional abundance of each patch type squared.

166
Shannon's Evenness Index - SHEI
Units: None
Range: 0  SHEI  1
SHDI = 0 when the landscape contains only 1 patch (i.e., no diversity) and approaches 0 as
the distribution of area among the different patch types becomes increasingly uneven (i.e.,
dominated by 1 type). SHDI = 1 when distribution of area among patch types is perfectly even
(i.e., proportional abundances are the same).
Description: SHEI equals minus the sum, across all patch types, of the proportional
abundance of each patch type multiplied by that proportion, divided by the logarithm of the
number of patch types. In other words, the observed Shannon's Diversity Index divided by the
maximum Shannon's Diversity Index for that number of patch types.
Simpson's Evenness Index - SIEI
Units: None
Range: 0  SIEI  1
SIDI = 0 when the landscape contains only 1 patch (i.e., no diversity) and approaches 0 as
dominated by 1 type). SIDI = 1 when distribution of area among patch types is perfectly even
(i.e., proportional abundances are the same).
Description: SIEI equals 1 minus the sum, across all patch types, of the proportional
abundance of each patch type squared, divided by 1 minus 1 divided by the number of patch
types. In other words, the observed Simpson's Diversity Index divided by the maximum
Simpson's Diversity Index for that number of patch types.

167
Modified Simpson's Evenness Index - MSIEI
Units: None
Range: 0  MSIEI  1
MSIDI = 0 when the landscape contains only 1 patch (i.e., no diversity) and approaches 0 as
dominated by 1 type). MSIDI = 1 when distribution of area among patch types is perfectly
even (i.e., proportional abundances are the same).
Description: MSIEI equals minus the logarithm of the sum, across all patch types, of the
proportional abundance of each patch type squared, divided by the logarithm of the number
of patch types. In other words, the observed modified Simpson's diversity index divided by
the maximum modified Simpson's diversity index for that number of patch types.
Patch Richness - PR
Units: None
Range: PR  1, without limit
Description: PR equals the number of different patch types present within the landscape
boundary.
Patch Richness Density - PRD
Range: PRD > 0, without limit
Description: PR equals the number of different patch types present within the landscape
boundary divided by total landscape area (m2
), multiplied by 10,000 and 100 (to convert to
100 hectares).

168
Relative Patch Richness - RPR
Units: Percent
Range: 0 < RPR  100
RPR approaches 0 when the landscape contains a single patch type, yet the number of
potential patch types is very large. RPR = 100 when all possible patch types are represented
in the landscape. RPR is reported as "NA" in the "basename".full file and a dot "." in the
"basename".land file if the maximum number of classes is not specified by the user.
Description: RPR equals the number of different patch types present within the landscape
boundary divided by the maximum potential number of patch types based on the patch type
classification scheme, multiplied by 100 (to convert to percent).

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