Cover photograph courtesy of Scott Bradley
Inside cover photograph courtesy of David Larson
Technical Report Documentation Page
1. Report No.
2.
3. Recipients Accession No.
MN/RC – 2008-34
4. Title and Subtitle
5. Report Date
Perceptions of the View from the Road
AIMS II: A Statewide Web Survey
Aesthetic Initiative Measurement System Phase II
June 2006
7. Author(s)
8. Performing Organization Report No.
6.
Joan Iverson Nassauer, Erik S. Dayrell, Zhifang Wang
9. Performing Organization Name and Address
10. Project/Task/Work Unit No.
University of Michigan
School of Natural Resources & Environment
Dana Building, 430 East University
Ann Arbor, Michigan 48109-1115
11. Contract (C) or Grant (G) No.
12. Sponsoring Organization Name and Address
13. Type of Report and Period Covered
Minnesota Department of Transportation
Research Services Section
395 John Ireland Boulevard Mail Stop 330
St. Paul, Minnesota 55155
Final Report of Mn/DOT’s AIMS Phase
II Research Survey. 12/2004 - 06/2006
87036
14. Sponsoring Agency Code
15. Supplementary Notes
http://www.lrrb.org/PDF/200834.pdf
Related report: http://www.lrrb.org/PDF/2001-04
16. Abstract (Limit: 200 words)
The Aesthetic Initiative Measurement System Part II (AIMS II) is an image-supported web survey tool for
measuring highway corridor landscape (HCL) perceptions. It allows quick, efficient, and repeated
measurements of responses from a large sample making it possible to conduct a survey over the entire state.
AIMS II image-support capabilities allow digital imaging simulations of a controlled factorial selection of HCL
visual characteristics combining the efficiency of web surveys with the validity of visual stimuli as a basis for
measuring aesthetic response to landscape characteristics.
AIMS II provides highly generalizable, quantitative results from a large sample population. This complements
results from the AIMS I (2001) focus-group method, which have high construct validity but are less
generalizable. AIMS II digital imaging simulations show variation on selected landscape variables of interest in
multiple landscape settings, allowing results to be generalized to other landscapes.
A full range of landscape characteristics (Urban & Rural, Wall Design, Mowing Patterns, Vegetation Design)
was seen by 1108 licensed MN drivers who participated in the December, 2005 survey. They rated images of
114 different HCL views on their attractiveness, and their perceived naturalness, safety, and maintenance.
Another 3 images allowed respondents to compare views with different bridge rails and rate their preference.
17. Document Analysis/Descriptors
18.Availability Statement
Aesthetics, Visual Quality,
Perception, Context, Attractive,
Unattractive, Landscape
No restrictions. Document available
from: National Technical Information
Services, Springfield, Virginia 22161
19. Security Class (this report)
20. Security Class (this page)
Unclassified
Unclassified
21. No. of Pages
274
22. Price
The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of
the Minnesota Department of Transportation.
Prepared for the
Minnesota Department of Transportation
395 John Ireland Boulevard Mail Stop 330
St. Paul, Minnesota 55155
Landscape Ecology, Perception and Design Lab
www-personal.umich.edu/~nassauer/Labfinal.htm
School of Natural Resources and Environment
University of Michigan
Joan Iverson Nassauer, Erik S. Dayrell, and Zhifang Wang
June 2006
Aesthetic Initiative Measurement System: Phase II
AIMS II: A Statewide Web Survey
Perceptions of the View from the Road
Donald Appleyard, Kevin Lynch and John Myer.
The View From the Road.
1963
“Design involves a balanced judgment about many factors, of which visual
requirements are only one set. We are convinced, however, that these
requirements are among the most important that a road must satisfy.”
8
AIMS II Goals and Summary
11
11
AIMS II Development
Stakeholder Participation
9
7
Other related literature
Methods
4
Aims I
4
Summary of Related Literature
1
Introduction
3
XIII
Executive Summary
Key Concepts and Definitions
XI
Acknowledgements
3
VI
List of Figures
Purpose of Project
IV
List of Tables
Contents
Bridge rails
Woody islands
Naturalized woodland
Evergreens and deciduous
Evergreens
Sumac
Prairie flowers
Brome
Weedy
AIMS II • 12/2005
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Vegetation
All mown turf
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15
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Mowing
Walls
Context
Landscape Variables and Treatments
i
ii
AIMS II • 12/2005
46
Comparing the Effects of Landscape Treatments
48
50
Urban context and effects of
vegetation
Rural context and effects of
vegetation
46
45
Comparing Perceptions of Attractiveness,
Naturalness, Maintenance and Safety
Landscape views as cases
41
39
Overall Trends in Landscape Attractiveness
Analysis of Results (Chapter Heading)
37
31
Survey Sample of Minnesota Drivers
Differences by gender
30
Avoiding Bias in Treatment Evaluation
32
24
Web Questionnaire
Representativeness of the survey sample
23
Simulation of Landscape Views
70
Effects of Mowing
References
Conclusions and Recommendations
84
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72
64
Effects of Wall Design
Effects of Mowing and Vegetation
Combinations
62
60
56
53
52
Bridges and the Panoramic View
Individual respondent differences
Rural context and effects of
vegetation
Urban context and effects of
vegetation
Responses as cases
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145
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153
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161
169
219
Appendix 2: Web Questionnaire
Appendix 3: View Order for the Six
Randomizations of the Web Questionnaire
Appendix 4: Tests for Significant Differences
among Randomizations
Appendix 5: Invitation Email to Web Survey
Respondents
Appendix 6: Minnesota County Respondent
Frequencies Compared with 2000 US Census
Appendix 7: Bivariate correlations for the
112 highway corridor landscape views
Appendix 8: Statistical Results Comparing
Wall Designs
Appendix 9: Statistical Results Comparing
Mowing and Vegetation Combinations
85
Appendix 1: Simulated Landscape Views
in the Factorial Design Table
Appendices
AIMS II • 12/2005
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iv
Household income for the 2000 US Census.
Household income for respondents of the AIMS II web-based survey.
Bivariate correlations of the preference ratings showed that all were positively correlated.
Effect of vegetation on attractiveness of urban HCL – views as cases.
Effect of vegetation on perceived naturalness of urban HCL – views as cases.
Effect of vegetation on perceived maintenance of urban HCL – views as cases.
Effect of vegetation on perceived safety of urban HCL – views as cases.
Effect of vegetation on attractiveness of rural HCL – views as cases.
Effect of vegetation on perceived naturalness of rural HCL – views as cases.
Effect of vegetation on perceived maintenance of rural HCL – views as cases.
Effect of vegetation on perceived safety of rural HCL – views as cases.
Effect of vegetation on attractiveness of urban HCL – responses as cases.
Effect of vegetation on attractiveness of urban HCL – gender balanced response as cases.
Effect of vegetation on perceived naturalness of urban HCL – responses as cases.
Effect of vegetation on perceived naturalness of urban HCL – gender balanced responses as cases.
Effect of vegetation on perceived maintenance of urban HCL – responses as cases.
Effect of vegetation on perceived maintenance of urban HCL – gender balanced responses as cases.
Effect of vegetation on perceived safety of urban HCL – responses as cases.
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7
Table 8
Table 9
Table 10
Table 11
Table 12
Table 13
Table 14
Table 15
Table 16
Table 17
Table 18
Table 19
AIMS II • 12/2005
The combination of variables used to produce the HCL views.
Table 1
Tables
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Effect of vegetation on perceived safety of urban HCL – gender balanced responses as cases.
Effect of vegetation on attractiveness of rural HCL – responses as cases.
Effect of vegetation on attractiveness of rural HCL – gender balanced responses as cases.
Effect of vegetation on perceived naturalness of rural HCL – responses as cases.
Effect of vegetation on perceived naturalness of rural HCL – gender balanced responses as cases.
Effect of vegetation on perceived maintenance of rural HCL – responses as cases.
Effect of vegetation on perceived maintenance of rural HCL – gender balanced responses as cases.
Effect of vegetation on perceived safety of rural HCL – responses as cases.
Effect of vegetation on perceived safety of rural HCL – gender balanced responses as cases.
Effect of respondent on perceptions of HCL views in a rural context.
Effect of respondent on perceptions of HCL views in an urban context.
High and low rated wall treatments that were significantly different.
ANOVA results for the urban context without the weedy treatment.
ANOVA results for the rural context without the weedy treatment.
Combinations of vegetation and mowing treatments within the three contexts.
Table 20
Table 21
Table 22
Table 23
Table 24
Table 25
Table 26
Table 27
Table 28
Table 29
Table 30
Table 31
Table 32
Table 33
Table 34
AIMS II • 12/2005
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Panoramic views were not a focus because past research makes their importance obvious.
The southern rural landscape was represented as fields of soybeans. Appendix 1, view 94.
The northern rural landscape was represented as boreal forest. Appendix 1, view 51.
A cast-in-place retaining wall. Appendix 1, view 57.
A cast-in-place retaining wall. Appendix 1, view 72.
A mechanically stabilized earth retaining wall. Appendix 1, view 83.
A wooden post-and-plank noise wall. Appendix 1, view 1.
A pre-cast concrete panel noise wall with form-liner pattern. Appendix 1, view 16.
A noise wall with the highway right-of-way entirely mown. Appendix 1, view 27.
A single pass of the mower is shown with brome grass and a retaining wall. Appendix 1, view 59.
The curved mowing treatment with brome grass and a retaining wall. Appendix 1, view 65.
Thistle and woody volunteer tree species represent the weedy treatment. Appendix 1, view 58.
Brome grass with a curved mowing treatment within the boreal context. Appendix 1, view 111.
Flowering prairie species grow within the highway right-of-way. Appendix 1, view 79.
Thick masses of sumac grow along the highway corridor. Appendix 1, view 61.
White spruce are planted with brome grass. Appendix 1, view 88.
Deciduous trees and shrubs and planted between white spruce groupings. Appendix 1, view 99.
An increase in plant species aims at naturalizing the highway right-of-way. Appendix 1, view 112.
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
AIMS II • 12/2005
AIMS II built upon the results of AIMS I (2001).
Figure 1
Figures
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Aspen grow as the prairie becomes an early successional savanna. Appendix 1, view 106.
A bridge approach view depicting a concrete bridge rail. Appendix 1, view 113.
A bridge approach view depicting a bridge with a t-rail. Appendix 1, view 117.
A view of a river from the top of a bridge with concrete guard rail. Appendix 1, view 114.
A view of a river from the top of a bridge with t-rail style guard rail. Appendix 1, view 116.
The elevated view of a river with a bridge t-rail style guard rail. Appendix 1, view 115.
A sample page of the web questionnaire showing the HCL views and four rating scales.
The three sets of context images viewed by respondents in the AIMS II web survey.
A sample page from the web questionnaire about compatibility of views.
A sample page from the web questionnaire about bridge approach preference.
A sample page from the web questionnaire about bridge view preference.
A view from replicate 1 showing the rural agriculture context. Appendix 1, view 97.
A view from replicate 2 showing the rural agriculture context. Appendix 1, view 108.
State map of Minnesota indicating the five counties with no respondents in the AIMS II survey.
Employment status of Minnesota from the 2000 US Census.
Employment status of survey respondents.
Household income of Minnesota residents (2000 US Census).
Distribution of household income as reported by survey respondents.
Educational attainment of Minnesota residents (2000 US Census).
Educational attainment survey respondents.
Racial distribution of Minnesota residents according to 2000 US Census.
Racial distribution of survey respondents.
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
AIMS II • 12/2005
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vii
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Distribution of age among survey respondents.
Marital status of Minnesota residents (2000 US Census).
Marital status of survey respondents.
Distribution of gender in Minnesota (2000 US Census).
Distribution of respondent gender in the online survey.
The view that received the highest ratings for attractiveness. Appendix 1, view 46.
An urban view that was among the highest rated views. Appendix 1, view 79.
Continuous mean groupings for the 112 HCL views that were rated for attractiveness.
Symmetry may have been why this was one of the most highly attractive. Appendix 1, view 110.
A naturalized woodland view within the boreal context. Appendix 1, view 56.
Views in the second to lowest group were often from the urban context. Appendix 1, view 27.
Views in the lowest mean group often contained brome and darker walls. Appendix 1, view 9.
This view received the lowest ratings for attractiveness. Appendix 1, view 57.
An example of the nine vegetation treatments used in the regression analysis.
The concrete bridge rail provides the smallest panoramic view. Appendix 1, view 114.
The T-rail shown above provides a panoramic view of the landscape below. Appendix 1, view 116.
This T-rail provides the largest panoramic view. Appendix 1, view 115.
The most attractive wall type. Appendix 1, view 16.
A retaining wall that was also seen as attractive. Appendix 1, view 79.
The MSE retaining wall was seen as one of the least attractive. Appendix 1, view 83.
The cast-in-place retaining wall was seen as one of the least attractive. Appendix 1, view 57.
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50
Figure 51
Figure 52
Figure 53
Figure 54
Figure 55
Figure 56
Figure 57
Figure 58
Figure 59
Figure 60
Figure 61
Figure 62
Figure 63
AIMS II • 12/2005
Distribution of age among Minnesota residents in 2000 US Census.
Figure 42
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The wooden post-and-plank noise wall was seen as the most well maintained. Appendix 1, view 1.
Wall 1 and Wall 3 (above) were perceived as being the safest. Appendix 1, view 27.
This noise wall was most attractive with the brome treatment. Appendix 1, view 18.
This retaining wall was seen as the least attractive with brome grass. Appendix 1, view 59.
The MSE wall was seen as the least attractive wall with the prairie flowers. Appendix 1, view 91.
This retaining wall was seen as the most attractive with woody islands. Appendix 1, view 82.
Within the urban context, naturalized woodland was most preferred. Appendix 1, view 14.
Within the rural context, prairie flowers vegetation was most preferred. Appendix 1, view 102.
All mown turf with no other vegetation was seen as the least natural. Appendix 1, view 51.
Naturalized woodlands with straight mown edge was seen as most natural. Appendix 1, view 56.
All mown turf with no other vegetation was seen as the most well maintained. Appendix 1, view 1.
Figure 64
Figure 65
Figure 66
Figure 67
Figure 68
Figure 69
Figure 70
Figure 71
Figure 72
Figure 73
Figure 74
AIMS II • 12/2005
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AIMS II • 12/2005
AIMS II • 12/2005
From the beginning, we all conceived of AIMS as Mn/DOT’s process – a pair of systems, one primarily qualitative and the other
broadly representative and quantitative, that would engage Mn/DOT staff in considering the public aesthetic benefits of Mn/
DOT decision-making. From the many Mn/DOT staff who took AIMS I training and then implemented its focus-groups-invans technique to the many who participated in determining the topics for the AIMS II questionnaire, tested the image-based
web survey, and reviewed its results – Mn/DOT staff have encouraged us by their engagement and concern about how they can
be even more effective in serving Minnesota travelers. We thank them for helping us to conceive and conduct research that is
relevant to real Mn/DOT opportunities and concerns – now and for the future.
The AIMS II image-based web survey program was developed with the essential assistance of Phil Ray, Manager of IT Services
for the School of Natural Resources and Environment, and the University of Michigan Web Access, Technology, and Solutions
(WATS) group. As the Landscape Ecology, Perception, and Design Laboratory (http://www-personal.umich.edu/~nassauer/
Labfinal.htm) continues to develop image-based web survey applications, we are very grateful to the WATS team for helping us
gain new sophistication in technology innovation and management.
Ten years ago Scott Bradley anticipated the need for a system that would allow decision-makers greater insight into the public’s
perceptions of landscapes created by Mn/DOT design and management decisions. Since that time, he has consistently advocated
for and contributed to ideas driving the Aesthetic Initiative Measurement System (AIMS I and II). His national leadership in
the study of context sensitive solutions now brings his vision to a larger arena.
David Larson made essential and enriching contributions to both this project, AIMS II, and its predecessor, AIMS I. As
manager of both projects for the Minnesota Department of Transportation, he was clear about Mn/DOT’s goals for the projects,
responsive to the research questions and needs of our team, very substantially and energetically engaged in the content of both
projects, and steady and effective in linking this research with the needs of Mn/DOT decision-makers. Always, it has been our
great good luck to be able to work with him.
Acknowledgements
xi
xii
AIMS II • 12/2005
Wall design: comparing six different designs for noise walls or retaining walls in urban settings.
Mowing area and pattern: comparing an all mown turf right-of-way and two other selected mow patterns.
Vegetation design: comparing nine different vegetation compositions in areas that are not mown.
Bridge rails and view extent: comparing two different bridge rail designs at two different heights that allowed different
view extents.
•
•
•
•
AIMS II • 12/2005
Regional context: comparing urban, southern Minnesota rural agriculture and northern Minnesota rural boreal settings.
•
AIMS I results pointed to the following key issues affecting the aesthetic experience of HCLs for the urban landscapes: good
fit with context, design within the right-of-way, perceived maintenance, and perceived naturalness. Consequently, these issues
guided Mn/DOT stakeholders’ selection of topics for the 2005 AIMS II survey, including:
AIMS II provides highly generalizable, quantitative results from a large sample population. This complements results from the
AIMS I (2001) focus-group method, which have high construct validity but are less generalizable. AIMS II digital imaging
simulations show variation on selected landscape variables of interest in multiple landscape settings, allowing results to be
generalized to other landscapes. The wide variation in demographic characteristics of AIMS II respondents also allows results
to be generalized across the state to similar populations.
The Aesthetic Initiative Measurement System – Part II (AIMS II) is an image-supported web survey tool for measuring highway
corridor landscape (HCL) perceptions of a large sample of Minnesotans. Web survey technology allows quick, efficient, and
repeated measurements of responses from a large sample making it possible to conduct a survey over the entire state and to
monitor changing HCL perceptions over time. AIMS II image-support capabilities allow digital imaging simulations of a
controlled factorial selection of HCL visual characteristics to be included in web surveys – combining the efficiency of web
surveys with the validity of visual stimuli as a basis for measuring aesthetic response to landscape characteristics.
Executive Summary
xiii
xiv
AIMS II • 12/2005
Results show that context and vegetation design affect driver perception of HCLs much more than other variables, and that
flowery prairie vegetation is perceived as very attractive and natural, as well as adequately safe and well-maintained in both urban
and rural settings. In addition, naturalized mixed species woodlands are perceived as very attractive in rural settings. Results also
show that a single straight swath of mown turf adjacent to the roadway is seen as more attractive than any other mowing pattern,
much more attractive than a right-of-way that is completely mown turf. Compared with other landscape variables, wall design
has less effect on perceptions. However, some walls consistently are perceived as more attractive than others. In general, walls
that are lightly colored and have a regular rhythm of columns are perceived as more attractive than others. Finally, considering
the view from highway bridges to the landscape below, rail designs that allow a broader panorama of the landscape below are
perceived as far more attractive than those that obscure more of the view. Taken together, the results suggest that some of Mn/
DOT’s leading past design and management innovations, like using native plants, selective mowing, and the T-rail for bridges,
have been good investments in enhanced public perception. They also suggest several conclusions and recommendations for
future Mn/DOT decision-making.
The full range of each of the landscape characteristics was seen by the 1108 licensed Minnesota drivers who participated in the
AIMS II web survey in December 2005. They rated images of 114 different HCL views on their attractiveness, and their perceived
naturalness, safety, and maintenance. Another 3 images allowed respondents to compare alternative views with different bridge
rails; these were rated for relative preference and importance of having the preferred view.
AIMS II • 12/2005
Bridge rail designs that allow a broader panorama
of the landscape below are perceived as far more
attractive than those that obscure more of the view.
Walls that are lightly colored and have a regular rhythm
of columns are perceived as more attractive than others.
A single straight swath of mown turf adjacent to the
roadway is seen as more attractive than any other
mowing pattern.
Naturalized mixed species woodlands are perceived as
very attractive in rural settings.
Flowery prairie vegetation is perceived as very
attractive and natural, as well as adequately safe and
well-maintained in both urban and rural settings.
Context and vegetation design affect driver perception
much more than other variables.
xv
INTRODUCTION
2
AIMS II • 12/2005
Both AIMS I and AIMS II were developed to help Mn/
DOT understand and document how travelers perceive the
aesthetic quality of Minnesota’s highway corridor landscapes
(HCL). Highway corridor landscapes include the highway,
the right-of-way, and natural and built elements within the
right-of-way, as well as its context, landscapes viewed beyond
the right-of-way.
Phase II of Mn/DOT’s Aesthetic Initiative Measurement
System (AIMS II) builds on the 2001 AIMS I methods and
findings (Final Report # 2001-04). AIMS II develops an
image-based web survey instrument with digital imaging
simulations of a controlled factorial combination of highway
corridor aesthetic variables. It then implements the web
survey with a large sample of Minnesotans from the entire
state (n>1000). In this way, AIMS II provides highly
generalizable quantitative results that complement AIMS
I results, which have high construct validity and are more
qualitative. While AIMS I is a straightforward focus group
tool for the Minnesota Department of Transportation (Mn/
DOT) to explore what Minnesota travelers notice and find
visually attractive or unattractive along highway corridors,
AIMS II is a survey methodology for investigating highway
corridor aesthetic issues in a way that is highly generalizable
– to the larger population of Minnesota travelers and,
importantly, to many different highway corridor landscapes.
Together, AIMS I and AIMS II are powerful tools that Mn/
DOT can use to better inform and target transportation
corridor design and management decisions based on public
expectations and preferences.
AIMS II • 12/2005
AIMS defines aesthetic quality as visual experience.
Specifically, AIMS identifies and measures what people notice
and find attractive or unattractive about the appearance of
highway corridor landscapes. Associated characteristics
(derived from results of AIMS I as well as from the broader
landscape perception literature cited in the AIMS I report)
include the degree to which a landscape appears to be natural,
safe, and well-maintained.
Key Concepts and Definitions
Purpose of Project
3
4
A tool to analyze public perception of existing and
proposed highway corridor landscape views.
AIMS II • 12/2005
•
AIMS was conceived as a two-phase system for measuring
public aesthetic response to highway corridor landscapes
(HCL). Results of AIMS I were key building blocks for
selection of HCL aesthetic issues investigated in AIMS II.
The three main functions of AIMS I and II together were to
provide:
Aims I
In 1964, Appleyard, Lynch and Myer’s classic book, The
View from the Road, set highway design in the context of
the broader design concern of aesthetics. Momentum from
this seminal book grew with the passage of the Highway
Beautification Act (1965) and later the assurance of
aesthetically pleasing surroundings as a federal goal in the
National Environmental Policy Act (1969).
A phalanx
of landscape aesthetic perception methods and research
programs followed and have been widely discussed (e.g. Sell,
et al. 1984, Nassauer 1995, Daniel 2001). Beginning with
the 1991 Intermodal Surface Transportation Efficiency Act,
a series of federal transportation laws have reinforced the
importance of environmental aesthetics considered from
the perspective of context sensitive design and management
solutions. The aesthetic initiative measurement system
(AIMS I and II) grows out of that design and research
tradition (Nassauer and Larson, 2004).
Summary of Related Literature
A monitoring system for traveler’s aesthetic
experience.
A tool for decision-making about highway design
and management.
The AIMS I method was developed to learn what travelers
actually perceive while they were traveling on Minnesota
highways. Urban highway corridors were the theme for the
first AIMS I implementation in summer of 1999. Volunteers
were driven in vans along selected segments of highways in
metropolitan areas of Minnesota. Routes were selected by
Mn/DOT staff in consultation with the research team to
emphasize aesthetic issues that could be important to Mn/
Dot decision-making. The first route was in Rochester,
Minnesota, the second was in Minneapolis - St. Paul, and
the third route was in the greater Duluth, Minnesota area.
AIMS I (2001) provided a new, high validity methodology
and straightforward tool for Mn/DOT to assess what travelers
notice and find attractive or unattractive along Minnesota
HCLs. It adapted focus group methods to qualitatively and
quantitatively identify, document, and analyze travelers’
perceptions of the aesthetic characteristics of highway
corridor landscapes. It provides landscape perception data of
high construct validity, describing what travelers themselves
notice as attractive or unattractive. However, because this
“focus groups in vans” approach gathers much information
from each traveler in real landscapes, it necessarily includes a
relatively small number of travelers.
•
•
Functional aspects. Functional aspects of good
design contributed to what people noticed as attractive.
Perceived safety, for example, is one functional aspect that
contributed to perceived attractiveness.
2.
Good design within the right-of-way. Good design
within the right-of-way accounted for what viewers saw as
attractive for the entire range of landscape attractiveness.
Vegetation design and design of architectural details, such
as railings, and wall and bridge materials, and form were
associated with HCL views that were perceived as highly
attractive.
Skillful design decisions created landscape
attractiveness that was valued as highly as were broad
landscape vistas.
1.
Good fit with context. The most attractive landscape
views typically display a good fit between highway design
and an attractive landscape context. Where highway design
creates and emphasizes large landscape vistas-whether of
urban skylines, hills covered by trees, landmark buildings,
bridges, or lakes or rivers - these vistas are perceived as highly
attractive. Where something in the right-of-way blocks these
vistas, it is seen as unattractive.
Sixty three volunteers in vans participated in AIMS I days
during 1999. During the drive, volunteers would note views
they found attractive or unattractive, and the locations were
recorded by a trained recorder/driver. Volunteers who had
noticed the corresponding view note were asked to rate the
view on an attractiveness scale. In AIMS I, 727 landscape
views were noted, described, and rated. AIMS I results
suggested four main factors that affect HCL aesthetics:
AIMS II • 12/2005
1.
Inadequate maintenance. The more unattractive
an HCL is to travelers, the more likely that the HCL is
perceived as poorly maintained. Weedy areas, unmown
stretches of lawn, and the presence of trash all contributed
to unattractive ratings. Lack in maintenance of structures,
either in the right-of-way or outside the right-of-way, were
also perceived as unattractive.
AIMS I results suggest that when any of the following three
factors was lacking, the public tended to perceive the HCL
as unattractive.
4.
Good maintenance.
Good maintenance was
perceived as attractive wherever it was seen – in attractive
landscapes and in less attractive landscapes. In that way, good
maintenances is highly influential in supporting perceptions
of highway attractiveness. While maintenance alone cannot
create the perception that a landscape is very attractive, poor
maintenance can make an otherwise attractive landscape
look less attractive, and good maintenance can add value to a
landscape that might otherwise be ordinary or unattractive.
3.
Nature. Respondents were far more likely to mention
“wildlife, green, environmental, natural” to explain what
made a very attractive landscape attractive. Some of these
characteristics were created by design (e.g., wildflowers
that attract birds and butterflies), and some were the result
of design that emphasized inherent characteristics of the
landscape (e.g., rock outcroppings or views of rolling hills).
Natural characteristics introduced or emphasized by design
were associated with very high attractiveness.
5
6
AIMS II • 12/2005
3.
Fit of right-of-way with its context. Poor fit with
context often was associated with what viewers saw as
unattractive within a landscape that they saw as attractive
overall. For example, people might find a sign unattractive
in an attractive landscape, or they might object to the
positioning and size of a bridge as it relates to the overall
vista of a river.
Functional aspects. People perceived some segments
of the highway as dangerous, and that contributed to their
unattractiveness.
2.
Design within the right-of-way. Even if a landscape
is well maintained, people may see it as very unattractive
if they see it as poorly designed. This can be related to
choice of materials that people see as inherently unattractive:
chain link fence and painted concrete were mentioned.
Unattractiveness also may relate to a lack of trees, shrubs, or
plantings beyond a simple mown lawn.
Figure 1: AIMS I (2001) provided a new, high validity methodology and
straightforward tool for Mn/DOT. AIMS II built upon these results.
Related to the AIMS II vegetation designs, when travelers
were asked if they would prefer to see “attractive vegetation
like wildflowers along the road,” they consistently agreed.
Responses were negative, however, when travelers were
asked if they would like to see more trees growing along the
road (Gartner and Erkkila. 2004). Meyers (1999) suggests
In a discussion of studies that focus on scenic byways, Gartner
and Erkkila (2004) note a general lack of research measuring
roadway characteristics that affect user experience. Their
study addresses this lack by examining 11 road segments
throughout Minnesota. Visitors traveling along the road
segments were intercepted and those who were willing (n
= 200 + per segment) completed interview questionnaires
about their preferences for Minnesota highways. Like the
AIMS I results for attractive segments of urban highways,
Gartner and Erkkila found that natural scenery was rated
as the most interesting aspect of every scenic byway road
segment. Related to AIMS II regional context comparisons,
forests like boreal northern Minnesota were highly rated, but
farms like those found in southern Minnesota were seen as
less interesting. Related to AIMS II mowing alternatives,
the question “I prefer to see the grass adjacent to the road
mowed,” tended to be rated positively. Nassauer (1993) has
repeatedly shown that the proportion and pattern of mowing
in a landscape view affects perceptions of attractiveness, and
a highly related variable, perceived care. Having at least
some mown area apparent to the viewer acts as a cue to care
(Nassauer 1995, 1997).
Other related literature
AIMS II • 12/2005
Vegetation may also have a psychological influence on
travelers. In a study of the effects of roadside vegetation on
anger and frustration levels by Cackowski and Nasar (2003),
participants were stressed and then shown one of three
videotapes of drives along a highway. These videos depicted
drives with highway right-of-ways that ranged in the amount
of vegetation and urban structure. While viewing roadways
with more vegetation relative to built structure along the
edges did not significantly affect anger, participants did
have greater frustration tolerance when there was more
vegetation.
native plants are an appropriate plant palette for assisting
in context sensitive design. The use of native plant species
helps blend road rights-of-way back into the surrounding
plants communities (Landis et al, 2005). Different from the
Garner and Erkkila results related to tree preference, Meyers
found that 92% of respondents found trees near the edge of
the road to be “quite attractive” or “very attractive” in a study
of 199 motorists using the Blue Ridge Parkway, She reports
that large trees grow as close as five or six feet from the edge
of the pavement along the Blue Ridge Parkway. When asked
about safety, Meyers reported that most respondents (86%)
considered the presence of trees to be “relatively safe” or “not
a hazard at all.”
7
8
AIMS II • 12/2005
In total, 1108 licensed drivers from all over the state of
Minnesota participated in AIMS II 2005. They rated
landscape views that had been computer-simulated to show
variation across landscape aesthetic variables of interest to
Mn/DOT stakeholders, including issues that emerged from
AIMS I. Landscape context, architectural elements, mowing
patterns, and vegetative treatments were selected for AIMS
II. While AIMS I examined HCLs in an urban context,
AIMS II examined HCLs in urban, rural agriculture, and
rural boreal contexts. In total, 114 different landscape
views were simulated and rated by Minnesota drivers for
their attractiveness, and perceived safety, naturalness, and
maintenance. An additional 3 were simulated and rated for
view preference. AIMS II also asked respondents to consider
“good fit with context” for each highway corridor landscape
rated.
These rating scales all followed from landscape
perception issues that had been identified by participants in
AIMS I.
AIMS II complements AIMS I by building on its high validity
conclusions and testing those conclusions with a large,
representative sample of Minnesota travelers. AIMS II is an
image-supported web survey method that efficiently gathers
data from a large number of respondents. Using the internet,
Minnesota drivers rate HCL views that are simulated to
exhibit specific combinations of landscape characteristics.
AIMS II Goals and Summary
5. Vegetative Treatments
4. Mowing Patterns
3. Architectural Elements:
Bridge Rails
2. Architectural Elements:
Walls
1. Landscape Context
Aesthetic Variables Selected
for AIMS II
METHODS
10
AIMS II • 12/2005
The January meeting included a larger group of Mn/DOT
Informed by AIMS I results, Mn/DOT stakeholders met with
the research team in August of 2004 and January of 2005 to
select highway aesthetics design and maintenance topics for
investigation in AIMS II. Participants in the August meeting
included David Larson, Scott Bradley, Rick Arnebeck, Val
Svenson, Kimberly Sannes, Tim Quinn, Dave Hall, Rod
Garver, and Jim Rosenow. Design and management topics as
well as traveler populations were discussed. Panoramic views,
views of nature, and screening were noted as crucial elements
of the HCL, based on AIMS I results. Planting design
comprehensiveness, architectural details, and maintenance
also were discussed based on AIMS I results. A statewide
sample of Minnesota licensed drivers from rural and urban
areas was stated as an expectation. The August meeting
concluded with the understanding that testing for preference
of panoramic views was not necessary because the aesthetic
effect of such views is solidly supported by past research;
there was no need to investigate this again. There also was
agreement that mowing patterns and a variety of wall designs
and treatments would probably be of interest for AIMS II.
It also was established that inclusion of both rural and urban
settings was needed for AIMS II. Finally, the group discussed
the fact that the number of images seen by each web survey
respondent would need to be limited to about 35 to reduce
respondent fatigue.
S ta k e h o l d e r p a r t i c i p a t i o n
AIMS II Development
AIMS II • 12/2005
Discussion of vegetation treatments for AIMS II focused on
species and planting design. Native species were a popular
suggestion for low maintenance design, including selective
mowing patterns. An important question was “how little
Architectural elements discussed included guard rails,
retaining walls, noise walls, and fencing. Particular interest
was focused on the aesthetics of retaining walls and noise
walls – partly because these elements also could have cost
effects. Cast in place, mechanically stabilized earth, and
modular blocks were wall construction methods mentioned
by stakeholders for further investigation. Bridge rails were
also a topic of discussion, partly because different bridge rail
designs allow travelers to see more or less of the landscape
below the bridge. Stakeholders were interested in discovering
whether or not the T-rail design that allows broader landscape
views was favored over the commonly used concrete guard
rail.
stakeholders (David Larson, Rick Arnebeck, Kimberly Sannes,
Scott Bradley, Paul Walvatne, Rod Garver, Rebecca Novak,
Dave Hall, Tina Markeson, Tim Quinn, and Jim Rosenow).
The first topic of discussion was landscape context. While
urban areas were the focus of AIMS I, Mn/DOT stakeholders
requested that AIMS II include not only urban landscape
contexts, but also rural settings typical of both the southern
and northern regions of Minnesota. This would allow Mn/
DOT to make more regionally context sensitive decisions.
Within each context, three major design topics were selected
for AIMS II investigation; architectural elements, vegetative
treatments, and mowing patterns.
11
12
AIMS II • 12/2005
Figure 2: Panoramic views, like those seen along many sections of Minnesota highways, were not a focus of AIMS II because past
research makes their importance obvious.
mowing can we do, and at what point will it appear to be neglect?” Mowing patterns that were curved or ranged in width were
mentioned as ways of creating aesthetically interesting designs while cutting down on the amount of mowing required. Indigenous
trees and shrubs, and native grass and wildflower species were discussed. Continuity and level of care were also mentioned as
design and management issues. Planting in masses, species composition, and the height and width of vegetation in the highway
right-of-way were important considerations. The effect of weeds in the right-of-way was also discussed as an aesthetic concern.
Straight
Straight
Woody Islands
Curved
Straight
MOWING
PATTERN
All
Straight
Curved
Straight
Curved
Straight
Curved
Straight
Curved
Straight
Curved
Biodiversity
Evergreen +
Deciduous
Evergreen
Sumac
Flowers
Brome
Weedy
Turf
VEGETATION
1
2
6
Agriculture Agriculture
Site 1
Site 2
Walls
3
4
5
Rural
Urban
CONTEXT
Boreal
Site 1
Boreal
Site 2
T-Rail
Concrete
Elevated
Conventional
Conventional
View
AIMS II • 12/2005
Approach View
Table 1: The combination of variables (context, walls, vegetation, mowing pattern, bridge approach and bridge view) and specific treatments of each variable used to
produce the HCL views. Each cell is a view simulated for AIMS II. (Appendix 1).
From stakeholder participation and based on the results of AIMS I, five landscape variables were selected to test HCL aesthetic
preferences for AIMS II. Within each variable, different treatments were defined and the treatments for all variables were
combined to produce a widely generalizable range of HCL views (Table 1).
Landscape Variables and Treatments
Bridge Rails
13
14
AIMS II • 12/2005
AIMS I focused on urban highway settings, and concluded
that the most attractive landscape views typically are related
to a good fit between highway design and an attractive
landscape context, including broad panoramic views.
AIMS II broadened the investigation of context to urban
and rural, and to both southern Minnesota and northern
Minnesota rural settings. The urban context contained a
variety of retaining and noise walls indicative of the urban
landscape. An agricultural context was chosen to represent
the rural landscape of southern Minnesota (Figure 3). Views
of the agriculture treatment depicted flat, expansive fields
planted with soybeans. Field boundaries were parallel to the
road, roughly 50 feet from the roadside. Deciduous trees
and shrubs were visible on the horizon line. Boreal forest
was chosen to represent the rural landscape of northern
Minnesota (Figure 4). Views of the boreal forest depicted
thick stands of mature white spruce (Picea glauca). Forest
stands were shown growing parallel to the road 40-50 feet
from the roadside.
Cont e xt
Figure 4: The northern rural landscape was represented in AIMS II as boreal
forest. Appendix 1, view 51.
Figure 3: The southern rural landscape was represented in AIMS II as fields of
soybeans. Appendix 1, view 94.
In AIMS I good design within the right-of-way accounted
for what viewers saw as attractive across the entire range
of landscape attractiveness. In particular, well designed
architectural details were associated with highway views that
were perceived as highly attractive in urban areas. In AIMS
II the urban context was depicted with six different wall
treatments characterized by different construction practices
and costs. Three retaining walls and three noise walls were
used in the study. The retaining walls consisted of two castin-place walls, one with rustication grooves and a single color
stain (Figure 5), and one with form-liner patterns and two
color stains (Figure 6). The third wall was a mechanically
stabilized earth wall (MSE) (Figure 7). The noise walls
consisted of a wooden post-and-plank wall (Figure 8), a precast concrete panel wall with form-liner pattern (Figure 9),
and a pre-cast concrete block and panel wall (Figure 10). The
general order of cost for the noise wall choices is: wooden
post & plank ($15-17 per ft 2), pre-cast concrete panel with
form-liner pattern(s) ($18-20 per ft 2), and the pre-cast
concrete block and panel wall ($25-28 per ft 2). The general
order of cost for the retaining wall choices is: mechanically
stabilized earth ($45 per ft 2), cast-in-place with rustication
grooves and single stain ($55 per ft 2), and the cast-in-place
with form-liner patterns and two colors ($65 per ft 2).
Wa lls
AIMS II • 12/2005
Figure 6: A cast-in-place retaining wall with form-liner pattern and two color
stains. Appendix 1, view 72.
Figure 5: A cast-in-place retaining wall with rustication grooves and a single
color stain. Appendix 1, view 57.
15
16
Figure 10: A pre-cast concrete block and panel noise wall with the highway
right-of-way entirely mown. Appendix 1, view 27.
Figure 9: A pre-cast concrete panel noise wall with form-liner pattern.
Appendix 1, view 16.
AIMS II • 12/2005
Figure 8: A wooden post-and-plank noise wall. Appendix 1, view 1.
Figure 7: A mechanically stabilized earth (MSE) retaining wall. Appendix 1,
view 83.
Since mowing is known to be a cue to care, which affects
perceived attractiveness by signaling human intentions for
landscape appearance (Nassauer 1995), AIMS II included
a mowing variable with three treatments. The first mowing
treatment represents the least cost alternative for energy
and maintenance time. It shows a single pass of the mower
directly adjacent to the roadside (Figure 11). Current Mn/
DOT maintenance practices use a mower with an adjustable,
single pass cut width of 8 to 14 feet. AIMS II simulated this
maintenance practice by showing a mow strip 10 feet wide.
The second mowing practice increased the amount of mowing
by adding a second pass of the mower. In this alternative,
maintenance staff would begin with the 10-foot pass along
the roadside and then mow a sinuous path adjacent to the
strip (Figure 12). The curved path would undulate out from
the first pass to a maximum distance of 10 feet, then curve
back to the first mown pass. The final mowing practice is
the highest cost alternative: everything within the highway
right-of-way is mowed (Figure 10).
In AIMS I results, good maintenance was perceived as
attractive whether it was seen in attractive landscapes or in less
attractive landscapes. This suggests that while maintenance
alone cannot create the perception that a landscape is very
attractive, poor maintenance can make an otherwise attractive
landscape look less attractive, and good maintenance can
add value to a landscape that might otherwise be ordinary
or unattractive. In addition, adjusting mowing widths
and patterns may also affect mowing time, fuel usage, and
maintenance costs.
Mowing
AIMS II • 12/2005
Figure 12: Shown here with brome grass and a retaining wall, the curved
mowing treatment adds an additional pass of the mower in a sinuous, variable
curve. Appendix 1, view 65
Figure 11: A 10-foot wide, single pass of the mower is shown here in a view
depicting brome grass and a retaining wall. Appendix 1, view 59.
17
18
AIMS II • 12/2005
Brome: Vegetation is monoculture brome grass, as typically
planted along Minnesota highways (Figure 14).
Weedy: Vegetation is a mix of burdock, thistle, Queen
Anne’s lace and volunteer woody saplings like quaking aspen
(Populus tremuloides). This treatment shows a minimalist
management approach in which volunteer herbaceous and
woody species are controlled only by mowing once every
two to three years (Figure 13). However, the immediate
roadside is maintained by regular mowing, as described by
the mowing variable.
All mown turf: The all mown turf treatment consists
of completely mown turf grass, without trees, shrubs, or
unmown perennial herbaceous vegetation in the right-ofway. (Figure 10).
Vegetation that enhanced landscape context and planting
designs that were well designed within the right-of-way
contributed to HCL attractiveness in AIMS I. AIMS II
included 9 alternative treatments for vegetation within the
right-of-way. Vegetative treatments show plantings that
start as close as 10 feet from the roadside. Treatments that
contained large trees are planted 30 feet from the roadside edge
to accommodate the 30-foot Clear Recovery Zone (CRZ),
as dictated by the Inspection and Contract Administration
Guidelines for Mn/DOT Landscape Projects manual.
Vege tation
Figure 14: Brome grass, common along many Minnesota highway corridors, is
shown here with a curved mowing treatment within the rural boreal context.
Appendix 1, view 111.
Figure 13: Burdock, thistle, and woody volunteer tree species grow within the
highway right-of-way in the weedy treatment. Appendix 1, view 58.
Naturalized woodland: The naturalized woodland
treatment increased species richness and roadside habitats
by including Minnesota native woody and herbaceous plant
species in contiguous patches. Woody species are planted at
least 30 feet from the roadside with brome grass elsewhere
(Figure 19).
Evergreen and deciduous: Deciduous trees and
shrubs are planted between white spruce groupings. American
Linden (Tilia americana), Red oak (Quercus rubra), red twig
dogwood (Cornus sericea) and Amur maple (Acer ginnala) are
shown. All woody material was planted at least 30 feet from
the roadside. Brome grass is planted elsewhere. Plants are
shown at roughly 10 years after planting 5-year old saplings
(Figure 18).
Evergreens: White spruce (Picea glauca) are planted in
groups of odd numbers, about 30 feet from the roadside.
Brome grass grows between the trees. Evergreens are shown
roughly 10 years after planting a 5-year old sapling (Figure
17).
Sumac: Smooth sumac (Rhus glabra) is shown growing in
dense masses, roughly 10 years after planting (Figure 16).
Prairie flowers: Vegetation is a mix of native flowering
prairie species and grasses such as grey coneflower (Ratibida
pinnata), ironweed (Vernonia altissima), big bluestem
(Adropogon gerardii), and monarda (Figure 15).
AIMS II • 12/2005
Figure 16: Thick masses of sumac grow along the highway corridor, shown
here with the straight mowing treatment. Appendix 1, view 61.
Figure 15: Flowering prairie species grow within the highway right-of-way
depicted here with the curved mowing treatment. Appendix 1, view 79.
19
20
Figure 20: Quaking aspen grow in small groups as the prairie becomes an early
successional savanna. Appendix 1, view 106.
Figure 19: An increase in plant species aims at naturalizing the highway rightof-way. Appendix 1, view 112.
AIMS II • 12/2005
Figure 18: Deciduous trees and shrubs and planted between white spruce
groupings; depicted here in a rural agricultural setting. Appendix 1, view 99.
Figure 17: White spruce are planted in clusters with brome grass growing
between groups. Appendix 1, view 88.
AIMS I found that functional aspects of good design
contributed to what people noticed as attractive. Bridge rails
may be attractive in and of themselves. In addition, bridge
rail design and viewer height may increase the attractiveness
of HCLs by providing panoramic views of the landscape
below. Panoramic views were consistently perceived as
highly attractive in AIMS I. To provide information on
the relationship between bridge rail design and the perceived
aesthetic value of panoramic views, AIMS II focused on
two bridge rail designs; the traditional concrete bridge rail,
which blocks more of the view, and the T-rail bridge design,
which allows a view through to the landscape. An approach
view was simulated with each of the two bridge rail designs
to show the surrounding context of the bridge (Figures 21
and 22); they depict a bridge with a six lane highway and
concrete center divider. Another view shows the rail and
landscape beyond as seen from a car while driving in the
right lane at the center of the bridge. The concrete bridge
rail shows the very top of the tree canopy with the remainder
of the view blocked (Figure 23). The first T-rail bridge view
Bridge r ails
Woody Islands: The woody island treatment contains
the same flowering prairie and grass species as the prairie
flowers treatment, along with early successional native tree
species. Groupings of 15 year-old quaking aspen (Populus
tremuloides) planted at least 30 feet from the roadside. This
treatment allows an established prairie ecosystem to succeed
into a savanna ecosystem (Figure 20).
AIMS II • 12/2005
Figure 22: A bridge approach view depicting a bridge with a t-rail. Appendix
1, view 117.
Figure 21: A bridge approach view depicting a concrete bridge rail. Appendix
1, view 113.
21
22
AIMS II • 12/2005
Figure 23: A view of a river from the top of a bridge with concrete guard rail.
Appendix 1, view 114.
places the top of the rail at the same height as the concrete
rail, showing about 1/3 of the tree canopy and a river (Figure
24), while the second T-rail bridge view shows the rail as seen
by a viewer with an elevated view (e.g., from a truck), with
roughly 2/3 of the tree canopy and river (Figure 25).
Figure 25: The elevated view of a river from the top of a bridge with a t-rail
style guard rail. Appendix 1, view 115.
Figure 24: A view of a river from the top of a bridge with t-rail style guard rail.
Appendix 1, view 116.
AIMS II • 12/2005
Views were simulated to show all plausible combinations of treatments for all variables with at least two replicates of each
treatment in a factorial experimental design (Appendix 1). Other characteristics that could affect perception of the simulated
views were held constant. These included: road surface and angle of view, sky, and absence of people, animals, and other cars.
In total, 117 views were simulated for inclusion in the web-base questionnaire.
All HCL views were simulated for a flat, 60 mph road with tangent alignment to the right-of-way, where a Clear Recovery
Zone (CRZ) of 30 feet is dictated by the Inspection and Contract Administration Guidelines for Mn/DOT Landscape Projects
manual. Views were simulated with late summer vegetation. The viewpoint of all simulations is from the front seat passenger
side window, roughly four feet from the ground. The only exception is the elevated viewer’s perspective from the bridge, which
is compared with the normal viewer elevation.
Simulation of landscape Views
23
24
AIMS II • 12/2005
For the bridge views, respondents were presented with one of
the two bridge approach views (concrete or T-rail) and asked
to imagine that they were driving on that particular highway
The questionnaire consisted of two sections and took 15
minutes to complete (Appendix 2). In the first section,
respondents rated landscape views grouped together by
urban, agricultural or boreal context. Each respondent saw
all nine vegetative treatments (weedy, brome, etc.) in either
the straight or curved mowing pattern for all three landscape
contexts (urban, rural agriculture and rural boreal). Each
“context group” presented three to five HCL views, and
then asked respondents to rate the compatibility of each
view with a landscape context that was depicted by three
images of urban, rural agriculture or rural boreal landscapes.
Respondents were first presented with the HCL views, one
at a time, and asked to rate the views according to their past
experience of Minnesota highways on a seven-point Likert
scales for attractiveness, naturalness, maintenance, and safety
(Figure 26). Then they were presented with three context
images depicting one of the landscape contexts and asked to
imagine that they were traveling down a highway through
a landscape that looked like the context images (Figure
27). Respondents were asked whether or not each of the
views that they had rated earlier was compatible with the
context images (Figure 28). This way AIMS II yielded both
perception ratings and context compatibility classifications
for each view.
Web Questionnaire
To avoid respondent fatigue, not all respondents viewed all
117 HCL views. Rather, each respondent viewed and rated
25 to 27 views that displayed a wide array of treatment and
context combinations for the landscape variables of interest
(Appendix 3). Because AIMS II efficiently samples a large
The second section of the questionnaire asked respondents
to answer background questions about themselves, and asks
their opinions about other Mn/DOT practices. Questions
on driving habits, commuting preference and highway use
were asked as well.
bridge (Figure 29). They were then asked to rate the view
to show their own perception of the view for attractiveness,
naturalness, maintenance, and safety. On the next page,
they saw two alternative bridge views, each of which revealed
a different extent of panoramic view over the bridge rail.
Respondents were asked to imagine that they were looking
out from the bridge they had just rated and to indicate which
of the two views they would prefer to have from the bridge
(Figure 30). They were also asked to rate how much more
attractive they found their preferred view compared with the
other view. Then they were asked to rate how important it
was for them to have their preferred view from the bridge
compared to the other view. For each rating, respondents
could choose “the same” or “not at all important” if the
two alternatives were essentially the same. These choices
and ratings provide information not only about the effect
of bridge rail design, but also about the effect of different
bridge rail heights on the public perception of the view from
the bridge.
AIMS II • 12/2005
Figure 26: A sample page of the web questionnaire. It shows the HCL view and 4 scales measuring attractiveness, naturalness, maintenance,
and safety.
number of respondents and the AIMS II sample was very large (n= 1108), statistical inferences could be made for all treatment
and context combinations without all respondents having to rate all views.
25
26
AIMS II • 12/2005
Figure 27: These are the three sets of context images viewed by respondents in the AIMS II web survey. The top row shows the rural boreal context, the middle shows
the rural agriculture context, and the bottom row shows the urban context.
AIMS II • 12/2005
Figure 28: A sample page from the web questionnaire, in which respondents judged the compatibility of views with three context images.
27
28
AIMS II • 12/2005
Figure 29: A sample page from the web questionnaire that shows a bridge approach view that asks respondents to rate the view on the four perception scales of attractiveness, naturalness, maintenance, and safety.
Figure 30: A sample page from the web questionnaire that asks respondents to choose their most preferred view from the bridge.
AIMS II • 12/2005
29
30
AIMS II • 12/2005
Figure 31: A view from replicate 1 showing the rural agriculture context with
the prairie flower treatment and a straight mown edge. Appendix 1, view 41.
Figure 32: A view from replicate 2 showing the rural agriculture context with
the prairie flower treatment and a straight mown edge. Appendix 1, view 97.
To avoid bias that might be introduced by image order within the questionnaire, each of the two replicates was viewed by
different respondents in one of three random orders. Each random order was different to allow testing for the effects of image
order, and each respondent saw only one of the six random orders. To test for effect of view order, view ratings were compared
for different random orders. Results showed that only 34 of the 912 rating scales, or 3.7%, had significantly different mean
ratings for different view orders. Most of these (26 of the 34, or 2.85% of the 912 rating scales) showed a rural or boreal context
or dense evergreen plantings. Four particular views accounted for more than half of the 34 rating scales that were significantly
different based on order, but no pattern of rating differences related to fatigue was seen even for those four views (Appendix 4).
Overall, view order did not appear to bias response.
For the AIMS II web questionnaire, two landscape setting replicates of each view treatment were measured (See Figures 31 and
32 and Appendix 1). This allows analysis to check for effects of landscape setting, separate from the experimental landscape
variables of interest. Each replicate showed the same combination of planting treatments and context. While the rural context
showed identical treatments between replicates, the urban context replicates varied in architectural treatments. In the urban
context, replicate 1 contained noise walls and replicate 2 contained retaining walls.
Avoiding Bias in Treatment Evaluation
When the AIMS II survey was conducted in December of
2005, Survey Spot had 41,000 volunteers in Minnesota. For
AIMS II, the volunteers were “Census balanced” by selecting
volunteers in Census blocks proportionate to the 2000
population of Minnesota as well as balanced for gender and
age. The invitations then were supplemented for ethnicity
to increase response rate of minority populations. In total,
the balanced panel consisted of 10,941 volunteers and the
supplemented panel consisted of 19,988 Minnesota licensed
drivers who received invitations to the AIMS II web site. On
December 5, 2005, Survey Spot sent panelists an invitation email that contained a link to the web questionnaire (Appendix
5). SSI reported 1000 survey responses by December 12,
2005, and stopped collecting responses. Before the survey
The population targeted for the survey to represent Minnesota
highway travelers was Minnesota residents age 18 and above
who had valid driver’s licenses. Responses by this population
would help Mn/DOT understand how Minnesota highway
travelers perceived the HCL when they drove or rode as
passengers viewing Minnesota landscapes. Survey Sample
International (SSI), a supplier of online population samples,
was used to reach the target population. Survey Spot, SSI’s
web sampling service stores demographic and socioeconomic
information on panelists who have volunteered to receive
invitations to participate in web surveys. For AIMS II, SSI
selected only panelists who were Minnesota residents over
age 18 and held a valid drivers license.
Survey Sample of
Minnesota drivers
AIMS II • 12/2005
Figure 33: State map of Minnesota indicating the five counties with no
respondents in the AIMS II survey sample.
web site was closed, a total of 1108 respondents completed
the survey.
31
32
AIMS II • 12/2005
Survey respondents were employed at levels similar to the State
of Minnesota as indicated by the 2000 Census (Figures 34 and
35), with 67.8% of respondents holding at least a part time
job, compared to 71.2% employed in Minnesota. Household
income of survey respondents was recorded in aggregated
categories (Table 3). The largest percentage (22.2%) of survey
respondents were in the $50,000 to $74,999 income class.
The 2000 US Census reported 22.4% in the same income
class. A total of 13.6% of respondents had incomes less than
$20,000 and the lowest percentage of respondents (1.6%)
had incomes above $150,000. Similar categories in the 2000
Survey respondents represented 82 of the 87 counties of
Minnesota. Big Stone, Chippewa, Lake of the Woods,
Stevens, and Traverse were the five counties not represented
in the survey population (Figure 33). Since the combined
population of these five counties is only 0.8% of the entire
Minnesota population, survey results are not affected for the
state overall. Appendix 6 shows the frequency of respondents
by county as well as the breakdown of population by county
for the state of Minnesota.
Comparison with the 2000 US Census suggests that the
survey sample is representative of the Minnesota population
in several important ways. The comparison also suggests
that the sample differs from the Minnesota population in
some ways, and we tested for the possible significance of
these differences in results of data analysis.
R e p r e s e n ta t i v e n e s s o f t he
surv e y s ampl e
Figure 35: Employment status of survey respondents.
Figure 34: Employment status of Minnesota from the 2000 US Census.
127,955
234,300
322,529
424,867
228,834
156,565
82,865
1,896,209
Number
23.5
12.4
17
22.4
12.1
8.3
4.3
100
Percent
Figure 36: Household income of Minnesota residents (2000 US Census).
2000 US Census
Income
Less than $24,999
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 +
Total
Table 2: Household income for the 2000 US Census.
Number
151
160
141
246
116
59
18
891
13.6
14.4
12.7
22.2
10.5
5.3
1.6
80.4
Percent
AIMS II • 12/2005
Figure 37: Distribution of household income as reported by survey respondents.
Less than $29,999
$30,000 - $39,999
$40,000 - $49,999
$50,000 to $74,999
$75,000 - $99,999
$100,000 - $149,999
$150,000 +
Total
Respondent Income
Table 3: Household income for respondents of the AIMS II web-based survey.
33
34
AIMS II • 12/2005
AIMS II slightly undersampled minority racial groups
compared with 2000 Census data for Minnesota. Whites
accounted for 94.8% of the survey sample, compared with
89.4% in the 2000 Census. However, all racial groups
included in the Census were represented in the AIMS survey.
American Indians/Native Alaskans, Asians and Blacks/African
Americans each had representations of 1%. Pacific Islanders
had very low representation (0.1%) but this is consistent
with 2000 US Census data (Figures 40 and 41). Analysis of
frequency of minorities by county showed that minorities in
Educational attainment of the survey sample is similar to
that of the Minnesota population (Figures 38 and 39). The
highest percentage of respondents (42.2%) indicated that
they had some college experience but had not received a
degree. Some college experience was followed by Bachelor’s
degrees (23%) and then high school graduates (19.9%).
Respondents with graduate or professional degrees accounted
for 12.3% of the sample. The 2000 US Census reported that
31.7% of Minnesota residents had received some college but
no degree. Some college experience was followed by high
school graduates (28.8%), those with a Bachelor’s degree
(19.1%) and those with a graduate or professional degree
(8.3%).
US Census reported 23.5% of households in Minnesota made
less than $24,999 and 4.3% made $150,000 and above. This
slight under sampling of the least wealthy and very wealthy
households is typical of consumer surveys. Figures 34 and 35
show the income distribution of the survey respondents and
Minnesota residents.
Figure 39: Educational attainment survey respondents.
Figure 38: Educational attainment of Minnesota residents (2000 US Census).
Respondent age ranged from 18 to 86, with a mean age
of 45. Respondents under the age of 30 accounted for
16.8% of responses, with 47.8% between the ages of 30 and
50, and 35.4% above 50 years of age. Figures 42 and 43
show the comparison of survey respondent ages with the
2000 US Census for Minnesota; differences were expected
since AIMS II surveyed only licensed drivers over age 18.
Consequently, children and very old people would be under
sampled compared with the Census. Marital status of the
survey population closely matched the status for Minnesota
residents (Figures 44 and 45). The largest percentage of
survey respondents was married (56.5%) with those that
were never married coming in at 20.2%. Individuals who
were separated, divorced or widowed accounted for 15.9%
and 7.4% were in a domestic partnership.
the AIMS II survey tended to come from metropolitan regions
such as Hennepin, Ramsey, Stearns and Anoka counties.
Figure 41: Racial distribution of survey respondents.
races listed.
AIMS II • 12/2005
Figure 40: Racial distribution of Minnesota residents according to 2000 US
Census. *Individuals can designate themselves as Hispanic or Latino in addition to the other
35
36
Figure 45: Marital status of survey respondents.
Figure 44: Marital status of Minnesota residents (2000 US Census).
AIMS II • 12/2005
Figure 43: Distribution of age among survey respondents.
Figure 42: Distribution of age among Minnesota residents in 2000 US Census.
However, different perceptions of 13 of the 114 views
may be related to demographic factors other than gender.
To explore this, we tested for possible covariance between
gender and other background variables that might possibly
affect response to HCLs. Chi-square analysis of gender with
educational attainment and household income showed that
males in the survey population had higher household incomes
The 2000 US Census for Minnesota shows an even split for
gender; females are 50.5% of the state’s population (Figure
46). In comparison, females were over-represented in the
AIMS II survey sample, accounting for 73.2% of responses
(Figure 47). While an overrepresentation of females is a
common phenomenon for consumer surveys, we evaluated
our sample for bias in perception of highway corridor
landscapes (HCLs) in three ways. First, we investigated
whether males and females had significantly different
perceptions of the attractiveness of each landscape view.
Comparing genders on rating of the attractiveness of 114
HCL views, only 13 images, or 11.4% of the landscape
views, were significantly different for male and females, but
there was no clear pattern of landscape treatments among
these 13 images. Males had significantly higher ratings of
11 views, including multiple images of each of the different
mowing patterns in both urban and rural boreal landscape
contexts. The bridge approach view with a concrete guard
rail (Figure 21) also was rated significantly higher by men
than by women (t=3.65, p<.000). Females had higher ratings
for two views that showed prairie plantings.
Differences by gender
AIMS II • 12/2005
Figure 47: Distribution of respondent gender in the online survey.
Figure 46: Distribution of gender in Minnesota (2000 US Census).
37
38
AIMS II • 12/2005
Then, to assure that gender bias did not affect results, we
ran two analyses for all tests of the relationship between
HCL treatments and traveler perceptions. The first analysis
included all 1108 respondents.
The second analysis
included all male respondents and an equal number of
female respondents, which was a random subsample of
female respondents to the survey. Tables of both analyses are
provided in the analyses portion of this report. They show
that gender did not substantially affect results, and would
not affect research conclusions.
than females (p>.05, Chi-square = 13.88) and a significantly
higher level of education (p < .000, Chi-square = 39.82).
An analysis of gender frequency in minorities showed that
68.4% of minorities sampled were females. To maintain
representation of broad range of race, income, and education
classes (similar to the Minnesota population), we accepted
our complete survey sample with its overrepresentation of
females.
and
AN ALYS I S
RESULTS
40
AIMS II • 12/2005
To understand overall trends in traveler perceptions, we
studied the distribution of mean attractiveness ratings for the
112 HCL views (excluding bridge views, which were rated on
different scales), and aggregated the distributions into seven
groups that had common landscape treatment characteristics
(Figure 50). Mean score breakpoints of at least 0.03 were
used to create classes of views with similar ratings. Most
of the 11 views with the highest mean attractiveness ratings
(Mean=6.04 to 5.61) were in the rural agricultural landscape
context of southern Minnesota and had prairie plantings and
straight or curved mowing patterns (Figure 48). Eight of
these 11 views had prairie plantings, and eight of the 11
were located in a rural context (either agricultural or boreal).
Three of these very most attractive views were in an urban
context; they included walls that were white or light-tan
in color (Figure 49). Views that increased naturalness by
providing a mix of deciduous and evergreen plantings or tree
cover in prairie treatments also were present among the top
views. An unexpected view within the highest rated group
Respondents’ ratings of HCL views on the four scales of
attractiveness, perceived naturalness, perceived maintenance,
and perceived safety were highly intercorrelated. Attractiveness
was most strongly correlated with all the other perception
variables. For this reason, perceived attractiveness can be used
as a helpful overall summary variable for understanding how
Minnesota travelers perceive highway corridor landscapes.
Overall Trends in
Landscape Attractiveness
AIMS II • 12/2005
Figure 49: An example of an urban view that was among the 11 highest rated
views. Mean rating = 5.71. Appendix 1, view 79.
Figure 48: This view received the highest ratings for attractiveness. Mean rating = 6.04. Appendix 1, view 46.
41
42
AIMS II • 12/2005
Figure 50: Continuous mean groupings for the 112 HCL views that were rated for attractiveness.
Figure 51: Vertical symmetry may have been one reason why this weedy view
was one of the most highly attractive. Mean rating = 5.63. Appendix 1, view
110.
In contrast, the group with the second lowest mean ratings
(4.61 to 4.05) tended to show an urban context (Figure 53).
The group with the second highest mean ratings (5.55 to
5.30) contained 32 views. Views in this group tended to be
within the rural context and wooded, including either higher
biodiversity tree and shrub plantings or boreal woodlands
(Figure 52). Weedy herbaceous treatments were also common
in this grouping when they were in the boreal context.
Prairie plantings and sumac within the urban context were
also present in this group.
showed a weedy vegetation treatment. This view was within
the boreal context and showed the curved mowing pattern
(Figure 51).
AIMS II • 12/2005
Figure 53: The majority of images that had the second to lowest ratings for
attractiveness were within the urban context. Mean rating = 3.72. Appendix
1, view 27.
Figure 52: A naturalized view within the boreal context; an example that represents the second highest mean rating group. Mean rating = 5.55. Appendix 1,
view 56.
43
44
AIMS II • 12/2005
Views in the lowest mean attractiveness rating group (3.95
to 2.96) were all urban and typically showed darker colored
walls. Seven of the 12 views contained brome grass, most
with a curved mowing pattern (Figure 54). Five of the
views displayed only mown turf with the walls. The most
unattractive view was the “mow all” turf with the cast-inplace retaining wall with rustication grooves and a single
color stain (Figure 55).
Sixteen of the 17 images were urban, the other two being
rural agriculture. Of the two images in the rural context,
one showed the “mow all” treatment and the other showed a
weedy treatment with a straight mown edge. Twelve of these
views contained a straight edge and eight were weedy. Prairie
with young aspen saplings and brome grass were also present
in this group.
Figure 55: This view received the lowest ratings for attractiveness. Mean rating
= 2.96. Appendix 1, view 57.
Figure 54: Views in the lowest mean rating group typically contained brome
grass and darker colored walls. Mean rating = 3.85. Appendix 1, view 9.
Table 4: Bivariate correlations of the preference ratings showed that attractiveness, naturalness, maintenance and
safety were all positively correlated (See Appendix 7 for p values).
AIMS II • 12/2005
Perceptions of attractiveness, naturalness, maintenance and safety are strongly related to each other. Bivariate correlations for
the 112 views, excluding the bridge views, showed that attractiveness, naturalness, maintenance and safety all were positively
correlated (p<.001) (Table 4). All were significantly correlated (p<.001) within each landscape context (urban and rural) as well
(Appendix 7).
Comparing Perceptions of Attractiveness, Naturalness,
Maintenance and Safety
45
46
AIMS II • 12/2005
First we used the 112 views (excluding bridge views) as cases,
an analysis method that is most frequently used in landscape
perception research. Using this technique, the mean score on
each rating scale (attractiveness, naturalness, maintenance, or
safety) is a dependent variable, and treatments in the factorial
design table (Appendix 1 and Figure 56) are independent
variables in regression analyses.
We conducted both
unsupervised regression and stepwise forward regressions to
measure relationships among these variables. In all of the
analyses, the stepwise forward regression produced results that
were substantially the same as the unsupervised regression;
forward regression results are reported in the tables below.
La ndscape vie ws a s ca ses
We compared the effects of landscape treatments using
multiple methods. Because the AIMS II data set is so
large and the number of views rated is also large, we were
able to analyze the data in ways that could investigate
differences among respondents as well as differences among
highway corridor landscape views as they were perceived by
respondents. Regardless of the analysis method, results suggest
that different contexts produce very different perceptions of
attractiveness, naturalness, maintenance, and safety. Within
each context, vegetation design is more important than any
other variable in its affect on perception.
Comparing the Effects of
Landscape Treatments
Different
contexts
produce
very different perceptions of
attractiveness,
naturalness,
maintenance,
and
safety.
Within each context, vegetation
design is more important
than any other variable in its
affect on perception.
Very interestingly, the fully mown turf treatments and the
brome grass treatments were the most powerful in predicting
both attractiveness and naturalness, but they tended to
be seen as unattractive and not natural. In contrast, the
prairie flower, naturalized woodland, and mixed evergreen/
deciduous treatments were the next most powerful in
predicting both attractiveness and naturalness, and they
Overall, regression analyses indicated that the vegetation
variable was a very powerful predictor of public perceptions
of attractiveness, naturalness, and maintenance in both
urban and rural contexts – accounting for 80 – 90% of the
variance in ratings in urban contexts and 50 – 80% of the
ratings in rural contexts. It was a statistically significant,
but somewhat less powerful predictor of perceived safety in
both contexts – accounting for about 20% of the variance
in rating of safety in urban context and about 30% of the
variance in safety ratings of rural contexts.
Sumac
Naturalized Woodland
Prairie Flowers
Evergreen and Deciduous
Woody Islands
Evergreen
Brome Grass
AIMS II • 12/2005
Figure 56: An example of the nine vegetation treatments used in the regression analysis, shown here with one of the more attractive noise walls; Wall 3.
Weedy
All Mown Turf
47
48
AIMS II • 12/2005
Urban context and effects of vegetation:
Here are those results in more detail. Within the urban context
(Table 5), the strongest predictor of attractiveness was the
brome grass treatment, negatively influencing attractiveness
ratings (r 2 change = .313, B= -0.715, p<.001). Following
brome grass was turf, once again negatively influencing
attractiveness ratings (r 2 change = .312, B= - 0.943, p<.001).
Prairie flowers was the third strongest predictor, and had a
positive correlation (r 2 change = .090, B= - 0.855, p<.001).
The cumulative r 2 of the seven treatments (brome grass, turf,
prairie flowers, evergreen and deciduous trees, naturalized,
sumac, and evergreen trees) was 0.838.
tended to be seen as attractive and natural. The fully mown
turf, prairie flowers, and sumac treatments were all powerful
in predicting perceived maintenance, with fully mown turf
being the most powerful. In contrast, the woody island in
prairie flowers treatment was also very powerful in predicting
perceived maintenance, but was seen as not well maintained.
Finally, prairie flowers and brome grass treatments were most
powerful in predicting perceived safety. Overall, in urban
settings the prairie flower treatment was the only one that
was very consistently seen as very attractive, very natural,
very well-maintained, and very safe compared with the other
treatments. In addition, neither the mow pattern variable nor
the wall treatment variable (for urban contexts) was nearly as
powerful as the vegetation variable in predicting any of the
perceived characteristics. This would suggest that planting
design is the most powerful way to affect public perceptions
of highway corridor landscapes within the right-of-way.
Table 6: Effect of vegetation treatments on perceived naturalness of urban
HCL – views as cases.
Table 5: Effect of vegetation treatments on attractiveness of urban HCL
– views as cases.
The fully mown turf and the
brome grass only treatments
were
most
powerful
in
predicting both attractiveness
and naturalness, but they
tended to be seen as unattractive
and not natural.
Perceptions of urban safety were predicted by prairie flowers,
brome grass and the pre-cast concrete panel noise wall with
form-liner pattern (wall 2). Prairie flowers and brome grass
were both positively correlated to perceptions of safety,
while the noise wall was negatively correlated (See Table
8). The cumulative r 2 of the three predictors was 0.217.
Using this method, with views as cases, the predictive
power of the regression models for the urban context was
robust. With the exception of urban safety (r 2 = 0.217), the
The two highest predictors for urban maintenance were
turf and woody islands. Turf was positively correlated to
maintenance ratings while woody islands was negatively
correlated (See Table 7). The third highest predictor
was prairie flowers (r 2 change = .058, B= 0.752, p<.001).
Naturalized was a less powerful predictor of perceptions of
urban maintenance (r 2 change = .080, B= 0.797, p<.001).
The cumulative r 2 value of turf, woody islands, prairie flowers,
sumac, evergreen and deciduous trees, evergreen trees, brome
grass, and naturalized woodland was equal to 0.818,
Similar to attractiveness, the strongest predictors of
naturalness in the urban context were turf and brome grass
(See Table 6 for r 2 change and B values, p<.001). Both
were once again negatively correlated. The third strongest
predictor was naturalized woodland with a positive correlation
(r 2 change = .052, B= 1.160, p<.001), while prairie flowers
was the weakest predictor (r 2 change = .034, B= 0.365,
p<.001). Together, turf, brome grass, naturalized, evergreen
and deciduous trees, woody islands, and prairie flowers had
a cumulative r 2 of 0.907.
AIMS II • 12/2005
Table 8: Effect of vegetation treatments on perceived safety of urban HCL
– views as cases.
Table 7: Effect of vegetation treatments on perceived maintenance of urban
HCL – views as cases.
The prairie flower, naturalized
woodland, and mixed evergreen
and
deciduous
treatments
were the next most powerful in
predicting both attractiveness
and naturalness, and they
tended to be seen as attractive
and natural.
49
50
AIMS II • 12/2005
Turf was the strongest predictor of preference of rural
naturalness with a negative correlation (See Table 10, r 2
change = .303). Naturalized woodland and prairie flowers
also were significant. Curved mowing pattern was also
Rural context and effects of vegetation:
Within the rural context, prairie flower vegetation was the
strongest predictor of attractiveness (See Table 9, r 2 change
= .302, B= 0.720, p<.001). The difference between rural
agriculture views in southern Minnesota and rural boreal
views in northern Minnesota was the second strongest
predictor (r 2 change = .081, B= 0.337, p<.01), with southern
Minnesota agricultural landscape views being more preferred.
The final two predictors were turf (r 2 change = .088, B=
- 0.485, p<.01) and brome grass (r 2 change = .077, B=
- 0.292, p<.05), both of which were negatively correlated to
attractiveness. Cumulatively these four treatments predicted
55% (r 2 = 0.548) of the variance in attractiveness.
predictive power was over 80%. It is interesting to note that
mowing pattern (curved or straight) and wall type (with the
exception of urban safety) were not significant predictors of
Minnesota driver perceptions. Brome grass was the most
common powerful predictor of perceptions, negatively
influencing attractiveness and naturalness ratings, while
positively influencing maintenance and safety. Turf was also
a common predictor, negatively correlated to attractiveness
and naturalness, but positively correlated to maintenance.
Prairie flower vegetation was also important – positively
related to attractiveness, naturalness, maintenance and
safety.
Table 10: Effect of vegetation treatments on perceived naturalness of rural
HCL – views as cases.
Table 9: Effect of vegetation treatments on attractiveness of rural HCL – views
as cases.
Prairie flower vegetation was
also
important – positively
related
to
attractiveness,
naturalness, maintenance and
safety.
As with the urban settings, the relationship of landscape
treatments to driver perceptions in rural landscapes was
strong, but perceptions of safety were less strongly related
to the landscape treatments. Turf and prairie flowers were
the most common predictors. All mown turf was positively
correlated with perceptions of maintenance and safety, while
negatively correlated with perceptions of attractiveness
and naturalness. Prairie flower vegetation had positive
correlations with all four perceived characteristics. Once
again, mowing pattern was not a significant predictor except
within naturalness, where it was marginal.
The strongest predictor of rural maintenance was the turf
treatment (See Table 11, r 2 change = .221). Different from
attractiveness and naturalness, turf was positively correlated
with perceived maintenance. Prairie flowers (r 2 change =
.095, B= 0.925, p<.001) and evergreen trees (r 2 change =
.097, B= 0.880, p<.001) were also significant predictors.
The weakest significant predictor for rural maintenance was
whether or not the context was rural boreal or rural agriculture
(r 2 change = .047, B= 0.206, p<.05); boreal settings were
seen as more well-maintained. The eight predictors of turf,
prairie flowers, evergreen trees, brome grass, evergreen and
deciduous trees, naturalized woodland, woody islands, and
boreal or rural had a strong cumulative predictive power (r 2=
0.802). Perceptions of rural safety were predicted by turf,
brome grass, and prairie flowers (See Table 12, all were p
<.05). The cumulative r 2 of all three treatments was 0.333.
statistically significant, but had a negative correlation with
naturalness (r2 change = .056, B= - 0.081, p<.05). The four
treatments had a cumulative r 2 of 0.561.
AIMS II • 12/2005
Table 12: Effect of vegetation treatments on perceived safety of rural HCL
– views as cases.
Table 11: Effect of vegetation treatments on perceived maintenance of rural
HCL – views as cases.
Within the rural context,
prairie flower vegetation was
the strongest predictor of
attractiveness. The difference
between rural agriculture views
and rural boreal views was the
second strongest predictor.
51
52
AIMS II • 12/2005
The analyses in this section of the report also were run
with two alternative samples, as a final test for the effect
of gender. In the first analysis, the entire sample of 1108
respondents was included. In the gender-balanced sample,
In addition to analyzing the data in the conventional way
described previously, we analyzed the data in another way
that maintains the variance among respondents in their
perceptions of the views. In this second method, each view as
rated by each respondent is analyzed as a case. So, the number
of cases is equal to the number of views (112) multiplied
by the number of respondents (1108). With this approach,
the AIMS II data set maintains the enormous statistical
power of its large sample (n < 112,000). In addition, this
approach allows the effect of differences among respondents
to be measured in comparison with the effect of independent
variables, the landscape treatments. The effect of respondent
differences is discussed in the next section of the report.
Responses as cases
For both rural and urban contexts, all mown turf, prairie
flower vegetation, naturalized woodlands, and brome
grass were the most common predictors in explaining
traveler perceptions. All mown turf reduced attractiveness
and perceived naturalness within both contexts, while
it enhanced perceived maintenance. The prairie flowers
treatment was positively correlated with preference ratings
for attractiveness, maintenance, and safety for both contexts,
while the naturalized woodland treatment was stronger for
naturalness.
The most noticeable difference between the two methods
of analysis was in their overall predictive power. When
variance among individuals is removed, as in analysis method
1 above, the predictive power of the landscape treatments is
very high. When variance among individuals is maintained,
Overall, the responses as cases analyses were entirely
consistent with the views as cases analyses reported earlier.
However, the responses as cases analyses reported here
provide more detail about some more subtle effects of the
treatment. Treatments that tended to make a big difference
for some respondents, but might not have been influential to
the perceptions of most of the respondents, were more likely
to appear in the analyses below than they were to appear in
the analyses reported in the previous section.
all males and an equal number of females was subsampled for
the analysis.
For both rural and urban
contexts,
all
mown
turf,
prairie
flower
vegetation,
naturalized woodlands, and
brome grass were the strong
predictors
in
explaining
traveler perceptions.
Regression analysis of urban naturalness showed that all
mown turf (Table 15, r 2 change = .150, B = -1.644, p<
.001) and then brome grass (r 2 change = .035, B = -0.628,
p< .001) were the strongest predictors; both were negatively
correlated. The third strongest predictor was naturalized
Urban context and effects of vegetation:
The responses as cases regression for urban attractiveness
showed that all mown turf was the strongest predictor and
negatively correlated (See Table 13, r 2 change = 0.074).
Brome grass (r 2 change = .049, B= - 0.651, p<.001) was the
second strongest predictor and also negatively correlated.
The prairie flowers treatment was the third strongest
predictor and was positively correlated (r 2 change = .008,
B= 0.919, p<.001), and the naturalized woodland treatment
also was positively related to attractiveness (r 2 change = .007,
B= 1.103, p<.001). Different from the first analysis, wall
treatments accounted for some differences in attractiveness
in this analysis. While wall 2 and 3 significantly enhanced
attractiveness, wall 4 significantly decreased attractiveness.
The cumulative r 2 for 11 treatments used in the regression
was 0.156. The gender balanced analysis had similar results
(Table 14). Cumulative r 2 for the 10 treatments within
the regression was 0.141. Mowing pattern did not have a
significant influence in predicting urban attractiveness.
as in analysis method 2 below, the predictive power of the
landscape treatments is statistically significant, but overall
much lower. This is not surprising and suggests that a separate
analysis of the relationship between respondent background
characteristics and view perceptions could provide insight.
AIMS II • 12/2005
Table 14: Effect of vegetation treatments on attractiveness of urban HCL
– gender balanced response as cases.
Table 13: Effect of vegetation treatments on attractiveness of urban HCL
– responses as cases.
Overall, the responses as
cases analyses were entirely
consistent with the views as
cases analyses.
53
54
AIMS II • 12/2005
In the urban setting, landscape treatments predicted little
In contrast, all mown turf was positively correlated with
urban maintenance (Table 17, r 2 change = .035, B = 1.264, p<
.001). Woody islands was the second strongest predictor and
negatively correlated (r 2 change = .014, B = -0.157, p< .01).
Prairie flowers and then sumac were the next two predictors
and positive (See Table 17 for r 2 change and B values. Both
were p< .001). Brome grass, curved mowing pattern and
walls (walls 1, 2, and 5) were also significant and positive in
predicting perceived urban maintenance (r 2 change <.007,
p< .05). The cumulative r 2 for the 12 treatments was quite
low (0.077). This is the same value reported by the gender
balance regression analysis (Table 18), although three walls
(walls 1, 2, and 5) were not statistically significant and
therefore not included in the regression.
The gender balanced sample was similar, but the curved
mowing treatment was not statistically significant. And the
cumulative r 2 was somewhat less (0.213) (Table 16). Nine of
the same treatments were used in the balanced regression as
compared to the full population sample. The two treatments
that were not significant were the cast-in-place retaining wall
with form-liner patterns and two color stains (wall 5) and
mowing pattern.
woodlands (r 2 change = .016, B= 1.075, p<.000). Wall type
(walls 1, 4, and 5) and evergreen trees were also significant
and positive in affecting urban naturalness ratings (all r 2
change < .001, p<.05), while a curved mowing pattern was
negative. Cumulative r 2 for the 11 treatments was 0.221.
Table 16: Effect of vegetation treatments on perceived naturalness of urban
HCL – gender balanced responses as cases.
Table 15: Effect of vegetation treatments on perceived naturalness of urban
HCL – responses as cases.
All mown turf was the strongest
predictor
and
negatively
correlated to attractiveness.
Table 19: Effect of vegetation treatments on perceived safety of urban HCL
– responses as cases.
Table 17: Effect of vegetation treatments on perceived maintenance of urban
HCL – responses as cases.
In the urban setting, landscape
treatments predicted little of
the variance in perceived safety.
AIMS II • 12/2005
Mowing
pattern
did
not
have a significant influence
in
predicting
urban
attractiveness.
Table 20: Effect of vegetation treatments on perceived safety of urban HCL
– gender balanced responses as cases.
Table 18: Effect of vegetation treatments on perceived maintenance of urban
HCL – gender balanced responses as cases.
55
56
AIMS II • 12/2005
Rural context and effects of vegetation:
Regression analysis of attractiveness within the rural context
shows that prairie flower vegetation was the strongest
predictor and positively correlated (Table 21, r 2 change =
.022, B = 0.849, p< .001). Turf was the second strongest
predictor and negatively correlated (r 2 change = .008, B =
-0.200, p< .001). The evergreen and deciduous tree treatment
(r 2 change = .002, B = 0.297, p< .001) and the woody islands
treatment (r 2 change = .002, B = 0.421, p< .001) were
also significant. The cumulative r 2 for the full population
sample regression was 0.040. Analysis of the gender balance
regression resulted in a cumulative r 2 of 0.054 (Table 22).
This regression reported that the difference between boreal
or agricultural rural landscape contexts was significant (r 2
change = .006, B = 0.493, p< .001).
of the variance in perceived safety using analysis method 2
(cumulative r 2 = 0.016). The pre-cast concrete panel noise
wall with form-liner pattern (wall 2) was the strongest
indicator of urban safety and negatively correlated (Table 19,
r 2 change = .007, B = -0.306, p< .000). The next strongest
predictors were prairie flowers (r 2 change = .004, B = 0.271,
p< .001), and brome grass (r 2 change = .003, B = 0.203, p<
.001). Mowing pattern (r 2 change = .000, B = 0.071, p<
.01) and the retaining wall with form-liner patterns and two
color stains (w4) were also predictors (r 2 change = .000, B =
0.066, p< .05). The gender balanced sample was similar, a
cumulative r 2 of 0.017 (Table 20). Included in the gender
balanced regression was turf grass (r 2 change = .001, B =
0.105, p< .05), which had a negative correlation. It was not
significant for the full population sample.
Table 22: Effect of vegetation treatments on attractiveness of rural HCL
– gender balanced responses as cases.
Table 21: Effect of vegetation treatments on attractiveness of rural HCL
– responses as cases.
The difference between boreal
or agricultural rural landscape
contexts was significant.
Regression analysis of ratings for rural maintenance reported
that all mown turf (Table 25, r 2 change = .019, B = 1.021,
p< .001), prairie flower vegetation (r 2 change = .008, B =
0.823, p< .001), and evergreen trees (r 2 change = .006, B =
0.717, p< .001) were the top three predictors, all of which
were positively correlated. The evergreen and deciduous
treatment (r 2 change = .007, B = 0.488, p< .001), as well
as woody islands (r 2 change = .002, B = 0.331, p< .001),
were the weakest predictors of rural maintenance ratings.
The cumulative r 2 for the 7 variables was 0.050, nearly the
same as the cumulative r 2 for the gender balanced regression
(r 2 = 0.051). The gender balanced regression included two
additional treatments; whether or not the treatment was
boreal or agriculture, as well as mowing pattern (Table 26).
The strongest predictor of rural naturalness was the all
mown turf treatment, with a negative correlation (Table 23,
r 2 change = .009, B = - 0.264, p< .001). The naturalized
woodland treatment (r 2 change = .001, B = 0.159, p< .001)
and prairie flowers (r 2 change = .002, B = 0.173, p< .001)
were the second and third strongest predictors, both positively
correlated with naturalness. The evergreen and deciduous
tree treatment also had a positive correlation (r 2 change =
.001, B = 0.173, p< .001). The cumulative r 2 of 0.014 was
nearly equal to the gender balanced regression (Table 24, r 2
= 0.015). All mown turf, naturalized woodland and prairie
flowers were the only three treatments that had significant
predictive power in the gender balanced regression. Mowing
pattern and the evergreen and deciduous tree treatment were
not included.
AIMS II • 12/2005
Table 24: Effect of vegetation treatments on perceived naturalness of rural
HCL – gender balanced responses as cases.
Table 23: Effect of vegetation treatments on perceived naturalness of rural
HCL – responses as cases.
All mown turf, prairie flower
vegetation, and evergreen trees
were the top three predictors for
rural maintenance.
57
58
AIMS II • 12/2005
Comparison of the regressions for the four rating scales within
the rural context show that all mown turf, prairie flower
vegetation, and brome grass were the strongest predictors.
Just as was with the urban context, turf and brome grass
were positively correlated with safety and maintenance, but
negatively correlated with attractiveness and naturalness.
Prairie flower vegetation was once again the only treatment
positively correlated with all four ratings.
The regression for the full sample reported all mown turf
as the strongest predictor of ratings for rural safety with a
positive correlation (Table 27, r 2 change = .008, B = 0.560,
p< .001). The second and third strongest predictors were
prairie flowers (r 2 change = .004, B = 0.462, p< .001) and
brome grass (r 2 change = .004, B = 0.392, p< .001). The
weakest significant predictor was naturalized woodlands (r 2
change = .003, B = 0.245, p< .001). The cumulative r 2 for
the five treatments was 0.023 (Table 28). The cumulative
r 2 for the gender balanced regression was 0.028 with the
addition of the evergreen and deciduous tree and woody
island treatments.
All mown turf and brome grass
were positively correlated with
safety and maintenance, but
negatively
correlated
with
attractiveness and naturalness.
Prairie flower vegetation was
once again the only treatment
positively correlated with all
four ratings.
Table 28: Effect of vegetation treatments on perceived safety of rural HCL
– gender balanced responses as cases.
Table 27: Effect of vegetation treatments on perceived safety of rural HCL
– responses as cases.
AIMS II • 12/2005
Table 26: Effect of vegetation treatments on perceived maintenance of rural
HCL – gender balanced responses as cases.
Table 25: Effect of vegetation treatments on perceived maintenance of rural
HCL – responses as cases.
59
60
Model one: no consideration of the
random effects of respondents
Model Two: addressing the random
effects of respondents
Model one: no consideration of the
random effects of respondents
Model Two: addressing the random
effects of respondents
Model one: no consideration of the
random effects of respondents
Model Two: addressing the random
effects of respondents
Naturalness
Maintenance
Safety
AIMS II • 12/2005
Model one: no consideration of the
random effects of respondents
Model Two: addressing the random
effects of respondents
Residual
Intercept
[subject = id]
Residual
Intercept
[subject = id]
Residual
Residual
Intercept
[subject = id]
Residual
Residual
Intercept
[subject = id]
Residual
Residual
Parameter
Rural Context
Attractiveness
Dependent variable
0.02672
0.013333
0.046398
0.991273
0.045881
0.968011
1.886519
0.887639
0.02775
0.014792
0.030023
0.641208
1.959285
0.984787
0.017085
0.008542
0.046304
0.943527
1.206275
0.568601
0.031273
0.019085
Std. Error
2.207999
1.270377
Estimate
21.365
70.605
66.576
21.098
70.605
66.576
21.357
70.605
66.566
20.377
70.605
66.564
Wald Z
Table 29: Mixed effects linear model measuring the effect of respondent on perceptions of HCL views in a rural context.
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Sig.
Two models were run for each dependent variable (attractiveness, naturalness, safety and maintenance). Independent variables
were the landscape treatments, the same as those reported in the previous section of this report. However, one mixed model
included the random effects of respondents and the other did not. Random effects of respondent were significant for all
dependent variables (attractiveness, naturalness, safety and maintenance) under both contexts.
Using mixed effects linear models, we measured the effect of differences among respondents on highway corridor landscape
perceptions, and found that differences among respondents had a statistically significant effect on perceptions of attractiveness,
naturalness, maintenance and safety in both rural (Table 29) and urban (Table 30) contexts. At the same time we found that the
statistical significance of the models that included only the treatment variables remained unchanged when the random effect of
respondent was added. In other words, while differences among respondents are very important, they do not change the effects
of landscape treatments, which are also very important to perceptions of HCL views.
Individual respondent differences
Safety
Maintenance
Naturalness
Attractiveness
Residual
Model Two: addressing the random
effects of respondents
Intercept
[subject = id]
Residual
Model one: no consideration of the
random effects of respondents
Intercept
[subject = id]
Residual
Model Two: addressing the random
effects of respondents
0.861422
1.37235
2.236381
0.72339
1.380353
2.106669
0.870085
Intercept
[subject = id]
Residual
1.618946
Residual
Model Two: addressing the random
effects of respondents
Model one: no consideration of the
random effects of respondents
2.494389
0.700993
Intercept
[subject = id]
Residual
1.983729
Residual
Model Two: addressing the random
effects of respondents
Model one: no consideration of the
random effects of respondents
2.689543
Estimate
Residual
Parameter
Urban Context
Model one: no consideration of the
random effects of respondents
Dependent variable
0.04055
0.015593
0.02455
0.0347
0.015683
0.023126
0.041606
0.018394
0.027382
0.035451
0.022537
0.029524
Std. Error
21.243
88.012
91.096
20.847
88.013
91.096
20.912
88.015
91.096
19.773
88.019
91.096
Wald Z
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Sig.
Table 30: Mixed effects linear model measuring the effect of respondent on perceptions of HCL views in an urban
context.
While differences among respondents are very
important, they do not change the effects of
landscape treatments.
AIMS II • 12/2005
61
62
AIMS II • 12/2005
Some respondents were asked to choose between two views
through T-rails, one with a more elevated panoramic view
(Figure 59) and one from the same height as the concrete
rail. Of those presented with the choice between the two Trails, 17 preferred the less extensive view, and 538 preferred
the more extensive panoramic view. When respondents rated
views according to attractiveness, the more extensive view
was rated as much more attractive (M = 5.83, sd = 1.144)
than the less extensive view (M = 4.35, sd = 1.869). When
asked how important it was to have their preferred bridge
view, the T-rail with the more extensive view had a higher
Respondents prefer the panoramic views provided by the Trail over the concrete bridge rail. Respondents also prefer a
maximized view of the surrounding landscape made possible
with a T-rail. The differences are dramatic and entirely
consistent with a long series of landscape perception studies
that show that panoramic views have high aesthetic value
and importance.
Respondents do prefer more extensive panoramic views over
more limited views of the same scene. When respondents
were asked to chose which view they would prefer to see
from a bridge: one with a concrete bridge rail (Figure 57) or
one with a T-rail at the same height (Figure 58) that allowed
a wider panoramic view, only 16 respondents chose the
concrete bridge rail, while 537 chose the T-rail. When asked
to rate the attractiveness of their chosen view compared with
the view they did not choose, those who chose the T-rail
view, found it more attractive by almost 6 out of 7 points
(M = 5.95, sd = 1.098). The 16 who preferred the concrete
bridge rail preferred it by less than 3 out of 7 points (M =
2.56, sd = 1.548). When asked how important it was to have
their preferred bridge rail view, the 537 who chose the T-rail
view rated the view more important (M = 4.99, sd = 1.491)
than the 16 who preferred the concrete bridge view (M =
4.38, sd = 2.156).
Respondents do prefer wider
panoramic views over more
limited views of the same scene.
More specifically, they prefer the
panoramic views provided by
the T-rail over the concrete
bridge rail.
mean importance (M = 4.59, sd = 1.546) than the T-rail with
the less extensive view (M =4.12, sd = 1.616).
Bridges and the Panoramic View
Figure 59: This T-rail provides the largest panoramic view of the surrounding
landscape. Appendix 1, view 115.
Figure 57: The concrete bridge rail shown above provides the smallest
panoramic view. Appendix 1, view 114.
AIMS II • 12/2005
Figure 58: The T-rail shown above provides a panoramic view of the landscape
below. Appendix 1, view 116.
63
64
w1, w2 w4, w6
w1, w3 w6, w2 w1, w3 w5, w2
w1
AIMS II • 12/2005
Safety
Maintenance
w1
w1, w5 w6, w3
Naturalness
Low
w2, w5 w6, w4 w2, w3 w5, w4
High
Mow All
Attractiveness
All Veg
Treatments
High
Low
w2
Weedy
Straight
High Low
w5
w4
w4
Weedy
Curved
High Low
w1
w4
w5, w2 w3, w4
w2, w3 w5, w4
Brome
Straight
High
Low
w5
w3
Brome
Curved
High Low
w4
Flowers
Straight
High Low
w6
w6
w4, w1 w6, w2
w5
Flowers
Curved
High
Low
Table 31: High and low rated wall treatments that were significantly different from at least two or more other walls.
Evergreen
Straight
High Low
w1
w5, w2 w6, w1
w5, w2 w6, w1
w4
w5, w2
Evergreen
Woody Islands
Curved
High Low High
Low
Looking at all vegetation treatments combined, the most and least attractive wall types were significantly different from others
by the criterion above. The most attractive wall type was the pre-cast concrete noise wall with form-liner pattern, or Wall 2
(Figure 60). The second most attractive wall was the cast-in-place retaining wall with form-liner patterns and two color stains
(Wall 5). Looking at how walls were rated for individual vegetation treatments, Wall 2 was typically significantly more attractive
Perhaps most useful for Mn/DOT decision-making was a pair-wise comparison of driver’s ratings of the wall treatments. Significant
differences among the group of walls as a whole do not mean that ratings of each wall alone were significantly different from other
walls, and most individual wall treatments were not significantly different from others (Tukey’s HSD, p<.05). To distinguish
which wall treatments did make a difference for traveler perceptions, we ranked the mean ratings of the different wall treatments
in which either high ranking or low ranking wall treatments were significantly different from at least two or more of the other
walls as characterized below and in Table 31.
Compared with vegetation treatments, wall treatments had much less effect on attractiveness, perceived naturalness, perceived
safety, and perceived maintenance. However, comparisons among the wall treatments alone provide useful information for
making choices among walls Appendix 8 lists rankings of each wall treatment for each of the four driver perception rating scales.
One-way Analysis of Variances (ANOVA) were performed to compare travelers’ perceptions of different walls for each vegetation
treatment separately as well as for all vegetation treatments combined. Considering ratings of attractiveness and naturalness,
there were significant differences among the six walls as a group (p<.05), for every vegetation treatment (Appendix 8). For
perceptions of safety and maintenance, there were significant differences among the six walls as a group (p<.05) for some, but
not all, vegetation treatments (Appendix 8).
Effects of Wall Design
Considering perceived maintenance, the wooden postand-plank noise wall (Wall 1) ranked first and was rated
significantly higher than at least two other walls for all
vegetation treatments combined. However, for the woody
islands vegetation treatment, it ranked lowest and was
significantly lower than at least two other walls. For all
vegetation treatments combined, the safest looking walls
were Walls 1 and 3, and they were significantly higher than
others. Interestingly, the least safe looking wall also was
considered the most attractive (Wall 2). A wall perceived as
unattractive, the mechanically stabilized earth wall (Wall 6),
was also seen as one of the least safe. There were significant
differences in the perceived naturalness of some wall types.
The most natural looking walls were Walls 1 and 5, while
the least natural looking walls were Wall 6 and the pre-cast
For all vegetation treatments combined, the least attractive
walls, in respective order, were the mechanically stabilized
earth wall (Wall 6, Figure 62) and the retaining wall with
rustication grooves and a single color stain (Wall 4, Figure
63) – both significantly different from at least two other
walls. Wall 4 was seen as unattractive in more “plain”
settings, mown turf and brome grass settings, while Wall 6
was seen as significantly less attractive than other walls in
flowery settings.
than others in settings that were not flowery. Wall 5 was
significantly more attractive only in settings that were flowery
(Figure 61), and was sometimes significantly less attractive
than others in more “plain” settings like brome grass alone or
mown turf alone.
AIMS II • 12/2005
Figure 61: The cast-in-place retaining wall with form-liner patterns and
two color stains (wall 5) was also seen as attractive, especially with the
prairie flowers treatment. Appendix 1, view 79.
Figure 60: The most attractive wall type (wall 2) was the pre-cast concrete noise wall with form-liner pattern. Appendix 1, view 16.
65
66
AIMS II • 12/2005
For the weedy settings, there were very few individual
To help Mn/DOT make choices among wall types in
different settings, Table 31 also shows which walls tended
to rate high or low in different vegetation settings. While
the data analysis shows that vegetation is far more important
than wall type in affecting perception, different wall types
seem to be better fits with different vegetation settings. For
example, for the mow all treatment, the most attractive wall
was once again Wall 2. The second most attractive wall was
Wall 3. The notable regular columns of both Wall 2 and 3
may add visual interest that is particularly appreciated in the
even green setting of mown turf. For mown turf settings, the
two walls that ranked the lowest for attractiveness were Walls
4 and 5. Possibly those walls appeared to be too even in tone
for a setting that was also even. For naturalness, Wall 1 was
ranked the highest, and no walls were ranked as the lowest
because they were not significantly different from each other.
Walls 1 and 2 were perceived as the most well maintained in
a turf setting (Figure 64), and Walls 4 and 6 were perceived
as least well maintained. For safety, Walls 1 and 3 were seen
as the safest (Figure 65), while Walls 5 and 2 were seen as
the least safe. Overall, results suggest than in a setting of
even mown turf, Wall 2 would be perceived as attractive and
well-maintained and Wall 3 would be perceived as attractive
and safe.
concrete block and panel noise wall (Wall 3). Interestingly, a
natural looking wall, Wall 5, was perceived as most attractive
in a natural looking setting, among prairie flowers or with
wooded islands among prairie flowers.
Figure 63: The cast-in-place retaining wall (wall 4) with rustication grooves
and a single color stain was seen as one of the least attractive. Appendix 1,
view 57.
Figure 62: The mechanically stabilized earth retaining wall (wall 6) was also
one of the walls that was seen as the least attractive. Appendix 1, view 83.
Among flowery settings the prairie flower vegetation with a
straight mown edge had only one wall that was significant:
Wall 4 was seen as the most natural wall. For the prairie flower
Of the other non-flower settings, the evergreen treatment
with either the straight or curved mowing pattern did not
have two or more significant differences between walls.
These results are not surprising considering how little of the
wall is visible through the evergreen tree plantings.
Brome grass settings were perceived similarly to mown turf
settings – probably for similar reasons. Like turf, an even
unmown brome grass cover may be seen as rather bland
and the rhythm of wall columns may enhance perceived
attractiveness. Walls 2 and 3 were perceived as the most
attractive (Figure 66), while Walls 5 and 4 were the least
attractive (Figure 67). For naturalness, Walls 2 and 5 are
seen as the most natural, and Walls 3 and 4 are the least
natural. For maintenance, Wall 1 was the only wall that
met the significance criteria and was seen as the most well
maintained wall. Similarly, only one wall met the criteria
for perceived safety: Wall 4 was seen as the least safe wall.
For the curved brome grass treatment the only two walls that
were significantly different from at least two other walls were
Walls 3 and 5. Wall 3 was perceived as the least natural wall,
while Wall 5 was seen as the least safe wall. Overall, for
brome grass settings, Wall 2 would be perceived as attractive
and natural, and Wall 3 would be seen as attractive.
significant differences among wall treatments. Wall type
makes very little difference to perception of weedy settings.
AIMS II • 12/2005
Figure 65: Wall 1 and Wall 3 (above) were perceived as being the safest.
Appendix 1, view 27.
Figure 64: The wooden post-and-plank noise wall (wall 1) was seen as the most
well maintained. Appendix 1, view 1.
67
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AIMS II • 12/2005
For naturalness, Walls 1, 4, and 5 were perceived as significantly
more natural in different vegetation settings, and Wall 6 was
perceived as significantly less natural. Perhaps the wooden,
post-and plank construction of Wall 1 contributes to its
Looking across all treatments Walls 2, 3 and 5 were seen
as significantly more attractive than others in at least one
setting. These walls all share common characteristics of being
light in color (cream to medium beige), having prominent
vertical columns, and having detailing within the panels.
These characteristics may be good aesthetic investments for
Mn/DOT wall design. Those walls that consistently seem to
be the least attractive were Walls 4 and 6. These walls lack
prominent vertical columns. Wall 4 is a very bright white,
cast-in-place wall while the other is a very dark brown, MSE
wall.
The woody islands among prairie flowers setting was similar.
Walls 2 and 5 were seen as the most attractive, the most well
maintained, and safest (Figure 69). Wall 1 was seen as the
least attractive, while Walls 1 and 6 were seen as the least
maintained and the least safe. Wall 4 was seen as the most
natural wall. For this setting, either Wall 5 or 2 might be a
good choice.
vegetation with a curved mown edge, Wall 5 was seen as the
most attractive and Wall 6 was seen as the least attractive
(Figure 68). However, Wall 6 also was seen as significantly
less safe. For naturalness, Walls 4 and 1 were perceived as
most natural, while Walls 6 and 2 were seen as least natural.
Overall, for flowery settings, Wall 5 may be a good choice.
Figure 67: The cast-in-place retaining wall with rustication grooves and a
single color stain (wall 4) was seen as the least attractive with the brome grass
treatment. Appendix 1, view 59.
Figure 66: The pre-cast concrete noise wall (wall 1) with form-liner
pattern was most attractive with the brome grass treatment. Appendix 1,
view 18.
Being light in color, having
prominent vertical columns,
and
having
detailing
within panels may be good
aesthetic
investments
for
Mn/DOT wall design.
Considering these results for the wall types, attractiveness
rankings may be most important to consider since there were
many significant differences between different walls in their
attractiveness.
natural look. Both maintenance and safety had relatively
few repeated significant differences among individual wall
treatments. For maintenance, Walls 1 and 2 sometimes were
perceived as significantly more well-maintained. While these
two walls differ drastically in color, the prominent vertical
columns and simple design may help them appear to be well
maintained. Wall 6 was perceived as least maintained in
two settings. Dark color and the lack of prominent vertical
columns may contribute to this impression. For safety, Walls
1 and 3 sometimes had significantly higher ratings. Possibly
their very prominent columns contributed to this impression.
Wall 6 sometimes was perceived as the least safe wall.
AIMS II • 12/2005
Figure 69: The cast-in-place retaining wall with form-liner patterns and
two color stains was seen as the most attractive with the woody islands
treatment. Appendix 1, view 82.
Figure 68: The mechanically stabilized earth retaining wall (wall 6) was seen as
the least attractive wall with the prairie flowers treatment. Appendix 1, view
91.
69
70
AIMS II • 12/2005
ANOVA analysis within the rural context showed that
selective mowing is preferred over all mown turf. There was
no significant difference, however, between the straight and
curved mowing treatments (See Table 33). For naturalness,
respondents rated the straight mowing treatment significantly
higher than the other treatments (M = 6.33, sd = 1.013,
p<.001). Curved was next, which was rated significantly
higher than mow all (M = 6.22, sd = 1.047, p<.001). For
rural maintenance, the mow all treatment was rated the
highest (M = 5.95, sd = 1.216, p<.001), then curved (M
= 5.59, sd = 1.353, p<.001), and then straight (M = 5.48,
sd = 1.374, p<.001). All mown turf was perceived as the
Within the urban context (Table 32) respondents found the
straight-edged mowing treatment to be the most attractive,
significantly more than the curved and all mown turf
treatments (M = 4.98, sd = 1.643, p<.001). Curved was seen
as the next most attractive (M = 4.86, sd = 1.663, p<.001)
and was preferred significantly more than all mown turf. For
naturalness, straight once again had a statistically significant
higher mean than curved and mow all (M = 5.56. sd = 1.514,
p<.001). Curved was next with a statistically higher mean
than all mown (M = 5.20, sd = 1.592, p<.001). All mown
turf was perceived as most well maintained (M = 6.01, sd
= 1.284, p<.001) and then curved (M = 5.43, sd = 1.429,
p<.001). The curved mowing treatment was perceived
as most safe (M=5.53, sd = 1.396, p<.001). The straight
mowing treatment was perceived as next most safe , but not
significantly different from all mown turf.
Effects of Mowing
safe
maint
nat
att
all
straight
curved
Total
all
straight
curved
Total
all
straight
curved
Total
all
straight
curved
Total
N
2,948
7,004
4,424
14,376
2,948
7,004
4,424
14,376
2,948
7,004
4,424
14,376
2,948
7,004
4,424
14,376
Std.
Mean
F
Deviation
3.59
1.971
4.98
1.643
716.767
4.86
1.663
4.66
1.805
3.57
2.044
5.56
1.514
1,517.587
5.20
1.592
5.04
1.826
6.01
1.284
5.31
1.507
247.259
5.43
1.429
5.49
1.464
5.41
1.775
5.43
1.466
8.177
5.53
1.396
5.45
1.515
ANOVA for Urban Without Weedy
0.000
0.000
0.000
0.000
Sig.
straight
all
Mean difference not
sig to .05 level
Table 32: ANOVA results for the urban context without the weedy treatment.
Within the urban context
respondents found the straightedged mown strip to be the most
attractive.
Selective mowing
is preferred over all mown turf
within the rural context.
To consider how mowing might allow for a very low
maintenance regime, we conducted a separate analysis of
mowing patterns around weedy vegetation. Within the
urban context, mowing treatments did not significantly
affect attractiveness or perceived safety of weedy vegetation.
However, straight-edged mowing was perceived as more
natural, but less well-maintained than the curved mowing
pattern around weedy vegetation in urban contexts. No
statistical difference was found for perceptions of mowing
practices around weedy vegetation within the rural context.
Urban and rural contexts differ in respondents’ perceptions of
safety. Respondents perceive the curved mown edge as more
safe in the urban setting, and all mown turf as more safe in
the rural setting. Respondents may prefer the curved mowing
treatment within the urban setting because it provides extra
space to pull off of the road within the right-of-way but also
offers a buffer between the road and the wall. The rural
context does not have noise or retaining walls; respondents
may perceive all mown turf as safer because it is free of
obstructions.
safest over selective mowing practices (M = 5.96, sd = 1.265,
p<.001). The curved and straight mowing treatments were
once again not significantly different from each other.
safe
maint
nat
att
all
straight
curved
Total
all
straight
curved
Total
all
straight
curved
Total
all
straight
curved
Total
N
1,468
4,084
2,572
8,124
1,468
4,084
2,572
8,124
1,468
4,084
2,572
8,124
1,468
4,084
2,572
8,124
Std.
Mean
Deviation
4.91
1.722
5.48
1.410
5.45
1.438
5.37
1.495
5.97
1.338
6.33
1.013
6.22
1.047
6.23
1.097
5.95
1.216
5.48
1.374
5.59
1.353
5.60
1.351
5.96
1.265
5.68
1.376
5.66
1.345
5.72
1.351
28.245
68.189
59.220
85.259
F
ANOVA for Rural Without Weedy
curved
straight
curved
straight
Mean difference not
sig to .05 level
AIMS II • 12/2005
0.000
0.000
0.000
0.000
Sig.
Table 33: ANOVA results for the rural context without the weedy treatment.
Respondents
perceive
the
curved mown edge as more safe
in the urban setting, and all
mown turf as more safe in the
rural setting.
71
72
urban
agriculture
boreal
Mow all
AIMS II • 12/2005
Curved mowing
Straight mowing
No
vegetation
Mow/vegetation
urban
agriculture
boreal
urban
agriculture
boreal
Weedy
urban
agriculture
boreal
urban
agriculture
boreal
Brome
urban
agriculture
urban
agriculture
Flowers
Table 34: Combinations of vegetation and mowing treatments within the three contexts.
urban
urban
Sumac
urban
agriculture
urban
agriculture
Evergreen
urban
agriculture
urban
agriculture
urban
agriculture
boreal
Evergreen +
Naturalized
Deciduous
urban
agriculture
Woody
Islands
Respondents perceived the combination of a straight-mown edge with naturalized woodlands as the most attractive within both
the urban context (M=5.52, sd =1.423) and rural boreal context (M=5.59, sd =1.338) (Appendix 9). Within the urban context,
the straight-mown naturalized woodland combination (Figure 70) was perceived as significantly more attractive than other
combinations (p<0.01), except for either the straight or curved mown edge with prairie flower vegetation (M=5.44/5.45, sd
=1.439/1.349), or any of the evergreen and deciduous tree vegetation treatments with a straight or curved mown edge combinations
(M=5.45/5.44, sd =1.426/1.379). In the boreal context, the straight-mowing/naturalized woodland treatment combination was
perceived as more attractive than the mow all/no vegetation treatment combination and the curved mowing/brome combination
(p<0.01), but not significantly different from others (straight/curved weedy and straight-mowing/brome). Similarly, all mown
turf with no other vegetation was least attractive in comparison to all other vegetation and mowing combinations in both urban
(M=3.59, sd = 1.971) and boreal contexts (M=5.23, sd = 1.606). However, mean ratings of all mown turf with no other vegetation
varied with landscape context. Within the urban context, all mown turf with no other vegetation was perceived as significantly less
Interaction effects of the nine vegetation treatments and three mowing treatments were used to test the influences of vegetation
planting compositions combined with maintenance practices on perceived attractiveness, naturalness, maintenance or safety.
Different landscape contexts had slightly different plausible vegetation and mowing combinations. The urban context has 15
combinations; the rural agricultural context has 13, and the rural boreal context has six (Table 34).
Effects of Mowing and Vegetation Combinations
Figure 73: The naturalized treatment with straight mowing pattern was seen as
the most natural in the rural boreal and urban contexts. Appendix 1, view 56.
Figure 72: The all mown turf with no other vegetation in the right-of-way was
seen as the least natural combination in all three contexts. Appendix 1, view
51.
AIMS II • 12/2005
Figure 71: Within the rural context, the prairie flowers treatment with the
curved or straight mowing pattern was seen as the most attractive combination.
Appendix 1, view 102.
Figure 70: Within the urban context, the naturalized treatment with a straight
mowing pattern was the most preferred planting combination. Appendix 1,
view 14.
73
74
AIMS II • 12/2005
All mown turf with no other vegetation also was perceived as
least natural in all three contexts (Figure 72). Particularly in
the urban context it was perceived as significantly less natural
than any other combinations (M=3.57, sd = 2.044, p<0.01).
However, rural boreal and rural agricultural settings seemed
to enhance perceptions of naturalness regardless of rightof-way vegetation. Even the least natural combination (all
mown turf with no other vegetation ) was perceived as quite
natural (mean= 6.08 within the boreal context, mean=5.86
in the rural agriculture context). Within the urban context,
a straight mown swath along a naturalized woodland was
perceived as most natural (M=6.38, sd = 1.006), significantly
more than other combinations (p<0.01) except combinations
attractive than all other vegetation and mowing combinations
(p<0.01), and its mean rating of 3.59 suggests that typically
it was not attractive in an urban context. However, in the
rural boreal context, all mown turf with no other vegetation
in the right of way was rated significantly lower than only
the curved/weedy combination and the straight/naturalized
combination, and its mean attractiveness rating of 5.23 was
relatively high: only 0.37 lower than the most attractive
combination (straight-mowing/naturalized woodland). In the
rural agricultural context, prairie flower vegetation combined
with either a curved or straight swath of mowing (Figure 71)
was perceived as significantly more attractive than all other
combinations (M=5.99, sd =1.185, p<0.01) and (M=5.91, sd
= 1.363 p<0.01). The straight/weedy combination had the
lowest mean rating for attractiveness (M=4.45, sd = 1.719)
in the rural agricultural context, significantly lower than the
others, except mow all/no vegetation (M=4.60, sd=1.779).
All mown turf with no other vegetation in the right of way
was perceived as the most well-maintained in all contexts,
with a mean rating of 6.01 (sd =1.284), 5.97 (sd=1.216),
and 5.94 (sd=1.217) in the urban, rural agricultural and
rural boreal contexts respectively (Figure 74). Straight mown
swaths combined with woody islands was perceived as being
the least well-maintained in the urban context (M=4.59, sd
= 1.736), significantly lower than all other combinations
(p<0.01) except straight mown swath along weedy vegetation
(M=4.66, sd= 1.699). A straight mown swath along weedy
that contained straight mowing and evergreen and deciduous
trees (M=6.23, sd =1.071). Once again, a straight mown
swath along a naturalized woodland was rated as most natural
in the boreal context (M=6.45, sd = 1.086), but significantly
more than only the mow all/no vegetation and the straight/
brome combinations (Figure 73). Straight and curved prairie
flower landscapes were perceived as most natural under the
rural agricultural context with a mean of 6.37 (sd=1.022)
and 6.34 (sd=0.972) respectively, significantly more than all
other combination (p<0.01).
A straight-mown edge with
naturalized woodlands was
perceived as most attractive
within both the urban context
and rural boreal context.
In all landscape contexts, perceived safety was rated
relatively high (mean ratings no lower than 5.0) with a
low variance in all three contexts (no higher than 1.0) for
any combination of mowing and vegetation. All of these
treatments consistently were perceived as relatively safe.
The curved/prairie flower combination was perceived most
safe in the urban context (M=5.68, sd=1.264), significantly
more than other combinations (p<0.01) except straight/
prairie flower combinations, straight or curved and brome
combinations, and curved with evergreen and deciduous
tree combinations. The straight/sumac combinations had
the lowest safety rating in the urban context (M=5.19, sd
=1.687). The straight/naturalized woodland combination
was perceived as most safe in the boreal context (M=5.91,
sd=1.358), while in the rural agricultural context, the safest
combination was all mown turf with no other vegetation
(M=6.07, sd=1.215). In general, the straight mown swath
along weedy vegetation was considered less safe in all three
contexts, with nearly the lowest rating for perceived safety
in the urban context (M=5.32, sd=1.504), and the lowest
ratings in both rural agricultural (M=5.14, sd=1.668) and
boreal context (M=5.47, sd=1.460).
vegetation was perceived as least well-maintained in both
rural agricultural (M=4.40, sd=1.683) and boreal contexts
(M=5.13, sd=1.605), and a curved mown swath along weedy
vegetation was similar with a mean of 4.76 within the rural
agricultural context and 5.26 within the rural boreal context.
Generally, weedy vegetation was perceived as significantly less
well maintained than other vegetation in rural agricultural
and boreal contexts.
AIMS II • 12/2005
Figure 74: The mow all/no vegetation combination was seen as the most wellmaintained for all three contexts. Appendix 1, view 1.
All mown turf with no other
vegetation was least attractive
in comparison to all other
vegetation
and
mowing
combinations in both urban
and boreal contexts.
75
CONCLUSIONS and
RECOMMENDATIONS
78
AIMS II • 12/2005
Architectural elements: walls
Architectural elements: bridge rails
Mowing patterns
Vegetative treatments
2.
3.
4.
5.
Using landscape views as cases
Using individual respondents’ perceptions of individual landscape views as cases
Distinguishing the relative effect of differences among respondents from the effects of differences among
landscape views
1.
2.
3.
To draw the most reliable conclusions, AIMS II data were analyzed by multiple methods:
AIMS II • 12/2005
The AIMS II web survey was implemented in December 2005; with 1108 Minnesota licensed drivers over age 18 responding.
The survey sample is representative of the Minnesota population in many key ways. However, the proportion of women in the
sample was high compared with the Minnesota population overall, and this difference was accounted for in data analysis.
Landscape context
1.
AIMS II developed an image-supported web survey instrument to investigate Minnesota travelers’ perceptions of highway corridor
landscape design and management choices. These choices were suggested by the results of the 1999 AIMS I focus-groups-invans study and specified further in a series of stakeholder meetings with Mn/DOT professional staff. Five types of design and
management choices became the key independent variables for the AIMS II survey. They were:
CONCLUSIONS AND RECOMMENDATIONS
79
80
AIMS II • 12/2005
4.
Overall, the mowing treatment that was most preferred in both rural and urban contexts was a single mown swath along
the roadway. While a curved mowing pattern was preferred over an entirely mown right-of-way, it was less preferred than a
single, straight mown swath in nearly every setting. This suggests that the least cost mowing alternative has the greatest aesthetic
benefit.
3.
While an entirely mown turf right-of way without any other planting is seen as unattractive and unnatural everywhere,
and brome grass without any other planting is seen as unattractive and unnatural in urban settings, virtually all other planting
designs have a positive effect on public perception. This suggests that mowing of the entire right-of-way for aesthetic reasons is
unnecessary.
2.
However, regardless of context, vegetation composition and design are far more important to highway landscape perceptions
than are structures or mowing patterns (as long as there is some mowing along the roadside). This suggests that in projects
where structural design treatments OR vegetation treatments are being considered for their aesthetic effects, vegetation design
treatments are likely to produce greater benefits.
1.
Context powerfully affects travelers’ perceptions of highway corridor landscapes. Landscape treatments that are attractive
in urban areas might be less attractive in rural MN. Some landscape treatments that are very attractive in rural northern MN
might be less attractive in rural southern MN, and vice versa.
Results from the first and second methods suggest the following key conclusions:
Results from the first and second methods are highly consistent; the second method provides more detail, but supports the
results from the first method. Results from the third method showed that differences among landscape views significantly
affect perceptions of the view from the road, even accounting for the highly significant effects of individual differences among
respondents.
When the second method was used, two analyses were conducted for each model to account for the possible effect of a
disproportionate gender distribution in the AIMS II sample: models based on the entire sample were compared with models
based on a subsample of equal numbers of male and female respondents. There were no substantive differences in results for
these paired analyses; gender did not substantially affect the relationship between landscape views and public perception.
AIMS II • 12/2005
1.
Make vegetation composition and design a high priority to assure that the public perceives Minnesota highway landscapes
as attractive. No other design or management variable tested in AIMS II was more powerful in its effect on public perception.
AIMS II results are a very rich source of conclusions and recommendations for Mn/DOT. Depending on what design and
management decisions are being considered, these results can be consulted in detail. However, an overarching pattern of results
and conclusions suggests some general recommendations that may be helpful to Mn/DOT. They include:
8.
On bridges, the extent of view over the bridge rail is very important and dramatically affects perceived attractiveness.
This suggests that bridge rail configurations and heights that allow travelers to see over the rail greatly enhance driver aesthetic
experience.
7.
In urban settings, wall design has relatively little effect on overall perceptions of attractiveness, naturalness, maintenance,
or safety. However, walls that were consistently perceived as more attractive had the following characteristics: light color and a
strong pattern of verticals (like columns) at regular intervals. Where vegetation is very simple (all mown turf or all brome grass
right-of-way), the presence of strong verticals, as in Wall 3 in AIMS II, may be especially important to attractiveness. Where
vegetation is more varied, a more subtle but regular pattern of verticals may be preferred.
6.
In rural settings, a woodland planting design that allows woody species to naturalize within a matrix of coniferous and
early successional deciduous species is perceived as the most attractive and natural planting treatment. In urban settings, it is
perceived as attractive, but less so than the prairie flower planting design. It is not perceived as being as well maintained or safe
as the prairie flower design in either setting. While weedy vegetation is typically unattractive in any setting, weedy herbaceous
vegetation that includes some colorful flowers sometimes may be perceived as attractive in boreal settings where coniferous trees
characterize the landscape beyond the right-of-way.
5.
Prairie flower vegetation is the only vegetation treatment that has a powerful positive effect on attractiveness, naturalness,
maintenance, and safety in all contexts. This suggests that prairie flower roadside plantings could be widely used for predictably
positive aesthetic effects.
81
82
Design bridge rails to maximize the extent of landscape viewed by travelers on the roadway.
AIMS II • 12/2005
The results suggest that some of Mn/DOT’s leading past design and management innovations, like using native plants, selective
mowing, and the T-rail for bridges, have been good investments in enhanced public perception. In the future, the AIMS II
method can be used to effectively monitor changes in public perception of these past innovations, as well as to make comparisons
Together, AIMS II conclusions and recommendations suggest how Mn/DOT can make more effective comparisons among different
alternatives for highway corridor landscape investments. Knowing what investments the public will notice and appreciate most
may help Mn/DOT develop new design and maintenance choices, as well as more comprehensively assess the comparative value
of current choices.
8.
7.
Design urban sound and retaining walls using a light colored material in a regular pattern of verticals (like columns) to
enhance their attractiveness from the road.
6.
Where walls are being constructed in the highway corridor landscape and budget is limited, to achieve the greatest effect
on public perception, consider a lower cost wall and a larger investment in vegetation installation and maintenance.
5.
Respect public perception of rural Minnesota landscapes as natural. Enhance these perceptions with design and management
choices.
4.
Consider adopting mowing regimes that limit mowing to a single swath along the roadside. This pattern is generally
perceived as most attractive and natural, and as well as adequately maintained and safe. It also would seem to require less fuel
and staff time to implement compared with other, less attractive alternatives.
3.
Avoid using only mown turf or only brome grass with no other vegetation in the right-of-way. Both tend to be perceived
as unattractive in any setting.
2.
Consider widely adopting a flowery mix of native herbaceous plants or a dense naturalized mixed species woodland. Both
are perceived as very attractive and very natural in many landscape contexts. The flowery mix of native herbaceous plants also
tends to be perceived as well-maintained and safe. Both vegetation compositions would probably be relatively inexpensive to
maintain once they were established, and they would be likely to provide greater biodiversity benefits than other compositions.
AIMS II • 12/2005
Ten years ago Mn/DOT professional staff had the great foresight to anticipate the need for objective tools that could be used
to understand the effect of Mn/DOT decisions on travelers’ perceptions of highway corridor landscapes. With the completion
and implementation of AIMS I and AIMS II, Mn/DOT now has a complementary pair of tools to assess and monitor change in
public perception, a key benefit of highway design for the state of Minnesota. AIMS I, a high validity focus group technique to
gain authentic insight into what the public notices and perceives as attractive in their view from the road, was recognized by the
Federal Highway Administration as being the most excellent example of environmental research in the nation in 2003. AIMS II
is unparalleled internationally as an image-supported web survey tool. It’s effectiveness in reaching a broadly distributed sample
of Minnesotans and providing sound, pragmatic conclusions has been demonstrated in this project. The two tools together can
help Mn/DOT remain at the cutting edge of highway landscape design with efficiency and sensitivity to the public it serves.
with new ideas that may arise, in part, from the results of this study. Many decisions that could affect public perception of
highway corridor landscapes could not be included in AIMS II. These include, for example, maintenance of structures and
urban landscape settings that are not limited by walls. In addition, the method could be used to contribute to the assessment
of new design and management innovations before they are implemented in the field. Future AIMS II projects could address
these and other topics that might arise as the AIMS I method is implemented in other settings and as Mn/DOT operates with
new budgetary and environmental factors.
83
84
AIMS II • 12/2005
Sell,J. L. Taylor, J. and Zube, E. (1984). Towards a theoretical framework for landscape perception. Environmental
Perception and Behavior. Saarinen, T. Seamon, D.,Sell, J. L (eds.), University of Chicago Press, Chicago, IL
pp. 61- 83.
Nassauer, J. I. and Larson, D. (2004). Aesthetic initiative measurement system: A means to achieve context sensitive design.
Transportation Research Record. No. 1890: 88-96.
Nassauer, J. I. (1993). Ecological function and the perception of suburban residential landscapes. In Gobster, P.H., ed.,
Managing Urban and High Use Recreation Settings, General Technical Report, USDA Forest Service North
Central Forest Exp. Sta., St. Paul, MN.
Nassauer, J. I. (1995). Messy ecosystems, orderly frames. Landscape Journal. 14 :( 2), pp. 161-170.
Nassauer, J. I. (1995). Culture and changing landscape structure. Landscape Ecology. 10:4, pp. 229-237
Myers, M. E. (2006). Power of the picturesque: Motorists’ perceptions of the Blue Ridge Parkway.
Landscape Journal. 25(1): 38 - 53.
Landis, T. D., Wilikinson, Kim M., Steinfeld, David E., Tiley, Scott A., and Fekaris, George N. (2005). Roadside
revegetation for forest highways: new applications for native plants. Native Plants. 6(3): 297 - 305.
Gartner, W. C., and Erkkila, Daniel, L. (2004). Attributes and amenities of highway systems important to tourists.
Transportation Research Record: Journal of the Transportation Research Board No. 1890: 97 - 104.
Daniel, T. (2001). Whither scenic beauty? Visual landscape quality assessment in the 21st century.
Landscape and Urban Planning. 54: 267-281.
Cackowski, J. M., and Nasar, Jack, L. (2003). The restorative effects of roadside vegetation. Implications for automobile
driver anger and frustration. Environment and Behavior. 35(6): 736 - 751.
Appleyard, D., Lynch, K. & Myer, JR (1964) The View from the Road. MIT Press, Cambridge, Massachusetts.
References
APPENDICES
86
AIMS II • 12/2005
AIMS II • 12/2005
Simulated Landscape Views in the
Factorial Design Table
Ap p endix 1
87
88
AIMS II • 12/2005
T-rail bridge
Replicate 2
Concrete
bridge rail
Replicate 1
117
Bridge Approach
113
Bridge Approach
116
116
AIMS II • 12/2005
115
View from Bridge
114
View from Bridge
89
90
AIMS II • 12/2005
Rural Boreal
(b1)
Rural
Agriculture
(r1)
Urban Noise
Wall (w3)
Urban Noise
Wall (w2)
Urban Noise
Wall (w1)
Context
Replicate 1
51
38
27
16
1
No Vegetation
All Mown
52
39
28
17
2
Weedy
53
40
29
18
3
Brome
41
30
19
4
Flowers
31
5
Sumac
Mow 10' Straight
42
32
20
6
Evergreen
43
7
Evergreen +
Deciduous
Rural Boreal
(b1)
Rural
Agriculture
(r1)
Urban Noise
Wall (w3)
Urban Noise
Wall (w2)
Urban Noise
Wall (w1)
Context
Replicate 1
54
44
33
21
8
Weedy
55
45
34
22
9
Brome
46
35
23
10
Flowers
24
11
Sumac
Mow 10' - 20' Curved
47
36
25
12
Evergreen
48
13
Evergreen +
Deciduous
56
49
14
Naturalized
AIMS II • 12/2005
50
37
26
15
Woody Islands
91
92
AIMS II • 12/2005
Rural Boreal
(b2)
Rural
Agriculture
(r2)
Urban
Retaining Wall
(w6)
Urban
Retaining Wall
(w5)
Urban
Retaining Wall
(w4)
Context
Replicate 2
107
94
83
72
57
No Vegetation
All Mown
108
95
84
73
58
Weedy
109
96
85
74
59
Brome
97
86
75
60
Flowers
87
61
Sumac
Mow 10' Straight
98
88
76
62
Evergreen
99
63
Evergreen +
Deciduous
Rural Boreal
(b2)
Rural
Agriculture
(r2)
Urban
Retaining Wall
(w6)
Urban
Retaining Wall
(w5)
Urban
Retaining Wall
(w4)
Context
110
100
89
77
64
Weedy
111
101
90
78
65
Brome
102
91
79
66
Flowers
80
67
Sumac
Mow 10' - 20' Curved
103
92
81
68
Evergreen
104
69
Evergreen +
Deciduous
112
105
70
Naturalized
AIMS II • 12/2005
106
93
82
71
Woody Islands
93
94
AIMS II • 12/2005
AIMS II • 12/2005
Web Questionnaire
Ap p endix 2
95
96
AIMS II • 12/2005
I will not participate in this survey.
Start survey
If you have other questions about this research, please contact:
Joan Nassauer, University of Michigan, 440 Church St., Ann Arbor, MI 48109-1041 email: roadscapes@umich.edu
3/27/2006
AIMS II • 12/2005
If you have questions regarding your rights as a participant in research, please contact:
Institutional Review Board, Kate Keever, 540 East Liberty Street, Suite 202, Ann Arbor, MI 48104-2210 734-936-0933 email:
irbhsbs@umich.edu
This questionnaire is divided into two sections. In the first section you will see images of roadside landscapes with different vegetation and
walls, and you will indicate your own perceptions of these landscapes. In the second section, we will ask you a few questions about yourself.
All of your responses are confidential and anonymous. SSI does not share your personal identification data with the University of Michigan.
Participating in this survey should take you less than 15 minutes. Please complete the entire questionnaire. Your participation is voluntary
and you have the right to not answer any question. If you do not answer a question on one page, the "next page" button will not appear, and
you will not be able to go to the next page and complete the questionnaire. However, there will be no consequence (you will still be eligible
for prizes and SSI will note that you did try to do our questionnaire). Participating in the survey will create no risks or discomforts for you.
http://landscapesurvey.snre.umich.edu/
„
„
„
Welcome!
Page 1 of 1
Thank you for taking part in the highway views survey conducted by the University of Michigan. This is a fun survey in which you'll be asked
to pretend you are traveling down a Minnesota highway and rate a series of images that show a variety of roadside landscapes. This
research will be used to measure the quality of travel on Minnesota highways and freeways.
SNRE
97
98
Page 1 of 1
I will not participate in this survey.
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?do=snresurvey
Survey Progress
3/27/2006
To help you compare these very similar images, you can use the BACK button at the bottom of each page to review the images and change your
ratings.
In the following pages you will see larger images of roadside landscapes like those shown above. Please rate your own perception of that view on
4 separate scales for safety, maintenance, naturalness and attractiveness. For each scale just give your own perceptions, comparing the view
with your own past experiences of Minnesota highways.
In this survey you will see a variety of images of the roadside. Some of the views you will rate will look very similar - like those you see below. But
the arrangement and combination of vegetation and walls in each image ARE different.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=1&do=snresurvey&next.x=27&next.y=15
Survey Progress
AIMS II • 12/2005
4/6/2006
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
99
100
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?005_att=7&005_nat=7&005_maint=7&005_safe=7&pageNumber=2&do=s... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?015_att=7&015_nat=7&015_maint=7&015_safe=7&pageNumber=3&do=s... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
101
102
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?007_att=7&007_nat=7&007_maint=7&007_safe=7&pageNumber=4&do=s... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?001_att=7&001_nat=7&001_maint=7&001_safe=7&pageNumber=5&do=s... 4/6/2006
Survey Progress
Now imagine you are traveling down a highway through a landscape that looks like the images shown in the blue box below. On the
next page we will ask you to decide whether or not the roadside landscapes you just rated are compatible with the nearby landscapes
you see below. When you are finished looking at these images, please click next page below.
SNRE Landscape Survey
103
104
Page 1 of 1
Not compatible
Not compatible
All images are compatible with the landscape views
Not compatible
Not compatible
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=6&do=snresurvey&next.x=61&next.y=17
Survey Progress
Nearby Landscapes
4/6/2006
The nearby landscapes are shown again in the blue box on the left. Do any of the roadside landscapes on the right look like they ARE
NOT COMPATIBLE with those nearby? Please click beneath any roadside landscape you perceive as incompatible with the nearby
landscapes on the left. You may click more than one image.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
http://landscapesurvey.snre.umich.edu/Main.php?allcompt1=1&pageNumber=7&do=snresurvey&next.x=34&next.y=13
Survey Progress
AIMS II • 12/2005
4/6/2006
Now you will see a new set of roadside landscapes and later we will ask you about their compatibility with nearby landscapes. Please
rate the roadsides to show your own perceptions along a Minnesota highway. You can go back and forth to compare these images and
revise your ratings by using the BACK button.
SNRE Landscape Survey
105
106
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?012_att=7&012_nat=7&012_maint=7&012_safe=7&pageNumber=8&do=s... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?010_att=7&010_nat=7&010_maint=7&010_safe=7&pageNumber=9&do=s... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
107
108
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?014_att=7&014_nat=7&014_maint=7&014_safe=7&pageNumber=10&do=... 4/6/2006
Survey Progress
Now imagine you are traveling down a highway through a landscape that looks like the images shown in the blue box below. On the
next page we will ask you to decide whether or not the roadside landscapes you just rated are compatible with the nearby landscapes
you see below. When you are finished looking at these images, please click next page below.
SNRE Landscape Survey
Page 1 of 1
Not compatible
4/6/2006
AIMS II • 12/2005
All images are compatible with the landscape views
Not compatible
Not compatible
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=11&do=snresurvey&next.x=31&next.y=17
Survey Progress
Nearby Landscapes
The nearby landscapes are shown again in the blue box on the left. Do any of the roadside landscapes on the right look like they ARE
NOT COMPATIBLE with those nearby? Please click beneath any roadside landscape you perceive as incompatible with the nearby
landscapes on the left. You may click more than one image.
SNRE Landscape Survey
109
110
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?allcompt2=1&pageNumber=12&do=snresurvey&next.x=10&next.y=13
Survey Progress
4/6/2006
Now you will see a new set of roadside landscapes and later we will ask you about their compatibility with landscape views. Please
rate them to show your own perceptions of roadsides along a Minnesota highway. You can go back and forth to compare these images
and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?046_att=7&046_nat=7&046_maint=7&046_safe=7&pageNumber=13&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
111
112
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?048_att=6&048_nat=6&048_maint=6&048_safe=6&pageNumber=14&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?049_att=7&049_nat=7&049_maint=7&049_safe=7&pageNumber=15&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
113
114
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?038_att=7&038_nat=7&038_maint=7&038_safe=7&pageNumber=16&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?042_att=7&042_nat=7&042_maint=7&042_safe=7&pageNumber=17&do=... 4/6/2006
Survey Progress
Now imagine you are traveling down a highway through a landscape that looks like the images shown in the blue box below. On the
next page we will ask you to decide whether or not the roadside landscapes you just rated are compatible with the nearby landscapes
you see below. When you are finished looking at these images, please click next page below.
SNRE Landscape Survey
115
116
Page 1 of 2
Not compatible
Not compatible
AIMS II • 12/2005
4/6/2006
All images are compatible with the landscape views
Not compatible
Not compatible
Not compatible
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=18&do=snresurvey&next.x=35&next.y=10
Nearby Landscapes
The nearby landscapes are shown again in the blue box on the left. Do any of the roadside landscapes on the right look like they ARE
NOT COMPATIBLE with those nearby? Please click beneath any roadside landscape you perceive as incompatible with the nearby
landscapes on the left. You may click more than one image.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
http://landscapesurvey.snre.umich.edu/Main.php?allcompt3=1&pageNumber=19&do=snresurvey&next.x=42&next.y=11
Survey Progress
AIMS II • 12/2005
4/6/2006
Now you will see a new set of roadside landscapes and later we will ask you about their compatibility with landscape views. Please
rate them to show your own perceptions of roadsides along a Minnesota highway. You can go back and forth to compare these images
and revise your ratings by using the BACK button.
SNRE Landscape Survey
117
118
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?016_att=7&016_nat=7&016_maint=7&016_safe=7&pageNumber=20&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?017_att=6&017_nat=6&017_maint=6&017_safe=6&pageNumber=21&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
119
120
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?022_att=7&022_nat=7&022_maint=7&022_safe=7&pageNumber=22&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?024_att=7&024_nat=6&024_maint=6&024_safe=6&pageNumber=23&do=... 4/6/2006
Survey Progress
Now imagine you are traveling down a highway through a landscape that looks like the images shown in the blue box below. On the
next page we will ask you to decide whether or not the roadside landscapes you just rated are compatible with the nearby landscapes
you see below. When you are finished looking at these images, please click next page below.
SNRE Landscape Survey
121
122
Page 1 of 1
Not compatible
Not compatible
All images are compatible with the landscape views
Not compatible
Not compatible
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=24&do=snresurvey&next.x=57&next.y=16
Survey Progress
Nearby Landscapes
4/6/2006
The nearby landscapes are shown again in the blue box on the left. Do any of the roadside landscapes on the right look like they ARE
NOT COMPATIBLE with those nearby? Please click beneath any roadside landscape you perceive as incompatible with the nearby
landscapes on the left. You may click more than one image.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
http://landscapesurvey.snre.umich.edu/Main.php?allcompt4=1&pageNumber=25&do=snresurvey&next.x=48&next.y=17
Survey Progress
AIMS II • 12/2005
4/6/2006
Now you will see a new set of roadside landscapes and later we will ask you about their compatibility with landscape views. Please
rate them to show your own perceptions of roadsides along a Minnesota highway. You can go back and forth to compare these images
and revise your ratings by using the BACK button.
SNRE Landscape Survey
123
124
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?054_att=7&054_nat=7&054_maint=7&054_safe=7&pageNumber=26&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?055_att=7&055_nat=7&055_maint=7&055_safe=7&pageNumber=27&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
125
126
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?051_att=7&051_nat=7&051_maint=7&051_safe=7&pageNumber=28&do=... 4/6/2006
Survey Progress
Now imagine you are traveling down a highway through a landscape that looks like the images shown in the blue box below. On the
next page we will ask you to decide whether or not the roadside landscapes you just rated are compatible with the nearby landscapes
you see below. When you are finished looking at these images, please click next page below.
SNRE Landscape Survey
Page 1 of 1
Not compatible
4/6/2006
AIMS II • 12/2005
All images are compatible with the landscape views
Not compatible
Not compatible
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=29&do=snresurvey&next.x=27&next.y=19
Survey Progress
Nearby Landscapes
The nearby landscapes are shown again in the blue box on the left. Do any of the roadside landscapes on the right look like they ARE
NOT COMPATIBLE with those nearby? Please click beneath any roadside landscape you perceive as incompatible with the nearby
landscapes on the left. You may click more than one image.
SNRE Landscape Survey
127
128
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?allcompt5=1&pageNumber=30&do=snresurvey&next.x=24&next.y=8
Survey Progress
4/6/2006
Now you will see a new set of roadside landscapes and later we will ask you about their compatibility with landscape views. Please
rate them to show your own perceptions of roadsides along a Minnesota highway. You can go back and forth to compare these images
and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?029_att=7&029_nat=7&029_maint=7&029_safe=7&pageNumber=31&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
129
130
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?032_att=7&032_nat=7&032_maint=7&032_safe=7&pageNumber=32&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?027_att=7&027_nat=7&027_maint=7&027_safe=7&pageNumber=33&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
131
132
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?034_att=7&034_nat=7&034_maint=7&034_safe=7&pageNumber=34&do=... 4/6/2006
Survey Progress
Rate the image to show your own perceptions of this roadside compared with your own past experiences of Minnesota highways. You
can go back and forth to compare these images and revise your ratings by using the BACK button.
SNRE Landscape Survey
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?030_att=7&030_nat=7&030_maint=7&030_safe=7&pageNumber=35&do=... 4/6/2006
Survey Progress
Now imagine you are traveling down a highway through a landscape that looks like the images shown in the blue box below. On the
next page we will ask you to decide whether or not the roadside landscapes you just rated are compatible with the nearby landscapes
you see below. When you are finished looking at these images, please click next page below.
SNRE Landscape Survey
133
134
Page 1 of 2
Not compatible
Not compatible
AIMS II • 12/2005
4/6/2006
All images are compatible with the landscape views
Not compatible
Not compatible
Not compatible
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=36&do=snresurvey&next.x=24&next.y=16
Nearby Landscapes
The nearby landscapes are shown again in the blue box on the left. Do any of the roadside landscapes on the right look like they ARE
NOT COMPATIBLE with those nearby? Please click beneath any roadside landscape you perceive as incompatible with the nearby
landscapes on the left. You may click more than one image.
SNRE Landscape Survey
Unsafe
Poorly maintained
Artificial
Unattractive
1
2
3
4
5
6
7
Safe
Well maintained
Natural
Attractive
Page 1 of 1
http://landscapesurvey.snre.umich.edu/Main.php?allcompt6=1&pageNumber=37&do=snresurvey&next.x=41&next.y=18
Survey Progress
AIMS II • 12/2005
4/6/2006
For this last set of images, please imagine you are driving on the highway bridge below. Rate the image to show your own perception
of this roadside compared with your past experiences of Minnesota highways.
SNRE Landscape Survey
135
136
1
2
3
4
5
6
7
Much more attractive
1
2
3
4
5
6
7
Very important
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?113_att=7&113_nat=7&113_maint=7&113_safe=7&pageNumber=38&do=... 4/6/2006
Survey Progress
Not at all important
HOW IMPORTANT is it to you to have your preferred view from the bridge compared with the other view?
The same attractiveness
HOW MUCH MORE attractive do you find the view you prefer compared with the other view ?
Which view would you prefer to see from the bridge? Please choose only one.
The image you just rated is located in the blue box on the right. Now imagine that when you are looking out
from that bridge, you see the view as it appears in one of the images below. First, click on the circle
beneath the view that you would prefer to see from the bridge. Choose only one.
SNRE Landscape Survey
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?bridge_view=116&attr_bridge=6&imp_bridge=6&pageNumber=39&do=snresurvey&... 3/27/2006
Survey Progress
You?re nearly finished. In this final section we would like to ask you a few questions about yourself. All of your answers are confidential and
anonymous.
SNRE Landscape Survey
137
138
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?pageNumber=40&do=snresurvey&next.x=39&next.y=16
Survey Progress
Over an hour
46-60 minutes
31-45 minutes
21-30 minutes
15-20 minutes
Less than 15 minutes
4. Normally, how long does it take you to commute to work or school?
No
Both
School
Work
3. Do you commute either to work or school during the week on a regular basis?
Other -- Please specify
Taxi
Bus
Car pool
Car
2. What is your primary mode of transportation to get to work, school, go shopping or run errands in your area?
No
Yes
1. Do you yourself drive at all to get to work, school, go shopping or run errands in your area?
SNRE Landscape Survey
3/27/2006
Page 1 of 1
Page 1 of 1
2
3
4
5
6
7
8
9
10
Highly effective
Don't Know
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?drive=0&trans_mode=3&trans_others=&commute=4&com_time=2&pageNumber=4... 3/27/2006
Survey Progress
Not at all effective
1
7. On the rating scale below, how effective do you think the Minnesota Department of Transportation has been in design and maintenance of
roadside landscapes?
7 days per week
Varies
None
4 days per week
5 days per week
6 days per week
2 days per week
3 days per week
1 day per week
6. Typically, how many days per week do you use major highways or freeway routes?
Yes
No
5. Do you typically use a major highway or freeway as part of your trip route?
For the next few questions, please think about roads maintained by Minnesota Department of Transportation. That would include highways and
freeways maintained by the state, but would not include county roads or city streets.
SNRE Landscape Survey
139
140
Page 1 of 1
2
3
4
5
6
7
8
9
10
Important
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?hwyuse_yn=0&hwyuse_fre=1&eff_mangmt=1&pageNumber=42&do=snresurvey&n... 3/27/2006
Survey Progress
Several times a year
Never
Several times a week
At least once a week
At least once a month
9. How often do you take a different route to get to your destination because the landscape is more pleasing, even if it might take you longer to
get to your destination?
Safety
Views of surroundings
Naturalness of roadside
Maintenance
Walls
Plantings
1
Unimportant
8. For each of the roadside characteristics listed below, please rate how important each is to your experience of Minnesota Department of
Transportation roadside landscapes:
SNRE Landscape Survey
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?exp_plant=9&exp_walls=9&exp_main=9&exp_nat=9&exp_views=9&exp_safe=9&di... 3/27/2006
Survey Progress
Never
1-3 times a month
Less than once a month
1-3 times a week
Daily/Almost daily
12. Instead of driving, how often do you use other travel modes (walk, public transportation, biking etc.) for work, school or shopping?
Manufacturing or warehouse businesses
Multi-family homes like apartments, duplexes or condominiums
Retail businesses
Farms
Single family homes
11. Which of the following are nearby the home in which you currently live? Check all that apply.
10. How many vehicles are owned or leased by adults 18 or over in your household?
SNRE Landscape Survey
141
142
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?num_vehi=1&nearby_farm=1&pubtran=5&pageNumber=44&do=snresurvey&next.x... 3/27/2006
Survey Progress
No
Yes
17. During the past 12 months, have you driven for pleasure?
Yes
No
16. During the past 12 months, did you go sightseeing?
Yes
No
15. Do you currently have a valid drivers? license?
No
Yes -- Please specify
14. Do you belong to any environmental groups?
No
Yes
13. Is your work or profession involved with environmental or conservation issues?
SNRE Landscape Survey
Please click the Submit button below to return to SurveySpot and be entered in the sweepstakes.
Thank you very much for your time!
3/27/2006
Page 1 of 1
AIMS II • 12/2005
http://landscapesurvey.snre.umich.edu/Main.php?envi_work=1&envi_grps_name=&envi_grps=0&driver=1&sightsee=1&drive_plea=1...
SNRE
143
144
AIMS II • 12/2005
AIMS II • 12/2005
View Order for the Randomizations of the
Web Questionnaire
Ap p endix 3
145
146
AIMS II • 12/2005
Replicate 2
Replicate 1
6
5
4
3
2
1
Randomization
Number
Context
Group
Urban
Boreal
Urban
Rural
Urban
Urban
group7
group8
Urban
Urban
Rural
Urban
Boreal
Urban
group7
group8
Urban
Rural
Rural
Urban
Boreal
Urban
group7
group8
Urban
Boreal
Urban
Rural
Urban
Urban
group7
group8
Urban
Urban
Rural
Urban
Boreal
Urban
group7
group8
Urban
Rural
Rural
Urban
Boreal
Urban
group7
group8
View Number
26,23,18,21
56,51,52
27,36,33,28
40,44,41,43,47
11,6,4,9
13,2,14,1
113
116, 114
5,15,7,1
12,10,14
46,48,49,38,42
16,17,22,24
54,55,51
29,32,27, 34,30
113
116, 114
27,35,31,37
42,38,46,49
48,45,39,50
25,19,20,16
56,52,53
14,8,3,1,7
113
114, 116
82,79,74,77
112,107,108
83,92,89,84
96,100,97,99,103
67,62,60,65
69,58,70,57
117
115,116
61,71,63, 57
68,66,70
102,104,105,94,98
72,73,78,80
110,111,107
85,88,83, 90,86
117
115,116
83,91,87,93
98,94,102,105
104,101,95,106
81,75,76,72
112,108,109
70,64,59,57,63
117
116,115
AIMS II • 12/2005
147
148
AIMS II • 12/2005
AIMS II • 12/2005
Tests for Significant Differences
Among Randomizations
Ap p endix 4
149
150
AIMS II • 12/2005
Context
Group
2
5
3
2
3
2
2
5
Randomization
Number
1
2
5
6
5
6
4
6
u,r,r,u,b,u
u,r,r,u,b,u
u,b,u,r,u,u
u,r,r,u,b,u
u,u,r,u,b,u
u,u,r,u,b,u
u,u,r,u,b,u
u,b,u,r,u,u
Context Group
Sequence*
Mean
5.48
5.32
5.80
5.79
5.65
5.03
6.12
5.41
x
x
x
6.22
x
5.78
6.46
6.10
5.13
5.19
5.59
5.64
5.65
5.97
5.91
6.20
AIMS II • 12/2005
5.21
5.26
5.57
x
5.65
5.71
x
6.40
Attractiveness Naturalness Maintenance Safety
* For context group sequences, u = urban, b = boreal, and r = rural agriculture
112
105
102
51
View
Number
One might also say that views seen prior to any particular view could influence respondent ratings. If this was the case we would
assume that rural views seen after another rural context group would have lower mean ratings because respondents have seen
similar images and have a bias for what they find attractive, natural, etc. This is indeed the case for views 51 and 112, where
mean ratings were lower when respondents had seen another rural context group prior to rating that particular view. Views 102
and 105, however, have higher mean ratings when respondents did not see another rural context group prior to rating the view.
The four views do not share a common occurrence that might have influenced rating scores, and difference between mean ratings
for the same view does not appear to be caused by randomization
Ratings of the same view between two randomizations could be different for a number of reasons. The number of views rated
could increase fatigue and therefore influence ratings. If fatigue is an issue we would assume that images in a higher context
group (i.e., respondents saw the view later in the survey) would have lower means than those rated in a lower context group.
This is indeed the case for views 51 and 112. However, views 102 and 105 have higher mean ratings for images seen in higher
context groups.
Four of the 112 views had three or more significantly different rating means between randomizations. The four views were
within the rural context (2 in boreal and 2 in agriculture) and it was necessary to analyze the data from these views to make sure
that the differences in mean ratings were not a product of the randomizations. The table below shows the process that was used
to identify any potential cause of the view rating discrepancy.
151
152
AIMS II • 12/2005
AIMS II • 12/2005
Invitation Email to
Web Survey Respondents
Ap p endix 5
153
154
AIMS II • 12/2005
Thanks for your participation!
AIMS II • 12/2005
This survey is strictly for University of Michigan research purposes; please be assured that
your individual answers are COMPLETELY CONFIDIENTIAL and there will be no sales followup. To ensure confidentiality, Surveyspot has been asked to conduct the survey.
We must have your completed survey by December 10. After you complete the survey, you will
be entered in SSI’s sweepstakes rewards.
To participate, go to: (LINK GOES HERE)
What do you think of the roadside landscapes you see in Minnesota? This fun survey invites
you to look at a variety of images of Minnesota roadsides and asks for your opinions of
them. Participating in this survey will take you about 15 minutes. To participate, you must be a
licensed driver over age 18.
Dear Panelist-
Invitation Text:
Fun survey for Minnesota drivers
Subject Line:
155
156
AIMS II • 12/2005
AIMS II • 12/2005
Minnesota County Respondent Frequencies
Compared with 2000 US Census
Ap p endix 6
157
158
AIMS II • 12/2005
Aitkin
Anoka
Becker
Beltrami
Benton
Big Stone
Blue Earth
Brown
Carlton
Carver
Cass
Chippewa
Chisago
Clay
Clearwater
Cook
Cottonwood
Crow Wing
Dakota
Dodge
Douglas
Faribault
Fillmore
Freeborn
Goodhue
Grant
Hennepin
Houston
Hubbard
Isanti
Itasca
Jackson
Kanabec
Kandiyohi
Kittson
Koochiching
Lac qui Parle
Lake
Lake of the Woods
Le Sueur
Lincoln
Lyon
McLeod
Mahnomen
Marshall
County
6
80
6
11
8
0
15
5
7
8
2
0
5
7
1
1
1
12
93
2
8
2
8
6
13
3
230
4
7
9
7
2
1
16
1
5
3
7
0
7
1
9
8
3
2
Frequency
0.5
7.2
0.5
1
0.7
0
1.4
0.5
0.6
0.7
0.2
0
0.5
0.6
0.1
0.1
0.1
1.1
8.4
0.2
0.7
0.2
0.7
0.5
1.2
0.3
20.8
0.4
0.6
0.8
0.6
0.2
0.1
1.4
0.1
0.5
0.3
0.6
0
0.6
0.1
0.8
0.7
0.3
0.2
Percent of
Respondents
15301
298084
30000
39650
34226
5820
55941
26911
31671
70205
27150
13088
41101
51229
8423
5168
12167
55099
355904
17731
32821
16181
21122
32584
44127
6289
1116200
19718
18376
31287
43992
11268
14996
41203
5285
14355
8067
11058
4522
25426
6429
25425
34898
5190
10155
County
Pop Total
0.3
6.1
0.6
0.8
0.7
0.1
1.1
0.5
0.6
1.4
0.6
0.3
0.8
1
0.2
0.1
0.2
1.1
7.2
0.4
0.7
0.3
0.4
0.7
0.9
0.1
22.7
0.4
0.4
0.6
0.9
0.2
0.3
0.8
0.1
0.3
0.2
0.2
0.1
0.5
0.1
0.5
0.7
0.1
0.2
Percent of
Minnesota
Total
Martin
Meeker
Mille Lacs
Morrison
Mower
Murray
Nicollet
Nobles
Norman
Olmsted
Otter Tail
Pennington
Pine
Pipestone
Polk
Pope
Ramsey
Red Lake
Redwood
Renville
Rice
Rock
Roseau
St. Louis
Scott
Sherburne
Sibley
Stearns
Steele
Stevens
Swift
Todd
Traverse
Wabasha
Wadena
Waseca
Washington
Watonwan
Wilkin
Winona
Wright
Yellow Medicine
Unreported
County
1,108.00
6
6
11
8
7
2
4
5
3
29
7
1
7
2
5
2
97
1
3
2
12
3
3
53
17
17
1
30
7
0
2
5
0
3
4
1
58
4
5
16
23
3
1
100
0.5
0.5
1
0.7
0.6
0.2
0.4
0.5
0.3
2.6
0.6
0.1
0.6
0.2
0.5
0.2
8.8
0.1
0.3
0.2
1.1
0.3
0.3
4.8
1.5
1.5
0.1
2.7
0.6
0
0.2
0.5
0
0.3
0.4
0.1
5.2
0.4
0.5
1.4
2.1
0.3
0.1
Percent of
Frequency Respondents
100
0.4
0.5
0.5
0.6
0.8
0.2
0.6
0.4
0.2
2.5
1.2
0.3
0.5
0.2
0.6
0.2
10.4
0.1
0.3
0.3
1.2
0.2
0.3
4.1
1.8
1.3
0.3
2.7
0.7
0.2
0.2
0.5
0.1
0.4
0.3
0.4
4.1
0.2
0.1
1
1.8
0.2
Percent of
Minnesota
AIMS II • 12/2005
4919479
21802
22644
22330
31712
38603
9165
29771
20832
7442
124277
57159
13584
26530
9895
31369
11236
511035
4299
16815
17154
56665
9721
16338
200528
89498
64417
15356
133166
33680
10053
11956
24426
4134
21610
13713
19526
201130
11876
7138
49985
89986
11080
County
Pop Total
159
160
AIMS II • 12/2005
AIMS II • 12/2005
Bivariate Correlations for the
112 Highway Corridor Landscape Views
Ap p endix 7
161
162
AIMS II • 12/2005
boreal
rural ag
context
urban
Correlations between the four rating scales looking at one context at a time
att
nat
maint
Pearson Correlation
att
1
0.611
0.498
Sig. (2-tailed)
0.000
0.000
N
16,612
16,612
16,612
Pearson Correlation
nat
0.611
1
0.248
Sig. (2-tailed)
0.000
0.000
N
16,612
16,612
16,612
Pearson Correlation
maint
0.498
0.248
1
Sig. (2-tailed)
0.000
0.000
N
16,612
16,612
16,612
Pearson Correlation
safe
0.377
0.335
0.537
Sig. (2-tailed)
0.000
0.000
0.000
N
16,612
16,612
16,612
Pearson Correlation
att
1
0.525
0.655
Sig. (2-tailed)
0.000
0.000
N
6,656
6,656
6,656
Pearson Correlation
nat
0.525
1
0.463
Sig. (2-tailed)
0.000
0.000
N
6,656
6,656
6,656
Pearson Correlation
maint
0.655
0.463
1
Sig. (2-tailed)
0.000
0.000
N
6,656
6,656
6,656
Pearson Correlation
safe
0.456
0.427
0.649
Sig. (2-tailed)
0.000
0.000
0.000
N
6,656
6,656
6,656
Pearson Correlation
att
1
0.536
0.659
Sig. (2-tailed)
0.000
0.000
N
3,324
3,324
3,324
Pearson Correlation
nat
0.536
1
0.451
Sig. (2-tailed)
0.000
0.000
N
3,324
3,324
3,324
Pearson Correlation
maint
0.659
0.451
1
Sig. (2-tailed)
0.000
0.000
N
3,324
3,324
3,324
Pearson Correlation
safe
0.498
0.447
0.623
Sig. (2-tailed)
0.000
0.000
0.000
N
3,324
3,324
3,324
3,324
6,656
0.498
0.000
3,324
0.447
0.000
3,324
0.623
0.000
3,324
1
16,612
0.456
0.000
6,656
0.427
0.000
6,656
0.649
0.000
6,656
1
0.377
0.000
16,612
0.335
0.000
16,612
0.537
0.000
16,612
1
safe
AIMS II • 12/2005
163
164
AIMS II • 12/2005
safe
maint
nat
att
safe
maint
nat
att
none
w1
w2
w3
w4
w5
w6
Total
none
w1
w2
w3
w4
w5
w6
Total
none
w1
w2
w3
w4
w5
w6
Total
none
w1
w2
w3
w4
w5
w6
Total
urban
rural ag
boreal
Total
urban
rural ag
boreal
Total
urban
rural ag
boreal
Total
urban
rural ag
boreal
Total
Std.
F
Deviation
Mean
4.64
1.785
5.29
1.543
507.792
5.39
1.502
4.90
1.726
5.06
1.789
6.22
1.107
1,709.096
6.23
1.105
5.50
1.666
5.40
1.510
5.48
1.438
9.048
5.47
1.437
5.43
1.484
5.44
1.507
5.66
1.391
72.036
5.68
1.384
5.53
1.468
0.000
0.000
0.000
0.000
Sig.
N
9,980
3,708
2,212
2,397
3,680
2,220
2,395
26,592
9,980
3,708
2,212
2,397
3,680
2,220
2,395
26,592
9,980
3,708
2,212
2,397
3,680
2,220
2,395
26,592
9,980
3,708
2,212
2,397
3,680
2,220
2,395
26,592
Std.
Mean
Deviation
5.32
1.530
4.81
1.750
4.78
1.649
4.55
1.756
4.67
1.862
4.55
1.803
4.39
1.812
4.90
1.726
6.22
1.107
5.36
1.700
4.86
1.758
4.76
1.776
5.33
1.795
4.96
1.782
4.77
1.829
5.50
1.666
5.48
1.438
5.50
1.459
5.38
1.497
5.37
1.506
5.40
1.516
5.37
1.534
5.29
1.566
5.43
1.484
5.67
1.389
5.56
1.396
5.13
1.692
5.54
1.407
5.47
1.522
5.46
1.490
5.40
1.541
5.53
1.468
47.517
8.146
657.393
188.137
F
0.000
0.000
0.000
0.000
Sig.
ANOVA Results with Walls as the Factor
N
16,612
6,656
3,324
26,592
16,612
6,656
3,324
26,592
16,612
6,656
3,324
26,592
16,612
6,656
3,324
26,592
ANOVA Results with Context as the Factor
w3, w4, w5
w1, w4, w5
w1, w3, w5, w6
w1, w3, w4, w6
w4, w5
w1, w2, w4
none, w2, w4
none, w1, w3, w4, w5, w6
w2, w4, w5, w6
none, w1, w2, w3, w5, w6
w2, w3, w4, w6
w2, w3, w4, w5
w4
w3, w5, w6
w2, w6
w1
w2
w2, w3
Mean difference not sig to .05
level
w2
w1, w4
w4, w5
w2, w3, w5
w3, w4
-
boreal
rural ag
boreal
rural ag
boreal
rural ag
Mean difference not sig to .05
level
safe
maint
nat
att
all
straight
curved
Total
all
straight
curved
Total
all
straight
curved
Total
all
straight
curved
Total
N
4,416
13,320
8,856
26,592
4,416
13,320
8,856
26,592
4,416
13,320
8,856
26,592
4,416
13,320
8,856
26,592
Std.
Mean
Deviation
4.03
1.991
5.10
1.607
5.02
1.622
4.90
1.726
4.37
2.159
5.82
1.407
5.57
1.493
5.50
1.666
5.99
1.262
5.27
1.518
5.38
1.469
5.43
1.484
5.59
1.644
5.49
1.454
5.55
1.391
5.53
1.468
10.479
406.410
1,410.402
705.866
F
0.000
0.000
0.000
0.000
Sig.
ANOVA Results with Mowing as the Factor
AIMS II • 12/2005
curved
all
-
-
Mean difference not sig to .05
level
-
165
166
AIMS II • 12/2005
safe
maint
nat
att
evergreen +
deciduous
biodiversity
woody islands
Total
evergreen
brome
flowers
sumac
woody islands
Total
none
weedy
biodiversity
evergreen +
deciduous
evergreen
sumac
woody islands
Total
none
weedy
brome
flowers
sumac
evergreen
evergreen +
deciduous
biodiversity
woody islands
Total
none
weedy
brome
flowers
none
weedy
brome
flowers
sumac
evergreen
evergreen +
deciduous
biodiversity
6.39
5.86
5.50
5.99
4.85
5.35
5.60
2,588
1,480
26,592
4,416
4,092
3,684
3,324
5.42
5.56
5.25
5.53
2,216
2,588
1,480
26,592
5.51
5.30
1,468
3,324
5.68
5.73
5.40
4,092
3,684
3,324
4.76
5.43
5.59
5.47
5.50
5.46
1,480
26,592
4,416
2,588
2,216
3,324
5.54
6.20
2,216
1,468
4.93
4.90
4.37
5.63
5.20
5.84
5.18
5.61
5.51
2,588
1,480
26,592
4,416
4,092
3,684
3,324
1,468
3,324
5.43
1.439
1.534
1.468
1.444
1.336
1.593
1.384
1.304
1.484
1.691
1.484
1.644
1.373
1.369
1.347
1.480
1.372
0.981
1.257
1.666
1.262
1.658
1.468
1.087
1.629
1.726
2.159
1.449
1.768
1.284
1.643
1.369
1.410
1.420
Std.
Mean
Deviation
4.03
1.991
4.79
1.678
4.37
1.777
5.62
1.369
5.10
1.658
5.10
1.435
2,216
N
4,416
4,092
3,684
3,324
1,468
3,324
30.908
216.709
519.108
367.511
F
0.000
0.000
0.000
0.000
Sig.
ANOVA Results with Vegetation as the Factor
weedy, sumac, evergreen
none, evergreen
sumac
sumac, evergreen + deciduous
none, flowers
brome
weedy, evergreen + deciduous,
woody islands
none, evergreen + deciduous,
biodiversity
brome, evergreen, biodiversity
sumac, evergreen + deciduous
flowers, evergreen, evergreen +
deciduous, biodiversity
sumac, evergreen + deciduous,
biodiversity
flowers, sumac, evergreen,
biodiversity
sumac, evergreen, evergreen +
deciduous
weedy
woody islands
-
flowers
evergreen
sumac
woody islands
brome
weedy
flowers, evergreen + deciduous
weedy, sumac
biodiversity
Mean difference not sig to .05
level
woody islands
biodiversity
woody islands, evergreen
sumac
AIMS II • 12/2005
167
168
AIMS II • 12/2005
AIMS II • 12/2005
Statistical Results Comparing
Wall Designs
Ap p endix 8
169
170
AIMS II • 12/2005
safe
maint
nat
att
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
1
6
2
3
4
5
1
2
4
5
3
6
1
4
6
3
1
5
4
1
3
6
2
5
rank
Mean
4.41
4.75
4.50
4.19
4.51
4.33
4.44
4.95
4.85
4.71
4.87
4.95
4.72
4.84
5.44
5.37
5.35
5.29
5.36
5.28
5.35
5.63
5.13
5.56
5.52
5.47
5.42
5.46
Std.
Deviation
1.812
1.646
1.755
1.918
1.801
1.819
1.804
1.789
1.768
1.788
1.911
1.785
1.845
1.818
1.505
1.502
1.518
1.579
1.532
1.579
1.537
1.382
1.709
1.390
1.490
1.478
1.534
1.506
29.314
3.098
7.512
24.018
F
0.000
0.008
0.000
0.000
Sig.
Oneway for all veg treatments
rank
1
6
2
3
5
4
1
1
4
5
3
6
1
5
3
2
6
4
3
1
2
6
5
4
139.506
9.202
3.411
40.776
F
0.000
0.000
0.004
0.000
Sig.
rank
1
5
4
2
3
5
2
5
3
4
1
5
2
6
3
1
4
5
3
5
1
4
2
6
Mean
4.58
4.38
4.72
4.49
4.66
4.18
4.50
5.43
4.89
5.38
5.45
5.25
5.14
5.26
4.83
4.49
4.72
4.61
4.86
4.49
4.66
5.48
5.22
5.28
5.44
5.30
5.22
5.32
0.995
1.700
3.687
2.509
F
Std.
Deviation
1.594
1.677
1.610
1.675
1.796
1.799
1.698
1.427
1.595
1.370
1.496
1.651
1.557
1.526
1.700
1.726
1.652
1.645
1.701
1.751
1.699
1.511
1.410
1.522
1.503
1.517
1.557
1.504
Std.
Deviation
1.968
1.791
1.913
1.870
1.983
1.898
1.971
2.064
1.984
1.985
2.166
2.013
1.991
2.044
1.132
1.042
1.214
1.471
1.215
1.424
1.284
1.394
1.984
1.376
1.667
1.671
1.588
1.775
Mean
3.55
4.76
3.72
2.96
3.38
3.53
3.59
3.83
3.38
3.57
3.61
3.34
3.55
3.57
6.22
6.22
5.97
5.85
6.05
5.83
6.01
5.92
3.38
5.84
5.59
5.50
5.57
5.41
Oneway for weedy straight
Oneway for mow all
0.420
0.132
0.003
0.029
Sig.
rank
1
2
4
6
3
5
1
3
2
6
5
4
5
2
3
6
1
4
4
2
1
6
3
5
0.969
1.541
6.090
4.828
F
0.436
0.174
0.000
0.000
Sig.
AIMS II • 12/2005
Std.
Mean
Deviation
4.61
1.610
4.75
1.566
4.81
1.551
4.15
1.575
4.64
1.540
4.36
1.646
4.55
1.595
4.93
1.583
5.30
1.448
5.26
1.400
4.74
1.602
5.45
1.367
4.98
1.548
5.11
1.511
5.08
1.539
4.97
1.578
5.01
1.637
4.71
1.619
4.78
1.634
4.82
1.562
4.89
1.598
5.53
1.357
5.50
1.436
5.43
1.495
5.26
1.345
5.49
1.356
5.40
1.426
5.43
1.404
Oneway for weedy curved
171
172
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
1
2
4
6
3
5
1
2
4
5
5
3
3
2
5
6
1
4
3
1
2
6
5
4
rank
Mean
4.17
4.31
4.21
3.45
3.47
4.05
3.94
4.72
4.90
4.24
4.10
5.17
4.36
4.58
5.49
5.41
5.21
5.01
5.01
5.35
5.24
5.84
5.77
5.56
5.36
5.70
5.53
5.62
AIMS II • 12/2005
safe
maint
nat
att
Std.
Deviation
1.700
1.813
1.663
1.678
1.662
1.975
1.782
1.708
1.765
1.757
1.816
1.638
2.052
1.828
1.378
1.546
1.415
1.552
1.655
1.489
1.519
1.149
1.341
1.314
1.538
1.321
1.538
1.381
3.003
3.458
9.987
F
9.104
0.011
0.004
0.000
Sig.
0.000
Oneway for brome straight
rank
2
3
4
1
6
5
1
6
2
4
5
3
1
4
6
2
3
5
3
1
2
6
5
4
Mean
3.85
4.19
3.95
3.47
3.66
3.77
3.82
4.83
4.32
4.19
4.76
4.62
4.30
4.51
5.43
5.09
5.23
5.17
5.13
5.19
5.21
5.77
5.58
5.51
5.79
5.30
5.39
5.57
Std.
Deviation
1.876
1.704
1.674
1.809
1.750
1.733
1.770
1.773
1.653
1.702
1.835
1.976
1.943
1.827
1.463
1.440
1.513
1.518
1.688
1.659
1.547
1.344
1.270
1.449
1.348
1.683
1.664
1.472
3.346
1.153
3.979
F
3.593
Oneway for brome curved
0.005
0.330
0.001
Sig.
0.003
rank
2
2
5
1
6
4
3
2
5
4
5
1
2
4
6
1
5
3
3
2
5
1
4
6
Mean
5.50
5.54
5.31
5.66
5.34
5.30
5.44
5.70
5.45
5.41
5.84
5.44
5.62
5.58
5.57
5.58
5.41
5.46
5.41
5.59
5.50
5.74
5.74
5.55
5.76
5.50
5.58
5.64
Std.
Deviation
1.353
1.390
1.532
1.397
1.366
1.574
1.439
1.262
1.450
1.453
1.324
1.328
1.384
1.373
1.320
1.346
1.438
1.467
1.409
1.435
1.402
1.320
1.229
1.294
1.314
1.416
1.498
1.349
1.363
0.731
3.000
F
1.910
Oneway for flowers straight
0.236
0.600
0.011
Sig.
0.090
rank
5
3
2
4
1
6
5
4
1
3
1
6
2
6
4
1
3
5
4
5
2
3
1
6
Std.
Mean
Deviation
5.34
1.382
5.30
1.354
5.67
1.223
5.46
1.376
5.71
1.238
5.25
1.444
5.45
1.349
5.79
1.261
5.37
1.400
5.56
1.305
5.87
1.133
5.60
1.372
5.39
1.236
5.59
1.300
5.40
1.286
5.58
1.287
5.72
1.297
5.60
1.385
5.72
1.317
5.35
1.531
5.56
1.359
5.64
1.148
5.73
1.285
5.80
1.173
5.66
1.315
5.90
1.099
5.38
1.465
5.68
1.264
3.748
2.478
4.692
F
3.942
Oneway for flowers curved
0.002
0.030
0.000
Sig.
0.002
safe
maint
nat
att
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
w1
w2
w3
w4
w5
w6
Total
2
5
4
1
6
3
1
6
5
2
4
3
2
3
6
1
4
5
2
6
5
1
4
3
rank
Mean
5.09
4.70
4.88
5.19
4.99
5.06
4.99
5.32
5.27
4.99
5.55
5.26
5.23
5.27
5.59
5.29
5.31
5.55
5.39
5.49
5.44
5.56
5.24
5.33
5.57
5.22
5.35
5.38
Std.
Deviation
1.452
1.444
1.414
1.399
1.383
1.433
1.426
1.469
1.407
1.383
1.375
1.343
1.503
1.419
1.215
1.311
1.371
1.259
1.359
1.347
1.313
1.312
1.344
1.275
1.362
1.455
1.482
1.378
2.352
1.627
2.961
F
2.720
0.039
0.150
0.012
Sig.
0.019
Oneway for evergreen straight
rank
3
5
1
4
6
2
6
3
5
1
3
2
3
2
6
1
5
4
5
6
3
2
4
1
Mean
4.81
4.71
4.83
4.92
4.82
4.95
4.84
5.37
5.40
5.16
5.48
5.17
5.31
5.31
5.17
5.31
5.28
5.39
5.31
5.35
5.30
5.35
5.31
5.63
5.41
5.29
5.55
5.42
Std.
Deviation
1.475
1.447
1.452
1.592
1.356
1.348
1.444
1.231
1.383
1.403
1.462
1.380
1.363
1.373
1.467
1.421
1.396
1.446
1.369
1.355
1.407
1.311
1.360
1.287
1.455
1.436
1.318
1.365
1.927
0.534
1.613
F
0.628
Oneway for evergreen curved
0.087
0.750
0.154
Sig.
0.678
rank
6
2
4
3
1
5
6
2
4
3
1
4
2
4
5
1
3
6
6
2
4
3
1
5
4.641
10.994
2.875
F
11.194
0.000
0.000
0.014
Sig.
0.000
AIMS II • 12/2005
Std.
Mean
Deviation
4.29
1.735
4.91
1.579
4.54
1.735
4.75
1.666
5.41
1.376
4.46
1.653
4.72
1.665
5.79
1.423
5.68
1.285
5.63
1.299
6.03
1.147
5.70
1.312
5.56
1.264
5.73
1.297
4.11
1.700
4.99
1.681
4.37
1.814
4.51
1.768
5.18
1.621
4.37
1.608
4.59
1.736
4.94
1.571
5.44
1.551
5.14
1.495
5.18
1.524
5.54
1.448
4.98
1.583
5.20
1.543
Oneway for woody islands
173
174
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-1.208
0.129
-0.166
0.115
0.589
0.115
0.168
0.128
0.024
0.115
1.208
0.129
1.042
0.129
1.798
0.129
1.376
0.141
1.232
0.129
0.166
0.115
-1.042
0.129
0.756
0.115
0.334
0.128
0.190
0.115
-0.589
0.115
-1.798
0.129
-0.756
0.115
-0.422
0.128
-0.566
0.114
-0.168
0.128
-1.376
0.141
-0.334
0.128
0.422
0.128
-0.144
0.128
-0.024
0.115
-1.232
0.129
-0.190
0.115
0.566
0.114
0.144
0.128
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.000
-1.58
-0.84
0.696
-0.49
0.16
0.000
0.26
0.92
0.780
-0.20
0.53
1.000
-0.30
0.35
0.000
0.84
1.58
0.000
0.67
1.41
0.000
1.43
2.16
0.000
0.97
1.78
0.000
0.86
1.60
0.696
-0.16
0.49
0.000
-1.41
-0.67
0.000
0.43
1.08
0.096
-0.03
0.70
0.560
-0.14
0.52
0.000
-0.92
-0.26
0.000
-2.16
-1.43
0.000
-1.08
-0.43
0.013
-0.79
-0.06
0.000
-0.89
-0.24
0.780
-0.53
0.20
0.000
-1.78
-0.97
0.096
-0.70
0.03
0.013
0.06
0.79
0.871
-0.51
0.22
1.000
-0.35
0.30
0.000
-1.60
-0.86
0.560
-0.52
0.14
0.000
0.24
0.89
0.871
-0.22
0.51
Multiple Comparisons for Mow All
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.453
0.138
0.260
0.123
0.221
0.123
0.488
0.137
0.275
0.123
-0.453
0.138
-0.192
0.138
-0.232
0.138
0.035
0.151
-0.177
0.138
-0.260
0.123
0.192
0.138
-0.039
0.123
0.227
0.137
0.015
0.123
-0.221
0.123
0.232
0.138
0.039
0.123
0.267
0.137
0.054
0.122
-0.488
0.137
-0.035
0.151
-0.227
0.137
-0.267
0.137
-0.213
0.137
-0.275
0.123
0.177
0.138
-0.015
0.123
-0.054
0.122
0.213
0.137
Sig.
0.013
0.276
0.464
0.005
0.218
0.013
0.731
0.545
1.000
0.792
0.276
0.731
1.000
0.559
1.000
0.464
0.545
1.000
0.373
0.998
0.005
1.000
0.559
0.373
0.630
0.218
0.792
1.000
0.998
0.630
95% Confidence Interval
Lower Bound Upper Bound
0.06
0.85
-0.09
0.61
-0.13
0.57
0.10
0.88
-0.07
0.62
-0.85
-0.06
-0.59
0.20
-0.62
0.16
-0.39
0.47
-0.57
0.22
-0.61
0.09
-0.20
0.59
-0.39
0.31
-0.16
0.62
-0.33
0.36
-0.57
0.13
-0.16
0.62
-0.31
0.39
-0.12
0.66
-0.30
0.40
-0.88
-0.10
-0.47
0.39
-0.62
0.16
-0.66
0.12
-0.60
0.18
-0.62
0.07
-0.22
0.57
-0.36
0.33
-0.40
0.30
-0.18
0.60
AIMS II • 12/2005
175
176
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.001
0.086
0.250
0.077
0.372
0.077
0.163
0.086
0.390
0.077
0.001
0.086
0.251
0.086
0.373
0.086
0.164
0.094
0.391
0.086
-0.250
0.077
-0.251
0.086
0.122
0.077
-0.087
0.086
0.140
0.077
-0.372
0.077
-0.373
0.086
-0.122
0.077
-0.209
0.086
0.018
0.077
-0.163
0.086
-0.164
0.094
0.087
0.086
0.209
0.086
0.227
0.086
-0.390
0.077
-0.391
0.086
-0.140
0.077
-0.018
0.077
-0.227
0.086
Sig.
1.000
0.015
0.000
0.400
0.000
1.000
0.042
0.000
0.504
0.000
0.015
0.042
0.600
0.914
0.444
0.000
0.000
0.600
0.142
1.000
0.400
0.504
0.914
0.142
0.085
0.000
0.000
0.444
1.000
0.085
95% Confidence Interval
Lower Bound Upper Bound
-0.25
0.24
0.03
0.47
0.15
0.59
-0.08
0.41
0.17
0.61
-0.24
0.25
0.01
0.50
0.13
0.62
-0.10
0.43
0.15
0.64
-0.47
-0.03
-0.50
-0.01
-0.10
0.34
-0.33
0.16
-0.08
0.36
-0.59
-0.15
-0.62
-0.13
-0.34
0.10
-0.45
0.03
-0.20
0.24
-0.41
0.08
-0.43
0.10
-0.16
0.33
-0.03
0.45
-0.02
0.47
-0.61
-0.17
-0.64
-0.15
-0.36
0.08
-0.24
0.20
-0.47
0.02
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
2.548
0.108
0.087
0.096
0.333
0.096
0.427
0.107
0.358
0.096
-2.548
0.108
-2.462
0.108
-2.215
0.108
-2.122
0.118
-2.190
0.108
-0.087
0.096
2.462
0.108
0.246
0.096
0.340
0.107
0.271
0.096
-0.333
0.096
2.215
0.108
-0.246
0.096
0.094
0.107
0.025
0.096
-0.427
0.107
2.122
0.118
-0.340
0.107
-0.094
0.107
-0.068
0.107
-0.358
0.096
2.190
0.108
-0.271
0.096
-0.025
0.096
0.068
0.107
Sig.
0.000
0.946
0.007
0.001
0.003
0.000
0.000
0.000
0.000
0.000
0.946
0.000
0.106
0.019
0.053
0.007
0.000
0.106
0.953
1.000
0.001
0.000
0.019
0.953
0.988
0.003
0.000
0.053
1.000
0.988
95% Confidence Interval
Lower Bound Upper Bound
2.24
2.86
-0.19
0.36
0.06
0.61
0.12
0.73
0.08
0.63
-2.86
-2.24
-2.77
-2.15
-2.52
-1.91
-2.46
-1.78
-2.50
-1.88
-0.36
0.19
2.15
2.77
-0.03
0.52
0.03
0.65
0.00
0.55
-0.61
-0.06
1.91
2.52
-0.52
0.03
-0.21
0.40
-0.25
0.30
-0.73
-0.12
1.78
2.46
-0.65
-0.03
-0.40
0.21
-0.37
0.24
-0.63
-0.08
1.88
2.50
-0.55
0.00
-0.30
0.25
-0.24
0.37
AIMS II • 12/2005
177
178
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.197
0.175
-0.136
0.173
0.089
0.175
-0.082
0.177
0.397
0.175
-0.197
0.175
-0.333
0.175
-0.108
0.176
-0.279
0.178
0.200
0.176
0.136
0.173
0.333
0.175
0.225
0.175
0.054
0.177
0.533
0.175
-0.089
0.175
0.108
0.176
-0.225
0.175
-0.171
0.178
0.308
0.176
0.082
0.177
0.279
0.178
-0.054
0.177
0.171
0.178
0.479
0.178
-0.397
0.175
-0.200
0.176
-0.533
0.175
-0.308
0.176
-0.479
0.178
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.869
-0.30
0.70
0.970
-0.63
0.36
0.996
-0.41
0.59
0.997
-0.59
0.42
0.204
-0.10
0.90
0.869
-0.70
0.30
0.396
-0.83
0.16
0.990
-0.61
0.39
0.622
-0.79
0.23
0.866
-0.30
0.70
0.970
-0.36
0.63
0.396
-0.16
0.83
0.790
-0.27
0.72
1.000
-0.45
0.56
0.028
0.04
1.03
0.996
-0.59
0.41
0.990
-0.39
0.61
0.790
-0.72
0.27
0.931
-0.68
0.34
0.498
-0.19
0.81
0.997
-0.42
0.59
0.622
-0.23
0.79
1.000
-0.56
0.45
0.931
-0.34
0.68
0.079
-0.03
0.99
0.204
-0.90
0.10
0.866
-0.70
0.30
0.028
-1.03
-0.04
0.498
-0.81
0.19
0.079
-0.99
0.03
Multiple Comparisons for Weedy Curved
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.537
0.156
0.052
0.155
-0.019
0.156
0.184
0.159
0.289
0.156
-0.537
0.156
-0.485
0.156
-0.557
0.158
-0.354
0.160
-0.249
0.158
-0.052
0.155
0.485
0.156
-0.072
0.156
0.131
0.159
0.236
0.156
0.019
0.156
0.557
0.158
0.072
0.156
0.203
0.160
0.308
0.158
-0.184
0.159
0.354
0.160
-0.131
0.159
-0.203
0.160
0.105
0.160
-0.289
0.156
0.249
0.158
-0.236
0.156
-0.308
0.158
-0.105
0.160
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.008
0.09
0.98
0.999
-0.39
0.50
1.000
-0.47
0.43
0.857
-0.27
0.64
0.437
-0.16
0.74
0.008
-0.98
-0.09
0.024
-0.93
-0.04
0.006
-1.01
-0.11
0.233
-0.81
0.10
0.614
-0.70
0.20
0.999
-0.50
0.39
0.024
0.04
0.93
0.997
-0.52
0.37
0.963
-0.32
0.58
0.657
-0.21
0.68
1.000
-0.43
0.47
0.006
0.11
1.01
0.997
-0.37
0.52
0.802
-0.25
0.66
0.370
-0.14
0.76
0.857
-0.64
0.27
0.233
-0.10
0.81
0.963
-0.58
0.32
0.802
-0.66
0.25
0.986
-0.35
0.56
0.437
-0.74
0.16
0.614
-0.20
0.70
0.657
-0.68
0.21
0.370
-0.76
0.14
0.986
-0.56
0.35
AIMS II • 12/2005
179
180
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.341
0.175
0.105
0.174
0.216
0.175
-0.030
0.177
0.341
0.175
-0.341
0.175
-0.236
0.175
-0.124
0.176
-0.371
0.179
0.000
0.176
-0.105
0.174
0.236
0.175
0.112
0.175
-0.135
0.177
0.236
0.175
-0.216
0.175
0.124
0.176
-0.112
0.175
-0.246
0.179
0.124
0.176
0.030
0.177
0.371
0.179
0.135
0.177
0.246
0.179
0.371
0.179
-0.341
0.175
0.000
0.176
-0.236
0.175
-0.124
0.176
-0.371
0.179
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.374
-0.16
0.84
0.991
-0.39
0.60
0.819
-0.28
0.72
1.000
-0.54
0.48
0.374
-0.16
0.84
0.374
-0.84
0.16
0.757
-0.74
0.26
0.981
-0.63
0.38
0.303
-0.88
0.14
1.000
-0.50
0.50
0.991
-0.60
0.39
0.757
-0.26
0.74
0.988
-0.39
0.61
0.974
-0.64
0.37
0.757
-0.26
0.74
0.819
-0.72
0.28
0.981
-0.38
0.63
0.988
-0.61
0.39
0.741
-0.76
0.26
0.981
-0.38
0.63
1.000
-0.48
0.54
0.303
-0.14
0.88
0.974
-0.37
0.64
0.741
-0.26
0.76
0.303
-0.14
0.88
0.374
-0.84
0.16
1.000
-0.50
0.50
0.757
-0.74
0.26
0.981
-0.63
0.38
0.303
-0.88
0.14
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.255
0.155
0.199
0.154
0.039
0.155
0.179
0.157
0.255
0.155
-0.255
0.155
-0.056
0.155
-0.216
0.156
-0.076
0.159
0.000
0.156
-0.199
0.154
0.056
0.155
-0.160
0.155
-0.020
0.157
0.056
0.155
-0.039
0.155
0.216
0.156
0.160
0.155
0.141
0.159
0.216
0.156
-0.179
0.157
0.076
0.159
0.020
0.157
-0.141
0.159
0.076
0.159
-0.255
0.155
0.000
0.156
-0.056
0.155
-0.216
0.156
-0.076
0.159
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.570
-0.19
0.70
0.789
-0.24
0.64
1.000
-0.40
0.48
0.865
-0.27
0.63
0.570
-0.19
0.70
0.570
-0.70
0.19
0.999
-0.50
0.39
0.738
-0.66
0.23
0.997
-0.53
0.38
1.000
-0.45
0.45
0.789
-0.64
0.24
0.999
-0.39
0.50
0.907
-0.60
0.28
1.000
-0.47
0.43
0.999
-0.39
0.50
1.000
-0.48
0.40
0.738
-0.23
0.66
0.907
-0.28
0.60
0.950
-0.31
0.59
0.738
-0.23
0.66
0.865
-0.63
0.27
0.997
-0.38
0.53
1.000
-0.43
0.47
0.950
-0.59
0.31
0.997
-0.38
0.53
0.570
-0.70
0.19
1.000
-0.45
0.45
0.999
-0.50
0.39
0.738
-0.66
0.23
0.997
-0.53
0.38
AIMS II • 12/2005
181
182
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.139
0.165
-0.201
0.165
0.456
0.164
-0.033
0.166
0.253
0.166
0.139
0.165
-0.063
0.162
0.595
0.161
0.105
0.163
0.392
0.163
0.201
0.165
0.063
0.162
0.658
0.161
0.168
0.163
0.455
0.163
-0.456
0.164
-0.595
0.161
-0.658
0.161
-0.489
0.162
-0.203
0.162
0.033
0.166
-0.105
0.163
-0.168
0.163
0.489
0.162
0.286
0.164
-0.253
0.166
-0.392
0.163
-0.455
0.163
0.203
0.162
-0.286
0.164
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.960
-0.61
0.33
0.827
-0.67
0.27
0.061
-0.01
0.92
1.000
-0.51
0.44
0.649
-0.22
0.73
0.960
-0.33
0.61
0.999
-0.52
0.40
0.003
0.14
1.05
0.987
-0.36
0.57
0.156
-0.07
0.86
0.827
-0.27
0.67
0.999
-0.40
0.52
0.001
0.20
1.12
0.907
-0.30
0.63
0.060
-0.01
0.92
0.061
-0.92
0.01
0.003
-1.05
-0.14
0.001
-1.12
-0.20
0.031
-0.95
-0.03
0.812
-0.67
0.26
1.000
-0.44
0.51
0.987
-0.57
0.36
0.907
-0.63
0.30
0.031
0.03
0.95
0.504
-0.18
0.76
0.649
-0.73
0.22
0.156
-0.86
0.07
0.060
-0.92
0.01
0.812
-0.26
0.67
0.504
-0.76
0.18
Multiple Comparisons for Brome Curved
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.371
0.156
-0.324
0.156
0.194
0.155
-0.516
0.157
-0.052
0.157
0.371
0.156
0.047
0.153
0.565
0.152
-0.145
0.154
0.320
0.154
0.324
0.156
-0.047
0.153
0.518
0.152
-0.192
0.154
0.273
0.154
-0.194
0.155
-0.565
0.152
-0.518
0.152
-0.710
0.153
-0.245
0.153
0.516
0.157
0.145
0.154
0.192
0.154
0.710
0.153
0.465
0.155
0.052
0.157
-0.320
0.154
-0.273
0.154
0.245
0.153
-0.465
0.155
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.163
-0.82
0.07
0.298
-0.77
0.12
0.812
-0.25
0.64
0.013
-0.96
-0.07
0.999
-0.50
0.40
0.163
-0.07
0.82
1.000
-0.39
0.48
0.003
0.13
1.00
0.936
-0.58
0.29
0.301
-0.12
0.76
0.298
-0.12
0.77
1.000
-0.48
0.39
0.009
0.08
0.95
0.814
-0.63
0.25
0.486
-0.17
0.71
0.812
-0.64
0.25
0.003
-1.00
-0.13
0.009
-0.95
-0.08
0.000
-1.15
-0.27
0.599
-0.68
0.19
0.013
0.07
0.96
0.936
-0.29
0.58
0.814
-0.25
0.63
0.000
0.27
1.15
0.034
0.02
0.91
0.999
-0.40
0.50
0.301
-0.76
0.12
0.486
-0.71
0.17
0.599
-0.19
0.68
0.034
-0.91
-0.02
AIMS II • 12/2005
183
184
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.105
0.167
0.074
0.167
0.371
0.166
0.295
0.168
0.257
0.168
-0.105
0.167
-0.031
0.163
0.266
0.162
0.190
0.165
0.152
0.165
-0.074
0.167
0.031
0.163
0.298
0.162
0.221
0.165
0.184
0.165
-0.371
0.166
-0.266
0.162
-0.298
0.162
-0.076
0.164
-0.114
0.164
-0.295
0.168
-0.190
0.165
-0.221
0.165
0.076
0.164
-0.038
0.166
-0.257
0.168
-0.152
0.165
-0.184
0.165
0.114
0.164
0.038
0.166
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.989
-0.37
0.58
0.998
-0.40
0.55
0.220
-0.10
0.84
0.492
-0.18
0.77
0.642
-0.22
0.74
0.989
-0.58
0.37
1.000
-0.50
0.43
0.573
-0.20
0.73
0.858
-0.28
0.66
0.940
-0.32
0.62
0.998
-0.55
0.40
1.000
-0.43
0.50
0.446
-0.17
0.76
0.760
-0.25
0.69
0.875
-0.29
0.65
0.220
-0.84
0.10
0.573
-0.73
0.20
0.446
-0.76
0.17
0.997
-0.54
0.39
0.982
-0.58
0.35
0.492
-0.77
0.18
0.858
-0.66
0.28
0.760
-0.69
0.25
0.997
-0.39
0.54
1.000
-0.51
0.44
0.642
-0.74
0.22
0.940
-0.62
0.32
0.875
-0.65
0.29
0.982
-0.35
0.58
1.000
-0.44
0.51
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.028
0.147
0.102
0.147
0.275
0.146
0.039
0.148
0.131
0.148
-0.028
0.147
0.073
0.144
0.246
0.143
0.011
0.145
0.103
0.145
-0.102
0.147
-0.073
0.144
0.173
0.143
-0.063
0.145
0.029
0.145
-0.275
0.146
-0.246
0.143
-0.173
0.143
-0.235
0.144
-0.144
0.144
-0.039
0.148
-0.011
0.145
0.063
0.145
0.235
0.144
0.092
0.146
-0.131
0.148
-0.103
0.145
-0.029
0.145
0.144
0.144
-0.092
0.146
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.39
0.45
0.983
-0.32
0.52
0.412
-0.14
0.69
1.000
-0.38
0.46
0.949
-0.29
0.55
1.000
-0.45
0.39
0.996
-0.34
0.48
0.517
-0.16
0.65
1.000
-0.40
0.42
0.981
-0.31
0.52
0.983
-0.52
0.32
0.996
-0.48
0.34
0.832
-0.24
0.58
0.998
-0.48
0.35
1.000
-0.38
0.44
0.412
-0.69
0.14
0.517
-0.65
0.16
0.832
-0.58
0.24
0.576
-0.65
0.18
0.919
-0.55
0.27
1.000
-0.46
0.38
1.000
-0.42
0.40
0.998
-0.35
0.48
0.576
-0.18
0.65
0.989
-0.32
0.51
0.949
-0.55
0.29
0.981
-0.52
0.31
1.000
-0.44
0.38
0.919
-0.27
0.55
0.989
-0.51
0.32
AIMS II • 12/2005
185
186
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.139
0.183
-0.041
0.184
0.723
0.182
0.699
0.184
0.118
0.187
0.139
0.183
0.098
0.181
0.863
0.178
0.839
0.181
0.257
0.183
0.041
0.184
-0.098
0.181
0.765
0.180
0.741
0.182
0.159
0.185
-0.723
0.182
-0.863
0.178
-0.765
0.180
-0.024
0.180
-0.605
0.182
-0.699
0.184
-0.839
0.181
-0.741
0.182
0.024
0.180
-0.581
0.185
-0.118
0.187
-0.257
0.183
-0.159
0.185
0.605
0.182
0.581
0.185
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.973
-0.66
0.38
1.000
-0.57
0.48
0.001
0.20
1.24
0.002
0.17
1.22
0.989
-0.41
0.65
0.973
-0.38
0.66
0.994
-0.42
0.61
0.000
0.35
1.37
0.000
0.32
1.35
0.723
-0.27
0.78
1.000
-0.48
0.57
0.994
-0.61
0.42
0.000
0.25
1.28
0.001
0.22
1.26
0.955
-0.37
0.69
0.001
-1.24
-0.20
0.000
-1.37
-0.35
0.000
-1.28
-0.25
1.000
-0.54
0.49
0.012
-1.13
-0.09
0.002
-1.22
-0.17
0.000
-1.35
-0.32
0.001
-1.26
-0.22
1.000
-0.49
0.54
0.021
-1.11
-0.05
0.989
-0.65
0.41
0.723
-0.78
0.27
0.955
-0.69
0.37
0.012
0.09
1.13
0.021
0.05
1.11
Multiple Comparisons for Prairie Flowers Cuved
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.172
0.187
0.480
0.188
0.621
0.186
-0.444
0.188
0.363
0.191
0.172
0.187
0.652
0.185
0.793
0.182
-0.272
0.185
0.535
0.188
-0.480
0.188
-0.652
0.185
0.141
0.184
-0.924
0.186
-0.117
0.189
-0.621
0.186
-0.793
0.182
-0.141
0.184
-1.065
0.184
-0.257
0.187
0.444
0.188
0.272
0.185
0.924
0.186
1.065
0.184
0.808
0.189
-0.363
0.191
-0.535
0.188
0.117
0.189
0.257
0.187
-0.808
0.189
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.941
-0.71
0.36
0.112
-0.06
1.02
0.011
0.09
1.15
0.172
-0.98
0.09
0.402
-0.18
0.91
0.941
-0.36
0.71
0.006
0.12
1.18
0.000
0.27
1.31
0.682
-0.80
0.26
0.050
0.00
1.07
0.112
-1.02
0.06
0.006
-1.18
-0.12
0.973
-0.38
0.67
0.000
-1.46
-0.39
0.990
-0.66
0.42
0.011
-1.15
-0.09
0.000
-1.31
-0.27
0.973
-0.67
0.38
0.000
-1.59
-0.54
0.739
-0.79
0.28
0.172
-0.09
0.98
0.682
-0.26
0.80
0.000
0.39
1.46
0.000
0.54
1.59
0.000
0.27
1.35
0.402
-0.91
0.18
0.050
-1.07
0.00
0.990
-0.42
0.66
0.739
-0.28
0.79
0.000
-1.35
-0.27
AIMS II • 12/2005
187
188
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.072
0.158
0.280
0.159
0.481
0.157
0.475
0.159
0.132
0.161
-0.072
0.158
0.208
0.156
0.408
0.154
0.403
0.156
0.059
0.158
-0.280
0.159
-0.208
0.156
0.200
0.155
0.195
0.157
-0.149
0.159
-0.481
0.157
-0.408
0.154
-0.200
0.155
-0.006
0.155
-0.349
0.157
-0.475
0.159
-0.403
0.156
-0.195
0.157
0.006
0.155
-0.343
0.159
-0.132
0.161
-0.059
0.158
0.149
0.159
0.349
0.157
0.343
0.159
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.997
-0.38
0.52
0.488
-0.17
0.73
0.027
0.03
0.93
0.034
0.02
0.93
0.964
-0.33
0.59
0.997
-0.52
0.38
0.765
-0.24
0.65
0.085
-0.03
0.85
0.102
-0.04
0.85
0.999
-0.39
0.51
0.488
-0.73
0.17
0.765
-0.65
0.24
0.790
-0.24
0.64
0.817
-0.25
0.64
0.937
-0.60
0.31
0.027
-0.93
-0.03
0.085
-0.85
0.03
0.790
-0.64
0.24
1.000
-0.45
0.44
0.229
-0.80
0.10
0.034
-0.93
-0.02
0.102
-0.85
0.04
0.817
-0.64
0.25
1.000
-0.44
0.45
0.259
-0.80
0.11
0.964
-0.59
0.33
0.999
-0.51
0.39
0.937
-0.31
0.60
0.229
-0.10
0.80
0.259
-0.11
0.80
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.067
0.143
0.274
0.145
0.472
0.143
0.139
0.145
0.305
0.147
-0.067
0.143
0.207
0.142
0.406
0.140
0.072
0.142
0.238
0.144
-0.274
0.145
-0.207
0.142
0.198
0.141
-0.135
0.143
0.031
0.145
-0.472
0.143
-0.406
0.140
-0.198
0.141
-0.333
0.141
-0.167
0.143
-0.139
0.145
-0.072
0.142
0.135
0.143
0.333
0.141
0.166
0.145
-0.305
0.147
-0.238
0.144
-0.031
0.145
0.167
0.143
-0.166
0.145
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.997
-0.34
0.48
0.406
-0.14
0.69
0.012
0.06
0.88
0.930
-0.27
0.55
0.299
-0.11
0.72
0.997
-0.48
0.34
0.688
-0.20
0.61
0.044
0.01
0.81
0.996
-0.33
0.48
0.562
-0.17
0.65
0.406
-0.69
0.14
0.688
-0.61
0.20
0.725
-0.20
0.60
0.935
-0.54
0.27
1.000
-0.38
0.44
0.012
-0.88
-0.06
0.044
-0.81
-0.01
0.725
-0.60
0.20
0.171
-0.74
0.07
0.852
-0.58
0.24
0.930
-0.55
0.27
0.996
-0.48
0.33
0.935
-0.27
0.54
0.171
-0.07
0.74
0.863
-0.25
0.58
0.299
-0.72
0.11
0.562
-0.65
0.17
1.000
-0.44
0.38
0.852
-0.24
0.58
0.863
-0.58
0.25
AIMS II • 12/2005
189
190
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.341
0.182
-0.098
0.182
0.378
0.182
0.185
0.184
0.077
0.184
0.341
0.182
0.243
0.183
0.719
0.183
0.526
0.186
0.418
0.186
0.098
0.182
-0.243
0.183
0.476
0.183
0.283
0.186
0.175
0.186
-0.378
0.182
-0.719
0.183
-0.476
0.183
-0.193
0.186
-0.301
0.186
-0.185
0.184
-0.526
0.186
-0.283
0.186
0.193
0.186
-0.109
0.188
-0.077
0.184
-0.418
0.186
-0.175
0.186
0.301
0.186
0.109
0.188
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.416
-0.86
0.18
0.995
-0.62
0.42
0.298
-0.14
0.90
0.916
-0.34
0.71
0.998
-0.45
0.60
0.416
-0.18
0.86
0.769
-0.28
0.77
0.001
0.20
1.24
0.053
0.00
1.06
0.216
-0.11
0.95
0.995
-0.42
0.62
0.769
-0.77
0.28
0.098
-0.05
1.00
0.648
-0.25
0.81
0.936
-0.36
0.70
0.298
-0.90
0.14
0.001
-1.24
-0.20
0.098
-1.00
0.05
0.905
-0.72
0.34
0.584
-0.83
0.23
0.916
-0.71
0.34
0.053
-1.06
0.00
0.648
-0.81
0.25
0.905
-0.34
0.72
0.993
-0.65
0.43
0.998
-0.60
0.45
0.216
-0.95
0.11
0.936
-0.70
0.36
0.584
-0.23
0.83
0.993
-0.43
0.65
Multiple Comparisons for Evergreen Curved
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.503
0.187
0.633
0.187
0.065
0.187
0.204
0.190
0.524
0.190
-0.503
0.187
0.130
0.189
-0.438
0.189
-0.299
0.191
0.021
0.191
-0.633
0.187
-0.130
0.189
-0.568
0.189
-0.428
0.191
-0.108
0.191
-0.065
0.187
0.438
0.189
0.568
0.189
0.139
0.191
0.459
0.191
-0.204
0.190
0.299
0.191
0.428
0.191
-0.139
0.191
0.320
0.194
-0.524
0.190
-0.021
0.191
0.108
0.191
-0.459
0.191
-0.320
0.194
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.079
-0.03
1.04
0.010
0.10
1.17
0.999
-0.47
0.60
0.891
-0.34
0.75
0.065
-0.02
1.07
0.079
-1.04
0.03
0.983
-0.41
0.67
0.186
-0.98
0.10
0.625
-0.84
0.25
1.000
-0.52
0.57
0.010
-1.17
-0.10
0.983
-0.67
0.41
0.032
-1.11
-0.03
0.221
-0.97
0.12
0.993
-0.65
0.44
0.999
-0.60
0.47
0.186
-0.10
0.98
0.032
0.03
1.11
0.978
-0.41
0.69
0.157
-0.09
1.01
0.891
-0.75
0.34
0.625
-0.25
0.84
0.221
-0.12
0.97
0.978
-0.69
0.41
0.566
-0.23
0.87
0.065
-1.07
0.02
1.000
-0.57
0.52
0.993
-0.44
0.65
0.157
-1.01
0.09
0.566
-0.87
0.23
AIMS II • 12/2005
191
192
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.343
0.160
0.202
0.160
0.267
0.160
0.303
0.162
0.246
0.162
-0.343
0.160
-0.141
0.161
-0.076
0.161
-0.040
0.163
-0.097
0.163
-0.202
0.160
0.141
0.161
0.065
0.161
0.101
0.163
0.044
0.163
-0.267
0.160
0.076
0.161
-0.065
0.161
0.036
0.163
-0.021
0.163
-0.303
0.162
0.040
0.163
-0.101
0.163
-0.036
0.163
-0.057
0.165
-0.246
0.162
0.097
0.163
-0.044
0.163
0.021
0.163
0.057
0.165
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.264
-0.11
0.80
0.803
-0.25
0.66
0.550
-0.19
0.72
0.420
-0.16
0.77
0.652
-0.22
0.71
0.264
-0.80
0.11
0.953
-0.60
0.32
0.997
-0.53
0.38
1.000
-0.51
0.43
0.992
-0.56
0.37
0.803
-0.66
0.25
0.953
-0.32
0.60
0.999
-0.39
0.52
0.990
-0.36
0.57
1.000
-0.42
0.51
0.550
-0.72
0.19
0.997
-0.38
0.53
0.999
-0.52
0.39
1.000
-0.43
0.50
1.000
-0.49
0.44
0.420
-0.77
0.16
1.000
-0.43
0.51
0.990
-0.57
0.36
1.000
-0.50
0.43
0.999
-0.53
0.41
0.652
-0.71
0.22
0.992
-0.37
0.56
1.000
-0.51
0.42
1.000
-0.44
0.49
0.999
-0.41
0.53
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.191
0.151
0.261
0.151
-0.020
0.151
0.472
0.153
0.381
0.153
-0.191
0.151
0.070
0.152
-0.211
0.152
0.281
0.154
0.189
0.154
-0.261
0.151
-0.070
0.152
-0.281
0.152
0.211
0.154
0.119
0.154
0.020
0.151
0.211
0.152
0.281
0.152
0.492
0.154
0.400
0.154
-0.472
0.153
-0.281
0.154
-0.211
0.154
-0.492
0.154
-0.091
0.156
-0.381
0.153
-0.189
0.154
-0.119
0.154
-0.400
0.154
0.091
0.156
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.804
-0.24
0.62
0.512
-0.17
0.69
1.000
-0.45
0.41
0.026
0.03
0.91
0.129
-0.06
0.82
0.804
-0.62
0.24
0.997
-0.36
0.50
0.736
-0.65
0.22
0.453
-0.16
0.72
0.823
-0.25
0.63
0.512
-0.69
0.17
0.997
-0.50
0.36
0.436
-0.72
0.15
0.748
-0.23
0.65
0.972
-0.32
0.56
1.000
-0.41
0.45
0.736
-0.22
0.65
0.436
-0.15
0.72
0.019
0.05
0.93
0.100
-0.04
0.84
0.026
-0.91
-0.03
0.453
-0.72
0.16
0.748
-0.65
0.23
0.019
-0.93
-0.05
0.992
-0.54
0.36
0.129
-0.82
0.06
0.823
-0.63
0.25
0.972
-0.56
0.32
0.100
-0.84
0.04
0.992
-0.36
0.54
AIMS II • 12/2005
193
194
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.039
0.150
0.184
0.148
-0.167
0.148
0.154
0.146
0.195
0.150
0.039
0.150
0.223
0.151
-0.128
0.151
0.193
0.149
0.234
0.153
-0.184
0.148
-0.223
0.151
-0.351
0.149
-0.030
0.147
0.011
0.151
0.167
0.148
0.128
0.151
0.351
0.149
0.321
0.147
0.362
0.151
-0.154
0.146
-0.193
0.149
0.030
0.147
-0.321
0.147
0.041
0.150
-0.195
0.150
-0.234
0.153
-0.011
0.151
-0.362
0.151
-0.041
0.150
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.47
0.39
0.816
-0.24
0.61
0.869
-0.59
0.26
0.900
-0.26
0.57
0.788
-0.23
0.62
1.000
-0.39
0.47
0.678
-0.21
0.65
0.958
-0.56
0.30
0.788
-0.23
0.62
0.646
-0.20
0.67
0.816
-0.61
0.24
0.678
-0.65
0.21
0.174
-0.78
0.07
1.000
-0.45
0.39
1.000
-0.42
0.44
0.869
-0.26
0.59
0.958
-0.30
0.56
0.174
-0.07
0.78
0.248
-0.10
0.74
0.160
-0.07
0.79
0.900
-0.57
0.26
0.788
-0.62
0.23
1.000
-0.39
0.45
0.248
-0.74
0.10
1.000
-0.39
0.47
0.788
-0.62
0.23
0.646
-0.67
0.20
1.000
-0.44
0.42
0.160
-0.79
0.07
1.000
-0.47
0.39
Multiple Comparisons for All Vegetation Treatments
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.244
0.143
0.291
0.141
-0.147
0.141
0.255
0.139
0.073
0.143
-0.244
0.143
0.047
0.144
-0.391
0.144
0.011
0.142
-0.171
0.146
-0.291
0.141
-0.047
0.144
-0.438
0.142
-0.036
0.140
-0.217
0.144
0.147
0.141
0.391
0.144
0.438
0.142
0.402
0.140
0.220
0.144
-0.255
0.139
-0.011
0.142
0.036
0.140
-0.402
0.140
-0.182
0.142
-0.073
0.143
0.171
0.146
0.217
0.144
-0.220
0.144
0.182
0.142
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.523
-0.16
0.65
0.307
-0.11
0.69
0.904
-0.55
0.26
0.444
-0.14
0.65
0.996
-0.33
0.48
0.523
-0.65
0.16
1.000
-0.36
0.46
0.072
-0.80
0.02
1.000
-0.39
0.42
0.850
-0.59
0.25
0.307
-0.69
0.11
1.000
-0.46
0.36
0.026
-0.84
-0.03
1.000
-0.44
0.36
0.659
-0.63
0.19
0.904
-0.26
0.55
0.072
-0.02
0.80
0.026
0.03
0.84
0.048
0.00
0.80
0.645
-0.19
0.63
0.444
-0.65
0.14
1.000
-0.42
0.39
1.000
-0.36
0.44
0.048
-0.80
0.00
0.797
-0.59
0.22
0.996
-0.48
0.33
0.850
-0.25
0.59
0.659
-0.19
0.63
0.645
-0.63
0.19
0.797
-0.22
0.59
AIMS II • 12/2005
195
196
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.016
0.146
0.160
0.145
0.101
0.145
0.160
0.143
-0.029
0.147
0.016
0.146
0.177
0.148
0.117
0.148
0.177
0.146
-0.012
0.150
-0.160
0.145
-0.177
0.148
-0.059
0.146
0.000
0.144
-0.189
0.148
-0.101
0.145
-0.117
0.148
0.059
0.146
0.060
0.144
-0.129
0.148
-0.160
0.143
-0.177
0.146
0.000
0.144
-0.060
0.144
-0.189
0.146
0.029
0.147
0.012
0.150
0.189
0.148
0.129
0.148
0.189
0.146
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.43
0.40
0.879
-0.25
0.57
0.983
-0.31
0.51
0.872
-0.25
0.57
1.000
-0.45
0.39
1.000
-0.40
0.43
0.839
-0.24
0.60
0.969
-0.30
0.54
0.830
-0.24
0.59
1.000
-0.44
0.41
0.879
-0.57
0.25
0.839
-0.60
0.24
0.999
-0.48
0.36
1.000
-0.41
0.41
0.798
-0.61
0.23
0.983
-0.51
0.31
0.969
-0.54
0.30
0.999
-0.36
0.48
0.998
-0.35
0.47
0.953
-0.55
0.29
0.872
-0.57
0.25
0.830
-0.59
0.24
1.000
-0.41
0.41
0.998
-0.47
0.35
0.788
-0.61
0.23
1.000
-0.39
0.45
1.000
-0.41
0.44
0.798
-0.23
0.61
0.953
-0.29
0.55
0.788
-0.23
0.61
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.002
0.141
0.192
0.139
-0.024
0.139
0.236
0.137
0.161
0.141
0.002
0.141
0.194
0.142
-0.022
0.142
0.238
0.140
0.163
0.144
-0.192
0.139
-0.194
0.142
-0.216
0.140
0.043
0.138
-0.031
0.142
0.024
0.139
0.022
0.142
0.216
0.140
0.260
0.138
0.185
0.142
-0.236
0.137
-0.238
0.140
-0.043
0.138
-0.260
0.138
-0.075
0.140
-0.161
0.141
-0.163
0.144
0.031
0.142
-0.185
0.142
0.075
0.140
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.40
0.40
0.737
-0.20
0.59
1.000
-0.42
0.37
0.520
-0.16
0.63
0.864
-0.24
0.56
1.000
-0.40
0.40
0.745
-0.21
0.60
1.000
-0.43
0.38
0.534
-0.16
0.64
0.867
-0.25
0.57
0.737
-0.59
0.20
0.745
-0.60
0.21
0.636
-0.62
0.18
1.000
-0.35
0.44
1.000
-0.44
0.37
1.000
-0.37
0.42
1.000
-0.38
0.43
0.636
-0.18
0.62
0.417
-0.14
0.65
0.784
-0.22
0.59
0.520
-0.63
0.16
0.534
-0.64
0.16
1.000
-0.44
0.35
0.417
-0.65
0.14
0.995
-0.48
0.33
0.864
-0.56
0.24
0.867
-0.57
0.25
1.000
-0.37
0.44
0.784
-0.59
0.22
0.995
-0.33
0.48
AIMS II • 12/2005
197
198
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.031
0.138
-0.332
0.141
-0.122
0.141
-0.378
0.139
0.084
0.138
-0.031
0.138
-0.363
0.140
-0.153
0.140
-0.410
0.138
0.052
0.136
0.332
0.141
0.363
0.140
0.210
0.143
-0.047
0.141
0.415
0.139
0.122
0.141
0.153
0.140
-0.210
0.143
-0.256
0.141
0.206
0.140
0.378
0.139
0.410
0.138
0.047
0.141
0.256
0.141
0.462
0.138
-0.084
0.138
-0.052
0.136
-0.415
0.139
-0.206
0.140
-0.462
0.138
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.36
0.43
0.174
-0.73
0.07
0.955
-0.53
0.28
0.073
-0.78
0.02
0.990
-0.31
0.48
1.000
-0.43
0.36
0.099
-0.76
0.04
0.884
-0.55
0.25
0.036
-0.80
-0.02
0.999
-0.34
0.44
0.174
-0.07
0.73
0.099
-0.04
0.76
0.686
-0.20
0.62
0.999
-0.45
0.36
0.034
0.02
0.81
0.955
-0.28
0.53
0.884
-0.25
0.55
0.686
-0.62
0.20
0.457
-0.66
0.15
0.680
-0.19
0.60
0.073
-0.02
0.78
0.036
0.02
0.80
0.999
-0.36
0.45
0.457
-0.15
0.66
0.010
0.07
0.85
0.990
-0.48
0.31
0.999
-0.44
0.34
0.034
-0.81
-0.02
0.680
-0.60
0.19
0.010
-0.85
-0.07
Multiple Comparisons for Weedy Staight
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.423
0.133
0.224
0.136
-0.085
0.136
0.189
0.134
0.399
0.132
-0.423
0.133
-0.198
0.134
-0.508
0.135
-0.234
0.133
-0.023
0.131
-0.224
0.136
0.198
0.134
-0.309
0.137
-0.035
0.136
0.175
0.134
0.085
0.136
0.508
0.135
0.309
0.137
0.274
0.136
0.485
0.134
-0.189
0.134
0.234
0.133
0.035
0.136
-0.274
0.136
0.210
0.132
-0.399
0.132
0.023
0.131
-0.175
0.134
-0.485
0.134
-0.210
0.132
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.019
0.04
0.80
0.562
-0.16
0.61
0.989
-0.47
0.30
0.720
-0.19
0.57
0.031
0.02
0.78
0.019
-0.80
-0.04
0.680
-0.58
0.19
0.002
-0.89
-0.12
0.495
-0.61
0.15
1.000
-0.40
0.35
0.562
-0.61
0.16
0.680
-0.19
0.58
0.215
-0.70
0.08
1.000
-0.42
0.35
0.780
-0.21
0.56
0.989
-0.30
0.47
0.002
0.12
0.89
0.215
-0.08
0.70
0.333
-0.11
0.66
0.004
0.10
0.87
0.720
-0.57
0.19
0.495
-0.15
0.61
1.000
-0.35
0.42
0.333
-0.66
0.11
0.606
-0.17
0.59
0.031
-0.78
-0.02
1.000
-0.35
0.40
0.780
-0.56
0.21
0.004
-0.87
-0.10
0.606
-0.59
0.17
AIMS II • 12/2005
199
200
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.176
0.140
-0.318
0.142
-0.200
0.143
-0.324
0.141
0.046
0.139
0.176
0.140
-0.142
0.141
-0.024
0.142
-0.148
0.140
0.222
0.138
0.318
0.142
0.142
0.141
0.118
0.144
-0.007
0.142
0.364
0.141
0.200
0.143
0.024
0.142
-0.118
0.144
-0.124
0.143
0.246
0.141
0.324
0.141
0.148
0.140
0.007
0.142
0.124
0.143
0.370
0.139
-0.046
0.139
-0.222
0.138
-0.364
0.141
-0.246
0.141
-0.370
0.139
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.807
-0.57
0.22
0.225
-0.72
0.09
0.727
-0.61
0.21
0.194
-0.73
0.08
0.999
-0.35
0.44
0.807
-0.22
0.57
0.917
-0.55
0.26
1.000
-0.43
0.38
0.896
-0.55
0.25
0.592
-0.17
0.62
0.225
-0.09
0.72
0.917
-0.26
0.55
0.965
-0.29
0.53
1.000
-0.41
0.40
0.102
-0.04
0.77
0.727
-0.21
0.61
1.000
-0.38
0.43
0.965
-0.53
0.29
0.954
-0.53
0.28
0.502
-0.16
0.65
0.194
-0.08
0.73
0.896
-0.25
0.55
1.000
-0.40
0.41
0.954
-0.28
0.53
0.083
-0.03
0.77
0.999
-0.44
0.35
0.592
-0.62
0.17
0.102
-0.77
0.04
0.502
-0.65
0.16
0.083
-0.77
0.03
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.085
0.130
-0.159
0.132
-0.020
0.132
-0.259
0.131
0.259
0.129
0.085
0.130
-0.075
0.131
0.065
0.131
-0.175
0.130
0.343
0.128
0.159
0.132
0.075
0.131
0.139
0.134
-0.100
0.132
0.418
0.130
0.020
0.132
-0.065
0.131
-0.139
0.134
-0.240
0.132
0.278
0.131
0.259
0.131
0.175
0.130
0.100
0.132
0.240
0.132
0.518
0.129
-0.259
0.129
-0.343
0.128
-0.418
0.130
-0.278
0.131
-0.518
0.129
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.987
-0.45
0.29
0.835
-0.54
0.22
1.000
-0.40
0.36
0.350
-0.63
0.11
0.339
-0.11
0.63
0.987
-0.29
0.45
0.993
-0.45
0.30
0.996
-0.31
0.44
0.756
-0.54
0.19
0.079
-0.02
0.71
0.835
-0.22
0.54
0.993
-0.30
0.45
0.904
-0.24
0.52
0.974
-0.48
0.28
0.017
0.05
0.79
1.000
-0.36
0.40
0.996
-0.44
0.31
0.904
-0.52
0.24
0.459
-0.62
0.14
0.274
-0.10
0.65
0.350
-0.11
0.63
0.756
-0.19
0.54
0.974
-0.28
0.48
0.459
-0.14
0.62
0.001
0.15
0.89
0.339
-0.63
0.11
0.079
-0.71
0.02
0.017
-0.79
-0.05
0.274
-0.65
0.10
0.001
-0.89
-0.15
AIMS II • 12/2005
201
202
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.394
0.148
0.219
0.147
-0.095
0.147
0.099
0.145
0.037
0.149
-0.394
0.148
-0.175
0.149
-0.489
0.149
-0.294
0.147
-0.357
0.151
-0.219
0.147
0.175
0.149
-0.314
0.148
-0.119
0.146
-0.181
0.150
0.095
0.147
0.489
0.149
0.314
0.148
0.194
0.146
0.132
0.150
-0.099
0.145
0.294
0.147
0.119
0.146
-0.194
0.146
-0.062
0.148
-0.037
0.149
0.357
0.151
0.181
0.150
-0.132
0.150
0.062
0.148
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.085
-0.03
0.82
0.670
-0.20
0.64
0.987
-0.51
0.32
0.983
-0.31
0.51
1.000
-0.39
0.46
0.085
-0.82
0.03
0.850
-0.60
0.25
0.014
-0.92
-0.06
0.346
-0.72
0.13
0.174
-0.79
0.08
0.670
-0.64
0.20
0.850
-0.25
0.60
0.276
-0.74
0.11
0.964
-0.54
0.30
0.831
-0.61
0.25
0.987
-0.32
0.51
0.014
0.06
0.92
0.276
-0.11
0.74
0.767
-0.22
0.61
0.951
-0.30
0.56
0.983
-0.51
0.31
0.346
-0.13
0.72
0.964
-0.30
0.54
0.767
-0.61
0.22
0.998
-0.48
0.36
1.000
-0.46
0.39
0.174
-0.08
0.79
0.831
-0.25
0.61
0.951
-0.56
0.30
0.998
-0.36
0.48
Multiple Comparisons for Brome Straight
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.059
0.147
0.335
0.146
-0.221
0.146
0.063
0.144
0.096
0.148
-0.059
0.147
0.276
0.149
-0.280
0.149
0.004
0.147
0.037
0.151
-0.335
0.146
-0.276
0.149
-0.557
0.147
-0.272
0.145
-0.239
0.149
0.221
0.146
0.280
0.149
0.557
0.147
0.284
0.145
0.317
0.149
-0.063
0.144
-0.004
0.147
0.272
0.145
-0.284
0.145
0.033
0.147
-0.096
0.148
-0.037
0.151
0.239
0.149
-0.317
0.149
-0.033
0.147
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.999
-0.36
0.48
0.194
-0.08
0.75
0.652
-0.64
0.19
0.998
-0.35
0.47
0.987
-0.33
0.52
0.999
-0.48
0.36
0.428
-0.15
0.70
0.411
-0.70
0.14
1.000
-0.41
0.42
1.000
-0.39
0.47
0.194
-0.75
0.08
0.428
-0.70
0.15
0.002
-0.98
-0.14
0.416
-0.69
0.14
0.594
-0.66
0.19
0.652
-0.19
0.64
0.411
-0.14
0.70
0.002
0.14
0.98
0.366
-0.13
0.70
0.273
-0.11
0.74
0.998
-0.47
0.35
1.000
-0.42
0.41
0.416
-0.14
0.69
0.366
-0.70
0.13
1.000
-0.39
0.45
0.987
-0.52
0.33
1.000
-0.47
0.39
0.594
-0.19
0.66
0.273
-0.74
0.11
1.000
-0.45
0.39
AIMS II • 12/2005
203
204
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.293
0.137
0.273
0.135
0.035
0.135
0.197
0.133
0.101
0.137
-0.293
0.137
-0.020
0.138
-0.258
0.138
-0.096
0.136
-0.192
0.140
-0.273
0.135
0.020
0.138
-0.238
0.136
-0.076
0.135
-0.172
0.138
-0.035
0.135
0.258
0.138
0.238
0.136
0.162
0.135
0.066
0.138
-0.197
0.133
0.096
0.136
0.076
0.135
-0.162
0.135
-0.096
0.137
-0.101
0.137
0.192
0.140
0.172
0.138
-0.066
0.138
0.096
0.137
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.268
-0.10
0.68
0.333
-0.11
0.66
1.000
-0.35
0.42
0.682
-0.18
0.58
0.978
-0.29
0.49
0.268
-0.68
0.10
1.000
-0.41
0.37
0.422
-0.65
0.14
0.981
-0.48
0.29
0.743
-0.59
0.21
0.333
-0.66
0.11
1.000
-0.37
0.41
0.503
-0.63
0.15
0.993
-0.46
0.31
0.814
-0.57
0.22
1.000
-0.42
0.35
0.422
-0.14
0.65
0.503
-0.15
0.63
0.837
-0.22
0.55
0.997
-0.33
0.46
0.682
-0.58
0.18
0.981
-0.29
0.48
0.993
-0.31
0.46
0.837
-0.55
0.22
0.982
-0.49
0.29
0.978
-0.49
0.29
0.743
-0.21
0.59
0.814
-0.22
0.57
0.997
-0.46
0.33
0.982
-0.29
0.49
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.323
0.143
0.230
0.142
-0.007
0.142
0.345
0.140
0.206
0.144
-0.323
0.143
-0.092
0.144
-0.330
0.144
0.022
0.143
-0.117
0.146
-0.230
0.142
0.092
0.144
-0.238
0.143
0.114
0.141
-0.025
0.145
0.007
0.142
0.330
0.144
0.238
0.143
0.352
0.141
0.213
0.145
-0.345
0.140
-0.022
0.143
-0.114
0.141
-0.352
0.141
-0.139
0.143
-0.206
0.144
0.117
0.146
0.025
0.145
-0.213
0.145
0.139
0.143
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.214
-0.09
0.73
0.581
-0.17
0.63
1.000
-0.41
0.40
0.135
-0.05
0.74
0.707
-0.20
0.62
0.214
-0.73
0.09
0.988
-0.50
0.32
0.200
-0.74
0.08
1.000
-0.39
0.43
0.968
-0.53
0.30
0.581
-0.63
0.17
0.988
-0.32
0.50
0.555
-0.65
0.17
0.966
-0.29
0.52
1.000
-0.44
0.39
1.000
-0.40
0.41
0.200
-0.08
0.74
0.555
-0.17
0.65
0.125
-0.05
0.75
0.682
-0.20
0.63
0.135
-0.74
0.05
1.000
-0.43
0.39
0.966
-0.52
0.29
0.125
-0.75
0.05
0.927
-0.55
0.27
0.707
-0.62
0.20
0.968
-0.30
0.53
1.000
-0.39
0.44
0.682
-0.63
0.20
0.927
-0.27
0.55
AIMS II • 12/2005
205
206
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.099
0.152
-0.022
0.149
-0.109
0.152
-0.010
0.148
-0.141
0.150
-0.099
0.152
-0.121
0.151
-0.208
0.154
-0.109
0.150
-0.239
0.152
0.022
0.149
0.121
0.151
-0.088
0.151
0.012
0.147
-0.119
0.149
0.109
0.152
0.208
0.154
0.088
0.151
0.099
0.150
-0.031
0.152
0.010
0.148
0.109
0.150
-0.012
0.147
-0.099
0.150
-0.131
0.148
0.141
0.150
0.239
0.152
0.119
0.149
0.031
0.152
0.131
0.148
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.987
-0.33
0.53
1.000
-0.45
0.40
0.980
-0.54
0.33
1.000
-0.43
0.41
0.937
-0.57
0.29
0.987
-0.53
0.33
0.968
-0.55
0.31
0.756
-0.65
0.23
0.979
-0.54
0.32
0.615
-0.67
0.19
1.000
-0.40
0.45
0.968
-0.31
0.55
0.992
-0.52
0.34
1.000
-0.41
0.43
0.968
-0.54
0.31
0.980
-0.33
0.54
0.756
-0.23
0.65
0.992
-0.34
0.52
0.986
-0.33
0.53
1.000
-0.47
0.40
1.000
-0.41
0.43
0.979
-0.32
0.54
1.000
-0.43
0.41
0.986
-0.53
0.33
0.951
-0.55
0.29
0.937
-0.29
0.57
0.615
-0.19
0.67
0.968
-0.31
0.54
1.000
-0.40
0.47
0.951
-0.29
0.55
Multiple Comparisons for Prairie Flowers Straight
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.034
0.144
0.205
0.141
-0.112
0.145
0.198
0.141
0.054
0.143
0.034
0.144
0.239
0.143
-0.079
0.146
0.232
0.142
0.088
0.144
-0.205
0.141
-0.239
0.143
-0.318
0.143
-0.007
0.140
-0.151
0.141
0.112
0.145
0.079
0.146
0.318
0.143
0.311
0.143
0.166
0.145
-0.198
0.141
-0.232
0.142
0.007
0.140
-0.311
0.143
-0.144
0.141
-0.054
0.143
-0.088
0.144
0.151
0.141
-0.166
0.145
0.144
0.141
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.45
0.38
0.696
-0.20
0.61
0.971
-0.53
0.30
0.722
-0.20
0.60
0.999
-0.35
0.46
1.000
-0.38
0.45
0.553
-0.17
0.65
0.995
-0.50
0.34
0.579
-0.17
0.64
0.991
-0.32
0.50
0.696
-0.61
0.20
0.553
-0.65
0.17
0.232
-0.73
0.09
1.000
-0.41
0.39
0.894
-0.56
0.25
0.971
-0.30
0.53
0.995
-0.34
0.50
0.232
-0.09
0.73
0.250
-0.10
0.72
0.860
-0.25
0.58
0.722
-0.60
0.20
0.579
-0.64
0.17
1.000
-0.39
0.41
0.250
-0.72
0.10
0.910
-0.55
0.26
0.999
-0.46
0.35
0.991
-0.50
0.32
0.894
-0.25
0.56
0.860
-0.58
0.25
0.910
-0.26
0.55
AIMS II • 12/2005
207
208
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.138
0.148
-0.110
0.145
-0.227
0.149
-0.140
0.145
-0.178
0.146
0.138
0.148
0.028
0.147
-0.089
0.150
-0.003
0.146
-0.041
0.148
0.110
0.145
-0.028
0.147
-0.117
0.147
-0.030
0.143
-0.068
0.145
0.227
0.149
0.089
0.150
0.117
0.147
0.087
0.147
0.048
0.149
0.140
0.145
0.003
0.146
0.030
0.143
-0.087
0.147
-0.038
0.145
0.178
0.146
0.041
0.148
0.068
0.145
-0.048
0.149
0.038
0.145
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.939
-0.56
0.29
0.975
-0.52
0.30
0.647
-0.65
0.20
0.928
-0.55
0.27
0.828
-0.60
0.24
0.939
-0.29
0.56
1.000
-0.39
0.45
0.991
-0.52
0.34
1.000
-0.42
0.41
1.000
-0.46
0.38
0.975
-0.30
0.52
1.000
-0.45
0.39
0.969
-0.54
0.30
1.000
-0.44
0.38
0.997
-0.48
0.35
0.647
-0.20
0.65
0.991
-0.34
0.52
0.969
-0.30
0.54
0.992
-0.33
0.51
1.000
-0.38
0.47
0.928
-0.27
0.55
1.000
-0.41
0.42
1.000
-0.38
0.44
0.992
-0.51
0.33
1.000
-0.45
0.37
0.828
-0.24
0.60
1.000
-0.38
0.46
0.997
-0.35
0.48
1.000
-0.47
0.38
1.000
-0.37
0.45
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.046
0.143
-0.277
0.141
-0.060
0.144
0.064
0.140
-0.195
0.142
-0.046
0.143
-0.323
0.142
-0.106
0.145
0.018
0.141
-0.241
0.143
0.277
0.141
0.323
0.142
0.217
0.143
0.341
0.139
0.082
0.141
0.060
0.144
0.106
0.145
-0.217
0.143
0.124
0.142
-0.135
0.144
-0.064
0.140
-0.018
0.141
-0.341
0.139
-0.124
0.142
-0.259
0.140
0.195
0.142
0.241
0.143
-0.082
0.141
0.135
0.144
0.259
0.140
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.36
0.46
0.360
-0.68
0.12
0.998
-0.47
0.35
0.997
-0.33
0.46
0.743
-0.60
0.21
1.000
-0.46
0.36
0.206
-0.73
0.08
0.978
-0.52
0.31
1.000
-0.39
0.42
0.544
-0.65
0.17
0.360
-0.12
0.68
0.206
-0.08
0.73
0.650
-0.19
0.62
0.137
-0.05
0.74
0.992
-0.32
0.48
0.998
-0.35
0.47
0.978
-0.31
0.52
0.650
-0.62
0.19
0.952
-0.28
0.53
0.937
-0.54
0.28
0.997
-0.46
0.33
1.000
-0.42
0.39
0.137
-0.74
0.05
0.952
-0.53
0.28
0.433
-0.66
0.14
0.743
-0.21
0.60
0.544
-0.17
0.65
0.992
-0.48
0.32
0.937
-0.28
0.54
0.433
-0.14
0.66
AIMS II • 12/2005
209
210
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.342
0.055
-0.092
0.054
0.225
0.054
-0.098
0.055
0.081
0.054
0.342
0.055
0.250
0.055
0.566
0.055
0.244
0.056
0.422
0.055
0.092
0.054
-0.250
0.055
0.317
0.054
-0.006
0.055
0.173
0.054
-0.225
0.054
-0.566
0.055
-0.317
0.054
-0.323
0.055
-0.144
0.054
0.098
0.055
-0.244
0.056
0.006
0.055
0.323
0.055
0.179
0.055
-0.081
0.054
-0.422
0.055
-0.173
0.054
0.144
0.054
-0.179
0.055
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.000
-0.50
-0.18
0.525
-0.25
0.06
0.000
0.07
0.38
0.477
-0.25
0.06
0.669
-0.07
0.23
0.000
0.18
0.50
0.000
0.09
0.41
0.000
0.41
0.72
0.000
0.08
0.40
0.000
0.26
0.58
0.525
-0.06
0.25
0.000
-0.41
-0.09
0.000
0.16
0.47
1.000
-0.16
0.15
0.018
0.02
0.33
0.000
-0.38
-0.07
0.000
-0.72
-0.41
0.000
-0.47
-0.16
0.000
-0.48
-0.17
0.083
-0.30
0.01
0.477
-0.06
0.25
0.000
-0.40
-0.08
1.000
-0.15
0.16
0.000
0.17
0.48
0.015
0.02
0.34
0.669
-0.23
0.07
0.000
-0.58
-0.26
0.018
-0.33
-0.02
0.083
-0.01
0.30
0.015
-0.34
-0.02
Multiple Comparisons for Evergreen Straight
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.100
0.056
0.243
0.054
0.082
0.055
0.008
0.056
0.235
0.055
-0.100
0.056
0.143
0.056
-0.018
0.056
-0.092
0.057
0.135
0.056
-0.243
0.054
-0.143
0.056
-0.161
0.055
-0.235
0.056
-0.008
0.055
-0.082
0.055
0.018
0.056
0.161
0.055
-0.075
0.056
0.153
0.055
-0.008
0.056
0.092
0.057
0.235
0.056
0.075
0.056
0.228
0.056
-0.235
0.055
-0.135
0.056
0.008
0.055
-0.153
0.055
-0.228
0.056
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.472
-0.06
0.26
0.000
0.09
0.40
0.659
-0.07
0.24
1.000
-0.15
0.17
0.000
0.08
0.39
0.472
-0.26
0.06
0.106
-0.02
0.30
1.000
-0.18
0.14
0.585
-0.25
0.07
0.150
-0.02
0.29
0.000
-0.40
-0.09
0.106
-0.30
0.02
0.038
-0.32
-0.01
0.000
-0.39
-0.08
1.000
-0.16
0.15
0.659
-0.24
0.07
1.000
-0.14
0.18
0.038
0.01
0.32
0.763
-0.23
0.08
0.058
0.00
0.31
1.000
-0.17
0.15
0.585
-0.07
0.25
0.000
0.08
0.39
0.763
-0.08
0.23
0.001
0.07
0.39
0.000
-0.39
-0.08
0.150
-0.29
0.02
1.000
-0.15
0.16
0.058
-0.31
0.00
0.001
-0.39
-0.07
AIMS II • 12/2005
211
212
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.069
0.047
0.097
0.046
0.148
0.046
0.083
0.047
0.158
0.046
-0.069
0.047
0.027
0.047
0.079
0.047
0.014
0.048
0.088
0.047
-0.097
0.046
-0.027
0.047
0.052
0.046
-0.013
0.047
0.061
0.046
-0.148
0.046
-0.079
0.047
-0.052
0.046
-0.065
0.047
0.009
0.046
-0.083
0.047
-0.014
0.048
0.013
0.047
0.065
0.047
0.074
0.047
-0.158
0.046
-0.088
0.047
-0.061
0.046
-0.009
0.046
-0.074
0.047
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.686
-0.07
0.20
0.290
-0.03
0.23
0.016
0.02
0.28
0.487
-0.05
0.22
0.009
0.03
0.29
0.686
-0.20
0.07
0.992
-0.11
0.16
0.547
-0.06
0.21
1.000
-0.12
0.15
0.422
-0.05
0.22
0.290
-0.23
0.03
0.992
-0.16
0.11
0.872
-0.08
0.18
1.000
-0.15
0.12
0.775
-0.07
0.19
0.016
-0.28
-0.02
0.547
-0.21
0.06
0.872
-0.18
0.08
0.737
-0.20
0.07
1.000
-0.12
0.14
0.487
-0.22
0.05
1.000
-0.15
0.12
1.000
-0.12
0.15
0.737
-0.07
0.20
0.615
-0.06
0.21
0.009
-0.29
-0.03
0.422
-0.22
0.05
0.775
-0.19
0.07
1.000
-0.14
0.12
0.615
-0.21
0.06
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.509
0.046
0.074
0.045
0.118
0.045
0.160
0.046
0.214
0.045
-0.509
0.046
-0.436
0.046
-0.392
0.046
-0.350
0.047
-0.296
0.046
-0.074
0.045
0.436
0.046
0.044
0.045
0.086
0.046
0.140
0.045
-0.118
0.045
0.392
0.046
-0.044
0.045
0.042
0.046
0.096
0.045
-0.160
0.046
0.350
0.047
-0.086
0.046
-0.042
0.046
0.054
0.046
-0.214
0.045
0.296
0.046
-0.140
0.045
-0.096
0.045
-0.054
0.046
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.000
0.38
0.64
0.574
-0.05
0.20
0.094
-0.01
0.25
0.007
0.03
0.29
0.000
0.09
0.34
0.000
-0.64
-0.38
0.000
-0.57
-0.30
0.000
-0.52
-0.26
0.000
-0.48
-0.22
0.000
-0.43
-0.16
0.574
-0.20
0.05
0.000
0.30
0.57
0.925
-0.08
0.17
0.413
-0.04
0.22
0.023
0.01
0.27
0.094
-0.25
0.01
0.000
0.26
0.52
0.925
-0.17
0.08
0.941
-0.09
0.17
0.270
-0.03
0.22
0.007
-0.29
-0.03
0.000
0.22
0.48
0.413
-0.22
0.04
0.941
-0.17
0.09
0.850
-0.08
0.19
0.000
-0.34
-0.09
0.000
0.16
0.43
0.023
-0.27
-0.01
0.270
-0.22
0.03
0.850
-0.19
0.08
AIMS II • 12/2005
213
214
AIMS II • 12/2005
DV
att
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-1.208
0.129
-0.166
0.115
0.589
0.115
0.168
0.128
0.024
0.115
1.208
0.129
1.042
0.129
1.798
0.129
1.376
0.141
1.232
0.129
0.166
0.115
-1.042
0.129
0.756
0.115
0.334
0.128
0.190
0.115
-0.589
0.115
-1.798
0.129
-0.756
0.115
-0.422
0.128
-0.566
0.114
-0.168
0.128
-1.376
0.141
-0.334
0.128
0.422
0.128
-0.144
0.128
-0.024
0.115
-1.232
0.129
-0.190
0.115
0.566
0.114
0.144
0.128
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.000
-1.58
-0.84
0.696
-0.49
0.16
0.000
0.26
0.92
0.780
-0.20
0.53
1.000
-0.30
0.35
0.000
0.84
1.58
0.000
0.67
1.41
0.000
1.43
2.16
0.000
0.97
1.78
0.000
0.86
1.60
0.696
-0.16
0.49
0.000
-1.41
-0.67
0.000
0.43
1.08
0.096
-0.03
0.70
0.560
-0.14
0.52
0.000
-0.92
-0.26
0.000
-2.16
-1.43
0.000
-1.08
-0.43
0.013
-0.79
-0.06
0.000
-0.89
-0.24
0.780
-0.53
0.20
0.000
-1.78
-0.97
0.096
-0.70
0.03
0.013
0.06
0.79
0.871
-0.51
0.22
1.000
-0.35
0.30
0.000
-1.60
-0.86
0.560
-0.52
0.14
0.000
0.24
0.89
0.871
-0.22
0.51
Multiple Comparisons for Woody Islands
DV
nat
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
0.453
0.138
0.260
0.123
0.221
0.123
0.488
0.137
0.275
0.123
-0.453
0.138
-0.192
0.138
-0.232
0.138
0.035
0.151
-0.177
0.138
-0.260
0.123
0.192
0.138
-0.039
0.123
0.227
0.137
0.015
0.123
-0.221
0.123
0.232
0.138
0.039
0.123
0.267
0.137
0.054
0.122
-0.488
0.137
-0.035
0.151
-0.227
0.137
-0.267
0.137
-0.213
0.137
-0.275
0.123
0.177
0.138
-0.015
0.123
-0.054
0.122
0.213
0.137
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.013
0.06
0.85
0.276
-0.09
0.61
0.464
-0.13
0.57
0.005
0.10
0.88
0.218
-0.07
0.62
0.013
-0.85
-0.06
0.731
-0.59
0.20
0.545
-0.62
0.16
1.000
-0.39
0.47
0.792
-0.57
0.22
0.276
-0.61
0.09
0.731
-0.20
0.59
1.000
-0.39
0.31
0.559
-0.16
0.62
1.000
-0.33
0.36
0.464
-0.57
0.13
0.545
-0.16
0.62
1.000
-0.31
0.39
0.373
-0.12
0.66
0.998
-0.30
0.40
0.005
-0.88
-0.10
1.000
-0.47
0.39
0.559
-0.62
0.16
0.373
-0.66
0.12
0.630
-0.60
0.18
0.218
-0.62
0.07
0.792
-0.22
0.57
1.000
-0.36
0.33
0.998
-0.40
0.30
0.630
-0.18
0.60
AIMS II • 12/2005
215
216
AIMS II • 12/2005
DV
maint
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
-0.001
0.086
0.250
0.077
0.372
0.077
0.163
0.086
0.390
0.077
0.001
0.086
0.251
0.086
0.373
0.086
0.164
0.094
0.391
0.086
-0.250
0.077
-0.251
0.086
0.122
0.077
-0.087
0.086
0.140
0.077
-0.372
0.077
-0.373
0.086
-0.122
0.077
-0.209
0.086
0.018
0.077
-0.163
0.086
-0.164
0.094
0.087
0.086
0.209
0.086
0.227
0.086
-0.390
0.077
-0.391
0.086
-0.140
0.077
-0.018
0.077
-0.227
0.086
95% Confidence Interval
Sig.
Lower Bound Upper Bound
1.000
-0.25
0.24
0.015
0.03
0.47
0.000
0.15
0.59
0.400
-0.08
0.41
0.000
0.17
0.61
1.000
-0.24
0.25
0.042
0.01
0.50
0.000
0.13
0.62
0.504
-0.10
0.43
0.000
0.15
0.64
0.015
-0.47
-0.03
0.042
-0.50
-0.01
0.600
-0.10
0.34
0.914
-0.33
0.16
0.444
-0.08
0.36
0.000
-0.59
-0.15
0.000
-0.62
-0.13
0.600
-0.34
0.10
0.142
-0.45
0.03
1.000
-0.20
0.24
0.400
-0.41
0.08
0.504
-0.43
0.10
0.914
-0.16
0.33
0.142
-0.03
0.45
0.085
-0.02
0.47
0.000
-0.61
-0.17
0.000
-0.64
-0.15
0.444
-0.36
0.08
1.000
-0.24
0.20
0.085
-0.47
0.02
DV
safe
w6
w5
w4
w3
w2
(I) wall
w1
(J) wall
w2
w3
w4
w5
w6
w1
w3
w4
w5
w6
w1
w2
w4
w5
w6
w1
w2
w3
w5
w6
w1
w2
w3
w4
w6
w1
w2
w3
w4
w5
Difference
(I-J)
Std. Error
2.548
0.108
0.087
0.096
0.333
0.096
0.427
0.107
0.358
0.096
-2.548
0.108
-2.462
0.108
-2.215
0.108
-2.122
0.118
-2.190
0.108
-0.087
0.096
2.462
0.108
0.246
0.096
0.340
0.107
0.271
0.096
-0.333
0.096
2.215
0.108
-0.246
0.096
0.094
0.107
0.025
0.096
-0.427
0.107
2.122
0.118
-0.340
0.107
-0.094
0.107
-0.068
0.107
-0.358
0.096
2.190
0.108
-0.271
0.096
-0.025
0.096
0.068
0.107
95% Confidence Interval
Sig.
Lower Bound Upper Bound
0.000
2.24
2.86
0.946
-0.19
0.36
0.007
0.06
0.61
0.001
0.12
0.73
0.003
0.08
0.63
0.000
-2.86
-2.24
0.000
-2.77
-2.15
0.000
-2.52
-1.91
0.000
-2.46
-1.78
0.000
-2.50
-1.88
0.946
-0.36
0.19
0.000
2.15
2.77
0.106
-0.03
0.52
0.019
0.03
0.65
0.053
0.00
0.55
0.007
-0.61
-0.06
0.000
1.91
2.52
0.106
-0.52
0.03
0.953
-0.21
0.40
1.000
-0.25
0.30
0.001
-0.73
-0.12
0.000
1.78
2.46
0.019
-0.65
-0.03
0.953
-0.40
0.21
0.988
-0.37
0.24
0.003
-0.63
-0.08
0.000
1.88
2.50
0.053
-0.55
0.00
1.000
-0.30
0.25
0.988
-0.24
0.37
AIMS II • 12/2005
217
218
AIMS II • 12/2005
AIMS II • 12/2005
Statistical Results Comparing
Mowing and Vegetation Combinations
Ap p endix 9
219
220
AIMS II • 12/2005
context
urban
DV
att
Tukey HSD
straight_brome
straight_weedy
mow_veg (I)
mowall_nonveg
mow_veg (J)
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-0.909
0.058
-0.343
0.058
-1.849
0.058
-1.385
0.068
-1.393
0.058
-1.852
0.068
-1.927
0.058
-1.129
0.058
-0.958
0.058
-0.222
0.058
-1.856
0.058
-1.632
0.068
-1.247
0.058
-1.850
0.090
0.909
0.058
0.566
0.070
-0.940
0.070
-0.476
0.078
-0.484
0.070
-0.943
0.078
-1.018
0.070
-0.220
0.070
-0.049
0.070
0.687
0.070
-0.947
0.070
-0.723
0.078
-0.338
0.070
-0.941
0.098
0.343
0.058
-0.566
0.070
-1.505
0.070
-1.042
0.078
-1.050
0.070
-1.509
0.078
-1.584
0.070
-0.786
0.070
-0.615
0.070
0.121
0.070
-1.513
0.070
-1.289
0.078
-0.903
0.070
-1.506
0.098
Multiple Comparisons of Mowing and Vegetation Combinations
95% Confidence Interval
Sig. Lower Bound Upper Bound
0.000
-1.11
-0.71
0.000
-0.54
-0.15
0.000
-2.04
-1.65
0.000
-1.62
-1.15
0.000
-1.59
-1.20
0.000
-2.08
-1.62
0.000
-2.12
-1.73
0.000
-1.33
-0.93
0.000
-1.15
-0.76
0.011
-0.42
-0.02
0.000
-2.05
-1.66
0.000
-1.86
-1.40
0.000
-1.44
-1.05
0.000
-2.15
-1.54
0.000
0.71
1.11
0.000
0.33
0.80
0.000
-1.18
-0.70
0.000
-0.74
-0.21
0.000
-0.72
-0.25
0.000
-1.21
-0.68
0.000
-1.25
-0.78
0.101
-0.46
0.02
1.000
-0.28
0.19
0.000
0.45
0.92
0.000
-1.18
-0.71
0.000
-0.99
-0.46
0.000
-0.57
-0.10
0.000
-1.27
-0.61
0.000
0.15
0.54
0.000
-0.80
-0.33
0.000
-1.74
-1.27
0.000
-1.31
-0.78
0.000
-1.29
-0.81
0.000
-1.77
-1.24
0.000
-1.82
-1.35
0.000
-1.02
-0.55
0.000
-0.85
-0.38
0.924
-0.12
0.36
0.000
-1.75
-1.28
0.000
-1.55
-1.02
0.000
-1.14
-0.67
0.000
-1.84
-1.17
AIMS II • 12/2005
221
222
AIMS II • 12/2005
context
urban
DV
att
straight_everdeciduous
straight_evergreen
straight_sumac
mow_veg (I)
straight_flowers
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
1.849
0.058
0.940
0.070
1.505
0.070
0.464
0.078
0.456
0.070
-0.004
0.078
-0.079
0.070
0.719
0.070
0.891
0.070
1.627
0.070
-0.007
0.070
0.216
0.078
0.602
0.070
-0.001
0.098
1.385
0.068
0.476
0.078
1.042
0.078
-0.464
0.078
-0.008
0.078
-0.467
0.086
-0.542
0.078
0.256
0.078
0.427
0.078
1.163
0.078
-0.471
0.078
-0.247
0.086
0.138
0.078
-0.465
0.104
1.393
0.058
0.484
0.070
1.050
0.070
-0.456
0.070
0.008
0.078
-0.459
0.078
-0.534
0.070
0.264
0.070
0.435
0.070
1.171
0.070
-0.463
0.070
-0.240
0.078
0.146
0.070
-0.457
0.098
1.852
0.068
0.943
0.078
1.509
0.078
0.004
0.078
0.467
0.086
0.459
0.078
-0.075
0.078
0.723
0.078
0.894
0.078
1.630
0.078
-0.004
0.078
0.220
0.086
0.606
0.078
0.003
0.104
Sig. Lower Bound Upper Bound
0.000
1.65
2.04
0.000
0.70
1.18
0.000
1.27
1.74
0.000
0.20
0.73
0.000
0.22
0.69
1.000
-0.27
0.26
0.999
-0.32
0.16
0.000
0.48
0.96
0.000
0.65
1.13
0.000
1.39
1.86
1.000
-0.24
0.23
0.265
-0.05
0.48
0.000
0.37
0.84
1.000
-0.33
0.33
0.000
1.15
1.62
0.000
0.21
0.74
0.000
0.78
1.31
0.000
-0.73
-0.20
1.000
-0.27
0.26
0.000
-0.76
-0.18
0.000
-0.81
-0.28
0.074
-0.01
0.52
0.000
0.16
0.69
0.000
0.90
1.43
0.000
-0.74
-0.21
0.204
-0.54
0.04
0.911
-0.13
0.40
0.001
-0.82
-0.11
0.000
1.20
1.59
0.000
0.25
0.72
0.000
0.81
1.29
0.000
-0.69
-0.22
1.000
-0.26
0.27
0.000
-0.72
-0.19
0.000
-0.77
-0.30
0.013
0.03
0.50
0.000
0.20
0.67
0.000
0.93
1.41
0.000
-0.70
-0.23
0.130
-0.50
0.03
0.739
-0.09
0.38
0.000
-0.79
-0.12
0.000
1.62
2.08
0.000
0.68
1.21
0.000
1.24
1.77
1.000
-0.26
0.27
0.000
0.18
0.76
0.000
0.19
0.72
1.000
-0.34
0.19
0.000
0.46
0.99
0.000
0.63
1.16
0.000
1.36
1.90
1.000
-0.27
0.26
0.396
-0.07
0.51
0.000
0.34
0.87
1.000
-0.35
0.36
95% Confidence Interval
context
urban
DV
att
curved_brome
curved_weedy
straight_islands
mow_veg (I)
straight_biodiv
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
1.927
0.058
1.018
0.070
1.584
0.070
0.079
0.070
0.542
0.078
0.534
0.070
0.075
0.078
0.798
0.070
0.969
0.070
1.705
0.070
0.071
0.070
0.295
0.078
0.681
0.070
0.078
0.098
1.129
0.058
0.220
0.070
0.786
0.070
-0.719
0.070
-0.256
0.078
-0.264
0.070
-0.723
0.078
-0.798
0.070
0.171
0.070
0.907
0.070
-0.727
0.070
-0.503
0.078
-0.117
0.070
-0.720
0.098
0.958
0.058
0.049
0.070
0.615
0.070
-0.891
0.070
-0.427
0.078
-0.435
0.070
-0.894
0.078
-0.969
0.070
-0.171
0.070
0.736
0.070
-0.898
0.070
-0.674
0.078
-0.289
0.070
-0.892
0.098
0.222
0.058
-0.687
0.070
-0.121
0.070
-1.627
0.070
-1.163
0.078
-1.171
0.070
-1.630
0.078
-1.705
0.070
-0.907
0.070
-0.736
0.070
-1.634
0.070
-1.410
0.078
-1.025
0.070
-1.628
0.098
Sig. Lower Bound Upper Bound
0.000
1.73
2.12
0.000
0.78
1.25
0.000
1.35
1.82
0.999
-0.16
0.32
0.000
0.28
0.81
0.000
0.30
0.77
1.000
-0.19
0.34
0.000
0.56
1.03
0.000
0.73
1.21
0.000
1.47
1.94
1.000
-0.17
0.31
0.014
0.03
0.56
0.000
0.44
0.92
1.000
-0.26
0.41
0.000
0.93
1.33
0.101
-0.02
0.46
0.000
0.55
1.02
0.000
-0.96
-0.48
0.074
-0.52
0.01
0.013
-0.50
-0.03
0.000
-0.99
-0.46
0.000
-1.03
-0.56
0.468
-0.06
0.41
0.000
0.67
1.14
0.000
-0.96
-0.49
0.000
-0.77
-0.24
0.939
-0.35
0.12
0.000
-1.05
-0.39
0.000
0.76
1.15
1.000
-0.19
0.28
0.000
0.38
0.85
0.000
-1.13
-0.65
0.000
-0.69
-0.16
0.000
-0.67
-0.20
0.000
-1.16
-0.63
0.000
-1.21
-0.73
0.468
-0.41
0.06
0.000
0.50
0.97
0.000
-1.13
-0.66
0.000
-0.94
-0.41
0.003
-0.52
-0.05
0.000
-1.22
-0.56
0.011
0.02
0.42
0.000
-0.92
-0.45
0.924
-0.36
0.12
0.000
-1.86
-1.39
0.000
-1.43
-0.90
0.000
-1.41
-0.93
0.000
-1.90
-1.36
0.000
-1.94
-1.47
0.000
-1.14
-0.67
0.000
-0.97
-0.50
0.000
-1.87
-1.40
0.000
-1.68
-1.14
0.000
-1.26
-0.79
0.000
-1.96
-1.29
95% Confidence Interval
AIMS II • 12/2005
223
224
AIMS II • 12/2005
context
urban
DV
att
curved_everdeciduous
curved_evergreen
curved_sumac
mow_veg (I)
curved_flowers
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
-0.078
0.720
0.892
1.628
-0.006
0.217
0.603
-0.003
0.098
0.098
0.098
0.098
0.098
0.104
0.098
0.104
Mean Difference
(I-J)
Std. Error
1.856
0.058
0.947
0.070
1.513
0.070
0.007
0.070
0.471
0.078
0.463
0.070
0.004
0.078
-0.071
0.070
0.727
0.070
0.898
0.070
1.634
0.070
0.223
0.078
0.609
0.070
0.006
0.098
1.632
0.068
0.723
0.078
1.289
0.078
-0.216
0.078
0.247
0.086
0.240
0.078
-0.220
0.086
-0.295
0.078
0.503
0.078
0.674
0.078
1.410
0.078
-0.223
0.078
0.386
0.078
-0.217
0.104
1.247
0.058
0.338
0.070
0.903
0.070
-0.602
0.070
-0.138
0.078
-0.146
0.070
-0.606
0.078
-0.681
0.070
0.117
0.070
0.289
0.070
1.025
0.070
-0.609
0.070
-0.386
0.078
-0.603
0.098
1.850
0.090
0.941
0.098
1.506
0.098
0.001
0.098
0.465
0.104
0.457
0.098
1.000
0.000
0.000
0.000
1.000
0.745
0.000
1.000
-0.41
0.39
0.56
1.29
-0.34
-0.14
0.27
-0.36
0.26
1.05
1.22
1.96
0.33
0.57
0.94
0.35
Sig. Lower Bound Upper Bound
0.000
1.66
2.05
0.000
0.71
1.18
0.000
1.28
1.75
1.000
-0.23
0.24
0.000
0.21
0.74
0.000
0.23
0.70
1.000
-0.26
0.27
1.000
-0.31
0.17
0.000
0.49
0.96
0.000
0.66
1.13
0.000
1.40
1.87
0.216
-0.04
0.49
0.000
0.37
0.85
1.000
-0.33
0.34
0.000
1.40
1.86
0.000
0.46
0.99
0.000
1.02
1.55
0.265
-0.48
0.05
0.204
-0.04
0.54
0.130
-0.03
0.50
0.396
-0.51
0.07
0.014
-0.56
-0.03
0.000
0.24
0.77
0.000
0.41
0.94
0.000
1.14
1.68
0.216
-0.49
0.04
0.000
0.12
0.65
0.745
-0.57
0.14
0.000
1.05
1.44
0.000
0.10
0.57
0.000
0.67
1.14
0.000
-0.84
-0.37
0.911
-0.40
0.13
0.739
-0.38
0.09
0.000
-0.87
-0.34
0.000
-0.92
-0.44
0.939
-0.12
0.35
0.003
0.05
0.52
0.000
0.79
1.26
0.000
-0.85
-0.37
0.000
-0.65
-0.12
0.000
-0.94
-0.27
0.000
1.54
2.15
0.000
0.61
1.27
0.000
1.17
1.84
1.000
-0.33
0.33
0.001
0.11
0.82
0.000
0.12
0.79
95% Confidence Interval
context
urban
DV
nat
straight_flowers
straight_brome
straight_weedy
mow_veg (I)
mowall_nonveg
mow_veg (J)
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-1.688
0.056
-1.011
0.056
-2.008
0.056
-1.674
0.065
-1.701
0.056
-2.663
0.065
-2.813
0.056
-2.159
0.056
-1.541
0.055
-0.939
0.056
-2.023
0.056
-1.542
0.065
-1.742
0.056
-2.383
0.087
1.688
0.056
0.677
0.067
-0.320
0.067
0.014
0.075
-0.013
0.067
-0.975
0.075
-1.125
0.067
-0.471
0.067
0.147
0.067
0.749
0.067
-0.335
0.067
0.146
0.075
-0.054
0.067
-0.695
0.094
1.011
0.056
-0.677
0.067
-0.996
0.067
-0.663
0.075
-0.690
0.067
-1.652
0.075
-1.802
0.067
-1.148
0.067
-0.530
0.067
0.072
0.067
-1.012
0.067
-0.531
0.075
-0.731
0.067
-1.372
0.094
2.008
0.056
0.320
0.067
0.996
0.067
0.334
0.075
0.307
0.067
-0.656
0.075
-0.806
0.067
-0.152
0.067
0.466
0.067
1.069
0.067
-0.015
0.067
0.465
0.075
0.265
0.067
-0.375
0.094
Sig. Lower Bound Upper Bound
0.000
-1.88
-1.50
0.000
-1.20
-0.82
0.000
-2.20
-1.82
0.000
-1.90
-1.45
0.000
-1.89
-1.51
0.000
-2.88
-2.44
0.000
-3.00
-2.62
0.000
-2.35
-1.97
0.000
-1.73
-1.35
0.000
-1.13
-0.75
0.000
-2.21
-1.83
0.000
-1.76
-1.32
0.000
-1.93
-1.55
0.000
-2.68
-2.09
0.000
1.50
1.88
0.000
0.45
0.90
0.000
-0.55
-0.09
1.000
-0.24
0.27
1.000
-0.24
0.21
0.000
-1.23
-0.72
0.000
-1.35
-0.90
0.000
-0.70
-0.24
0.667
-0.08
0.37
0.000
0.52
0.98
0.000
-0.56
-0.11
0.832
-0.11
0.40
1.000
-0.28
0.17
0.000
-1.01
-0.38
0.000
0.82
1.20
0.000
-0.90
-0.45
0.000
-1.22
-0.77
0.000
-0.92
-0.41
0.000
-0.92
-0.46
0.000
-1.91
-1.40
0.000
-2.03
-1.57
0.000
-1.38
-0.92
0.000
-0.76
-0.30
0.999
-0.16
0.30
0.000
-1.24
-0.78
0.000
-0.79
-0.28
0.000
-0.96
-0.50
0.000
-1.69
-1.05
0.000
1.82
2.20
0.000
0.09
0.55
0.000
0.77
1.22
0.001
0.08
0.59
0.000
0.08
0.53
0.000
-0.91
-0.40
0.000
-1.03
-0.58
0.622
-0.38
0.08
0.000
0.24
0.69
0.000
0.84
1.30
1.000
-0.24
0.21
0.000
0.21
0.72
0.007
0.04
0.49
0.006
-0.70
-0.06
95% Confidence Interval
AIMS II • 12/2005
225
226
AIMS II • 12/2005
context
urban
DV
nat
straight_biodiv
straight_everdeciduous
straight_evergreen
mow_veg (I)
straight_sumac
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
1.674
0.065
-0.014
0.075
0.663
0.075
-0.334
0.075
-0.027
0.075
-0.989
0.083
-1.140
0.075
-0.485
0.075
0.133
0.075
0.735
0.075
-0.349
0.075
0.132
0.083
-0.068
0.075
-0.709
0.100
1.701
0.056
0.013
0.067
0.690
0.067
-0.307
0.067
0.027
0.075
-0.962
0.075
-1.113
0.067
-0.458
0.067
0.160
0.067
0.762
0.067
-0.322
0.067
0.158
0.075
-0.042
0.067
-0.682
0.094
2.663
0.065
0.975
0.075
1.652
0.075
0.656
0.075
0.989
0.083
0.962
0.075
-0.150
0.075
0.504
0.075
1.122
0.075
1.724
0.075
0.640
0.075
1.121
0.083
0.921
0.075
0.280
0.100
2.813
0.056
1.125
0.067
1.802
0.067
0.806
0.067
1.140
0.075
1.113
0.067
0.150
0.075
0.654
0.067
1.272
0.067
1.874
0.067
0.791
0.067
1.271
0.075
1.071
0.067
0.431
0.094
Sig. Lower Bound Upper Bound
0.000
1.45
1.90
1.000
-0.27
0.24
0.000
0.41
0.92
0.001
-0.59
-0.08
1.000
-0.28
0.23
0.000
-1.27
-0.71
0.000
-1.39
-0.88
0.000
-0.74
-0.23
0.910
-0.12
0.39
0.000
0.48
0.99
0.000
-0.60
-0.09
0.960
-0.15
0.41
1.000
-0.32
0.19
0.000
-1.05
-0.37
0.000
1.51
1.89
1.000
-0.21
0.24
0.000
0.46
0.92
0.000
-0.53
-0.08
1.000
-0.23
0.28
0.000
-1.22
-0.71
0.000
-1.34
-0.89
0.000
-0.69
-0.23
0.527
-0.07
0.39
0.000
0.53
0.99
0.000
-0.55
-0.09
0.730
-0.10
0.41
1.000
-0.27
0.19
0.000
-1.00
-0.36
0.000
2.44
2.88
0.000
0.72
1.23
0.000
1.40
1.91
0.000
0.40
0.91
0.000
0.71
1.27
0.000
0.71
1.22
0.800
-0.41
0.10
0.000
0.25
0.76
0.000
0.87
1.38
0.000
1.47
1.98
0.000
0.38
0.90
0.000
0.84
1.40
0.000
0.67
1.18
0.250
-0.06
0.62
0.000
2.62
3.00
0.000
0.90
1.35
0.000
1.57
2.03
0.000
0.58
1.03
0.000
0.88
1.39
0.000
0.89
1.34
0.800
-0.10
0.41
0.000
0.43
0.88
0.000
1.05
1.50
0.000
1.65
2.10
0.000
0.56
1.02
0.000
1.02
1.53
0.000
0.84
1.30
0.000
0.11
0.75
95% Confidence Interval
context
urban
DV
nat
curved_flowers
curved_brome
curved_weedy
mow_veg (I)
straight_islands
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
2.159
0.056
0.471
0.067
1.148
0.067
0.152
0.067
0.485
0.075
0.458
0.067
-0.504
0.075
-0.654
0.067
0.618
0.067
1.220
0.067
0.136
0.067
0.617
0.075
0.417
0.067
-0.224
0.094
1.541
0.055
-0.147
0.067
0.530
0.067
-0.466
0.067
-0.133
0.075
-0.160
0.067
-1.122
0.075
-1.272
0.067
-0.618
0.067
0.602
0.067
-0.482
0.067
-0.001
0.075
-0.201
0.067
-0.842
0.094
0.939
0.056
-0.749
0.067
-0.072
0.067
-1.069
0.067
-0.735
0.075
-0.762
0.067
-1.724
0.075
-1.874
0.067
-1.220
0.067
-0.602
0.067
-1.084
0.067
-0.603
0.075
-0.803
0.067
-1.444
0.094
2.023
0.056
0.335
0.067
1.012
0.067
0.015
0.067
0.349
0.075
0.322
0.067
-0.640
0.075
-0.791
0.067
-0.136
0.067
0.482
0.067
1.084
0.067
0.481
0.075
0.281
0.067
-0.360
0.094
Sig. Lower Bound Upper Bound
0.000
1.97
2.35
0.000
0.24
0.70
0.000
0.92
1.38
0.622
-0.08
0.38
0.000
0.23
0.74
0.000
0.23
0.69
0.000
-0.76
-0.25
0.000
-0.88
-0.43
0.000
0.39
0.84
0.000
0.99
1.45
0.781
-0.09
0.36
0.000
0.36
0.87
0.000
0.19
0.64
0.536
-0.54
0.10
0.000
1.35
1.73
0.667
-0.37
0.08
0.000
0.30
0.76
0.000
-0.69
-0.24
0.910
-0.39
0.12
0.527
-0.39
0.07
0.000
-1.38
-0.87
0.000
-1.50
-1.05
0.000
-0.84
-0.39
0.000
0.37
0.83
0.000
-0.71
-0.25
1.000
-0.26
0.25
0.151
-0.43
0.03
0.000
-1.16
-0.52
0.000
0.75
1.13
0.000
-0.98
-0.52
0.999
-0.30
0.16
0.000
-1.30
-0.84
0.000
-0.99
-0.48
0.000
-0.99
-0.53
0.000
-1.98
-1.47
0.000
-2.10
-1.65
0.000
-1.45
-0.99
0.000
-0.83
-0.37
0.000
-1.31
-0.86
0.000
-0.86
-0.35
0.000
-1.03
-0.57
0.000
-1.76
-1.12
0.000
1.83
2.21
0.000
0.11
0.56
0.000
0.78
1.24
1.000
-0.21
0.24
0.000
0.09
0.60
0.000
0.09
0.55
0.000
-0.90
-0.38
0.000
-1.02
-0.56
0.781
-0.36
0.09
0.000
0.25
0.71
0.000
0.86
1.31
0.000
0.23
0.74
0.003
0.05
0.51
0.011
-0.68
-0.04
95% Confidence Interval
AIMS II • 12/2005
227
228
AIMS II • 12/2005
DV
nat
maint
context
urban
urban
mowall_nonveg
curved_everdeciduous
curved_evergreen
mow_veg (I)
curved_sumac
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
1.542
0.065
-0.146
0.075
0.531
0.075
-0.465
0.075
-0.132
0.083
-0.158
0.075
-1.121
0.083
-1.271
0.075
-0.617
0.075
0.001
0.075
0.603
0.075
-0.481
0.075
-0.200
0.075
-0.841
0.100
1.742
0.056
0.054
0.067
0.731
0.067
-0.265
0.067
0.068
0.075
0.042
0.067
-0.921
0.075
-1.071
0.067
-0.417
0.067
0.201
0.067
0.803
0.067
-0.281
0.067
0.200
0.075
-0.641
0.094
2.383
0.087
0.695
0.094
1.372
0.094
0.375
0.094
0.709
0.100
0.682
0.094
-0.280
0.100
-0.431
0.094
0.224
0.094
0.842
0.094
1.444
0.094
0.360
0.094
0.841
0.100
0.641
0.094
1.342
0.051
0.765
0.051
0.506
0.051
0.599
0.060
0.569
0.051
0.483
0.060
0.429
0.051
1.414
0.051
1.113
0.051
0.797
0.051
0.448
0.051
0.339
0.060
0.708
0.051
0.339
0.079
Sig. Lower Bound Upper Bound
0.000
1.32
1.76
0.832
-0.40
0.11
0.000
0.28
0.79
0.000
-0.72
-0.21
0.960
-0.41
0.15
0.730
-0.41
0.10
0.000
-1.40
-0.84
0.000
-1.53
-1.02
0.000
-0.87
-0.36
1.000
-0.25
0.26
0.000
0.35
0.86
0.000
-0.74
-0.23
0.330
-0.45
0.05
0.000
-1.18
-0.50
0.000
1.55
1.93
1.000
-0.17
0.28
0.000
0.50
0.96
0.007
-0.49
-0.04
1.000
-0.19
0.32
1.000
-0.19
0.27
0.000
-1.18
-0.67
0.000
-1.30
-0.84
0.000
-0.64
-0.19
0.151
-0.03
0.43
0.000
0.57
1.03
0.003
-0.51
-0.05
0.330
-0.05
0.45
0.000
-0.96
-0.32
0.000
2.09
2.68
0.000
0.38
1.01
0.000
1.05
1.69
0.006
0.06
0.70
0.000
0.37
1.05
0.000
0.36
1.00
0.250
-0.62
0.06
0.000
-0.75
-0.11
0.536
-0.10
0.54
0.000
0.52
1.16
0.000
1.12
1.76
0.011
0.04
0.68
0.000
0.50
1.18
0.000
0.32
0.96
0.000
1.17
1.52
0.000
0.59
0.94
0.000
0.33
0.68
0.000
0.40
0.80
0.000
0.40
0.74
0.000
0.28
0.69
0.000
0.26
0.60
0.000
1.24
1.59
0.000
0.94
1.29
0.000
0.62
0.97
0.000
0.27
0.62
0.000
0.14
0.54
0.000
0.53
0.88
0.002
0.07
0.61
95% Confidence Interval
context
urban
DV
maint
straight_sumac
straight_flowers
straight_brome
mow_veg (I)
straight_weedy
mow_veg (J)
mowall_nonveg
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-1.342
0.051
-0.577
0.062
-0.836
0.062
-0.743
0.069
-0.773
0.062
-0.859
0.069
-0.913
0.062
0.073
0.062
-0.229
0.061
-0.545
0.062
-0.894
0.062
-1.003
0.069
-0.634
0.062
-1.003
0.087
-0.765
0.051
0.577
0.062
-0.259
0.062
-0.165
0.069
-0.196
0.062
-0.281
0.069
-0.336
0.062
0.650
0.062
0.349
0.061
0.032
0.062
-0.317
0.062
-0.425
0.069
-0.057
0.062
-0.426
0.087
-0.506
0.051
0.836
0.062
0.259
0.062
0.094
0.069
0.063
0.062
-0.022
0.069
-0.077
0.062
0.909
0.062
0.608
0.061
0.291
0.062
-0.058
0.062
-0.166
0.069
0.202
0.062
-0.167
0.087
-0.599
0.060
0.743
0.069
0.165
0.069
-0.094
0.069
-0.031
0.069
-0.116
0.076
-0.171
0.069
0.815
0.069
0.514
0.069
0.197
0.069
-0.152
0.069
-0.260
0.076
0.108
0.069
-0.260
0.092
Sig. Lower Bound Upper Bound
0.000
-1.52
-1.17
0.000
-0.79
-0.37
0.000
-1.05
-0.63
0.000
-0.98
-0.51
0.000
-0.98
-0.56
0.000
-1.09
-0.62
0.000
-1.12
-0.70
0.998
-0.14
0.28
0.016
-0.44
-0.02
0.000
-0.75
-0.34
0.000
-1.10
-0.69
0.000
-1.24
-0.77
0.000
-0.84
-0.43
0.000
-1.30
-0.71
0.000
-0.94
-0.59
0.000
0.37
0.79
0.002
-0.47
-0.05
0.522
-0.40
0.07
0.095
-0.40
0.01
0.004
-0.52
-0.05
0.000
-0.54
-0.13
0.000
0.44
0.86
0.000
0.14
0.56
1.000
-0.18
0.24
0.000
-0.53
-0.11
0.000
-0.66
-0.19
1.000
-0.27
0.15
0.000
-0.72
-0.13
0.000
-0.68
-0.33
0.000
0.63
1.05
0.002
0.05
0.47
0.991
-0.14
0.33
1.000
-0.15
0.27
1.000
-0.26
0.21
0.996
-0.29
0.13
0.000
0.70
1.12
0.000
0.40
0.82
0.000
0.08
0.50
1.000
-0.27
0.15
0.508
-0.40
0.07
0.071
-0.01
0.41
0.841
-0.46
0.13
0.000
-0.80
-0.40
0.000
0.51
0.98
0.522
-0.07
0.40
0.991
-0.33
0.14
1.000
-0.27
0.20
0.972
-0.37
0.14
0.465
-0.40
0.06
0.000
0.58
1.05
0.000
0.28
0.75
0.222
-0.04
0.43
0.670
-0.39
0.08
0.044
-0.52
0.00
0.965
-0.13
0.34
0.231
-0.57
0.05
95% Confidence Interval
AIMS II • 12/2005
229
230
AIMS II • 12/2005
context
urban
DV
maint
straight_islands
straight_biodiv
straight_everdeciduous
mow_veg (I)
straight_evergreen
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-0.569
0.051
0.773
0.062
0.196
0.062
-0.063
0.062
0.031
0.069
-0.085
0.069
-0.140
0.062
0.846
0.062
0.544
0.061
0.228
0.062
-0.121
0.062
-0.229
0.069
0.139
0.062
-0.230
0.087
-0.483
0.060
0.859
0.069
0.281
0.069
0.022
0.069
0.116
0.076
0.085
0.069
-0.054
0.069
0.931
0.069
0.630
0.069
0.313
0.069
-0.035
0.069
-0.144
0.076
0.224
0.069
-0.144
0.092
-0.429
0.051
0.913
0.062
0.336
0.062
0.077
0.062
0.171
0.069
0.140
0.062
0.054
0.069
0.986
0.062
0.684
0.061
0.368
0.062
0.019
0.062
-0.090
0.069
0.279
0.062
-0.090
0.087
-1.414
0.051
-0.073
0.062
-0.650
0.062
-0.909
0.062
-0.815
0.069
-0.846
0.062
-0.931
0.069
-0.986
0.062
-0.301
0.061
-0.618
0.062
-0.967
0.062
-1.075
0.069
-0.707
0.062
-1.075
0.087
Sig. Lower Bound Upper Bound
0.000
-0.74
-0.40
0.000
0.56
0.98
0.095
-0.01
0.40
1.000
-0.27
0.15
1.000
-0.20
0.27
0.996
-0.32
0.15
0.614
-0.35
0.07
0.000
0.64
1.05
0.000
0.34
0.75
0.019
0.02
0.44
0.821
-0.33
0.09
0.062
-0.46
0.00
0.625
-0.07
0.35
0.334
-0.52
0.06
0.000
-0.69
-0.28
0.000
0.62
1.09
0.004
0.05
0.52
1.000
-0.21
0.26
0.972
-0.14
0.37
0.996
-0.15
0.32
1.000
-0.29
0.18
0.000
0.70
1.17
0.000
0.40
0.86
0.001
0.08
0.55
1.000
-0.27
0.20
0.854
-0.40
0.11
0.078
-0.01
0.46
0.966
-0.46
0.17
0.000
-0.60
-0.26
0.000
0.70
1.12
0.000
0.13
0.54
0.996
-0.13
0.29
0.465
-0.06
0.40
0.614
-0.07
0.35
1.000
-0.18
0.29
0.000
0.78
1.19
0.000
0.48
0.89
0.000
0.16
0.58
1.000
-0.19
0.23
0.994
-0.32
0.14
0.001
0.07
0.49
0.999
-0.38
0.20
0.000
-1.59
-1.24
0.998
-0.28
0.14
0.000
-0.86
-0.44
0.000
-1.12
-0.70
0.000
-1.05
-0.58
0.000
-1.05
-0.64
0.000
-1.17
-0.70
0.000
-1.19
-0.78
0.000
-0.51
-0.09
0.000
-0.83
-0.41
0.000
-1.18
-0.76
0.000
-1.31
-0.84
0.000
-0.92
-0.50
0.000
-1.37
-0.78
95% Confidence Interval
context
urban
DV
maint
curved_sumac
curved_flowers
curved_brome
mow_veg (I)
curved_weedy
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-1.113
0.051
0.229
0.061
-0.349
0.061
-0.608
0.061
-0.514
0.069
-0.544
0.061
-0.630
0.069
-0.684
0.061
0.301
0.061
-0.317
0.062
-0.665
0.061
-0.774
0.069
-0.405
0.061
-0.774
0.086
-0.797
0.051
0.545
0.062
-0.032
0.062
-0.291
0.062
-0.197
0.069
-0.228
0.062
-0.313
0.069
-0.368
0.062
0.618
0.062
0.317
0.062
-0.349
0.062
-0.457
0.069
-0.089
0.062
-0.458
0.087
-0.448
0.051
0.894
0.062
0.317
0.062
0.058
0.062
0.152
0.069
0.121
0.062
0.035
0.069
-0.019
0.062
0.967
0.062
0.665
0.061
0.349
0.062
-0.108
0.069
0.260
0.062
-0.109
0.087
-0.339
0.060
1.003
0.069
0.425
0.069
0.166
0.069
0.260
0.076
0.229
0.069
0.144
0.076
0.090
0.069
1.075
0.069
0.774
0.069
0.457
0.069
0.108
0.069
0.368
0.069
0.000
0.092
Sig. Lower Bound Upper Bound
0.000
-1.29
-0.94
0.016
0.02
0.44
0.000
-0.56
-0.14
0.000
-0.82
-0.40
0.000
-0.75
-0.28
0.000
-0.75
-0.34
0.000
-0.86
-0.40
0.000
-0.89
-0.48
0.000
0.09
0.51
0.000
-0.53
-0.11
0.000
-0.87
-0.46
0.000
-1.01
-0.54
0.000
-0.61
-0.20
0.000
-1.07
-0.48
0.000
-0.97
-0.62
0.000
0.34
0.75
1.000
-0.24
0.18
0.000
-0.50
-0.08
0.222
-0.43
0.04
0.019
-0.44
-0.02
0.001
-0.55
-0.08
0.000
-0.58
-0.16
0.000
0.41
0.83
0.000
0.11
0.53
0.000
-0.56
-0.14
0.000
-0.69
-0.22
0.984
-0.30
0.12
0.000
-0.75
-0.16
0.000
-0.62
-0.27
0.000
0.69
1.10
0.000
0.11
0.53
1.000
-0.15
0.27
0.670
-0.08
0.39
0.821
-0.09
0.33
1.000
-0.20
0.27
1.000
-0.23
0.19
0.000
0.76
1.18
0.000
0.46
0.87
0.000
0.14
0.56
0.965
-0.34
0.13
0.002
0.05
0.47
0.996
-0.40
0.18
0.000
-0.54
-0.14
0.000
0.77
1.24
0.000
0.19
0.66
0.508
-0.07
0.40
0.044
0.00
0.52
0.062
0.00
0.46
0.854
-0.11
0.40
0.994
-0.14
0.32
0.000
0.84
1.31
0.000
0.54
1.01
0.000
0.22
0.69
0.965
-0.13
0.34
0.000
0.13
0.60
1.000
-0.31
0.31
95% Confidence Interval
AIMS II • 12/2005
231
232
AIMS II • 12/2005
DV
maint
safe
context
urban
urban
straight_weedy
mowall_nonveg
curved_everdeciduous
mow_veg (I)
curved_evergreen
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-0.708
0.051
0.634
0.062
0.057
0.062
-0.202
0.062
-0.108
0.069
-0.139
0.062
-0.224
0.069
-0.279
0.062
0.707
0.062
0.405
0.061
0.089
0.062
-0.260
0.062
-0.368
0.069
-0.369
0.087
-0.339
0.079
1.003
0.087
0.426
0.087
0.167
0.087
0.260
0.092
0.230
0.087
0.144
0.092
0.090
0.087
1.075
0.087
0.774
0.086
0.458
0.087
0.109
0.087
0.000
0.092
0.369
0.087
0.088
0.053
-0.213
0.053
-0.232
0.053
0.221
0.062
0.033
0.053
-0.003
0.062
-0.028
0.053
0.208
0.053
-0.022
0.053
-0.155
0.053
-0.273
0.053
-0.007
0.062
-0.011
0.053
-0.107
0.082
-0.088
0.053
-0.302
0.064
-0.321
0.064
0.133
0.071
-0.055
0.064
-0.091
0.071
-0.116
0.064
0.120
0.064
-0.110
0.064
-0.243
0.064
-0.361
0.064
-0.096
0.071
-0.100
0.064
-0.196
0.090
Sig. Lower Bound Upper Bound
0.000
-0.88
-0.53
0.000
0.43
0.84
1.000
-0.15
0.27
0.071
-0.41
0.01
0.965
-0.34
0.13
0.625
-0.35
0.07
0.078
-0.46
0.01
0.001
-0.49
-0.07
0.000
0.50
0.92
0.000
0.20
0.61
0.984
-0.12
0.30
0.002
-0.47
-0.05
0.000
-0.60
-0.13
0.002
-0.66
-0.08
0.002
-0.61
-0.07
0.000
0.71
1.30
0.000
0.13
0.72
0.841
-0.13
0.46
0.231
-0.05
0.57
0.334
-0.06
0.52
0.966
-0.17
0.46
0.999
-0.20
0.38
0.000
0.78
1.37
0.000
0.48
1.07
0.000
0.16
0.75
0.996
-0.18
0.40
1.000
-0.31
0.31
0.002
0.08
0.66
0.942
-0.09
0.27
0.005
-0.39
-0.03
0.001
-0.41
-0.05
0.028
0.01
0.43
1.000
-0.15
0.21
1.000
-0.21
0.21
1.000
-0.21
0.15
0.007
0.03
0.39
1.000
-0.20
0.16
0.192
-0.33
0.03
0.000
-0.45
-0.09
1.000
-0.22
0.20
1.000
-0.19
0.17
0.994
-0.39
0.17
0.942
-0.27
0.09
0.000
-0.52
-0.09
0.000
-0.54
-0.10
0.873
-0.11
0.38
1.000
-0.27
0.16
0.995
-0.33
0.15
0.892
-0.33
0.10
0.864
-0.10
0.34
0.921
-0.33
0.11
0.012
-0.46
-0.03
0.000
-0.58
-0.15
0.992
-0.34
0.15
0.967
-0.32
0.12
0.676
-0.50
0.11
95% Confidence Interval
context
urban
DV
safe
straight_evergreen
straight_sumac
straight_flowers
mow_veg (I)
straight_brome
mow_veg (J)
mowall_nonveg
straight_weedy
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.213
0.053
0.302
0.064
-0.019
0.064
0.435
0.072
0.246
0.064
0.211
0.072
0.186
0.064
0.421
0.064
0.191
0.064
0.059
0.064
-0.060
0.064
0.206
0.071
0.202
0.064
0.106
0.090
0.232
0.053
0.321
0.064
0.019
0.064
0.454
0.072
0.265
0.064
0.230
0.072
0.205
0.064
0.440
0.064
0.210
0.064
0.078
0.064
-0.041
0.064
0.225
0.071
0.221
0.064
0.125
0.090
-0.221
0.062
-0.133
0.071
-0.435
0.072
-0.454
0.072
-0.188
0.072
-0.224
0.079
-0.249
0.072
-0.013
0.072
-0.243
0.071
-0.376
0.072
-0.494
0.072
-0.229
0.078
-0.232
0.072
-0.329
0.095
-0.033
0.053
0.055
0.064
-0.246
0.064
-0.265
0.064
0.188
0.072
-0.036
0.072
-0.060
0.064
0.175
0.064
-0.055
0.064
-0.188
0.064
-0.306
0.064
-0.040
0.071
-0.044
0.064
-0.140
0.090
Sig. Lower Bound Upper Bound
0.005
0.03
0.39
0.000
0.09
0.52
1.000
-0.24
0.20
0.000
0.19
0.68
0.010
0.03
0.46
0.176
-0.03
0.45
0.190
-0.03
0.40
0.000
0.21
0.64
0.150
-0.02
0.41
1.000
-0.16
0.28
1.000
-0.28
0.16
0.203
-0.04
0.45
0.098
-0.01
0.42
0.998
-0.20
0.41
0.001
0.05
0.41
0.000
0.10
0.54
1.000
-0.20
0.24
0.000
0.21
0.70
0.003
0.05
0.48
0.087
-0.01
0.47
0.086
-0.01
0.42
0.000
0.22
0.66
0.065
-0.01
0.43
0.997
-0.14
0.29
1.000
-0.26
0.18
0.102
-0.02
0.47
0.039
0.00
0.44
0.988
-0.18
0.43
0.028
-0.43
-0.01
0.873
-0.38
0.11
0.000
-0.68
-0.19
0.000
-0.70
-0.21
0.348
-0.43
0.05
0.218
-0.49
0.04
0.038
-0.49
-0.01
1.000
-0.26
0.23
0.047
-0.49
0.00
0.000
-0.62
-0.13
0.000
-0.74
-0.25
0.189
-0.49
0.04
0.077
-0.48
0.01
0.042
-0.65
-0.01
1.000
-0.21
0.15
1.000
-0.16
0.27
0.010
-0.46
-0.03
0.003
-0.48
-0.05
0.348
-0.05
0.43
1.000
-0.28
0.21
1.000
-0.28
0.16
0.278
-0.04
0.39
1.000
-0.27
0.16
0.182
-0.40
0.03
0.000
-0.52
-0.09
1.000
-0.28
0.20
1.000
-0.26
0.17
0.966
-0.44
0.16
95% Confidence Interval
AIMS II • 12/2005
233
234
AIMS II • 12/2005
context
urban
DV
safe
curved_weedy
straight_islands
straight_biodiv
mow_veg (I)
straight_everdeciduous
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.003
0.062
0.091
0.071
-0.211
0.072
-0.230
0.072
0.224
0.079
0.036
0.072
-0.025
0.072
0.211
0.072
-0.019
0.071
-0.152
0.072
-0.270
0.072
-0.005
0.078
-0.008
0.072
-0.105
0.095
0.028
0.053
0.116
0.064
-0.186
0.064
-0.205
0.064
0.249
0.072
0.060
0.064
0.025
0.072
0.236
0.064
0.005
0.064
-0.127
0.064
-0.245
0.064
0.020
0.071
0.016
0.064
-0.080
0.090
-0.208
0.053
-0.120
0.064
-0.421
0.064
-0.440
0.064
0.013
0.072
-0.175
0.064
-0.211
0.072
-0.236
0.064
-0.230
0.064
-0.363
0.064
-0.481
0.064
-0.215
0.071
-0.219
0.064
-0.316
0.090
0.022
0.053
0.110
0.064
-0.191
0.064
-0.210
0.064
0.243
0.071
0.055
0.064
0.019
0.071
-0.005
0.064
0.230
0.064
-0.132
0.064
-0.251
0.064
0.015
0.071
0.011
0.064
-0.085
0.089
Sig. Lower Bound Upper Bound
1.000
-0.21
0.21
0.995
-0.15
0.33
0.176
-0.45
0.03
0.087
-0.47
0.01
0.218
-0.04
0.49
1.000
-0.21
0.28
1.000
-0.27
0.22
0.175
-0.03
0.45
1.000
-0.26
0.22
0.724
-0.39
0.09
0.013
-0.51
-0.03
1.000
-0.27
0.26
1.000
-0.25
0.23
0.999
-0.43
0.22
1.000
-0.15
0.21
0.892
-0.10
0.33
0.190
-0.40
0.03
0.086
-0.42
0.01
0.038
0.01
0.49
1.000
-0.16
0.28
1.000
-0.22
0.27
0.018
0.02
0.45
1.000
-0.21
0.22
0.807
-0.34
0.09
0.010
-0.46
-0.03
1.000
-0.22
0.26
1.000
-0.20
0.23
1.000
-0.38
0.22
0.007
-0.39
-0.03
0.864
-0.34
0.10
0.000
-0.64
-0.21
0.000
-0.66
-0.22
1.000
-0.23
0.26
0.278
-0.39
0.04
0.175
-0.45
0.03
0.018
-0.45
-0.02
0.023
-0.45
-0.01
0.000
-0.58
-0.15
0.000
-0.70
-0.26
0.147
-0.46
0.03
0.043
-0.44
0.00
0.033
-0.62
-0.01
1.000
-0.16
0.20
0.921
-0.11
0.33
0.150
-0.41
0.02
0.065
-0.43
0.01
0.047
0.00
0.49
1.000
-0.16
0.27
1.000
-0.22
0.26
1.000
-0.22
0.21
0.023
0.01
0.45
0.751
-0.35
0.08
0.007
-0.47
-0.04
1.000
-0.23
0.26
1.000
-0.20
0.23
1.000
-0.39
0.22
95% Confidence Interval
context
urban
DV
safe
curved_evergreen
curved_sumac
curved_flowers
mow_veg (I)
curved_brome
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_sumac
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.155
0.053
0.243
0.064
-0.059
0.064
-0.078
0.064
0.376
0.072
0.188
0.064
0.152
0.072
0.127
0.064
0.363
0.064
0.132
0.064
-0.118
0.064
0.147
0.072
0.143
0.064
0.047
0.090
0.273
0.053
0.361
0.064
0.060
0.064
0.041
0.064
0.494
0.072
0.306
0.064
0.270
0.072
0.245
0.064
0.481
0.064
0.251
0.064
0.118
0.064
0.266
0.071
0.262
0.064
0.165
0.090
0.007
0.062
0.096
0.071
-0.206
0.071
-0.225
0.071
0.229
0.078
0.040
0.071
0.005
0.078
-0.020
0.071
0.215
0.071
-0.015
0.071
-0.147
0.072
-0.266
0.071
-0.004
0.071
-0.100
0.095
0.011
0.053
0.100
0.064
-0.202
0.064
-0.221
0.064
0.232
0.072
0.044
0.064
0.008
0.072
-0.016
0.064
0.219
0.064
-0.011
0.064
-0.143
0.064
-0.262
0.064
0.004
0.071
-0.096
0.090
Sig. Lower Bound Upper Bound
0.192
-0.03
0.33
0.012
0.03
0.46
1.000
-0.28
0.16
0.997
-0.29
0.14
0.000
0.13
0.62
0.182
-0.03
0.40
0.724
-0.09
0.39
0.807
-0.09
0.34
0.000
0.15
0.58
0.751
-0.08
0.35
0.877
-0.34
0.10
0.764
-0.10
0.39
0.636
-0.07
0.36
1.000
-0.26
0.35
0.000
0.09
0.45
0.000
0.15
0.58
1.000
-0.16
0.28
1.000
-0.18
0.26
0.000
0.25
0.74
0.000
0.09
0.52
0.013
0.03
0.51
0.010
0.03
0.46
0.000
0.26
0.70
0.007
0.04
0.47
0.877
-0.10
0.34
0.016
0.02
0.51
0.004
0.05
0.48
0.879
-0.14
0.47
1.000
-0.20
0.22
0.992
-0.15
0.34
0.203
-0.45
0.04
0.102
-0.47
0.02
0.189
-0.04
0.49
1.000
-0.20
0.28
1.000
-0.26
0.27
1.000
-0.26
0.22
0.147
-0.03
0.46
1.000
-0.26
0.23
0.764
-0.39
0.10
0.016
-0.51
-0.02
1.000
-0.25
0.24
0.999
-0.42
0.22
1.000
-0.17
0.19
0.967
-0.12
0.32
0.098
-0.42
0.01
0.039
-0.44
0.00
0.077
-0.01
0.48
1.000
-0.17
0.26
1.000
-0.23
0.25
1.000
-0.23
0.20
0.043
0.00
0.44
1.000
-0.23
0.20
0.636
-0.36
0.07
0.004
-0.48
-0.05
1.000
-0.24
0.25
0.999
-0.40
0.21
95% Confidence Interval
AIMS II • 12/2005
235
236
AIMS II • 12/2005
DV
safe
rural ag att
context
urban
straight_brome
straight_weedy
mowall_nonveg
mow_veg (I)
curved_everdeciduous
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_sumac
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_sumac
curved_evergreen
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.107
0.082
0.196
0.090
-0.106
0.090
-0.125
0.090
0.329
0.095
0.140
0.090
0.105
0.095
0.080
0.090
0.316
0.090
0.085
0.089
-0.047
0.090
-0.165
0.090
0.100
0.095
0.096
0.090
0.152
0.094
-0.402
0.094
-1.308
0.094
-0.887
0.077
-0.957
0.094
-0.801
0.077
-0.934
0.094
-0.223
0.094
-0.224
0.094
-1.389
0.077
-0.827
0.094
-0.734
0.077
-0.152
0.094
-0.554
0.108
-1.461
0.108
-1.039
0.094
-1.110
0.108
-0.953
0.094
-1.086
0.108
-0.376
0.108
-0.376
0.108
-1.542
0.094
-0.979
0.108
-0.886
0.094
0.402
0.094
0.554
0.108
-0.907
0.108
-0.485
0.094
-0.556
0.108
-0.399
0.094
-0.532
0.108
0.178
0.108
0.177
0.108
-0.988
0.094
-0.426
0.108
-0.332
0.094
Sig. Lower Bound Upper Bound
0.994
-0.17
0.39
0.676
-0.11
0.50
0.998
-0.41
0.20
0.988
-0.43
0.18
0.042
0.01
0.65
0.966
-0.16
0.44
0.999
-0.22
0.43
1.000
-0.22
0.38
0.033
0.01
0.62
1.000
-0.22
0.39
1.000
-0.35
0.26
0.879
-0.47
0.14
0.999
-0.22
0.42
0.999
-0.21
0.40
0.924
-0.16
0.46
0.001
-0.71
-0.09
0.000
-1.62
-1.00
0.000
-1.14
-0.63
0.000
-1.27
-0.65
0.000
-1.06
-0.55
0.000
-1.24
-0.62
0.453
-0.53
0.09
0.453
-0.54
0.09
0.000
-1.64
-1.13
0.000
-1.14
-0.52
0.000
-0.99
-0.48
0.924
-0.46
0.16
0.000
-0.91
-0.20
0.000
-1.82
-1.10
0.000
-1.35
-0.73
0.000
-1.47
-0.75
0.000
-1.26
-0.64
0.000
-1.44
-0.73
0.029
-0.73
-0.02
0.029
-0.73
-0.02
0.000
-1.85
-1.23
0.000
-1.34
-0.62
0.000
-1.20
-0.57
0.001
0.09
0.71
0.000
0.20
0.91
0.000
-1.26
-0.55
0.000
-0.80
-0.18
0.000
-0.91
-0.20
0.001
-0.71
-0.09
0.000
-0.89
-0.17
0.911
-0.18
0.53
0.916
-0.18
0.53
0.000
-1.30
-0.68
0.005
-0.78
-0.07
0.023
-0.64
-0.02
95% Confidence Interval
context DV
rural ag att
straight_biodiv
straight_everdeciduous
straight_evergreen
mow_veg (I)
straight_flowers
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
1.308
0.094
1.461
0.108
0.907
0.108
0.422
0.094
0.351
0.108
0.508
0.094
0.375
0.108
1.085
0.108
1.084
0.108
-0.081
0.094
0.481
0.108
0.575
0.094
0.887
0.077
1.039
0.094
0.485
0.094
-0.422
0.094
-0.071
0.094
0.086
0.077
-0.047
0.094
0.663
0.094
0.662
0.094
-0.503
0.077
0.060
0.094
0.153
0.077
0.957
0.094
1.110
0.108
0.556
0.108
-0.351
0.108
0.071
0.094
0.157
0.094
0.024
0.108
0.734
0.108
0.733
0.108
-0.432
0.094
0.130
0.108
0.224
0.094
0.801
0.077
0.953
0.094
0.399
0.094
-0.508
0.094
-0.086
0.077
-0.157
0.094
-0.133
0.094
0.577
0.094
0.576
0.094
-0.589
0.077
-0.027
0.094
0.067
0.077
Sig. Lower Bound Upper Bound
0.000
1.00
1.62
0.000
1.10
1.82
0.000
0.55
1.26
0.000
0.11
0.73
0.058
-0.01
0.71
0.000
0.20
0.82
0.030
0.02
0.73
0.000
0.73
1.44
0.000
0.73
1.44
1.000
-0.39
0.23
0.001
0.13
0.84
0.000
0.26
0.88
0.000
0.63
1.14
0.000
0.73
1.35
0.000
0.18
0.80
0.000
-0.73
-0.11
1.000
-0.38
0.24
0.996
-0.17
0.34
1.000
-0.36
0.26
0.000
0.35
0.97
0.000
0.35
0.97
0.000
-0.76
-0.25
1.000
-0.25
0.37
0.743
-0.10
0.41
0.000
0.65
1.27
0.000
0.75
1.47
0.000
0.20
0.91
0.058
-0.71
0.01
1.000
-0.24
0.38
0.904
-0.15
0.47
1.000
-0.33
0.38
0.000
0.38
1.09
0.000
0.38
1.09
0.000
-0.74
-0.12
0.993
-0.23
0.49
0.449
-0.09
0.53
0.000
0.55
1.06
0.000
0.64
1.26
0.001
0.09
0.71
0.000
-0.82
-0.20
0.996
-0.34
0.17
0.904
-0.47
0.15
0.971
-0.44
0.18
0.000
0.27
0.89
0.000
0.27
0.89
0.000
-0.84
-0.33
1.000
-0.34
0.28
1.000
-0.19
0.32
95% Confidence Interval
AIMS II • 12/2005
237
238
AIMS II • 12/2005
context DV
rural ag att
curved_flowers
curved_brome
curved_weedy
mow_veg (I)
straight_islands
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.934
0.094
1.086
0.108
0.532
0.108
-0.375
0.108
0.047
0.094
-0.024
0.108
0.133
0.094
0.710
0.108
0.710
0.108
-0.455
0.094
0.107
0.108
0.200
0.094
0.223
0.094
0.376
0.108
-0.178
0.108
-1.085
0.108
-0.663
0.094
-0.734
0.108
-0.577
0.094
-0.710
0.108
-0.001
0.108
-1.166
0.094
-0.604
0.108
-0.510
0.094
0.224
0.094
0.376
0.108
-0.177
0.108
-1.084
0.108
-0.662
0.094
-0.733
0.108
-0.576
0.094
-0.710
0.108
0.001
0.108
-1.165
0.094
-0.603
0.108
-0.509
0.094
1.389
0.077
1.542
0.094
0.988
0.094
0.081
0.094
0.503
0.077
0.432
0.094
0.589
0.077
0.455
0.094
1.166
0.094
1.165
0.094
0.562
0.094
0.656
0.077
Sig. Lower Bound Upper Bound
0.000
0.62
1.24
0.000
0.73
1.44
0.000
0.17
0.89
0.030
-0.73
-0.02
1.000
-0.26
0.36
1.000
-0.38
0.33
0.971
-0.18
0.44
0.000
0.35
1.07
0.000
0.35
1.07
0.000
-0.77
-0.14
0.999
-0.25
0.46
0.640
-0.11
0.51
0.453
-0.09
0.53
0.029
0.02
0.73
0.911
-0.53
0.18
0.000
-1.44
-0.73
0.000
-0.97
-0.35
0.000
-1.09
-0.38
0.000
-0.89
-0.27
0.000
-1.07
-0.35
1.000
-0.36
0.36
0.000
-1.48
-0.86
0.000
-0.96
-0.25
0.000
-0.82
-0.20
0.453
-0.09
0.54
0.029
0.02
0.73
0.916
-0.53
0.18
0.000
-1.44
-0.73
0.000
-0.97
-0.35
0.000
-1.09
-0.38
0.000
-0.89
-0.27
0.000
-1.07
-0.35
1.000
-0.36
0.36
0.000
-1.48
-0.85
0.000
-0.96
-0.25
0.000
-0.82
-0.20
0.000
1.13
1.64
0.000
1.23
1.85
0.000
0.68
1.30
1.000
-0.23
0.39
0.000
0.25
0.76
0.000
0.12
0.74
0.000
0.33
0.84
0.000
0.14
0.77
0.000
0.86
1.48
0.000
0.85
1.48
0.000
0.25
0.87
0.000
0.40
0.91
95% Confidence Interval
rural ag nat
context DV
rural ag att
straight_weedy
mowall_nonveg
curved_everdeciduous
mow_veg (I)
curved_evergreen
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.827
0.094
0.979
0.108
0.426
0.108
-0.481
0.108
-0.060
0.094
-0.130
0.108
0.027
0.094
-0.107
0.108
0.604
0.108
0.603
0.108
-0.562
0.094
0.093
0.094
0.734
0.077
0.886
0.094
0.332
0.094
-0.575
0.094
-0.153
0.077
-0.224
0.094
-0.067
0.077
-0.200
0.094
0.510
0.094
0.509
0.094
-0.656
0.077
-0.093
0.094
-0.294
0.070
-0.446
0.070
-0.508
0.070
-0.463
0.057
-0.423
0.070
-0.474
0.057
-0.399
0.070
-0.170
0.070
-0.240
0.070
-0.482
0.057
-0.271
0.070
-0.391
0.057
0.294
0.070
-0.153
0.080
-0.214
0.080
-0.169
0.070
-0.129
0.080
-0.180
0.070
-0.105
0.080
0.124
0.080
0.054
0.080
-0.188
0.070
0.023
0.080
-0.097
0.070
Sig. Lower Bound Upper Bound
0.000
0.52
1.14
0.000
0.62
1.34
0.005
0.07
0.78
0.001
-0.84
-0.13
1.000
-0.37
0.25
0.993
-0.49
0.23
1.000
-0.28
0.34
0.999
-0.46
0.25
0.000
0.25
0.96
0.000
0.25
0.96
0.000
-0.87
-0.25
0.999
-0.22
0.40
0.000
0.48
0.99
0.000
0.57
1.20
0.023
0.02
0.64
0.000
-0.88
-0.26
0.743
-0.41
0.10
0.449
-0.53
0.09
1.000
-0.32
0.19
0.640
-0.51
0.11
0.000
0.20
0.82
0.000
0.20
0.82
0.000
-0.91
-0.40
0.999
-0.40
0.22
0.002
-0.53
-0.06
0.000
-0.68
-0.22
0.000
-0.74
-0.28
0.000
-0.65
-0.27
0.000
-0.65
-0.19
0.000
-0.66
-0.28
0.000
-0.63
-0.17
0.415
-0.40
0.06
0.034
-0.47
-0.01
0.000
-0.67
-0.29
0.007
-0.50
-0.04
0.000
-0.58
-0.20
0.002
0.06
0.53
0.795
-0.42
0.11
0.273
-0.48
0.05
0.428
-0.40
0.06
0.929
-0.39
0.14
0.324
-0.41
0.05
0.986
-0.37
0.16
0.946
-0.14
0.39
1.000
-0.21
0.32
0.255
-0.42
0.04
1.000
-0.24
0.29
0.977
-0.33
0.13
95% Confidence Interval
AIMS II • 12/2005
239
240
AIMS II • 12/2005
context DV
rural ag nat
straight_everdeciduous
straight_evergreen
straight_flowers
mow_veg (I)
straight_brome
mow_veg (J)
mowall_nonveg
straight_weedy
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.446
0.070
0.153
0.080
-0.061
0.080
-0.017
0.070
0.024
0.080
-0.028
0.070
0.048
0.080
0.277
0.080
0.206
0.080
-0.036
0.070
0.176
0.080
0.056
0.070
0.508
0.070
0.214
0.080
0.061
0.080
0.045
0.070
0.085
0.080
0.034
0.070
0.109
0.080
0.338
0.080
0.268
0.080
0.025
0.070
0.237
0.080
0.117
0.070
0.463
0.057
0.169
0.070
0.017
0.070
-0.045
0.070
0.041
0.070
-0.011
0.057
0.064
0.070
0.293
0.070
0.223
0.070
-0.019
0.057
0.192
0.070
0.072
0.057
0.423
0.070
0.129
0.080
-0.024
0.080
-0.085
0.080
-0.041
0.070
-0.051
0.070
0.024
0.080
0.253
0.080
0.182
0.080
-0.060
0.070
0.152
0.080
0.032
0.070
Sig. Lower Bound Upper Bound
0.000
0.22
0.68
0.795
-0.11
0.42
1.000
-0.33
0.20
1.000
-0.25
0.21
1.000
-0.24
0.29
1.000
-0.26
0.20
1.000
-0.22
0.31
0.032
0.01
0.54
0.328
-0.06
0.47
1.000
-0.27
0.19
0.596
-0.09
0.44
1.000
-0.17
0.29
0.000
0.28
0.74
0.273
-0.05
0.48
1.000
-0.20
0.33
1.000
-0.19
0.28
0.998
-0.18
0.35
1.000
-0.20
0.26
0.980
-0.16
0.37
0.002
0.07
0.60
0.047
0.00
0.53
1.000
-0.21
0.26
0.138
-0.03
0.50
0.903
-0.11
0.35
0.000
0.27
0.65
0.428
-0.06
0.40
1.000
-0.21
0.25
1.000
-0.28
0.19
1.000
-0.19
0.27
1.000
-0.20
0.18
0.999
-0.17
0.30
0.002
0.06
0.52
0.072
-0.01
0.45
1.000
-0.21
0.17
0.221
-0.04
0.42
0.989
-0.12
0.26
0.000
0.19
0.65
0.929
-0.14
0.39
1.000
-0.29
0.24
0.998
-0.35
0.18
1.000
-0.27
0.19
1.000
-0.28
0.18
1.000
-0.24
0.29
0.080
-0.01
0.52
0.537
-0.08
0.45
1.000
-0.29
0.17
0.799
-0.11
0.42
1.000
-0.20
0.26
95% Confidence Interval
context DV
rural ag nat
curved_brome
curved_weedy
straight_islands
mow_veg (I)
straight_biodiv
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.474
0.057
0.180
0.070
0.028
0.070
-0.034
0.070
0.011
0.057
0.051
0.070
0.075
0.070
0.304
0.070
0.234
0.070
-0.008
0.057
0.203
0.070
0.083
0.057
0.399
0.070
0.105
0.080
-0.048
0.080
-0.109
0.080
-0.064
0.070
-0.024
0.080
-0.075
0.070
0.229
0.080
0.159
0.080
-0.084
0.070
0.128
0.080
0.008
0.070
0.170
0.070
-0.124
0.080
-0.277
0.080
-0.338
0.080
-0.293
0.070
-0.253
0.080
-0.304
0.070
-0.229
0.080
-0.070
0.080
-0.312
0.070
-0.101
0.080
-0.221
0.070
0.240
0.070
-0.054
0.080
-0.206
0.080
-0.268
0.080
-0.223
0.070
-0.182
0.080
-0.234
0.070
-0.159
0.080
0.070
0.080
-0.242
0.070
-0.031
0.080
-0.151
0.070
Sig. Lower Bound Upper Bound
0.000
0.28
0.66
0.324
-0.05
0.41
1.000
-0.20
0.26
1.000
-0.26
0.20
1.000
-0.18
0.20
1.000
-0.18
0.28
0.997
-0.16
0.31
0.001
0.07
0.53
0.045
0.00
0.47
1.000
-0.20
0.18
0.153
-0.03
0.43
0.966
-0.11
0.27
0.000
0.17
0.63
0.986
-0.16
0.37
1.000
-0.31
0.22
0.980
-0.37
0.16
0.999
-0.30
0.17
1.000
-0.29
0.24
0.997
-0.31
0.16
0.179
-0.04
0.49
0.752
-0.11
0.43
0.993
-0.32
0.15
0.933
-0.14
0.39
1.000
-0.22
0.24
0.415
-0.06
0.40
0.946
-0.39
0.14
0.032
-0.54
-0.01
0.002
-0.60
-0.07
0.002
-0.52
-0.06
0.080
-0.52
0.01
0.001
-0.53
-0.07
0.179
-0.49
0.04
1.000
-0.34
0.20
0.001
-0.54
-0.08
0.989
-0.37
0.16
0.077
-0.45
0.01
0.034
0.01
0.47
1.000
-0.32
0.21
0.328
-0.47
0.06
0.047
-0.53
0.00
0.072
-0.45
0.01
0.537
-0.45
0.08
0.045
-0.47
0.00
0.752
-0.43
0.11
1.000
-0.20
0.34
0.031
-0.47
-0.01
1.000
-0.30
0.24
0.624
-0.38
0.08
95% Confidence Interval
AIMS II • 12/2005
241
242
AIMS II • 12/2005
rural ag maint
context DV
rural ag nat
mowall_nonveg
curved_everdeciduous
curved_evergreen
mow_veg (I)
curved_flowers
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.482
0.057
0.188
0.070
0.036
0.070
-0.025
0.070
0.019
0.057
0.060
0.070
0.008
0.057
0.084
0.070
0.312
0.070
0.242
0.070
0.211
0.070
0.092
0.057
0.271
0.070
-0.023
0.080
-0.176
0.080
-0.237
0.080
-0.192
0.070
-0.152
0.080
-0.203
0.070
-0.128
0.080
0.101
0.080
0.031
0.080
-0.211
0.070
-0.120
0.070
0.391
0.057
0.097
0.070
-0.056
0.070
-0.117
0.070
-0.072
0.057
-0.032
0.070
-0.083
0.057
-0.008
0.070
0.221
0.070
0.151
0.070
-0.092
0.057
0.120
0.070
1.572
0.088
0.366
0.088
0.398
0.088
0.350
0.072
0.451
0.088
0.607
0.072
0.704
0.088
1.204
0.088
0.508
0.088
0.116
0.072
0.254
0.088
0.596
0.072
Sig. Lower Bound Upper Bound
0.000
0.29
0.67
0.255
-0.04
0.42
1.000
-0.19
0.27
1.000
-0.26
0.21
1.000
-0.17
0.21
1.000
-0.17
0.29
1.000
-0.18
0.20
0.993
-0.15
0.32
0.001
0.08
0.54
0.031
0.01
0.47
0.112
-0.02
0.44
0.932
-0.10
0.28
0.007
0.04
0.50
1.000
-0.29
0.24
0.596
-0.44
0.09
0.138
-0.50
0.03
0.221
-0.42
0.04
0.799
-0.42
0.11
0.153
-0.43
0.03
0.933
-0.39
0.14
0.989
-0.16
0.37
1.000
-0.24
0.30
0.112
-0.44
0.02
0.887
-0.35
0.11
0.000
0.20
0.58
0.977
-0.13
0.33
1.000
-0.29
0.17
0.903
-0.35
0.11
0.989
-0.26
0.12
1.000
-0.26
0.20
0.966
-0.27
0.11
1.000
-0.24
0.22
0.077
-0.01
0.45
0.624
-0.08
0.38
0.932
-0.28
0.10
0.887
-0.11
0.35
0.000
1.28
1.86
0.002
0.07
0.66
0.000
0.11
0.69
0.000
0.11
0.59
0.000
0.16
0.74
0.000
0.37
0.85
0.000
0.41
1.00
0.000
0.91
1.50
0.000
0.21
0.80
0.930
-0.12
0.36
0.162
-0.04
0.55
0.000
0.36
0.84
95% Confidence Interval
context DV
rural ag maint
straight_evergreen
straight_flowers
straight_brome
mow_veg (I)
straight_weedy
mow_veg (J)
mowall_nonveg
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-1.572
0.088
-1.206
0.101
-1.174
0.101
-1.222
0.088
-1.121
0.101
-0.965
0.088
-0.868
0.102
-0.368
0.101
-1.065
0.102
-1.456
0.088
-1.318
0.101
-0.976
0.088
-0.366
0.088
1.206
0.101
0.032
0.101
-0.016
0.088
0.085
0.101
0.240
0.088
0.338
0.101
0.838
0.101
0.141
0.101
-0.250
0.088
-0.112
0.101
0.229
0.088
-0.398
0.088
1.174
0.101
-0.032
0.101
-0.048
0.088
0.053
0.101
0.208
0.088
0.306
0.101
0.806
0.101
0.109
0.101
-0.282
0.088
-0.144
0.101
0.198
0.088
-0.350
0.072
1.222
0.088
0.016
0.088
0.048
0.088
0.102
0.088
0.257
0.072
0.354
0.088
0.854
0.088
0.158
0.088
-0.234
0.072
-0.095
0.088
0.246
0.072
Sig. Lower Bound Upper Bound
0.000
-1.86
-1.28
0.000
-1.54
-0.87
0.000
-1.51
-0.84
0.000
-1.51
-0.93
0.000
-1.46
-0.78
0.000
-1.26
-0.67
0.000
-1.21
-0.53
0.017
-0.70
-0.03
0.000
-1.40
-0.73
0.000
-1.75
-1.16
0.000
-1.65
-0.98
0.000
-1.27
-0.68
0.002
-0.66
-0.07
0.000
0.87
1.54
1.000
-0.30
0.37
1.000
-0.31
0.28
1.000
-0.25
0.42
0.236
-0.05
0.53
0.048
0.00
0.67
0.000
0.50
1.17
0.975
-0.19
0.48
0.183
-0.54
0.04
0.997
-0.45
0.22
0.305
-0.06
0.52
0.000
-0.69
-0.11
0.000
0.84
1.51
1.000
-0.37
0.30
1.000
-0.34
0.24
1.000
-0.28
0.39
0.466
-0.08
0.50
0.119
-0.03
0.64
0.000
0.47
1.14
0.997
-0.23
0.45
0.070
-0.57
0.01
0.971
-0.48
0.19
0.558
-0.09
0.49
0.000
-0.59
-0.11
0.000
0.93
1.51
1.000
-0.28
0.31
1.000
-0.24
0.34
0.995
-0.19
0.39
0.024
0.02
0.50
0.004
0.06
0.65
0.000
0.56
1.15
0.856
-0.13
0.45
0.066
-0.47
0.01
0.997
-0.39
0.20
0.039
0.01
0.49
95% Confidence Interval
AIMS II • 12/2005
243
244
AIMS II • 12/2005
context DV
rural ag maint
curved_weedy
straight_islands
straight_biodiv
mow_veg (I)
straight_everdeciduous
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-0.451
0.088
1.121
0.101
-0.085
0.101
-0.053
0.101
-0.102
0.088
0.155
0.088
0.253
0.101
0.753
0.101
0.056
0.101
-0.335
0.088
-0.197
0.101
0.144
0.088
-0.607
0.072
0.965
0.088
-0.240
0.088
-0.208
0.088
-0.257
0.072
-0.155
0.088
0.097
0.088
0.597
0.088
-0.099
0.088
-0.490
0.072
-0.352
0.088
-0.011
0.072
-0.704
0.088
0.868
0.102
-0.338
0.101
-0.306
0.101
-0.354
0.088
-0.253
0.101
-0.097
0.088
0.500
0.101
-0.196
0.102
-0.588
0.088
-0.449
0.101
-0.108
0.088
-1.204
0.088
0.368
0.101
-0.838
0.101
-0.806
0.101
-0.854
0.088
-0.753
0.101
-0.597
0.088
-0.500
0.101
-0.696
0.101
-1.088
0.088
-0.949
0.101
-0.608
0.088
Sig. Lower Bound Upper Bound
0.000
-0.74
-0.16
0.000
0.78
1.46
1.000
-0.42
0.25
1.000
-0.39
0.28
0.995
-0.39
0.19
0.867
-0.14
0.45
0.381
-0.08
0.59
0.000
0.42
1.09
1.000
-0.28
0.39
0.009
-0.63
-0.04
0.768
-0.53
0.14
0.917
-0.15
0.44
0.000
-0.85
-0.37
0.000
0.67
1.26
0.236
-0.53
0.05
0.466
-0.50
0.08
0.024
-0.50
-0.02
0.867
-0.45
0.14
0.997
-0.20
0.39
0.000
0.31
0.89
0.996
-0.39
0.19
0.000
-0.73
-0.25
0.004
-0.64
-0.06
1.000
-0.25
0.23
0.000
-1.00
-0.41
0.000
0.53
1.21
0.048
-0.67
0.00
0.119
-0.64
0.03
0.004
-0.65
-0.06
0.381
-0.59
0.08
0.997
-0.39
0.20
0.000
0.16
0.84
0.778
-0.53
0.14
0.000
-0.88
-0.30
0.001
-0.79
-0.11
0.992
-0.40
0.18
0.000
-1.50
-0.91
0.017
0.03
0.70
0.000
-1.17
-0.50
0.000
-1.14
-0.47
0.000
-1.15
-0.56
0.000
-1.09
-0.42
0.000
-0.89
-0.31
0.000
-0.84
-0.16
0.000
-1.03
-0.36
0.000
-1.38
-0.80
0.000
-1.28
-0.61
0.000
-0.90
-0.32
95% Confidence Interval
context DV
rural ag maint
curved_everdeciduous
curved_evergreen
curved_flowers
mow_veg (I)
curved_brome
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
Mean Difference
(I-J)
Std. Error
-0.508
0.088
1.065
0.102
-0.141
0.101
-0.109
0.101
-0.158
0.088
-0.056
0.101
0.099
0.088
0.196
0.102
0.696
0.101
-0.391
0.088
-0.253
0.101
0.088
0.088
-0.116
0.072
1.456
0.088
0.250
0.088
0.282
0.088
0.234
0.072
0.335
0.088
0.490
0.072
0.588
0.088
1.088
0.088
0.391
0.088
0.138
0.088
0.480
0.072
-0.254
0.088
1.318
0.101
0.112
0.101
0.144
0.101
0.095
0.088
0.197
0.101
0.352
0.088
0.449
0.101
0.949
0.101
0.253
0.101
-0.138
0.088
0.341
0.088
-0.596
0.072
0.976
0.088
-0.229
0.088
-0.198
0.088
-0.246
0.072
-0.144
0.088
0.011
0.072
0.108
0.088
0.608
0.088
-0.088
0.088
-0.480
0.072
-0.341
0.088
Sig. Lower Bound Upper Bound
0.000
-0.80
-0.21
0.000
0.73
1.40
0.975
-0.48
0.19
0.997
-0.45
0.23
0.856
-0.45
0.13
1.000
-0.39
0.28
0.996
-0.19
0.39
0.778
-0.14
0.53
0.000
0.36
1.03
0.001
-0.68
-0.10
0.377
-0.59
0.08
0.999
-0.20
0.38
0.930
-0.36
0.12
0.000
1.16
1.75
0.183
-0.04
0.54
0.070
-0.01
0.57
0.066
-0.01
0.47
0.009
0.04
0.63
0.000
0.25
0.73
0.000
0.30
0.88
0.000
0.80
1.38
0.001
0.10
0.68
0.939
-0.15
0.43
0.000
0.24
0.72
0.162
-0.55
0.04
0.000
0.98
1.65
0.997
-0.22
0.45
0.971
-0.19
0.48
0.997
-0.20
0.39
0.768
-0.14
0.53
0.004
0.06
0.64
0.001
0.11
0.79
0.000
0.61
1.28
0.377
-0.08
0.59
0.939
-0.43
0.15
0.007
0.05
0.63
0.000
-0.84
-0.36
0.000
0.68
1.27
0.305
-0.52
0.06
0.558
-0.49
0.09
0.039
-0.49
-0.01
0.917
-0.44
0.15
1.000
-0.23
0.25
0.992
-0.18
0.40
0.000
0.32
0.90
0.999
-0.38
0.20
0.000
-0.72
-0.24
0.007
-0.63
-0.05
95% Confidence Interval
AIMS II • 12/2005
245
246
AIMS II • 12/2005
context DV
rural ag safe
straight_flowers
straight_brome
straight_weedy
mow_veg (I)
mowall_nonveg
mow_veg (J)
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
0.930
0.087
-0.159
0.086
0.259
0.086
0.359
0.071
0.599
0.086
0.689
0.071
0.691
0.087
0.596
0.086
0.228
0.087
0.178
0.071
0.306
0.086
0.723
0.071
-0.930
0.087
-1.089
0.099
-0.671
0.099
-0.571
0.087
-0.331
0.099
-0.241
0.087
-0.239
0.100
-0.334
0.099
-0.702
0.100
-0.752
0.087
-0.624
0.099
-0.207
0.087
0.159
0.086
1.089
0.099
0.418
0.099
0.518
0.086
0.758
0.099
0.848
0.086
0.850
0.099
0.755
0.099
0.387
0.099
0.337
0.086
0.465
0.099
0.882
0.086
-0.259
0.086
0.671
0.099
-0.418
0.099
0.101
0.086
0.340
0.099
0.430
0.086
0.432
0.099
0.338
0.099
-0.030
0.099
-0.081
0.086
0.048
0.099
0.464
0.086
Sig. Lower Bound Upper Bound
0.000
0.64
1.22
0.827
-0.44
0.13
0.124
-0.03
0.54
0.000
0.12
0.59
0.000
0.31
0.88
0.000
0.45
0.92
0.000
0.40
0.98
0.000
0.31
0.88
0.288
-0.06
0.52
0.375
-0.06
0.41
0.023
0.02
0.59
0.000
0.49
0.96
0.000
-1.22
-0.64
0.000
-1.42
-0.76
0.000
-1.00
-0.34
0.000
-0.86
-0.28
0.048
-0.66
0.00
0.208
-0.53
0.05
0.444
-0.57
0.09
0.044
-0.66
0.00
0.000
-1.03
-0.37
0.000
-1.04
-0.47
0.000
-0.95
-0.29
0.448
-0.49
0.08
0.827
-0.13
0.44
0.000
0.76
1.42
0.002
0.09
0.75
0.000
0.23
0.80
0.000
0.43
1.09
0.000
0.56
1.13
0.000
0.52
1.18
0.000
0.43
1.08
0.007
0.06
0.72
0.006
0.05
0.62
0.000
0.14
0.79
0.000
0.60
1.17
0.124
-0.54
0.03
0.000
0.34
1.00
0.002
-0.75
-0.09
0.995
-0.19
0.39
0.034
0.01
0.67
0.000
0.14
0.72
0.001
0.10
0.76
0.037
0.01
0.67
1.000
-0.36
0.30
0.999
-0.37
0.20
1.000
-0.28
0.38
0.000
0.18
0.75
95% Confidence Interval
context DV
rural ag safe
straight_islands
straight_biodiv
straight_everdeciduous
mow_veg (I)
straight_evergreen
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
curved_weedy
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
Mean Difference
(I-J)
Std. Error
-0.359
0.071
0.571
0.087
-0.518
0.086
-0.101
0.086
0.240
0.086
0.329
0.071
0.331
0.087
0.237
0.086
-0.131
0.087
-0.182
0.071
-0.053
0.086
0.363
0.071
-0.599
0.086
0.331
0.099
-0.758
0.099
-0.340
0.099
-0.240
0.086
0.090
0.086
0.092
0.099
-0.003
0.099
-0.371
0.099
-0.421
0.086
-0.293
0.099
0.124
0.086
-0.689
0.071
0.241
0.087
-0.848
0.086
-0.430
0.086
-0.329
0.071
-0.090
0.086
0.002
0.087
-0.092
0.086
-0.460
0.087
-0.511
0.071
-0.382
0.086
0.034
0.071
-0.691
0.087
0.239
0.100
-0.850
0.099
-0.432
0.099
-0.331
0.087
-0.092
0.099
-0.002
0.087
-0.094
0.099
-0.462
0.100
-0.513
0.087
-0.384
0.099
0.032
0.087
Sig. Lower Bound Upper Bound
0.000
-0.59
-0.12
0.000
0.28
0.86
0.000
-0.80
-0.23
0.995
-0.39
0.19
0.213
-0.05
0.53
0.000
0.09
0.56
0.008
0.04
0.62
0.228
-0.05
0.52
0.953
-0.42
0.16
0.337
-0.42
0.05
1.000
-0.34
0.23
0.000
0.13
0.60
0.000
-0.88
-0.31
0.048
0.00
0.66
0.000
-1.09
-0.43
0.034
-0.67
-0.01
0.213
-0.53
0.05
0.998
-0.20
0.38
0.999
-0.24
0.42
1.000
-0.33
0.33
0.012
-0.70
-0.04
0.000
-0.71
-0.14
0.140
-0.62
0.04
0.969
-0.16
0.41
0.000
-0.92
-0.45
0.208
-0.05
0.53
0.000
-1.13
-0.56
0.000
-0.72
-0.14
0.000
-0.56
-0.09
0.998
-0.38
0.20
1.000
-0.28
0.29
0.998
-0.38
0.19
0.000
-0.75
-0.17
0.000
-0.75
-0.28
0.001
-0.67
-0.10
1.000
-0.20
0.27
0.000
-0.98
-0.40
0.444
-0.09
0.57
0.000
-1.18
-0.52
0.001
-0.76
-0.10
0.008
-0.62
-0.04
0.999
-0.42
0.24
1.000
-0.29
0.28
0.999
-0.42
0.24
0.000
-0.79
-0.13
0.000
-0.80
-0.23
0.007
-0.71
-0.05
1.000
-0.25
0.32
95% Confidence Interval
AIMS II • 12/2005
247
248
AIMS II • 12/2005
context DV
rural ag safe
curved_evergreen
curved_flowers
curved_brome
mow_veg (I)
curved_weedy
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_brome
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_flowers
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_evergreen
curved_everdeciduous
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_everdeciduous
Mean Difference
Std. Error
(I-J)
-0.596
0.086
0.334
0.099
-0.755
0.099
-0.338
0.099
-0.237
0.086
0.003
0.099
0.092
0.086
0.094
0.099
-0.368
0.099
-0.419
0.086
-0.290
0.099
0.126
0.086
-0.228
0.087
0.702
0.100
-0.387
0.099
0.030
0.099
0.131
0.087
0.371
0.099
0.460
0.087
0.462
0.100
0.368
0.099
-0.051
0.087
0.078
0.099
0.494
0.087
-0.178
0.071
0.752
0.087
-0.337
0.086
0.081
0.086
0.182
0.071
0.421
0.086
0.511
0.071
0.513
0.087
0.419
0.086
0.051
0.087
0.129
0.086
0.545
0.071
-0.306
0.086
0.624
0.099
-0.465
0.099
-0.048
0.099
0.053
0.086
0.293
0.099
0.382
0.086
0.384
0.099
0.290
0.099
-0.078
0.099
-0.129
0.086
0.416
0.086
Sig. Lower Bound Upper Bound
0.000
-0.88
-0.31
0.044
0.00
0.66
0.000
-1.08
-0.43
0.037
-0.67
-0.01
0.228
-0.52
0.05
1.000
-0.33
0.33
0.998
-0.19
0.38
0.999
-0.24
0.42
0.014
-0.70
-0.04
0.000
-0.70
-0.13
0.150
-0.62
0.04
0.964
-0.16
0.41
0.288
-0.52
0.06
0.000
0.37
1.03
0.007
-0.72
-0.06
1.000
-0.30
0.36
0.953
-0.16
0.42
0.012
0.04
0.70
0.000
0.17
0.75
0.000
0.13
0.79
0.014
0.04
0.70
1.000
-0.34
0.24
1.000
-0.25
0.41
0.000
0.21
0.78
0.375
-0.41
0.06
0.000
0.47
1.04
0.006
-0.62
-0.05
0.999
-0.20
0.37
0.337
-0.05
0.42
0.000
0.14
0.71
0.000
0.28
0.75
0.000
0.23
0.80
0.000
0.13
0.70
1.000
-0.24
0.34
0.958
-0.16
0.41
0.000
0.31
0.78
0.023
-0.59
-0.02
0.000
0.29
0.95
0.000
-0.79
-0.14
1.000
-0.38
0.28
1.000
-0.23
0.34
0.140
-0.04
0.62
0.001
0.10
0.67
0.007
0.05
0.71
0.150
-0.04
0.62
1.000
-0.41
0.25
0.958
-0.41
0.16
0.000
0.13
0.70
95% Confidence Interval
curved_brome
curved_weedy
straight_biodiv
straight_brome
straight_weedy
mowall_nonveg
boreal
att
mow_veg (I)
curved_everdeciduous
context DV
rural ag safe
mow_veg (J)
mowall_nonveg
straight_weedy
straight_brome
straight_flowers
straight_evergreen
straight_everdeciduous
straight_biodiv
straight_islands
curved_weedy
curved_brome
curved_flowers
curved_evergreen
straight_weedy
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_weedy
Mean Difference
(I-J)
Std. Error
-0.723
0.071
0.207
0.087
-0.882
0.086
-0.464
0.086
-0.363
0.071
-0.124
0.086
-0.034
0.071
-0.032
0.087
-0.126
0.086
-0.494
0.087
-0.545
0.071
-0.416
0.086
-0.169
0.078
-0.102
0.095
-0.368
0.078
-0.294
0.096
-0.038
0.096
0.169
0.078
0.066
0.095
-0.199
0.077
-0.125
0.096
0.130
0.096
0.102
0.095
-0.066
0.095
-0.266
0.095
-0.191
0.111
0.064
0.111
0.368
0.078
0.199
0.077
0.266
0.095
0.074
0.096
0.330
0.096
0.294
0.096
0.125
0.096
0.191
0.111
-0.074
0.096
0.256
0.112
0.038
0.096
-0.130
0.096
-0.064
0.111
-0.330
0.096
-0.256
0.112
Sig. Lower Bound Upper Bound
0.000
-0.96
-0.49
0.448
-0.08
0.49
0.000
-1.17
-0.60
0.000
-0.75
-0.18
0.000
-0.60
-0.13
0.969
-0.41
0.16
1.000
-0.27
0.20
1.000
-0.32
0.25
0.964
-0.41
0.16
0.000
-0.78
-0.21
0.000
-0.78
-0.31
0.000
-0.70
-0.13
0.251
-0.39
0.05
0.891
-0.37
0.17
0.000
-0.59
-0.15
0.028
-0.57
-0.02
0.999
-0.31
0.24
0.251
-0.05
0.39
0.982
-0.20
0.34
0.104
-0.42
0.02
0.784
-0.40
0.15
0.752
-0.14
0.40
0.891
-0.17
0.37
0.982
-0.34
0.20
0.058
-0.54
0.01
0.512
-0.51
0.12
0.992
-0.25
0.38
0.000
0.15
0.59
0.104
-0.02
0.42
0.058
-0.01
0.54
0.972
-0.20
0.35
0.008
0.06
0.60
0.028
0.02
0.57
0.784
-0.15
0.40
0.512
-0.12
0.51
0.972
-0.35
0.20
0.198
-0.06
0.57
0.999
-0.24
0.31
0.752
-0.40
0.14
0.992
-0.38
0.25
0.008
-0.60
-0.06
0.198
-0.57
0.06
95% Confidence Interval
AIMS II • 12/2005
249
250
maint
boreal
AIMS II • 12/2005
DV
nat
context
boreal
straight_weedy
mowall_nonveg
curved_brome
curved_weedy
straight_biodiv
straight_brome
straight_weedy
mow_veg (I)
mowall_nonveg
mow_veg (J)
straight_weedy
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_weedy
straight_weedy
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_brome
straight_biodiv
curved_weedy
curved_brome
Mean Difference
(I-J)
Std. Error
-0.112
0.057
-0.117
0.070
-0.370
0.057
-0.218
0.071
-0.043
0.071
0.112
0.057
-0.005
0.070
-0.258
0.057
-0.106
0.070
0.069
0.070
0.117
0.070
0.005
0.070
-0.253
0.070
-0.101
0.081
0.074
0.081
0.370
0.057
0.258
0.057
0.253
0.070
0.152
0.070
0.327
0.070
0.218
0.071
0.106
0.070
0.101
0.081
-0.152
0.070
0.175
0.082
0.043
0.071
-0.069
0.070
-0.074
0.081
-0.327
0.070
-0.175
0.082
0.813
0.073
0.421
0.090
0.526
0.073
0.679
0.091
0.424
0.091
-0.813
0.073
-0.392
0.089
-0.287
0.073
-0.134
0.090
-0.390
0.090
Sig. Lower Bound Upper Bound
0.359
-0.27
0.05
0.544
-0.32
0.08
0.000
-0.53
-0.21
0.024
-0.42
-0.02
0.990
-0.24
0.16
0.359
-0.05
0.27
1.000
-0.20
0.19
0.000
-0.42
-0.10
0.660
-0.31
0.09
0.925
-0.13
0.27
0.544
-0.08
0.32
1.000
-0.19
0.20
0.004
-0.45
-0.05
0.815
-0.33
0.13
0.944
-0.16
0.31
0.000
0.21
0.53
0.000
0.10
0.42
0.004
0.05
0.45
0.258
-0.05
0.35
0.000
0.13
0.53
0.024
0.02
0.42
0.660
-0.09
0.31
0.815
-0.13
0.33
0.258
-0.35
0.05
0.268
-0.06
0.41
0.990
-0.16
0.24
0.925
-0.27
0.13
0.944
-0.31
0.16
0.000
-0.53
-0.13
0.268
-0.41
0.06
0.000
0.60
1.02
0.000
0.17
0.68
0.000
0.32
0.73
0.000
0.42
0.94
0.000
0.17
0.68
0.000
-1.02
-0.60
0.000
-0.65
-0.14
0.001
-0.50
-0.08
0.675
-0.39
0.12
0.000
-0.65
-0.13
95% Confidence Interval
DV
maint
safe
context
boreal
boreal
curved_brome
curved_weedy
straight_biodiv
straight_brome
straight_weedy
mowall_nonveg
curved_brome
curved_weedy
straight_biodiv
mow_veg (I)
straight_brome
mow_veg (J)
mowall_nonveg
straight_weedy
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_weedy
straight_weedy
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_brome
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_biodiv
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
curved_weedy
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_brome
mowall_nonveg
straight_weedy
straight_brome
straight_biodiv
curved_weedy
Mean Difference
(I-J)
Std. Error
-0.421
0.090
0.392
0.089
0.104
0.089
0.258
0.104
0.002
0.104
-0.526
0.073
0.287
0.073
-0.104
0.089
0.153
0.090
-0.102
0.090
-0.679
0.091
0.134
0.090
-0.258
0.104
-0.153
0.090
-0.256
0.105
-0.424
0.091
0.390
0.090
-0.002
0.104
0.102
0.090
0.256
0.105
0.387
0.071
0.281
0.087
-0.052
0.071
0.328
0.088
0.337
0.088
-0.387
0.071
-0.106
0.087
-0.439
0.071
-0.059
0.088
-0.050
0.088
-0.281
0.087
0.106
0.087
-0.332
0.087
0.047
0.101
0.056
0.101
0.052
0.071
0.439
0.071
0.332
0.087
0.380
0.088
0.388
0.088
-0.328
0.088
0.059
0.088
-0.047
0.101
-0.380
0.088
0.008
0.102
-0.337
0.088
0.050
0.088
-0.056
0.101
-0.388
0.088
-0.008
0.102
Sig. Lower Bound Upper Bound
0.000
-0.68
-0.17
0.000
0.14
0.65
0.853
-0.15
0.36
0.133
-0.04
0.55
1.000
-0.30
0.30
0.000
-0.73
-0.32
0.001
0.08
0.50
0.853
-0.36
0.15
0.535
-0.10
0.41
0.869
-0.36
0.16
0.000
-0.94
-0.42
0.675
-0.12
0.39
0.133
-0.55
0.04
0.535
-0.41
0.10
0.145
-0.56
0.04
0.000
-0.68
-0.17
0.000
0.13
0.65
1.000
-0.30
0.30
0.869
-0.16
0.36
0.145
-0.04
0.56
0.000
0.18
0.59
0.017
0.03
0.53
0.979
-0.25
0.15
0.003
0.08
0.58
0.002
0.08
0.59
0.000
-0.59
-0.18
0.829
-0.35
0.14
0.000
-0.64
-0.24
0.986
-0.31
0.19
0.993
-0.30
0.20
0.017
-0.53
-0.03
0.829
-0.14
0.35
0.002
-0.58
-0.08
0.997
-0.24
0.34
0.994
-0.23
0.35
0.979
-0.15
0.25
0.000
0.24
0.64
0.002
0.08
0.58
0.000
0.13
0.63
0.000
0.14
0.64
0.003
-0.58
-0.08
0.986
-0.19
0.31
0.997
-0.34
0.24
0.000
-0.63
-0.13
1.000
-0.28
0.30
0.002
-0.59
-0.08
0.993
-0.20
0.30
0.994
-0.35
0.23
0.000
-0.64
-0.14
1.000
-0.30
0.28
95% Confidence Interval
AIMS II • 12/2005
251
Landscape Ecology, Perception and Design Lab
www-personal.umich.edu/~nassauer/Labfinal.htm
School of Natural Resources and Environment
University of Michigan
Joan Iverson Nassauer, Erik S. Dayrell, and Zhifang Wang