1. Examining Spatial Correlation
of Resident’s Wildlife-friendly
landscaping in Residential Tran-
sects in Chicago
Mike Bularz
Amy Belaire
BIOS 399 - Inde-
pentent Study
3. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects
3
Examining Spatial Correlation of Residents’ Wildlife-friendly landscaping
in Forest-residential transects in Chicago.
Mike Bularz
1. Introduction
1.1 Residential Urban Ecology
Studies in urban ecology have traditionally focused on availability and quality of open space in
urbanized regions in the context of public spaces such as parks, forest preserves, and right of ways
(Rowntree, 2008). While this accounts for some of the wildlife habitat in urban areas, recent studies
in urban ecology have suggested that the design and composition of residential areas may also benefit
local wildlife species. Researchers have attempted to define the distribution patterns of yard vegetation
and wildlife-friendly features on privately-owned property in residential areas (Zmyslony and Gagnon,
1997; Daniels and Kirpatrick, 2006). Previous studies on these patterns indicate that neighbor mimicry
or the idea of “keeping up with the Joneses” may be a powerful motivation for certain types of yard
designs (Zmyslony and Gagnon, 1997; Daniels and Kirpatrick, 2006; Kirpatrick et al., 2006; Nassauer
et al., 2009; Goddard et al., 2012).
Urban ecological researchers in residential systems have examined the distribution patterns and
plausible causes of yard design from a few different perspectives: spatial proximity of like features,
social or socioeconomic factors, or both.
Studies focusing on spatial proximity of wildlife-friendly features look at geographic concentrations
of features such as bird feeders, bird houses, water features, multiple layers of vegetation, and
native-plant landscaping. For example, Zmyslony and Gagnon analyze distance between properties
and similarities using Mantel Correlograms, while Daniels and Kirpatrick break down front-yard
composition of flora and man-made landscape features such as walkways and driveways. Statistically
significant concentrations of these features can indicate the degree of how one property affects
adjacent neighbors’ gardening habits. (Zmyslony and Gagnon, 1997).
Studies increasingly seem to suggest spatial proximity affects the distribution of these features to
some degree, both positively and negatively. Research considering social factors affecting neighbor
similarities is adding another dimension to our understanding of yard compositions in urban
landscapes. For instance, research by Nassauerand others implies that social factors and similarity
of neighbors’ cultural background are being made and are leading to research into why neighbors or
social trends in greater society impact residents to conform, imitate, or sometimes contradict their
neighbors (Nassauer et al.; 2009). Further, studies by assert that broader socioeconomic indicators
of social stratification affect the quality of neighborhood landscape characteristics and yard upkeep
(Grove et al., 2006).
We further examine the question of whether neighbors’ gardening habits affect each other by
examining similarities and differences between the landscaping of properties within residential
transects. Our study addresses the following research questions:
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1) Are front yards and back yards more alike between nearby neighbors than between more
distant neighbors? We compare the number of vegetative layers between neighboring properties’ front
and back yards to determine this. We hypothesized that there might be notable differences between
back yard care and front, as these offer different degrees of privacy.
2) Do neighbors affect each other’s yard landscaping in other respects, such as their adoption
of wild-life friendly features? We hypothesized that nearby neighbors have an affect on each others’
implementation of wildlife-friendly features.
Understanding these fine-scale differences will help inform social aspect of neighborhood
characteristics, such as whether back or front yards are more likely to mimic each other. This study
helps explain the mechanisms that drive residents to design their yards in various ways, which
ultimately has implications for biodiversity in urban ecosystems. We addressed these questions
through interdisciplinary methods including a social survey instrument, GIS (Geographic Information
Systems), and statistical packages such as PC Ord and Passage to calculate spatial statistics on the
property characteristics.
2. Methods
2.1 Study Sites
Our study sites are 1km transects in residential areas near forest preserves in Cook County, Illinois
(Figure 1). The study sites were chosen to represent relatively homogenous residential transects
(Figure 2). We selected sites where land use was primarily residential with a canopy cover of greater
than 15% (determined using the USDA / USGS National Land Cover Dataset, 2006). We looked for
transect sites adjacent to forest preserves of uniform size and shape as well as minimal variation in
other characteristics such as streams.
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Figure 1: Transect Sites
Transect sites, titled after the street name which they follow: Sibley, Lemai, Bloomingdale, Hull,
Southcote, 44th, Bonita, Wildwood, Lawn, Augusta, Cleveland, Everett, Linden, Forest, Ash, Wilmette,
Bristol, Clausen, Oak, Delaplaine, Grant, Lindenwood, Keota, Central, Gage, Keystone.
Fig2. Transect bounds
Common structure of a Residential Transect. Paths follow the road direction out to 1km from a forest
preserve and are 50m wide from the road centerline.
2.2 Survey Method
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Necessary information about residents’ yards was collected through a survey instrument, delivered in
a DOPU (Drop-off, Pick-up) method at transect residents’ homes (SEE APPENDIX). Questions were
asked in 4 broad categories: 1) Respondent’s yard management practices and vegetation composition
of front and back yard, 2) Presence of wildlife-friendly features such as bird feeders, 3) perception
/ attitudes towards birds and nature, and 4) demographic details. The collected information used for
the purpose of this study includes the number of vegetation layers (to quantify vegetation structural
diversity) and wildlife-friendly features.
2.3 Analysis Method
2.3.1 Data Preparation
Survey responses were coded into special survey software and were then appended to parcel (property
boundary) shapefiles of Cook County in GIS. Parcel centroids (central point in the property) were
determined in ArcGIS, and then geographic distances between all pairs of properties were calculated
using the Geospatial Modeling Environment tool (Geospatial Modeling Environment tool).
Corresponding tables for the properties were generated representing our comparison variables: 1)
Structural Diversity in front yards, 2) Strucutral Diversity in backyards, 3) Wildlife Friendliness Index,
and 4) Simple WFI
For front and back yards, we determined the difference in yard vegetation structural diversity for pairs
of yards within each transect. This was done by calculating the difference between yard pairs by adding
up number of vegetative layers respondents reported in their front and back yards. These were used as
an input distance matrix in Mantel tests.
For WFI and Simple WFI, we used indices devised to compare wildlife-friendliness of properties. Our
primary Wildlife – Friendliness Index (WFI) consisted of totaling the number of the following of self-
reported features: flowers, fruit-yielding vegetation, shrubs, evergreen trees, deciduous trees, vegetation
planted with the goal of attracting birds, native plant species, water features, brush pile / open compost,
birdhouses, and bird feeders. The WFI value for a particular parcel, then, could range from zero to 11,
depending on the number of features a respondent reported in the survey. A second index was devised
from a subset of these features that are easier and less time-intensive to implement: water features,
brush pile/open compost areas, birdhouses, and bird feeders. We termed this second index, “Simple
WFI”.
2.3.2 Mantel Test
We used Mantel tests to determine the relationship between geographic distance and differences
between yards. In other words, Mantel tests reveal whether yards that are closer together are more
similar in terms of our four indices: front yard vegetation structural diversity, back yard vegetation
structural diversity, WFI, and Simple WFI.
The Mantel test compares the values between the matrices and summarizes the relationship with an
R-value (and associated p-value) indicating level of correlation. The Mantel test will indicate the degree
of spatial correlation of the four feature distributions we are examining, and can help us determine
whether neighbors affect each others’ back and front yards, as well as wildlife-friendliness of the
property.
The tests were repeated for each transect, providing a localized statistic. A global calculation for all
transects overall was not performed as we’re not testing a correlation between neighborhoods far
removed throughout the study area. Instead, we aggregated the level of correlation by calculating the
average R-value across the whole set of local results, this gives an overall representation of the degree
7. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects
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of similarity or dissimilarity in neighbors yards.
Figure 3: Calculated Matrices and Three Mantel Tests
2.3.3 Mantel Correlelograms
Mantel Correlelograms depict Mantel scores over increasing distances between yards. Producing
Mantel Correlograms further enhanced our result set by illustrating variation Mantel R- values across
60 distance classes (Figures 6-10). Distance classes, measured about 16.67m each for the transects. This
distance was chosen because it was an average between nearest neighbors. Breaking down the degree
of correlation by distance classes helped visualize the extent of neighbor likeness and at what distance
neighbor effect ceases to have influence on yard composition.
3. RESULTS
3.1 Mantel Scores for Transects
When examining spatial dependency at the transect level, overall correlation was low, with R-value
either close to 0, or negative on average. The highest score a Mantel test for correlation can
theoretically achieve is 1. Front yards, on average across all transects, achieved an R- score of 0.023,
and back yards 0.035. There were transects which consistently showed higher scores, such as Wilmette,
Southcote, and Clausen, as well as some that had a high discrepancy between front and back yard
scores: Keystone, Gage, Central, and Keota.
Overall, Mantel R-values for WFI scores were low, except for Wilmette and 44th transects. The Simple
WFI score aligns with the overall WFI score for the most part, with slightly less distance from 0 than
the overall index.
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Figure 4a: Mantel R Scores between Front Yards, Figure 4c: Mantel R Scores for Wildlife-
per Transect Friendliness Index
Figure 4b: Mantel R Scores between Back Yards Figure 4d: Mantel R scores for Easily-
Implementable Wildlife-Friendly Features
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3.2 Mantel Correlograms (select transects illustrating typical patterns):
Figure 6: Wilmette Transect Mantel Correlograms
6b: Front Yard
6a: Back Yard
6c:
WFI
6d: WFI - Easy
Wilmette carried higher R-value across longer distance classes than other data sets. The R-score
maintains a higher value overall, but does go below 0 (meaning no correlation) or negative (negative
correlation) within the first 10-15 distance classes. Back yards maintain a higher score than front
yards, and the WFI doesn’t reach negative or 0 until the farthest distance classes.
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Figure 9: Everett Transect Mantel Correlograms
9b: Front Yard
9a: Back Yard
9c: WFI 9d: WFI - Easy
The Everett Transect has WFI scores close to 0, with high variation between back yard scores as
compared to the rest of the measures.
V
11. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects
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Figure10: Lawn Transect Mantel Correlograms
Back Yard Front Yard
WFI - Easy
WFI
Lawn Transect has high variation in back yard scores, as compared to front yards. The WFI and easy WFI for Lawn reach
high scores compared to the overall scores of all transects within the first 10 to 15 distance classes.
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Figure 11: Average of all Mantel Correlograms
The above is an average of corellograms for all transects. Front and back yards overall stay above 0,
while the WFI and WFI easy vary greatly, and often fall into the negative. The average of all of these
measures is represented as the thick black line.
3. DISCUSSION
A substantial number of transect received higher R-values for front yard correlation than back yards.
This might suggest that front yards are more curated to appeal to neighbors and neighborhood. This
reaffirms studies suggesting that the front yard is designed as a face to the outer world, and is considered
more of a public space than a back yard. Comparison of neighbors based on further indicators, such as
socio-economic status, preferences for / against birds, and other methods to compare likelihood may
further explain these results. Future study of similarity of neighbors yards and similarity of neighbors
themselves should be undertaken with the data we collected. It would also be valuable to determine
which factor, such as income, profession, value of wildlife, and other indicators collected have the
highest effect on presence / absence of features through relating these factors to WFI.
The Mantel Correlelograms highlight how rapidly the Mantel value drops over distance classes for the
majority of transects. This may suggests that only the closest neighbors affect each other. For certain
Transects, the R-value maintains a higher score over further distance classes. Examining indicators for
these, whether they be socioeconomic or values-based may tell if there is an underlying cause for this,
and may also explain rapid drops below zero and overall negative R-values for certain other transects.
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The Simple WFI did not seem to have significant difference from the regular WFI, other than that it
was often lower. This may be because features that are harder to implement, such as large trees and
shrubs, may be part of a neighborhood’s general composition, and not present because neighbors were
mimicking each other. Examining the other features only may indicate neighborhood age and stability
from change over time.
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