Final draft mikebularz

Examining Spatial Correlation of Resident’s Wildlife-friendly landscaping in Residential Tran-sects in Chicago

Mike BularzAmy Belaire

BIOS 399 - Inde-pentent Study

3Spatial Correlation of Wildlife Friendly Habitats in Residential Transects

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:

4BIOS 399 - INDEPENDENT STUDY - Mike Bularz

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.


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


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 PreparationSurvey 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


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.


Figure 4a: Mantel R Scores between Front Yards, per Transect

Figure 4c: Mantel R Scores for Wildlife-Friendliness Index

Figure 4b: Mantel R Scores between Back Yards Figure 4d: Mantel R scores for Easily-Implementable Wildlife-Friendly Features


3.2 Mantel Correlograms (select transects illustrating typical patterns):

Figure 6: Wilmette Transect Mantel Correlograms

6a: Back Yard6b: Front Yard

6c: WFI

6d: WFI - EasyWilmette 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.


Figure 9: Everett Transect Mantel Correlograms

9a: Back Yard

9b: Front 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


Figure10: Lawn Transect Mantel Correlograms

Back Yard Front Yard

WFI

WFI - Easy

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.


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.


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.

REFERENCES

Daniels, G.D., and J.B. Kirpatrick. “Comparing the Characteristics of Front and Back Domestic Gardens in Hobart, Tasmania, Australia.” Landscape and Urban Planning78.4 (2006): 344-52. Print.

Daniels, G.D., and J.B. Kirpatrick, and T. Zagorski. “Explaining variation in front gardens between suburbs

of Hobart, Tasmania, Australia” Landscape and Urban Planning79.3-4 (2007): 314-22. Web.

Grove, J. M., A. R. Troy, J. P. M. O’Neil-Dunne, W. R. Burch, Jr., M. L. Cadenasso, and S. T. A. Pickett. “Characterization of Households and Its Implications for the Vegetation of Urban Ecosystems.” Ecosystems 9 (2006): 578-97. Print.

Hunter, Mary Carol R., and Daniel G. Brown. Landscape and Urban Planning 105.3 (2012): 407-16. Print.

Nassauer, Joan Iverson, Zhifang Wang, and Erik Dayrell. “What Will the Neighbors Think? Cultural Norms and Ecological Design.” Landscape and Urban Planning 92.3-4 (2009): 282-92. Web.

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