Final draft mikebularz

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Final draft mikebularz

  1. 1. Examining Spatial Correlationof Resident’s Wildlife-friendlylandscaping in Residential Tran-sects in Chicago Mike Bularz Amy Belaire BIOS 399 - Inde- pentent Study
  2. 2. 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 Bularz1. Introduction1.1 Residential Urban EcologyStudies in urban ecology have traditionally focused on availability and quality of open space inurbanized 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 studiesin urban ecology have suggested that the design and composition of residential areas may also benefitlocal wildlife species. Researchers have attempted to define the distribution patterns of yard vegetationand 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 mimicryor the idea of “keeping up with the Joneses” may be a powerful motivation for certain types of yarddesigns (Zmyslony and Gagnon, 1997; Daniels and Kirpatrick, 2006; Kirpatrick et al., 2006; Nassaueret al., 2009; Goddard et al., 2012).Urban ecological researchers in residential systems have examined the distribution patterns andplausible 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 concentrationsof features such as bird feeders, bird houses, water features, multiple layers of vegetation, andnative-plant landscaping. For example, Zmyslony and Gagnon analyze distance between propertiesand similarities using Mantel Correlograms, while Daniels and Kirpatrick break down front-yardcomposition of flora and man-made landscape features such as walkways and driveways. Statisticallysignificant concentrations of these features can indicate the degree of how one property affectsadjacent neighbors’ gardening habits. (Zmyslony and Gagnon, 1997).Studies increasingly seem to suggest spatial proximity affects the distribution of these features tosome degree, both positively and negatively. Research considering social factors affecting neighborsimilarities is adding another dimension to our understanding of yard compositions in urbanlandscapes. For instance, research by Nassauerand others implies that social factors and similarityof neighbors’ cultural background are being made and are leading to research into why neighbors orsocial trends in greater society impact residents to conform, imitate, or sometimes contradict theirneighbors (Nassauer et al.; 2009). Further, studies by assert that broader socioeconomic indicatorsof 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 byexamining similarities and differences between the landscaping of properties within residentialtransects. Our study addresses the following research questions:
  3. 3. BIOS 399 - INDEPENDENT STUDY - Mike Bularz4 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.
  4. 4. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects 5 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
  5. 5. BIOS 399 - INDEPENDENT STUDY - Mike Bularz6 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
  6. 6. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects 7of similarity or dissimilarity in neighbors yards. Figure 3: Calculated Matrices and Three Mantel Tests2.3.3 Mantel CorrelelogramsMantel Correlelograms depict Mantel scores over increasing distances between yards. ProducingMantel Correlograms further enhanced our result set by illustrating variation Mantel R- values across60 distance classes (Figures 6-10). Distance classes, measured about 16.67m each for the transects. Thisdistance was chosen because it was an average between nearest neighbors. Breaking down the degreeof correlation by distance classes helped visualize the extent of neighbor likeness and at what distanceneighbor effect ceases to have influence on yard composition.3. RESULTS3.1 Mantel Scores for TransectsWhen examining spatial dependency at the transect level, overall correlation was low, with R-valueeither close to 0, or negative on average. The highest score a Mantel test for correlation cantheoretically 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 yardscores: Keystone, Gage, Central, and Keota. Overall, Mantel R-values for WFI scores were low, except for Wilmette and 44th transects. The SimpleWFI score aligns with the overall WFI score for the most part, with slightly less distance from 0 thanthe overall index.
  7. 7. BIOS 399 - INDEPENDENT STUDY - Mike Bularz8 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
  8. 8. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects 9 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.
  9. 9. BIOS 399 - INDEPENDENT STUDY - Mike Bularz10 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
  10. 10. Spatial Correlation of Wildlife Friendly Habitats in Residential Transects 11 Figure10: Lawn Transect Mantel Correlograms Back Yard Front Yard WFI - Easy WFILawn Transect has high variation in back yard scores, as compared to front yards. The WFI and easy WFI for Lawn reachhigh scores compared to the overall scores of all transects within the first 10 to 15 distance classes.
  11. 11. BIOS 399 - INDEPENDENT STUDY - Mike Bularz12 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.
  12. 12. BIOS 399 - INDEPENDENT STUDY - Mike Bularz13 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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