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Relationships among socioeconomic affluence,
yard management, and biodiversity
Speaker: Vitor Vieira Vasconcelos
Co-authors: Jesse C. Jones, Olesya Malakhova, Nicholas W. Taylor, Ricky W. Telg, Wendy L. Wilber, M. Jennison Kipp, Eben N.
Broadbent, Jiangxiao Qiu, Adam G. Dale, Kacey A. Russo, and Basil V. Iannone III
IALE – North America Annual Meeting, April 12 – 16, 2021
The world is urbanizing…
World Population
People’s contact with nature
and access to ecosystem
services shift from wild areas to
their yards
Socio-environmental (In)justice
• Socioeconomic inequality
• Cultural diversity
• Unequal access to ecosystem
services
• Unequal impact on supply of
ecosystem services
Leong, M., et al., 2018. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biology Letters, 14(5),
p.20180082.
Lin, B.B., et al. 2017. How green is your garden?: Urban form and socio-demographic factors influence yard vegetation,
visitation, and ecosystem service benefits. Landscape and Urban Planning, 157, pp.239-246.
Morera, M.C., et al. 2020. Determinants of landscape irrigation water use in Florida-friendly yards. Environmental
management, 65(1), pp.19-31.
What influences people’s design and
management choices in their yards?
•Socioeconomic affluence
•Lifestyle / cultural values
•Expectations from neighboors
Hope, D., et al. 2003. Socioeconomics drive urban plant diversity. PNAS, 100:8788–8792
Locke, D.H., et al. , 2018. Social norms, yard care, and the difference between front and back yard management: examining
the landscape mullets concept on urban residential lands. Society & Natural Resources, 31(10), pp.1169-1188.
Harris, E.M., Martin, D.G., Polsky, C., Denhardt, L. and Nehring, A., 2013. Beyond “Lawn People”: The role of emotions in
suburban yard management practices. The Professional Geographer, 65(2), pp.345-361.
Affects yards
biodiversity
Survey
• 102 respondents
• Perceived species richness
• Maintenance (practices, inputs, intensity)
• Water usage (H2OSAV)
• Socioeconomic data (Alachua Co. Appraisal)
Monterey & The Valley
Westmoreland
Debra Height
Greater
Northeast
Community
Florida
Alachua
County
Gainesville
Comparing intensity of yard management tasks
Legend: Mean Intensity
Number of answers for each activity/intensity (proportional to bar length)
N = 102 residents
House value
House size
(sqft)
Parcel
acreage
Percentage of
irrigated yard
Water use
per irrigable
acre
Sprinkler
irrigation
Barrel/cistern
irrigation
Socio-
economic
Water use
Automatic
inground
irrigation
Raking
leaves
intensity
Lawn
fertilization
intensity
Lawn
fertilization
frequency
Nutrient
management
Loadings:
Loadings:
Loadings:
Average variance
extracted:
48%
Average variance
extracted:
37%
Average variance
extracted:
21%
r2 = 0.32***
r2 = 0.37***
0.54***
[0.30, 0.88]
0.80***
[0.67, 1.02]
0.71***
[0.51, 0.99]
0.60***
[0.45, 0.96]
0.70***
[0.46, 1.09]
0.50**
[0.22, 0.89]
0.37*
[0.07, 0.78]
0.52***
[0.30, 0.88]
P-values:
* ≤ 0.05
** ≤ 0.01
*** ≤ 0.001
0.44**
[0.33, 0.96]
0.52***
[0.30, 0.90]
- 0.40*
[-0.79, -0.17]
Socioeconomic affluence increases natural resources demand in yards
Structural Equation Model with Consistent Partial Least Squares (cPLS) Path Modelling
Redundancy Analysis:
Socioeconomic affluence on
Nutrient Management
parcel acreage
property
value
house size
fertilization
frequency
fertilization intensity
raking
leaves
RDA 1
RDA
2
Partition of Variation
Residual variance = 85.36%
Socioeconomic
14.64
Redundancy Analysis
Explained
variance (%)
P-value
(Anova)
Socioeconomic
All socioeconomic variables
(house price, size and parcel
acreage)
14.64 0.0003
House value 3.52 0.03
House size 8.48 0.0004
Parcel acreage 11.05 0.00001
Space
4 neighborhoods together (3
binary variables)
8.59 0.007
Center/border of the city 8.26 0.001
Greater northeast community 4.24 0.01
Debra heights 1.31 0.25
Shadowlawn Estates 4.49 0.01
Westmoreland 1.74 0.15
Partition of Variation
Redundancy Analysis
Explained
variance (%)
P-value
(Anova)
Socioeconomic
All socioeconomic variables
(house price, size and parcel
acreage)
14.64 0.0003
House value 3.52 0.03
House size 8.48 0.0004
Parcel acreage 11.05 0.00001
Space
4 neighborhoods together (3
binary variables)
8.59 0.007
Center/border of the city 8.26 0.001
Greater northeast community 4.24 0.01
Debra heights 1.31 0.25
Shadowlawn Estates 4.49 0.01
Westmoreland 1.74 0.15
Monterey & The Valley
Westmoreland
Debra Height
Greater
Northeast
Community
Partition of Variation
Redundancy Analysis
Explained
variance (%)
P-value
(Anova)
Socioeconomic
All socioeconomic variables
(house price, size and parcel
acreage)
14.64 0.0003
House value 3.52 0.03
House size 8.48 0.0004
Parcel acreage 11.05 0.00001
Space
4 neighborhoods together (3
binary variables)
8.59 0.007
East/West of the city 8.26 0.001
Greater northeast community 4.24 0.01
Debra heights 1.31 0.25
Shadowlawn Estates 4.49 0.01
Westmoreland 1.74 0.15
Monterey & The Valley
Westmoreland
Debra Height
Greater
NE
Community
East/
West
East
West
Partition of Variation
Redundancy Analysis
Explained
variance (%)
P-value
(Anova)
Socioeconomic
All socioeconomic variables
(house price, size and parcel
acreage)
14.64 0.0003
House value 3.52 0.03
House size 8.48 0.0004
Parcel acreage 11.05 0.00001
Space
4 neighborhoods together (3
binary variables)
8.59 0.007
East/West of the city 8.26 0.001
Greater northeast community 4.24 0.01
Debra heights 1.31 0.25
Monterey & The Valley 4.49 0.01
Westmoreland 1.74 0.15
Monterey & The Valley
Westmoreland
Debra Height
East
West
East/
West
4.22
Greater
Northeast
Community
Greater
NE
Community
Luxury effect not evident
•No clear effect of property value on homeowner-
reported plant species richness
•Kendall correlation tests
Front yards: rt = 0.04, p = 0.60
Back yards: rt = 0.07, p = 0.35
Professional maintenance negatively related to
plant species richness in back yards
1.82 times more likely to report less plant species
richness in back yards maintained by professionals
Cumulative link model, controlling for the 4 neighborhoods as nominal effects
Mean
Front yards Back yards
p = 0.03
p = 0.50
Conclusions
•Socioeconomic affluence increases natural resources
consumption (fertilizers and water)
•Yard biodiversity
Less related to socioeconomic affluence
More related to maintenance style and aesthetic preferences
•Socioeconomic patterns may partially overlap cultural
spatial patterns
Integrating Landscape Ecology and Socioeconomics
• Structural Equation Modeling is a promissing technique for socio-
ecological systems
 Still lacks extensions to incorporate spatial information
• Redundancy analysis
 Can be used with socioeconomic vs ecological data
 Variance partitioning in nested spatial designs
• Cumulative link models
 Potential to use with ordinal data from surveys and citizen science
 Incorporation of space as nominal, random or scale effects
Next steps
• Expanding the survey to other neighborhoods
• Other socioeconomic and cultural characteristics
• With homeowners association (HOA) vs without
• Relationships of socioeconomic variables on perceived vs
actual biodiversity
• Other statistical approaches for spatial analysis of
socioecological systems:
• Dimensionality reduction
• Cluster analysis
Acknowledgments
• SEEDIT funding: UF Florida Agricultural Experiment Station
• UF|IFAS Program for Resource Efficient Communities
• USDA McIntire-Stennis Capacity Grant (#1012120)
• Residents that agreed to collaborate (IRB approved n. IRB202002223)

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Relationships among socioeconomic affluence, yard management, and biodiversity

  • 1. Relationships among socioeconomic affluence, yard management, and biodiversity Speaker: Vitor Vieira Vasconcelos Co-authors: Jesse C. Jones, Olesya Malakhova, Nicholas W. Taylor, Ricky W. Telg, Wendy L. Wilber, M. Jennison Kipp, Eben N. Broadbent, Jiangxiao Qiu, Adam G. Dale, Kacey A. Russo, and Basil V. Iannone III IALE – North America Annual Meeting, April 12 – 16, 2021
  • 2. The world is urbanizing… World Population
  • 3. People’s contact with nature and access to ecosystem services shift from wild areas to their yards
  • 4. Socio-environmental (In)justice • Socioeconomic inequality • Cultural diversity • Unequal access to ecosystem services • Unequal impact on supply of ecosystem services Leong, M., et al., 2018. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biology Letters, 14(5), p.20180082. Lin, B.B., et al. 2017. How green is your garden?: Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits. Landscape and Urban Planning, 157, pp.239-246. Morera, M.C., et al. 2020. Determinants of landscape irrigation water use in Florida-friendly yards. Environmental management, 65(1), pp.19-31.
  • 5. What influences people’s design and management choices in their yards? •Socioeconomic affluence •Lifestyle / cultural values •Expectations from neighboors Hope, D., et al. 2003. Socioeconomics drive urban plant diversity. PNAS, 100:8788–8792 Locke, D.H., et al. , 2018. Social norms, yard care, and the difference between front and back yard management: examining the landscape mullets concept on urban residential lands. Society & Natural Resources, 31(10), pp.1169-1188. Harris, E.M., Martin, D.G., Polsky, C., Denhardt, L. and Nehring, A., 2013. Beyond “Lawn People”: The role of emotions in suburban yard management practices. The Professional Geographer, 65(2), pp.345-361. Affects yards biodiversity
  • 6. Survey • 102 respondents • Perceived species richness • Maintenance (practices, inputs, intensity) • Water usage (H2OSAV) • Socioeconomic data (Alachua Co. Appraisal) Monterey & The Valley Westmoreland Debra Height Greater Northeast Community Florida Alachua County Gainesville
  • 7. Comparing intensity of yard management tasks Legend: Mean Intensity Number of answers for each activity/intensity (proportional to bar length) N = 102 residents
  • 8. House value House size (sqft) Parcel acreage Percentage of irrigated yard Water use per irrigable acre Sprinkler irrigation Barrel/cistern irrigation Socio- economic Water use Automatic inground irrigation Raking leaves intensity Lawn fertilization intensity Lawn fertilization frequency Nutrient management Loadings: Loadings: Loadings: Average variance extracted: 48% Average variance extracted: 37% Average variance extracted: 21% r2 = 0.32*** r2 = 0.37*** 0.54*** [0.30, 0.88] 0.80*** [0.67, 1.02] 0.71*** [0.51, 0.99] 0.60*** [0.45, 0.96] 0.70*** [0.46, 1.09] 0.50** [0.22, 0.89] 0.37* [0.07, 0.78] 0.52*** [0.30, 0.88] P-values: * ≤ 0.05 ** ≤ 0.01 *** ≤ 0.001 0.44** [0.33, 0.96] 0.52*** [0.30, 0.90] - 0.40* [-0.79, -0.17] Socioeconomic affluence increases natural resources demand in yards Structural Equation Model with Consistent Partial Least Squares (cPLS) Path Modelling
  • 9. Redundancy Analysis: Socioeconomic affluence on Nutrient Management parcel acreage property value house size fertilization frequency fertilization intensity raking leaves RDA 1 RDA 2
  • 10. Partition of Variation Residual variance = 85.36% Socioeconomic 14.64 Redundancy Analysis Explained variance (%) P-value (Anova) Socioeconomic All socioeconomic variables (house price, size and parcel acreage) 14.64 0.0003 House value 3.52 0.03 House size 8.48 0.0004 Parcel acreage 11.05 0.00001 Space 4 neighborhoods together (3 binary variables) 8.59 0.007 Center/border of the city 8.26 0.001 Greater northeast community 4.24 0.01 Debra heights 1.31 0.25 Shadowlawn Estates 4.49 0.01 Westmoreland 1.74 0.15
  • 11. Partition of Variation Redundancy Analysis Explained variance (%) P-value (Anova) Socioeconomic All socioeconomic variables (house price, size and parcel acreage) 14.64 0.0003 House value 3.52 0.03 House size 8.48 0.0004 Parcel acreage 11.05 0.00001 Space 4 neighborhoods together (3 binary variables) 8.59 0.007 Center/border of the city 8.26 0.001 Greater northeast community 4.24 0.01 Debra heights 1.31 0.25 Shadowlawn Estates 4.49 0.01 Westmoreland 1.74 0.15 Monterey & The Valley Westmoreland Debra Height Greater Northeast Community
  • 12. Partition of Variation Redundancy Analysis Explained variance (%) P-value (Anova) Socioeconomic All socioeconomic variables (house price, size and parcel acreage) 14.64 0.0003 House value 3.52 0.03 House size 8.48 0.0004 Parcel acreage 11.05 0.00001 Space 4 neighborhoods together (3 binary variables) 8.59 0.007 East/West of the city 8.26 0.001 Greater northeast community 4.24 0.01 Debra heights 1.31 0.25 Shadowlawn Estates 4.49 0.01 Westmoreland 1.74 0.15 Monterey & The Valley Westmoreland Debra Height Greater NE Community East/ West East West
  • 13. Partition of Variation Redundancy Analysis Explained variance (%) P-value (Anova) Socioeconomic All socioeconomic variables (house price, size and parcel acreage) 14.64 0.0003 House value 3.52 0.03 House size 8.48 0.0004 Parcel acreage 11.05 0.00001 Space 4 neighborhoods together (3 binary variables) 8.59 0.007 East/West of the city 8.26 0.001 Greater northeast community 4.24 0.01 Debra heights 1.31 0.25 Monterey & The Valley 4.49 0.01 Westmoreland 1.74 0.15 Monterey & The Valley Westmoreland Debra Height East West East/ West 4.22 Greater Northeast Community Greater NE Community
  • 14. Luxury effect not evident •No clear effect of property value on homeowner- reported plant species richness •Kendall correlation tests Front yards: rt = 0.04, p = 0.60 Back yards: rt = 0.07, p = 0.35
  • 15. Professional maintenance negatively related to plant species richness in back yards 1.82 times more likely to report less plant species richness in back yards maintained by professionals Cumulative link model, controlling for the 4 neighborhoods as nominal effects Mean Front yards Back yards p = 0.03 p = 0.50
  • 16. Conclusions •Socioeconomic affluence increases natural resources consumption (fertilizers and water) •Yard biodiversity Less related to socioeconomic affluence More related to maintenance style and aesthetic preferences •Socioeconomic patterns may partially overlap cultural spatial patterns
  • 17. Integrating Landscape Ecology and Socioeconomics • Structural Equation Modeling is a promissing technique for socio- ecological systems  Still lacks extensions to incorporate spatial information • Redundancy analysis  Can be used with socioeconomic vs ecological data  Variance partitioning in nested spatial designs • Cumulative link models  Potential to use with ordinal data from surveys and citizen science  Incorporation of space as nominal, random or scale effects
  • 18. Next steps • Expanding the survey to other neighborhoods • Other socioeconomic and cultural characteristics • With homeowners association (HOA) vs without • Relationships of socioeconomic variables on perceived vs actual biodiversity • Other statistical approaches for spatial analysis of socioecological systems: • Dimensionality reduction • Cluster analysis
  • 19. Acknowledgments • SEEDIT funding: UF Florida Agricultural Experiment Station • UF|IFAS Program for Resource Efficient Communities • USDA McIntire-Stennis Capacity Grant (#1012120) • Residents that agreed to collaborate (IRB approved n. IRB202002223)