Exploring the Effects of Yard Management and Neighborhood Influence on Carbon Storage in Residential Subdivisions

The dramatic land-use shift from forest and agricultural to exurban residential land uses creates an excellent opportunity for ecosystem restoration and carbon sequestration through yard design and management. Yard management in a residential subdivision is rarely an autonomous endeavor. Cultural and local norms play an important role in how residents design and maintain their yards. Studies show that residents are influenced by the behavior of their neighbors. Yet, social influence has rarely been incorporated into carbon sequestration studies in residential landscapes. Agent-based modeling offers an ideal framework for exploring how social complexities among humans could affect their environment. An agent-based model called ELMST (Exploratory Land Management and Carbon Storage), was developed to explore how management of individual yards and neighborhood influence could affect carbon storage at the scale of a residential subdivision. The model was run under four scenarios: (tier-0) no management, (tier-1) individual management without influence (tier-2) individual management with opportunity to adapt based on neighbor behaviors, and (tier-3) adaptive management, as in tier-2, but several residents were given an incentive to innovate their yard to a native prairie design upon model start-up. The model was parameterized with interview and fieldwork data from exurban homes Southeast Michigan. Total carbon within the subdivision was compared among scenarios for year 30. Tier-1 showed a significantly higher quantity of carbon than all others, including tier-0 (no management). Results from tier-2 and tier-3 showed a greater variability of carbon storage at the subdivision level, suggesting that a wide range of outcomes can emerge as a result of neighborhood influence and divergent local norms. Considering model sensitivity of individual management behaviors, the model showed that turfgrass fertilization and mowing the lawn while allowing grass clippings to decompose on-site dramatically increased carbon stored at the parcel level, when compared with the no management scenario. Comparatively, removing grass clippings dramatically decreased carbon stored at the parcel level, when compared with the no management scenario. The native prairie innovation was able to propagate through the subdivision in tier-3 in the ELMST model. Prairie-based parcels were shown to store less carbon overall than the conventional lawn-based parcels that were fertilized or mown while allowing grass clippings to remain on-site, but stored more carbon than if grass clippings were removed all together. Model results imply that trade-off between carbon storage and other ecosystem services may need to be considered when developing policies for environmentally-friendly residential landscapes. The ELMST model was developed to be expanded and re-used for a variety of locales, cultures and climates. Results from this study may be used to formulate better research questions and hypothesis, inform data collection, expand intuition of policy makers, and advance the development of agent-based models with regards to coupled human and natural systems.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/78211/1/Hutchins-Thesis-Final-20101013.pd

Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics

Abstract Background Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.http://deepblue.lib.umich.edu/bitstream/2027.42/134740/1/12874_2016_Article_274.pd

Multi‐scale heterogeneity in vegetation and soil carbon in exurban residential land of southeastern Michigan, USA

Exurban residential land (one housing unit per 0.2–16.2 ha) is growing in importance as a human‐dominated land use. Carbon storage in the soils and vegetation of exurban land is poorly known, as are the effects on C storage of choices made by developers and residents. We studied C storage in exurban yards in southeastern Michigan, USA, across a range of parcel sizes and different types of neighborhoods. We divided each residential parcel into ecological zones (EZ) characterized by vegetation, soil, and human behavior such as mowing, irrigation, and raking. We found a heterogeneous mixture of trees and shrubs, turfgrasses, mulched gardens, old‐field vegetation, and impervious surfaces. The most extensive zone type was turfgrass with sparse woody vegetation (mean 26% of parcel area), followed by dense woody vegetation (mean 21% of parcel area). Areas of turfgrass with sparse woody vegetation had trees in larger size classes (> 50 cm dbh) than did areas of dense woody vegetation. Using aerial photointerpretation, we scaled up C storage to neighborhoods. Varying C storage by neighborhood type resulted from differences in impervious area (8–26% of parcel area) and area of dense woody vegetation (11–28%). Averaged and multiplied across areas in differing neighborhood types, exurban residential land contained 5240 ± 865 g C/m2 in vegetation, highly sensitive to large trees, and 13 800 ± 1290 g C/m2 in soils (based on a combined sampling and modeling approach). These contents are greater than for agricultural land in the region, but lower than for mature forest stands. Compared with mature forests, exurban land contained more shrubs and less downed woody debris and it had similar tree size‐class distributions up to 40 cm dbh but far fewer trees in larger size classes. If the trees continue to grow, exurban residential land could sequester additional C for decades. Patterns and processes of C storage in exurban residential land were driven by land management practices that affect soil and vegetation, reflecting the choices of designers, developers, and residents. This study provides an example of human‐mediated C storage in a coupled human–natural system.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/122437/1/eap1313.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122437/2/eap1313_am.pd

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Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics

Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results: In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions: The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily