The first 12 years of the Baltimore Ecosystem Study (BES) were guided by three overarching questions that addressed 1) structure of ecological, physical, and social components of the urban ecosystem, 2) fluxes of materials, energy, human-, social-, and built-capital, and 3) development and use of ecological understanding of the metropolitan system.
Characterization of social patch structure enabled us to 1) assess changes in social structure over time (Boone et al. 2009b, Lord & Norquist 2010, Boone et al. 2011), 2) analyze cause and effect relationships between social processes and biophysical structures and processes (Band et al. 2006, Pickett et al. 2008, Troy et al. 2007, Troy & Grove 2008, Zhou et al. 2008, 2009a, 2009b, Boone et al. 2009a, 2009b, Vemuri et al. 2009), 3) evaluate temporal complexity such as lags, legacies, and slow processes (Boone et al. 2009, Lord & Norquist 2010, Boone et al. 2011, Boone et al. 2014), and 4) elucidate system resiliency (Boone 2002, 2003, Grove 2009). We enhanced Claritas' PRIZM lifestyle market classification to include social and biophysical characteristics of neighborhoods and test the theory of an Ecology of Prestige (Grove et al. 2005, 2006a, 2006b, Troy et al. 2007, Baker et al. 2007, Boone et al. 2009b, Zhou et al. 2008, 2009a, 2009b, Fraser et al. 2013) in Baltimore, Plum Island LTER (PIE: Harris et al. 2012, Giner et al. 2013) and New York City (Locke et al. Submitted).
In BES III, we seek to improve our understanding of social structure and hierarchical complexity of the Baltimore Metropolitan Region by adopting a multi-scale approach (Figure below: Grove et al. 2005, Roy Chowdhury et al. 2011). This approach is relevant to two new themes in BES III, 1) Governance and Social Networks; and 2) Locational Choice and Land Change.
Example of social science theories of urban-environmental dynamics at multiple scales (Chowdhury et al. 2011, adapted from Grove et al. 2005)
Ecological Understanding and Practice
The U.S. Forest Service's urban tree canopy (UTC) assessment program grew out of BES research (Troy et al., 2007). The Troy et al. data were used by then-mayor Martin O'Malley to establish a UTC goal and program to increase the tree canopy for the City of Baltimore, in part through educational outreach to residents, businesses and institutions (Galvin et al. 2006, Raciti et al. 2006). Through networks such Urban Ecology Collaborative (UEC), this program has expanded to 64 UTC projects, which cover 8,780 sq. miles and 837 communities, and includes over 28 million people-from small towns to large counties in the U.S. and Canada. Of note is New York City's 1 Million Trees initiative, a goal established using scientific protocols developed by BES researchers.
Based upon work in Baltimore, the UTC program has expanded to include Assessments, Prioritization, and Marketing tools for analyses (Locke et al. 2011, and Locke et al. 2013). An important outcome in general is that these tools have facilitated a shift to towards an "all lands, all people approach," (Grove, In Press) that promotes the integration of social and ecological knowledge and data, and multi-agency and stakeholder collaboration to achieve urban sustainability goals.
In the third phase of the Baltimore Ecosystem Study (BES III), we seek to 1) improve our knowledge of human-natural system interactions and 2) examine the mechanisms by which the social and the biogeophysical components of the Baltimore ecosystem can adjust to ongoing or future changes. BES III exploits the growing interest in sustainability in urban systems and the recognition that integration between social and biogeophysical components underlies adaptive strategies and processes that control the resilience of coupled socio-ecological systems in a changing world. Our research, education, community engagement, and outreach builds upon our watershed-based, spatially explicit, and geographically extensive social and biogeophysical studies, extends into newly developing exurban lands, adds locational choice as a socio-economic modeling approach, and enhances the interaction among our complementary social, ecohydrological, and ecological models.
We have identified two new major areas of demographic and socioeconomic research 1) Governance and Social Networks; and 2) Locational Choice and Land Change. Our work on locational choice theory is new to the project, but connects with our on-going research to understand the social ecological structure and function of the Baltimore Metropolitan Region.
An essential institutional adaptation to address sustainability and climate change may be changes in governance structures and social networks. The last century saw the traditional emphasis on centralized, top-down government management practices increasingly constrained by decentralized, bottom-up management. Initial sustainability and climate change adaptation efforts have focused on learning efforts through multiple networks, e.g., building capacity through generating new information, and the ability to process and to act upon information effectively. As urban systems address these complex and rapidly changing social-ecological issues, sustainable systems may require adaptive management strategies best provided by a hybrid, polycentric approach to governance with an array of interacting institutions having overlapping and varying objectives, authorities, and strengths of linkages. This diversity of interests and perspectives may allow for greater adaptability to promote sustainability and adapt to climate change.
Thus, we ask what are the social and biogeophysical causes that affect who initiates sustainability and climate change adaptations; what are the motivations for these adaptations; and what adaptations result? We ask these questions in a social network context in order to assess how interactions among network actors affect information flows, and in turn, how this information affects social behaviors and outcomes (Dalton 2001, Romolini, 2012, Romolini et al. 2013). This leads us to specify the feedback hypothetical models behind our work.
Locational Choice Theory
The location choices of households and firms and the economic, social, and institutional constraints to these choices are fundamental processes underlying the spatial dynamics of urban socio-ecological systems. Well-established social science theories of location, segregation, and social inequality provide the cornerstones of an interdisciplinary and synthetic theory of locational choice that builds upon previous BES social science research and guides BES III. Location theory identifies three fundamental economic forces that influence location: (1) natural advantages that attract firms and households; (2) economies of concentration that enhance the productive efficiency of firms that cluster; and (3) transportation and communication costs that spatially differentiate markets and their geographical extent. Important extensions of this theory have demonstrated the importance of amenities and disamenities to explain the location of population and jobs. These include urban amenities, such as per capita cultural activities; disamenities, such as crime; and natural amenities, such as climate and coastlines. Locational choice theory informs land change science by providing a modeling framework for the demand and supply of land in a particular use at a particular location.
Locational advantages and amenities influence individual location choices of households, but social, economic, and institutional constraints to these choices are critical to understanding the enduring patterns of spatial segregation in American cities. Racial segregation, while still high in the United States, has slowly declined since the 1970s; whereas segregation by socioeconomic status, educational attainment, and political affiliation has steadily increased in recent decades. Structural theories of segregation and neighborhood differentiation emphasize different social dynamics but share the principle that actions are constrained by forces larger than individual choice, leading to uneven and unequal chances in where people live and work. Examples include the role of capital accumulation; racial and ethnic discrimination; and lifestyle preferences.
Theories of location, social inequality, and spatial interaction suggest a range of economic, social, and institutional factors that influence the location of households and economic activity. These are described generally as "push" and "pull" factors, which can be classified as formal versus informal and exogenous versus endogenous. Formal push and pull factors originate from social institutions or policies, e.g., government built roads or public parks; whereas informal factors emerge from social arrangements, e.g., exclusionary practices by neighborhood associations and other social processes. Exogenous factors or "drivers" of the system originate from forces outside the system under study: e.g., an economic or natural disaster or federal policy. Endogenous factors are dynamic feedbacks generated by the cumulative effects of individual location and land use decisions within the system. These feedbacks often reinforce existing patterns by acting as a constraint to some households' location choices while reinforcing the location choices of others. As a result, urban SES's are often path dependent and reflect historical legacies in current outcomes (Boone 2003, Lord & Norquist, 2010). Modeling these feedbacks must explicitly account for exogenous and endogenous factors in terms of cross-scale, spatio-temporal interactions that can multiply localized shocks or changes across the larger CSE region.
A novel link between social science theories of locational choice and the social-ecological context of BES III is to connect push/pull drivers with ecosystem services over the long term. This permits an examination of dynamic feedbacks, cross-scale interactions, and adaptive processes across social and biophysical systems with an explicit focus on ecosystem services. The following questions are three examples. First, which ecosystem services affect locational choices and by how much over the long term? Do some push/pull drivers become more important over time, while others decline? Will the transition from the sanitary to the sustainable city result in an increasing recognition, valuation, or dependence on ecosystem services that will alter push/pull dynamics, location choices, and future policy (Chapin et al. 2009)? Second, do changes in understanding and perception of ecosystem services affect locational choices over the long term? As households, NGOs, and public agencies' understanding and valuation of ecosystem services increase, do interactions between where households and firms choose to locate and the institutional incentives and constraints to those choices change over the long term? Third, do social processes adapt to diminished biophysical adaptive processes due to urbanization and climate change. How have social processes adapted to diminished biophysical functioning such as stream degradation and biodiversity loss in the past through urbanization and how might this change in the future as a result of climate change? Have past practices created path dependencies and legacies, which influence or constrain the choices that are available now and in the future?
Sources of Data
The types of data needed to support the research efforts are both extensive and intensive. Both types of data are necessary to model spatial behavioral processes and to better understand how location and land use decisions interact with policies and biophysical processes:
Extensive spatial data on all land use, subdivisions, parcels, housing price, population and employment variables from 1990 to the present to estimate reduced form models of landowner land use and household location choices.
Extensive spatial data on current and historical zoning and variances, infrastructure for sewers, roads, schools and transportation.
Intensive data, e.g., collected via surveys or other specialized data sources on individual landowners, developers, households and other key agents, which can be used to estimate more fully specified structural models of these key decision making processes.
Intensive histories of neighborhood change and stability. Extensive data will be complemented with in-depth histories of processes and mechanisms of change.
We propose to concentrate our data collection efforts in neighborhoods that best illustrate the broad spectrum of changes Baltimore experienced from ca. 1910 to 2008 to construct a sample of neighborhoods that is demographically, economically, and geographically diverse and that captures the social and environmental stability and instability of the time period. Archival and library collections of non-profit organizations, civic leagues, government agencies, newspapers, and oral histories of residents will be analyzed for these neighborhoods.
A second major area of focus in BES III will be to assess institutional responses to climate change by evaluating changing approaches by four key planning agencies/entities. Research will chart the evolution of the types of adaptation plans and efforts through triangulating interviews and documents from the Baltimore Commission on Sustainable Development, the Baltimore City Planning Commission, the Baltimore County Planning Commission, and the Maryland State Planning Department. Interviews will address adaptation actions planned, distribution of burdens and benefits, perceived information priorities, capacity to process new information, flexibility to act, mainstreaming, multiple stresses including relation between adaptation and mitigation, and first efforts prioritized for implementation.
Agency interviews will be conducted in conjunction with the replication and enhancement of an existing survey and methods for assessing governance and social networks associated with natural resource stewardship and sustainability (Dalton 2001). Using 1999 BES data on organizations (Dalton 2001) and relevant long-term social and ecological datasets, we will examine changes to and the effectiveness of polycentric networks in the Baltimore CSE. We will seek to understand how network relationships form and adapt in response to changing social and ecological conditions, how information is transmitted among network actors, and whether network structure affects social and ecological outcomes.
Baker L.; Brazel A,; Byrne L.; Felson A.; Grove J.M.; Hill H.; Nelson K.C.; Walker J.; Shandas V. (2007). Effects of human choices on characteristics of urban ecosystems. Bull Ecol Soc Am. October: 404-409.
Band, L.E.; Cadenasso, M.L.; Grimmond, S.; Grove, J.M. (2005). Heterogeneity in Urban Ecosystems: Pattern and Process. In: Lovett, G.; Jones, C.G.; Turner, M.G.; Weathers, K.G., eds. Ecosystem Function in Heterogeneous Landscapes. New York: Springer-Verlag: 257-278.
Boone, C. G. (2002). An Assessment and Explanation of Environmental Inequity in Baltimore. Urban Geography 23, 6: 581-595.
Boone, C. G. (2003). Obstacles to Infrastructure Provision: The Struggle to Build Comprehensive Sewer Works in Baltimore. Historical Geography 31: 151-168.
Boone, C., Buckley, G., Grove, J. M., & Sister, C. (2009a). Parks and People: An Environmental Justice Inquiry in Baltimore, Maryland. Annals of the Association of American Geographers, 99(4), 767-787.
Boone, C.; Cadenasso, M.L.; and Grove, J.M. (2009b). Landscape, vegetation characteristics, and group identity in an urban and suburban watershed: why the 60s matter. Urban Ecosystems. 13: 255-271.
Boone, C.G.; Gragson, T.L.; Grove, J.M. (2011). Long-term trends in human population growth and economy across sites. In: Peters, D.P.C.; Laney C.M.; Lugo A.E.; Collins S.L.; Driscoll C.T.; Groffman P.M.; Grove J.M.; Knapp A.K.; Kratz T.K.; Ohman M.D.; Waide R.B.; Yao J., eds. Long-term trends in ecological systems: a basis for understanding responses to global change. USDA Agricultural Research Service Publication No. XX. Washington, D.C.: Chapter 8.
Boone, C. G., Fragkias, M., Buckley, G. L., & Grove, J. M. (2014). A long view of polluting industry and environmental justice in Baltimore. Cities, 36, 41-49.
Chapin, F.S.; Carpenter, III, S.R.; Kofinas, G.P.; Folke, C.; Abel, N.; Clark, W.C.; Olsson, P.; Stafford Smith, D.M.; Walker, B.; Young, O.R.; Berkes, F.; Biggs, R.; Grove, J.M.; Naylor, R.L.; Pinkerton, E.; Steffen, W.; Swanson, F.J. (2009). Ecosystem stewardship: sustainability strategies for a rapidly changing planet. Trends in Ecology and Evolution. 25(4):241-249.
Chowdhury, R. R., Larson, K., Grove, M., Polsky, C., & Cook, E. (2011). A Multi-Scalar Approach to Theorizing Socio- Ecological Dynamics of Urban Residential Landscapes A Multi-Scalar Approach to Theorizing Socio-Ecological Dynamics of, 4(1).
Dalton, S.E. (2001). The Gwynns Falls Watershed: a case study of public and non-profit sector behavior in natural resource management. Doctoral dissertation. The Johns Hopkins University; MSE Library, Baltimore, MD.
Fraser, J. C., Bazuin, J. T., Band, L. E., & Grove, J. M. (2013). Covenants, cohesion, and community: The effects of neighborhood governance on lawn fertilization. Landscape and Urban Planning, 115, 30-38. doi:10.1016/j.landurbplan.2013.02.013
Galvin, M.F.; Grove, J.M.; O'Neil-Dunne, J.P.M. (2006). A Report on City of Baltimore's Present and Potential Urban Tree Canopy, Maryland Department of Natural Resources, Forest Service: 17.
Giner, N. M., Polsky, C., Pontius, R. G., & Runfola, D. M. (2013). Understanding the social determinants of lawn landscapes: A fine-resolution spatial statistical analysis in suburban Boston, Massachusetts, USA. Landscape and Urban Planning, 111, 25-33.
Grove, J.M. (2009). Cities: Managing densely settled social-ecological systems. In: Chapin, F.S.I.; Kofinas, G.; Folke, C., eds. Principles of Ecosystem Stewardship: resilience-based natural resource management in a changing world. New York: Springer-Verlag.
Grove, J.M. (2014). Expanding the Vision of the Experimental Forest Network to Urban Areas. Research for the Long-Term: The interplay of societal need and research on USDA Forest Service Experimental Forests and Ranges. Springer-Verlag.
Grove, J.M.; Burch, W.R.; Pickett, S.T.A. (2005). Social Mosaics and Urban Forestry in Baltimore, Maryland. In: Lee, R.G.; Field, D.R., eds., Communities and Forests: Where People Meet the Land. Corvalis: Oregon State University Press: 248-273.
Grove, J.M.; Cadenasso, M.L.; Burch, W.R. Jr.; Pickett, S.T.A.; O'Neil-Dunne, J.P.M.; Schwarz, K.; Wilson, M.; Troy, A.R.; Boone, C. (2006). Data and Methods Comparing Social Structure and Vegetation Structure of Urban Neighborhoods in Baltimore, Maryland. Society & Natural Resources. 19(2):117-136.
Grove, J.M.; Troy, A.R.; O'Neil-Dunne, J.P.M.; Burch, W.R.; Cadenasso, M.L.; Pickett, S.T.A. (2006). Characterization of Households and Its Implications for the Vegeta(tion of Urban Ecosystems. Ecosystems. 9:578-597.
Harris, E. M., Polsky, C., Larson, K. L., Garvoille, R., Martin, D. G., Brumand, J., & Ogden, L. (2012). Heterogeneity in Residential Yard Care: Evidence from Boston, Miami, and Phoenix. Human Ecology.
Lord, C. H., & Norquist, K. (2010). "Cities as Emergent Systems: Race as a Fule in Organized Complexity. Environmental Law, 40, 551-597.
Locke, D. H., Grove, J. M., Lu, J. W. T., Troy, A., Neil-dunne, J. P. M. O., & Beck, B. D. (2010a). Prioritizing Preferable Locations for Increasing Urban Tree Canopy in New York City, 3(1), 1-18.
Locke, D. H., Grove, J.M. Galvin, M. O’Neil-Dunne, J.P.M., Murphy, C. (2013). Applications of Urban Tree Canopy Assessment and Prioritization Tools: Supporting Collaborative Decision Making to Achieve Urban Sustainability Goals Applications of Urban Tree Canopy Assessment and Prioritization Tools: Cities and the Environment, 6(1-26).
Locke, D.H., Grove, J.M. O'Neil-Dunne, J. P. M.. (Submitted). "Social Status and Trees: Testing The Ecology of Prestige in New York City." Environmental Management.
Pickett, S.T.A.; Cadenasso, M.L.; Grove, J.M.; Groffman, P.; Band, L.E.; Boone, C.; Burch, W.R.; Grimmond, S.; Hom, J.; Jenkins, J.C.; Law, N.L.; Nilon, C.H.; Pouyat, R.V.; Szlavecz, K.; Warren, P.S.; Wilson, M.A. )2008). Beyond Urban Legends: an emerging framework of urban ecology as illustrated by the Baltimore Ecosystem Study. Bioscience. 58(2):139-150.
Raciti, S.; Galvin, M.F.; Grove, J.M.; O'Neil-Dunne, J.P.M.; Todd, A.; Clagett, S. (2006). Urban Tree Canopy Goal Setting: A Guide for Chesapeake Bay Communities, United States Department of Agriculture, Forest Service, Northeastern State & Private Forestry, Chesapeake Bay Program Office, Annapolis, MD.
Romolini, M. (2012). Governance of 21st century sustainable cities: Examining stewardship networks in Baltimore & Seattle. Doctoral Dissertation. University of Vermont.
Romolini, M., Grove, J. M., & Locke, D. H. (2013). Assessing and comparing relationships between urban environmental stewardship networks and land cover in Baltimore and Seattle. Landscape and Urban Planning, 120, 190-207. doi:10.1016/j.landurbplan.2013.08.008
Troy, A.R.; Grove, J.M.; O'Neil-Dunne, J.P.M.; Cadenasso, M.L.; Pickett, S.T.A. (2007). Predicting Patterns of Vegetation and Opportunities for Greening on Private Urban Lands. Environmental Management. 40:394-412.
Troy, A.R.; Grove, J.M. (2008). Property values, parks, and crime: a hedonic analysis in Baltimore, MD. Landscape and Urban Planning. 87:233-245.
Vemuri, A. W., Morgan Grove, J., Wilson, M. A., & Burch, W. R. (2009). A Tale of Two Scales: Evaluating the Relationship Among Life Satisfaction, Social Capital, Income, and the Natural Environment at Individual and Neighborhood Levels in Metropolitan Baltimore. Environment and Behavior, 43(1), 3-25.
Zhou, W.; Troy, A.R.; Grove, J.M. (2008). Object-based Land Cover Classification and Change Analysis in the Baltimore Metropolitan Area Using Multi-temporal High Resolution Remote Sensing Data. Sensors. 8:1613-1636.
Zhou, W.; Troy, A.R.; Grove, J.M. (2009a). Modeling Residential Lawn Fertilization Practices: Integrating high resolution remote sensing with socioeconomic data. Environmental Management. 41:742-752.
Zhou, W.; Grove, J.M.; Troy, A.; Jenkins, J.C. (2009b). Can Money Buy Green?: Demographic and socioeconomic predictors of lawncare expenditures and lawn greenness in urban residential areas. Society & Natural Resources. 22:744-760.