Iterative scenarios for social-ecological systems.

Managing social-ecological systems toward desirable regimes requires learning about the system being managed while preparing for many possible futures. Adaptive management (AM) and scenario planning (SP) are two systems management approaches that separately use learning to reduce uncertainties and employ planning to manage irreducible uncertainties, respectively. However, each of these approaches have limitations that confound management of social-ecological systems. Here, we introduce iterative scenarios (IS), a systems management approach that is a hybrid of the scopes and relationships to uncertainty and controllability of AM and SP that combines the "iterativeness" of AM and futures planning of SP. Iterative scenarios is appropriate for situations with high uncertainty about whether a management action will lead to intended outcomes, the desired benefits are numerous and cross-scale, and it is difficult to account for the social implications around the natural resource management options. The value of iterative scenarios is demonstrated by applying the approach to green infrastructure futures for a neighborhood in the city of Cleveland, Ohio, U.S., that had experienced long-term, systemic disinvestment. The Cleveland green infrastructure project was particularly well suited to the IS approach given that learning about environmental factors was necessary and achievable, but what would be socially desirable and possible was unknown. However, iterative scenarios is appropriate for many social-ecological systems where uncertainty is high as IS accommodates real-world complexity faced by management.

The Provision of Urban Ecosystem Services Throughout the Private-Social-Public Domain: A Conceptual Framework.

As cities are largely private systems, recent investigations have assessed the provision of ecosystem services from the private realm. However, these assessments are largely based on the concept of ownership and fail to capture the complexity of service provision mediated by interactions between people and ecological structures. In fact, people interact with ecological structures in their role of land tenants and stewards, further modulating the provision of ecosystem services. We devise a theoretical framework based on the concepts of ownership, tenancy, and stewardship, in which people, as mediators of ecosystem services, regulate the provision of services throughout the private-social-public domain. We survey relevant literature describing these dimensions and propose a comprehensive framework focused on the private-social-public domain. Our framework can advance ecosystem service research and enhance the provision of ecosystems services. The inclusion of people's individual, social and public roles in the mediation of ecosystem services could improve how benefits are planned for, prioritized, and optimized across cities.

Agroecology for the Shrinking City.

Many cities are experiencing long-term declines in population and economic activity. As a result, frameworks for urban sustainability need to address the unique challenges and opportunities of such shrinking cities. Shrinking, particularly in the U.S., has led to extensive vacant land. The abundance of vacant land reflects a loss of traditional urban amenities, economic opportunity, neighbors, businesses, and even basic city services and often occurs in neighborhoods with socially and economically vulnerable or underserved populations. However, vacant land also provides opportunities, including the space to invest in green infrastructure that can provide ecosystem services and support urban sustainability. Achieving desirable amenities that provide ecosystem services from vacant land is the central tenet of a recent urban sustainability framework termed ecology for the shrinking city. An agroecological approach could operationalize ecology for the shrinking city to both manage vacancy and address ecosystem service goals. Developing an agroecology in shrinking cities not only secures provisioning services that use an active and participatory approach of vacant land management but also transforms and enhances regulating and supporting services. The human and cultural dimensions of agroecology create the potential for social-ecological innovations that can support sustainable transformations in shrinking cities. Overall, the strength of agroecological principles guiding a green infrastructure strategy stems from its explicit focus on how individuals and communities can shape their environment at multiple scales to produce outcomes that reflect their social and cultural context. Specifically, the shaping of the environment provides a pathway for communities to build agency and manage for resilience in urban social-ecological systems. Agroecology for the shrinking city can support desirable transformations, but to be meaningful, we recognize that it must be part of a greater strategy that addresses larger systemic issues facing shrinking cities and their residents.

Legacies of Lead in Charm City's Soil: Lessons from the Baltimore Ecosystem Study.

Understanding the spatial distribution of soil lead has been a focus of the Baltimore Ecosystem Study since its inception in 1997. Through multiple research projects that span spatial scales and use different methodologies, three overarching patterns have been identified: (1) soil lead concentrations often exceed state and federal regulatory limits; (2) the variability of soil lead concentrations is high; and (3) despite multiple sources and the highly heterogeneous and patchy nature of soil lead, discernable patterns do exist. Specifically, housing age, the distance to built structures, and the distance to a major roadway are strong predictors of soil lead concentrations. Understanding what drives the spatial distribution of soil lead can inform the transition of underutilized urban space into gardens and other desirable land uses while protecting human health. A framework for management is proposed that considers three factors: (1) the level of contamination; (2) the desired land use; and (3) the community's preference in implementing the desired land use. The goal of the framework is to promote dialogue and resultant policy changes that support consistent and clear regulatory guidelines for soil lead, without which urban communities will continue to be subject to the potential for lead exposure.

Ecology for the Shrinking City.

This article brings together the concepts of shrinking cities-the hundreds of cities worldwide experiencing long-term population loss-and ecology for the city. Ecology for the city is the application of a social-ecological understanding to shaping urban form and function along sustainable trajectories. Ecology for the shrinking city therefore acknowledges that urban transformations to sustainable trajectories may be quite different in shrinking cities as compared with growing cities. Shrinking cities are well poised for transformations, because shrinking is perceived as a crisis and can mobilize the social capacity to change. Ecology is particularly well suited to contribute solutions because of the extent of vacant land in shrinking cities that can be leveraged for ecosystem-services provisioning. A crucial role of an ecology for the shrinking city is identifying innovative pathways that create locally desired amenities that provide ecosystem services and contribute to urban sustainability at multiple scales.

Trees grow on money: urban tree canopy cover and environmental justice.

This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman's correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns.

A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities.

In many older US cities, lead (Pb) contamination of residential soil is widespread; however, contamination is not uniform. Empirically based, spatially explicit models can assist city agencies in addressing this important public health concern by identifying areas predicted to exceed public health targets for soil Pb contamination. Sampling of 61 residential properties in Baltimore City using field portable X-ray fluorescence revealed that 53 % had soil Pb that exceeded the USEPA reportable limit of 400 ppm. These data were used as the input to three different spatially explicit models: a traditional general linear model (GLM), and two machine learning techniques: classification and regression trees (CART) and Random Forests (RF). The GLM revealed that housing age, distance to road, distance to building, and the interactions between variables explained 38 % of the variation in the data. The CART model confirmed the importance of these variables, with housing age, distance to building, and distance to major road networks determining the terminal nodes of the CART model. Using the same three predictor variables, the RF model explained 42 % of the variation in the data. The overall accuracy, which is a measure of agreement between the model and an independent dataset, was 90 % for the GLM, 83 % for the CART model, and 72 % for the RF model. A range of spatially explicit models that can be adapted to changing soil Pb guidelines allows managers to select the most appropriate model based on public health targets.