In this article, I offer an institutional history of the ecosystem concept, tracing shifts in its meaning and application as it has become the key organizing principle for the Everglades restoration program in Florida. Two institutional forms are analyzed here: (1) quasi- governmental organizations, a term I use to describe interagency science collaboratives and community stakeholder organizations, and (2) government bureaucracies, which are the administrative agencies tasked with Everglades restoration planning and implementation. In analyzing these knowledge trajectories, I both document the complex networks of relations that facilitate the ecosystem’s emergence as an object of knowledge and examine the bureaucratic claims to authority that circumscribe the ecosystem’s transformation into policy.

ABSTRACT: This study assesses the impact of Glen Canyon Dam releases on rafting (white-water boating and day-use rafters) and angling recreationists in Glen Canyon and Grand Canyon National Park using attribute and contingent valuation surveys. Several sources of information were utilized in this study: knowledgeable people (fishing quides, rafting guides, resource managers, and GCES researchers), seven formal surveys (including attribute surveys), and contingent valuation survey to quantify, in dollars, the effects of dam releases on the recreational exoerience. The goal of the study was to assess the impact of alternative annual flow release patterns for Glen Canyon Dam on recreationists in the aggregate. Flow regimes combining high constant flows in the summer months with moderate or low flows during the remainder of the year would be likely to produce the largest recreational benefits. Extreme high or low flows will adversely affect all river recreation, with flows below approximately 5,000 cubic feet per second and above 35,000 cubic feet oer second to both boaters and anglers.

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.