Social and economic considerations are among the most important drivers of landscape change, yet few studies have addressed economic and environmental influences on landscape structure, and how land ownership may affect landscape dynamics. Watersheds in the Olympic Peninsula, Washington, and the southern Appalachian highlands of western North Carolina were studied to address two questions: (1) Does landscape pattern vary among federal, state, and private lands? (2) Do land‐cover changes differ among owners, and if so, what variables explain the propensity of land to undergo change on federal, state, and private lands? Landscape changes were studied between 1975 and 1991 by using spatial databases and a time series of remotely sensed imagery. Differences in landscape pattern were observed between the two study regions and between different categories of land ownership. The proportion of the landscape in forest cover was greatest in the southern Appalachians for both U.S. National Forest and private lands, compared to any land‐ownership category on the Olympic Peninsula. Greater variability in landscape structure through time and between ownership categories was observed on the Olympic Peninsula. On the Olympic Peninsula, landscape patterns did not differ substantially between commercial forest and state Department of Natural Resources lands, both of which are managed for timber, but differed between U.S. National Forest and noncommercial private land ownerships. In both regions, private lands contained less forest cover but a greater number of small forest patches than did public lands. Analyses of land‐cover change based on multinomial logit models revealed differences in land‐cover transitions through time, between ownerships, and between the two study regions. Differences in land‐cover transitions between time intervals suggested that additional factors (e.g., changes in wood products or agricultural prices, or changes in laws or policies) cause individuals or institutions to change land management. The importance of independent variables (slope, elevation, distance to roads and markets, and population density) in explaining land‐cover change varied between ownerships. This methodology for analyzing land‐cover dynamics across land units that encompass multiple owner types should be widely applicable to other landscapes.
Boat collisions with manatees ( Trichechus manatus latirostris ) account for about one‐quarter of manatee deaths annually in Florida. This emphasizes the need to influence boaters' behavior through an understanding of their knowledge, beliefs, attitudes, and behavioral intentions toward manatees and their conservation. We conducted a telephone survey of a random sample of boat users whose boats were observed in Tampa Bay, Florida. Five hundred and four boaters completed the survey, a cooperation rate of 55%. Respondents had a mean score of 6.2 on a 10‐question knowledge scale, and supported manatee conservation efforts with a mean score of 3.84 on a 5‐point support scale based on seven statements. Boaters indicated more support for increased public education than for stringent regulations such as speed and wake limits in sea grass areas, no‐entry areas, or increased patrols. Greater knowledge about manatees was positively correlated with support for manatee conservation. To understand boating behaviors, we used the sociopsychological theory of reasoned action to analyze boaters' disregard for speed zones. Results indicate a strong normative influence on boaters' behavioral intention to follow speed zones, with respondents highly motivated to comply with law enforcement. The survey results provide a basis for recommendations about public communication interventions.
In this article, we present an approach based on generalized additive models (GAMs) to predict species’ distributions and abundance in Florida estuaries with habitat suitability modeling. Environmental data gathered by fisheries‐independent monitoring in Tampa Bay from 1998 to 2008 were interpolated to create seasonal habitat maps for temperature, salinity, and dissolved oxygen and annual maps for depth and bottom type. We used delta‐GAM models assuming either zero‐adjusted gamma or beta‐inflated‐at‐zero distributions to predict catch per unit effort (CPUE) from five habitat variables plus gear type for each estuarine species by life stage and season. Bottom type and gear type were treated as categorical predictors with reference parameterization. Three spline‐fitting procedures (the penalized B‐spline, cubic smoothing spline, and restricted cubic spline) were applied to the continuous predictors. Two additive, linear submodels on the log and logistic scales were used to predict CPUEs >0 and CPUEs = 0, respectively, across environmental gradients. The best overall model among those estimated was identified based on the lowest Akaike information criterion. A stepwise routine was used to omit continuous predictors that had little predictive power. The model developed was then applied to interpolated habitat data to predict CPUEs across the estuary using GIS. The statistical models, coupled with the use of GIS, provide a method for predicting spatial distributions and population numbers of estuarine species’ life stages. An example is presented for juvenile pink shrimp Farfantepenaeus duorarum during the summer in Tampa Bay, Florida. Received February 10, 2015; accepted August 11, 2015
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.Wiley is collaborating with JSTOR to digitize, preserve and extend access to Ecological Applications This content downloaded from 128.205.114.91 on Sun, 26 Jun 2016 12:46:33 UTC All use subject to http://about.jstor.org/termsAbstract. Ecosystem management is emerging as an organizing theme for land use and resource management in the United States. However, while this subject is dominating professional and policy discourse, little research has examined how such system-level goals might be formulated and implemented. Effective ecosystem management will require insights into the functioning of ecosystems at appropriate scales and their responses to human interventions, as well as factors such as resource markets and social preferences that hold important influence over land and resource use. In effect, such management requires an understanding of ecosystem processes that include human actors and social choices. We examine ecosystem management issues using spatial models that simulate landscape change for a study site in the southern Appalachian -highlands of the United States. We attempt to frame a set of ecosystem management issues by examining how this landscape could develop under a number of different scenarios designed to reflect historical land-cover dynamics as well as hypothetical regulatory approaches to ecosystem management. Scenarios based on historical change show that recent shifts in social forces that drive land cover change on both public and private lands imply a more stable and a more forested landscape. Scenarios based on two hypothetical regulatory instruments indicate that public land management may have only limited influence on overall landscape pattern and that spatially targeted approaches on public and private lands may be more efficient than blanket regulation for achieving landscape-level goals.
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