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Stressor gradients and spatial narratives of the St. Louis River Estuary, a joint Minnesota and Wisconsin Sea Grant study, connected aquatic science research with spatially-explicit stories of local resource issues and place-based geo-quests to enhance spatial awareness and stewardship of the estuary. The goal of this paper is to report and reflect on an integrated study that combined environmental humanities and technology with aquatic science in a spatial context. Our study was organized into three objectives around research, outreach, and evaluation. First, we summarized anthropogenic stressors within high resolution watersheds and linked the watershed stress estimates to aquatic habitats within the estuary. Second, we designed tools to deliver place-based environmental science and technology to targeted users to increase awareness, learning, and the potential for long-term stewardship. And third, we evaluated the responses of targeted end users to their interaction with the project’s integrated science and innovative delivery methods. Finally, central to all three objectives, we created a dynamic website to facilitate regional to national coastal outreach and education goals. We found significant correlations between the stressor index and the water quality and biotic data, along with variability attributed to landscape elements. Connecting this science with the place-based experiences we collected is expected to expand the scope and reach of state, bi-national and non-governmental outreach programs. The project also has direct applications to classroom science education. Developing this integrated project contributed to our shared knowledge of environmental and cultural aspects of the estuary for place-based education, and offers several lessons for future work of this nature.

Developing indicators of ecosystem condition is a priority in the Great Lakes, but little is known about appropriate spatial scales to characterize disturbance or response for most indicators. We surveyed birds, fish, amphibians, aquatic macroinvertebrates, wetland vegetation, and diatoms at 276 coastal wetland locations throughout the U.S. Great Lakes coastal region during 2002-2004. We assessed the responsiveness of 66 candidate indicators to human disturbance (agriculture, urban devel- opment, and point source contaminants) characterized at multiple spatial scales (100, 500, 1,000, and 5,000 m buffers and whole watersheds) using classification and regression tree analysis (CART). Non- stressor covariables (lake, ecosection, watershed, and wetland area) accounted for a greater proportion of variance than disturbance variables. Row-crop agriculture and urban development, especially at

The City of Duluth, the University of Minnesota-Duluth (Natural Resources Research Institute and Minnesota Sea Grant) and other regional partners collaborated to develop Lakesuperiorstreams.org, a website that utilizes real time data (temperature, flow, precipitation, turbidity, specific conductivity) in an easy to understand animated graphical format to educate the public about (1) how streams function, and (2) the impacts of human activities on water quality, habitat, and aquatic organisms. In support of the data, the website provides a comprehensive library of information on stream parameters and information on local streams and surface water issues. It covers land use impacts on streams and positive approaches to protecting water quality. For example, a site design tool kit provides information and examples of approaches to building and maintaining homes and businesses while reducing stormwater impacts. The website also serves as a repository for regionally relevant school curricula for K-12 schools and colleges. The website has been the catalyst that has brought together regional agencies and communities, who have established the Regional Stormwater Protection Team (RSPT), designed to develop one voice for surface water education and technical assistance for communities at the head of Lake Superior.

Identification of reference conditions for ecological assessments of coastal ecosystems poses a challenging problem in highly modified landscapes. A method is described for characterizing disturbance in coastal ecosystems using remotely sensed land classification and other publicly available GIS data. Within ecoregions bordering the US Great Lakes coast, aquatic habitats bordering the shoreline were classified into five ecological types: high‐energy shoreline, embayments, open‐coast, river‐influenced and protected wetlands. Degree of anthropogenic disturbance in contributing areas to these ecosystems was assessed using a watershed approach for wetland types or a moving window approach for high‐energy shorelines. Anthropogenic stress variables included proportions of agricultural or residential land use, information on population and road density, and distance from the nearest point source. Polygons (wetlands) or pixels (high‐energy shoreline) were categorized as ‘reference’ if the magnitude of the most se...

Understanding the relationship between human disturbance and ecological response is essential to the process of indicator development. For large-scale observational studies, sites should be selected across gradients of anthropogenic stress, but such gradients are often unknown for a population of sites prior to site selection. Stress data available from public sources can be used in a geographic information system (GIS) to partially characterize environmental conditions for large geographic areas without visiting the sites. We divided the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then summarized over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute sites across the range of environmental variation in our GIS data. This design has broad applicability when the goal is to develop ecological indicators using observational data from large-scale surveys.

The interaction between physical environment and land ownership in creating spatial heterogeneity was studied in largely forested landscapes of northern Wisconsin, USA. A stratified random approach was used in which 2500-ha plots representing two ownerships (National Forest and private non-industrial) were located within two regional ecosystems (extremely well-drained outwash sands and moderately well-drained moraines). Sixteen plots were established, four within each combination of ownership and ecosystem, and the land cover on the plots was classified from aerial photographs using a modified form of the Anderson (U.S. Geological Survey) land use and land cover classification system. Upland deciduous forests dominated by northern hardwoods were common on the moraines for both ownerships. On the outwash, the National Forest was dominated by pine plantations, upland deciduous forests, and upland regenerating forests (as defined by

A generic bioenergetics model for chinook salmon was modified to estimate solid wastes from a commercial net-pen aquaculture operation in a Minnesota mine-pit lake. The model was calibrated using data from the operation on growth, ration, and temperature. Multiple simulations were run to form three-dimensional response surfaces for consumption, egestion, excretion and respiration as a function of fish size and water temperature. These formed the basis for the Fish Simulation Culture (FIS-C) Model. Predictions for food consumption and solids load were compared with actual ration levels and sedimentation within the mine-pit lake from 1989 to 1992, and compared well with the general trends of the observed data. However, the actual predictive power of FIS-C was very sensitive to our initial model assumption that aquaculture operations are predicated on maximizing the growth of their stock. FIS-C currently does not account for management decisions electing sub-optimal stock growth, but under these conditions does estimate a worst case loading scenario for the system. The annual phosphorus load to the system predicted by FIS-C was not significantly different from that of the mean of 17 values of annual P-load estimated empirically from the literature. However, FIS-C's estimate of P-loading shows a pronounced seasonal pattern to the annual loading. FIS-C offers substantial benefits to users by estimating seasonal and shorter term food wastage and wasteloads to receiving waters under particular operating conditions. Then, other operational scenarios can be created to examine the effects of changing fish inventory, feeding schedule, food composition, etc., in order to examine the impacts on production, environmental and/or regulatory requirements, prior to costly implementation.

Ecological land classification systems have recently been developed at continental, regional, state, and landscape scales. In most cases, the map units of these systems result from subjectively drawn boundaries, often derived by consensus and with unclear choice and weighting of input data. Such classifications are of variable accuracy and are not reliably repeatable. We combined geographic information systems (GIS) with multivariate statistical analyses to integrate climatic, physiographic, and edaphic databases and produce a classification of regional landscape ecosystems on a 29 340-km2 quadrangle of northwestern Wisconsin. Climatic regions were identified from a high-resolution climatic database consisting of 30-yr mean monthly temperature and precipitation values interpolated over a 1-km2 grid across the study area. Principal component analysis (PCA) coupled with an isodata clustering algorithm was used to identify regions of similar seasonal climatic trends. Maps of Pleistocene geology and major soil morphosequences were sued to identify the major physiographic and soil regions within the landscape. Climatic and physiographic coverages were integrated to identify regional landscape ecosystems, which potentially differ in characteristic forest composition, successional dynamics, potential productivity, and other ecosystem-level processes. Validation analysis indicated strong correspondence between forest cover classes from an independently derived Landsat Thematic Mapper classification and ecological region. The development of more standardized data sets and analytical methods for ecoregional classification provides a basis for sound interpretations of forest management at multiple spatial scales.

In this paper we present the proposed conceptual approach and essential subsystems of SYLVATICA, a landscape-based simulation model which will integrate several resource-management technologies in a visual interactive environment. We believe this approach incorporates several critical technologies which should be common to any integrated landscape-based simulation and visualization environment.

ECOPHYS is an ecophysiological whole-tree growth process model designed to simulate the growth of poplar in the establishment year. This microcomputer-based model may be used to test the influence of genetically determined physiological or morphological attributes on plant growth. This manual describes the installation, file structures, and operational procedures for using the model.

During the week of 24 October, 1994, an international group of scientists met in Uppsala, Sweden, to discuss the problems of understanding and modelling the growth of short rotation forests for energy purposes. Three working groups were formed to study specific aspects of the problem: ecophysiology, competition and modelling strategies. This special issue of Biomass and Bioenergy reports the findings of those groups and presents a series of papers related to the ecophysiology and process modelling of Salix .