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Many streams along the Minnesota coast of Lake Superior have been listed as impaired from either high turbidity or high fish mercury concentrations or both. Both turbidity and total mercury have been shown to be strongly correlated to total suspended sediment in many disturbed watersheds. Turbidity and total mercury concentrations and loads were estimated in four western Lake Superior watersheds from 2005–2006 using automated in-stream turbidity measurements. Regression models were developed relating this near-continuous turbidity data to grab sample measures of mercury during differing flow regimes. Total mercury values ranged from 1 to 28 ng l−1 throughout the open water season and showed a close relationship to total suspended sediment (r2 = 0.85, n = 23; p < 0.001) and a less robust but still significant relationship with turbidity (r2 = 0.40, n = 34; p < 0.001) for all four streams. Mercury loads to Lake Superior were estimated to range from 8 to 97 g yr−1 with watershed yields ranging from 0.5 to 4.3 μg m−2 yr−1. Continuous turbidity monitoring appears to be a reasonable surrogate for both suspended sediment and total mercury concentration, providing information when manual sample collection is cost-prohibitive or logistically difficult, and across a wide range of flows.

Predicting ozone-induced reduction of carbon sequestration of forests under elevated tropospheric ozone concentrations requires robust mechanistic leaf-level models, scaled up to whole tree and stand level. As ozone effects depend on genotype, the ability to predict these effects on forest carbon cycling via competitive response between genotypes will also be required. This study tests a process-based model that predicts the relative effects of ozone on the photosynthetic rate and growth of an ozone-sensitive aspen clone, as a first step in simulating the competitive response of genotypes to atmospheric and climate change. The resulting composite model simulated the relative above ground growth response of ozone-sensitive aspen clone 259 exposed to square wave variation in ozone concentration. This included a greater effect on stem diameter than on stem height, earlier leaf abscission, and reduced stem and leaf dry matter production at the end of the growing season. Further development of the model to reduce predictive uncertainty is discussed.

Quantitative structure-activity relationship (QSAR) models are routinely used in predicting toxicologic and ecotoxi- cologic effects of untested chemicals. One critical factor in QSAR-based risk assessment is the proper assignment of a chemical to a mode of action and associated QSAR. In this paper, we used molecular similarity, neural networks, and discriminant analysis methods to predict acute toxic modes of action for a set of 283 chemicals. The majority of these molecules had been previously determined through toxicodynamic studies in fish to be narcotics (two classes), electrophiles/proelectrophiles, uncouplers of oxidative phosphorylation, acetylcholinesterase inhibitors, and neurotoxicants. Nonempirical parameters, such as topological indices and atom pairs, were used as structural descriptors for the development of similarity-based, statistical, and neural network models. Rates of correct classification ranged from 65 to 95% for these 283 chemicals. Keywords—Toxic mode prediction Topological indices Molecular similarity Neural network Discriminant func- tion analysis

1. Landscape characteristics of sixty-two subcatchments within the Saginaw Bay Catchment of central Michigan were examined to identify relationships with stream water chemistry. Land use, land cover and elevation were quantified for both entire catchments and the upland–river ecotone (100 m stream buffer strip). Catchment and ecotone data were then empirically compared with stream water chemistry using multivariate and regression analyses. Redundancy analysis was used to partition variance among land use, geology, and the shared influence of land use and geology. 2. Major catchments dominated by rowcrop agriculture had the highest alkalinity, total dissolved solids and nitrate + nitrite concentrations. 3. Strong seasonal differences were observed in total nitrogen and nitrite + nitrate, but not in total phosphorus or suspended solids. Land use and landscape structure factors such as slope and patch density (number of land use patches per km2) accounted for most of the observed variance in summer. 4. In both autumn and summer, landscape factors accounted for much of the observed variation in total dissolved solids and alkalinity. During autumn, geological factors and the shared influence of geology/landscape structure plus land use exerted more influence than did land use alone. 5. Total phosphorus and total suspended solids were much better explained by land use within the stream ecotone in summer than in other seasons. However, total nitrogen, nitrate, orthophosphate and alkalinity were equally well explained by land use within the ecotone and throughout the whole catchment. Only total dissolved solids in summer and ammonium in autumn were explained better by the whole catchment than the ecotone. 6. Our results show that relatively coarse spatial databases can provide useful descriptors of regional water quality.

The balance of mechanistic detail with mathematical simplicity contributes to the broad use of the Farquhar, von Caemmerer and Berry (FvCB) photosynthetic rate model. Here the FvCB model was coupled with a stomatal conductance model to form an [A,g(s)] model, and parameterized for mature Populus tremuloides leaves under varying CO(2) and temperature levels. Data were selected to be within typical forest light, CO(2) and temperature ranges, reducing artifacts associated with data collected at extreme values. The error between model-predicted photosynthetic rate (A) and A data was measured in three ways and found to be up to three times greater for each of two independent data sets than for a base-line evaluation using parameterization data. The evaluation methods used here apply to comparisons of model validation results among data sets varying in number and distribution of data, as well as to performance comparisons of [A,g(s)] models differing in internal-process components.

A model of photosynthate production is the central component of a larger whole-tree ecophysiological growth process model for Populus (ECOPHYS). This photosynthesis model was validated by comparing predicted photosynthate production values for individual leaves and the total tree with hourly field measurements collected on four days spaced throughout a growing season. Simulated trees had identical numbers of leaves and leaf areas as the sample trees studied in the field, and hourly weather data collected on the plantation site were supplied as a model input. Total production for the four sample days ranged between 200 and 4900 mg CO(2) tree(-1) day(-1). Model predictions of total daily photosynthate production were within 12% of the observed rates for three of the four sampling days. Diurnal variations in stomatal conductance and ambient CO(2) concentrations and seasonal variations in area leaf weight were the primary sources of error. Total leaf area, proportion of sunlit leaf area, and photosynthetic efficiency were the most important factors influencing carbon dioxide exchange rates.

Predicting ozone-induced reduction of carbon sequestration of forests under elevated tropospheric ozone concentrations requires robust mechanistic leaf-level models, scaled up to whole tree and stand level. As ozone effects depend on genotype, the ability to predict these effects on forest carbon cycling via competitive response between genotypes will also be required. This study tests a process-based model that predicts the relative effects of ozone on the photosynthetic rate and growth of an ozone-sensitive aspen clone, as a first step in simulating the competitive response of genotypes to atmospheric and climate change. The resulting composite model simulated the relative above ground growth response of ozone-sensitive aspen clone 259 exposed to square wave variation in ozone concentration. This included a greater effect on stem diameter than on stem height, earlier leaf abscission, and reduced stem and leaf dry matter production at the end of the growing season. Further development of the model to reduce predictive uncertainty is discussed.

Potential nitrogen mineralization and nitrification were studied in three upland forest ecosystems to develop an understanding of nitrogen turnover on a landscape basis. The northern Michigan forests studied were an oak ecosystem primarily associated with glacial outwash features and two sugar maple ecosystems that occurred on morainal landforms but differed in the diversity and abundance of ground flora species. Four randomly chosen stands separated by at least 6 km were sampled within each of the three ecosystems. Potential net nitrogen mineralization and nitrification were determined by an aerobic laboratory incubation. Litter was collected from all ecosystems during autumn. Litter production, nitrogen returned to the forest floor, and net mineralization differed by a factor of two between the oak and sugar maple ecosystems. The species-rich sugar maple ecosystems exhibited a fourfold increase in potential nitrification compared with the species-poor sugar maple ecosystem. Nitrification was virtually absent in the oak ecosystem. The distribution of ecosystems could be used to predict differences in potential mineralization and nitrification. Areas susceptible to nitrate loss following intensive forest management practices may be related to the occurrence of plant associations. In this upland landscape, high nitrification potentials appear to be confined to species-rich sugar maple forests.

Potential net nitrogen (N) mineralization, potential net nitrification, and overstory (boles and branches) biomass were measured in nine forest ecosystems commonly found within the well-drained uplands of northern Lower Michigan. The ecosystem types ranged from oak-dominated forests on coarse-textured outwash sands to mesic northern hardwood forests on sandy glacial till. Overstory biomass was calculated using species-specific allometric equations developed for Lake States hardwood species. Potential net N mineralization and potential net nitrification were measured by a 30-day aerobic laboratory soil incubation. Analyses of (co)variance were used to determine differences in potential N mineralization, net nitrification, overstory biomass, and biomass increment among the nine ecosystem types. Linear and nonlinear regression analyses were used to predict overstory biomass and biomass increment using potential net N mineralization as the independent variable. Overstory biomass ranged from 92 t•ha−1 in a xeric oak ecosystem to 243 t•ha−1 in a northern hardwood ecosystem; annual biomass production ranged from 1.3 to 3.5 t•ha−1 year−1, respectively. Potential net N mineralization was lowest in the xeric oak ecosystem (52.0 μg N•g−1) and greatest in the mesic northern hardwood ecosystem (127.8 μg N•g−1). Potential net nitrification was 45.5 μg NO3−-N•g−1 in the northern hardwood ecosystem; 10 to 230 times greater than in other ecosystems. A saturating exponential model (y = a(1–e−kx) + c) produced the smallest residual mean squares in predicting overstory biomass (R2 = 0.822) and annual biomass increment (R2 = 0.847) from potential net N mineralization. Maximum overstory biomass and biomass increment predicted from this equation were 247 t•ha−1 and 3.7 t•ha−1, respectively. In addition, laboratory net N mineralization potentials were highly correlated with annual rates of N mineralization determined by insitu incubation (r2 = 0.849). Overstory biomass and woody biomass increment were poorly correlated with potential net nitrification. The exponential function used to predict biomass increment from N mineralization suggests that the productivity of some northern hardwood forests in northern Lower Michigan is not limited by N availability.