Field responses of Prunus serotina and Asclepias syriaca to ozone around southern Lake Michigan

A classification and regression tree model of controls on dissolved inorganic nitrogen leaching from European forests

Often, there is a non-linear relationship between atmospheric dissolved inorganic nitrogen (DIN) input and DIN leaching that is poorly captured by existing models. We present the first application of the non-parametric classification and regression tree approach to evaluate the key environmental drivers controlling DIN leaching from European forests. DIN leaching was classified as low (<3), medium (3-15) or high (>15kg N ha(-1) year(-1)) at 215 sites across Europe. The analysis identified throughfall NO(3)(-) deposition, acid deposition, hydrology, soil type, the carbon content of the soil, and the legacy of historic N deposition as the dominant drivers of DIN leaching for these forests. Ninety four percent of sites were successfully classified into the appropriate leaching category. This approach shows promise for understanding complex ecosystem responses to a wide range of anthropogenic stressors as well as an improved method for identifying risk and targeting pollution mitigation strategies in forest ecosystems.

Seasonal and diurnal gas exchange differences in ozone-sensitive common milkweed (Asclepias syriaca L.) in relation to ozone uptake

Stomatal conductance and net photosynthesis of common milkweed (Asclepias syriaca L.) plants in two different soil moisture regimes were directly quantified and subsequently modeled over an entire growing season. Direct measurements captured the dynamic response of stomatal conductance to changing environmental conditions throughout the day, as well as declining gas exchange and carbon assimilation throughout the growth period beyond an early summer maximum. This phenomenon was observed in plants grown both with and without supplemental soil moisture, the latter of which should theoretically mitigate against harmful physiological effects caused by exposure to ozone. Seasonally declining rates of stomatal conductance were found to be substantial and incorporated into models, making them less susceptible to the overestimations of effective exposure that are an inherent source of error in ozone exposure indices. The species-specific evidence presented here supports the integration of dynamic physiological processes into flux-based modeling approaches for the prediction of ozone injury in vegetation.

Temporal patterns of foliar ozone symptoms on tall milkweed (Asclepias exaltata L.) in Great Smoky Mountains National Park

Incidence and severity of ozone-induced foliar symptoms on tall milkweed (Asclepias exaltata L.) along selected trails in Great Smoky Mountains National Park (GRSM) were determined by two surveys/season conducted from 1992 through 1996. Overall incidence was 73%, and was 84%, 44%, 90%, 58%, and 82% for 1992-1996, respectively for the same clusters. Average incidence was 61% and 84% for the 1st and 2nd surveys, respectively. Seasonal comparisons showed two distinct injury groupings regarding incidence and severity of injury: 1992, 1994 and 1996 (high injury); 1993 and 1995 (low injury). No discernible patterns were observed between symptomatic and asymptomatic plants regarding height, herbivory or flowering. Regression analyses indicated no differentiation in foliar symptoms regarding topographic position, aspect, slope or elevation over the 5-year study period. Our findings indicate other micro-site or genetic factors may control ozone sensitivity of tall milkweed in GRSM.

A neural-fuzzy approach to classify the ecological status in surface waters

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive.