Stomatal uptake of O3 in aspen and aspen-birch forests under free-air CO2 and O3 enrichment

Rising atmospheric carbon dioxide (CO₂) may alleviate the toxicological impacts of concurrently rising tropospheric ozone (O₃) during the present century if higher CO₂ is accompanied by lower stomatal conductance (g_s), as assumed by many models. We investigated how elevated concentrations of CO₂ and O₃, alone and in combination, affected the accumulated stomatal flux of O₃ (AFst) by canopies and sun leaves in closed aspen and aspen-birch forests in the free-air CO₂-O₃ enrichment experiment near Rhinelander, Wisconsin. Stomatal conductance for O₃ was derived from sap flux data and AFst was estimated either neglecting or accounting for the potential influence of non-stomatal leaf surface O₃ deposition. Leaf-level AFst (AFst_l) was not reduced by elevated CO₂. Instead, there was a significant CO₂ × O₃ interaction on AFst_l, as a consequence of lower values of g_s in control plots and the combination treatment than in the two single-gas treatments. In addition, aspen leaves had higher AFst_l than birch leaves, and estimates of AFst_l were not very sensitive to non-stomatal leaf surface O₃ deposition. Our results suggest that model projections of large CO₂-induced reductions in g_s alleviating the adverse effect of rising tropospheric O₃ may not be reasonable for northern hardwood forests.     

Pollution magnet: nano-magnetite for arsenic removal from drinking water

Arsenic contamination in groundwater is a severe global problem, most notably in Southeast Asia where millions suffer from acute and chronic arsenic poisoning. Removing arsenic from groundwater in impoverished rural or urban areas without electricity and with no manufacturing infrastructure remains a significant challenge. Magnetite nanocrystals have proven to be useful in arsenic remediation and could feasibly be synthesized by a thermal decomposition method that employs refluxing of FeOOH and oleic acid in 1-octadecene in a laboratory setup. To reduce the initial cost of production, $US 2600/kg, and make this nanomaterial widely available, we suggest that inexpensive and accessible “everyday” chemicals be used. Here we show that it is possible to create functional and high-quality nanocrystals using methods appropriate for manufacturing in diverse and minimal infrastructure, even those without electricity. We suggest that the transfer of this knowledge is best achieved using an open source concept.     

The myth of nitrogen fertilization for soil carbon sequestration

Intensive use of N fertilizers in modern agriculture is motivated by the economic value of high grain yields and is generally perceived to sequester soil organic C by increasing the input of crop residues. This perception is at odds with a century of soil organic C data reported herein for the Morrow Plots, the world's oldest experimental site under continuous corn (Zea mays L.). After 40 to 50 yr of synthetic fertilization that exceeded grain N removal by 60 to 190%, a net decline occurred in soil C despite increasingly massive residue C incorporation, the decline being more extensive for a corn-soybean (Glycine max L. Merr.) or corn-oats (Avena sativa L.)-hay rotation than for continuous corn and of greater intensity for the profile (0-46 cm) than the surface soil. These findings implicate fertilizer N in promoting the decomposition of crop residues and soil organic matter and are consistent with data from numerous cropping experiments involving synthetic N fertilization in the USA Corn Belt and elsewhere, although not with the interpretation usually provided. There are important implications for soil C sequestration because the yield-based input of fertilizer N has commonly exceeded grain N removal for corn production on fertile soils since the 1960s. To mitigate the ongoing consequences of soil deterioration, atmospheric CO(2) enrichment, and NO(3)(-) pollution of ground and surface waters, N fertilization should be managed by site-specific assessment of soil N availability. Current fertilizer N management practices, if combined with corn stover removal for bioenergy production, exacerbate soil C loss.     

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.     

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.