Physiological and developmental effects of O3 on cottonwood growth in urban and rural sites.

Previously we found that cloned cottonwood saplings (Populus deltoides) grew twice as large in New York, New York, USA, compared to surrounding rural environments and that soils, temperature, CO2, nutrient deposition, and microclimatic variables could not account for the greater urban plant biomass. Correlations between final season biomass and cumulative O3 exposures, combined with twofold growth reductions in an open-top chamber experiment provided strong evidence that higher cumulative O3 exposures in rural sites reduced growth in the country. Here, we assess the field gas exchange, growth and development, and allocation responses underlying the observed growth differences and compare them with isolated O3 responses documented in the open-top chamber experiment. Cottonwoods showed no visible foliar injury, reduced photosynthesis of recently expanded foliage, early leaf senescence, protective reduction in stomatal conductance, or compensatory allocation to shoot relative to root biomass for either the chamber or field experiment. Instead, O3-impacted chamber plants had significantly higher conductance and reduced photosynthesis of older foliage that led to reduced leaf area production and a twofold biomass reduction in the absence of visible injury. Rural-grown field plants showed the same pattern of significantly higher conductance in the absence of concomitant increases in photosynthesis that was indicative of a loss of stomatal control. Incremental changes in foliar production were also significantly inversely related to fluctuations in ambient O3 exposures. The similarity in biomass, gas exchange, phenological, and allocation responses between chamber and field experiments indicate that mechanisms accounting for reduced growth at rural sites were consistent with those in the open-top chamber O3 experiment. This study shows the limitation of visible symptoms as a sole diagnostic factor for documenting detrimental O3 impacts and points toward a new approach to show O3 impacts when visible injury is not present. Namely, O3-impacted vegetation showed an unusual inverse relationship of increased conductance with lower photosynthesis of older foliage that was indicative of a loss of stomatal control. This increased stomatal conductance of O3-impacted vegetation accentuates pollutant flux into affected foliage and has important implications for system water balance during warm, dry portions of the growing season when O3 concentrations are highest.

Combining sources in stable isotope mixing models: alternative methods.

Stable isotope mixing models are often used to quantify source contributions to a mixture. Examples include pollution source identification; trophic web studies; analysis of water sources for soils, plants; or water bodies, and many others. A common problem is having too many sources to allow a unique solution. We discuss two alternative procedures for addressing this problem. One option is a priori to combine sources with similar signatures so the number of sources is small enough to provide a unique solution. Aggregation should be considered only when isotopic signatures of clustered sources are not significantly different, and sources are related so the combined source group has some functional significance. For example, in a food web analysis, lumping several species within a trophic guild allows more interpretable results than lumping disparate food sources, even if they have similar isotopic signatures. One result of combining mixing model sources is increased uncertainty of the combined end-member isotopic signatures and consequently the source contribution estimates; this effect can be quantified using the IsoError model (http://www.epa.gov/wed/pages/models/isotopes/isoerror1_04.htm). As an alternative to lumping sources before a mixing analysis, the IsoSource mixing model (http://www.epa.gov/wed/pages/models/isosource/isosource.htm) can be used to find all feasible solutions of source contributions consistent with isotopic mass balance. While ranges of feasible contributions for each individual source can often be quite broad, contributions from functionally related groups of sources can be summed a posteriori, producing a range of solutions for the aggregate source that may be considerably narrower. A paleo-human dietary analysis example illustrates this method, which involves a terrestrial meat food source, a combination of three terrestrial plant foods, and a combination of three marine foods. In this case, a posteriori aggregation of sources allowed strong conclusions about temporal shifts in marine versus terrestrial diets that would not have otherwise been discerned.

Urbanization effects on tree growth in the vicinity of New York City.

Plants in urban ecosystems are exposed to many pollutants and higher temperatures, CO2 and nitrogen deposition than plants in rural areas. Although each factor has a detrimental or beneficial influence on plant growth, the net effect of all factors and the key driving variables are unknown. We grew the same cottonwood clone in urban and rural sites and found that urban plant biomass was double that of rural sites. Using soil transplants, nutrient budgets, chamber experiments and multiple regression analyses, we show that soils, temperature, CO2, nutrient deposition, urban air pollutants and microclimatic variables could not account for increased growth in the city. Rather, higher rural ozone (O3) exposures reduced growth at rural sites. Urban precursors fuel the reactions of O3 formation, but NO(x) scavenging reactions resulted in lower cumulative urban O3 exposures compared to agricultural and forested sites throughout the northeastern USA. Our study shows the overriding effect of O3 despite a diversity of altered environmental factors, reveals 'footprints' of lower cumulative urban O3 exposures amidst a background of higher regional exposures, and shows a greater adverse effect of urban pollutant emissions beyond the urban core.

Source partitioning using stable isotopes: coping with too many sources.

Stable isotopes are increasingly being used as tracers in environmental studies. One application is to use isotopic ratios to quantitatively determine the proportional contribution of several sources to a mixture, such as the proportion of various pollution sources in a waste stream. In general, the proportional contributions of n+1 different sources can be uniquely determined by the use of n different isotope system tracers (e.g., delta13C, delta15N, delta18O) with linear mixing models based on mass balance equations. Often, however, the number of potential sources exceeds n+1, which prevents finding a unique solution of source proportions. What can be done in these situations? While no definitive solution exists, we propose a method that is informative in determining bounds for the contributions of each source. In this method, all possible combinations of each source contribution (0-100%) are examined in small increments (e.g., 1%). Combinations that sum to the observed mixture isotopic signatures within a small tolerance (e.g., +/-0.1 per thousand ) are considered to be feasible solutions, from which the frequency and range of potential source contributions can be determined. To avoid misrepresenting the results, users of this procedure should report the distribution of feasible solutions rather than focusing on a single value such as the mean. We applied this method to a variety of environmental studies in which stable isotope tracers were used to quantify the relative magnitude of multiple sources, including (1) plant water use, (2) geochemistry, (3) air pollution, and (4) dietary analysis. This method gives the range of isotopically determined source contributions; additional non-isotopic constraints specific to each study may be used to further restrict this range. The breadth of the isotopically determined ranges depends on the geometry of the mixing space and the similarity of source and mixture isotopic signatures. A sensitivity analysis indicated that the estimated ranges vary only modestly with different choices of source increment and mass balance tolerance parameter values. A computer program (IsoSource) to perform these calculations for user-specified data is available at http://www.epa.gov/wed/pages/models.htm.

Uncertainty in source partitioning using stable isotopes.

Stable isotope analyses are often used to quantify the contribution of multiple sources to a mixture, such as proportions of food sources in an animal's diet, or C3 and C4 plant inputs to soil organic carbon. Linear mixing models can be used to partition two sources with a single isotopic signature (e.g., δ(13)C) or three sources with a second isotopic signature (e.g., δ(15)N). Although variability of source and mixture signatures is often reported, confidence interval calculations for source proportions typically use only the mixture variability. We provide examples showing that omission of source variability can lead to underestimation of the variability of source proportion estimates. For both two- and three-source mixing models, we present formulas for calculating variances, standard errors (SE), and confidence intervals for source proportion estimates that account for the observed variability in the isotopic signatures for the sources as well as the mixture. We then performed sensitivity analyses to assess the relative importance of: (1) the isotopic signature difference between the sources, (2) isotopic signature standard deviations (SD) in the source and mixture populations, (3) sample size, (4) analytical SD, and (5) the evenness of the source proportions, for determining the variability (SE) of source proportion estimates. The proportion SEs varied inversely with the signature difference between sources, so doubling the source difference from 2‰ to 4‰ reduced the SEs by half. Source and mixture signature SDs had a substantial linear effect on source proportion SEs. However, the population variability of the sources and the mixture are fixed and the sampling error component can be changed only by increasing sample size. Source proportion SEs varied inversely with the square root of sample size, so an increase from 1 to 4 samples per population cut the SE in half. Analytical SD had little effect over the range examined since it was generally substantially smaller than the population SDs. Proportion SEs were minimized when sources were evenly divided, but increased only slightly as the proportions varied. The variance formulas provided will enable quantification of the precision of source proportion estimates. Graphs are provided to allow rapid assessment of possible combinations of source differences and source and mixture population SDs that will allow source proportion estimates with desired precision. In addition, an Excel spreadsheet to perform the calculations for the source proportions and their variances, SEs, and 95% confidence intervals for the two-source and three-source mixing models can be accessed at http://www.epa.gov/wed/pages/models.htm.