Trace-level environmental data typically include values near or below detection and quantitation thresholds where health effects may result from low-concentration exposures to one chemical over time or to multiple chemicals. In a cook stove case study, bias in dibenzo[a,h]anthracene concentration means and standard deviations (SDs) was assessed following censoring at thresholds for selected analysis approaches: substituting threshold/2, maximum likelihood estimation, robust regression on order statistics, Kaplan–Meier, and omitting censored observations. Means and SDs for gas chromatography–mass spectrometry-determined concentrations were calculated after censoring at detection and calibration thresholds, 17% and 55% of the data, respectively. Threshold/2 substitution was the least biased. Measurement values were subsequently simulated from two log-normal distributions at two sample sizes. Means and SDs were calculated for 30%, 50%, and 80% censoring levels and compared to known distribution counterparts. Simulation results illustrated (1) threshold/2 substitution to be inferior to modern after-censoring statistical approaches and (2) all after-censoring approaches to be inferior to including all measurement data in analysis. Additionally, differences in stove-specific group means were tested for uncensored samples and after censoring. Group differences of means tests varied depending on censoring and distributional decisions. Investigators should guard against censoring-related bias from (explicit or implicit) distributional and analysis approach decisions.
Aims In western North America ectomycorrhizal fungi are critical to establishment of conifers in low nitrogen soils. Fire can affect both ectomycorrhizal fungi and soil properties, and inoculation with ectomycorrhizal fungi is recommended when planting on burns for restoration. The aim of this study was to examine how Suillus species used in inoculation affect whitebark pine (Pinus albicaulis L.) seedlings planted in fire-impacted soil. Methods In a greenhouse experiment, Suillus-colonized and uncolonized whitebark pine seedlings were planted in unsterilized and sterilized (control) soil from a recent burn. After 6 months, foliar nitrogen and carbon content, concentration, and stable isotope values were assessed, along with growth parameters. Results When seedlings were colonized, biomass was 61% greater, foliar nitrogen content 25% higher, foliar nitrogen concentration 30-63% lower; needles had lower δ 15 N and higher δ 13 C. Differences were more pronounced in sterilized soil where colonization was higher. Foliar N content was negatively correlated with δ 15 N values. Conclusions Colonization by host-specific fungi produced larger seedlings with higher foliar nitrogen content in both burn soils. The hypothesis that ectomycorrhizal fungi on roots fractionate nitrogen isotopes leading to lower δ 15 N in needles is supported. This helps explain restoration outcomes, and bridges the gap between field and in vitro investigations.
A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river’s length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site’s characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model’s results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.
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