Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

Desorption and Bioavailability of PAHs in Contaminated Soil Subjected to Long-Term In Situ Biostimulation

The distribution and potential bioavailability of polycyclic aromatic hydrocarbons (PAHs) in soil from a former manufactured-gas plant (MGP) site were examined before and after long-term biostimulation under simulated in situ conditions. Treated soil was collected from the oxygenated zones of two continuous-flow columns, one subjected to biostimulation and the other serving as a control, and separated into low- and high-density fractions. In the original soil, over 50% of the total PAH mass was associated with lower-density particles, which comprised < 2% of the total soil mass. However, desorbable fractions of PAHs were much lower in the low-density material than in the high-density material. After over 500 d of biostimulation, significant removal of total PAHs occurred in both the high- and low-density materials (77% and 53%, respectively), with three- and four-ring PAHs accounting for the majority of the observed mass loss. Total PAHs that desorbed over a 28-d period were substantially lower in treated soil from the biostimulated column than in the original soil for both the high-density material (23 versus 63%) and low-density material (5 versus 20%). The fast-desorbing fractions quantified by a two-site desorption model ranged from 0.1 to 0.5 for most PAHs in the original soil but were essentially zero in the biostimulated soil. The fast-desorbing fractions in the original soil underestimated the extent of PAH biodegradation observed in the biostimulated column, and thus was not a good predictor of PAH bioavailability after long-term, simulated in situ biostimulation

Pyrosequence analysis of bacterial communities in aerobic bioreactors treating polycyclic aromatic hydrocarbon-contaminated soil

Two aerobic, lab-scale, slurry-phase bioreactors were used to examine the biodegradation of polycyclic aromatic hydrocarbons (PAHs) in contaminated soil and the associated bacterial communities. The two bioreactors were operated under semi-continuous (draw-and-fill) conditions at a residence time of 35 days, but one was fed weekly and the other monthly. Most of the quantified PAHs, including high-molecular-weight compounds, were removed to a greater extent in the weekly-fed bioreactor, which achieved total PAH removal of 76%. Molecular analyses, including pyrosequencing of 16S rRNA genes, revealed significant shifts in the soil bacterial communities after introduction to the bioreactors and differences in the abundance and types of bacteria in each of the bioreactors. The weekly-fed bioreactor displayed a more stable bacterial community with gradual changes over time, whereas the monthly-fed bioreactor community was less consistent and may have been more strongly influenced by the influx of untreated soil during feeding. Phylogenetic groups containing known PAH-degrading bacteria previously identified through stable-isotope probing of the untreated soil were differentially affected by bioreactor conditions. Sequences from members of the Acidovorax and Sphingomonas genera, as well as the uncultivated ‘‘Pyrene Group 2’’ were abundant in the bioreactors. However, the relative abundances of sequences from the Pseudomonas, Sphingobium, and Pseudoxanthomonas genera, as well as from a group of unclassified anthracene degraders, were much lower in the bioreactors compared to the untreated soil

Linoleic acid participates in the response to ischemic brain injury through oxidized metabolites that regulate neurotransmission.

Linoleic acid (LA; 18:2 n-6), the most abundant polyunsaturated fatty acid in the US diet, is a precursor to oxidized metabolites that have unknown roles in the brain. Here, we show that oxidized LA-derived metabolites accumulate in several rat brain regions during CO2-induced ischemia and that LA-derived 13-hydroxyoctadecadienoic acid, but not LA, increase somatic paired-pulse facilitation in rat hippocampus by 80%, suggesting bioactivity. This study provides new evidence that LA participates in the response to ischemia-induced brain injury through oxidized metabolites that regulate neurotransmission. Targeting this pathway may be therapeutically relevant for ischemia-related conditions such as stroke

Evaluating the Effects of Bioremediation on Genotoxicity of Polycyclic Aromatic Hydrocarbon-Contaminated Soil Using Genetically Engineered, Higher Eukaryotic Cell Lines

Bioremediation is one of the commonly applied remediation strategies at sites contaminated with polycyclic aromatic hydrocarbons (PAHs). However, remediation goals are typically based on removal of the target contaminants rather than on broader measures related to health risks. We investigated changes in the toxicity and genotoxicity of PAH-contaminated soil from a former manufactured-gas plant site before and after two simulated bioremediation processes: a sequencing batch bioreactor system and a continuous-flow column system. Toxicity and genotoxicity of the residues from solvent extracts of the soil were determined by the chicken DT40 B-lymphocyte isogenic cell line and its DNA-repair-deficient mutants. Although both bioremediation processes significantly removed PAHs from the contaminated soil (bioreactor 69% removal; column 84% removal), bioreactor treatment resulted in an increase in toxicity and genotoxicity over the course of a treatment cycle, whereas long-term column treatment resulted in a decrease in toxicity and genotoxicity. However, when screening with a battery of DT40 mutants for genotoxicity profiling, we found that column treatment induced DNA damage types that were not observed in untreated soil. Toxicity and genotoxicity bioassays can supplement chemical analysis-based risk assessment for contaminated soil when evaluating the efficacy of bioremediation