Limnology and Oceanography: Methods最新文献

A highly specific electrochemical reduction method has been developed that enables the trace level measurement of dimethyl sulfoxide (DMSO) concentration in natural waters. Following the sparging of native dimethyl sulfide (DMS) from the sample, DMSO is reduced to DMS using a novel electrochemical workflow that relies upon CuSO₄ as a redox mediator. The DMS produced through DMSO reduction is collected, concentrated, and detected using a previously described Purge & Trap-Atmospheric Pressure Chemical Ionization-Tandem Mass Spectrometry (P&T-APCI-MS/MS) analytical workflow. The method provides a 0.5 pM detection limit for the analysis of DMSO in 10 mL sample volumes, with a demonstrated method precision of 5.4% for the analysis of consecutive 10 nM aqueous standards. The method selectivity for DMSO was evaluated using a range of commonly observed marine organosulfur compounds, none of which were found to interfere with the analysis at a reduction potential of 4 V. Method intercomparison confirmed that the electrochemical reduction provides results that are equivalent (at the 95% confidence level) to an established TiCl₃ reduction protocol for the analysis of both freshwater and seawater samples. Relative to established methods of DMSO reduction, the electrochemical method provides excellent selectivity and reproducibility, and offers the potential for automated, high-throughput analysis. In addition, the new electrochemical method does not require expensive, difficult to procure enzymes or hazardous, corrosive chemical reagents. Depth profile measurements of DMSO, DMS, and dimethylsulfoniopropionate (DMSP) for unfiltered seawater samples collected in Saanich Inlet, a coastal fjord in British Columbia, demonstrate the effectiveness of the DMSO reduction method in an oceanographic context.

The aquatic eddy covariance (AEC) technique is a versatile tool for understanding benthic fluxes, and calculating primary production, respiration, and net ecosystem metabolism rates of benthic communities. A limitation for researchers has been the length of deployments where the major constraints have primarily been sensor breakage and degradation over time and battery consumption. This paper evaluates the design and deployment of a long-term eddy covariance system (LECS) that was deployed in a temperate seagrass meadow for 6 months that resulted in reliable data 79% of the time. The system consisted of a fixed bottom lander that measured the AEC and a surface buoy that transmitted real time data and provided solar power. This study found a gradual reduction in sensor response time, likely due to fouling, that reduced the response time from 1 to 22 s and resulted in a normalized root square mean error of 8% when comparing the LECS with a second short-term AEC system. New spectral analysis techniques allow for these changes in sensor response time to be monitored in real time so the sensor can be replaced or cleaned as needed. This ensures future deployments will be able to collect high-quality data and allow for long-term analyses of benthic fluxes using the new technology and analyses of the presented LECS.

Analysis of lignin in seawater is essential to understanding the fate of terrestrial dissolved organic matter (DOM) in the ocean and its role in the carbon cycle. Lignin is typically quantified by gas or liquid chromatography, coupled with mass spectrometry (GC-MS or LC-MS). MS instrumentation can be relatively expensive to purchase and maintain. Here we present an improved approach for quantification of lignin phenols using LC and absorbance detection. The approach applies a modified version of parallel factor analysis (PARAFAC2) to 2^nd derivative absorbance chromatograms. It is capable of isolating individual elution profiles of analytes despite co-elution and overall improves sensitivity and specificity, compared to manual integration methods. For most lignin phenols, detection limits below 5 nmol L⁻¹ were achieved, which is comparable to MS detection. The reproducibility across all laboratory stages for our reference material showed a relative standard deviation between 1.47% and 16.84% for all 11 lignin phenols. Changing the amount of DOM in the reaction vessel for the oxidation (dissolved organic carbon between 22 and 367 mmol L⁻¹), did not significantly affect the final lignin phenol composition. The new method was applied to seawater samples from the Kattegat and Davis Strait. The total concentration of dissolved lignin phenols measured in the two areas was between 4.3–10.1 and 2.1–3.2 nmol L⁻¹, respectively, which is within the range found by other studies. Comparison with a different oxidation approach and detection method (GC-MS) gave similar results and underline the potential of LC and absorbance detection for analysis of dissolved lignin with our proposed method.

Pelagic photosynthesis and respiration serve critical roles in controlling the dissolved oxygen (DO) concentration in seawater. The consumption and production via pelagic primary production are of particular importance in the surface ocean and in freshwater ecosystems where photosynthetically active radiation is abundant. However, the dynamic nature and large degree of heterogeneity in these ecosystems pose substantial challenges for providing accurate estimates of marine primary production and metabolic state. The resulting lack of higher-resolution data in these systems hinders efforts in scaling and including primary production in predictive models. To bridge the gap, we developed and validated a novel automated water incubator that measures in situ rates of photosynthesis and respiration. The automated water incubation system uses commercially available optodes and microcontrollers to record continuous measurements of DO within a closed chamber at desired intervals. With fast response optodes, the incubation system produced measurements of photosynthesis and respiration with an hourly resolution, resolving diel signals in the water column. The high temporal resolution of the time series also enabled the development of Monte Carlo simulation as a new data analysis technique to calculate DO fluxes, with improved performance in noisy time series. Deployment of the incubator was conducted near Ucantena Island, Massachusetts, U.S.A. The data captured diel fluctuations in metabolic fluxes with an hourly resolution, allowed for a more accurate correlation between oxygen cycling and environmental conditions, and provided improved characterization of the pelagic metabolic state.

Application of high-frequency monitoring of dissolved organic carbon (DOC) is difficult in instances where training datasets are challenging to develop (e.g., remote locations) and the relationship between optical features and DOC concentration changes due to environmental or landscape shifts (e.g., climate or land-use change). We developed and compared three partial least squares (PLS) models using in situ water level measurements, conductivity, and UV–Vis spectral attenuation to predict DOC. Two site-specific models were developed using data from a hillslope-dominated forest or a low-relief wetland-pond-dominated stream catchment. The third model, using data from both sites, exhibited the best performance (DOC range = 4–15.5 mg C L⁻¹, mean = 8.38 mg C L⁻¹, training RMSE = 0.34 mg C L⁻¹, internal validation RMSE = 0.50 mg C L⁻¹, external validation RMSE = 2.43 mg C L⁻¹). We further demonstrate using PLS model statistics to monitor performance and elucidate when and how models should be updated. These statistics, Hotelling's T² and squared prediction errors, are useful consistency checks for the predictions made and detect underlying inconsistencies that, if undetected, can reduce the robustness of DOC prediction. For example, via the T² statistic, we identified the summer–autumn transition as a period when DOC composition differed from what was represented in the training dataset. We also determined that elevated SUVA₂₅₄ values contributed to the overall bias observed in predictions made during the subsequent year as part of the external validation. This enabled the application of a bias correction that reduced the RMSE from 2.43 to 0.89 mg C L⁻¹. The method presented here could be applied to future monitoring programs enabling model updates to monitor DOC fluxes accurately from optical datasets (e.g., attenuance or fluorescence) in the face of developing datasets in remote locations or environmental change. Implementation of this approach may also identify possible regime shifts or landscape and hydrologic change associated with climate and other environmental changes relevant to terrestrial to aquatic fluxes.

Marine dissolved organic carbon and nitrogen (DOC and DON) are major global carbon and nutrient reservoirs, and their characterization relies on extraction methods for preconcentration and salt removal. Existing methods optimize for capturing and describing DOC. Here, we report an optimized analytical strategy to recover marine DON for subsequent molecular characterization. Retention efficiencies between 5% and 95% are reported for seven solid phase extraction (SPE) sorbents, with PPL recovering 23% of marine DON compared to 95% recovered with C₁₈. Additional comparisons of the effect of varying sample volumes and elution speed, and the resulting molecular composition of DON extracts, were investigated using C₁₈ and PPL sorbents. Sample volumes > 200 mL decreased DON retention efficiency independent of SPE sorbent, and gravity elution recovered 1.7- to 4.2-fold more DON compared to vacuum elution. Characterization of extracted DON by negative-ion electrospray ionization Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) highlights compositional differences between DON species recovered by each method. DON isolated with optimized methods includes low molecular weight (< 600 Da) peptide-like compounds with low O:C ratios (0.2 to 0.5) that are not detected by other SPE sorbents (e.g., PPL). The majority of additional DON isolated with this approach was undetectable by direct infusion negative mode FT-ICR MS analysis.

Surveying coastal systems to estimate distribution and abundance of fish and benthic organisms is labor-intensive, often resulting in spatially limited data that are difficult to scale up to an entire reef or island. We developed a method that leverages the automation of a machine learning platform, CoralNet, to efficiently and cost-effectively allow a single observer to simultaneously generate georeferenced data on abundances of fish and benthic taxa over large areas in shallow coastal environments. Briefly, a researcher conducts a fish survey while snorkeling on the surface and towing a float equipped with a handheld GPS and a downward-facing GoPro, passively taking ~ 10 photographs per meter of benthos. Photographs and surveys are later georeferenced and photographs are automatically annotated by CoralNet. We found that this method provides similar biomass and density values for common fishes as traditional scuba-based fish counts on fixed transects, with the advantage of covering a larger area. Our CoralNet validation determined that while photographs automatically annotated by CoralNet are less accurate than photographs annotated by humans at the level of a single image, the automated approach provides comparable or better estimations of the percent cover of the benthic substrates at the level of a minute of survey (~ 50 m² of reef) due to the volume of photographs that can be automatically annotated, providing greater spatial coverage of the site. This method can be used in a variety of shallow systems and is particularly advantageous when spatially explicit data or surveys of large spatial extents are necessary.

Separating microplastics (MPs) (smaller particle size, < 1 mm) from complex environmental samples such as particulate organic matter (POM) is challenging, particularly for polyethylene and polypropylene, which are buoyant like POM. It is often done using a time-consuming procedure, often with hazardous waste generation. We developed a simple, low-cost procedure using a binary solvent mixture (ethanol–water) followed by water solvation to separate MPs from estuarine POM and surface water. The isolated MPs were quantified and characterized using μFT-IR and scanning electron microscopy, with particle sizes ranging from 30 to 2500 μm and percentage mass from 2.62–21.3% wt/wt in POM and 0.04–0.42% wt/vol for surface water, respectively. Different polymer types, colors, and shapes were observed. Method recovery assessed using spiking yielded 89–93.1% and the method was validated by visual sorting with dye staining. This method is low-cost, simple, and aligns with Green Chemistry approaches while efficiently separating plastics of various particle sizes, shapes, and compositions. Furthermore, this low-cost approach and the near-universal availability of ethanol make this method more accessible in research and education throughout regions of the world where plastic debris is a major challenge but resources to study the problem are limited.

Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP)⁻¹, TN had a MAE of 0.39 mg (L-TN)⁻¹, and water velocity had a MAE of 0.10 m s⁻¹. The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.