Limnology and Oceanography: Methods最新文献

Muddy marine sediments are elastic materials in which bubbles grow and worms extend their burrows by fracture. Bubble growth and burrowing behavior are dependent on the stiffness and fracture toughness (K_Ic) of these muds. This article describes a custom laboratory apparatus to measure the fracture toughness of muddy, cohesive sediments using a bubble injection method. The system induces fracture in sediment samples by incrementally injecting air through a needle inserted into the sediment. The increasing pneumatic pressure is monitored until it drops abruptly, indicating bubble formation. Fracture toughness is then calculated from the peak pressure at which fracture occurred, following cavitation rheology methods developed for soft gels. The system has produced measurements that compare well to previous data but with better spatial resolution, allowing for characterization of spatial heterogeneity on small scales.

Cyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long-range autonomous underwater vehicle (LRAUV) equipped with a 3^rd-generation Environmental Sample Processor (3G-ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search-and-sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web-based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.

A targeted method for the quantification of bioavailable amide N found in marine DON (bDON) is presented. The method utilizes mild acid hydrolysis to convert amide N found in proteins and N-acetyl amino polysaccharides to primary amine containing products that are measured using a highly sensitive (nanomolar range and precision) fluorometric technique with addition of O-phthaldialdehyde. We find amidic bDON concentrations ranging from 0.08 to 1.82 μM N within waters from the upper 300 m in the southern California Current, Southern California Bight, and subtropical North Pacific representing 15–33% of bulk DON concentrations. Bioassay experiments from the North Pacific revealed consumption of ~20% of the in situ bDON within 5 days. The method represents a simple and rapid tool for the quantification of bioavailable DON concentrations in seawater with improved analytical precision over traditional estimates of bulk DON concentrations.

The quantitatively most important process by which organic matter in marine sediments is mineralized is performed by sulfate-reducing bacteria, resulting in the accumulation of total dissolved (free) sulfide (S²⁻ = H₂S + HS⁻ + S²⁻) in porewater. S²⁻ is toxic to benthic animals and vascular plants and measurements serve as a proxy for the deleterious effects of organic enrichment on benthic habitat, biodiversity, and ecosystem function. Methodologies for measuring S²⁻ in water have been pursued for at least a century, and standard approaches employ colorimetry (methylene blue and iodometric titration) and potentiometry. These standard methods require between 1 and 200 mL of porewater, which can be laborious to obtain. The ion-selective electrode method is widely employed as a practical approach for sediment S²⁻ analysis but lacks analytical robustness and is highly prone to measurement biases that misinform research and environmental management decisions. A technically simple method is described, based on direct UV spectrophotometry, for the near real-time field analysis of small porewater samples. The procedure prevents known measurement biases associated with particulate sulfide interference, S²⁻ volatilization and oxidation, and represents a practical approach for monitoring organic enrichment and classifying benthic ecological quality status. Porewater concentrations between 200 and 15,000 μmol L⁻¹ can be measured and instrument calibration is highly stable. The method has the capacity to rapidly process and analyze sediment samples at low cost, which helps resolve the problem of chronic under-sampling associated with the use of traditional S²⁻ methods.

A comparison of three analytical methods (the indirect GC-FPD and MIMS, and direct LC-MS/MS) for dimethylsulfoniopropionate (DMSP) measurements was conducted to assess their accuracy and reliability. The three methods showed a linear response but are distinguished by their linearity range, the largest being for MIMS. All three methods showed good precision on Alexandrium minutum samples (2–12%). The variability between the three methods when comparing analyses of A. minutum replicates was 11%, with the DMSP measurements by LC-MS/MS being the highest. This result also confirms that indirect DMSP measurement after hydrolysis for GC or MIMS methods does not lead to an overestimation of DMSP values in A. minutum. A special focus was made on the more recent LC-MS/MS method including further assays in sample preparation and storage from cultures of the dinoflagellate A. minutum. Dinoflagellate cells should be harvested by gentle filtration (< 5 cm Hg) or slow centrifugation (500 × g) to retrieve the largest DMSP pool. For the LC-MS/MS method, MeOH used for cell extraction should be added prior to freezing (to prevent DMSP degradation). Samples will then be stable in frozen storage for at least 2 months. Finally, direct and indirect methods are complementary for identifying the exact DMSP fraction among dimethylsulfide-producing compounds that compose total and particulate DMSP pools issued from newly screened organisms or environmental samples.

Continuous measurements of dissolved oxygen (DO) are useful for quantifying ecosystem metabolism, which is critical for understanding estuarine biogeochemistry and ecology, but current methods applied to these data may lead to estimates that are physically impossible and poorly constrained errors. Here, we present a new approach for estimating estuarine metabolism: Estuarine BAyesian Single-station Estimation (EBASE). EBASE applies a Bayesian framework to a simple process-based model and DO observations, allowing the estimation of critical model parameters, specifically light efficiency and respiration, as informed by a set of prior distributions. EBASE improves upon the stream-based model from which it was derived by accommodating missing DO data and allowing the user to set the time period over which parameters are estimated. We demonstrate that EBASE can recover known metabolic parameters from a synthetic time series, even in the presence of noise (e.g., due to tidal advection) and when prior distributions are uninformed. Optimization periods of 7 and 30 d are more preferable than 1 d. A comparison with the more-conventional method of Odum reveals the ability of EBASE to avoid unphysical results (such as negative photosynthesis and respiration) and improves when the DO data are detided. EBASE is available using open-source software (R) and can be readily applied to multiple years of long-term monitoring data that are available in many estuaries. Overall, EBASE provides an accessible method to parameterize a simple metabolic model appropriate for estuarine systems and will provide additional understanding of processes that influence ecosystem status and condition.

The simulation of deep-sea conditions in laboratories is technically challenging but necessary for experiments that aim at a deeper understanding of physiological mechanisms or host-symbiont interactions of deep-sea organisms. In a proof-of-concept study, we designed a recirculating system for long-term culture (>2 yr) of deep-sea mussels Gigantidas childressi (previously Bathymodiolus childressi). Mussels were automatically (and safely) supplied with a maximum stable level of ~60 μmol L⁻¹ methane in seawater using a novel methane–air mixing system. Experimental animals also received daily doses of live microalgae. Condition indices of cultured G. childressi remained high over the years, and low shell growth rates could be detected, too, which is indicative of positive energy budgets. Using stable isotope data, we demonstrate that G. childressi in our culture system gained energy, both, from the digestion of methane-oxidizing endosymbionts and from digesting particulate food (microalgae). Limitations of the system, as well as opportunities for future experimental approaches involving deep-sea mussels, are discussed.

Simple and economical colorimetric strips for measuring manganese (Mn) in natural waters are described. For their construction, leucomalachite green (LMG) embedded in a Nafion® polymeric matrix was immobilized on a polyvinylchloride surface. Upon immersing the strips in the sample, any soluble manganese(II/III) present catalyzed the oxidation of the LMG base to malachite green by adding sodium periodate. The observed color change is related to the manganese concentration and can be quantified using a field device constructed using commercial red–green–blue sensors and an Arduino® board. A linear response from 0.1 to 0.6 μmol L⁻¹ manganese (limit of detection: 0.1 μmol L⁻¹) was observed. The signal for strips prepared on 7 d gave a relative standard deviation equal to 13%. The strips showed good agreement with results obtained by ICP-MS in porewater collected on Spiekeroog Island (North Sea). Therefore, the method is a tool for rapid measurements of manganese in porewater samples offering new possibilities in understanding biogeochemistry for high temporal and spatial resolution manganese surveys and providing an analytical technique that provides field results comparable to laboratory systems.

Salt marshes have been studied in the context of ecosystem services they can provide for coastal protection. In this study, monthly field campaigns focusing on Elymus spp. and its biomechanical properties were conducted from December 2021 to December 2022 on the German Barrier Island Spiekeroog. A total of 1390 specimens were investigated to determine their growth length, out of which 418 specimens were investigated mechanically with three-point bending tests to determine their biomechanical properties. To evaluate the interaction of hydraulic loads and vegetation, the challenge of modeling biomechanical plant properties to scale is addressed by using resin 3D printing with flexible material, while focusing on the materials mechanical properties. Based on the field data acquired and additional literature (adding up to 1959 measurements), a cylindrical plant model with an outer diameter of $��d_o�=�1.60��\mathrm{mm}�$ (scale 1 : 1) was developed. It was manufactured mixing two resin components with varying volume ratios resulting in surrogates with different flexural stiffnesses. The surrogates were characterized using three-point bending tests and image analysis of their bending behavior when subjected to currents between 0.4 and 1.2 m/s. With the average Young's modulus $��E�$ ranging from 8.45 to 1708.42 MPa, the bending angle varies from 0° to 77.4° displaying the influence of material stiffness and flow velocity. Applying the Cauchy scaling law, this study shows that resin 3D printing can be used to model Elymus sp. with respect to its biomechanical properties allowing for seasonally independent physical laboratory experiments with plant models.

It is empirically known that fish exposed to harmful algal blooms (HABs) exhibit abnormal behavior. This might serve as a method for early detection of HABs. There has been no report of the detection of behavioral abnormalities of fish exposed to harmful algae using machine learning. In this study, the behavior of Oryzias javanicus (Java medaka) exposed in a stepwise manner to the HAB species Karenia mikimotoi at densities of 0 cells mL⁻¹ (control), 1 × 10³ cells mL⁻¹ (nonlethal), and 5 × 10³ cells mL⁻¹ (sublethal) was recorded for 30 min at each cell density using two digital cameras connected to a software that tracked behavioral metrics of fish. The level of anomaly in the behavior of Java medaka was then analyzed using one-class support vector machines (OC-SVM) to determine whether the behavioral changes could be considered abnormal. The results revealed abnormal swimming behavior evidenced by an increase of swimming speed, a decrease of shoaling behavior, and a greater depth of swimming in Java medaka exposed especially to the sublethal K. mikimotoi density. The medaka exposed to K. mikimotoi also displayed physical deformities of their gills that were thought to have caused their abnormal behavior. This supposition was confirmed by further analysis using OC-SVM because the behavior of groups exposed to nonlethal and sublethal densities of K. mikimotoi were considered abnormal compared with that of the control groups. The results of this study show the possibility of using this system for early and real-time detection of HABs.