Limnology and Oceanography: Methods最新文献

A key assumption in trophic position (TP) estimation using stable isotope analysis is that consumers are in isotopic equilibrium with their resources. Here, we assess the degree to which time-varying resource dynamics and isotope incorporation rates of consumers influence consumer TP estimates across multiple trophic levels and aquatic ecosystems. We constructed a first-order kinetics model to explore consumer stable isotope incorporation rates and modeled the effect on TP calculations using bulk and compound-specific stable isotope data from previous experimental and observational studies. We found TP estimates of higher trophic level consumers are less accurate than lower trophic level consumers when applying bulk stable isotope analysis (BSIA) and using particulate organic matter as the stable isotope baseline. The accuracy of TP estimates depended on the time-varying dynamics of the stable isotope baseline. Tertiary consumers had the highest TP estimation error, and this error was not eliminated by sampling tissues with fast incorporation rates (i.e., blood) in the tertiary consumer. Compound-specific stable isotope analysis (CSIA) of individual amino acids was more accurate in estimating TP for all consumers and ecosystems compared to BSIA. Our analysis confirms that consideration for the dynamic nature of stable isotope ratios is necessary for accurate TP estimates. Finally, we show how first-order kinetics models can provide a useful framework for integrating prey and consumer incorporation rates in stable isotope studies to improve TP estimates.

This work assesses the effectiveness of sample preservation techniques for measurements of pH_T (total scale), total dissolved inorganic carbon (C_T), and total alkalinity (A_T) in organic-rich estuarine waters as well as the internal consistency of measurements and calculations (e.g., A_T, pH_T, and C_T) in these waters. Using mercuric chloride (HgCl₂)-treated and untreated water samples, measurements of these carbonate system parameters were examined over a period of 3 months. Respiration of dissolved organic matter in untreated samples created large discrepancies in C_T concentrations (~37 μmol kg⁻¹ increase, p < 0.0001), while C_T was effectively constant in treated samples (3095.0 ± 1.14 μmol kg⁻¹). A_T changes were observed for both treated and untreated samples, with HgCl₂-treated samples showing the greatest variation (~ 26 μmol kg⁻¹ decrease, p < 0.001). In response to changing A_T/C_T ratios, pH_T changes occurred in both treated and untreated samples but were relatively small in treated samples. Results in organic-rich estuarine waters that reflect the in situ carbonate system characteristics of the samples at the time of collection can be improved when samples obtained for C_T and A_T analysis are collected and stored separately. Accurate analyses of C_T can be obtained by filtration and preservation with HgCl₂. Accuracy of A_T analyses can be improved by filtration and storage without adding HgCl₂. The quality of pH_T measurements can be improved by prompt analysis in the field and, if this cannot be accomplished, then samples can be preserved with HgCl₂ and measured in the laboratory within 1 week.

Autonomous sensors for gravitational carbon flux in the ocean are critically needed, because of uncertainties in the projected response of the biological carbon pump (BCP) to climate change, and the proposed, engineered acceleration of the BCP to sequester carbon dioxide in the ocean. Optical sediment trap (OST) sensors directly sense fluxes of sinking particles in a manner that is independent of, and complementary to, other autonomous, sensor-derived estimates of BCP fluxes. However, limited intercalibrations of OSTs with traditional sediment traps and uncharacterized, potential biases have limited their broad adoption. A global field data set spanning three orders of magnitude in carbon flux was compiled and used to develop empirical models predicting particulate organic carbon flux from OST observations, and intercalibrating different sensor designs. These data provided valuable constraints on the uncertainty in the predicted carbon flux and showed a quantitative, theoretically consistent relationship between observations from OSTs with collimated and diffuse optical geometries. While not designed for this purpose, commercial beam transmissometers have been used as OSTs, so two models were developed quantifying the biases arising from the transmissometer's housing geometry and optical beam diameter. Finally, an algorithm for the quality control of beam transmissometer-derived OST data was optimized using sensitivity tests. The results of this study support the expansion of OST-based gravitational carbon flux measurements and provide a framework for interpretation of OST measurements alongside other gravitational particle flux observations. These findings also suggest key features that should be included in designs of future, purpose-built OST sensors.

Classification of suspended particles characterizes the composition of seawater, which helps the interpretation of remote sensing data and promotes the researches of the matter exchanges in ocean processes. In this article, an in situ prototype based on polarized light scattering is introduced, and its ability to classify the suspended particles is demonstrated. The experimental results show that the prototype can classify the sediments, microplastics, and phytoplankton in seawater with an accuracy larger than 85%, and further calculate their relative proportion in water. In the summer and winter of 2020, the prototype was deployed three times in Daya Bay and lasted for dozens of hours each time, along with the additional commercial sensors, that is, Environment X Observation (EXO) and Acoustic Doppler Current Profiler (ADCP). The chlorophyll content measured by EXO and the acoustic backscatter intensity measured by ADCP are respectively related to the number of algal cells and sediments in the water, which helps to interpret the data of the prototype. The results of field data show that the prototype can effectively classify phytoplankton and sediment particles in seawater and monitor their temporal variations. Besides, the retrieved information of the suspended particles is consistent with the analysis from the flow dynamics and season variations in Daya Bay. These results indicate the ability of this prototype to classify the suspended particles in seawater, which promises its potential contribution to particulate oceanography in the future.

Freshwaters are impacted by many pollutants. Scientists and resource managers need to reliably detect and monitor these pollutants by employing appropriate sampling techniques to quantify and mitigate their impacts. Emerging freshwater contaminants such as microplastics are difficult to monitor as effective sampling techniques have not been fully developed and lack standardization. Here, we propose a novel method, adapted from stream invertebrate kick-net sampling, which can be employed for long-term, standardized microplastic monitoring. We hypothesized that invertebrate kick-net sampling would be effective at collecting microplastics, capturing higher microplastic concentrations than standard microplastic sampling, due to collecting suspended and benthic microplastics simultaneously. We sampled 28 streams for microplastics using standard drift and sediment microplastic collection methods and invertebrate kick-netting. As predicted, kick-netting captured microplastics at higher concentrations than conventional sampling, regardless of whether values were expressed per volume of water (as in drift samples), per kg of sediment or per area sampled (as in benthic samples). Consequently, kick-net sampling has the potential to be a time- and cost-efficient tool for monitoring microplastic pollution. We recommend the employment of invertebrate kick-netting as a new, standardized means to investigate and routinely monitor microplastic concentrations worldwide. This would generate a more robust dataset of global freshwater microplastic pollution, making it possible to answer unresolved questions pertaining to changes in microplastic pollution profiles. Standardized, long-term records of microplastic concentrations in freshwaters would also allow a more accurate assessment of the ecological risks of microplastic pollution. Finally, long-term microplastic data could be used to inform much-needed regulatory decisions pertaining to microplastic pollution.

Underwater imaging enables nondestructive plankton sampling at frequencies, durations, and resolutions unattainable by traditional methods. These systems necessitate automated processes to identify organisms efficiently. Early underwater image processing used a standard approach: binarizing images to segment targets, then integrating deep learning models for classification. While intuitive, this infrastructure has limitations in handling high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and highly variable target sizes. To address these challenges, we introduce a new framework that starts with a scene classifier to capture large within-image variation, such as disparities in the layout of particles and dominant taxa. After scene classification, scene-specific Mask regional convolutional neural network (Mask R-CNN) models are trained to separate target objects into different groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal plankton images, we compared the scene-specific models to the Mask R-CNN model encompassing all scene categories as a single full model. Results showed that the scene-specific approach outperformed the full model by achieving a 20% accuracy improvement in complex noisy images. The full model yielded counts that were up to 78% lower than those enumerated by the scene-specific model for some small-sized plankton groups. We further tested the framework on images from a benthic video camera and an imaging sonar system with good results. The integration of scene classification, which groups similar images together, can improve the accuracy of detection and classification for complex marine biological images.

The ocean–atmosphere exchange of carbon largely depends on the balance between carbon export of particulate organic carbon (POC) as sinking marine particles, and POC remineralization by attached microbial communities. Despite the vast spectrum of types, sources, ages, shapes, and composition of individual sinking particles, they are usually considered as a bulk together with their associated microbial communities. This limits our mechanistic understanding of the biological carbon pump (BCP) and its feedback on the global carbon cycle. We established a method to sample individual particles while preserving their shape, structure, and nucleic acids by placing a jellified RNA-fixative at the bottom of drifting sediment traps. Coupling imaging of individual particles with associated 16S rRNA analysis reveals that active bacterial communities are highly heterogenous from one particles origin to another. In contrast to lab-made particles, we found that complex in situ conditions lead to heterogeneity even within the same particle type. Our new method allows to associate patterns of active prokaryotic and functional diversity to particle features, enabling the detection of potential remineralization niches. This new approach will therefore improve our understanding of the BCP and numerical representation in the context of a rapidly changing ocean.

Beryllium isotopes have emerged as a quantitative tracer of continental weathering, but accurate and precise determination of the cosmogenic ¹⁰Be and stable ⁹Be in seawater is challenging, because seawater contains high concentrations of matrix elements but extremely low concentrations of ⁹Be and ¹⁰Be. In this study, we develop a new, time-efficient procedure for the simultaneous preconcentration of ⁹Be and ¹⁰Be from (coastal) seawater based on the iron co-precipitation method. The concentrations of ⁹Be, ¹⁰Be, and the resulting ¹⁰Be/⁹Be ratio for Changjiang Estuary water derived from the new procedure agree well with those obtained from the conventional procedure requiring separate preconcentration for ⁹Be and ¹⁰Be determinations. By avoiding the separate preconcentration, our newly developed procedure contributes toward more time-efficient handling of samples, less sample cross-contamination, and a more reliable ¹⁰Be/⁹Be ratio. Prior to this, we validated the iron co-precipitation method using artificial seawater and natural water samples from the Amazon Estuary regarding: (1) the “matrix effect” for Be analysis, (2) its extraction efficiency for pg g⁻¹ levels Be in the presence and absence of organic matter, and (3) the data comparability with another preconcentration method. We calculated that for the determination of ⁹Be and ¹⁰Be in most open ocean seawater with typical ¹⁰Be concentrations of > 500 atoms g⁻¹, good precisions (< 5%) can be achieved using less than 3 liters of seawater compared to more than 20 liters routinely used previously. Even for coastal seawater with extremely low ¹⁰Be concentration (e.g., 100 atoms g⁻¹), we estimate a maximum amount of 10 liters to be adequate.

Reduced light is one of the primary threats to seagrass meadows in the coming decades, with reduced light reaching the benthos due to eutrophication. We assessed a multispectral photography technique using near-infrared photography to estimate chlorophyll content in the seagrass Zostera marina. Using near-infrared and red wavelength cameras in the lab environment, we measured normalized difference vegetation index (NDVI) in photographs of sampled seagrass leaves. In samples taken from three different environments, we found a positive correlation between lab-based NDVI and chlorophyll content, with variation attributable to leaf age. In samples grown under different light conditions, we found high levels of NDVI associated with lower light possibly due to seagrass photoacclimation. This method may be used in addition to existing seagrass monitoring methods to collect data on seagrass photic status and estimate chlorophyll content, and detect possible light limitation due to turbidity or high epibiota cover. The relatively low cost and time required for this method may make it useful where researchers are already collecting and imaging seagrass as part of routine monitoring.

The frequency of abnormally warm water events is increasing not only in surface waters, but also in subsurface layers, with major impacts on benthic ecosystems. Previous insights on heatwave effects have been obtained through field observations or manipulative laboratory experiments. Here, we introduce a system capable of inducing elevated water temperatures in benthic habitats in situ over several days. The system consists of a commercially available electric boiler, usually applied in domestic underfloor heating, and custom-designed benthic acrylic glass chambers connected to individual thermostats. Furthermore, the chambers are semi-open, allowing constant water exchange, maintaining otherwise near-natural conditions, including oxygen concentrations, while the temperature is elevated. The water exchange can be stopped to facilitate incubations measuring changes in benthic fluxes. We conducted a 15-d trial study in July 2021 on a bare-sediment habitat at 2.5 m depth, exposing five chambers to water temperatures 5°C above ambient temperatures for 6 d and comparing with five control chambers. In this assessment, we demonstrate that the temperature control and stability were reliable while maintaining natural oxygen conditions. The modular character of the system permits adaptations for various benthic habitats, facilitating the investigation of elevated temperatures in situ for future climate change scenarios.