Limnology and Oceanography: Methods最新文献

Measurements by the submersible ultraviolet nitrate analyzer (SUNA) can be used to derive high-resolution in situ nitrate concentration with reliable accuracy and precision. Here we report our operational practices for SUNA deployment (including pre-cruise instrument preparation and in-cruise instrument maintenance) and detailed post-cruise nitrate quality control procedures for SUNA integrated onto the CTD rosette. This work is based on experiences and findings from over 500 individual SUNA casts collected from 24 cruises (of which 14 cruises have been quality controlled so far) over the past 5 yr. After applying previously published spectral corrections for temperature, salinity, and pressure effects, we found residual biases in SUNA nitrate estimates compared to independently measured discrete samples. We further develop and assess a new two-step procedure to remove remaining biases: (1) a general temperature-dependent adjustment at low-nitrate concentrations; and (2) a cruise-specific full-range bias correction. Our final quality-controlled SUNA nitrate data achieve an accuracy of 0.34–0.78 μM, with a precision of 0.08–0.21 μM, at a vertical resolution of 1 m. Additional comparisons between the nitrate and density data confirm the high quality of the quality-controlled SUNA data. Although applying spectral correction algorithms increases the accuracy and precision of the instrument-output nitrate concentration, we emphasize that additional constraints of SUNA measurements against other independent sources (e.g., bottle data, temperature, and density) are irreplaceable to ensure the accuracy of final nitrate data.

Chemical signaling is ubiquitous in the marine environment. Plankton rely on chemical signals to find mates, hunt prey, and respond to threats, and these small-scale interactions can propagate into community-wide cascades and large-scale ecological changes. The chemical signaling exchange in the open ocean is poorly understood, and fundamental information about concentrations and spatiotemporal variability is lacking. Passive sampling has been used to monitor a wide range of dissolved chemicals, including anthropogenic pollutants and harmful algal toxins, but it is not generally applied to the study of marine chemical ecology. Here we test the compatibility of two resins commonly used for passive sampling via solid phase adsorption toxin tracking (SPATT), Diaion® HP20 and Sepabeads® SP207, with copepodamides, a group of polar lipid signaling compounds produced by copepods. We developed extraction and analysis methods that align with current SPATT practices for algal toxins and show the first measurements of copepodamides from Monterey Bay in California. In lab trials, mean copepodamide recovery from HP20 resin was approximately 240% greater than SP207. In addition, copepodamides were found to have a mean half-life of 34 h in seawater. Adsorption to HP20 stabilized dissolved copepodamides, increasing the mean recovery after 168 h from 0.62% in seawater to 65.2% from SPATT. Results suggest that SPATT is a sensitive and effective tool for obtaining integrated copepodamide concentrations, spotlighting a novel method to include information from copepod mesozooplankton in time series and field studies.

Predicting anthropogenic impacts on benthic marine ecosystems is of great importance for conservation. Climate change models have indicated that increasing seawater temperatures will drive shifts in the distribution of benthic organisms due to species-specific thermal tolerances. When combined with other stressors such as pollutants, interactive effects may lead to even greater impacts. Microplastics (MP), as a marine pollutant, have been shown to elicit responses in organisms but often at concentrations far greater than experienced in the environment and with short-term exposure times. Assessing long-term interactive effects of MP pollution and ocean warming on benthic marine organisms has not been previously addressed. A unique mesocosm facility was constructed on the island of Helgoland, in the southern North Sea, to explore the combined impacts of these two factors. The multi-factorial experimental system is composed of 16 independent benthic mesocosms, utilizing novel features and methods for the continuous generation of climate change and MP exposure scenarios, while retaining natural conditions for other environmental parameters. We provide a description of the system design and methods, followed by an operational performance assessment during a 10-month exposure experiment with European flat oysters (Ostrea edulis), evaluated on the accuracy of exposure scenario control and the degree of realism achieved. We demonstrate the novel application of kinetic modeling for generating environmentally relevant MP exposure conditions (+ 25 MP L⁻¹), and highlight the mesocosm systems suitability for studying chronic effects of MP pollution and ocean warming on benthic marine ecosystems through its real-world application.

Knowledge on the biodiversity and abundance of phytoplankton is key for many ecological and societal (e.g., blue growth) questions. Gathering temporal variation and spatial patterns on key indicators requires reliable and standardized protocols on sampling, species identification and counting. Numerous methods are used but consequences for comparing the biodiversity and abundance of phytoplankton of these different techniques are not well known. We evaluated the consequences of different counting protocols using light microscopy (i.e., subsampling transects or wedges within counting chambers) for these indices using samples collected weekly to bi-weekly (n = 398, 2009–2018) from the Wadden Sea (southern North Sea). Phytoplankton cells were counted (by one person under similar conditions) in a fixed number of viewing fields (58, 70, and 29) at three respective magnifications (10 × 100, 10 × 40, and 10 × 10). Patterns in the spatial distribution of phytoplankton cells varied among species and clustering of cells occurred in more than one-fifth of the samples. This will induce error in the conversion from counts (per viewing field) to abundance (cells mL⁻¹). Our present effort resulted in a high accuracy (95%) in overall cell abundances. This was not the case for species richness, for example, capturing 90% of all species present in the sample would require an almost threefold increase in effort for the 10 × 40 and 10 × 10 magnifications. We recommend that counting methods be tailored to the main research objectives and that counting protocols should quantify uncertainty as well as potential bias to provide an estimation of the error in phytoplankton abundance and species composition.

We assess whether a supervised machine learning algorithm, specifically a convolutional neural network (CNN), achieves higher accuracy on planktonic image classification when including non-plankton and ancillary plankton during the training procedure. We focus on the case of optimizing the CNN for a single planktonic image source, while considering ancillary images to be plankton images from other instruments. We conducted two sets of experiments with three different types of plankton images (from a Zooglider, Underwater Vision Profiler 5, and Zooscan), and our results held across all three image types. First, we considered whether single-stage transfer learning using non-plankton images was beneficial. For this assessment, we used ImageNet images and the 2015 ImageNet contest-winning model, ResNet-152. We found increased accuracy using a ResNet-152 model pretrained on ImageNet, provided the entire network was retrained rather than retraining only the fully connected layers. Next, we combined all three plankton image types into a single dataset with 3.3 million images (despite their differences in contrast, resolution, and pixel pitch) and conducted a multistage transfer learning assessment. We executed a transfer learning stage from ImageNet to the merged ancillary plankton dataset, then a second transfer learning stage from that merged plankton model to a single instrument dataset. We found that multistage transfer learning resulted in additional accuracy gains. These results should have generality for other image classification tasks.

Total lipid content is a valuable indicator of fish health, prey quality, survival potential, stock health, and ecosystem status. Here, we demonstrate an accurate method for measuring total lipids in fish tissues using the spectrophotometric sulfo-phospho-vanillin (SPV) assay, adapted to a 96-well plate format. Samples of dried homogenate were cross-analyzed via the SPV assay and standard gravimetric lipid analysis. Initial measurements of whole fish homogenates analyzed include Pacific herring (Clupea pallasii), Pacific cod (Gadus macrocephalus), walleye pollock (G. chalcogrammus), Pacific capelin (Mallotus villosus), Chinook (Oncorhynchus tshawytscha), and coho (O. kisutch) salmon. Samples of muscle tissue were analyzed from Chinook, pink (O. gorbuscha), sockeye (O. nerka), and chum (O. keta) salmon. All SPV measurements were calibrated using menhaden oil. The mean absolute and relative difference between gravimetric and SPV analysis was 0.5 and ~ 16.4%, respectively (n = 121). To improve the accuracy of SPV assay results, linear calibration models specific to taxa and tissue matrix type were developed, enabling calculation of corrected SPV assay values. The accuracy of using these calibration models was tested by analyzing additional fish samples (n = 16). The results of the corrected SPV assay were not statistically different (p > 0.05) from gravimetric analysis for any samples measured, and the mean absolute and relative difference between the two assays improved to 0.2% and 4.6%, respectively. The SPV assay provides a rapid (2 h), high-throughput (25 samples processed in triplicate), precise (interassay coefficient of variation = 5.6%), and accurate method for quantifying the total lipid content of homogenized fish tissue.

Blechinger, T., Link, D., Nelson, J.K.R. and Hansen, G.J.A. (2024), Estimating ethanol correction factors for δ¹³C and δ¹⁵N isotopic signatures of freshwater zooplankton from multiple lakes. Limnol Oceanogr Methods, 22: 464–472. https://doi.org/10.1002/lom3.10623

In the author affiliation section, the correct affiliation for the co-author “Jenna K. R. Nelson” is: “Minnesota Department of Natural Resources, Saint Paul, Minnesota, USA.”

We apologize for this error.

The geochemistry of marine sediment is a massive archive of (paleo)oceanographic information. Accessing that information requires “unmixing” the various influences on marine sediment geochemistry to understand individual sources and marine geochemical processes. Q-mode factor analysis (QFA) and independent component analysis (ICA) are multivariate statistical techniques that have successfully been applied to large datasets of marine sediment element concentrations to identify the number and composition of marine sediment sources or end-members. In this study, we apply both techniques to two datasets of marine sediment geochemistry, compare the output, and discuss the advantages of each approach. In both datasets, ICA identified a mixing trend between carbonates and dust, whereas QFA represented the end-members as two separate factors. In the Pacific and Indian Oceans dataset, both techniques produced three factors or independent components involving rare earth elements, but two of the QFA factors explained a small, almost negligible, amount of the variability of the dataset. Also, QFA identified more aluminosilicate end-members (dust or volcanic ash) than ICA. In the Indian Ocean Sites 738 and 752 dataset, ICA identified two processes affecting Sr and Ba concentrations as separate independent components, while QFA created a factor representing the covariation of Sr and Ba over intervals of the site's paleoceanographic history. The results of this study exemplify that QFA identifies covariances and finds discrete end-members contributing to the bulk mass of sediment. ICA works best with non-Gaussian element distributions and finds geochemical signals and mixing trends that constitute the characteristic structure of the multielemental data.

As global ocean monitoring programs and marine carbon dioxide removal methods expand, so does the need for scalable biogeochemical sensors. Currently, pH sensors are widely used to measure the ocean carbonate system on a variety of autonomous platforms. This paper assesses a commercially available optical pH sensor (optode) distributed by PyroScience GmbH for oceanographic applications. Results from this study show that the small, solid-state pH optode demonstrates a precision of 0.001 pH and relative accuracy of 0.01 pH using an improved calibration routine outlined in the manuscript. A consistent pressure coefficient of 0.029 pH/1000 dbar is observed across multiple pH optodes tested in this study. The response time is investigated for standard and fast-response versions over a range of temperatures and flow rates. Field deployments include direct comparison to ISFET-based pH sensor packages for both moored and profiling platforms where the pH optodes experience sensor-specific drift rates up to 0.006 pH d⁻¹. In its current state, the pH optode potentially offers a viable and scalable option for short-term field deployments and laboratory mesocosm studies, but not for long term deployments with no possibility for recalibration like on profiling floats.

Membrane inlet mass spectrometry (MIMS) provides detailed measures of dissolved ^28,29,30N₂, O₂, and argon (Ar) for estimating important gas fluxes and concentrations in aquatic ecosystems. Previous studies demonstrated a large O₂ scavenging effect while using a MIMS, where varying concentrations of O₂ can affect measured N₂ : Ar because O₂ interacts with N₂ in the ion source to produce NO⁺ (m/z = 30), potentially decreasing the detected current for ^28,29N₂ and increasing the detected current for ³⁰N₂. A common solution is to use a muffle furnace heated to 600°C with a copper reduction column to reduce the concentration of O₂ to minimal levels and accurately measure ^28,29,30N₂. However, this solution eliminates the detection of O₂ in environmental samples, which is a major benefit of using a MIMS. We questioned whether the MIMS was sensitive enough to provide accurate O₂ estimates when using the furnace and whether the O₂ scavenging effect was real and consistent among MIMS. We conducted four separate experiments on three different MIMS to test the O₂ scavenging effect and the potential detection of O₂ when using a MIMS with furnace. The furnace removed ~ 99% of O₂, and O₂ scavenging had little to no detectable effect on N₂ : Ar and an unclear effect on ²⁹N₂ : ²⁸N₂, but increased ³⁰N₂ : ²⁸N₂. In most cases, accurate O₂ data could be retrieved despite using the furnace. The need for O₂ reduction may be limited to measuring accurate ³⁰N₂ : ²⁸N₂ in isotope pairing studies, but without substantial loss of MIMS measurements used to describe O₂ dynamics.