Limnology and Oceanography: Methods最新文献

Methane (CH₄) emissions from aquatic ecosystems require accurate monitoring in the context of climate change. Among the several methods for CH₄ flux measurement, open dynamic chambers (ODC) are a reliable option. This method consists of a floating chamber through which a carrier gas is constantly flowing, providing accurate flux measurement with high temporal resolution. However, this method requires expensive and heavy CH₄ analyzers with high sensitivity, as well as a carrier gas system that comprises a gas cylinder and a gas flow controller, among other components. This system involves significant weight and cost challenges, limiting method implementation in certain settings and hindering its wider adoption. To address these limitations, we developed a simplified ODC configuration using atmospheric air as the carrier gas and a light and relatively less expensive detector. We applied this method to a 450-ha urban lake with CH₄ emissions ranging from moderate diffusive to high ebullitive fluxes. Concurrent measurements using a high-sensitivity CH₄ analyzer allowed us to compare the accuracy of the simplified ODC method and to assess its advantages and disadvantages. Results show that our method provides accurate CH₄ flux measurements with a spatial resolution comparable to high-sensitivity analyzers. This offers a more cost-effective, straightforward, and lightweight alternative to high-sensitivity detectors and carrier gas systems, simplifying ODC deployment in aquatic ecosystems.

We develop a novel technique to exploit the extensive data sets provided by underwater neutrino telescopes to gain information on bioluminescence in the deep sea. The passive nature of the telescopes gives us the unique opportunity to infer information on bioluminescent organisms without actively interfering with them. We propose a statistical method that allows us to reconstruct the light emission of individual organisms, as well as their location and movement. A mathematical model is built to describe the measurement process of underwater neutrino telescopes and the signal generation of the biological organisms. The Metric Gaussian Variational Inference algorithm is used to reconstruct the model parameters using photon counts recorded by photomultiplier tubes. We apply this method to synthetic data sets and data collected by the ANTARES neutrino telescope. The telescope is located 40 km off the French coast and fixed to the sea floor at a depth of 2475 m. The runs with synthetic data reveal that we can model the emitted bioluminescent flashes of the organisms. Furthermore, we find that the spatial resolution of the localization of light sources highly depends on the configuration of the telescope. Precise measurements of the efficiencies of the detectors and the attenuation length of the water are crucial to reconstruct the light emission. Finally, the application to ANTARES data reveals the first localizations of bioluminescent organisms using neutrino telescope data.

Microscopic biominerals are ubiquitous in the ocean, and several major taxa secrete them during early life stages or as adults. Organisms secrete an extracellular proteome incorporated within the biomineral to guide biomineralization remotely and enhance its material properties. This proteome has attracted the attention of extensive scientific research, but its characterization is challenging due to methodological constraints that limit the overall insight, particularly in small organisms. Therefore, we propose this straightforward and reproducible method development for preparing microscopic biominerals before proteome extraction. The method development can be tailored to other microscopic biominerals, and, importantly, it aims to integrate biomineral cleanliness and integrity without sacrificing proteome completeness. First, we suggest running an in-depth sample exploration to identify key sample characteristics and determine the magnitude of the sodium hypochlorite (NaOCl) treatment. Then, we recommend running a multiple time points experiment for biomineral cleaning treatment with a fixed NaOCl concentration. The time points are evaluated using qualitative (visual assessment) and quantitative methods (biomineral loss, elemental composition, and organic structural components removal). Finally, critical time points are identified for method validation using shotgun proteomics. This approach was tested using Hong Kong oyster larval shells as a model organism. Our study discovered that surprisingly, longer treatments and partial biomineral damage are preferred for Hong Kong oyster larvae and do not lead to protein diversity loss but enrichment. This microscopic biomineral cleaning method development can facilitate harnessing information from increasingly diverse biomineral proteomes.

Time series analyses of pigment concentrations are key to understanding past aquatic ecosystem dynamics. As lake sediments provide a window into longer-term changes, innovative paleolimnological chlorophyll quantification could provide impactful insights into past environmental processes. Lab-based hyperspectral imaging of sediment cores is an emerging technique to develop rapid, non-destructive, high-resolution chlorophyll inferences but it requires more extensive vetting. Despite recent advances in model development, there is still a knowledge gap about the reliability of chlorophyll models when applied in lakes with diverse properties, as well as the potential confounding effects of physical sediment properties on these models. We assessed the performance of 23 chlorophyll indices based on paired measurements collected via hyperspectral imaging and spectrophotometry for 202 samples spread across seven Canadian lake sediment cores. The best performance was by a new index based on the wavelength of the red-edge minimum point (λREMP). We tested the applicability of λREMP to a broad range of sediment cores using a database of 116 cores, and found the index to provide reliable reconstructions of ƩChl (i.e., chlorophyll a and b and their degradation products) trends in 84% of sites. Further analyses indicated that sediment characteristics including particle size, organic matter content, water content, and density had no systematic impact on ƩChl, but greater sediment brightness did increase ƩChl inferences from hyperspectral images. Hyperspectral core scanning is poised to facilitate the generation of high-resolution chlorophyll time series data, which could greatly improve our understanding of trajectories of change from the local to global scales.

Zooplankton analysis represents a bottleneck in marine ecology studies due to the difficulty to obtain zooplankton data. The last decades have seen the intense development of zooplankton imaging systems, to increase the zooplankton data spatio-temporal resolution as well as enabling the combination of size, taxonomy, and functional traits in aquatic ecology studies. Here, we propose a benchmark between the ZooScan, a commercially available, laboratory-based scanner, which analyses zooplankton preserved samples, and the ZooCAM, an in-flow imaging system designed for on-board live zooplankton imaging. Sixty-one zooplankton samples collected over the Bay of Biscay in environments ranging from estuarine to offshore blue waters were imaged with both instruments. Zooplankton Normalized Biovolume-Size Spectra slopes, mean sizes, abundances, and zooplankton community biogeographical patterns were computed for each instrument and compared at the taxonomic group, the sampling stations and the Bay of Biscay scales. Both instruments produced similar zooplankton variables by stations and by taxa and described similar zooplankton community compositions and biogeographical patterns, on the large mesozooplankton size range, i.e., [0.3–3.39] mm ESD. We conclude that the ZooCAM and the ZooScan data can be combined to generate long term or spatially resolved zooplankton time series. Our study shows that benchmarking imaging instruments or techniques (1) offers a robust assessment of interoperability between instruments, mitigating possible instrumental biases, and (2) may be of great interest in the case of instrumental obsolescence or breakdown, to choose the most conservative replacement solution in a long term time series framework.

Diatom community composition has a critical influence on global ocean health and ecological processes. Developing accurate and efficient methods for diatom identification under dynamic environmental conditions is essential to understanding the implications of diatom community changes. Two developing methods for identifying and enumerating phytoplankton, cell imaging and molecular sequencing, are experiencing rapid advancements. This study aims to compare diatom taxonomic composition results within natural assemblages derived from rapidly advancing methods, FlowCam imaging and metabarcoding of the V4 region of the 18S rRNA gene, with traditional light microscopy cell counting techniques. All three methods were implemented in tandem to analyze changes in dynamic diatom assemblages within simulated upwelling experiments conducted in the California upwelling zone. The results of this study indicate that, summed across all samples, DNA sequencing detected four times as many genera as morphology-based methods, thus supporting previous findings that DNA sequencing is the most powerful method for analyzing species richness. Results indicate that all three methods returned comparable relative abundance for the most abundant genera. However, the three methods did not return comparable absolute abundance, primarily due to barriers in deriving quantities in equal units. Overall, this study indicates that at the semi-quantitative level of relative abundance measurements, FlowCam imaging and metabarcoding of the V4 region of the 18S rRNA gene yield comparable results with light microscopy but at the qualitative and quantitative levels, enumeration metrics diverge, and thus method selection and cross-method comparison should be performed with caution.

Lake morphometric features like surface area, volume, mean, and maximum depth are important predictors of many physical, biological, and ecological processes. Lake bathymetric maps that present the lake basin contours are thus an integral part of limnological investigations. Accurate but cumbersome traditional bathymetric surveys measure the depth using a lead line or echosounder. Recently, airborne bathymetric mapping using imagery or laser scanning has been attempted in shallow freshwater and coastal habitats. However, these methods depend on the ability of light to penetrate the water column, which can be problematic in eutrophic lakes and shallow lakes. To alleviate these issues, we developed and tested a deep learning model (based on the U-net) using data from 153 lakes in Denmark to predict bathymetry using the topography of the surrounding terrain. The deep learning model performed much better (pixel-wise mean absolute error: validation set = 1.75 and test set = 2.15 m) than baseline interpolation approaches (validation set = 3.12 m). In addition, the deep learning model generated more realistic bathymetry maps that did not suffer from interpolation artifacts. We find that the model performance improves slightly with increasing model size (number of trainable parameters) and the extent of the surrounding terrain. In addition, our pretraining procedure improved performance and reduced the time for model convergence. Because the model only relies on digital elevation data which are widely available, it can be fine-tuned and used to predict lake bathymetry in other geographical regions.

To broaden our understanding of pelagic ecosystem responses to environmental change, it is essential that we improve the spatiotemporal resolution of in situ monitoring of phytoplankton communities. A key challenge for existing methods is in classifying and quantifying cells within the nanophytoplankton size range (2–20 μm). This is particularly difficult when there are similarities in morphology, making visual differentiation difficult for both trained taxonomists and machine learning-based approaches. Here we present a rapid fluoro-electrochemical technique for classifying nanophytoplankton, and using a library of 52 diverse strains of nanophytoplankton we assess the accuracy of this technique based on two measurements at the individual level: charge required to reduce per cell chlorophyll a fluorescence by 50% and cell radius. We demonstrate a high degree of accuracy overall (92%) in categorizing cells belonging to widely recognized key functional groups; however, this is reduced when we consider the broader diversity of “nano-phytoflagellates'.” Notably, we observe that some groups, for example, calcifying Isochrysidales, have much greater resilience to electrochemically driven oxidative conditions relative to others of a similar size, making them more easily categorized by the technique. The findings of this study present a promising step forward in advancing our toolkit for monitoring phytoplankton communities. We highlight that, for improved categorization accuracy, future iterations of the method can be enhanced by measuring additional predictor variables with minimal adjustments to the set-up. In doing so, we foresee this technique being highly applicable, and potentially invaluable, for in situ classification and enumeration of the nanophytoplankton size fraction.