Limnology and Oceanography: Methods最新文献

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.

Accurate measurements of seawater carbonate chemistry are crucial for marine carbon cycle research. Certified reference materials (CRMs) are typically analyzed alongside samples to correct measurements for calibration drift. However, the COVID-19 pandemic led to a limited access to CRMs. In response to this shortage, we prepared and monitored in-house reference materials (IHRMs) for total alkalinity (TA) and dissolved inorganic carbon (DIC), over 12 and 15 months, respectively. Overall, TA was stable, but a slight increase in DIC of about 2 μmol kg⁻¹ occurred over 15 months. The increase could potentially be attributed to bacterial growth, despite mercuric chloride fixation and repeated UV exposure. It is noted that this small increase was most likely within our instrument and measurements uncertainties. Our repeated measurements also identified a few bottles that had TA or DIC concentrations 4–5 μmol kg⁻¹ higher than the rest, indicating issues during cleaning, fixation, or storage of individual bottles. This study emphasizes the importance of careful and continuous monitoring if self-prepared IHRMs are used. Given that the amount of work required is very high, IHRM preparation is only recommended when CRMs are not available.

In shallow coastal systems, sediments are exposed to dramatic and complex variability in environmental conditions that influences sediment processes on short timescales. Sediment oxygen demand (SOD), or consumption of oxygen by sediment-dwelling organisms and chemical reactions within sediments, is one such process and an important metric of aquatic ecosystem functioning and health. The most common instruments used to measure SOD in situ are batch-style benthic chambers, which generally require long measurement periods to resolve fluxes and thus do not capture the high temporal variability in SOD that can be driven by dynamic coastal processes. These techniques also preclude linking changes in SOD through time to specific features of the sediment, for example, shifts in sediment faunal activities which can vary on short time scales and can also be affected by ambient oxygen concentrations. Here we present an in situ semi-flow through instrument to repeatedly measure SOD in discrete areas of sediment. The system isolates patches of sediment in replicate benthic chambers, and measures and records oxygen decrease for a short time before refreshing the overlying water in the chamber with water from the external environment. This results in a sawtooth pattern in which each tooth is an incubation, providing an automated method to produce direct measurements of in situ SOD that can be directly linked to an area of sediment and related to rapid shifts in environmental conditions.

Biodiversity conservation and management requires surveillance that captures the full spectrum of taxa. Here, we showcase the potential for a portfolio of visual, extractive, and molecular methods for detecting previously hidden components of tropical fish biodiversity in an economically and culturally valuable marine site that spans a tropical-temperate ecotone—the Ningaloo Coast World Heritage Area. With scale and practicality in mind, we demonstrate how environmental DNA (eDNA) methods deployed in a stratified sampling design can yield a more comprehensive monitoring program for species presence than current alternatives (e.g., extractive sampling via anesthetic). eDNA from filtered water samples detected up to six times as many cryptobenthic fish species per site than samples collected with anesthetic, indicating it is a potentially powerful tool for assessing biodiversity of tropical fishes. However, there were also species that were only found when using anesthetic and the contribution of cryptobenthic species to overall diversity of the fish assemblage was unexpectedly low, suggesting not all cryptobenthic fish species have been detected with eDNA. There were also distinct differences in cryptobenthic assemblages both among sites and sample depths (2–3 m) when using eDNA from filtered water, suggesting this technique may be able to identify fine scale spatial differences in cryptobenthic fish assemblage. eDNA collected from water detects the most cryptobenthic species and is therefore an efficient tool for rapidly assessing biodiversity, but extractive techniques may still be required for biological and monitoring studies, and when combined with eDNA sampling provides the most comprehensive assessment of cryptobenthic fishes.

Lake morphometry is a driver of limnological processes, yet digitized bathymetry is lacking for most lakes. Here, we describe a method for efficiently extracting hypsography from bathymetric maps using ImageJ. To validate our method, we compared results generated from two independent users to those obtained from digital elevation models for 100 lakes. The mean absolute difference between hypsographic curves extracted using ImageJ vs. digital elevation models (DEMs) was 0.049 (95% CI 0.041–0.056) proportion of lake area, suggesting that ImageJ provides accurate hypsography. We calculated the mean absolute difference between the two users (0.016; 95% CI: 0.011–0.021), which suggests high interobserver reliability. Finally, we compared DEMs to an interpolated hypsography using only the maximum lake depth and found large differences. We apply this method to extract data for 1012 lakes. Our data and approach will be useful where bathymetric maps exist but are not digitized.

Intense biogeochemical transformations in sediments and biofilms may occur over sub-mm distances. Our current understanding of those transformations in such narrowly stratified environments has been facilitated by the introduction of microsensors. Until now most studies have been conducted using individual sensors for the various chemical species, and careful vertical alignment of the sensor tips is then essential for the meaningful interpretation of the resulting data. For instance, the determination of total dissolved sulfide (TDS) at high resolution requires perfect alignment of sensors for H₂S and pH, as the pKa for H₂S is close to ambient pH. In this study, we show how a recently developed TDS sensor and a new combined H₂S/O₂ microsensor can improve the analysis of sulfidic environments including the oxygen–sulfide interface. The TDS sensor does not require pH correction unlike the conventional H₂S sensor, and it thus eliminates the need for a simultaneous pH measurement. The combined sensor allows for perfect alignment of H₂S and O₂ micro profiles and makes it possible to not only more accurately estimate fluxes, but also to determine overlapping zones of oxygen and dissolved sulfide at very high resolution.

Robust sampling of animals is necessary for understanding ocean ecology, but evaluating the effectiveness of our samplers is a challenge. Scientific echosounders were added to two robotic platforms carrying video imaging systems: a remotely operated vehicle (ROV) and an autonomous underwater vehicle (AUV). The vehicles were used to quantitatively sample midwater life in Monterey Bay along horizontal transects at incremental depths ranging from 25 to 1000 m. The echosounders allowed the bulk behavioral responses of animals to be observed up to 200 m forward of each platform. These responses observed included no response, continual avoidance, avoidance to a fixed range resulting in a patch, and attraction. There were strong and interacting effects of depth and platform type on behavioral responses. Measurements of acoustic backscatter showed that animals responded more strongly to the AUV than the ROV. During AUV surveys, there were effects of day/night and the use of artificial illumination on animal responses. Behavioral responses to our sampling were both species- and context-dependent. These data inspired the expansion of an existing mathematical framework that formalized the processes affecting the sampling of motile ocean organisms. Originally developed for net sampling, we generalized the equations to be platform- and sensor-agnostic and incorporated animal decision-making processes to allow for behaviors consistent with the full range of responses we observed. These results and the framework can help move toward more effective sampling of motile animals in the ocean.

Sensors that use ultraviolet (UV) light absorption to measure nitrate in seawater at in situ temperatures require a correction to the calibration coefficients if the calibration and sample temperatures are not identical. This is mostly due to the bromide molecule, which absorbs more UV light as temperature increases. The current correction applied to in situ ultraviolet spectrophotometer (ISUS) and submersible ultraviolet nitrate analyzer (SUNA) nitrate sensors generally follows Sakamoto et al. (2009, Limnol. Oceanogr. Methods 7, 132–143). For waters warmer than the calibration temperature, this correction model can lead to a 1–2 μmol kg⁻¹ positive bias in nitrate concentration. Here we present an updated correction model, which reduces this small but noticeable bias by at least 50%. This improved model is based on additional laboratory data and describes the temperature correction as an exponential function of wavelength and temperature difference from the calibration temperature. It is a better fit to the experimental data than the current model and the improvement is validated using two populations of nitrate profiles from Biogeochemical Argo floats navigating through tropical waters. One population is from floats equipped with ISUS sensors while the other arises from floats with SUNA sensors on board. Although this model can be applied to both ISUS and SUNA nitrate sensors, it should not be used for OPUS UV nitrate sensors at this time. This new approach is similar to that used for OPUS sensors (Nehir et al., 2021, Front. Mar. Sci. 8, 663800) with differing model coefficients. This difference suggests that there is an instrumental component to the temperature correction or that there are slight differences in experimental methodologies.

Bryozoans are becoming an increasingly popular study system in macroevolutionary, ecological, and paleobiological research. Members of this colonial invertebrate phylum display an exceptional degree of division of labor in the form of specialized modules, which allows for the inference of individual allocation of resources to reproduction, defense, and growth using simple morphometric tools. However, morphometric characterizations of bryozoans are notoriously labored. Here, we introduce DeepBryo, a web application for deep-learning-based morphometric characterization of cheilostome bryozoans. DeepBryo is capable of detecting objects belonging to six classes and outputting 14 morphological shape measurements for each object. The users can visualize the predictions, check for errors, and directly filter model outputs on the web browser. DeepBryo was trained and validated on a total of 72,412 structures in six different object classes from images of 109 different families of cheilostome bryozoans. The model shows high (> 0.8) recall and precision for zooid-level structures. Its misclassification rate is low (~ 4%) and largely concentrated in two object classes. The model's estimated structure-level area, height, and width measurements are statistically indistinguishable from those obtained via manual annotation. DeepBryo reduces the person-hours required for characterizing individual colonies to less than 1% of the time required for manual annotation. Our results indicate that DeepBryo enables cost-, labor,- and time-efficient morphometric characterization of cheilostome bryozoans. DeepBryo can greatly increase the scale of macroevolutionary, ecological, taxonomic, and paleobiological analyses, as well as the accessibility of deep-learning tools for this emerging model system.