Limnology and Oceanography: Methods最新文献

The use of camera and video technologies for conducting underwater surveys has rapidly expanded over the past several decades. However, the utility of these systems can be significantly hampered by numerous logistical factors, including limited underwater visibility, rough bottom topography, and ease of use for the operator. Video studies can be difficult to compare when methods and terminologies differ. Here, we describe the development of a cost-effective diver-propelled underwater ski-based video system for rapidly acquiring videos in challenging shallow, high-energy rocky benthic habitats for quantifying fish, macroalgae, and invertebrates in a coastal temperate system. The ski held the camera at a (relatively) fixed distance from the seafloor, we used parallel lasers to quantify our observations, and we used the standardized language of the Coastal and Marine Ecological Classification Standard to acquire consistent quantitative data to serve as an ecological baseline, also including archived images. Our results indicate that the ski proved to be an effective tool for capturing insightful data that would otherwise be very difficult and time-consuming to collect. Our baseline and repeatable methods can be used by other investigators at this or other locations for monitoring, re-evaluation, or comparisons to other sites.

Advances in active acoustic technology have outpaced the ability to process and analyze the data in a timely manner. Currently, scientists rely on manual scrutiny or limited automation to translate acoustic backscatter to biologically meaningful metrics useful for fisheries and ecosystem management. The National Oceanic and Atmospheric Administration Northeast Fisheries Science Center has monitored the Atlantic herring population in the Gulf of Maine and Georges Bank since 1999 due to the stocks' important economic and ecological role for the commercial lobster industry. Manual scrutinization to identify Atlantic herring schools from the water column sonar data is time-consuming and impractical for large-scale studies. To automate this process, a hybrid model with multiview learning was proposed for automatic Atlantic herring school detection, which consists of two steps: (1) region-of-interest (ROI) detection and (2) ROI classification. The ROI detection step was designed to detect school-like objects, and the ROI classification step was designed to distinguish Atlantic herring schools from other objects. The co-training algorithm was employed for multiview learning as well as semi-supervised learning. Within this framework, single-view vs. multiview learning and supervised vs. semi-supervised learning were evaluated and compared. Our results showed that multiview learning can improve the performance of the hybrid model in Atlantic herring school detection, and the utilization of unlabeled data is also helpful when the training set is small. The best-performed model achieved an F1-score of 0.804. This new framework provides an efficient and effective tool for automatic Atlantic herring school detection.

Dissolved gas concentrations in surface waters can have sharp gradients across marine and freshwater environments, which often prove challenging to capture with analytical measurement. Collecting discrete samples for laboratory analysis provides accurate results, but suffers from poor spatial resolution. To overcome this limitation, water equilibrators and gas membrane contactors (GMCs) have been used for the automated underway measurement of dissolved gas concentrations in surface water. However, while water equilibrators can provide continuous measurements, their analytical response times to changes in surface water concentration can be slow, lasting tens of minutes. This leads to spatial imprecisions in the dissolved gas concentration data. Conversely, while GMCs have proven to have much faster analytical response times, often lasting only a few minutes or less, they suffer from poor accuracy and thus require routine calibration. Here we present an analytical system for the high accuracy and high precision spatial mapping of dissolved methane concentration in surface waters. The system integrates a GMC with a cavity ringdown spectrometer for fast analytical response times, with a calibration method involving two Weiss-style equilibrators and discrete measurements in vials. Data from both the GMC and equilibrators are collected simultaneously, with discrete vial samples collected periodically throughout data collection. We also present a mathematical algorithm integrating all data collected for the routine calibration of the GMC dataset. The algorithm facilitates comparison between the GMC and equilibrator datasets despite the substantial differences in response times (0.7–2.1 and 4.1–17.6 min, respectively). This measurement system was tested with both systematic laboratory experiments and field data collected on a research cruise along the US Atlantic margin. Once calibrated, this system identified numerous sharp peaks of dissolved methane concentration in the US Atlantic margin dataset that would be poorly resolved, or outright missed with previous measurement techniques. Overall, the precision and accuracy for the technique presented here were determined to be 11.2% and 10.4%, respectively, the operating range was 0–1000 ppm methane, and the e-folding response time to changes in dissolved methane concentration was 0.7–2.1 min.

In vivo studies of the effects of molecules of interest, such as hormones or xenobiotics on corals, are essential to uncover their effects on coral biological processes. However, exposure to such molecules is very challenging in aquarium systems due to the duration of exposure, the high cost of the compounds, their quantity, and their diffusion in seawater. In this study, we provide a durable alternative method by in vivo injection. The aim of this study was to evaluate slow release and local injection as a novel method of delivering compounds to corals. In this method, coconut oil, which solidifies upon injection and has a melting point of about 24°C, is used as the vehicle for injection. Local diffusion of the injected products in the organism was followed using visual tracers. Specifically, two classes of fluorescent markers were used, one of which examined internalization into cells (rhodamine), while the others were used as an application to monitor the calcification process (alizarin, calcein). In parallel, we developed an analytical method to quantify the calcein and alizarin labeling of sclerites, which allowed us to determine calcification rates in different parts of the coral. Two octocorals were used to optimize these methods, with Sarcophyton sp. being the preferred organism to develop and validate the injection procedures and characterize the diffusion of the markers. Once the method was perfected, injections were performed on the precious coral Corallium rubrum to prove the transferability of the method.

The TPTZ (2,4,6-tripyridyl-s-triazine) method is used to detect monosaccharides from seawater and particulate matter samples because it is sensitive, precise, rapid and easy to perform. Contrary to mechanisms proposed in the literature, we provide evidence that the TPTZ method detects hydroxyl as well as aldehyde groups in monosaccharides when all reducing groups are fully deprotonated in alkaline medium. We use this insight to develop an optimized hydrolysis protocol to increase yields from polysaccharides while minimizing the dehydration of monosaccharides. Compared to the TPTZ method with commonly used hydrolysis protocols and the often-used phenol–sulfuric acid method, our new optimized method detects a wider range of carbohydrates with a more consistent yield relative to glucose and much lower coefficient of variation. When applied to phytoplankton cultures and marine particulate samples, our new method achieves significantly higher bulk carbohydrate yields.

The marine carbonate system is influenced by anthropogenic CO₂ uptake, biogeochemical processes, and physical changes that involve freshwater input and removal. Two frequently used parameters to quantify seawater carbonate system are total alkalinity (TA) and total dissolved inorganic carbon (DIC). To account for the physical changes, both TA and DIC are usually normalized to a reference salinity (i.e., nTA and nDIC), and then the relationship between nTA and nDIC is used to identify major biogeochemical processes that regulate the carbonate system, based on process-specific reaction stoichiometry. However, the theoretical basis of this interpretation has not been holistically examined. In this study, we validated this method under  idealized conditions and discussed the associated assumptions and limitations. Furthermore, we applied this method to interpret field TA and DIC data from a lagoonal estuary in the northwestern Gulf of Mexico. Our results demonstrated that evaluating field data that encompass multiple stations and time periods could be problematic. In addition, various combinations of biogeochemical processes can lead to the same nTA–nDIC relationship, even though the relative importance of each individual process may vary significantly. Therefore, the stoichiometric relationship relying solely on TA and DIC data is not a definitive approach for uncovering dominant biogeochemical processes. Instead, measurements of process-specific parameters are necessary.

Diadromous fishes migrate between marine and fresh waters for reproduction. For anadromous species, which spawn in freshwater, improved access to freshwater spawning and nursery habitats and ability of juveniles to emigrate to the ocean may support population recovery. Despite the potentially enormous influence of early life stage survival on adult population size, managers and scientists have limited capacity to assess numbers of juvenile anadromous fishes leaving freshwater ecosystems. Such data are critical for evaluating reproductive success and habitat suitability and have been identified as a top priority in anadromous fish research and management. We developed a state-of-the-art underwater video and computational system to collect videos to estimate abundances and migration timing for juvenile river herring (Alosa pseudoharengus; Alosa aestivalis). We collected continuous video in the Monument River (Bourne, Massachusetts, USA) from June to November 2017. We trained three types of neural network models to detect and count fish in video frames and evaluated model performance by comparing human counts to model outputs. Our top model assessed presence and absence (F1 = 87%) and counted fish (counting error 9.4%) with an accuracy comparable to human counters (F1 = 88%). Our system's capability to collect accurate counts of emigrating juveniles will provide critical information that could be related to the numbers of spawning adults, system-specific productivity, and spawning and nursery habitat suitability. Both the video collection system and computational model may be transferrable to other sites and for other species where tracking juvenile emigration may inform management efforts.

While many ecology studies require estimations of species abundance, doing so for mobile animals in an accurate, non-invasive manner remains a challenge. One popular stopgap method involves the use of remote video-based surveys using several cameras, but abundance estimates derived from this method are computed with conservative metrics (e.g., maxN computed as the maximum number of individuals seen simultaneously on a single video). We propose a novel methodological framework based on a remote-camera network characterized by known positions and non-overlapping field-of-views. This approach involves a temporal synchronization of videos and a maximal speed estimate for studied species. Such a design allows computing a new abundance metric called Synchronized maxN (SmaxN). We provide a proof-of-concept of this approach with a network of nine remote underwater cameras that recorded fish for three periods of 1 h on a fringing reef in Mayotte (Western Indian Ocean). We found that abundance estimation with SmaxN yielded up to four times higher values than maxN among the six fish species studied. SmaxN performed better with an increasing number of cameras or longer recordings. We also found that using a network of synchronized cameras for a short time period performed better than using a few cameras for a long duration. The SmaxN algorithm can be applied to many video-based approaches. We built an open-sourced R package to encourage its use by ecologists and managers using video-based censuses, as well as to allow for replicability with SmaxN metric.

Early life stages of fish are widely used for regulatory toxicity testing, and marine fish display high sensitivity to pollutant exposure. Exposure to pollutants during embryogenesis causes acute effects on embryonic development and survival, but also sub-lethal impacts manifested as maldeveloped larvae. Acquiring time- and exposure-dependent responses to pollutant exposure and other stressors in small organisms is labor intensive and often subjective. This leads to studies obtaining small sample sizes, with measurements often made infrequently during development. Automated monitoring methods can maintain consistency between measurements and allow many more measurements to be made, improving the quantity and quality of such data. We exposed Atlantic cod embryos to 3,4-dichloroaniline, a reference chemical widely used as a positive control agent in regulatory fish embryo toxicity testing. We monitored their growth through daily imaging with an automated flow-through imaging system. Biologically relevant sublethal endpoints were estimated from these images with a neural network and traditional machine vision methods. We demonstrate the automated capture and analysis of tens of thousands of images, producing detailed morphometric data from hundreds of fish over a 10-d study period, and assess the effectiveness of the automated system. The automated method presented allows measurements to be made frequently without sacrificing the sampled organisms, making detailed time series of development obtainable. We show dose-dependent effects of the toxicant on development and capture nonlinear responses that would not be attainable under a conventional manual sampling regime.

Advancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high-resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE-DOM); however, quantitative biases against select components were observed. Further linkages with FTICR-MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE-DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high-resolution mass spectrometry datasets in freshwater lotic environments.