Limnology and Oceanography: Methods最新文献

{"title":"Producing plankton classifiers that are robust to dataset shift","authors":"C. Chen,&nbsp;S. P. Kyathanahally,&nbsp;M. Reyes,&nbsp;S. Merkli,&nbsp;E. Merz,&nbsp;E. Francazi,&nbsp;M. Hoege,&nbsp;F. Pomati,&nbsp;M. Baity-Jesi","doi":"10.1002/lom3.10659","DOIUrl":"https://doi.org/10.1002/lom3.10659","url":null,"abstract":"<p>Modern plankton high-throughput monitoring relies on deep learning classifiers for species recognition in water ecosystems. Despite satisfactory nominal performances, a significant challenge arises from dataset shift, which causes performances to drop during deployment. In our study, we integrate the ZooLake dataset, which consists of dark-field images of lake plankton (Kyathanahally et al. 2021a), with manually annotated images from 10 independent days of deployment, serving as <i>test cells</i> to benchmark out-of-dataset (OOD) performances. Our analysis reveals instances where classifiers, initially performing well in in-dataset conditions, encounter notable failures in practical scenarios. For example, a MobileNet with a 92% nominal test accuracy shows a 77% OOD accuracy. We systematically investigate conditions leading to OOD performance drops and propose a preemptive assessment method to identify potential pitfalls when classifying new data, and pinpoint features in OOD images that adversely impact classification. We present a three-step pipeline: (i) identifying OOD degradation compared to nominal test performance, (ii) conducting a diagnostic analysis of degradation causes, and (iii) providing solutions. We find that ensembles of BEiT vision transformers, with targeted augmentations addressing OOD robustness, geometric ensembling, and rotation-based test-time augmentation, constitute the most robust model, which we call <i>BEsT</i>. It achieves an 83% OOD accuracy, with errors concentrated on container classes. Moreover, it exhibits lower sensitivity to dataset shift, and reproduces well the plankton abundances. Our proposed pipeline is applicable to generic plankton classifiers, contingent on the availability of suitable test cells. By identifying critical shortcomings and offering practical procedures to fortify models against dataset shift, our study contributes to the development of more reliable plankton classification technologies.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 1","pages":"39-66"},"PeriodicalIF":2.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive software for processing, compilation, and interpretation of seafloor heat-flow data, including analysis of measurement uncertainties","authors":"K. L. Dickerson,&nbsp;A. T. Fisher,&nbsp;R. N. Harris,&nbsp;M. Hutnak","doi":"10.1002/lom3.10655","DOIUrl":"https://doi.org/10.1002/lom3.10655","url":null,"abstract":"<p>We present software for processing and interpretation of marine heat-flow data. These data commonly include in situ measurements of the thermal gradient and thermal conductivity as a function of subseafloor depth, and are used to calculate vertical heat flow. New software includes <i>SlugPen</i>, for parsing and correcting datasets for each penetration, and <i>SlugHeat</i>, for calculating equilibrium temperatures (<span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>T</mi>\u0000 <mi>eq</mi>\u0000 </msub>\u0000 </mrow></math>) and in situ thermal conductivity (<i>k</i>) with depth, and using these values to calculate heat flow and uncertainty metrics. <i>SlugHeat</i> is based on an established approach that solves iteratively for <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>T</mi>\u0000 <mi>eq</mi>\u0000 </msub>\u0000 </mrow></math> and <i>k</i> and introduces several new tools and a graphical user interface to help identify and resolve data issues and improve interpretation of results. Notably, <i>SlugHeat</i> incorporates multiple options for determining the uncertainty of calculated heat-flow values, including a Monte Carlo sensitivity analysis of layer properties. Results from reprocessing heat-flow data from earlier marine surveys show the benefits of <i>SlugHeat</i>'s flexibility and interactivity for managing and interpreting data from multiple settings. We find that incorporating ambiguity in the thickness and thermal conductivity of individual sediment layers significantly increases the uncertainty in heat flow measurements; the statistical fit of observations to a linear model, as has been historically relied on to define heat-flow uncertainty, tends to understate this value. Assessment of data quality and testing reduction options requires making thoughtful decisions, aided using an interactive interface, and is therefore not fully automated. <i>SlugPen</i> and <i>SlugHeat</i> are accessible for public use with documentation and source code that is readily adaptable to include alternative processing algorithms.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 1","pages":"1-25"},"PeriodicalIF":2.1,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bias-corrected high-resolution vertical nitrate profiles from the CTD rosette-mounted submersible ultraviolet nitrate analyzer","authors":"Bofu Zheng,&nbsp;E. Taylor Crockford,&nbsp;Weifeng (Gordon) Zhang,&nbsp;Rubao Ji,&nbsp;Heidi M. Sosik","doi":"10.1002/lom3.10656","DOIUrl":"https://doi.org/10.1002/lom3.10656","url":null,"abstract":"<p>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 <i>μ</i>M, with a precision of 0.08–0.21 <i>μ</i>M, 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.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 12","pages":"889-902"},"PeriodicalIF":2.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Field monitoring of copepodamides using a new application for solid phase adsorption toxin tracking","authors":"Aubrey Trapp,&nbsp;Erik Selander,&nbsp;Melissa Peacock,&nbsp;Raphael M. Kudela","doi":"10.1002/lom3.10654","DOIUrl":"https://doi.org/10.1002/lom3.10654","url":null,"abstract":"<p>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.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 12","pages":"877-888"},"PeriodicalIF":2.1,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10654","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modular shadowgraph imaging for zooplankton ecological studies in diverse field and mesocosm settings","authors":"Adam T. Greer,&nbsp;Patrick I. Duffy,&nbsp;Tim J. W. Walles,&nbsp;Charles Cousin,&nbsp;Laura M. Treible,&nbsp;Kyle D. Aaron,&nbsp;Jens C. Nejstgaard","doi":"10.1002/lom3.10657","DOIUrl":"https://doi.org/10.1002/lom3.10657","url":null,"abstract":"<p>Various iterations of shadowgraph imaging have been used to quantify zooplankton in situ with high spatial resolution. Because these systems can image relatively large volumes of water, they are especially useful for resolving less common meso- or macrozooplankton taxa (&lt; 50 ind. m⁻³), such as larval fishes and gelatinous animals. However, larger volume imagers are typically integrated with heavy towed vehicles and deployed from research vessels, which introduces high costs and limits sampling approaches. Here we demonstrate that versatile configurations of shadowgraph imaging, including modular benchtop, handheld, and towed, compact vehicle systems (along with customizable software), allow for tailoring sampling to a variety of marine and freshwater settings (including mesocosms). These systems encompass a suite of possible architectures, designed for adapting the imaging depth of field, acquisition rates, sensor configuration, and deployment method to fit a wide range of sampling protocols, with high vertical resolution (~ 5 cm) and adequate taxonomic capabilities for &gt; 0.5 mm organisms. The benchtop system facilitates an interactive approach to observe and quantify zooplankton behaviors and optical properties. Video footage from the benchtop system generates thousands of regions of interest min⁻¹ for target organisms with variable orientations and swimming postures. When used in conjunction with in situ imaging, the benchtop system can build large machine learning training libraries targeted toward rare or morphologically diverse zooplankton, which often includes the larval stages of economically valuable taxa. These modular hardware and software components increase affordability and versatility while broadening the scope of scientific questions addressed by plankton imaging systems.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 1","pages":"67-86"},"PeriodicalIF":2.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A benthic mesocosm system for long-term multi-factorial experiments applying predicted warming and realistic microplastic pollution scenarios","authors":"Nicholas Mackay-Roberts,&nbsp;Christian Bock,&nbsp;Gisela Lannig,&nbsp;Magnus Lucassen,&nbsp;Nina Paul,&nbsp;Elisa Schaum,&nbsp;Bernadette Pogoda,&nbsp;Gunnar Gerdts","doi":"10.1002/lom3.10653","DOIUrl":"https://doi.org/10.1002/lom3.10653","url":null,"abstract":"<p>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 (<i>Ostrea edulis</i>), 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.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 12","pages":"910-929"},"PeriodicalIF":2.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of counting protocols for light microscopy on estimates of biodiversity and algal density of phytoplankton","authors":"Pascalle Jacobs,&nbsp;Léon Serre-Fredj,&nbsp;Reinoud P. T. Koeman,&nbsp;Anneke van den Oever,&nbsp;Myron A. Peck,&nbsp;Catharina J. M. Philippart","doi":"10.1002/lom3.10651","DOIUrl":"https://doi.org/10.1002/lom3.10651","url":null,"abstract":"<p>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 (<i>n</i> = 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.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 12","pages":"930-942"},"PeriodicalIF":2.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond transfer learning: Leveraging ancillary images in automated classification of plankton","authors":"Jeffrey S. Ellen,&nbsp;Mark D. Ohman","doi":"10.1002/lom3.10648","DOIUrl":"https://doi.org/10.1002/lom3.10648","url":null,"abstract":"<p>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 <i>Zooglider</i>, 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.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 12","pages":"943-952"},"PeriodicalIF":2.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10648","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-throughput determination of total lipids from North Pacific marine fishes via the sulfo-phospho-vanillin microplate assay","authors":"Cody Pinger,&nbsp;Drew Porter,&nbsp;Bryan Cormack,&nbsp;Corey Fugate,&nbsp;Matthew Rogers","doi":"10.1002/lom3.10649","DOIUrl":"https://doi.org/10.1002/lom3.10649","url":null,"abstract":"<p>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 (<i>Clupea pallasii</i>), Pacific cod (<i>Gadus macrocephalus</i>), walleye pollock (<i>G. chalcogrammus</i>), Pacific capelin (<i>Mallotus villosus</i>), Chinook (<i>Oncorhynchus tshawytscha</i>), and coho (<i>O. kisutch</i>) salmon. Samples of muscle tissue were analyzed from Chinook, pink (<i>O. gorbuscha</i>), sockeye (<i>O. nerka</i>), and chum (<i>O. keta</i>) 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 (<i>n</i> = 121). To improve the accuracy of SPV assay results, linear calibration models specific to taxa and tissue matrix type were developed, enabling calculation of <i>corrected</i> SPV assay values. The accuracy of using these calibration models was tested by analyzing additional fish samples (<i>n</i> = 16). The results of the <i>corrected</i> SPV assay were not statistically different (<i>p</i> &gt; 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.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 12","pages":"903-909"},"PeriodicalIF":2.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Estimating ethanol correction factors for δ13C and δ15N isotopic signatures of freshwater zooplankton from multiple lakes”","authors":"","doi":"10.1002/lom3.10647","DOIUrl":"10.1002/lom3.10647","url":null,"abstract":"<p>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, <b>22</b>: 464–472. https://doi.org/10.1002/lom3.10623</p><p>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.”</p><p>We apologize for this error.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"22 10","pages":"789"},"PeriodicalIF":2.1,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}