Limnology and Oceanography: Methods最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 3","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086893","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 3","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148049617","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":"Exploiting algal strains for robust cross-domain phytoplankton classification via deep learning","authors":"Ladislav Hodač,&nbsp;Susanne Dunker,&nbsp;Matthias Schmal,&nbsp;Edwin Carreño,&nbsp;Patrick Mäder,&nbsp;Maike Lorenz,&nbsp;Marie Jamroszczyk,&nbsp;David Šubrt,&nbsp;Sandra Meier,&nbsp;Claus-Dieter Dürselen,&nbsp;Jana Wäldchen","doi":"10.1002/lom3.10723","DOIUrl":"10.1002/lom3.10723","url":null,"abstract":"<p>Phytoplankton species are essential bioindicators for evaluating the status of freshwater ecosystems in accordance with the EU Water Framework Directive. However, manual identification of phytoplankton is time-consuming and requires taxonomic expertise. Deep learning (DL) offers promising tools for automating the identification, but challenges remain due to imaging biases, morphological diversity, and the lack of validated benchmark datasets. In this study, we trained a DL model on microphotographs of controlled laboratory strains from 20 phytoplankton species and tested its performance on independent environmental image datasets. We assessed which species are suitable for cross-dataset classification and explored whether computer vision–based image representations (DL features) reflect species similarity across datasets. Additionally, we combined shape analysis with DL features to determine whether feature-based species distances correspond to morphological similarity. The model trained on strain images achieved reliable cross-dataset classification for over half of the species. Classification performance declined with increasing feature/domain shifts between training and test images but improved when environmental images enriched the training set. Morphologically distinctive species, such as star-like forms and those with lobes or bristles, exhibited higher classification rates, whereas rectangular or roundish forms posed greater challenges. DL features consistently clustered species across datasets, and the distances in DL feature space aligned with those in simplified shape space. Our findings demonstrate that using strains as references in DL models enables effective cross-dataset classification while capturing morphological patterns. Integrating taxonomic expertise with computer vision is crucial for developing robust, interpretable phytoplankton bioindicator systems for ecological monitoring and biodiversity research.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 11","pages":"815-833"},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537978","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 novel in situ benthic photorespirometer for estimating photosynthesis rates of benthic primary producers","authors":"Chang Hwa Lee,&nbsp;Hyun Min Baek,&nbsp;Jung-Ho Hyun,&nbsp;Karl M. Attard,&nbsp;Sung-Han Kim,&nbsp;Won-Gi Min,&nbsp;Dong Mun Choi,&nbsp;Minsu Woo,&nbsp;Hyunho An,&nbsp;Jae Seong Lee","doi":"10.1002/lom3.10720","DOIUrl":"10.1002/lom3.10720","url":null,"abstract":"<p>We developed a novel benthic photorespirometer (BP) to overcome limitations of existing benthic incubation chambers for in situ photosynthesis–irradiance (P–I) curve analysis, including prolonged measurement times, uneven light distribution, and poor sealing. The BP integrates an artificial light source, an oxygen optode sensor, and a neoprene skirt to enable rapid P–I curve construction through 2-h incubations. Field tests at four coastal sites in Korea validated uniform light delivery and thermal stability during incubation, which are essential for reliable measurements. The compact and rigid design allowed single-operator deployment with minimal habitat disturbance, while the neoprene skirt ensured effective sealing across diverse substrates, including uneven rocky bottoms. Single deployments yielded reproducible oxygen responses and community-specific P–I curves under uniform light conditions, reflecting distinct photosynthetic characteristics among benthic algal communities and demonstrating the BP's capability to assess benthic photosynthesis. These results validate the BP as a reliable, field-deployable tool for high-resolution assessment of benthic primary production in coastal ecosystems.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 11","pages":"850-861"},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537873","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148083756","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":"Machine learning approach to study microboring assemblage dynamics in two living massive coral genera","authors":"Diego Alaguarda,&nbsp;Julien Brajard,&nbsp;Redouane Lguensat,&nbsp;Aline Tribollet","doi":"10.1002/lom3.10714","DOIUrl":"10.1002/lom3.10714","url":null,"abstract":"<p>The coral host comprises various microorganisms including those living in its skeleton. In coral skeletons, bioeroding microflora (cyanobacteria, algae, and fungi), which play an important role in reefs and coral resilience, produce specific traces (microborings) by actively dissolving the carbonate. To date, only a few highly time-consuming methods that rely on the observer allow microborings' study, limiting the number of samples that can be analyzed. Recently, a machine-learning approach based on the analysis of scanning electronic microscope images via a modified convolutional neural network (CNN) was developed to evaluate accurately microborings abundance along a core of a massive <i>Diploastrea</i> sp. from Mayotte. The aim here was to test this CNN on another massive coral species, <i>Porites</i> sp., to verify that it can be applied to other massive corals. We found that the classification accuracy decreased by 8% while the other metrics dropped significantly down to 26% on average. To improve our CNN (training step especially), we tested diverse loss functions. We also developed a specific CNN for <i>Porites</i> sp. and obtained a similar accuracy (94%) to that for the CNN for <i>Diploastrea</i> sp. (93%). Despite this result, we developed a CNN-Mixed model combining images collected from both coral genera to propose a unique and accurate model. As we obtained an accuracy above 90% when applying the mixed model to either massive coral genus, we strongly suggest that it can be used to better understand the role of microborers in living massive corals and reefs over long term.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 11","pages":"862-881"},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537940","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":"Spatial mapping of dissolved methane using an in situ sensor in Puget Sound","authors":"Alexandra M. Padilla,&nbsp;William Pardis,&nbsp;Jason Kapit,&nbsp;Tor A. Bjorklund,&nbsp;Nicholas D. Ward,&nbsp;Daniel J. Fornari,&nbsp;Susan Hautala,&nbsp;William F. Waite,&nbsp;H. Paul Johnson,&nbsp;Anna P. M. Michel","doi":"10.1002/lom3.10717","DOIUrl":"10.1002/lom3.10717","url":null,"abstract":"<p>Release of methane, as gas bubbles or in the dissolved phase, from the seafloor has been observed in coastal waters (&lt; 200 m) and deep ocean basins (&gt; 1000 m). Methane dissolution within the water column affects the geochemistry of the surrounding water, leading to localized oxygen loss and potential escape to the atmosphere, particularly from shallower sites. Traditional methods for detecting and quantifying dissolved methane rely on collecting discrete water samples for ship- or land-based ex situ analysis and post processing. Here, we report on the use of a reduced response time, in situ methane sensor, the Sensor for Aqueous Gases in the Environment (SAGE), for detecting and quantifying dissolved methane concentrations in a wide range of seafloor environments. During a Fall 2022 research cruise on the R/V <i>Thomas G. Thompson</i> in Puget Sound, SAGE was integrated onto a towed conductivity/temperature/depth rosette and deep-sea camera system with live-stream 1 Hz telemetry and used to spatially map the concentration of methane approximately 1 m above the seafloor. The site had been previously identified as an active methane plume field characterized by gas bubbles, fluid venting, and a faulted seabed. The widespread background dissolved concentration of methane measured by SAGE was 83 nM, and a range of 78–670 nM was observed throughout the survey. The results highlight the capacity of SAGE to map the spatial and temporal variability of dissolved methane concentrations in situ and to identify and localize sites of variable methane emissions from the seafloor.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 11","pages":"804-814"},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537977","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"24 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148083757","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":"A novel artificial intelligence–powered cell counting tool coupled with digital microscopy for rapid field-assessment of harmful cyanobacterial blooms","authors":"Igor Mrdjen,&nbsp;Zacharias J. Smith,&nbsp;Abby M. Webster,&nbsp;Christopher C. Nack,&nbsp;Bryan Arndt,&nbsp;Danara Dormaeva,&nbsp;Gregory L. Boyer,&nbsp;N. Roxanna Razavi,&nbsp;Stephen B. Shaw","doi":"10.1002/lom3.10725","DOIUrl":"10.1002/lom3.10725","url":null,"abstract":"<p>Historical quantification of cyanobacterial harmful algal blooms (cHABs) typically involved labor-intensive manual cell counting. We developed a novel, cost-effective, field-validated system to perform cell counts of six common toxin-producing cyanobacterial genera within 30 s of upload with 10-min sample preparation. Using a portable field microscope, users can quickly evaluate the type and quantity of freshwater cyanobacteria for use in ecological monitoring, human health, and water quality. Participating groups (<i>n</i> = 21) received digital microscopes and sampling equipment and submitted images from 170 cHAB events occurring in 36 US lakes for machine learning (ML) assisted cHAB analysis via a smartphone app. The accuracy of ML identification was compared to human taxonomic identification, while cell concentrations were compared against FluoroProbe (bbe Moldaenke GmbH) blue-green algal (BGA) chlorophyll fluorescence. Machine learning classification of 497 photos containing 4002 colonies performed as well as, or better than, human taxonomic analysis in 94% of cases. There was a weak correlation between BGA chlorophyll and ML-derived cell counts (<i>R</i>² = 0.33), biovolume (<i>R</i>² = 0.13) or total pixel counts (<i>R</i>² = 0.32) across all samples, but there was a strong correlation (<i>R</i>² = 0.76) between ML cell concentrations and BGA chlorophyll in samples not subject to overnight shipping and handling, where stress induced by transport and dark conditioning of cyanobacteria was hypothesized to drive more error in quantification by fluorescence. Cell counting minimizes errors introduced by fluorescence measurements and could improve risk assessment. The described quantification tool is easy-to-use and readily accessible to users with different levels of expertise in cHAB science.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 11","pages":"788-803"},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145538068","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":"TEOS10 compliant salinity and density equations for sound speed instruments","authors":"J. T. Allen,&nbsp;P. W. Keen,&nbsp;J. Nicholson,&nbsp;M. Quartley,&nbsp;I. Slade,&nbsp;C. Quartley","doi":"10.1002/lom3.10715","DOIUrl":"10.1002/lom3.10715","url":null,"abstract":"<p>TEOS-10 compliant equations are presented for the direct calculation of salinity, density, and Sigma0 from triplets of temperature, pressure, and sound speed for marine and estuarine waters. The 73, 71, and 71 term, respectively, 6^th-order equations, are valid over an environmental range of 0–40°C in situ temperature, 0–6000 dBar pressure, and 0–40 practical salinity. The equations can reproduce a TEOS-10 speed of sound equation–derived reference parameter space to a root mean square (RMS) error of ± 0.002383 g kg⁻¹, ± 0.002814 kg m⁻³, and ± 0.002812 kg m⁻³, respectively. The limitation for practical use is the order ± 0.05 m s⁻¹ accuracy of the TEOS-10 equation for calculating sound speed. Four new in situ reference datasets are subsequently used to improve on this limitation. Adding the reference datasets leads to 70, 72, and 72 term, respectively, 6^th-order equations, to an overall RMS error of ± 0.012251 g kg⁻¹, ± 0.010023 kg m⁻³, and ± 0.010209 kg m⁻³, and extends the temperature validity range to −1.772 to 40.000°C in situ temperature.</p>","PeriodicalId":18145,"journal":{"name":"Limnology and Oceanography: Methods","volume":"23 11","pages":"834-849"},"PeriodicalIF":1.9,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lom3.10715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537941","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}