Evaluating Plankton Assemblages in the Hudson River: Microscopy and Molecular Methods

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Due to its proximity to a large metropolitan area, the HRE is heavily impacted by anthropogenic stressors which intensify over time, stressing the need for high-resolution plankton monitoring as a tool managing estuarine resilience in a changing environment. This study used a combination of microscopy and gene sequencing of 18S and 23S rRNA regions to explore the plankton community of the HRE over a four-week period. Across all three methods, a total of 27 phytoplankton genera and 15 zooplankton genera were identified in the samples. The Simpson Index and the Shannon–Weaver Index were consistently higher for the 18S sequencing data compared to microscopy counts or 23S sequencing data. Across samples, 18S rRNA recovered higher diversity values and captured key taxa in agreement with microscopy, suggesting that 18S is the most effective molecular marker for broad eukaryotic plankton monitoring in the HRE. There was substantial variation in community composition, which reflects the dynamic nature of the HRE, where short-term shifts in freshwater discharge, nutrient input, and turbidity may influence local plankton structure in narrow temporal windows. Continued paired sampling will be critical for detecting long-term ecological trends, guiding management strategies, and advancing our understanding of estuarine health in urbanized environments. Urban Estuary Plankton Diversity Microscopy 18S 23S Figures Figure 1 Figure 2 Figure 3 Introduction Estuarine ecosystems are among the most dynamic and productive aquatic environments on Earth, supporting complex food webs and substantial biogeochemical cycling (Kennish 2002 , McLusky & Elliott, 2004 ). Increasingly detrimental anthropogenic forces render estuaries among some of the most vulnerable marine systems, especially those near cities (Lotze et al., 2006 ). The Hudson River Estuary (HRE), a tidal freshwater-to-saline gradient system adjacent to one of the most densely populated urban regions in the United States, flows between New Jersey and New York into the Atlantic Ocean (Strayer et al., 2014 ). With more than 12 million people living adjacent to the HRE (Myers et al. 2024 ), the urban runoff and waste discharge consistently saturates the system with inorganic nutrients (Kleppel et al., 2019 ). The system's surface waters are nutrient-enriched (O’Shea & Brosnan, 1997, O’Shea & Brosnan, 2000 ) at levels that are consistent with eutrophic conditions in coastal ecosystems (Bricker et al., 2008 ). Long-term studies of the Hudson River fish community have revealed substantial shifts in species composition over recent decades, linked to climate-driven changes in flow, salinity, and temperature (Strayer et al., 2004 ; Strayer et al., 2008 ). These shifts underscore the sensitivity of upper trophic levels to underlying changes in planktonic dynamics and highlight the need for high-resolution monitoring at the base of the food web. The HRE has started to see an increase in water temperature increases, sea level rise and changes in the average freshwater flow (Strayer et al. 2008 , Seekell & Pace, 2011 , Strayer et al 2014 ). This has had lasting impacts on the plankton community composition and larval fish abundances (Strayer et al 2004 ). Plankton communities are essential to HRE food webs, acting as primary producers and trophic intermediaries that mediate energy transfer to higher consumers (Beaugrand et al., 2002 ; Richardson, 2008 ). Pace et al. ( 1992 ) demonstrated that zooplankton biomass in the Hudson River (HR) was inversely correlated with freshwater discharge, with elevated flows contributing to increased flushing and reduced biomass even during peak summer productivity. As climate and anthropogenic influences intensify in the HRE (Strayer et al 2014 ), high-resolution plankton monitoring becomes critical for interpreting ecosystem responses and managing estuarine resilience (Möllmann et al., 2005 ; Richardson, 2008 ). These changing dynamics have affected fluctuations in both phyto- and zooplankton (Garzke et al., 2015 ), though previous studies on climate influences have often focused on primary producers, overlooking consequences in zooplankton who function as trophic intermediaries (Caron & Hutchins, 2013 ). Ecosystem monitoring programs rely on plankton community composition data and with the advances in molecular tools, such as 18S and 23S rRNA sequencing, we are now able to resolve microbial eukaryotes and cryptic species not easily identified through traditional microscopy alone (Stoeck et al., 2010 ; Abad et al., 2017 ). By combining microscopy abundance counts with high-throughput sequencing, our study aims to characterize the plankton assemblages across four sampling events in the lower HRE. In the present study, we used the 23S rRNA gene region, the hyper-variable V9 region of the nuclear 18S rDNA gene and microscopic counts to characterize the planktonic eukaryotic community assemblage in the HR over a four-week period. The findings from this study hope to elucidate the structure of the HRE plankton communities in a heavily urban influenced and under monitored system. Materials & Methods 3.1 Sample Collection and Processing The sampling was conducted at the West Harlem Pier (41.20865065898373° N, -73.04492021313243° W, Fig. 1 ) in the HRE. The sampling site has an average depth of ~ 10 m and a submerged topography characterized by shallow, muddy sediments (Nitsche et. al., 2007 ). Samples (n = 4) were collected weekly from June 27, 2023 to July 19, 2023, using a SeaGear plankton tow net 3:1 with a mesh size of 50µm. The net was rinsed in the sampling location three times before finally being submerged in the water for collection. The net was towed for 10 minutes at the surface water (0-1m). The river's flow rate was recorded before and after each tow using a flow meter. Subsequent to each tow, the samples were transported to Barnard College of Columbia University for laboratory processing. Samples were inverted and split evenly with half being used for dry weights and the other half were preserved in a neutrally buffered 10% formalin solution. Specifically, 5% of the total sample volume was augmented with formalin and transferred to large-scale sampling containers. To obtain plankton biomass, each sample was placed in a pre-weighed foil dish and dried for 24h at 70°C then re-weighed with the biomass expressed as dry mass per filtered water volume (mg m − 3 ). To enumerate the plankton community, each sample was subsampled (1ml) five times and identified using an Olympus CX21 Microscope with a Sedwick rafter following the protocol established by the National Institute of Oceanography (Dhargalkar & Verlecar 2004 ). Specimens were identified to the lowest taxonomic level possible, ranging from general groups to genera (López-Figueroa et al., 2023). The molecular samples were collected from the plankton tow using a sterile 60ml plastic syringe. Between 60ml to 120ml of the water was pushed through a 1.0µm pore size nylon syringe filter until the filter was effectively clogged, as defined as a filtration flow rate of 0.05 ml per second. The samples were then immediately frozen and shipped to Jonah Ventures (Boulder, CO) for analysis, which included next-generation sequencing of phytoplankton and zooplankton. Briefly, the genomic DNA from samples was extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol and sequenced on an Illumina (San Diego, CA) MiSeq using the v2 500-cycle kit. The plastid-encoded 23S rRNA gene was amplified using primers P23SrV_f1 (GGACAGAAAGACCCTATGAA) and Diam23Sr1 (TGAGTGACGGCCTTTCCACT) (Sherwood & Presting 2007 ). The 18S rRNA gene was amplified using primers F1391 (5′-GTACACCGCCCGTC-3′) and REukBr (5′-TGATCCTTCTGCAGGTTCACCTAC-3′) (Ramirez et al 2014 ). Sequences were matched to both NCBI and SILVA database with a ≥ 97% certainty. 3.2 Statistical Analysis The statistical analysis for this study was completed using JMP Pro 17.0.0 (SAS Institute Inc., Cary, NC) and R (version 4.4.2). Several diversity indices were calculated for each sample summing abundances across all samples. Shannon-Weaver Index (H′) was derived as a measure of diversity and the Simpson index (D), which was used as a measure of species richness, and an associated index (E) was used as a measure of evenness. Alpha-diversity indices, including the Shannon-Weaver Index and Simpson’s Index, were calculated in the vegan package in R (version 2.6–10) using relative abundance counts identified through light microscopy, 18s DNA analysis, and 23s DNA analysis. Due to differences in taxonomic coverage across the three methods, each sample’s total taxonomic abundance was collapsed into a single cumulative value per method to allow direct comparison of community profiles across techniques (Earl et al. 2018 ). Analysis of similarities (ANOSIM) was performed to assess whether community structure differed significantly between microscopy and 18S sequencing, microscopy and 23S sequencing, and 18S and 23S sequencing. Each comparison involved separate pairwise ANOSIM tests. Significance was assessed using 999 permutations. R-values and P-values were recorded for each comparison, with P-values less than 0.01 considered statistically significant and highlighted accordingly. The environmental conditions, water temperature (°C), DO (mg/L), and pH, during sampling were collected from nearby U.S. Geological Survey’s monitoring stations (01376515 and 01376520) and the tidal recordings were obtained from a National Oceanic and Atmospheric Administration station (8518750). To identify the environmental factors influence on the phytoplankton community composition, a Principal Component Analysis (PCA) was used to reduce the dimensionality of the physico-chemistry data. The PCA was statistically assessed using permutational analysis of variance (PERMANOVA), based on Bray-Curtis similarity matrices (McArdle & Anderson, 2001 ). Results 4.1 Total and Relative Abundance Across all three sampling methods there were a total of 27 genera of phytoplankton represented belonging to 5 phyla. Among these, Bacillariophyta and Chlorophyta were the most common. Comparatively, there were 15 genera of zooplankton identified belonging to 6 phyla with Arthropoda and Mollusca being the most common. There were 5 unknown sequences across 18S and 23S that did not have an associated taxonomic match. These were identified as uncultured marine eukaryotes in the NCBI database (DQ103803.1, LC109016.1, AB252776.1, KY554513.1, DQ020204.1). Relative abundance patterns in the HRE varied between methods, with no single taxon consistently dominant across all approaches (Fig. 2 ). The 18S sequencing data indicated that copepods comprised roughly 50% of the eukaryotic community in early July, but their contribution declined to about 25% by late July. Conversely, chlorophyte algae in the class Trebouxiophyceae increased in relative abundance from only ~ 5% of sequences in late June to nearly 30% by the final mid-July sample in the 18S dataset. The 23S sequences revealed a different pattern with the June sample being overwhelmingly dominated by uncultured marine eukaryotes (constituting the majority of 23S sequences), whereas samples in July showed increasing contributions from algal groups, particularly Trebouxiophyceae and Eustigmatophyceae . 4.2 Plankton Diversity The highest and lowest diversity in the HRE were observed in the same late June sample, depending on the marker used. Analysis via 18S sequencing yielded a Shannon diversity (H′) of 2.54 and a Simpson’s diversity (D) of 0.91 (Table 1 ). In contrast, 23S sequencing detected almost exclusively a single uncultured marine eukaryote, producing near-zero diversity values (H′ ≈ 0.00, D ≈ 0.00). Averages of the alpha diversity metrics (Table 1 ) reflected richness differences between the HRE collection methods. The Shannon diversity (H′) was consistently higher for the 18S (1.67–2.54) than for microscopy counts (1.55–1.92) or 23S data (0.00–0.83). Similarly, the Simpson’s diversity (D) was highest for 18S (0.67–0.91), intermediate for microscopy (0.76–0.84), and lowest for 23S (0.00–0.51).</p ANOSIM revealed significant differences in overall HRE community composition between microscopy and 18S (R = 0.174, P = 0.001; Table 1 ), indicating distinct taxonomic profiles between the morphological and molecular approaches. The comparison between microscopy and 23S also showed moderate separation (R = 0.174), though the result was not significant (P = 0.065). In contrast, the 18S and 23S datasets exhibited high overlap in community composition (R = 0.091, P = 0.155), consistent with expectations for molecular markers targeting overlapping subsets of eukaryotic taxa. The overall low R-values across comparisons reflect substantial within-group variability. 4.3 Physicochemical Drivers The PCA showed two dominant axes of variation, with the total variance of 89.1%. (Fig. 3 ). The first principal component (58.7%) was defined primarily by high loadings of pH and dissolved oxygen. The second component (30.4%) captured variation in plankton dry weight, tidal height, and temperature. Notably, dry weight was uncorrelated with pH and DO, indicating that biomass accumulation varied independently of chemical conditions. Discussion 5.1 Comparing Molecular and Microscopy Approaches Comparison of phyto- and zooplankton taxonomic profiles across the HRE samples highlights key differences between microscopy and molecular approaches in effective community monitoring. The semi-quantitative relative abundance results were similar, but not consistent across all three methods for the commonly shared genera. Overall, there were 29 genera identified with 18S, 24 genera with microscopy and 5 genera with 23S. Microscopy and 18S revealed the greatest taxonomic similarities and resolution (Fig. 2 ), detecting commonly seen HRE plankton like Bacillariophyceae (e.g. Chaetoceros, Melosira, Navicula, Skeletonema ), Trebouxiophyceae (e.g. Chlorella) and Copepoda (e.g. Acartia ). With all samples, 18S rRNA consistently recovered higher diversity values (Table 1 ) and captured key taxa in agreement with microscopy, suggesting that 18S may be one of the most effective molecular markers for broad eukaryotic phyto- and zooplankton monitoring in the HRE. Based on previous analyses of microscopy and 18S analyzing plankton biodiversity, this result was expected based on previous findings (Caron et al. 2009; Johnson and Martiny 2015 , Pierce et al. 2023 ). This is consistent with other urban estuary studies that found 18S to be more inclusive of both phototrophic and heterotrophic taxa, providing better resolution across trophic groups (Xu et al. 2023 ). These findings echo results from Berdjeb et al. ( 2018 ), who used 18S sequencing to track protist community changes over daily to weekly periods across a spring–summer transition. Similar to our study in timescale and season, they observed rapid shifts in community composition highlighting the increase of temporal turnover of coastal plankton communities in early summer via 18S (Berdjeb et al. 2018 ). Further, ANOSIM results revealed a significant distinction between microscopy and 18S datasets, while comparisons involving 23S showed weaker or non-significant separation, reflecting moderate overlap across methods. The generally low R-values suggest greater variability within each method than between them, a result that may stem from high-frequency changes in community structure driven by environmental pulses such as HRE tidal mixing or nutrient variability, which has been observed in other short-term microbial surveys (e.g., Berdjeb et al., 2018 ). In contrast to the HRE 18S, the 23S data showed significant underrepresentation of key groups (Fig. 2 ), this likely reflects well-documented biases in 23S primers, which preferentially amplify phytoplankton and plastid sequences, often at the expense of other eukaryotic plankton like copepods and ciliates (Sherwood & Presting, 2007 ). In addition, low read depth or high sequence variability in 23S targets may contribute to the poor taxonomic resolution observed, making it a weaker choice for fine-scale or comprehensive planktonic community profiling (Sherwood & Presting, 2007 , Kezlya et al., 2023 ). These limitations illustrate the importance of marker selection within molecular studies that work in highly variable systems, like urban estuaries. 5.2 Environmental Drivers In coastal marine waters with increased cultural eutrophication the impact of physiochemical variables directly influences phytoplankton biomass and community composition (Seitzinger et al. 2002 ). The HRE PCA of environmental variables suggests two dominant gradients: one defined by pH and dissolved oxygen (PC1), and a second associated with plankton dry weight, temperature, and tide stage (PC2) (Fig. 3 ). This structure indicates possible coupling between physical drivers and short-term biomass variation in the estuary. Taylor et al ( 2003 ) found the HRE is a net heterotrophic balance with the flow rate influencing plankton production and the export into the Atlantic Ocean. 5.3 Implications Despite the limited sample size and potential primer-specific differences in detection, this study contributes valuable data from a heavily urbanized estuary that lacks consistent plankton monitoring. This reflects the dynamic nature of the HRE, where short-term shifts in freshwater discharge, nutrient input, or turbidity may influence local plankton structure, even within small spatial or temporal windows (Strayer et al. 2008 ). These short-duration studies can yield meaningful ecological and methodological insights (Chen et al. 2024 ), particularly when used to evaluate community changes in dynamic or understudied environments (Wang et al. 2021 ). Overall, these results support the need for multimodal, long-term plankton monitoring in the HRE and similar systems in metropolitan cities. Microscopy and 18S sequencing together offer a more complete representation of both microalgal and metazoan taxa, while 23S alone appears insufficient for characterizing total eukaryotic community structure. Continued paired sampling will be essential for detecting long-term trends, informing management decisions, and improving our understanding of ecosystem health in urbanized estuaries. Declarations Acknowledgements This student research project was made possible by Barnard College of Columbia University’s Summer Research Initiative. We would like to thank the Barnard Biology Department for their support. Funding This research was supported by Barnard College of Columbia University’s Summer Research Initiative student support. Without it the data collection and processing would not be possible. Conflicts of Interest The author declares no conflicts of interest. Ethics Approval Not applicable. This study did not involve human participants or animal subjects. Consent to Participate Not applicable. Consent for Publication Not applicable. Availability of Data and Materials Sequencing data and raw environmental measurements will be made available in a public repository (e.g., NCBI) upon publication. Author Contributions Dr. Corradino conceived and designed the study and all other authors conducted field sampling, performed laboratory analysis, analyzed data, and assisted with writing the manuscript. References Abad, D., A. Albaina, M. Aguirre, and A. Estonba. 2017. 18S V9 metabarcoding correctly depicts plankton estuarine community drivers. Marine Ecology Progress Series 584:31–43. Beaugrand, G., P. C. Reid, F. Ibañez, J. A. Lindley, and M. Edwards. 2002. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296(5573):1692–1694. Berdjeb, L., A. Parada, D. M. Needham, and J. A. Fuhrman. 2018. Short-term dynamics and interactions of marine protist communities during the spring–summer transition. 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Diversity, community structure, and quantity of eukaryotic phytoplankton revealed using 18S rRNA and plastid 16S rRNA genes and pigment markers: a case study of the Pearl River Estuary. Marine Life Science & Technology 5(3):415–430. Tables Table 1. Summary of taxonomic richness and alpha diversity indices with the sample mean (± standard deviation) values of Shannon diversity index (H′), Pielou’s Evenness (J), and Simpson’s diversity index (D) for plankton communities identified using microscopy, 18S rRNA, and 23S rRNA methods across the HR samples. Indices were calculated at the genus level. Method Shannon Diversity (H′) Pielou's Evenness (J) Simpson Diversity (D) 18S rRNA 2.00 ± 0.40 0.85 ± 0.16 0.67 ± 0.11 23S rRNA 0.59 ± 0.39 0.62 ± 0.13 0.46 ± 0.04 Microscopy 1.76 ± 0.18 0.73 ± 0.14 0.82 ± 0.04 Table 2. Pairwise Analysis of Similarities (ANOSIM) comparing plankton community composition across three analytical methods: microscopy, 18S rRNA sequencing, and 23S rRNA sequencing. R-values indicate the degree of dissimilarity between methods, with values closer to 1 representing greater separation. R P-value Microscopy vs 18S 0.174 0.001 Microscopy vs 23S 0.174 0.065 18S vs 23S 0.091 0.155 Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Estuaries and Coasts → Version 1 posted Reviewers agreed at journal 11 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor invited by journal 03 Aug, 2025 Editor assigned by journal 29 Jul, 2025 First submitted to journal 29 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7247027","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495468779,"identity":"52bb5c69-ef87-49e8-9eee-c85b91b0b5a5","order_by":0,"name":"Gabrielle Corradino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYPACZh5+CQiLsYGBwYA4LZIzSNXCYHCDWC3y7oePfa6osZYxvt387HNBzT3ZBvbmbRL4tBieSUueeeZYOo/ZnWPGs2ccKzZu4DlWhl/LDB5jxga2wzxmNxKMmXnYEhIbJHLMiNDy7zCP8Yz0z8w8/4Ba5N/g1yIvAdTS2HaYx0Aix5iZtw1kCw9+LQY8acmMjX3pPBJ3zhQz8/YlGLfxpBVb4LWl/fBhxoZv1vb8s9s3M/N8S5DtZz+88QZeWw6gi7DhUw62pYGQilEwCkbBKBgFAPFCQqiH0cVyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0008-4809-441X","institution":"Barnard College","correspondingAuthor":true,"prefix":"","firstName":"Gabrielle","middleName":"","lastName":"Corradino","suffix":""},{"id":495468780,"identity":"226bb17d-4dcb-4f9c-8520-6580885cefb1","order_by":1,"name":"Hannah Laufer","email":"","orcid":"","institution":"Barnard College","correspondingAuthor":false,"prefix":"","firstName":"Hannah","middleName":"","lastName":"Laufer","suffix":""},{"id":495468781,"identity":"34fb7a4f-fe8d-4108-b430-f8aabf8a9090","order_by":2,"name":"Megan Rivera","email":"","orcid":"","institution":"Barnard College","correspondingAuthor":false,"prefix":"","firstName":"Megan","middleName":"","lastName":"Rivera","suffix":""},{"id":495468782,"identity":"3db491df-11b8-4eb8-a3b9-48216a3655b5","order_by":3,"name":"Thia Ostrander","email":"","orcid":"","institution":"Barnard College","correspondingAuthor":false,"prefix":"","firstName":"Thia","middleName":"","lastName":"Ostrander","suffix":""},{"id":495468783,"identity":"b8beba18-dec8-496c-9f31-1e38a4a6d54f","order_by":4,"name":"Margaret Ireland","email":"","orcid":"","institution":"Barnard College","correspondingAuthor":false,"prefix":"","firstName":"Margaret","middleName":"","lastName":"Ireland","suffix":""},{"id":495468784,"identity":"442edd5d-7d5d-48a2-980e-3b91a037c38b","order_by":5,"name":"Savannah Rose Eklund","email":"","orcid":"","institution":"Columbia University Graduate School of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"Savannah","middleName":"Rose","lastName":"Eklund","suffix":""}],"badges":[],"createdAt":"2025-07-29 23:33:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7247027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7247027/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12237-026-01693-7","type":"published","date":"2026-03-19T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88470573,"identity":"310924a0-fc65-40be-ad5c-64b86df6e555","added_by":"auto","created_at":"2025-08-06 18:55:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24246,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Hudson River Estuary sampling site at the West Harlem Piers, located along the western shoreline of Manhattan, New York, USA. Scale bar represents 2 km.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7247027/v1/1634be9fed83d2d1090c3192.png"},{"id":88470784,"identity":"28f2b2a8-6f3f-4f1e-96fd-9600cf9b0261","added_by":"auto","created_at":"2025-08-06 19:03:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57046,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative abundance (%) of major plankton groups in four HRE plankton tow samples (x-axis labels) as determined by three methods: (A) microscopy, (B) 18S rRNA gene sequencing, and (C) 23S rRNA gene sequencing. Taxonomic categories include \u003cem\u003eCopepoda, Vampyrellidae, Trebouxiophyceae, Ulvophyceae, Bacillariophyceae, Eustigmatophyceae, Malacostraca, Thecostraca\u003c/em\u003e, and an “Other” category comprising less abundant taxa.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7247027/v1/2b65c61cb25928e7e06e69c7.png"},{"id":88470568,"identity":"4ca3ba6c-7fb7-46f1-9521-33d863c854e5","added_by":"auto","created_at":"2025-08-06 18:55:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19210,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) biplot of environmental variables measured during the sampling period in the Hudson River Estuary. The first two principal components explain 89.1% of the total variance (PC1 = 58.7%, PC2 = 30.4%). Vectors represent the strength and direction of correlations among environmental variables, including dissolved oxygen (DO), pH, temperature, tide height, and plankton dry weight (DW).\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7247027/v1/e7118c6d61e3f382fa50a6f9.jpeg"},{"id":105223254,"identity":"dfc23b8e-4e86-45c1-8ce7-f42dcf278853","added_by":"auto","created_at":"2026-03-23 16:00:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":696446,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7247027/v1/c62a0c01-a452-473e-bab5-ce0c7022b2fb.pdf"}],"financialInterests":"","formattedTitle":"Evaluating Plankton Assemblages in the Hudson River: Microscopy and Molecular Methods","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEstuarine ecosystems are among the most dynamic and productive aquatic environments on Earth, supporting complex food webs and substantial biogeochemical cycling (Kennish \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, McLusky \u0026amp; Elliott, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Increasingly detrimental anthropogenic forces render estuaries among some of the most vulnerable marine systems, especially those near cities (Lotze et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The Hudson River Estuary (HRE), a tidal freshwater-to-saline gradient system adjacent to one of the most densely populated urban regions in the United States, flows between New Jersey and New York into the Atlantic Ocean (Strayer et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). With more than 12\u0026nbsp;million people living adjacent to the HRE (Myers et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the urban runoff and waste discharge consistently saturates the system with inorganic nutrients (Kleppel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The system's surface waters are nutrient-enriched (O\u0026rsquo;Shea \u0026amp; Brosnan, 1997, O\u0026rsquo;Shea \u0026amp; Brosnan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) at levels that are consistent with eutrophic conditions in coastal ecosystems (Bricker et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Long-term studies of the Hudson River fish community have revealed substantial shifts in species composition over recent decades, linked to climate-driven changes in flow, salinity, and temperature (Strayer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Strayer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These shifts underscore the sensitivity of upper trophic levels to underlying changes in planktonic dynamics and highlight the need for high-resolution monitoring at the base of the food web.\u003c/p\u003e\u003cp\u003eThe HRE has started to see an increase in water temperature increases, sea level rise and changes in the average freshwater flow (Strayer et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Seekell \u0026amp; Pace, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Strayer et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This has had lasting impacts on the plankton community composition and larval fish abundances (Strayer et al \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Plankton communities are essential to HRE food webs, acting as primary producers and trophic intermediaries that mediate energy transfer to higher consumers (Beaugrand et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Richardson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Pace et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) demonstrated that zooplankton biomass in the Hudson River (HR) was inversely correlated with freshwater discharge, with elevated flows contributing to increased flushing and reduced biomass even during peak summer productivity. As climate and anthropogenic influences intensify in the HRE (Strayer et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), high-resolution plankton monitoring becomes critical for interpreting ecosystem responses and managing estuarine resilience (M\u0026ouml;llmann et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Richardson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These changing dynamics have affected fluctuations in both phyto- and zooplankton (Garzke et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), though previous studies on climate influences have often focused on primary producers, overlooking consequences in zooplankton who function as trophic intermediaries (Caron \u0026amp; Hutchins, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEcosystem monitoring programs rely on plankton community composition data and with the advances in molecular tools, such as 18S and 23S rRNA sequencing, we are now able to resolve microbial eukaryotes and cryptic species not easily identified through traditional microscopy alone (Stoeck et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Abad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By combining microscopy abundance counts with high-throughput sequencing, our study aims to characterize the plankton assemblages across four sampling events in the lower HRE. In the present study, we used the 23S rRNA gene region, the hyper-variable V9 region of the nuclear 18S rDNA gene and microscopic counts to characterize the planktonic eukaryotic community assemblage in the HR over a four-week period. The findings from this study hope to elucidate the structure of the HRE plankton communities in a heavily urban influenced and under monitored system.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sample Collection and Processing\u003c/h2\u003e\u003cp\u003eThe sampling was conducted at the West Harlem Pier (41.20865065898373\u0026deg; N, -73.04492021313243\u0026deg; W, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in the HRE. The sampling site has an average depth of ~\u0026thinsp;10 m and a submerged topography characterized by shallow, muddy sediments (Nitsche et. al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Samples (n\u0026thinsp;=\u0026thinsp;4) were collected weekly from June 27, 2023 to July 19, 2023, using a SeaGear plankton tow net 3:1 with a mesh size of 50\u0026micro;m. The net was rinsed in the sampling location three times before finally being submerged in the water for collection. The net was towed for 10 minutes at the surface water (0-1m). The river's flow rate was recorded before and after each tow using a flow meter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequent to each tow, the samples were transported to Barnard College of Columbia University for laboratory processing. Samples were inverted and split evenly with half being used for dry weights and the other half were preserved in a neutrally buffered 10% formalin solution. Specifically, 5% of the total sample volume was augmented with formalin and transferred to large-scale sampling containers. To obtain plankton biomass, each sample was placed in a pre-weighed foil dish and dried for 24h at 70\u0026deg;C then re-weighed with the biomass expressed as dry mass per filtered water volume (mg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eTo enumerate the plankton community, each sample was subsampled (1ml) five times and identified using an Olympus CX21 Microscope with a Sedwick rafter following the protocol established by the National Institute of Oceanography (Dhargalkar \u0026amp; Verlecar \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Specimens were identified to the lowest taxonomic level possible, ranging from general groups to genera (L\u0026oacute;pez-Figueroa et al., 2023).\u003c/p\u003e\u003cp\u003eThe molecular samples were collected from the plankton tow using a sterile 60ml plastic syringe. Between 60ml to 120ml of the water was pushed through a 1.0\u0026micro;m pore size nylon syringe filter until the filter was effectively clogged, as defined as a filtration flow rate of 0.05 ml per second. The samples were then immediately frozen and shipped to Jonah Ventures (Boulder, CO) for analysis, which included next-generation sequencing of phytoplankton and zooplankton. Briefly, the genomic DNA from samples was extracted using the DNeasy Blood \u0026amp; Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s protocol and sequenced on an Illumina (San Diego, CA) MiSeq using the v2 500-cycle kit. The plastid-encoded 23S rRNA gene was amplified using primers P23SrV_f1 (GGACAGAAAGACCCTATGAA) and Diam23Sr1 (TGAGTGACGGCCTTTCCACT) (Sherwood \u0026amp; Presting \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The 18S rRNA gene was amplified using primers F1391 (5\u0026prime;-GTACACCGCCCGTC-3\u0026prime;) and REukBr (5\u0026prime;-TGATCCTTCTGCAGGTTCACCTAC-3\u0026prime;) (Ramirez et al \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Sequences were matched to both NCBI and SILVA database with a\u0026thinsp;\u0026ge;\u0026thinsp;97% certainty.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis for this study was completed using JMP Pro 17.0.0 (SAS Institute Inc., Cary, NC) and R (version 4.4.2). Several diversity indices were calculated for each sample summing abundances across all samples. Shannon-Weaver Index (H\u0026prime;) was derived as a measure of diversity and the Simpson index (D), which was used as a measure of species richness, and an associated index (E) was used as a measure of evenness. Alpha-diversity indices, including the Shannon-Weaver Index and Simpson\u0026rsquo;s Index, were calculated in the vegan package in R (version 2.6\u0026ndash;10) using relative abundance counts identified through light microscopy, 18s DNA analysis, and 23s DNA analysis. Due to differences in taxonomic coverage across the three methods, each sample\u0026rsquo;s total taxonomic abundance was collapsed into a single cumulative value per method to allow direct comparison of community profiles across techniques (Earl et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Analysis of similarities (ANOSIM) was performed to assess whether community structure differed significantly between microscopy and 18S sequencing, microscopy and 23S sequencing, and 18S and 23S sequencing. Each comparison involved separate pairwise ANOSIM tests. Significance was assessed using 999 permutations. R-values and P-values were recorded for each comparison, with P-values less than 0.01 considered statistically significant and highlighted accordingly.\u003c/p\u003e\u003cp\u003eThe environmental conditions, water temperature (\u0026deg;C), DO (mg/L), and pH, during sampling were collected from nearby U.S. Geological Survey\u0026rsquo;s monitoring stations (01376515 and 01376520) and the tidal recordings were obtained from a National Oceanic and Atmospheric Administration station (8518750). To identify the environmental factors influence on the phytoplankton community composition, a Principal Component Analysis (PCA) was used to reduce the dimensionality of the physico-chemistry data. The PCA was statistically assessed using permutational analysis of variance (PERMANOVA), based on Bray-Curtis similarity matrices (McArdle \u0026amp; Anderson, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Total and Relative Abundance\u003c/h2\u003e\u003cp\u003eAcross all three sampling methods there were a total of 27 genera of phytoplankton represented belonging to 5 phyla. Among these, \u003cem\u003eBacillariophyta\u003c/em\u003e and \u003cem\u003eChlorophyta\u003c/em\u003e were the most common. Comparatively, there were 15 genera of zooplankton identified belonging to 6 phyla with \u003cem\u003eArthropoda\u003c/em\u003e and \u003cem\u003eMollusca\u003c/em\u003e being the most common. There were 5 unknown sequences across 18S and 23S that did not have an associated taxonomic match. These were identified as uncultured marine eukaryotes in the NCBI database (DQ103803.1, LC109016.1, AB252776.1, KY554513.1, DQ020204.1).\u003c/p\u003e\u003cp\u003eRelative abundance patterns in the HRE varied between methods, with no single taxon consistently dominant across all approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The 18S sequencing data indicated that copepods comprised roughly 50% of the eukaryotic community in early July, but their contribution declined to about 25% by late July. Conversely, chlorophyte algae in the class \u003cem\u003eTrebouxiophyceae\u003c/em\u003e increased in relative abundance from only\u0026thinsp;~\u0026thinsp;5% of sequences in late June to nearly 30% by the final mid-July sample in the 18S dataset. The 23S sequences revealed a different pattern with the June sample being overwhelmingly dominated by uncultured marine eukaryotes (constituting the majority of 23S sequences), whereas samples in July showed increasing contributions from algal groups, particularly \u003cem\u003eTrebouxiophyceae\u003c/em\u003e and \u003cem\u003eEustigmatophyceae\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Plankton Diversity\u003c/h2\u003e\u003cp\u003eThe highest and lowest diversity in the HRE were observed in the same late June sample, depending on the marker used. Analysis via 18S sequencing yielded a Shannon diversity (H\u0026prime;) of 2.54 and a Simpson\u0026rsquo;s diversity (D) of 0.91 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In contrast, 23S sequencing detected almost exclusively a single uncultured marine eukaryote, producing near-zero diversity values (H\u0026prime; \u0026asymp; 0.00, D\u0026thinsp;\u0026asymp;\u0026thinsp;0.00). Averages of the alpha diversity metrics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) reflected richness differences between the HRE collection methods. The Shannon diversity (H\u0026prime;) was consistently higher for the 18S (1.67\u0026ndash;2.54) than for microscopy counts (1.55\u0026ndash;1.92) or 23S data (0.00\u0026ndash;0.83). Similarly, the Simpson\u0026rsquo;s diversity (D) was highest for 18S (0.67\u0026ndash;0.91), intermediate for microscopy (0.76\u0026ndash;0.84), and lowest for 23S (0.00\u0026ndash;0.51).\u003c/p\u003cp\u003eANOSIM revealed significant differences in overall HRE community composition between microscopy and 18S (R\u0026thinsp;=\u0026thinsp;0.174, P\u0026thinsp;=\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating distinct taxonomic profiles between the morphological and molecular approaches. The comparison between microscopy and 23S also showed moderate separation (R\u0026thinsp;=\u0026thinsp;0.174), though the result was not significant (P\u0026thinsp;=\u0026thinsp;0.065). In contrast, the 18S and 23S datasets exhibited high overlap in community composition (R\u0026thinsp;=\u0026thinsp;0.091, P\u0026thinsp;=\u0026thinsp;0.155), consistent with expectations for molecular markers targeting overlapping subsets of eukaryotic taxa. The overall low R-values across comparisons reflect substantial within-group variability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Physicochemical Drivers\u003c/h2\u003e\u003cp\u003eThe PCA showed two dominant axes of variation, with the total variance of 89.1%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The first principal component (58.7%) was defined primarily by high loadings of pH and dissolved oxygen. The second component (30.4%) captured variation in plankton dry weight, tidal height, and temperature. Notably, dry weight was uncorrelated with pH and DO, indicating that biomass accumulation varied independently of chemical conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Comparing Molecular and Microscopy Approaches\u003c/h2\u003e\u003cp\u003eComparison of phyto- and zooplankton taxonomic profiles across the HRE samples highlights key differences between microscopy and molecular approaches in effective community monitoring. The semi-quantitative relative abundance results were similar, but not consistent across all three methods for the commonly shared genera. Overall, there were 29 genera identified with 18S, 24 genera with microscopy and 5 genera with 23S. Microscopy and 18S revealed the greatest taxonomic similarities and resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), detecting commonly seen HRE plankton like Bacillariophyceae (e.g. \u003cem\u003eChaetoceros, Melosira, Navicula, Skeletonema\u003c/em\u003e), Trebouxiophyceae (e.g. Chlorella) and Copepoda (e.g. \u003cem\u003eAcartia\u003c/em\u003e). With all samples, 18S rRNA consistently recovered higher diversity values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and captured key taxa in agreement with microscopy, suggesting that 18S may be one of the most effective molecular markers for broad eukaryotic phyto- and zooplankton monitoring in the HRE. Based on previous analyses of microscopy and 18S analyzing plankton biodiversity, this result was expected based on previous findings (Caron et al. 2009; Johnson and Martiny \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Pierce et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is consistent with other urban estuary studies that found 18S to be more inclusive of both phototrophic and heterotrophic taxa, providing better resolution across trophic groups (Xu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings echo results from Berdjeb et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who used 18S sequencing to track protist community changes over daily to weekly periods across a spring\u0026ndash;summer transition. Similar to our study in timescale and season, they observed rapid shifts in community composition highlighting the increase of temporal turnover of coastal plankton communities in early summer via 18S (Berdjeb et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther, ANOSIM results revealed a significant distinction between microscopy and 18S datasets, while comparisons involving 23S showed weaker or non-significant separation, reflecting moderate overlap across methods. The generally low R-values suggest greater variability within each method than between them, a result that may stem from high-frequency changes in community structure driven by environmental pulses such as HRE tidal mixing or nutrient variability, which has been observed in other short-term microbial surveys (e.g., Berdjeb et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to the HRE 18S, the 23S data showed significant underrepresentation of key groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), this likely reflects well-documented biases in 23S primers, which preferentially amplify phytoplankton and plastid sequences, often at the expense of other eukaryotic plankton like copepods and ciliates (Sherwood \u0026amp; Presting, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In addition, low read depth or high sequence variability in 23S targets may contribute to the poor taxonomic resolution observed, making it a weaker choice for fine-scale or comprehensive planktonic community profiling (Sherwood \u0026amp; Presting, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Kezlya et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These limitations illustrate the importance of marker selection within molecular studies that work in highly variable systems, like urban estuaries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Environmental Drivers\u003c/h2\u003e\u003cp\u003eIn coastal marine waters with increased cultural eutrophication the impact of physiochemical variables directly influences phytoplankton biomass and community composition (Seitzinger et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The HRE PCA of environmental variables suggests two dominant gradients: one defined by pH and dissolved oxygen (PC1), and a second associated with plankton dry weight, temperature, and tide stage (PC2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This structure indicates possible coupling between physical drivers and short-term biomass variation in the estuary. Taylor et al (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) found the HRE is a net heterotrophic balance with the flow rate influencing plankton production and the export into the Atlantic Ocean.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Implications\u003c/h2\u003e\u003cp\u003eDespite the limited sample size and potential primer-specific differences in detection, this study contributes valuable data from a heavily urbanized estuary that lacks consistent plankton monitoring. This reflects the dynamic nature of the HRE, where short-term shifts in freshwater discharge, nutrient input, or turbidity may influence local plankton structure, even within small spatial or temporal windows (Strayer et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These short-duration studies can yield meaningful ecological and methodological insights (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly when used to evaluate community changes in dynamic or understudied environments (Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, these results support the need for multimodal, long-term plankton monitoring in the HRE and similar systems in metropolitan cities. Microscopy and 18S sequencing together offer a more complete representation of both microalgal and metazoan taxa, while 23S alone appears insufficient for characterizing total eukaryotic community structure. Continued paired sampling will be essential for detecting long-term trends, informing management decisions, and improving our understanding of ecosystem health in urbanized estuaries.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis student research project was made possible by Barnard College of Columbia University\u0026rsquo;s Summer Research Initiative. We would like to thank the Barnard Biology Department for their support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003cbr\u003e\u0026nbsp;This research was supported by Barnard College of Columbia University\u0026rsquo;s Summer Research Initiative student support. Without it the data collection and processing would not be possible.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflicts of Interest\u003c/em\u003e\u003cbr\u003e\u0026nbsp;The author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics Approval\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Not applicable. This study did not involve human participants or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to Participate\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Sequencing data and raw environmental measurements will be made available in a public repository (e.g., NCBI) upon publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Dr. Corradino conceived and designed the study and all other authors conducted field sampling, performed laboratory analysis, analyzed data, and assisted with writing the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbad, D., A. Albaina, M. Aguirre, and A. 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Planktonic carbon cycling and transport in surface waters of the highly urbanized Hudson River estuary. \u003cem\u003eLimnology and Oceanography\u003c/em\u003e 48(5):1779\u0026ndash;1795.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, F., P. Cheng, N. Chen, and Y. M. Kuo. 2021. Tidal driven nutrient exchange between mangroves and estuary reveals a dynamic source-sink pattern. \u003cem\u003eChemosphere\u003c/em\u003e 270:128665.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, S., G. Li, C. He, Y. Huang, D. Yu, H. Deng, Z. Tong, Y. Wang, C. Dupuy, B. Huang, and Z. Shen. 2023. Diversity, community structure, and quantity of eukaryotic phytoplankton revealed using 18S rRNA and plastid 16S rRNA genes and pigment markers: a case study of the Pearl River Estuary. \u003cem\u003eMarine Life Science \u0026amp; Technology\u003c/em\u003e 5(3):415\u0026ndash;430.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Summary of taxonomic richness and alpha diversity indices with the sample mean (\u0026plusmn; standard deviation) values of Shannon diversity index (H\u0026prime;), Pielou\u0026rsquo;s Evenness (J), and Simpson\u0026rsquo;s diversity index (D) for plankton communities identified using microscopy, 18S rRNA, and 23S rRNA methods across the HR samples. Indices were calculated at the genus level.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.1399%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon Diversity (H\u0026prime;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7552%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePielou\u0026apos;s Evenness (J)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.4579%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimpson Diversity (D)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e18S rRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.1399%;\"\u003e\n \u003cp\u003e2.00 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7552%;\"\u003e\n \u003cp\u003e0.85 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.4579%;\"\u003e\n \u003cp\u003e0.67 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.6471%;\"\u003e\n \u003cp\u003e23S rRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.1399%;\"\u003e\n \u003cp\u003e0.59 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7552%;\"\u003e\n \u003cp\u003e0.62 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.4579%;\"\u003e\n \u003cp\u003e0.46 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.6471%;\"\u003e\n \u003cp\u003eMicroscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.1399%;\"\u003e\n \u003cp\u003e1.76 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.7552%;\"\u003e\n \u003cp\u003e0.73 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28.4579%;\"\u003e\n \u003cp\u003e0.82 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Pairwise Analysis of Similarities (ANOSIM) comparing plankton community composition across three analytical methods: microscopy, 18S rRNA sequencing, and 23S rRNA sequencing. R-values indicate the degree of dissimilarity between methods, with values closer to 1 representing greater separation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eMicroscopy vs 18S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eMicroscopy vs 23S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e18S vs 23S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urban Estuary, Plankton Diversity, Microscopy, 18S, 23S","lastPublishedDoi":"10.21203/rs.3.rs-7247027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7247027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Hudson River Estuary (HRE) is a tidal freshwater-to-saline system and is one of the most complex estuaries in the northeastern United States. Due to its proximity to a large metropolitan area, the HRE is heavily impacted by anthropogenic stressors which intensify over time, stressing the need for high-resolution plankton monitoring as a tool managing estuarine resilience in a changing environment. This study used a combination of microscopy and gene sequencing of 18S and 23S rRNA regions to explore the plankton community of the HRE over a four-week period. Across all three methods, a total of 27 phytoplankton genera and 15 zooplankton genera were identified in the samples. The Simpson Index and the Shannon\u0026ndash;Weaver Index were consistently higher for the 18S sequencing data compared to microscopy counts or 23S sequencing data. Across samples, 18S rRNA recovered higher diversity values and captured key taxa in agreement with microscopy, suggesting that 18S is the most effective molecular marker for broad eukaryotic plankton monitoring in the HRE. There was substantial variation in community composition, which reflects the dynamic nature of the HRE, where short-term shifts in freshwater discharge, nutrient input, and turbidity may influence local plankton structure in narrow temporal windows. Continued paired sampling will be critical for detecting long-term ecological trends, guiding management strategies, and advancing our understanding of estuarine health in urbanized environments.\u003c/p\u003e","manuscriptTitle":"Evaluating Plankton Assemblages in the Hudson River: Microscopy and Molecular Methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 18:55:05","doi":"10.21203/rs.3.rs-7247027/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-12T01:37:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T14:26:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2025-08-03T15:09:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T03:36:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2025-07-29T19:33:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e368c0db-161f-40b5-9118-e66d39add217","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:00:03+00:00","versionOfRecord":{"articleIdentity":"rs-7247027","link":"https://doi.org/10.1007/s12237-026-01693-7","journal":{"identity":"estuaries-and-coasts","isVorOnly":false,"title":"Estuaries and Coasts"},"publishedOn":"2026-03-19 15:57:31","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-08-06 18:55:05","video":"","vorDoi":"10.1007/s12237-026-01693-7","vorDoiUrl":"https://doi.org/10.1007/s12237-026-01693-7","workflowStages":[]},"version":"v1","identity":"rs-7247027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7247027","identity":"rs-7247027","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}