Adaptive sampling using multi-sensor fusion: Marine biodiversity assessments using eDNA metabarcoding and acoustic sensor data

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Davies, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4302016/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To achieve the aims of the Convention on Biological Diversity’s 2030 Global Biodiversity Framework, marine legislation and management requires the use of cost- and time- effective monitoring of indicator species. Marine observation platforms, which are increasing in popularity globally, are used for such monitoring activities. These platforms allow data to be collected from a variety of sensors simultaneously, providing the opportunity for adapting where and when sampling is performed based on real-time observational data. While some recent monitoring activities are following an adaptive sampling approach, most still employ a more opportunistic method. In this study, we applied an adaptive sampling approach to detect calanoid copepods at seasonally contrasting time points using real-time acoustic sensor data, traditional plankton net sampling, and eDNA metabarcoding. We demonstrate that there are ways to move from sampling opportunistically to a more adaptive sampling approach for more cost- and time- effective monitoring of indicators. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The pressing issue of biodiversity loss has reached a pivotal moment. Achieving the targets of the 2030 Global Biodiversity Framework and the European Biodiversity Strategy demands more than a commitment (see Guidetti et al., 2008 ; Rinaldi, 2021 ; Yates et al., 2019 ); it necessitates an in-depth comprehension of biodiversity patterns and ecological interdependencies, as well as vigilant monitoring of the biodiversity changes and effects of anthropogenic activities on our planet's varied habitats to develop effective conservation and mitigation strategies (e.g., Knight et al., 2020 ; Perino et al., 2022 ). The intensification of human activities within our marine environments including the expansion of offshore infrastructure for energy production are reshaping ecosystems, making effective monitoring crucial. Through regular monitoring of biodiversity, we can gain insights into the state, trends and subtle shifts within ecosystems, and pinpoint areas where conservation efforts are most urgently needed (Proença et al., 2017 ). Today, our ability to collect vast amounts of data on biodiversity has reached unprecedented levels. Many different technological solutions are currently used to monitor biodiversity in our oceans (e.g., acoustics Powell and Ohman, 2012 , remote sensing Basedow et al., 2019 , molecular methods Langlois et al., 2021 , in situ imagingPicheral et al., 2022 ) with the increasing popularity of (semi)-autonomous platforms (such as data buoys), which allow for the collection of data from several sensors simultaneously (e.g., SINTEF OceanLab Observatory, 2023). These new techniques, such as automated data collection from in-situ sensors and DNA-based techniques with high quality standard reference libraries, have improved and overcome laborious and costly traditional sampling and data acquisition techniques (see Capurso et al., 2023 ; Fu et al., 2021 ). As biodiversity varies across different spatial and temporal scales, selecting representative sites and time points that capture this variation is crucial to obtaining a comprehensive understanding of a given ecosystem. Biodiversity monitoring activities are also often resource limited and face practical constraints such as budget, time, and available resources. Balancing these limitations with the need for representative data is a constant challenge. Zooplankton in the ocean are incredibly diverse and abundant, and maintaining this diversity is crucial for maintaining the health of marine ecosystems (Charron, 2012 ). Zooplanktonic species have an intermediary role in the aquatic food webs, linking primary producers with higher trophic levels. Thus, they play an important role in biogeochemical cycling (Roman et al., 2002 ; Steinberg et al., 2012 ), contribute to energy transfer in pelagic food webs, and impact prey-predators dynamics and distributions (McQuatters-Gollop et al., 2019 ). Furthermore, zooplankton, and specifically copepods, are sensitive to changing environmental conditions, which makes them a suitable bioindicator (Batten et al., 2019 ; Thackeray et al., 2016 ). However, the abundance and distribution of zooplankton is known to be highly variable across spatial and temporal scales such as seasonal changes, diel vertical migration, and small-scale aggregations. This variability makes it difficult for monitoring programs to assess where and when to sample in a time- and cost-effective manner. Here, we use calanoid copepods as model taxon for testing a data-driven, adaptive sampling approach. This approach relies on the use of real-time data from an acoustic sensor to inform the sampling timing and depth. The sampling involves collecting water for environmental DNA (eDNA) metabarcoding to assess the presence of different marine Calanoida, and plankton net samples for traditional visual taxonomic classification to assess the age structure of sampled calanoid copepods. The adaptive sampling approach presented in this study aims to improve the quality of environmental monitoring while also reducing the time and cost expended for sampling. Materials and methods Sample and Data collection Two sampling campaigns were performed during environmentally contrasting time points at an Ocean Data Buoy Station (SINTEF OceanLab Observatoy, 2023) located near Munkholmen island in Trondheim fjord, Norway (located approximately at 63°27'26.921'', 10°22'20.179'''). The first sampling campaign was performed on February 1st 2023 (“winter”) and the second on May 08th 2023 (“summer”, see Fig. 1 ). An acoustic sensor (Nortek Signature 100 echosounder) was used to select the sampling timing for the two sampling campaigns and depths for sampling. The echosounder was attached to the Ocean Data Buoy at approximately a depth of − 2 m using the optional sensor beam (70–120 Hz). Real-time data collected from the acoustic sensor were visually inspected by researchers to determine the time of the sampling within a selected 24-hour window when the weather allowed for sampling. One week of continuous acoustic sensor data was also used to determine at which depths to collect samples using the plankton net and where approximately to collect water for eDNA metabarcoding based on time frames with high amount of activity in ecograms (see Fig. 2 ). Water samples were collected using a 11.300 series Van Dorn water sampler with a volume of 5 L. Zooplankton was collected using a custom-made plankton net (200 µm, Ø55 cm, 1 L non-filtering cod-end). All samplings were performed in triplicates, and samples were stored in the dark at the deck of the boat until return to the laboratory within ~ 2 h. Water samples were kept at 4°C until filtering using glass fiber filters with binder, 2.0 µm (Millipore). Filtering was performed within 3 h after retrieval, and each filter was stored at − 20°C in 4.05 ml Qiagen ATL buffer. Zooplankton samples were preserved with > 70% EtOH and stored at 4°C until further processing. Data on environmental conditions were collected using a conductivity, temperature, and depth (CTD; SeaCAT Plus V2) sensor on a profiler. CTD data was used to assess whether conditions were sufficiently seasonally contrasting. Profiles were taken from − 50 m to 0 m. Metabarcoding DNA from filtered water samples was extracted using NucleoSpin Plant II Midi kit (Macherey-Nagel), according to the producers' manuals with minor modifications. Final elution of DNA was performed in 200 µL PE-buffer. Filtering- and extraction controls were included. A ~ 313 bp fragment of the mitochondrial Cytochrome c Oxidase subunit I gene (mtCOI) were amplified using the degenerated Leary-XT primer set, consisting of the reverse primer jgHCO2198 5′-TAIACYTCIGGRTGICCRAARAAYCA-3′ (Geller et al., 2013) and the forward primer mlCOIintF-XT 5′-GGWACWRGWTGRACWITITAYCCYCC-3′ (Wangensteen 2018). Metabarcoding was performed using standard 16S Metagenomic Sequencing Library preparation protocol from Illumina (Illumina 2013 ) with some minor modifications. The 1st stage amplification mix contained 2x KAPA HiFi HotStart ReadyMix, (Roche) with 1.25 µl of each 5 µM forward and reverse primer (jgHCO2198 and mlCOIintF-XT) and 2.5 µl template DNA (≤ 10 ng/µl) and dH 2 O to a total volume of 25 µl. The PCR program included 5 min at 95°C, 16 cycles of 95°C for 10 sec, 62°C (touchdown, with 1°C reduction in temperature pr cycle) for 30 sec and 72°C for 1 min, followed by 25 cycles of 95°C for 10 sec, 46°C for 30 sec and 72°C for 1 min. The program ended with 72°C for 5 min. In the second amplification, samples were dual indexed using dual indexer (IDT for Illumina, Unique dual indexes), and a PCR program as described in the protocol. After PCR, the quality of amplifications was assessed by electrophoresis using Tape Station (Agilent), and PCR products were purified after each step using MagBind TotalPure NGS magnetic beads (Omega). Indexed amplicons were normalized and pooled into a library before sequencing, using NovaSeq SP paired end 250 bp (Illumina) at the Norwegian Sequencing Centre (NSC), Oslo, Norway. Paired end Ilumina Novaseq data was processed following the DADA2 pipeline (Callahan et al., 2016 ). Primers were removed using the cutadapt package(Martin, 2011 ) before sequences were filtered and trimmed. Reads with a length between 50 and 210 base pairs were retained for analyses. Following merging of forward and reverse sequences, amplicon sequence variant (ASV) tables were constructed. After the removal of chimeras, taxonomy was assigned to the remaining sequence variants. The highly curated MetaZooGene Barcode Atlas and Database(Bucklin et al., 2021 ) reference database was used for taxonomic assignment. Sequences retained in the assignment step were not limited to being collected in the North Atlantic Ocean, with the genus/species of sequences included in assignment being limited to having been observed in the North Atlantic Ocean. Within-sample richness estimates were not sub-sampled to an equal sequencing depth (i.e., rarefied) as sample-wise processing of data produced by DADA2 may show spurious correlations between richness metrics and sequencing depth (Bardenhorst et al., 2022 ). Analyses of data following taxonomic assignment were conducted on relative abundances within samples. Plankton net analysis The plankton net samples were used to validate and complement the results from eDNA metabarcoding by assessing the age structure of the sampled copepod population. Calanoid copepods were identified and counted using a stereomicroscope (Wild Heerbrugg, Leica Microsystems). The calanoid copepods were categorized as juvenile and adult, with females and males differentiated. Counts of individuals were aggregated across net samples for each sex and life-stage in each sampling campaign. Results and Discussion Adaptive sampling Water samples for DNA metabarcoding were collected at -2 m and − 10 m in both sampling campaigns. The sampling depth for water collection was in the vicinity of the points where high activity was identified in echograms (see Fig. 3 ). Collecting samples from two different depths near the area of high activity rather than the depth of high activity itself was preferred for the samples collected for metabarcoding due to uncertainty around rates of DNA sinking rates (Stewart, 2019 ). Plankton nets were placed at depths of -30 m in winter and − 10 m in summer where high activity was detected in the echograms in the week preceding each planned sampling campaign. The entire water column was sampled using the plankton net as the net used was not a closing net (which would allow for only sampling at a specific depth). Metabarcoding The Illumina run produced 19083277 reads from all samples collected in the Subarctic study area. Following filtering, the dataset consisted of 15109500 reads. These reads were then denoised in DADA2 resulting in an average of 1026101.5 reads (minimum = 474990, maximum = 1880193) in the final dataset and 97% of reads remained following the removal of chimeras. Following taxonomic assignment, Calanus finmarchichus, Calanus glacialis , and Calanus helgolandicus were differentiated in samples collected in both the summer and winter (see Fig. 4 panel A). Data was then filtered to contain ASVs that accounted for 5% or more of a given sample to determine the relative abundance of ASVs identified as Calanoida in each sampling campaign (Fig. 4 panel B). When data was filtered to contain ASVs that accounted for 5% or more of a given sample, four Calanoida genera were abundant in the samples collected in the winter (Oithona, Centropages, Acartia, and Microcalanus), and three genera were abundant in the samples collected in the summer (Oithona, Centropages, and Calanus) Plankton nets Calanoid copepods were detected in all net samples. We present the net-sample data as calanoid copepods by life stage, without species-level information as very small differences in morphological characteristics are used to differentiate Calanus species, which risks misidentification even by specialized taxonomist (see Nielsen et al. 2014 ). In the samples collected in the winter, the majority of calanoid copepods identified were adult females (66.70%, see Fig. 5 ), with a large amount of early life stage calanoid copepods being the next most represented group (28.50%) with adult males representing just 3.11% of the samples. In the samples collected in the summer, the majority of the sample was early life stage calanoid copepods (98.80%), with adult female calanoid copepods representing just 0.50% of samples and male adults 0.02%. Discussion In this study, we were able to monitor an important group of organisms, calanoid copepods, which are important bioindicator organisms for different stressors (Farkas et al., 2023; Hansen et al., 2011). We applied a data-driven sampling approach; the sampling timing and location of sampling was adapted based on acoustic sensor data (see Fig 2). We were successfully able to detect the three Calanus species known to occur in the North and Norwegian seas ( Calanus finmarchicus, C. glacialis, and C. helgolandicus ; Skreslet et al., 2000) using eDNA metabarcoding. However, in the plankton net samples calanoid copepods were only differentiated from other planktonic species. In traditional visual morphological analysis, the ability to identify taxa to the species-level can be challenging, especially for groups like Calanus , due to the small variations in their morphological characteristics. In addition, it is prohibitively laborious, requires highly specialised taxonomic knowledge, and it has been demonstrated to not be effective for distinguishing certain species (Lindeque et al. 1999; Choquet et al. 2018). Indeed, it is likely that Calanus finmarchicus can be over-estimated in studies that rely on net samples collected in Northern Seas as it has been reported that the morphological species identification systematically overestimates the abundance of C. finmarchicus (e.g. Gabrielsen et al. 2012; Lindeque et al. 2004) species, which allowed for the detection of seasonal changes to the age- and sex-structure of populations This is important information as it has been reported that different Calanus species have different life cycles and roles (Nielsen et al. 2014 and references therein). In addition, plankton net samples can be used for validating the occurrence of different taxa identified using eDNA metabarcoding (e.g. Djurhuus et al., 2018; Suter et al., 2021). The data-driven approach to sampling presented here is likely more time- and cost-effective compared to the more traditional opportunistic (or random) sampling approaches, where the sampling campaigns are oftentimes initiated without having certainty on the presence of the targeted group of organisms. In addition, opportunistic sampling may lead to false-negatives (where a target species is missed) and low reproducibility among replicates (Xiong et al., 2016; Zhou et al., 2011). This adaptive sampling approach can easily be coupled with relatively cost- and time- effective methods for biodiversity estimations such as (e)DNA metabarcoding (as described by Aylagas et al., 2018). Future developments of this approach to reduce manual work collecting water samples could include the use of autonomous underwater vehicles (see Hendricks et al., 2023; Yamahara et al., 2019). Future studies seeking to explore how to expand this n approach should include a random sampling to test whether samples collected opportunistically capture the same events. Conclusions These results demonstrate that real-time data collected using acoustic sensors can be used for potentially more accurate and resource-effective sampling campaigns than an opportunistic approach to sampling. As the number of automated monitoring platforms that host multiple sensors is increasing internationally, we encourage monitoring programs to apply an adaptive sampling approach to monitoring marine species. Declarations Competing Interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Funding statement This work is funded in part by SINTEF Ocean’s fundamental research funding (GM), the Research Council of Norway (315728), and this work made use of Norwegian National Research Infrastructure provided by OceanLab ( https://oceanlabobservatory.no/ ). Author Contribution Lara Veylit wrote the main manuscript and performed bioinformatic work. Emlyn Davies, Ralph Stevenson-Jones, Stefania Piarulli, and Sigrid Hakvåg collected data in the field. Marianne Aas performed laboratory analyses. All authors (Julia Farkas, Sanna Majaneva, Lara Veylit, Emlyn Davies, Marianne Aas, Ralph Stevenson-Jones, Stefania Piarulli, and Sigrid Hakvåg) reviewed the manuscript and provided comments. Acknowledgement We would like to sincerely thank the biology department at the Norwegian University of Science and Technology (NTNU) for access to their Calanus cultures during the planning phase of this project. 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Marine Biology, 163, 1–12 . https://doi.org/10.1007/s00227-016-2911-1 Yamahara, K.M., Preston, C.M., Birch, J., Walz, K., Marin, R., Jensen, S., Pargett, D., Roman, B., Ussler, W., Zhang, Y., Ryan, J., Hobson, B., Kieft, B., Raanan, B., Goodwin, K.D., Chavez, F.P., Scholin, C., (2019). In situ autonomous acquisition and preservation of marine environmental DNA using an autonomous underwater vehicle. Frontiers in Marine Science 6, 373 https://doi.org/10.3389/fmars.2019.00373 Yates, K.L., Clarke, B., Thurstan, R.H. (2019). Purpose vs performance: What does marine protected area success look like? Environmental Science and Policy 92, 76–86 https://doi.org/10.1016/j.envsci.2018.11.012 Zhou, J., Wu, L., Deng, Y., Zhi, X., Jiang, Y.H., Tu, Q., Xie, J., Van Nostrand, J.D., He, Z., Yang, Y. (2011). Reproducibility and quantitation of amplicon sequencing-based detection. ISME Journal 5 , 1303–1313. https://doi.org/10.1038/ismej.2011.11 Additional Declarations Competing interest reported. 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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-4302016","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314184114,"identity":"29cf3d08-8a07-4985-a010-fb45378b0eee","order_by":0,"name":"Lara Veylit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3QsQrCMBCA4QsH16WYNUV8h0gXB1F8kxahrrrpIp2yuvo4SqGT7oKCdelctw6CxgqKg7GjYP6lF8hHkwDYbD9Y4zkgsAyknnBlJvQcEFFqIoCCugSQhP7WIM72VDDVDZXD01k5Psw5AWWFibgjXzAVhQqR9q7MhafAaS+NB4v0WTbJg4BMhDzG1HRNhOdYwuZakUmpST+FL0RE+tbTVUXAvf+FvpKcOsF06GviNzXxlilTnukunEe4K2SvteDr07m8JJwrTIXpxaoCgEH8WrL4w773+rV22Ww22392AxfcO6qZkpRxAAAAAElFTkSuQmCC","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":true,"prefix":"","firstName":"Lara","middleName":"","lastName":"Veylit","suffix":""},{"id":314184116,"identity":"5d63959c-1a67-40a9-a928-b3362e90dbc0","order_by":1,"name":"Stefania Piarulli","email":"","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":false,"prefix":"","firstName":"Stefania","middleName":"","lastName":"Piarulli","suffix":""},{"id":314184118,"identity":"060750be-beac-40e3-8f01-3206392688c1","order_by":2,"name":"Julia Farkas","email":"","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Farkas","suffix":""},{"id":314184120,"identity":"424e3b58-eedf-4822-ad74-12e85e6cdf54","order_by":3,"name":"Emlyn J. Davies","email":"","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":false,"prefix":"","firstName":"Emlyn","middleName":"J.","lastName":"Davies","suffix":""},{"id":314184122,"identity":"332d9370-6a4a-4706-b3f4-f9cd23760265","order_by":4,"name":"Ralph Stevenson-Jones","email":"","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":false,"prefix":"","firstName":"Ralph","middleName":"","lastName":"Stevenson-Jones","suffix":""},{"id":314184124,"identity":"65a431a1-1dc2-4d69-bde9-913dcab74a09","order_by":5,"name":"Marianne Aas","email":"","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":false,"prefix":"","firstName":"Marianne","middleName":"","lastName":"Aas","suffix":""},{"id":314184128,"identity":"7c37e6a9-ee5e-4b40-b032-3f4a60d542cb","order_by":6,"name":"Sanna Majaneva","email":"","orcid":"","institution":"Akvaplan-niva AS","correspondingAuthor":false,"prefix":"","firstName":"Sanna","middleName":"","lastName":"Majaneva","suffix":""},{"id":314184129,"identity":"a4a338b0-130d-424b-8de2-59acbf1a209b","order_by":7,"name":"Sigrid Hakvåg","email":"","orcid":"","institution":"SINTEF Ocean","correspondingAuthor":false,"prefix":"","firstName":"Sigrid","middleName":"","lastName":"Hakvåg","suffix":""}],"badges":[],"createdAt":"2024-04-21 20:09:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4302016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4302016/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58523640,"identity":"8a79904c-92ce-48ef-a1a9-195c6978a78b","added_by":"auto","created_at":"2024-06-17 19:41:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1789913,"visible":true,"origin":"","legend":"\u003cp\u003eThe case study location was collected is shown in panel a. The case study is located near Munkholmen islet in Trondheimfjorden near the city of Trondheim. Temperature across water depths from CTD profiles is shown in panel b for the two seasonally contrasting sampling campaigns to demonstrate seasonal differences in water temperature.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4302016/v1/5b4606a5e96510644e91c902.jpg"},{"id":58523641,"identity":"ac122e12-177c-4275-865b-0040fb477e30","added_by":"auto","created_at":"2024-06-17 19:41:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3368374,"visible":true,"origin":"","legend":"\u003cp\u003eReal-time data from an acoustic sensor was used to inform the timing and depth of sampling both for eDNA metabarcoding and for plankton net sampling for validating the occurrence of calanoid copepods.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4302016/v1/cdc8fc245af788bb2028dc89.jpg"},{"id":58523644,"identity":"43b48360-3fe8-49d4-a03b-2c5cbea9735d","added_by":"auto","created_at":"2024-06-17 19:41:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4048527,"visible":true,"origin":"","legend":"\u003cp\u003eAn example of an echogram used for adapting sampling depth and timing. The two times with high activity are shows with alarm clocks and the depth chosen for sampling using the plankton net is indicated with the measuring stick (water sampling for eDNA metabarcoding being collected above and at this depth).\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4302016/v1/0d3bd98ca755e3977aa66728.jpg"},{"id":58523643,"identity":"eeebeb0f-4578-43f2-b7db-25ab43e753a9","added_by":"auto","created_at":"2024-06-17 19:41:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1314075,"visible":true,"origin":"","legend":"\u003cp\u003ePanel a depicts the relative abundance of all ASVs identified as Calanus with species-level information. Panel b depicts the relative abundances of taxa (ASVs identified at the genus level) in the order Calanoida that represent 5% or more of a sample, separated by sampling campaign. All other genera are grouped as “other”.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4302016/v1/6d1842ffa478cc50e5564807.jpg"},{"id":58523720,"identity":"482dc8ae-1f76-4218-9eb6-5aa8f8adbff4","added_by":"auto","created_at":"2024-06-17 19:49:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":199520,"visible":true,"origin":"","legend":"\u003cp\u003eRelative proportions of copepod early life stages, adult males, adult females, and other organisms from plankton net samples separated by sampling campaign.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4302016/v1/ce061246efcdd74255ab9408.png"},{"id":59272276,"identity":"24b12b73-efde-49ec-ba8f-2ce4ea7e1d77","added_by":"auto","created_at":"2024-06-28 13:01:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11207503,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4302016/v1/28bcfa33-f317-4745-9777-8b72788caaf8.pdf"}],"financialInterests":"Competing interest reported. The authors have no conflicts of interest to declare that are relevant to the content of this article.","formattedTitle":"Adaptive sampling using multi-sensor fusion: Marine biodiversity assessments using eDNA metabarcoding and acoustic sensor data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe pressing issue of biodiversity loss has reached a pivotal moment. Achieving the targets of the 2030 Global Biodiversity Framework and the European Biodiversity Strategy demands more than a commitment (see Guidetti et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rinaldi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yates et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); it necessitates an in-depth comprehension of biodiversity patterns and ecological interdependencies, as well as vigilant monitoring of the biodiversity changes and effects of anthropogenic activities on our planet's varied habitats to develop effective conservation and mitigation strategies (e.g., Knight et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perino et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The intensification of human activities within our marine environments including the expansion of offshore infrastructure for energy production are reshaping ecosystems, making effective monitoring crucial. Through regular monitoring of biodiversity, we can gain insights into the state, trends and subtle shifts within ecosystems, and pinpoint areas where conservation efforts are most urgently needed (Proen\u0026ccedil;a et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eToday, our ability to collect vast amounts of data on biodiversity has reached unprecedented levels. Many different technological solutions are currently used to monitor biodiversity in our oceans (e.g., acoustics Powell and Ohman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, remote sensing Basedow et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, molecular methods Langlois et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cem\u003ein situ\u003c/em\u003e imagingPicheral et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with the increasing popularity of (semi)-autonomous platforms (such as data buoys), which allow for the collection of data from several sensors simultaneously (e.g., SINTEF OceanLab Observatory, 2023). These new techniques, such as automated data collection from \u003cem\u003ein-situ\u003c/em\u003e sensors and DNA-based techniques with high quality standard reference libraries, have improved and overcome laborious and costly traditional sampling and data acquisition techniques (see Capurso et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As biodiversity varies across different spatial and temporal scales, selecting representative sites and time points that capture this variation is crucial to obtaining a comprehensive understanding of a given ecosystem. Biodiversity monitoring activities are also often resource limited and face practical constraints such as budget, time, and available resources. Balancing these limitations with the need for representative data is a constant challenge.\u003c/p\u003e \u003cp\u003eZooplankton in the ocean are incredibly diverse and abundant, and maintaining this diversity is crucial for maintaining the health of marine ecosystems (Charron, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Zooplanktonic species have an intermediary role in the aquatic food webs, linking primary producers with higher trophic levels. Thus, they play an important role in biogeochemical cycling (Roman et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Steinberg et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), contribute to energy transfer in pelagic food webs, and impact prey-predators dynamics and distributions (McQuatters-Gollop et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, zooplankton, and specifically copepods, are sensitive to changing environmental conditions, which makes them a suitable bioindicator (Batten et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Thackeray et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the abundance and distribution of zooplankton is known to be highly variable across spatial and temporal scales such as seasonal changes, diel vertical migration, and small-scale aggregations. This variability makes it difficult for monitoring programs to assess where and when to sample in a time- and cost-effective manner. Here, we use calanoid copepods as model taxon for testing a data-driven, adaptive sampling approach. This approach relies on the use of real-time data from an acoustic sensor to inform the sampling timing and depth. The sampling involves collecting water for environmental DNA (eDNA) metabarcoding to assess the presence of different marine Calanoida, and plankton net samples for traditional visual taxonomic classification to assess the age structure of sampled calanoid copepods. The adaptive sampling approach presented in this study aims to improve the quality of environmental monitoring while also reducing the time and cost expended for sampling.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample and Data collection\u003c/h2\u003e \u003cp\u003eTwo sampling campaigns were performed during environmentally contrasting time points at an Ocean Data Buoy Station (SINTEF OceanLab Observatoy, 2023) located near Munkholmen island in Trondheim fjord, Norway (located approximately at 63\u0026deg;27'26.921'', 10\u0026deg;22'20.179'''). The first sampling campaign was performed on February 1st 2023 (\u0026ldquo;winter\u0026rdquo;) and the second on May 08th 2023 (\u0026ldquo;summer\u0026rdquo;, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn acoustic sensor (Nortek Signature 100 echosounder) was used to select the sampling timing for the two sampling campaigns and depths for sampling. The echosounder was attached to the Ocean Data Buoy at approximately a depth of \u0026minus;\u0026thinsp;2 m using the optional sensor beam (70\u0026ndash;120 Hz). Real-time data collected from the acoustic sensor were visually inspected by researchers to determine the time of the sampling within a selected 24-hour window when the weather allowed for sampling. One week of continuous acoustic sensor data was also used to determine at which depths to collect samples using the plankton net and where approximately to collect water for eDNA metabarcoding based on time frames with high amount of activity in ecograms (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Water samples were collected using a 11.300 series Van Dorn water sampler with a volume of 5 L. Zooplankton was collected using a custom-made plankton net (200 \u0026micro;m, \u0026Oslash;55 cm, 1 L non-filtering cod-end). All samplings were performed in triplicates, and samples were stored in the dark at the deck of the boat until return to the laboratory within ~\u0026thinsp;2 h. Water samples were kept at 4\u0026deg;C until filtering using glass fiber filters with binder, 2.0 \u0026micro;m (Millipore). Filtering was performed within 3 h after retrieval, and each filter was stored at \u0026minus;\u0026thinsp;20\u0026deg;C in 4.05 ml Qiagen ATL buffer. Zooplankton samples were preserved with \u0026gt;\u0026thinsp;70% EtOH and stored at 4\u0026deg;C until further processing. Data on environmental conditions were collected using a conductivity, temperature, and depth (CTD; SeaCAT Plus V2) sensor on a profiler. CTD data was used to assess whether conditions were sufficiently seasonally contrasting. Profiles were taken from \u0026minus;\u0026thinsp;50 m to 0 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMetabarcoding\u003c/h2\u003e \u003cp\u003e DNA from filtered water samples was extracted using NucleoSpin Plant II Midi kit (Macherey-Nagel), according to the producers' manuals with minor modifications. Final elution of DNA was performed in 200 \u0026micro;L PE-buffer. Filtering- and extraction controls were included. A\u0026thinsp;~\u0026thinsp;313 bp fragment of the mitochondrial Cytochrome c Oxidase subunit I gene (mtCOI) were amplified using the degenerated Leary-XT primer set, consisting of the reverse primer jgHCO2198 5\u0026prime;-TAIACYTCIGGRTGICCRAARAAYCA-3\u0026prime; (Geller et al., 2013) and the forward primer mlCOIintF-XT 5\u0026prime;-GGWACWRGWTGRACWITITAYCCYCC-3\u0026prime; (Wangensteen 2018). Metabarcoding was performed using standard 16S Metagenomic Sequencing Library preparation protocol from Illumina (Illumina \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with some minor modifications. The 1st stage amplification mix contained 2x KAPA HiFi HotStart ReadyMix, (Roche) with 1.25 \u0026micro;l of each 5 \u0026micro;M forward and reverse primer (jgHCO2198 and mlCOIintF-XT) and 2.5 \u0026micro;l template DNA (\u0026le;\u0026thinsp;10 ng/\u0026micro;l) and dH\u003csub\u003e2\u003c/sub\u003eO to a total volume of 25 \u0026micro;l. The PCR program included 5 min at 95\u0026deg;C, 16 cycles of 95\u0026deg;C for 10 sec, 62\u0026deg;C (touchdown, with 1\u0026deg;C reduction in temperature pr cycle) for 30 sec and 72\u0026deg;C for 1 min, followed by 25 cycles of 95\u0026deg;C for 10 sec, 46\u0026deg;C for 30 sec and 72\u0026deg;C for 1 min. The program ended with 72\u0026deg;C for 5 min. In the second amplification, samples were dual indexed using dual indexer (IDT for Illumina, Unique dual indexes), and a PCR program as described in the protocol. After PCR, the quality of amplifications was assessed by electrophoresis using Tape Station (Agilent), and PCR products were purified after each step using MagBind TotalPure NGS magnetic beads (Omega). Indexed amplicons were normalized and pooled into a library before sequencing, using NovaSeq SP paired end 250 bp (Illumina) at the Norwegian Sequencing Centre (NSC), Oslo, Norway.\u003c/p\u003e \u003cp\u003ePaired end Ilumina Novaseq data was processed following the DADA2 pipeline (Callahan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Primers were removed using the cutadapt package(Martin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) before sequences were filtered and trimmed. Reads with a length between 50 and 210 base pairs were retained for analyses. Following merging of forward and reverse sequences, amplicon sequence variant (ASV) tables were constructed. After the removal of chimeras, taxonomy was assigned to the remaining sequence variants. The highly curated MetaZooGene Barcode Atlas and Database(Bucklin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reference database was used for taxonomic assignment. Sequences retained in the assignment step were not limited to being collected in the North Atlantic Ocean, with the genus/species of sequences included in assignment being limited to having been observed in the North Atlantic Ocean. Within-sample richness estimates were not sub-sampled to an equal sequencing depth (i.e., rarefied) as sample-wise processing of data produced by DADA2 may show spurious correlations between richness metrics and sequencing depth (Bardenhorst et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Analyses of data following taxonomic assignment were conducted on relative abundances within samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePlankton net analysis\u003c/h2\u003e \u003cp\u003eThe plankton net samples were used to validate and complement the results from eDNA metabarcoding by assessing the age structure of the sampled copepod population. Calanoid copepods were identified and counted using a stereomicroscope (Wild Heerbrugg, Leica Microsystems). The calanoid copepods were categorized as juvenile and adult, with females and males differentiated. Counts of individuals were aggregated across net samples for each sex and life-stage in each sampling campaign.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAdaptive sampling\u003c/h2\u003e \u003cp\u003eWater samples for DNA metabarcoding were collected at -2 m and \u0026minus;\u0026thinsp;10 m in both sampling campaigns. The sampling depth for water collection was in the vicinity of the points where high activity was identified in echograms (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Collecting samples from two different depths near the area of high activity rather than the depth of high activity itself was preferred for the samples collected for metabarcoding due to uncertainty around rates of DNA sinking rates (Stewart, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Plankton nets were placed at depths of -30 m in winter and \u0026minus;\u0026thinsp;10 m in summer where high activity was detected in the echograms in the week preceding each planned sampling campaign. The entire water column was sampled using the plankton net as the net used was not a closing net (which would allow for only sampling at a specific depth).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetabarcoding\u003c/h2\u003e \u003cp\u003eThe Illumina run produced 19083277 reads from all samples collected in the Subarctic study area. Following filtering, the dataset consisted of 15109500 reads. These reads were then denoised in DADA2 resulting in an average of 1026101.5 reads (minimum\u0026thinsp;=\u0026thinsp;474990, maximum\u0026thinsp;=\u0026thinsp;1880193) in the final dataset and 97% of reads remained following the removal of chimeras.\u003c/p\u003e \u003cp\u003eFollowing taxonomic assignment, \u003cem\u003eCalanus finmarchichus, Calanus glacialis\u003c/em\u003e, and \u003cem\u003eCalanus helgolandicus\u003c/em\u003e were differentiated in samples collected in both the summer and winter (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e panel A). Data was then filtered to contain ASVs that accounted for 5% or more of a given sample to determine the relative abundance of ASVs identified as Calanoida in each sampling campaign (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e panel B). When data was filtered to contain ASVs that accounted for 5% or more of a given sample, four Calanoida genera were abundant in the samples collected in the winter (Oithona, Centropages, Acartia, and Microcalanus), and three genera were abundant in the samples collected in the summer (Oithona, Centropages, and Calanus)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePlankton nets\u003c/h2\u003e \u003cp\u003eCalanoid copepods were detected in all net samples. We present the net-sample data as calanoid copepods by life stage, without species-level information as very small differences in morphological characteristics are used to differentiate \u003cem\u003eCalanus\u003c/em\u003e species, which risks misidentification even by specialized taxonomist (see Nielsen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the samples collected in the winter, the majority of calanoid copepods identified were adult females (66.70%, see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with a large amount of early life stage calanoid copepods being the next most represented group (28.50%) with adult males representing just 3.11% of the samples. In the samples collected in the summer, the majority of the sample was early life stage calanoid copepods (98.80%), with adult female calanoid copepods representing just 0.50% of samples and male adults 0.02%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we were able to monitor an important group of organisms, calanoid copepods, which are important bioindicator organisms for different stressors (Farkas et al., 2023; Hansen et al., 2011). We applied a data-driven sampling approach; the sampling timing and location of sampling was adapted based on acoustic sensor data (see\u0026nbsp;Fig 2). We were successfully able to detect the three \u003cem\u003eCalanus\u0026nbsp;\u003c/em\u003especies known to occur in the North and Norwegian seas (\u003cem\u003eCalanus finmarchicus, C.\u003c/em\u003e \u003cem\u003eglacialis,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eC. helgolandicus\u003c/em\u003e; Skreslet et al., 2000) using eDNA metabarcoding. However, in the plankton net samples calanoid copepods were only differentiated from other planktonic species. In traditional visual morphological analysis, the ability to identify taxa to the species-level can be challenging, especially for groups like \u003cem\u003eCalanus\u003c/em\u003e, due to the small variations in their morphological characteristics. In addition, it is prohibitively laborious, requires highly specialised taxonomic knowledge, and it has been demonstrated to not be effective for distinguishing certain species (Lindeque et al. 1999; Choquet et al. 2018). Indeed, it is likely that \u003cem\u003eCalanus finmarchicus\u0026nbsp;\u003c/em\u003ecan be over-estimated in studies that rely on net samples collected in Northern Seas as it has been reported that the morphological species identification systematically overestimates the abundance of \u003cem\u003eC. finmarchicus\u0026nbsp;\u003c/em\u003e(e.g. Gabrielsen et al. 2012; Lindeque et al. 2004) species, which allowed for the detection of seasonal changes to the age- and sex-structure of populations\u0026nbsp;This is important information as it has been reported that different \u003cem\u003eCalanus\u0026nbsp;\u003c/em\u003especies have different life cycles and roles (Nielsen et al. 2014 and references therein).\u0026nbsp;In addition, plankton net samples can be used for validating the occurrence of different taxa identified using eDNA metabarcoding (e.g. Djurhuus et al., 2018; Suter et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe data-driven approach to sampling presented here is likely more time- and cost-effective compared to the more traditional opportunistic (or random) sampling approaches, where the sampling campaigns are oftentimes initiated without having certainty on the presence of the targeted group of organisms. In addition, opportunistic sampling may lead to false-negatives (where a target species is missed) and low reproducibility among replicates (Xiong et al., 2016; Zhou et al., 2011). This adaptive sampling approach can easily be coupled with relatively cost- and time- effective methods for biodiversity estimations such as (e)DNA metabarcoding (as described by Aylagas et al., 2018). Future developments of this approach to reduce manual work collecting water samples could include the use of autonomous underwater vehicles (see Hendricks et al., 2023; Yamahara et al., 2019). Future studies seeking to explore how to expand this n approach should include a random sampling to test whether samples collected opportunistically capture the same events.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese results demonstrate that real-time data collected using acoustic sensors can be used for potentially more accurate and resource-effective sampling campaigns than an opportunistic approach to sampling. As the number of automated monitoring platforms that host multiple sensors is increasing internationally, we encourage monitoring programs to apply an adaptive sampling approach to monitoring marine species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis work is funded in part by SINTEF Ocean\u0026rsquo;s fundamental research funding (GM), the Research Council of Norway (315728), and this work made use of Norwegian National Research Infrastructure provided by OceanLab (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://oceanlabobservatory.no/\u003c/span\u003e\u003cspan address=\"https://oceanlabobservatory.no/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLara Veylit wrote the main manuscript and performed bioinformatic work. Emlyn Davies, Ralph Stevenson-Jones, Stefania Piarulli, and Sigrid Hakv\u0026aring;g collected data in the field. Marianne Aas performed laboratory analyses. All authors (Julia Farkas, Sanna Majaneva, Lara Veylit, Emlyn Davies, Marianne Aas, Ralph Stevenson-Jones, Stefania Piarulli, and Sigrid Hakv\u0026aring;g) reviewed the manuscript and provided comments.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to sincerely thank the biology department at the Norwegian University of Science and Technology (NTNU) for access to their Calanus cultures during the planning phase of this project. In addition, we would like to thank Ida \u0026Oslash;verjordet for her expertise with calanoid copepods. We would also like the to thank the NINAGEN laboratory, and Markus Antti Mikael Majaneva in particular, for providing DNA sequencing services and OceanLab (https://oceanlabobservatory.no/) for access to their ocean data buoy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAylagas, E., Borja, \u0026Aacute;., Muxika, I., Rodr\u0026iacute;guez-Ezpeleta, N. (2018). Adapting metabarcoding-based benthic biomonitoring into routine marine ecological status assessment networks. \u003cem\u003eEcological Indicators 95, 194\u0026ndash;202.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2018.07.044\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2018.07.044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBardenhorst, S.K., Vital, M., Karch, A., R\u0026uuml;bsamen, N. (2022). 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ISME Journal \u003cem\u003e5\u003c/em\u003e, 1303\u0026ndash;1313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ismej.2011.11\u003c/span\u003e\u003cspan address=\"10.1038/ismej.2011.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4302016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4302016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo achieve the aims of the Convention on Biological Diversity\u0026rsquo;s 2030 Global Biodiversity Framework, marine legislation and management requires the use of cost- and time- effective monitoring of indicator species. Marine observation platforms, which are increasing in popularity globally, are used for such monitoring activities. These platforms allow data to be collected from a variety of sensors simultaneously, providing the opportunity for adapting where and when sampling is performed based on real-time observational data. While some recent monitoring activities are following an adaptive sampling approach, most still employ a more opportunistic method. In this study, we applied an adaptive sampling approach to detect calanoid copepods at seasonally contrasting time points using real-time acoustic sensor data, traditional plankton net sampling, and eDNA metabarcoding. We demonstrate that there are ways to move from sampling opportunistically to a more adaptive sampling approach for more cost- and time- effective monitoring of indicators.\u003c/p\u003e","manuscriptTitle":"Adaptive sampling using multi-sensor fusion: Marine biodiversity assessments using eDNA metabarcoding and acoustic sensor data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 19:41:17","doi":"10.21203/rs.3.rs-4302016/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f657349a-55ff-44f3-a5c4-685e1651b6c1","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-28T12:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 19:41:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4302016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4302016","identity":"rs-4302016","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}