dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, some taxonomic datasets now have up to 90 years of data which provide a critical perspective in assessing long-term change and which is unparalleled by satellites. We therefore need to maintain direct monitoring approaches for a holistic view of the plankton, and northwest European waters are particularly well-blessed with these time series. Although there are a number of programmes that monitor plankton in northwest European waters, they operate at different spatial scales, from fixed-point sampling stations to long-distance continuously sampled ship transects  (O'Brien et al. , 2017). Furthermore, European plankton surveys employ different sampling methods, enumerate specimens at a variety of taxonomic levels, and employ different counting regimes  (Raybaud et al. , 2011). These methodological differences and the lack of direct comparability between datasets has meant that the tools to use all available datasets together to produce a comprehensive assessment have only recently been developed  (Bedford et al.
, 2020a; McQuatters-Gollop et al. , 2019). While most datasets are regularly submitted to appropriate data repositories (e. g. the Ocean Biodiversity Information System, OBIS; the British Oceanographic Data Centre, BODC; or the PANGAEA data publisher for earth and environmental science) and some are available through institutional websites or data centres, the aggregation of plankton data into functional groups  (or "lifeforms", e. g. diatoms, di-noflagellates, holoplankton, meroplankton)  has not yet been linked to traceable dataset versions or been possible to apply in an accessible, transparent, and centralized way. Accordingly, understanding of plankton change across multiple spatial and temporal scales has been limited. The International Group of Marine Ecological Time Series (IGMETS, https://igmets.
net, last access: 22 November 2021; O  'Brien et al. , 2017)  represents valuable progress towards this goal: it provides a global-scale compilation of pelagic time series, with a tool to summarize visualizations of trends across a variety of temporal and spatial scales. However, this initiative summarizes time trends of highly aggregated variables (e. g. total zooplankton) for multiple sites. Trajectories of the key component plankton functional groups are not described, and the underlying data products are not made available to users for further analysis. Aggregating these disparate plankton datasets increases the spatial-temporal scope of analysis, increases their robustness, and provides decision makers with more scientifically robust evidence. Building on previous work  (Gowen et al. , 2011; Scherer et al.
, 2014; Tett et al. , 2008 Tett et al. , , 2013)) , an indicator of shifts in plankton structure based on time series of broad plankton functional groups, called "lifeforms", has been developed for use in policy assessments  (McQuatters-Gollop et al. , 2019). The term "lifeform" is derived from work carried out by  Margalef (1978) , to distinguish between diatoms and dinoflagellates based on traits related to survival in specific hydrodynamic conditions. Lifeforms differ slightly from the term "plankton functional type" (PFT), in that PFTs are often used to describe plankton based on their ecosystem function and not on their traits. This indicator enables plankton datasets with different sample collection and analysis routines to be used congruently to investigate changes in pelagic habitat functioning.
By using these pre-defined lifeforms to group plankton taxa, the new Plankton Lifeform Extraction Tool (PLET), hosted by the Archive for Marine Species and Habitats Data (DASSH, https://www. dassh. ac. uk, last access: 22 November 2021), brings together disparate plankton datasets, increasing their accessibility and promoting compliance with the FAIR data principles  (Wilkinson et al. , 2016). The PLET enables the user to investigate multiple datasets to assess changes in plankton ecology at multiple spatiotemporal scales using a consistent plankton indicator for the first time. As time series grow in length and/or spatial distribution, and new plankton time series are established, additional plankton taxa and datasets can be added to the PLET in order to improve future biodiversity assessments.
Plankton datasets In its current form, the PLET integrates 155 000 samples containing over 44 million plankton records from nine different data providers around the UK and European seas, collected between 1924 and 2017 (Table  1 ). There are a number of plankton trait datasets and plankton compilation efforts that are complementary to the PLET with the potential to feed into future versions of the tool, such as the nutrient utilization trait dataset  (Edwards et al. , 2015) , the Baltic Sea phytoplankton trait dataset  (Klais et al. , 2017) , and the French lake phytoplankton trait database  (Laplace-Treyture et al. , 2021). While these are highly valuable resources, the authors are not aware of a platform to bring such information together and disseminate it in a consistent format. The design of PLET allows for this lifeform extraction and dissemination, with the aim to incorporate further plankton trait datasets in future versions.
Flexibility of the PLET design allows existing time series to be updated and new time series to be added, continuing the expansion of integrated datasets beyond the UK, where policy reporting motivated its initial development. Plankton time series have been collected both along transects and at fixed-point stations (Fig. 1 ). These datasets, which underpin the PLET lifeform outputs, enumerate plankton in taxon groupings (see the Plankton Lifeform Traits Master List, UK Pelagic Habitats Expert Group, 2020; https://www. dassh. ac. uk/doitool/data/1709, last access: 22 November 2021).
Plankton sampling and analysis methodology All individual datasets that have been added to the PLET have been pre-processed to ensure suitability for extraction of monthly-aggregated lifeform data products. Preprocessing was the responsibility of the individual data providers. Examples of pre-processing required are (i) the exclusion of instances of "double counting" where, for example, a taxon is included in both higher and lower taxonomic groupings within the same dataset and (ii) the removal of taxa that have not been looked for (recorded) over the entire time period to avoid apparent changes in lifeform abundance due to methodological changes. When new datasets are submitted to the PLET the data providers supply AphiaIDs of all of the taxa within their dataset. Following the pre-processing of the data by the data providers, the data manager of PLET and the manager of the Plankton Lifeform Traits Master List does a check of the submitted AphiaIDs to highlight any missing taxa.
Any taxa that are not included within the Plankton Lifeform Traits Master List are checked for compatibility with the lifeforms, and their traits are added in discussion with an expert group and the data providers. Existing datasets were gathered through a data call issued by OSPAR in 2016. The purpose of the data call was to gather plankton datasets to use for assessment and reporting for the European Union's and individual Member States' Marine Strategy Framework Directive initial biodiversity assessment in 2017 (https://oap. ospar. org/en/ospar-assessments/ intermediate-assessment-2017/biodiversity-status/habitats/ changes-phytoplankton-and-zooplankton-communities/,last access: 22 November 2021). A simple data submission template was developed as part of this process and is now available on the PLET website for wider use.
To make data submission as simple and easy as possible, the template allows data holders to submit the datasets in either list (long) or matrix (wide) formats. A data archiving and access permission agreement form is also available from the PLET website, which allows data holders to specify their preferred level of data access, such as full access to raw data or access to lifeform data products only. All plankton records currently included have been identified using light microscopy. For simplicity we use the term "phytoplankton" to mean protist cells, mindful that these in-clude a spectrum of auto-, mixo-, and heterotrophic forms  (Flynn et al. , 2013). This terminology is used to differentiate from "zooplankton", which are the metazoans usually counted from net haul samples. For quality assurance, analysts participate in NMBAQC (the Northeast Atlantic Marine Biological Analytical Quality Control Scheme) and the International Phytoplankton Intercomparison external identification ring trials, although these do not cover the full length of some of the historical datasets.
Although net design and methods of deployment have changed on several occasions, care has been taken to ensure that sampling characteristics have not altered appreciably. The 1 m 2 Young Fish Trawl (YFT), fitted with a 700 µm knitted mesh, is hauled for 20 min in an oblique profile to an ideal depth of ∼ 5 m above the seabed. Depth and temperature profiles are occasionally recorded, and the volume of filtered water is calculated using flow data recorded by a flowmeter fitted across the net mouth. The samples are preserved in 4 % buffered formalin and analysed as soon as possible after collection using a WILD M5 binocular microscope. Results are standardized to the number of individuals per 4000 m 3 in order to mitigate historical changes in sampling gear and deployment. A comprehensive summary of these macroplankton sampling methods and analysis is given in  Southward et al.
Species are identified and enumerated to the lowest possible taxonomic level, and counts are recorded in cells per litre. More detailed methodology is available in  Weston et al. (2008)  and  Greenwood (2019). Plankton and environmental parameters from the SmartBuoy monitoring programme are available from the Cefas Data Hub: https://www. cefas. co. uk/data-and-publications/smartbuoys/ (last access: 22 November 2021).
Most taxa are identified to the lowest taxonomic level possible, whilst other animals are recorded at the class or phylum level. More detailed methodology is available in  Bresnan et al. (2016). Phytoplankton monthly densities from Stonehaven, Loch Ewe, Scapa, and Scalloway and zooplankton weekly densities from Stonehaven and Loch Ewe are available from https://data. marine. gov. scot/search/type/ dataset (last access: 22 November 2021).
Spatio-temporal data distribution The plankton datasets currently available for lifeform extraction by the PLET have variable spatial and temporal extents, summarized herein into the four regions shown in Fig. 1. Within each region, the availability of plankton data over time differs between datasets (Fig. 2 ). Due to their high spatial coverage, the CPR and EA datasets contain the largest numbers of samples available for any month, within each region. The number of samples at fixed-point sampling stations shows variations in sampling frequency; in some cases this has changed over the course of the time series (for example, in the Celtic Seas both Cefas SmartBuoy and the SAMS dataset). While sampling is typically carried out weekly or monthly in order to capture the seasonal cycle of the plankton community structure and rapid changes associated with plankton bloom events, several datasets include sampling gaps and changes in sampling intensity for a variety of reasons (Fig.
3 ). For example, the EA dataset sampling frequency (and spatial distribution of samples) increased alongside the implementation of the Water Framework Directive  (EU, 2000)  in 2007, while the SAMS time series stopped between 1982 and 1999. Missing months in the Cefas SmartBuoy time series largely indicate failures of the automated sampling system, or sample loss related to logistical delays in buoy servicing. Ongoing sampling of all time series are at risk of additional reductions in sampling frequency and quality re-lated to funding  (McQuatters-Gollop et al. , 2017; Zingone et al. , 2015).
Plankton lifeforms The PLET uses a trait look-up table to aggregate plankton taxa into lifeforms. The lifeforms have been pre-defined using biological traits to represent groups of plankton which perform similar ecological functional roles  (McQuatters-Gollop et al. , 2019; Scherer et al. , 2014). Details of each lifeform, and lifeform pairings with ecological relevance for assessment, are given in Table  2. It should be noted that these traits have been developed for marine taxa only (see list of included taxon groups in UK Pelagic Habitats Expert Group, 2020; https://www. dassh. ac. uk/doitool/data/ 1709, last access: 22 November 2021, and traits in Table  A1 ), with the goal of simplifying plankton datasets for use in assessments; they are not intended as a fully comprehensive list of plankton traits.
The trait look-up table (Plankton Lifeform Traits Master List (UK Pelagic Habitats Expert Group, 2020) was developed by using a combination of extensive literature synthesis and expert opinion. The World Register of Marine Species (WoRMS Editorial Board, 2020) AphiaIDs are used to link the taxa to their associated traits. Confidence in the lifeforms extracted is assigned based on a combination of the ability to identify each of the taxa it comprises reliably by light microscopy and the ability to assign traits to each taxon (Table 3). The confidence associated with each lifeform is described in Table  4. Only lifeforms with a "high" confidence rating are provided in the PLET outputs. In some cases, confidence assignment reflects the limitations of identification by light microscopy by nature of the datasets around which the table was developed.
Similarly, the size-based lifeforms (as currently defined and used) reflect the size limitations of sampling and identification of the currently included datasets. For example, the lifeform "small phytoplankton", defined as phytoplankton with size < 20 µm, is more appropriately termed "small micro-phytoplankton" because while the lower size limit of identification by routine light microscopy will include some large nanophytoplankton, it excludes pico-plankton and the smaller nano-phytoplankton.
Plankton Lifeform Extraction Tool functionality The PLET is accessed through a web-based user interface (see Fig. 4 ). To generate custom plankton lifeform outputs, users select time-series start and end dates, a spatial area, and a dataset. Because of methodological differences in sampling and analysis, it is not appropriate to produce average lifeforms across the multiple datasets, as such all sample locations within the selected spatial area for any dataset are aggregated into a single lifeform data product, but stations from different datasets are never aggregated. The resulting data product, monthly averaged aggregated lifeform abundance, is generated either within the web browser or for download in. csv or. json format. The output data include the number of individual samples from which each monthly average was derived, as well as a list of component taxon groupings.
Blank output component taxon groupings indicate that the originally submitted sample data did not include information in the (optional) "taxon name" field. The PLET also has a simple API (application programming interface), which provides the option of bypassing the web page interface and sending queries to the tool using the URL only. The base URL is https://www. dassh. ac. uk/lifeforms/cgi-bin/get_form. py. The parameters are startdate (YYYY-MM-DD), enddate (YYYY-MM-DD), north (northern edge of bounding box, in decimal degrees), south (southern edge of bounding box, in decimal degrees), east (eastern edge of bounding box, in decimal degrees), west (western edge of bounding box, in decimal degrees), dataset (currently CPR, L4_phyto, L4_zoo, SMHI, CEFAS_SmartBuoy, EA_ PHYTO_2000-2017, MBA_E1_L5, MSS_phyto, MSS_zoo, SEPA_Zooplankton, SEPA_Phytoplankton or SAMS-LPO), and format (csv, json, or pretty).
For example, to retrieve results from the CPR dataset for May 1975 between 50 and 60 • of latitude and -5 and 5 • of longitude, and return in CSV format, the URL request is https://www. dassh. ac. uk/lifeforms/cgi-bin/get_form. py? startdate=1975-05-01&enddate=1975-05-30&north=60& south=50&east=5&west=-5&dataset=CPR&format=CSV (last access: 22 November 2021). Sending such URL commands via Curl or similar tools allows the PLET to be used programmatically if desired. There are a number of options for defining the spatial domain of the lifeform data product. A rectangular extent can be manually defined by the northern, southern, western, and eastern edges of a rectangular bounding box, by simply drawing a rectangle on the interactive map display which shows sample locations for each dataset. Similarly, a polygon-shaped extent can be manually defined in "wellknown text" (WKT) format or through the interactive map.
A query specifying spatial extent by polygon instead of bounding box can be constructed for the API by designating the parameter wkt instead of north, south, east, and west. A more complex area, for example a formal assessment region, can be used by uploading a shapefile to the tool. All integrated datasets are listed within the web interface, with full metadata. The trait look-up table can also be accessed and downloaded. To facilitate submission of new and updated plankton data, templates for data submission are also provided. Table  2. List of lifeforms and their ecological importance. The definitions of the lifeforms (see also  McQuatters-Gollop et al. , 2019)  based on the trait look-up table are given in Table  B1.
Lifeform outputs The spatial and temporal patterns of plankton lifeforms, based on the data currently held in PLET, are summarized to highlight seasonal patterns in Fig. 5  (phytoplankton) and Fig. 6  (zooplankton) and inter-annual patterns in Figs. 7  and 8. In order to facilitate visualization across the different lifeforms and datasets, where absolute lifeform abundances are extremely variable, within-lifeform and dataset changes are shown as standardized z scores that indicate the difference from the overall time-series mean values  (Glover et al. , 2005). Plankton abundance peaks in spring and summer are associated with nearly all plankton lifeforms, across all datasets. However, the timing, duration, and intensity of these peaks differ between lifeforms and datasets (see Figs. 5  and 6 ), in some cases partly because of spatial aggregation of the data.
In the CPR dataset, which samples furthest offshore, seasonal zooplankton lifeform abundance peaks last longer than those of the phytoplankton lifeforms. The EA datasets, which represent estuarine and coastal waters, include much shorter seasonal phytoplankton lifeform abundance peaks than the CPR for the corresponding regions, and differences in bloom timing are also evident, highlighting the small-scale spatial variability in plankton abundance. This is further evidenced in the comparison between seasonal patterns in PML's L4 station in the English Channel and both the EA and CPR data aggregation for the same larger region. This heterogeneity demonstrates the added value of integrating datasets to achieve a representative description of plankton community seasonal succession within even a relatively localized sea area, particularly where different programmes sample different subsets of the plankton community (in this case the fragile dinoflagellates are less well preserved by CPR compared to PML and EA sampling).
Comparing across larger spatial scales, differences in seasonal patterns between English and Scottish waters are likely influenced by the latitudinal gradients  (Fanjul et al. , 2017 (Fanjul et al. , , 2019;; Uriarte et al. , 2021)  as well as local hydrographic conditions (e. g. Atlantic inflow). The Swedish stations, located in the Kattegat, show the most divergent lifeform seasonality compared to the other datasets, notably in the timing of abundance peaks (e. g. the absence of April plankton blooms) which likely reflect their distinct Table  4. Lifeform confidences based on ability to identify and assign traits, applying rationale in Table  3. Only lifeforms with a "high" confidence rating are provided in the PLET outputs.
-The genus Alexandrium contains both paralytic shellfish toxin-and non-toxinproducing species/strains, and it is not possible to distinguish these using routine light microscopy; some toxin-and non-toxin-producing species are morphologically identical. -Currently it is unknown if the negative impact from Karenia mikimotoi in European waters is via toxin production or anoxia arising from high biomass blooms. -Not all datasets included in PLET reliably record key species (e. g. CPR does not record Alexandrium).
Potentially toxic and nuisance dinoflagellates Low Ciliates Low -Ecological function can be duplicated by heterotrophic and mixotrophic dinoflagellates. -Ciliates do not preserve well in the standard 0. 5 % Lugol iodine preservative used to preserve phytoplankton samples, and some (but not all) are too small and too fragile to be well sampled by many of the datasets currently in PLET. Holoplankton Medium -May not identify taxa specifically enough to determine traits. -Some of the rarer species are resuspended from the seabed, and definition of their holo-or meroplanktonic status is difficult.
e. at least monthly sampling), is robust against missing samples (months) so long as these are not biased to particular times of the year. The Pelagic Habitat Expert Group has recommended at least monthly sampling to adequately take account of seasonal changes in the balance of plankton lifeforms, while noting that higher temporal resolution would provide greater confidence that all transient bloom events (which may last less than a month) were observed. Given the tool's robustness against data loss, annual assessments can be made reliably when 1 to 3 months have been lost, so long as there is no persistent bias in lost months over several years. Despite missing months being an important consideration for annual aggregation (in some datasets in particular; see Fig. 4 ), the interannual trends in phytoplankton (Fig.
7 ) and zooplankton (Fig. 8 ) lifeforms show considerable changes in lifeform abundance among years in all datasets and regions. The longest time series  (MBA L5 and E1 since 1924 , CPR since 1960 , and SAMS phytoplankton since 1970)  capture decreases over several years followed by subsequent increases, which caution against over-interpretation of the shorter time series. For example, there have been decreases in all zooplankton lifeforms at the MSS Celtic Seas station since 2013 that cannot be seen in the nearby SEPA Celtic Seas station, which only has observations from 2014 onwards; while both in the Celtic Seas area, these two sites are characterized by very different hydrographical settings. The importance of considering both short-and long-term changes in plankton lifeforms is discussed in detail in  Bedford et al.
(2020b). Bringing diverse datasets together to extend both spatial and temporal coverage is a key tool for distinguishing small-scale, short-term fluctuations from larger-scale longerterm changes. For example,  Bedford et al. (2020a)  identified regional-scale trends in lifeform changes (increasing diatom abundance in the northern North Sea and increasing mesozooplankton abundance across almost the whole northwest European shelf) using time-series data from five different UK plankton surveys and linked some of these changes to changing sea surface temperature. Assessment of the status of the marine pelagic habitat  (McQuatters-Gollop et al. , 2019)  requires linking changes to pressures  (Scherer et al. , 2016) , which relies on high-temporal-and high-spatial-resolution, good-quality observations, such as climate  (Bedford et al.