dataesr/scientific-paragraphs-categorization huggingface dataset:
They exert strong predation pressure on larval fish, eggs, and crustacean zooplankton. Due to their nonquantitative capture by common sampling methods and poor fixation in formaldehyde, this group is not counted or underrepresented in some key zooplankton datasets including the spatially extensive CPR survey Fig. 1. CPR routes (a) and the locations of samples from all other datasets used in the assessment (b). Points are overlaid on the assessment units used to aggregate samples. Points are coloured according to the contracting party of the institute that provided the data. The total number of samples taken at each location is proportional to point opacity in (a) and point size in (b). (Aubert et al. , 2018; Boero et al. , 2008). The limited data available for gelatinous zooplankton have been included to highlight their poor representation in plankton datasets in comparison to other life-forms.
Spatial and temporal scale of analysis While pelagic habitats are primarily open systems with no clearly definable boundaries, they can be roughly delineated according to key water column features, such as bathymetry, freshwater influence, and stratification, and based on spatially consistent patterns in phytoplankton dynamics  (Enserink et al. , 2019; van Leeuwen et al. , 2015). The analysis of distributed datasets was performed across distinct pelagic habitats or "assessment units" (Fig. 2 ). Discrete fixed-point times series were each analysed separately and not extrapolated to any larger spatial scale. To identify regional variation in lifeform trends we used the "COMP4 assessment units" (Common Procedure for the Identification of the Eutrophication Status of the OSPAR Maritime Area, 4th application), an OSPAR data product, to spatially subdivide plankton samples  (Enserink et al.
Lifeform abundance trends All analyses were performed using R-programming language (R Core Team, 2020). For each dataset and sample, total lifeform abundances were grouped by assessment unit or fixed-point station to generate a distinct subset for each lifeform. Each record of a taxon, including very rare taxa, was classified into lifeforms based on a Masterlist of trait information, and subsequently the total abundance of each lifeform was summed for each sample  (Ostle et al. , 2021). Before total lifeform abundance could be log 10 transformed, a nominal value equivalent to half the minimum nonzero observed value for each time-series was added to each sample to remove zeros  (Ives, 2015). Rather than simply adding 1 to every value, this approach was used to ensure the magnitude of the adjustment matched the scale of each time-series, particularly for time-series which had many zeros recorded in winter months.
Common time resolution averaging for both data types was monthly, even when finer temporal resolution was available. For distributed CPR data, this occasionally resulted in localised Fig. 2. COMP4 assessment units developed by JMP-EUNOSAT  (Enserink et al. , 2019)  and OSPAR as polygons. Triangle symbols highlight the small COMP4 assessment units which contain river plumes, and round symbols indicate the locations of the fixed-point plankton monitoring stations used in this study. groupings of samples being extrapolated across larger assessment units (see the distribution of CPR samples in Fig. 1(a) ), as was the case for CPR data along the west of Scotland (Intermittently Stratified 1) and Ireland (Atlantic Seasonally Stratified). For months when there were no distributed samples within an assessment unit, gaps in the time-series constructed from spatially distributed datasets were filled by extracting a mean value from an inverse distance weighted (IDW) interpolated surface generated from samples adjacent to the assessment unit (<250 nmi) from the same dataset.
Sen's slope estimates were calculated to determine the median percent change in abundance per decade for each lifeform across all assessment units. Considering the range of start dates among datasets (e. g. 1960 for CPR, 1992 for PML, 1999 for NLWKN; Table  S 1) , we conducted an additional analysis to determine whether differences in time-series length affected the general spatial patterns in trend direction. We truncated all timeseries to the period 2000 to 2019 and re-ran the Kendall trend test on the shortened time-series.
Cluster analysis of lifeform abundance trends Spatially coherent patterns in long-term abundance trends across multiple lifeforms indicate possible changes occurring at the community level. To identify these patterns, a cluster analysis was conducted using Kmeans clustering on the Kendall's S-statistics for the 30 COMP4 assessment units and the 3 fixed-point stations that contained observations for all seven lifeforms (excluding gelatinous zooplankton due to limited data availability). Kendall's S-statistics were converted to matrix format with assessment unit and station names as rows and lifeform names as columns. Statistics for each lifeform (columns in the matrix) were normalised by subtracting their mean value and dividing by their standard deviation. To determine the optimal number of clusters to group the data, silhouette and within sum of squares (WSS) plots were generated from the matrix using the "factoextra" package for visualising multivariate data  (Kassambara and Mundt, 2020)  (see Supplemental methods: Determining the optimal number of clusters).
, 2019). To determine which lifeforms were important in driving the separation of data into clusters, the 'envfit' function was used to generate lifeform loadings as projected vectors on the NMDS ordination. A similar approach was taken to project loading vectors generated from Kendall statistics at the lowest taxonomic level available in each dataset onto the same ordination to study the alignment of abundance trends in taxa with those of the lifeforms they comprised. This approach also indicates which taxa have experienced the greatest change in abundance. In some cases, this was conducted at a coarser taxonomic resolution than the species level (e. g. up to phylum level) since this was occasionally the highest level of classification recorded. Some datasets recorded a mixture of taxonomic classifications (e.
g. Calanus finmarchicus and Calanus spp. ). Where such cases existed, the higher order classification accounted for samples which could not be identified to species level. Gelatinous zooplankton and fish larvae/eggs were excluded from this component of the analysis due to lack of data, and poor taxonomic resolution, respectively. Therefore, only six lifeforms were considered for this component. Finally, a matrix of per-taxa Kendall statistics was generated using the same combination of datasets to represent each assessment unit or fixedpoint station. Due to differences in which taxa were observed and reported among datasets, only taxa with observations recorded in at least 80 % of assessment units and fixed-point stations used in the lifeforms analysis were retained. The remaining missing values were replaced with the mean Kendall statistic for each taxon before the columns were normalised.
Patterns apparent from the widely distributed CPR data were largely reflected by the fixedpoint stations from adjacent coastal areas, except for dinoflagellates and, in some cases, diatoms, which demonstrate increasing trends in coastal waters around Scotland and Northern Europe, and decreasing trends offshore. Spatial patterns in the direction of change for both small and large copepods closely matched those of diatoms, with coastal areas of the English Channel and the North Sea often exhibiting abundance trends opposite to those from more offshore Atlantic assessment units. Due to the low availability of monitoring data, it was only possible to calculate trends in gelatinous zooplankton for three COMP4 assessment units and three fixed-point stations. For fixed-point stations that collect data on this lifeform, one station in western Scotland indicated a declining trend (Loch Ewe) while another station in the English Channel indicated an increasing trend (L4).
Discussion Our study represents the most comprehensive time-series compilation achieved for the North-East Atlantic to-date, charting the regionality of long-term change in key plankton lifeforms, from diatoms up to fish larvae. Our comparison of lifeform trends allows integration of diverse data sets, such as those from the CPR and water-bottle samples, which are difficult to combine directly. This large-scale, long-term integrative approach has identified widespread and serious declines in most of the assessed lifeforms over much of the North-East Atlantic, contrasting with less change or even increases across the North Sea (Fig. 6 ). This contrast is unexpected and counter-intuitive, because it is the North Sea that is the exemplar of a suite of major and well-documented pressure changes over the past 50 years.
Numerous studies have used long-term CPR datasets from the Northeast Atlantic and fringing seas to correlate changes in plankton with climate and environmental changes. Beaugrand et al. (2002)  related northwards shifts in copepod assemblages to increasing Northern Hemisphere Temperature (NHT). Alvarez-Fernandez et al. (2012)  explained major changes in phytoplankton and zooplankton abundance and seasonality as associated with three ecological regime shifts in the North Sea, driven by changes in air temperature, wind speed and inflow of North Atlantic water. Hinder et al. (2012)  ascribed a marked increase in the relative abundance of diatoms versus dinoflagellates to increasing sea surface temperatures combined with more windy conditions in summer. Similarly, our study revealed increasing diatom abundance within coastal areas where dinoflagellate abundance has decreased.
Recently,  Edwards et al. (2022)  related long-term trends in diatom abundance to the Atlantic Multidecadal Oscillation (AMO) index and NHT anomalies. Models provide another method for identifying causal links, albeit subject to correct formulation of these links within the model. Several studies have investigated energy flows through North Sea food webs with 'endto-end' models  (Heath and Lough, 2007; Thorpe et al. , 2022) , calculation schemes constrained by observations  (Frederiksen et al. , 2006; Greenstreet et al. , 1997; Heath, 2005a; Heath, 2005b) , or ECOPATH software  (Lynam and Mackinson, 2015). Most concluded that fish production depended on phytoplankton production, channelled through zooplankton in a 'bottom-up' fashion. Findings that planktivorous fish  (Maar et al. , 2014)  or their young  (Heath and Lough, 2007 ) consumed a substantial portion of zooplankton production might suggest that fisheries-related stock changes could reduce zooplankton abundance, as we observed in the North-East Atlantic; but  Heath (2005a)  concluded that the planktivore niche might be rapidly refilled by other species.
Thorpe et al. (2022)  found that simulated warming raised primary productivity; but also increased metabolic rates of omnivorous zooplankton and reduced their abundance. In summary, these studies suggest that long-term trends showing forced changes in plankton might be ascribed with most confidence to: (1) direct effects of climate change on SST and stratification; (2) indirect effects of climate change by way of run-off and oceanic circulation; and (3) in some coastal areas by changes in inputs of anthropogenic N and P. Effects of changes in fisheries might be harder to identify, and natural inter-annual and semi-cyclical variability in plankton, as well as in climate features such as the NAO, can mask long-term trends and lead to false conclusions about causality. There is a need for wide-area analyses of multi-decadal and multi-source datasets, such as we have reported here.
, 2022). Alternatively, this increase could be limited to small bodied species experiencing reduced mortality due to the removal of predators  (Daan et al. , 2005). However, since most zooplankton datasets do not identify larval fish to species level, it was not possible to conclude whether the increasing abundance trends in the North Sea extended to commercially exploited species. In the NEA oceanic and outer shelf waters,  Hátún et al. (2009)  related the increase  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005)  in the fishery of the pelagic planktivore blue whiting (Micromesistius poutassou) to richer zooplankton environments, as sampled by the CPR  (Hátún et al. , 2009). However, using different timeseries, a longer time-period, a larger area of study, and different numerical approaches, our analysis detected decreasing zooplankton abundance in the NEA cluster; this points to a general problem both in making temporal comparisons and in generalising/extrapolating conclusions.
By contrasts this decline was not so marked in the North Sea, with more stable or increasing links in this simplified food chain. Nutrient changes might also favour diatoms in the North Sea and constrain them in the North-East Atlantic. However, the warming trend (and associated extended seasonal stratification) may further decrease diatom and large copepod abundances in the North Sea relative to the North-East Atlantic. Finally, while we aimed to conduct a comprehensive assessment of the plankton community, there were of course important components, or lifeforms, which were poorly represented or absent from our datasets, including gelatinous zooplankton and small size taxa, pico-to micro-plankton. Until recently gelatinous zooplankton were largely considered a trophic dead end  (Richardson et al.
, 2009) , however, there is growing evidence that this group is an important vector for the transfer of energy from pico-plankton to higher trophic levels, including fish  (Aubert et al. , 2022; Jaspers et al. , 2015; Lamb et al. , 2017). Similarly, pico-and nanoplankton, along with micro-zooplankton, are difficult size categories to measure routinely and are thus absent from most datasets. However, they also make up a significant proportion of plankton biomass, diversity, and trophic roles  (De Vargas et al. , 2015)  relevant to marine food webs and carbon export  (Leblanc et al. , 2018). Adoption of improved sampling methods and new technology  (Aubert et al. , 2018)  will be necessary to improve holistic understanding of the plankton community.
Acknowledgements The authors would like to acknowledge the contributions from our networks of pelagic habitats experts who have enabled and progressed this work, to the OSPAR ICG-COBAM and BDC members and convenors, and to the OSPAR secretariat. We are grateful for the funding received from  DEFRA's marine Natural Capital and Ecosystem Assessment (mNCEA) Programme , and from the  European Maritime and Fisheries Fund  for the project: " North-east Atlantic project on biodiversity and eutrophication assessment integration and creation of effective measures (NEA PANACEA )", financed by the  European Union 's  DG ENV/MSFD 2020 , under agreement No. 110661/2020/839628/SUB/ENV. C. 2. AM-G was supported by the  UK Natural Environment Research Council (NERC)   NE/R002738/1   Knowledge Exchange fellowship  scheme.
, 2021). Research has been carried out to address these challenges along with other factors, such as a large and uncontrolled three-dimensional work area, and continuous mobility of nodes  (Gupta and Goyal 2021, Iqbal et al. , 2021). Most existing research that uses a single data forwarding technique is either localization-based or localization-free. For example, it could use hop-by-hop, multi-hop, or an autonomous underwater vehicle (AUV), where each approach has advantages and disadvantages. Every technique performs well in some scenarios but its utility may be compromised in other situations. For example, clustering performs poorly when water currents are high. The drawback of the multi-hop data forwarding technique is that the nodes near the surface sink face higher processing burdens, which reduces the lifespan of the network.
On the other hand, complete dependence on an AUV for data collection has limitations. It offers a limited coverage area, and a limited timespan for diving into the water and returning to the sink node. Therefore, it is inappropriate for delay-sensitive applications. A hybrid approach that integrates more than one scheme is likely to combine the advantages of respective schemes, ultimately helping to enhance network efficiency. This mechanism offers a more realistic approach when compared with general multi-hop schemes, especially in terms of energy efficiency and reliability, as its performance depends on the application's nature and requirements. Although proposing a hybrid approach is not a simple task, particularly when considering the multiple aspects, however, if done carefully, it can lead to an optimal solution for data forwarding.
This study proposes a hybrid routing scheme called Multilayer Dynamic Data Forwarding (MD2F) that aims to achieve load-balancing among all nodes and is suitable for large and deep underwater areas. During data collection, the inclusion of AUV and its mobility patterns balances the load on all participating nodes. Through balanced processing, the first failed node of the network will be delayed, which increases the network's reliability, throughput, and lifespan. The main objectives of the proposed work are as follows: Balancing resource utilization Minimizing energy consumption Maximizing the lifespan of the network.
In most of the techniques, an AUV is used as a mobile data collector, and instead of visiting each node, CH-nodes are introduced to reduce the travel distance and the end-to-end delay of the AUV. Data collection via AUV has proved more convenient in many applications as it helps to reduce overall energy consumption. For example,  (Ahmad et al. , 2013)  provided AUV-aided Energy Efficient Routing Protocol (AEERP), a data delivery mechanism that uses AUVs to collect data while travelling around a predefined horizontal trajectory. However, as compared to many other schemes, AEERP has some limitations, such as higher end-to-end delays. Further, in AEERP, the AUV travels at a constant speed during its visit, if some gateway nodes have more data to deliver, then the probability of data loss becomes high.
The performance of MD2F is not dependent on the number of layers as it could cover even deeper areas. Fig. 1  shows the network deployment and communication scenario under the proposed scheme. A buoyancy control mechanism is used to deploy sensor nodes at various layers and depths. The nodes deployed through the buoyancy mechanism at different depth levels can move horizontally. Although minor vertical shifts are expected, this is likely negligible in most scenarios. In addition, multiple sinks are deployed at the surface, where both acoustic and radio modems are installed. Radio frequency (RF) modems at these sinks are used to communicate with each other and to deliver data directly to the processing center. While, communication with AUVs and sensor nodes is carried out using the acoustic modems.
The location of sensor nodes is unknown, but the location of sinks is known as these are equipped with global positioning system (GPS) facility. The notations used during the proposed methodology are outlined in Table  1. A dynamic clustering method is used to collect data from the lower layers, where ordinary sensor nodes, called cluster members (CMs) forward the sensed data to the respective cluster heads (CHs). The CHs are responsible for receiving data from CMs, performing an aggregation process on collected data and forwarding this to AUVs. In some cases, CH can forward information to the nodes at top layers. The AUVs will run for a specific time and on defined routes to collect data from CHs. Only sensor nodes on the path of the AUV are told to enter the active mode for a specified period to conserve energy.
S-IM i ¼ 9 max hopcount of S-IM i ¼ 3 max hopcount of U-IM Nu v Received AUV hop_id (U-IM) Nw x AUV processing hop_id (CU À hop idÞ e Energy consumption of nodes a f ð Þ Absorption coefficient T coll: AUV's data collection delay CHset Set of cluster heads Overall, MD2F completes its task in three phases. Inquiry messages are used to assign dynamic hop_ids. Based on these dynamic IDs, the network is divided in two parts. The upper part does not participate in cluster formation, as it is done only in the lower part. In third phase, data packets are forwarded towards AUVs or to the surface sinks.
Dynamic clustering Clustering will be done in the lower part of the network to collect and forward the sensed data. AUV is expected to visit and collect the data from the CHs only. Despite the fact that AUV paths and visits are planned and pre-defined depending on various factors, e. g. , the nature of the mission, to cover a maximum area as possible with a minimum of visits, the sensor nodes are unaware of this. Existing clustering-based schemes are mainly proposed for static environments and, therefore, unsuitable for the present circumstances due to the frequent fluctuation or movement of the sensor nodes. Furthermore, if the same node becomes CH frequently, it has to face more processing burdens. To prevent this issue, we use dynamic clustering, where CHs are selected randomly when the data needs to be collected and forwarded.
Cluster head selection No predefined cluster heads are installed, demonstrating that none of the sensor nodes can be distinguished. Dynamic clustering is performed based on the AUV's inquiry message (beacon message). This means that AUVs play a significant role as cluster formation is performed considering their movement and visiting location. During cluster formation, it organizes many significant tasks, such as activating the nodes on its path, pushing them for cluster formation and CH selection and data collection from the selected CHs. During the cluster head selection process, AUV has no prior information about the sensor nodes and their location. After the tour planning, AUV follows the predefined path and broadcasts hello packets to start this process. Then, all the nearby sensor nodes will participate in cluster formation and data collection.
Said parameters are computed when various hello packets are exchanged between the source and possible next-hop forwarders and considered before the actual data forwarding. It is to be noted that these values are analyzed again after a particular event, e. g. , the next visit of AUV. Hence, load balancing is achieved based on estimated link quality by considering the more suitable channels, resulting with higher PDR and neighboring nodes with lower distances. For simplicity, all participated factors are considered of equal importance (absolute priority is 1 where a channel approaching 1 and higher than others will be considered more reliable).
Data packet format and data forwarding Other than IMs, another key factor is data packet and its format which is shown in Table  6. Upon completing the cluster head selection stage, data packets are collected by respective cluster heads at a fixed rate, independent of AUV activity. Whenever any node has a data packet and is looking to forward, it will check its own 'hop_id' or value of 'q'. If this value is equal to or less than 3, it will forward data toward the sink through a hop-by-hop method. If it is in the lower part of the network, it will try to forward through the AUV with the help of respective CH. If the node that wants to forward the data packet is a CH itself, then it will wait for a specified amount of time so that the member nodes of this cluster can forward their data packets to it.
Next, the CH will aggregate all the collected data before forwarding it either to AUV or to another suitable CH. As soon as the AUV arrives, it sends a control packet to the approaching CH. The CH will then force the suspension of communication with the cluster members to avoid collisions by notifying them regarding the arrival of the AUV. After collecting the aggregated data from a CH, the same procedure is repeated for the rest of the CHs formed on the particular tour route. The major key steps during the data packet forwarding are outlined in Algorithm 3 while its flow chart is provided in Fig. 6. Algorithm 3 (Data packet forwarding algorithm).