dataesr/scientific-paragraphs-categorization huggingface dataset:
Food web data Food web indicators differ in their data requirements according to the complexity of the indicator and the number of ecosystem components considered. Different types of phytoplankton primary production data were used to construct food web indicators. For Production of phytoplankton (FW2), for example, the estimation of primary production comes from different data collection and processing methods such as carbon or oxygen isotope techniques, fluorometric techniques and modelling methods. Since the time-series were collected from fixed stations and transects, a step-by-step approach was developed to integrate these datasets for the indicator calculation  (Kromkamp et al. , 2017). The data requirements for the Change in mean trophic level of consumers or marine predators (FW4) food web indicator can be classified into two main categories: a) biomass data per species for fish and invertebrates from surveys and landings and b) data on the trophic level of each species.
NIS data Most OSPAR parties provided data on NIS newly recorded within their waters between the 1st of January 2003 and the 31st of December 2014. A distinction was made between newly recorded NIS and new introductions to account for limitations in regional monitoring programmes (at time of data collection) and the frequent time delay between introduction and detection of NIS especially in systems. The period assessed was selected because it represents two six-year periods  (2003-2008 and 2009-2014) , from which Contracting Parties are most likely to have complete data sets, enabling comparison of new NIS records between these two six-year reporting periods. This was in order to test the indicator for further use within the six-year rolling programme scenario of the MSFD. Data included the date on which a NIS was first recorded within each of the regions and the location of the observation.
Although multiple time-series datasets were analysed (see Table  1 ), changes in phytoplankton biomass (Changes in phytoplankton biomass and zooplankton abundance (PH2)) were remarkably consistent across the OSPAR maritime area with anomalies of phytoplankton biomass proxies rapidly increasing from the 1980s, a manifestation of the welldocumented region-wide regime shift  (Beaugrand, 2004). Similarly, zooplankton abundance largely decreased across the whole area, although it did increase in some regions post-2000s. For plankton diversity (Changes in plankton diversity (PH3)), an increase in species dominance (the relative abundance of certain species compared to the overall community) was observed in 2007-2008 for the Greater North Sea stations, suggesting a shift in community composition, but the presence of a definitive trend in species dominance patterns is unclear.
Scientific uncertainty and data gaps have resulted in uncertain status addressed by this indicator (Fig. 2 , Table  A1 ). However, the pilot assessment illustrates the potential of this indicator, representing the main production source of organic matter in the marina environment, to provide valuable information to a broader and holistic ecosystem assessment. Typical length (Size composition in fish communities (FW3)) was used to assess the size structure within fish communities but as no threshold was proposed the status is uncertain. For demersal fish the indicator was generally increasing across all OSPAR regions since 2010, while no long-term trend was found for pelagic fish, suggesting overall that recovery is ongoing (Fig. 2 , Table  A1 ). However, this follows longterm decreases in the Greater North Sea between 1980s and 2000s, with particular declines in the southern and central North Sea.
For example, some marine mammal indicators could not be assessed with confidence since the available pressure data, i. e. harbour porpoise bycatch, and the biological data, i. e. the abundance of cetaceans other than bottlenose dolphins, were not at a wide enough spatial scale. In terms of the temporal scale of analysis, the length of the time-series is an important factor for the interpretation of the indicator and hence, biodiversity assessment. In the case of plankton, the available datasets have different timespans, from 14 to 60 years, making it challenging to establish a common reference period across datasets to assess potential large-scale long-term changes. Paucity of long-term benthic datasets hinders the development of reference points to measure change over time. In the past few years, however, the implementation of the Water Framework Directive (2000) and MSFD (  2008 ) and increased numbers of environmental impact studies and Marine Protected Areas have slightly increased the number of standardised surveys and datasets collected.
These datasets, used to assess benthic biodiversity status, improve quantitative and in situ validated knowledge on species and biotopes, but only for a limited number of geographic areas and for specific environmental management scales and objectives. This also highlights the need to develop more coordinated benthic monitoring. The variety of methods and protocols (gears, metadata, taxonomic detail, quality assurance and sampling designs) used to acquire the different datasets also limits the spatial aggregation and comparability of biodiversity data at (sub)regional scales. Likewise, the data collected for NIS were patchy both temporally and spatially due to limited, uncoordinated, and varying efforts in monitoring and species categories considered. The absence of a clear baseline from which assessments of new arrivals could be made has been indicated as an issue for NIS which has subsequently been addressed  (Tsiamis et al.
Some indicators, such as the Proportion of large fish-Large Fish Index (FC2) and Marine birds: non-breeding abundance (B1-A), have a history of assessment. While they were further developed for IA2017, the previous assessment experience, including an established network of experts and data flows, enabled the status category addressed by these indicators to be clearly determined. Most OSPAR common benthic, pelagic and food web indicators, were developed explicitly for the MSFD and were assessed for the first time for the OSPAR Intermediate Assessment 2017. As part of this assessment process, expert networks and data flows had to be established and appropriate indicators had to be selected and developed. An important yet often complex aspect of biodiversity indicators is a clear understanding of the nature and extent of potential pressures that could drive community changes.
This process enabled synergistic working between 188 nominated scientists, as well as policymakers from 15 Contracting Parties, to develop policy-useful biodiversity indicators, evaluate indicator change, assess environmental status where possible, and communicate the work appropriately (e. g. McQuatters-Gollop et al. , 2019b). Furthermore, the IA2017 process established data flows which increased the level of participation in the assessment from contracting parties. It was found that top-down data calls, issued by OSPAR through the heads of Contracting Party delegations, work better at mobilising data into the assessment process than bottom-up data calls among experts. Stabilising and formalising data flows has facilitated the whole assessment process, particularly for indicator datasets that rely on individual national or even finer scale monitoring programmes (e.
g. datasets that are maintained by nongovernmental research laboratories). The working relationships, information flows, cross-cutting issues, and lessons learned during the IA2017 process will underpin and facilitate future biodiversity assessments, and will be important to consider for future policy mechanisms such as the development of the CBD Post-2020 Global Biodiversity Framework. The process, however, does have room for improvement. For example, extending scientific expert contribution by enlarging the expert groups to gain an even more robust scientific consensus, and enabling official citable authorship of experts by OSPAR could contribute positively to the next assessment cycles.
Acknowledgements The authors of this paper are grateful to the networks of scientific experts who have enabled and progressed this work, to the OSPAR ICG-COBAM and BDC members and convenors, to the OSPAR secretariat, and to sunny bar and restaurant terraces across Europe which facilitated development of these indicators by fuelling discussions with wine and cheese. Benthic, food web, and pelagic authors are grateful to funding received from  European Maritime and Fisheries Fund  for the "Applying an  Ecosystem Approach  to (sub) Regional Habitat Assessments (EcApRHA): Addressing gaps in biodiversity indicator development for the OSPAR Region from data to ecosystem assessment", grant number:  11. 0661/2015/712630/SUB/ENVC. 2OSPAR. AM-G is supported by the  UK Natural Environment Research Council  ( NE/R002738/1 )  Knowledge Exchange fellowship  scheme.
AA, LG, SE, NN, GS, and IR received funding from the  French Ministry of Environment, Energy , and the  Sea (MEEM) , the  French National Museum of Natural History (MNHN)  and the  French National Centre for Scientific Research (CNRS-INEE ,  CNRS-INSU ). LFA was partly supported by a  CNRS-INSU  -MEEM convention n • 2101893310 for the MSFD implementation in France. AG and VD received funding from the  German Federal Agency for Nature Conservation. CPL received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  869300.
Investigations conducted during the GEOSECS program concluded that radium-226 (T 1/2 = 1602 y) and barium are tightly correlated in waters above 2500 m in the Atlantic, Pacific and Antarctic Oceans, with a fairly uniform 226 Ra/Ba ratio of 2. 3 ± 0. 2 dpm µmol -1 (4. 6 nmol 226 Ra/ mol Ba). Here, we report new 226 Ra and Ba data obtained at three different stations in the Pacific Ocean: stations K1 and K3 in the North-West Pacific and station old Hale Aloha, off Hawaii Island. The relationship between 226 Ra and Ba found at these stations is broadly consistent with that reported during the GEOSECS program. At the three investigated stations, however, we find that the 226 Ra/Ba ratios are significantly lower in the upper 500 m of the water column than at greater depths, a pattern that was overlooked during the GEOSECS program, either because of the precision of the measurements or because of the relatively low sampling resolution in the upper 500 m.
INTRODUCTION Seawater radium (Ra) and barium (Ba) distributions were measured extensively in all major ocean basins during the GEOSECS program  (Broecker et al. , 1967; Wolgemuth and Broecker, 1970; Bacon and Edmond, 1972; Broecker et al. , 1976; Chan et al. , 1976; Ku and Lin, 1976; Chung and Craig, 1980; Ku et al. , 1980)  and more recently during the GEOTRACES program (see e. g. Le  Roy et al. , 2018). Because of their similar chemical properties, it was argued that Ba and 226 Ra would not be biologically fractionated and that 226 Ra/Ba ratios would only be affected by the decay of 226 Ra, providing an independent clock that would complement the information obtained from 14 C/ 12 C ratios to yield important constraints on the circulation and mixing of abyssal waters (e. g. Ku and Luo, 1994).
, 2008). In particular, the Ba dataset (dissolved Ba and particulate Ba concentrations) obtained in the JPAC project on the entire water column can be compared to the Ba dataset collected during the VERTIGO project in the "twilight zone" (0-1000 m)  (Dehairs et al. , 2008). In addition, the vertical distribution of the 226 Ra activities reported here complement the study of  Kawakami and Kusakabe (2008)  who investigated the horizontal distribution of 226 Ra (and 228 Ra) in surface waters of the northwestern North Pacific Ocean. Time-series sediment trap moorings have been deployed in the northwestern North Pacific Ocean and provide information on sinking particle fluxes in the region  (Honda, 2003). The vertical profiles of potential temperature, salinity and potential density are reported in Figure  2  of the Supplementary Material for both stations K1 and K3.
228 Ra activities were determined using the 338, 911 and 969 keV peaks of 228 Ac. Counting times for each sample ranged from 2 to 5 days, in order to allow the detection of the low 228 Ra activities in addition to 226 Ra activities. Uncertainties for 226 Ra activities are errors due to counting statistics. The low counting background afforded by the counting facilities, the large sample volumes and the long counting times resulted in uncertainties for 226 Ra (ie. ± 1 to ± 3%) smaller than reported from most of the measurements performed during the GEOSECS program (± 3% to ± 10%). For dissolved Ba, aliquots of seawater were analysed by isotope dilution using 135 Ba. The determination of dissolved Ba concentrations at old station Hale Aloha was conducted using the ICP-MS facility (Element, Finnigan) at WHOI.
Radium and Barium in Seawater The 226 Ra activities and Ba concentrations determined in seawater at old station Hale Aloha, station K1, and station K3 are reported in Tables 1-3 of the Supplementary Material, and the vertical profiles are shown on Figure  2. Both increase with depth, reflecting uptake during particle formation in shallow waters and subsequent release at depth. Similar profiles were reported in the North Pacific during the GEOSECS program  (Chung, 1976; Chung and Craig, 1980). The 226 Ra and Ba profiles are similar at the three stations, but above 3000 m, Ba concentrations (and 226 Ra) are higher in the Subarctic Gyre (station K1) than at station K3 (Figure  2 ), while below 3000 m, there is a clear decrease in Ba concentrations at K1. The Ba concentrations at old station Hale Aloha are similar in June (lower sampling resolution) and August 2002 (Table  1  of the Supplementary Material).
, 2007). The data from the Mediterranean Sea also clearly highlight the temporal variability of the 226 Ra/Ba ratio at the DYFAMED station. Many of the vertical profiles obtained during GEOSECS do not show lower 226 Ra/Ba ratios in the upper water column. This could be attributed to the lower sampling resolution and precision of the GEOSECS Ba and 226 Ra measurements  (Ku and Luo, 1994). We note however, that when all the 226 Ra/Ba profiles obtained during the GEOSECS program for each ocean basins are plotted versus depth, the lowest 226 Ra/Ba ratios are invariably found in the upper 500 m of the water column (left of the vertical line on Figure  11 ). Likewise, as noted before  (Wolgemuth and Broecker, 1970; Chan et al. , 1976; Ku and Luo, 1994) , the linear regression of 226 Ra activity versus Ba concentration has a significant, positive intercept on the Ba axis (Figure  1  of the Supplementary Material).
The pattern reported here (i. e. , low 226 Ra/Ba ratios in the upper 500 m) does not appear universal, however. In particular, data from  Foster et al. (2004)  and  Staubwasser et al. (2004)  indicate that surface waters in the Ross Sea have 226 Ra/Ba ratios similar to deep waters. This pattern is consistent with the deep convective mixing that dominates this region, which may mask fractionation observed in other regions. This is further confirmed by the lack of an intercept when regressing 226 Ra activity and Ba concentration measured in samples from the Southern Ocean  (Li et al. , 1973).
ACKNOWLEDGMENTS We are grateful to the crews and captains of  RV WECOMA (USA), RV Ka'imikai-o-Kanaloa (USA)  and  RV Mirai (Japan). We thank  Mike Bacon , chief scientists on board RV WECOMA and RV KoK. We thank  Kazuhiro Hayashi  and  Markus Kienast  for their help during sampling on board RV Mirai. We thank  Larry Ball  and  David Schneider  at the  ICP/MS of WHOI, USA , and  Valeŕie Olive  at the  ICP/MS of East Kilbride, Scotland, UK. We also thank  Alan Fleer  and  Susan Brown-Leǵer  at  WHOI  for their help in the lab and at sea ( Hale Aloha ). We thank  Charlotte Riccio ,  Thierry Sampieri  and  Jean-Louis Saury at the underground laboratory of Modane (LSM). We thank  Reḿi Freydier  and  Marc Souhaut  for their help at the  ICP/MS at Observatoire Midi Pyreńeés. We thank  Steven Manganini  and  Jerzy Blusztajn  for constructive discussions on the project.
Introduction Monitoring the occupancy and distribution of populations is an integral part of ecological knowledge gathering and effective conservation management. Such studies are of interest in determining whether specific species occupy specific sites. For this purpose, data are collected from multiple sites, and species occupancies are recorded. In metacommunities, two types of species are most typical: satellite species are uncommon species found from only a few sites, whereas core species are common species found in most sites. In macro-and community ecology, species occupancy-frequency distribution (SOFD) and ranked speciesoccupancy curves (RSOCs) are often used to describe species distributions across multiple sites  (Hui, 2012; Jenkins, 2011; McGeoch & Gaston, 2002). The SOFD indicates the distribution of the number of species occupying different numbers of study sites, producing a data matrix (where rows indicate species, columns indicate different sites), and the element indicates the presence/absence of a species in a specific site.
Larger scales (e. g. , continent-wide) would follow a unimodal satellite dominant pattern, whereas data collected from smaller (e. g. , city-level) scales follow bimodal distributions  (Collins & Glenn, 1997 ; McGeoch & Gaston, 2002). Second, is there geographical variation in the SOFD patterns among European cities? We predicted differences in SOFD patterns among cities, given that our large-scale spatial approach potentially implies different local species pools. Third, does urban development type (LSH vs. LSP) affect SOFD patterns? A satellitedominant unimodal SOFD pattern was predicted to occur in the LSH urban areas. In contrast, LSP areas are predicted to exhibit bimodal SOFDs with many core (common) and satellite (rare) species. Finally, are there differences in the compositions of core and satellite species between urban development types and cities? This last question attempts to establish whether species can change their status from core to satellite or vice versa, depending on the urban development type or city.
Study areas Data were collected from nine cities (Fig. 2 ) (64,000 -3 170,000 inhabitants) located in three different vegetation zones along a 3700 km latitudinal gradient that expanded from the Mediterranean area up to the Arctic Circle in Europe (Table  1 , see more details in  Ibáñez-Alamo et al. , 2020; Jokimäki & Suhonen et al. , 2020). We selected several cities from each major climatic zone to capture a good representation of the variation in urban areas in Europe and quantified breeding bird communities in the LSH and LSP urban areas of nine European cities. Data on bird species occupancy was collected using a carefully planned and replicated study design, where we employed precisely the same grain size (500 m × 500 m), sample size, sampling cover, survey intensity, and bird survey methods in each studied city, thereby decreasing the possibility of methodological biases affecting the shape of SOFD patterns  (McGeoch & Gaston, 2002).
We estimated species accumulation curves with the "vegan" package version 2. 5-7 in R  (Oksanen et al. , 2020). In species accumulation calculation we used option "random". We followed standard statistical procedures performed in previous analyses of SOFD patterns  (Hui, 2012; Jenkins, 2011) , namely a multimodel inference approach based on ranked species-occupancy curves (RSOCs)  (Hui, 2012; Jenkins, 2011). First, we created binary (presence/ absence) species-by-site matrices for each dataset. We then calculated the occupancy frequency of each species as the sum of all the study sites in which it was found. The occupancy frequency of each bird species was divided by the number of study sites, and the relative occupancy values (O i ) were sorted in decreasing order. Each breeding bird species had a rank value, Ri, inversely correlated with the relative occupancy value.
We then performed five regression analyses (one for each of the currently described SOFD patterns, those depicted in Fig. 1 , and the random pattern) in which the relative occupancy of a species (O i ) was the dependent variable and R i was the independent variable. Finally, we determined which of the five core-satellite SOFD patterns provided the best fit for breeding bird species communities in urban habitats  (Hui, 2012; Jenkins, 2011). In the multi-model inference approach, we followed the statisticalranked species-occupancy curve (RSOCs) method described by  Jenkins (2011)  and  Hui (2012). First, we processed the 12 binary (presence/ absence) species-by-site matrices for each of the datasets: (i) combined data for large-scale effects; (ii) one set of each urban landscape development type data (LSH vs.
It is assumed that higher parsimony implies a higher likelihood of reality. The most parsimonious fitted model has the smallest AICc  (Burnham & Anderson, 2000). This approach is powerful for detecting differences between models if ΔAICc (=AICc i -AICc min values are greater than four; otherwise, the alternative models will fit the data almost equally well  (Anderson et al. , 2000; Burnham & Anderson, 2000; Jenkins, 2011). Following the recommendations of  McGeoch and Gaston (2002) , we used 10% occupancy classes (i. e. , 10%, 20%, 30%) and the number of bird species in each class in the figures. The satellite species occupied less than 20% of study sites, and core species at least 90% of study sites. We used a paired t-test to compare the unique species richness and total occupancy frequency in LSH and LSP squares within each of the nine study cities in Europe.
Continental-level scale We showed that a unimodal satellite-dominant SOFD pattern prevailed in urban breeding bird assemblages in our pooled European data. Our results match another pan-European study on the same topic (but focused on highly-urbanized core areas and heterogeneous data sets;  Jokimäki, Suhonen, & Kaisanlahti-Jokimäki, 2016). Moreover, our continental-scale results agree with most previously published studies indicating that the geographical extent modifies the SOFD pattern to a unimodal satellite-dominant pattern  (Collins & Glenn, 1997; Korkeamäki et al. , 2018; McGeoch & Gaston, 2002). In contrast,  Storch and Šizling (2002)  used continental European and national Czech datasets and found that the bird SOFD pattern was bimodal at both scales. The grain size (50 × 50 km squares) in their study was much larger than in our study (500 m × 500 m squares), which might explain the observed difference.
, 2006; Devictor et al. , 2007; McKinney, 2006). We showed that a unimodal satellite-dominant SOFD pattern prevailed in our pooled European data on urban breeding bird assemblages. Our results match another pan-European study, which tackled the same topic but focused on highly urbanized core areas and heterogeneous datasets  (Jokimäki, Suhonen, & Kaisanlahti-Jokimäki, 2016). Moreover, our results agree with those of most previously published studies  (Collins & Glenn, 1997; Ferenc et al. , 2014; Korkeamäki et al. , 2018; McGeoch & Gaston, 2002; Storch & Sizling, 2002). However, an urban wintering birds survey showed symmetric SOFD pattern which differs from our current study  (Suhonen & Jokimäki, 2019b). For examble,  Ferenc et al. (2014)  indicated that the regional species pool influences the urban breeding bird species richness.