dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcome and Way Forward The results from the LH programme are an important input into the annual risk assessment workshops, for the evaluation of the likelihood of non-compliance with the landing obligation. The outcome of the risk assessment is then used as a key input for the planning of the JDP for the upcoming year control activities. It should be noted that the objective of the collection of LH data is not to obtain precise discard ratios but to identify where problems are present, and in which magnitude, to focus monitoring and control efforts. The information collected by MS under their scientific discard observers' programmes is also taken into account when evaluating compliance. This information is compiled in a public database available at https:// stecf. jrc. ec. europa. eu/web/stecf/reports.
DNA-based identification methods offer fast, standardized and accurate tools for species assignment. The only prerequisites are that the focal species and its sisterspecies have been described taxonomically, that regions of their DNA have been ). This technique relies on region-specific DNA amplification using a method known as Polymerase Chain Reaction (PCR) and is typically followed by conventional Sanger DNA sequencing. Typically, for animals, the mitochondrial cytochrome oxidase subunit 1 gene (abbreviated COI) is sequenced and compared to a validated sequence reference database (Fig. 20. 2a). The amplified gene segment is approximately 650 base pairs long and was originally chosen based on the level of sequence variation, for most taxa showing only minute genetic variation within the species, while still enabling the discrimination of even closely related species  (Hebert et al.
PCR assay The specific combination of primers used in PCR. For qPCR and ddPCR the assay include primers and reporter molecule. DNA barcoding DNA barcoding relies on PCR using universal primers that amplifies a specific region, normally the mitochondrial COI gene, across taxa. Following amplification, the region can be purified, sequenced and finally compared to a database in order to establish species identity. (continued) TextBox 20. 1 (continued) Sanger sequencing Sanger sequencing is a technique that allows for DNA sequencing of one sequence at the time of up to around 1000 base pairs. Next generation sequencing Next generation sequencing or NGS refers to DNA sequencing, which allows individual sequencing of million of sequences in parallel. Many platforms only generate short sequences up to few hundred base pairs.
Meta-barcoding Meta-barcoding is a method that combines DNA based identification and Next generation Sequencing DNA sequencing. It relies on the use of universal primers, which allows amplification of a specific target region across species groups (e. g. fish) in environmental or other mixed species samples, which subsequently can be sequenced. Output sequences can following be matched to a reference database in order to analyse the number of species present in the sample. Moreover, a measure of relative species composition can be made by counting the number of sequences from each observed species. However, in some cases, the primers may work better for some species than other, which will bias such estimates. qPCR Real-time PCR, also called quantitative PCR or qPCR, is a method that allows for analysis of a PCR reaction in real-time, which enables enumeration of DNA copies.
Following PCR each droplet is analysed for positive amplification and number of initial DNA targets. Compared to qPCR, ddPCR provide an absolute count of target DNA copies per sample without the need for running a standard curve and enables more accurate measurements of small fold changes in target DNA amongst samples. When comparing a sequence from a specimen with unknown taxonomic identity to the database, it is important to make sure that the obtained sequence is of high quality and that the database includes all closely related species. If this is the case, the identity of the specimen can be inferred with very high precision, except in cases where diagnostic variants do not exist. A lack of diagnostic variants can be found e. g. between extremely closely related species and in groups of organisms with low levels of variation in the chosen barcoding region or due to introgression events where the DNA is shared between species because of hybridization.
When no close matches of the DNA sequence can be found in the database, the sample likely belongs to an unknown species or one that is not included in the database  (Ovenden et al. 2015). Genetics can also be used to assign individuals to populations within species  (Nielsen 2016). In these cases, several nuclear genetic markers are normally used in a probabilistic framework, combining the statistical power of individual genetic markers into an overall likelihood of origin. This is necessary, as populations are rarely fixed for specific (diagnostic) gene variants of either mitochondrial or nuclear genes.
Current Limitations The accuracy of DNA barcoding is generally high with most species showing distinctive COI sequences  (Hebert et al. 2003). For example, among 207 Australian fish species, the identification success rate was 93-98% across groups In DNA barcoding, the barcoding gene (e. g. COI) is amplified by PCR and the product subsequently sequenced and finally compared to a database for species-match. In qPCR species-specific assays are used. Such assays are initially tested for possible cross-amplification to other species in order to control for false-positive detections. Hence, a positive amplification signal denotes a positive identification for the tested species  (Ward et al. 2005) , and a 90% accuracy has been documented for North American freshwater fish  (April et al. 2011). Nonetheless, the overall precision of the methods critically depends on the reference database used  (Ovenden et al.
2015). To assure transparency, sequences should optimally be matched against DNA sequence data from voucher specimens or other curated museum specimens to ensure the reliability of the data  (Ratnasingham and Hebert 2007). Currently several public DNA databases exist. GenBank (https://www. ncbi. nlm. nih. gov/ genbank/) and the Barcode of Life (http://www. boldsystems. org/index. php/data bases) are probably the best known and contain sequences from thousands of species. While the Barcode of Life database consists of several recognized DNA barcoding genes with high quality assurance assessment, Genbank acts as a general repository for genetic data and has therefore a lower quality standard. Due to a general increase in the use of genetics for species identification and taxonomic purposes, as well as the establishment of databases for commercial species, the databases already contain sequences from many species and are constantly expanding.
For example, the Barcode of Life database (on the 16th of June 2018) contained 18,843 species of ray-finned fishes. However, the databases remain incomplete, especially for some cryptic, rare and non-commercial species and for geographical regions with high species diversity. Another limiting factor is the labour expenditure needed for sequencing and analysing of the data. However, other genetic methods can be used for performing species-level assignment that do not involve direct visualization of the genetic code, thereby potentially allowing faster and easier species identification. One such technique is real-time PCR, also known as quantitative PCR (qPCR)  (Fig. 20. 2b). Quantitative PCR relies on species-specific detection and utilizes a chemistry, which emit light when in contact with DNA from the target organism.
The technique allows for simultaneous analysis of one or multiple samples in parallel thereby permitting a fast, sensitive and a potentially cost-efficient alternative to conventional DNA barcoding for routine presence/absence applications. While the risk of false-positives in DNA barcoding both depends on the accuracy of the reference databases and the target region's ability to discriminate between species, false-positives in qPCR analysis depend on potential cross-amplification to non-target species (the method provides a signal of presence of the species, while it is not there). Hence, for qPCR analysis thorough testing for crossamplification is needed before an assay can be validated. This can be particularly laborious and expensive for marine species due to the often-large number of related and co-occurring species.
fish. Following this, DNA is sequenced using a Next-generation (NGS) platform generating millions of sequences that subsequently can be sorted, quantified and compared to a data base alternative. In meta-barcoding, a gene, like COI, is sequenced across one taxon (e. g. fish) using a high-throughput next generation sequencing (NGS) platform. This allows for the sequencing of millions of individual gene sequences, which can be matched to a reference database and counted, and hence combines DNA barcoding with possible quantification  (Fig. 20. 3c). This method has been used to assess species identity and composition for different kinds of mixed species samples. Meta-barcoding has been used for assessing animal diet by analysing the gut content (e. g. ,  Murray et al. 2011 ) and has been one of the favoured tools for analysing environmental DNA samples (see next section)  (Hansen et al.
Acknowledgements Acknowledgments We would like to thank two anonymous reviewers and the editorial team for their constructive comments and suggestions. Acknowledgements Part of this work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680. This support is gratefully acknowledged. Acknowledgments We are grateful to the contributions by and discussions with members of the ICES expert working group on methods for estimating discard survival (WGMEDS). Acknowledgments This work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680. This support is gratefully acknowledged. Acknowledgment The authors acknowledge the financial support from the  European COST Action "Ocean Governance for Sustainabilitychallenges , options and the role of science" and by the  ICES Science Fund  Project " Social Transformations of Marine Social-Ecological Systems ", and  MINOUW  (Grant Agreement  634495 ).
C. Pita acknowledges FCT/MEC national funds and FEDER co-funding, within the  PT2020 partnership Agreement and Compete 2020 , for the financial support to CESAM (Grant no  UID/AMB/50017/2013 ). C. M. Teixeira  had the support of the  Fundação para a Ciência e a Tecnologia (FCT)  ( Pest-OE/MAR/UI0199/2011 ); and  C. M. Teixeira  and  C. Pita  were supported by the  Research Project  " LESSisMORE -LESS discards and LESS fishing effort for BETTER efficiency on the small-scale fisheries" (Ref. " LISBOA-01-0145-FEDER-028179 "), support by the  FEDER Funds  through the  COMPETE 2020 , by the  PIDDAC through FCT/MCTES. TM thanks the support from the  European Union 's  Horizon 2020 research and innovation project DiscardLess  (Grant Agreement No  633680 ), the  Fundação para a Ciência e Tecnologia (FCT)  strategic project  UID/MAR/04292/2013  granted to MARE.
, et al. (2007) A pesca de arrasto-de-praianailha de  São Vincente ,  São Paulo ,  Brasil. Série Relatórios  Técnicos N 29. Faltas, S. N. (1997). Analysis of beach seine catch from  Abu Qir Bay (Egypt). Bulletin of the  National Institute of Oceanography and Fisheries ,  23 ,  69-82. Fauconnet, L. , Pham, C. ,  Canha , A. , Afonso, P. , Vandeperre, F. , Machete, M. , et al. (2016). Estimating total fisheries discards in an oceanic archipelago of the  NE Atlantic. 7th World Fisheries Congress, Busan, South Korea, 23-27th May 2016. Acknowledgements This work received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680  and from the  LIFE+Environmental Program  of the  European Union  under grant agreement iSEAS project,  Ref. LIFE13 ENV/ES/000131. This support is gratefully acknowledged.
Acknowledgments Part of this work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680. This support is gratefully acknowledged. TM and AS acknowledge support by the  Fundação para a Ciência e Tecnologia (FCT)  strategic project  UID/MAR/04292/2013  granted to MARE. TM is supported by the  Program Investigador FCT  ( IF/01194/2013/CP1199/CT0002 ). Acknowledgments Disclaimer: The views and opinions expressed in this chapter are those of the author and do not reflect the official position of any employer. Acknowledgements The ideas brought forward in this chapter are largely based on the BehavFish workshop funded by the  ICES Science Fund  and the  Fisheries Society of the British Isles  (Kraak et al. 2015), and we therefore thank all participants of this workshop:  Chris Anderson ,  Dorothy Dankel ,  Matteo Galizzi ,  Mark Gibson ,  Diogo Gonçalves ,  Katell Hamon ,  Ciaran Kelly ,  Jean-Jacques Maguire ,  Jeroen Nieboer ,  Ingrid van Putten ,  Andrew Reeson ,  Dave Reid ,  Andries Richter  and  Alessandro Tavoni.
Acknowledgments This work was funded by the  Project iSEAS, Ref. LIFE13 ENV/ES/000131 , " Knowledge-Based Innovative Solutions to Enhance Adding-Value Mechanisms towards Healthy and Sustainable EU Fisheries ", cofounded under the  LIFE+Environmental Program  of the  European Union. We are deeply grateful to all the partnerships on this project and particularly to the observers who gather the fishing data within the  IEO discarding observer program. The collection of fishery data has been co-funded by the  EU  through the  European Maritime and Fisheries Fund (EMFF)  within the  National Program  of collection, management and use of data in the fisheries sector and support for scientific advice regarding the  Common Fisheries Policy. Acknowledgments This work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680.
This support is gratefully acknowledged. Acknowledgements Part of this work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680. This support is gratefully acknowledged. References Anders, N. , Fernö, A. , Humborstad, O. -B. , Løkkeborg, S. , Rieucau, G. ,  Utne-Palm , A. C. (2017). Size-dependent social attraction and repulsion explains the decision of Atlantic cod Gadus morhua to enter baited pots. Journal of Fish Biology, 91(6),  1569-1581. Arimoto, T. , Glass, C. W. , Zhang, X. (2010). Fish vision and its role in fish capture. In P. He  (Ed. ), Behavior of marine fishes: Capture processes and conservation challenges. Oxford:  Wiley-Blackwell. Armstrong ,  M. J. , Payne, A. I. L. , Deas, B. , Catchpole, T. L. (2013). Involving stakeholders in the commissioning and implementation of fishery science projects: Experiences from the  UK Fisheries Science Partnership.
ICES Journal of  Marine Science ,  74 ,  1458-1468. Bryan, D. R. , Bosley, K. L. , Hicks, A. C. , Haltuch, M. A. , Wakefield, W. W. (2014). Quantitative video analysis of flatfish herding behavior and impact on effective area swept of a survey trawl. Fisheries Research,  154 ,  120-126. Acknowledgements The content of this chapter is largely based on work carried out in the  DISCARDLESS  project and the  Life iSEAS project. The  DISCARDLESS  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement DiscardLess No 633680. The Life iSEAS project has been co-funded under the LIFE +Environment programme of the  European Union  ( LIFE13 ENV/ES/000131 ). This support is gratefully acknowledged. Acknowledgments DiscardLess project has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under grant agreement no.
633680. Life iSEAS has been co-funded under the  LIFE+Environment Program  of the  European Union  ( LIFE13 ENV/ES/000131 ). Acknowledgments This chapter is largely based on work carried out in the  DiscardLess  project and the  Life iSEAS project. The  DiscardLess  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement DiscardLess No 633680. The Life iSEAS project has been co-funded under the  LIFE+Environment Program  of the  European Union  ( LIFE13 ENV/ES/000131 ). This support is gratefully acknowledged. Authors also want to thank  Ms. Esther Abad ,  Mr. Xesús Morales ,  Ms. Marta Quinzán  and  Mr. Julio Valeiras  for their contribution to the development of the iObserver. Acknowledgments This work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680.
Our results are robust to alternative modes of phylogenetic estimation and phylogenomic dataset construction. We show that two popular phylogenomic matrix construction pipelines yield profoundly different datasets, both in the identities and in the functional classes of the loci they include, but resolve the same topology. We then leverage our phylogenetic resolution of Cnidaria to understand the character histories of several critical organismal traits. Ancestral state reconstruction analyses based on our phylogeny establish several notable organismal transitions in the evolutionary history of Cnidaria and depict the ancestral cnidarian as a solitary, non-symbiotic polyp that lacked a medusa stage. In addition, Bayes factor tests strongly suggest that symbiosis has evolved multiple times independently across the cnidarian radiation.
Nevertheless, the topologies from these two independent phylogenomic studies were otherwise largely congruent, providing some prospect that large datasets and increased taxon sampling may settle long-standing questions about the evolutionary history of Cnidaria. Staurozoa (Fig. 1 ) is one of the most poorly understood cnidarian clades. These animals have unique life history attributes, including non-ciliated, creeping, larval planulae that develop into polyps, which later undergo metamorphosis (unique from strobilation or budding) into adult stauromedusae  [12, 13, 14]. In addition, the adult staurozoan body plan includes features common to both the polyp and medusa stages of other cnidarians, thus adding to their phylogenetic significance  [15, 16, 17, 18]. The phylogenetic position of staurozoans relative to other medusozoan lineages remains controversial and recent hypotheses  [7, 19, 10]  present different implications for the evolution of medusozoan body plans (Fig.
Prior to the publication of molecular phylogenetic analyses, this latter relationship had been predicted based on morphological traits (e. g. , mesentery arrangement). The recent publication of a study focused on toxin-related transcripts  [36]  allowed us to incorporate zoantharian transcriptomic data here, for the first time, in a phylogenomic study. Phylogenomic analyses of genome-scale datasets (i. e. , whole-genome-derived gene models or RNA-seq-derived transcripts) have recently been exploited to resolve a host of longstanding phylogenetic issues  [37, 38, 39]. A critical step common to these analyses is the identification of one-toone orthologs from genome-scale datasets for each taxon, which are then used as data partitions in large supermatrices. Various methods are available for the identification of such data partitions and methodological differences among them have been shown to impact phylogenetic inference  [40, 41, 42 ].
Yet, phylogenomic analyses frequently rely on a single method for data matrix construction and do not examine the impact of alternative approaches on phylogenetic reconstruction (e. g. [37, 43, 44] ). Here, we apply new phylogenomic data for Staurozoa, Ceriantharia and several other previously undersampled cnidarian clades to the construction and analyses of independent phylogenomic datasets for Cnidaria using two popular approaches: 1) Agalma  [45]  and 2) a custom pipeline based on Orthofinder  [46]  and PhyloTreePruner  [47]. We show that both procedures produce datasets with surprisingly little overlap in terms of data composition, but resolve the same topology under robust phylogenetic methods. We then leverage our highly resolved cnidarian phylogeny to address questions surrounding the origins and evolutionary histories of several key organismal innovations in Cnidaria.
Potential contamination identified in cnidarian transcriptome data We applied a strict filter to all datasets to remove potential contaminants. In total, we removed less than 5% of sequences from most datasets except for the following taxa: Alatina alata (7. 9%), Anemonia viridis (6%), Anthopleura elegantissima (7%), Gorgonia ventalina (6. 8%), Hydractinia polyclina (6. 8%), Platygyra carnosus (6. 7%), and Seriatopora hystrix (6. 9%). In addition, many sequences from the myxozoans Kudoa iwatai (39. 8%), Myxobolus cerebralis (25. 6%), M. pendula (40. 5%), and Thelohanellus kitauei (21. 4%), as well as the filiferan hydrozoan Podocoryna carnea (26. 7%) had best matches to bilaterian sequences and were subsequently removed (Additional file 1). Following the removal of these putative contaminants, preliminary phylogenetic analyses showed that the myxozoan sequences procured by the Agalma pipeline still retained many contaminants, as these species were positioned within the vertebrates (Additional file 3).
Further analysis of myxozoan-bearing partitions from both datasets showed that the Agalma pipeline was prone to include partitions with a single myxozoan species present and that these partitions were more likely to be comprised of contaminants, a situation not encountered in the OF-PTP dataset (Fig. 3 ). Further, when Agalma partitions with greater than three myxozoan species were selected for phylogenetic analysis (47 partitions), the myxozoan species were resolved in their expected position within the Endocnidozoa, and the remaining topology was largely consistent with all other results, see below (Additional file 4).
Phylogenomic matrix generation pipelines produced contrasting data matrices We built two preliminary, independent phylogenomic data matrices with Agalma (AG_62tx) and OF-PTP (OF-PTP_62tx). After selecting orthologous partition alignments that exceeded 50% taxon occupancy, the Agalma pipeline incorporated roughly three times as many genes and four times as much data (962 single-gene partitions, 233,568 data positions) as OF-PTP (372 single-gene partitions, 53,389 data positions) (Fig. 4 ). Furthermore, the average partition length was longer for the AG_62tx dataset than OF-PTP_62tx (Fig. 4a ). Comparisons of the across-partition N. vectensis complements of AG_62tx and OF-PTP_62tx revealed that only 53 loci are shared between the two datasets (Fig. 4c ). We also detected substantial differences in taxon occupancy between the datasets produced by Agalma and OF-PTP.
AG_62tx had, on average, significantly lower taxon occupancy (481/962 = 49%) compared to OF-PTP_62tx (232/372 = 62%; Fisher's Exact Test P = 0. 028) or OF-PTP75tx (225/357 = 63%; Fisher's Exact Test P = 0. 023). In addition, the AG_62tx dataset had exceedingly low coverage for several key groups (see below and   3  Relationship between sparse data representation and the retention of contaminated sequences in phylogenomic data matrices as illustrated by myxozoan species. We conducted BLAST similarity searches against a metazoan genome database for all myxozoan sequences present in both the AG_62tx and OF-PTP_62tx matrices. In addition, we noted how many myxozoan species were present in each partition. Myxozoans are internal parasites of teleost fishes and we noted significant contamination in transcriptome data from these host species.