dataesr/scientific-paragraphs-categorization huggingface dataset:
Systematic literature review Systematic searches were undertaken on Web of Science, Science Direct, and Google Scholar, with the search term '(sea OR marine) AND turtle* AND [country]'. The '[country]' field was replaced with Somalia, Kenya, Tanzania, Zanzibar, Mozambique, and South Africa, respectively. These searches were augmented with an exhaustive review of the contents of the Indian Ocean Turtle Newsletter and African Sea Turtle Newsletter, since these publications are not included in the online databases. This initial list of literature then yielded further sources through snowball and citation searches. Due to limited available data in peerreviewed literature, we decided to include grey literature sources, such as reports from government bodies or non-governmental organizations (NGOs), workshop reports, and theses.
OVERVIEW OF AVAILABLE DATA SOURCES Initial systematic literature searches yielded a total of 116 sources (95 peer-reviewed, 21 other sources). These were augmented by 46 sources from the references of the first publications (26 peer-reviewed, 20 other sources). Snowball and citation searches yielded a further 58 sources (11 peer-reviewed, 47 other sources), summing to 220 sources. The experts suggested 28 sources following sharing of the original draft of the manuscript (3 peer-reviewed, 25 other sources). Additionally, a database compiled for a previous unpublished literature review was provided by the WIO Marine Turtle Task Force (WIO-MTTF, established to promote implementation of the regional Conservation and Management Plan), which added a further 189 sources  (35 peer-reviewed, 154 other sources).
However, grey literature remains an important source of information on marine turtles along the continental coast. This presents a challenge since these grey literature sources, often in the form of technical reports, can be difficult to find: 154 grey literature sources that had not been found in the earlier searches were retrieved from the database provided by the WIO-MTTF. The exhaustive database of literature collated herein will therefore be invaluable as a reference library for future research efforts and has been shared here: https://doi. org/10. 6084/m9. figshare. 16904875. v1. Literature about green, loggerhead, and leatherback turtles was most common, followed by hawksbill turtles; olive ridley turtles were referred to least (Fig. 2B ). Literature about turtles in the pelagic environment was underrepresented (14% of articles) compared to beach and neritic habitats (48 and 38%, respectively; Fig.
South Africa. No hawksbill nesting events have been recorded in South Africa. Regional context. The majority of hawksbill nesting in the WIO is reported in the Seychelles and the Chagos Archipelago  (Mortimer et al. 2020; Fig. 3B). Further nesting areas are found on Madagascar, especially in the north-west  (Metcalf et al. 2007 , Humber et al. 2017) , Juan de Nova  (Lauret-Stepler et al. 2010 , Jean et al. pers. comm. cited in SWOT 2018) , and Mayotte  (Bourjea et al. 2007a , Quillard & Ballorain pers. comm. cited in SWOT 2018). Effectively protected rookeries have shown signs of recovery, and an estimated 12 000-16 000 clutches are laid in the WIO region per year  (Allen et al. 2010 , Mortimer et al. 2020). Data presented here, indicating minimal hawksbill nesting activity along the continental coast (< 30 clutches yr -1 ), match those in previous assessments  (Mortimer et al.
Green turtle Studies on green turtles in the WIO, mainly on post-nesting females, have revealed complex migratory patterns across the region. For brevity and clarity, only those routes linked to the continental coast are discussed here, but more green turtle migratory data exists  (Dalleau 2013 , Dalleau et al. 2016). Migratory data collected using flipper tags (n = 60) and satellite tags (n = 67) have demonstrated the importance of the continental coastal waters as migratory and foraging habitat for this species. In a frequently observed pattern, females nesting at the oceanic island rookeries utilize shallow coastal foraging habitat along the continent  (Bourjea et al. 2013a ,b, Dalleau 2013 , Dubernet et al. 2013 , Hays et al. 2014 , 2018 , Shimada et al. 2020 ; Fig. S1 ). Seagrass, an important dietary component for this species, is widespread along the continental coast  (Gullström et al.
Hawksbill turtle Data on the migratory behavior of hawksbill turtles linked to the continental coast are lacking, with only a limited number of flipper tag recoveries encountered in the reviewed literature (n = 4). One juvenile, tagged in the Seychelles and captured 11 mo later by a fisher on the Kenyan coast, migrated a distance of >1000 km  (von Brandis et al. 2017 ; Fig. 3B ). von Brandis et al. (  2017 ) also described a migration of an immature hawksbill from the south-western Seychelles to northern Mozambique. A juvenile tagged in the Cocos (Keeling) Islands was found dead in a fishing net in southern Tanzania, having traveled > 6000 km  (Whiting et al. 2010 , Vargas et al. 2016). One record of inshore coastal migration was found, where a juvenile hawksbill was tagged in Watamu, Kenya, and recaptured approximately 150 km south, near Funzi (Zanre 2005).
Further migration data were provided by the experts (n = 2); a flipper tag that was applied to a post-nesting female in the granitic Seychelles was recovered at Lindi in southern Tanzania (J. Mortimer unpubl. data), and an individual tagged in South Africa in 2013 was tracked to the north-east coast of Madagascar and remained in the same area for 1 yr, when the tag stopped working (R. Nel unpubl. data. ). Although relatively little information exists about hawksbill foraging habitat along the continental coast, several areas have been identified based on direct observations at Watamu (Zanre 2005), Vamizi Island (Anastácio et al. 2017), and the Primeiras and Segundas Archipelago  (Costa et al. 2007 ; Fig. S1 ). Tracking data from 3 immature individuals tagged at the iSimangaliso Wetland Park revealed extended residency in local coastal waters  (Rambaran 2020) , and juveniles are regularly sighted by divers (R.
2017). Data from Somalia are sparse but suggest that turtles are regularly caught and sold openly (Frazier   (Frazier 1980 , 1995b , Nel et al. 2013). However, in recent years illegal take was again identified as a problem there (IOSEA 2014). Satellite tracking data also indicated the possibility of illegal take when tags stopped transmitting prematurely near or on land, suggesting that the turtle had been taken  (Hays et al. 2003 , Dubernet et al. 2013 , Pereira et al. 2014b ). The extent of targeted illegal take along the continental coast is unknown and difficult to ascertain because of reticence by fishers and other stakeholders to divulge information regarding illegal activities  (Pilcher & Williams 2018 ). ). Specific responses were grouped into topics; the 6 most mentioned topics are presented.
Industrial fisheries bycatch Industrial fishing along the continental coast includes demersal fisheries, such as shallow-and deep-water trawl, and pelagic fisheries, such as longline and purse seine (van der Elst & Everett 2015). Apart from the domestic fleets, foreign vessels are also licensed to operate in the EEZs of Kenya, Tanzania, Mozambique, and South Africa, predominantly in the pelagic fisheries  (FAO 2007a ,b, 2010 , 2015 , Bourjea et al. 2014 , Riskas et al. 2018). As with the SSF sector, adequate data and resources are generally lacking in the WIO to allow effective management of the region's industrial fisheries or to make accurate estimates of turtle bycatch, and it has been highlighted as a significant threat to turtles along the continental coast (Nel et al. 2012, van der Elst & Everett 2015; Fig.
2009). Although loggerheads make up the majority of the bycaught turtles, leatherbacks are also encountered, and it is thought that the impact from the longline fishery is delaying their recovery at the Maputaland rookery  (Petersen et al. 2009 , Nel et al. 2013 , Harris et al. 2018). A preliminary study with observers onboard Mozambican longline vessels recorded bycatch of low numbers of leatherback and green turtles, which were released alive (Mutombene 2015). However, foreign longline vessels operating in Mozambican waters have been implicated in a practice whereby bycaught turtles are decapitated when the lines are recovered, and a spate of stranded headless turtles was reported  (Louro et al. 2006). Robust bycatch data from longlining, which is carried out along most of the continental coast, are lacking and the extent of this threat is not known.
Coastal mining Coastal mining is carried out in all 5 countries included in this review but robust current quantitative data is lacking (UNEP-Nairobi Convention & WIOMSA 2015). Formal and informal sand mining is carried out for construction material and minerals such as titanium, taking material from dunes, beaches, and offshore, which has resulted in signi -ficant erosion in several locations  (UNEP-Nairobi Convention & WIOMSA 2015 , Obura et al. 2017). Although the mining of live coral ceased around Mafia Island and Juani Island with the establishment of the Mafia Island Marine Park (L. West pers. obs. ), impacts of this activity, such as increased coastal erosion and reduced ecosystem productivity, will remain noticeable for a long time  (Dulvy et al. 1995).
2019). Spatial protection measures need to be expanded through a comprehensive regional network of MPAs that includes Locally Managed Marine Areas. The IOSEA's Sites of Importance for Marine Turtles in the Indian Ocean-South-East Asia Region (Site Network) program is an effective pathway to achieving this and collecting data to nominate appropriate sites is essential  (Harris et al. 2012 , IOSEA 2020). Sites that could achieve multi-species conservation targets, including species other than turtles, should be prioritized. Currently, 2 such sites exist along the continental coast, namely the Rufiji-Mafia Seascape in Tanzania and the iSimangaliso Wetland Park in South Africa, which includes the Maputaland rookeries (IOSEA 2020). Several transboundary MPAs have been established or proposed that encompass nesting and foraging areas, which should present opportunities to align legislation between respective countries  (Guerreiro et al.
Genome features Two genomes with similar coding capacities A total of 10,631 putative genes were predicted from the raw assembly (17,930 alternative splicings), which were split into two sets of similar size: 5270 genes in genome A (8895 alternative splicings) and 5361 genes (9035 alternative splicings) in genome B (Table  1 , Fig. S2 ). The completeness of both A and B genomes was assessed by using BUSCO software  [29]  on the Apicomplexa geneset (n = 446). Genomes A and B respectively showed completeness scores of 70% (n = 312) and 67. 7% (n = 302) (Fig. S4 ). The number of A and B orthologues was investigated. The predicted proteins of P. cf. gigantea A and B were split into 5656 orthogroups including 4443 groups (88%) which had at least one orthologous gene for both A and B. This percentage of common orthogroups between genomes A and B is higher than that observed between Plasmodium falciparum and Plasmodium berghei (70%), thought to have diverged around 33 Mya ago (TimeTree  [30] ), but similar to that observed between P.
Expanded superfamily of crustacean gregarines To assess the position of P. cf. gigantea A and B within Apicomplexa, we constructed a genome-wide phylogeny based on 312 concatenated proteins from the datasets published by  Salomaki et al. , 2021 [13]  and all recently published transcriptomic data from gregarines  [10, 11, 13]  (Fig. 3 ). This phylogeny grouped P. cf. gigantea A and B into one clade, placed as a sister group of other crustacean gregarines (Cephaloidophora communis, Heliospora caprellae), although having shorter branch lengths. In agreement to  Salomaki et al. (2021)   [13]  Cryptosporidium species remain at the base of A + G (Apicomplexa + gregarines), using a LG + C60 + G + F model in maximum likelihood phylogenomic analyses. However, the bayesian analysis using classical partitioned model LG + G + F is in favor of a A + C topology (Apicomplexans + Cryptosporidium) (average standard deviation of split frequencies = 0.
020977). More sampling of Cryptosporidium relatives is required to address the apicomplexan topology issue. The sequences of 18S small subunit ribosomal DNA, for which the largest taxonomic sampling for gregarines is available in databases, was also used to position P. cf. gigantea within the crustacean gregarines. Using a combination of amplifications with specific primers (initially based on  Simdyanov et al. (2015)   [39]  and  Schrével et al. (2016)   [40]  then partly redesigned (Fig. S6 , Table  S7 )) and in silico clustering, we were able to fully reconstruct complete ribosomal loci covering 18S-ITS1-5. 8S-ITS2-28S (5977 bp) for both A and B genomes. Thirty polymorphic positions were found between A and B, only one within the 18S sequence, and 29 within the 28S sequence (Fig. S6 ). Two phylogenetic studies were performed, one excluding environmental sequences (Fig.
S2. B ). For all contigs, the putative protein coding genes were then predicted using Augustus  [117]  (version 3. 3) and the Apicomplexa gene model for T. gondii. All the predicted proteins were thus compared with the NCBI non-redundant protein database using BLAST  [118]. The analysis of the taxonomic groups corresponding to the best hits, enabled us to identify five clusters as putative bacterial contaminants whereas the sixth cluster which included 1745 contigs (18. 0 Mb), was identified as organisms closely related to Apicomplexa, referred to as the "apicomplexa" cluster (Fig. S2. B).
The linear discriminant method (training and classification) was iterated 3 times until convergence. A similar analysis was carried out with 1-kb non-overlapping windows (instead of full-length contigs) to identify putative hybrid contigs. Contigs were thus classified to different genomes depending on the windows, then divided into sub-contigs which were re-assigned to their respective genomes. A detailed protocol with R scripts is available on github (see data and code availability). The nucleotidic divergence between genome A and genome B was estimated from the alignment of contigs built with Mummer3. 0  [122]. All alignments of the syntenic regions were parsed to compute the divergence using a home-made script. Assembly metrics were assessed by using QUAST  [123]  (version 5. 0).
Prediction and annotation All de novo assembled transcripts were aligned against the "apicomplexa" cluster contigs with GMAP  [124]  within the PASA program  [125]. Then, two ab initio gene prediction tools, SNAP  [126]  (version 2017-  [11] [12] [13] [14] [15]  and Augustus were trained using a subset of the PASA transcriptome assemblies. A specific gene model was trained with Augustus, including meta-parameter optimization and prediction of introns (allowing small intron length > 10 bp) using our "apicomplexan" cluster repeat-masked genome assembly as reference (Repeat-Masker  [127] , version 4. 0. 8). Gene predictions were then performed allowing for the prediction of alternative transcripts and noncanonical intron splice sites. An alternative model was also trained with SNAP (default protocol) and used for gene predictions.
The Augustus and SNAP outputs showed that some gene predictions were slightly different, so the predictions were parsed with a home-made script to keep as many alternative genes and transcripts as possible for each prediction made. The completeness of the gene prediction was assessed using BUSCO (version 4. 0. 6). The predicted proteins were automatically annotated by using i) the best hit of a BLASTP search against VEupathdb (version 2019-01-20), ii) the results of KoFam-Scam against the KEGG pathway database  [128]  (version 2019-05-11) and iii) the signature domains obtained with Interproscan  [129]  (version 5. 39-77. 0). The ortholog groups were identified with orthoMCL  [130]  (default parameters, version 2. 0. 9) applied to the proteome of a selection of representative organisms available on VEuPathDB (Table  S1 ).
The divergence time of genome A and genome B was estimated from the divergence time of P. falciparum and P. reichenowi as estimated in TimeTree. Then the coding sequences of the orthologous groups/quartets including a single gene each for genome A, genome B, P. falciparum and P. reichenowi were aligned using MacSE  [131]. For each alignment, the number of synonymous substitutions per site (dS) between genomes A/B and between P. falciparum/reichenowi were computed with the maximum likelihood method of Yang and Nielsen (2000)  [132]  implemented in PAML4  [133]. The Infernal software  [134]  (version 1. 3. 3) and the Rfam database  [135]  (version 14. 2) were used together to search for transfer RNAs, spliceosomal RNAs and ribosomal RNAs. The snoReport software  [136]  (version 2) was used to search C/D and H/ACA small nucleolar RNAs.
Host contaminants All "apicomplexa" cluster contigs were screened against the short reads available from the Homarus americanus (PRJNA486050) genome sequencing project, to identify closely-related host contaminants. This dataset was assumed to be free of sequences from apicomplexans, since it was obtained from DNA extracted from nonintestinal tissues (tail, leg or pleiopod appendices). Mapping was carried out with Bowtie2 and the coverages were calculated by using Samtools. The contigs thus identified that were covered over more than 60% of their length by Homarus short reads, were considered as host contaminants and were removed.
Prokaryotic and fungal contaminants In parallel, predicted genes in the "apicomplexa" cluster contigs were deeply analyzed for the presence of bacterial and fungal sequences. For each scaffold containing at least one predicted protein, a BLASTP against the NCBI NR database was launched. If the resulting hit had an e-value lower than 1e-30 and more than 30% of the length of the contig was covered by prokaryote/ fungi hits, an additional BLASTN against NCBI NR/NT was performed. For the remaining scaffolds without predicted proteins, a direct BLASTN vs NR/NT search was performed. At the end of this procedure, the contigs with prokaryotes/fungi hits covering more than 70% of the length were labeled as contaminants and were removed from the genome assembly.
Search for organellar genomes Organellar genomes were searched using the mitochondrial or apicoplastic genomes available in VEupathDB (version 2019-01-20) as well as with the contigs described in Janouškovec et al. (2019)  [10]  as reference sequences. Firstly, a similarity search using a TBLASTX and these sequences as query was applied on all assembled contigs (identified as P. gigantea or not). All hits with a bit score above 100 were considered as organellar candidates and were extracted (with 100 bp upstream and downstream). Secondly, these candidates were used in a reciprocal TBLASTX search against NCBI NR database to eliminate bacterial contamination. The regions exhibiting at least one hit against an eukaryotic sequence among the nine best hits were manually studied to check if the associated contigs could correspond to organellar genomes.
Experimental reconstruction of 18S/28S loci First, a partial SSU rDNA locus was amplified by using JS-470 gDNA (i. e. genome A only) as template and WL1 and EukP3 primers (Table  S7 ) in a conventional PCR reaction. The amplified bands were cloned and sequenced as previously described  [40]. The resulting partial SSU rDNA sequence was further extended in the 3′ direction still using JS-470 gDNA as template and novel primers designed or re-designed based on the molecular data published for Cephaloidophora cf. communis and Heliospora cf. longissima  [39]  (Fig. S6. A). The resulting sequence (> 4 kb) was then used as anchor to reconstruct a complete ribosomal locus with the program iSeGWalker  [137]. By clustering reads from JS-470 on this anchor, a 7322-bp theoretical sequence that corresponded to [partial 28S -18S -ITS1-5.
Phylogenomics of gregarines The phylogenomic tree was built from a super matrix of 312 orthologues from two datasets published by Salomaki et al. (2021)  [13]. These two datasets are composed by 246 and 299 orthologues respectively. For all orthologues, corresponding genes have been searched in the proteomes of P. cf. gigantea A and B by using BLASTP and candidates were aligned with known orthologues using mafft  [138]. Then, orthologous relationships were validated by visual inspection of all the single-protein phylogenetic tree using RaxML  [139]  with rapid bootstraps (-f a), -m PROTGAMMAAUTO. Orthologues for P. cf. gigantea A and/or B have been recovered for 201 and 256 orthologues in both initial datasets. Both datasets were grouped into a larger dataset composed by 312 non-redundant orthologues.
All orthologues were I) filtered with Prequal  [140]  to remove non-homologous residues, ii) aligned with mafft, iii) filtered with divvier  [141]  to remove alignment errors, iv) trimed with trimAl  [142]  and v) merged into the super matrix by using the script matrix_constructor. py available with PhyloFisher  [143]. The maximum likelihood tree was built with IQ-Tree2 under LG + C60 + G + F  [144]. The reliability of the phylogenetic tree was tested by the SH-aLRT and ultrafast bootstrap methods (repeated 1000 times). Bayesian phylogenetic tree was constructed with MrBayes  [145]  (version 3. 2. 3) using a LG + G + F model on a partitioned alignment: prset applyto = (all) aamodelpr = fixed(lg); prset applyto = (all) statefreqpr = fixed(empirical); lset applyto = (all) rates = gamma; unlink shape = (all) pinvar = (all) statefreq = (all); mcmc ngen = 500,000 samplefreq = 1000 printfreq = 10,000 nchains = 4 nruns = 2 savebrlens = yes; sump burnin = 25,000; sumt burnin = 25,000 contype = allcompat.
Environmental 18S phylogeny focused on crustacean gregarines The 189-sequence phylogeny was built from the 18S SSU rDNA sequences from genomes A and B aligned with 14 from crustacean gregarines, and 154 environmental sequences from several projects described in  Rueckert et al. (2011)   [41]  or gathered from NCBI Genbank. The sequences from the Gregarinoidae clade (n = 19) were used as the outgroup, as this clade has been placed as a sister group to the crustacean gregarine clade in recent literature  [10] [11] [12]. A total of 1135 sites were found to be conserved after selecting conserved blocks as defined by Gblocks with the following parameters: minimum number of sequences for a conserved position, 95; minimum number of sequences for a flanking position, 95; maximum number of contiguous non-conserved positions, 8; minimum length of a block, 3; allowed gap positions, all.
Expert annotation for glideosome proteins A reference apicomplexan glideosome protein dataset was written based on glideosome protein repertoires described in the literature mainly for T. gondii and P. falciparum  [26, 63, 67]. This reference dataset was used as a seed for parsing the orthogroups established for 25 reference proteomes (Table  S1 ) and the predicted proteomes of the two P. cf. gigantea genomes. These reference proteomes were selected by considering the most recent data and associated publications to have the most complete panorama of apicomplexan proteins and key functions/ structures documented to date. We also searched for potential orthologues within all recently published proteomes of gregarines using BLASTP (seed: reference proteins in T. gondii and P. falciparum). For each orthogroup containing at least one of the reference proteins, the list of proteins was extracted, and the protein sequences were recovered with their respective coding sequences for both P.