dataesr/scientific-paragraphs-categorization huggingface dataset:
Trichoderma spp. (Hypocreales, Sordariomycetes, Ascomycota) are widely known as representative fungal biocontrol agents of plant pathogens due to their diverse nutritional modes (e. g. , for being as saprotrophic, endophytic, and mycoparasitic)  [12] [13] [14]. Since the genome sequencing of T. reesei QM6a (GenBank accession no. AAIL00000000) was first presented  [15] , Trichoderma sequencing studies have actively increased to gain a better understanding of their biological and ecological roles for the more improved applications (e. g. , biofungicide, biofertilizer, and a producer of plant biomass hydrolyzing enzymes (CAZymes)); however, they were mainly focused on nuclear genomes  [16] [17] [18] [19] [20]. To date (as of January 2021), only five Trichoderma mitogenomes (T. asperellum B05 (GenBank accession no.
NC_037075), T. atroviride ATCC 26799 (GenBank accession no. MN125601), T. gamsii KUC1747 (GenBank accession no. KU687109), T. hamatum (GenBank accession no. MF287973), and T. reesei QM9414 (GenBank accession no. AF447590)) are available as a verified dataset in GenBank. In our previous report of the T. atroviride ATCC 26799 mitogenome, all of these five complete sequences were compared and reviewed for the first time  [11]. When considering the species complex of T. harzianum, initially it was suggested to be an aggregate species including several cryptic species  [21] , genomic studies of T. harzianum can provide more knowledge of their adaptive evolution and functional mechanisms in particular niches with both of systematic-and ecological perspectives  [19, 20]. In terms of the T. harzianum CBS 226.
95, an ex-neotype strain of the T. harzianum species complex and the most globally representative commercial fungal biocontrol agent, the nuclear genome of this fungus has already been sequenced (GenBank accession no. GCA_003025095)  [19] , but the mitogenome has not. Here, we present a complete de novo mitogenome of the fungus T. harzianum CBS 226. 95 with genomic characterization, comparative analyses, and the tracing of time-scaled evolutionary divergences.
De Novo Assembly and Annotation Only raw reads with a read-quality higher than 0. 85 were used for de novo mitogenome assembly using the hierarchical genome-assembly process (HGAP)  [25]  in the SMRT TM pipeline (Pacific Biosciences, USA). The de novo mitogenome assembly's accuracy was verified using the P-mapping module  [26] , and the circularity was confirmed using Gepard  [27]. The mitogenome was primarily annotated on webservers for Mfannot (Mfannot, http://megasun. bch. umontreal. ca/cgi-bin/mfannot/mfannotInterface. pl (accessed on 12 January 2021)  [28]  and MITOS (Mitos, http://mitos. bioinf. uni-leipzig. de/index. py (accessed on 12 January 2021))  [29] , under the Genetic Code 4 (the Mold, Protozoan, and Coelenterate Mitochondrial Code). Then, these annotations were modified by BLAST searching (default parameters) on the NCBI non-redundant (nr) database  [30]  and NCBI Open Reading Frame (ORF) Finder  [31].
All annotations relating to gene boundaries were also manually curated to avoid artificial frameshifts in the ORFs. The tRNAs were annotated using the webserver for RNAweasel (RNAweasel, http://megasun. bch. umontreal. ca/cgi-bin/RNAweasel/RNAweaselInterface. pl (accessed on 12 January 2021))  [32]  and tRNAScan-SE (v. 2. 0)  [33] , under the Genetic Code 4. A circular map of the mitogenome was illustrated using Circos  [34]. Finally, the complete de novo mitogenome sequence of T. harzianum CBS 226. 95 was deposited in GenBank under the accession number MN564945.
Genomic Analysis The base composition and codon usage were analyzed using MEGA (v. 7. 0)  [35]  and the EMBOSS package  [36]. The secondary structure of tRNAs was predicted by tRNAScan-SE (v. 2. 0)  [33] , and the asymmetric bias of nucleotide composition was calculated using the following formulas [37]: AT-skew = (A -T)/(A + T) and GC-skew = (G -C)/(G + C). The interspersed-/tandem repeats were analyzed using BLAST  [38]  via the self-comparison of mitogenome itself (parameter: E-values < 10 -10 ) and Tandem Repeats Finder (v. 4. 0; default parameters)  [39] , respectively. All intron loci and intron types were investigated on the webserver for RNAweasel (RNAweasel, http://megasun. bch. umontreal. ca/cgibin/RNAweasel/RNAweaselInterface. pl (accessed on 12 January 2021))  [32]  under the Genetic Code 4, and intronic ORFs were identified by BLAST searching on the NCBI-nr database  [30]  and NCBI-ORF Finder  [31].
Phylogenetic Analyses A total of 47 Sordariomycetes mitogenomes were downloaded from the GenBank database on NCBI (GenBank, https://www. ncbi. nlm. nih. gov/genbank/ (accessed on 12 January 2021))  [42]. Full nucleotide sequences of the 13 core PCGs (atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, and nad6; in this study, the atp9 gene was excluded because it has not been reported in the GenBank dataset of the T. gamsii KUC1747 mitogenome (GenBank accession no. KU687109)) were individually aligned using MAFFT (v. 7. 4; parameter: mafft -auto)  [43] , and these multiple alignments were concatenated in SequenceMatrix (v. 1. 8) [44]. The best-fit evolutionary model was determined using jModelTest (v. 2. 1) under the Information Criterion of Akaike (AIC) and Bayesian (BIC)  [45] , and the GTR + Gamma + I substitution model was selected as the best-fit model from all criteria.
Time-Scaled Bayesian Phylogeny Using the target mitogenomes of Hypocreales species, only exon sequences of the 13 core PCGs (atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, and nad6; the atp9 gene was excluded because it has not been reported in the GenBank dataset of the T. gamsii KUC1747 mitogenome (GenBank accession no. KU687109)) were individually aligned using MAFFT (v. 7. 4; parameter: mafft -auto)  [43]  and concatenated in SequenceMatrix (v. 1. 8) [44]. Under a best-fit of the GTR + Gamma substitution model determined by jModelTest (v. 2. 1)  [45] , MCMC analysis was counted for 4 × 10 7 generations (sampling for every 1000 gen. ) with a lognormal relaxed clock in BEAST2 (v. 2. 5)  [51]. Calibration nodes were defined by five ancestral nodes (i. e. , set clade ages for covering a central 95% probability range at the common ancestral node for (1) all species (272-288 Mya); (2) the order Hypocreales (185-201 Mya); (3) the family Nectriaceae (117-133 Mya); (4) the family Ophiocordycipitacea (109-125 Mya); and (5) the family Hypocreaceae (112-128 Mya)-based on the Bayesian chronogram previously reported using 638 core orthologous proteins by Hypocreales genomes  [20] , and species within the clades for each calibration node were constrained to be a monophyletic group.
All parameters of MCMC output were verified to meet the effective sample size (ESS; as above 200) in Tracer (v. 1. 7) [52]. After a burn-in for the first 25% of the sampled trees, only remaining trees were used to construct the consensus tree in TreeAnnotator (v. 2. 5; in BEAST2 package), and all estimated divergence times (million years ago, Mya) were visualized on the time-scaled BI tree for node ages in FigTree (FigTree, http://tree. bio. ed. ac. uk/software/figtree/ (accessed on 12 January 2021)) (v. 1. 4).
Selective Pressures Analysis The exon sequences of the 13 core PCGs (atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, and nad6; the atp9 gene was excluded because it has not been reported in the GenBank dataset of the T. gamsii KUC1747 mitogenome (GenBank accession no. KU687109)) were individually aligned using ClustalW (parameter: -codon option) in MEGA (v. 7. 0)  [35] , and these in-frame codon alignments were used to explore evolutionary pressures. The non-synonymous substitution rate (Ka), synonymous substitution rate (Ks), and Ka/Ks ratio were calculated on the pairwise alignments of each target gene between species using DnaSP (v. 6. 12) [53] , and obtained values were plotted using ggplot2  [40]  in R (v. 3. 4)  [41]. The codon sites evolving under the episodic diversifying positive selection were detected using the mixed effects model of evolution (MEME) method  [54]  in HyPhy (v.
Genomic Features of the T. harzianum CBS 226. 95 Mitogenome After filtering low-quality reads, a total of 1,806,923,812 read bases were used as seeds for the assembling process, and a complete mitogenome of T. harzianum CBS 226. 95 was assembled de novo with a length of 27,632 bp (coverage depth, 573. 59×; GenBank accession no. MN564945). A closed-circular DNA molecule of the T. harzianum CBS 226. 95 mitogenome was composed of A (36. 1%), T (36. 4%), G (15. 1%), and C (12. 4%), with the AT content (72. 45%) approximately 2. 63 times higher than the GC content. The asymmetric biases of these nucleotide compositions were inferred from the skewness, each for the negative value of AT-skew and the positive value of GC-skew, it showed more frequency of thymine (T) and guanine (G) than adenine (A) and cytosine (C) in the forward strand.
Phylogenetic Analyses of the T. harzianum CBS 226. 95 Mitogenome Excluding an unverified dataset, 47 complete mitogenomes (i. e. , 46 from Hypocreales species (belonging to the class Sordariomycetes) and one of N. crassa OR74A (belonging to the order Sordariales that composed of the class Sordariomycetes with the order Hypocreales)) were retrieved from the GenBank and used in the analyses (Table  S5 ). In both of the molecular phylogenies (ML-and BI tree), all Hypocreales species were well observed with high confidence, under the significant clades supported by seven major families of the order Hypocreales; interestingly, Paecilomyces penicillatus SAAS_ppe1, belonging to the family Clavicipitaceae, was found with a closer relationship with the family Hypocreaceae than those of native family (BS = 100, BPP = 1.
00). It may be resolved by improving the Hypocreales phylogeny resolution on accumulations of informative datasets of the mitochondrial genomes from closely related family species (Figures  2  and S2 ). The genus Trichoderma, including T. harzianum CBS 226. 95, was placed in a sister clade with the genus Hypomyces. Among hypocrealean fungi, a sister relationship between T. harzianum ATCC 26799 and T. gamsii KUC1747 was observed identically in both tree topologies (BS = 100, BPP = 1. 00) (Figure  2  and Figure  S2 ).
Comparative Analysis of the Trichoderma Mitogenomes The complete de novo mitogenome of T. harzianum CBS 226. 95 was compared with five complete Trichoderma mitogenomes that publicly available from the genus Trichoderma, excluding an unverified dataset (as of January 2021) (Table  1 ). The entire lengths of mitogenome ranged from 27,632 bp (T. harzianum CBS 226. 95) to 42,130 bp (T. reesei QM9414) with an average AT content (72. 2%). The intergenic regions ranged from 17. 58% (T. reesei QM9414) to 31. 73% (T. asperellum B05), and the mitogenome of T. harzianum CBS 226. 95 was shown to fall in the middle of this range. The mitogenomes of T. gamsii KUC1747, T. asperellum B05, and T. hamatum were presented with the negative value of AT-skew as those of T. harzianum CBS 226. 95. In contrast, only two mitogenomes, from T.
gamsii KUC1747 and T. harzianum CBS 226. 95 (Table  1  and Table  S8 ). Notably, an IA-type intron (i. e. , an intron of the subtype IA) was commonly found with an intronic ORF in the region of the rrnL gene of all Trichoderma species except T. asperellum B05 (i. e. , in this study, the likelihood of the rrnL gene being present in the T. asperellum B05 mitogenome was not considered because this gene was not reported in the GenBank dataset of the T. asperellum B05 mitogenome) (Figure  3A  and Table  S8 ). Finally, compared with other Trichoderma species, the mitogenome of T. harzianum CBS 226. 95 showed the highest identity of 95. 17% to T. reesei QM9414 (Table  1 ). The genus Trichoderma, including T. harzianum CBS 226. 95, was placed in a sister clade with the genus Hypomyces. Among hypocrealean fungi, a sister relationship between T.
Comparative Analysis of the Trichoderma Mitogenomes The complete de novo mitogenome of T. harzianum CBS 226. 95 was compared with five complete Trichoderma mitogenomes that publicly available from the genus Trichoderma, excluding an unverified dataset (as of January 2021) (Table  1 ). The entire lengths of mitogenome ranged from 27,632 bp (T. harzianum CBS 226. 95) to 42,130 bp (T. reesei QM9414) with an average AT content (72. 2%). The intergenic regions ranged from 17. 58% (T. reesei QM9414) to 31. 73% (T. asperellum B05), and the mitogenome of T. harzianum CBS 226. 95 was shown to fall in the middle of this range. The mitogenomes The tree was generated using concatenated sequences of 13 core genes (atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5 and nad6), and the mitochondrial genome of Neurospora crassa OR74A (Sordariales) was used as an outgroup.
aurantius from the genus Hypomyces, to use as a sub-outgroup for the genus Trichoderma within the Hypocreaceae clade, and (3) one strain N. crassa OR74A, belonging to the order Sordariales that composed of the class Sordariomycetes with the order Hypocreales, to use as an outgroup for all Hypocreales species. Based on the complete mitogenomes of the selected species (Table  S5 ), only exons sequences of the 13 core PCGs were used in the phylogenetic analysis to remove the potential of evolutionary effects by the presence/numbers of introns within the nucleotide sequences of the target gene. a Based on the GenBank database, NCBI (datasets as of, January 2021). b A number of putative trn genes predicted within the protein-coding genes were indicated inside the parentheses (details in Table  S7 ).
Meanwhile, IA-typed rrnL-i was commonly detected across all Trichoderma species except T. asperellum B05 (i. e. , in this study, the likelihood of the rrnL gene being present in the T. asperellum B05 mitogenome was not considered because this gene was not reported in the GenBank dataset of the T. asperellum B05 mitogenome (GenBank accession no. NC_037075)) (Figure  3A  and Table  S8 ). Although it should be comprehensively reflected in the unique features of other intron loci, such as (1) the intron locus harboring multiple intronic ORFs, or (2) the coexistence of different intron subtypes within a single coding gene (Table  S8 ); nevertheless, the rrnL-i is expected to play an essential role in revealing the dynamic history of Trichoderma mitochondrial introns, as one of the primitive factors that is assumed to be inherited from the most recent common ancestor of the Trichoderma genus.
Time-Scaled Bayesian Phylogeny with the Strain T. harzianum HB324 In addition to the six complete mitogenomes of Trichoderma species (including T. harzianum CBS 226. 95 of this study), there is additional mitogenomic information from the group of T. harzianum species; the 32,277 bp mitogenome of T. harzianum HB324 (Gen-Bank accession no. MT263519). Nonetheless, defined annotations of this mitogenome are unavailable in GenBank because it was publicly deposited as an unverified dataset (as of January 2021). In a recent study of this mitogenome that described mitogenomic features for the first time  [68] , some coding genes were suggested with multiple terminations of their coding regions. For these reasons, in this study, the core PCGs of the T. harzianum HB324 mitogenome were re-annotated to remove the positions of stop codons that were multiply predicted in the middle of coding genes and re-defined their gene lengths (Table  S9 ).
e. , including Trichoderma genomes)  [20]. However, despite belonging to the same species of T. harzianum, two strains of CBS 226. 95 and HB324 were placed separately (Figure  4A ). Compared to T. harzianum CBS 226. 95, the mitogenome of T. harzianum HB324 was approximately 4645 bp larger and exhibited a sequence identity of 92. 23% (query coverage 94%). The GC content was similar, but skewness patterns were different for all positive values in T. harzianum HB324. As described in the beginning of this discussion, putative trn genes entirely positioned within the coding genes (e. g. , tRNA Val of the nad6 gene, tRNA Met of the nad2 gene) were found across all Trichoderma species (Table  S7 )-however, not in the T. harzianum HB324 (Table  S9 ). In addition, when comparing genetic distances using exon sequences of the 13 core PCGs (atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, and nad6; the atp9 gene was excluded because it has not been reported in the GenBank dataset of the T.
gamsii KUC1747 mitogenome (GenBank accession no. KU687109)), T. harzianum HB324 was assumed to be more divergent from T. harzianum CBS 226. 95 (p-distance value, 0. 099) than other Trichoderma species (p-distance values, 0. 055-0. 057) (Figure  4B  and Table  S10 ). All these aspects showed a presence of mitogenomic differentiation between two T. harzianum strains, CBS 226. 95 and HB324. Nevertheless, it seems that one additional point should be also contemplated together as below. Since the first description of the name Trichoderma  [69] , phylogenetic concepts of the genus Trichoderma have been developed to systematically identify Trichoderma taxonomy  [70]. Particularly, in earlier studies, some Trichoderma strains were misidentified, and even the name of T. harzianum has been misused for many different species due to the species complex of Trichoderma  [71] [72] [73] [74].
Evolutionary Pressures for the Trichoderma Mitogenomes Related with the adaptive evolution of Trichoderma mitogenomes, selective pressures were investigated using the codon alignments of 13 core PCGs (atp6, atp8, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, and nad6; the atp9 gene was excluded because it has not been reported in the GenBank dataset of the T. gamsii KUC1747 mitogenome (GenBank accession no. KU687109)). Compared to the Ka/Ks ratios, most of the core PCGs were assessed with values lower than one (<1), which is supposed to be under purifying selection. However, interestingly, the cox3 gene was measured with a value greater than one (1>) in a pairwise comparison between T. harzianum CBS 226. 95 and T. reesei QM9414, implying a positive selection potential. When considering the Trichoderma clustering of the two BI topologies (Figures  2  and 3B ), the cox3 gene might be a site for adaptive changes that influenced the evolutionary speciation events between two strains, CBS 226.
Higher population densities were followed by reductions in population density the following year. Dotted horizontal line shows stability (no change in population density from one year to the next), and solid sloping line shows least-squares regression fitted to the combined dataset. See Table  1  for statistical tests of these data, incorporating weather covariates. suggests that populations frequently reach a point at which increased densities reduce average viability of individuals. That process may act by exacerbating competition for a finite food supply (the eggs of demersal-spawning fishes); but could also be mediated by higher rates of pathogen transfer at increased population densities, or if predators are attracted to sites of high prey availability. Our data do not allow us to distinguish among these alternatives; all we can say is that juvenile snakes appear to be disproportionately disadvantaged by increased population density.
Our study provides the first long-term dataset on population fluctuations in hydrophiine snakes, so we cannot compare the dynamics of our turtle-headed sea snake populations with those of other species. Worryingly, however, sea snakes have been reported to exhibit population declines in some areas, even on protected reefs, in some cases leading to local extinctions (with ambiguous causation  37, 38  ). Given the substantial diversity in life-history traits among hydrophiine species  29, 36, 39  , population dynamics of sea snakes are likely to be equally diverse, and generalisations therefore elusive. We urgently need research on long-term population dynamics of other species of sea snakes, perhaps using cost-effective methods such as video-camera stations  40. Until we understand the factors that drive annual variation in the abundance of these marine predators, we will struggle to understand the factors that are driving those widespread declines.
Methods Study site and species. Anse Vata and the Baie des Citrons (22° 16′ S, 166° 26′ E) are adjacent shallow bays beside Noumea, capital city of the Pacific archipelago of New Caledonia (Fig. 3a ). Sea snakes are found primarily in coral-reef habitats at either end of each bay, which are separated by sandy substrate  41. Thus, the turtle-headed sea snakes that we studied in this area form three discrete populations (north end of Anse Vata, north and south ends of Baie des Citrons). These snakes are so highly philopatric that interchange of individuals among the three populations is very rare (despite their proximity: < 1. 5 km apart) based on phenotypic traits  42  and genetic data  24. The three study areas that we survey for snakes are each 350 X 20 m in size, extending from the shoreline to about 4 m water depth, over a substrate dominated by live and dead coral, sand, and boulders  43.
6 per year, range 101-231). For the first several years we worked only at Anse Vata and the southern end of Baie des Citrons, but we began microchipping snakes at the third site (north end of Baie des Citrons) in 2016. Thus, we have 18 years of data for two sites, but only 6 years of data for the third. All of our statistical analyses (except those for differences among sites) include site as a random factor, to avoid pseudoreplication.
Independent variables. Jolly-Seber estimates of population sizes for each site in each year were calculated using the POPAN implementation in the MARK software package  50. Goodness-of-fit tests indicated that the mark-recapture data from each site were appropriate for analysis using Cormack-Jolly-Seber models (all P > 0. 06). For each site we constructed a set of candidate models in which parameters for survival (phi), recapture (p) and recruitment (pent) were allowed to vary among years (t) or to remain constant (•). We then selected the best model for each site based on corrected Akaike Information Criteria (AICc) values. At both Anse Vata and Baie des Citrons south the top-ranked model was fully time-dependant (Phi(t) p(t) pent (t); Table  2 ). At Baie des Citrons north the top model was one in which survival and recapture rates were both constant over time but with time-dependent recruitment (Phi(•) p(•) pent(t); Table  2 ).
A priori, we predicted that the two most important weather variables for snakes would be rainfall (access to freshwater enhances snake body condition  51  ) and wind speed (stronger winds create rough sea conditions, constraining shallow-water foraging but hiding snakes from aerial and aquatic predators). We extracted mean monthly values for these variables from the Météo-France climate database for Noumea (http:// www. meteo. nc/ donne es-publi ques/ publi theque) and calculated seasonal means from these mean monthly values. With these indices, we can ask whether the direction and degree of change in population density from one year to the next is related to the number of snakes initially present, and/or to weather conditions experienced in the year prior to our January sampling.
(2) Age structure. Quantified as the proportion of captured snakes that were adults (> 500 mm SVL), and lntransformed (ln [1 + X]) to improve normality. (3) Body condition of snakes. Quantified by taking residual scores from the general linear regression of ln body mass against ln snout-vent length. Positive scores represent snakes that were heavier-than-expected based on their body length; our analyses used means per population per year. (4) Reproductive frequency. Quantified as the proportion of adult-sized female snakes (> 500 mm SVL) that were gravid when palpated; our analyses used means per population per year. (5) Litter size. Determined by abdominal palpation of females; our analyses used means per population per year. Analyses. There has been considerable debate about methods for detecting density-dependence from timeseries data on animal abundances  2, 4, 53.