dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , habitat, trophic level, and diet content). We used a pool of 100 stochastic mappings with the make. simmap function in phytools to account for phylogenetic uncertainty. Next, we used a rjMCMC run over 20,000,000 generations with two independent runs and a burn-in of 10%. The convergence of the runs for each one of the regimes was assessed with Gelman and Rubin's diagnostic (Supplementary Table  S4 ). This analysis was implemented in the package ratematrix  125. Finally, we estimated the ancestral jaw shape considering all the averaged coordinates after GPA by species, for which we used the anc. recon function in Rphylopars  126. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Acknowledgements This research was funded in part by the  Austrian Science Fund (FWF)  [ P33820 ]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. F. A. L. R. is supported by the PhD Completion Grant from the  Vienna Doctoral School of Ecology and Evolution. A. P. is supported by the  ANR  grant " ANR  #  ANR-22-CE02-0015-01_MACHER ". We would like to thank  Thomas Guillerme , and three anonymous reviewers whose comments helped to improve the manuscript.
Data availability All data sources used in the study are indicated in the methods section. The ply files for the shark species can be found at https://doi. org/10. 6084/m9. figshare. c. 3662366. v1 and 10. 18563/journal. m3. 133 for C. milii. The tables, landmarks, and phylogenetic trees are deposited at https://github. com/Faviel-LR/Jaws_Sharks_Evolution.
FTIR-ATR Peat ash and potential source sediment samples (bulk and individual size fractions) were analysed by Fourier transform infrared spectroscopy, using a Thermo Scientific™ FTIR equipped with an iS7 ATR device with a single-reflection diamond crystal at the SLAM Laboratory at the Department of Geological Sciences, Stockholm University. All labware and the ATR-diamond were cleaned with ethanol between each sample or by using compressed air whenever possible. The measurements were obtained for the midinfrared wavelength spectral range of 4000e400 cm À1 (2. 5e25 mm) at a resolution of 4 cm À1. A background was measured before each sample  (Gåling, 2019). Reference spectra for major minerals (quartz, microcline, orthoclase, albite, anorthite, muscovite, biotite, calcite and amphibole) were obtained from the RRUFF database (http://rruff.
geo. arizona. edu). Second derivative spectra of the sediment samples, peat ash and reference minerals were obtained using the {anduri-nha} R package ( Alvarez-Fern andez and Martinez-Cortizas, 2020). Second derivative IR spectrum can enhance the MIR spectral resolution and amplify tiny differences in IR spectra. Many differences poorly visible in FTIR spectra such as frequency position, information on the width and the maximum absorption intensity become clearer. As part of the {andurinha} routine all data are standardised using Z-scores. The inspection of the spectra showed that almost all samples showed no or very low absorbance in the 4000-1900 cm À1 region (SD Fig. 2 ). Therefore, further descriptions and statistical procedures are based on the 1900-400 cm À1 region alone.
A PCA was then performed in correlation mode with a varimax rotation using the JMP Pro 14 software. Cp were extracted until they explained at least 85% of the variance in the data. To infer the timing of discrete changes in the peat paleostorm proxies, changepoint modelling was applied (see  Gallagher et al. , 2011). This approach allows us to infer the location of changepoints (i. e. , discontinuities where the mean of the data changes abruptly) without needing to specify in advance how many changepoints there are or where they are located. Furthermore, the approach does not require a priori knowledge of the noise in the observed values, but allows us to infer a noise distribution for each dataset. This noise can arise from analytical procedures and/or be introduced due to local geological or environmental processes.
The approach implements a Bayesian transdimensional Markov chain Monte Carlo (MCMC) algorithm to propose many potential solutions (with different numbers and locations of changepoints and noise estimates), which are either accepted or rejected, based on simple probabilistic rules. One attractive feature of the Bayesian approach is that it naturally balances the requirement of fitting noisy data while avoiding an unnecessarily complex changepoint structure. If we anticipate the same changepoint structure (number and locations of changepoints), the approach can be implemented on single datasets in turn and the inferred changepoint structures compared visually. Alternatively, we can model multiple datasets simultaneously, noting that the magnitude and sense of the change can vary between datasets.
This approach does not require the data to be sampled from the same depths/ages. An advantage of jointly modelling multiple datasets is that the common signal will tend to be reinforced while the noise component tends to cancel out (as it is generally random and independent across datasets). This again reduces the possibility of over-interpreting the changepoint structure. The output of the changepoint modelling method includes probability distributions on the number and location of changepoints, on the noise for each dataset and the inferred mean signal as a function of depth/age for each dataset. We note also that the inference of changepoints as a function of time assumes that the age models are reliable across the different datasets.
0017 and 38 s count time. Processing of the results (determination of background, peak and phase identification quantification of relative occurrence) was conducted in HighScore 4. 6, a PANalytical software  (Degen et al. , 2014) , hosting a reference mineral database (ICDS database 2012) and Rietveld quantification  (Rietveld, 1969). Quantification was restricted to results between 5 and 55 2q, using the default HighScore settings  (Degen et al. , 2014).
FTIR-ATR The individual IR absorbance spectra of the potential source sediment (bulk) and peat ash samples show a generally consistent pattern within each sample type (Fig. 4 ). The second derivative of the average absorbance spectra allows us to determine the precise location (wavenumber) of the most important peaks in the IR spectra, and it is particularly helpful to discriminate overlapping vibrations. Relatively high absorbances were obtained in the 1200-850 cm À1 region and in the <500 cm À1 region, with lower absorbances in the 800-500 cm À1 region. Relative maxima can be seen at approximately 1170e1150, 990e1000, 900 (shoulder), 800e770, 720, 690, 580 and 520 cm À1. The second derivative spectra enables identification of significant peaks that can be attributed mostly to silicates through comparison with spectra from the RRUFF database (SD Fig.
XRF-CS XRF-CS measures only relative changes in elemental intensity and not absolute concentrations. These data are used as secondary support for our interpretations. Based on analytical performance (counting statistics) Al, Si, Ti, P, K, Ca, Fe, Mn, Zn, Pb and Zr were analysed using PCA. The top seven samples were not included because of likely anthropogenic signal (see Discussion). Four Cp were extracted capturing 85% of the total variance in the dataset (denoted Cp XRF ; Table  4 ). Cp1 XRF explains 26% of the variance in the data and is positively associated with Mn, Fe and Ca. Cp2 XRF explains 25% of the variance in the data and represents the behaviour of Zr, Ti, Si and Al. Cp3 XRF explains 19% of the data and describes the behaviour of P, K and Al. Finally, Cp4 XRF accounts for 14% of the variance in the data and is associated with Pb and Zn.
9 ± 15. 3%). The behaviour of K-feldspar is not as obvious but seems to show marginally higher average values in the medium coarse (63e125 mm) fraction for the SMT samples (14. 9 ± 5. 3%) and the medium (38e63 mm) fraction for AS samples (11. 8 ± 4. 1%). Because Ca-feldspar explains such a small part of the variance (1. 4 ± 1. 6%), it is hard to define any grain-size relationship, but a tendency towards finer sizes is suggested. A single coastal dune sample from Halland (~90 km to the southwest) was analysed for Fig. 8. Partial communalities of the minerals identified by FTIR-ATR analysis of the SMDS sequence representing relative changes in mica (principal component 4, Cp4 Peat ) Cafeldspar (Cp6 Peat ), quartz (Cp2 Peat ) and K-feldspar (Cp5 Peat ). These latter two minerals, along with Cp2 XRF and Si/Al from the XRF-CS elemental dataset, representing lithogenic inputs and grain-size changes, respectively, are used as storm proxies for the SMDS sequence.
Several indicators of anthropogenic activity are shown including pollen-based records of pasture and disturbed ground from Store Mosse itself as well as indicators of agriculture from Lake Aveg€ ol, a site 70 km north-northeast. See text for references. Fig. 9. Individual storm proxies from sites in southern Sweden starting from inland sites and moving southwest towards the coast with changepoint modelling applied to each proxy individually (left to right, see inset). For SMDS quartz (principal component 2, Cp2 Peat ) and K-feldspar (Cp5 Peat ) from the FTIR-ATR analysis as well as Cp2 XRF and Si/Al from the XRF-CS elemental dataset were included. Although storm periods constrained by changepoint modelling of these four proxies individually vary in timing and duration, we have defined several local storm periods (LSP).
These are compared with Al and K mass accumulation rates (MAR) from SMS 2008 , a sequence taken ~5 km south of SMDS in the same bog complex. Davidsmosse is represented by Cp2 XRF and the Si/Ti ratio from a multi-element XRF dataset. Finally, at the two coastal sites, Undarsmosse and Hyltemosse, the aeolian sand index (ASI) for grains >200 mm and the maximum grain size are used. The original data are shown in colour. Changepoint modelling was applied to all proxies individually and peaks in the inferred functions (black line) were constrained temporally using the changepoint structure (thin grey line) where higher values indicate a greater probability of a changepoint occurring. This information was used to identify stormier periods in individual proxy records (shaded boxes). To generate a regional storm stack for southern Sweden (S Swe-StSt), quartz and Si/Al from SMDS was modelled jointly with the other available storm proxies (excluding SMS 2008 ) to find common changepoints (SD Fig.
These elements are all mobile in the acidic conditions of a bog  (Chesworth et al. , 2006)  and are expected to diffuse upwards from the basal sediment e like that seen in the Cp1 XRF pattern. A drop in Cp1 XRF thus provides another indication of ombrotrophy. Given this evidence, we are confident that the majority of the sequence is ombrotrophic and therefore atmospherically fed, with the fen-bog transition occurring ~4100 cal yr BP. The ash content at the top of the SMDS sequence from 250 cal yr BP to present is up to 5%, which is off trend in comparison to the rest of the profile (Fig. 3 ). There is also an increase in the bulk density, PAR, quartz and K-feldspar (Figs. 3  and 8 ). Peat mining a presence, below 0. 1% occurrence, LDH ¼ layered double-hydroxide; R wp is a discrepancy value between the data and the model  (Toby, 2006).
Cp2 XRF has the highest correlations with quartz (r ¼ 0. 68) and Ca-feldspar (r ¼ 0. 64), which agrees with the relative abundance suggested by the pXRD and confirms that these two minerals dominate the elemental signal (Table  3 , Fig. 8 ). In a review of contemporary dust deposition, Si and Al concentrations were found to be relatively unchanged in comparison to the upper continental crust during dust transport, supporting the use of these elements to represent mineral deposition  (Lawrence and Neff, 2009). The agreement between the MIR spectral data and Cp2 XRF , derived from two completely independent datasets also shows that the fen section of the deposit does not greatly impact the PCA analyses. In an analysis of the elemental variations in different grainsize fractions of tills, glaciofluvial and dune samples, Sj€ ostr€ om et al.
8 ). In order to assess local storm events as well as the potential storm activity information contained in each dataset, we applied changepoint modelling to each of these proxies individually. This allows us to examine the agreement between the different proxies in terms of their response to stormier periods and the noise level in a given dataset (Fig. 9 ). We expect stormier periods to express themselves as peaks (rather than a gradual or baseline changes) in the average weighted function where the timing of these peaks is identified using the probability distribution curves. Authors define events differently, often using the age interval from, and returning to, the baseline. With the approach used here the change is dated between the baseline and the first sample of the peak. The intervals given therefore provide conservative windows.
Regional context for storm reconstruction In order to assess and upscale our results to a more regional context, we compare the SMDS sequence with the available withinbog record SMS 2008 , but also with regionally relevant paleostorm records including Davidsmosse and the coastal sites Hyltemossen and Undarsmosse (~60, ~100 and ~95 km southwest, respectively; Fig. 1 ). Changepoint modelling was also applied to each proxy individually to test coherence across the transect (Fig. 9 ). The SMS 2008 study focused on trace elements acquired through acid digestion and ICP-MS/OES. With no Si data and poor analytical quality for Ti, we rely on K and Al MAR, which are both associated with mineral inputs  (Kylander et al. , 2013 (Kylander et al. , , 2016;; Martinez-Cortizas et al. , 2020). Potassium is hosted in K-feldspar and mica, which makes it more appropriate for comparison with the SMDS IRinferred mineralogy, while Al is a common mineral forming element  (Deer et al.
, 1992). The storm record from Davidsmosse is based on a multi-element PCA, where Cp2 XRF represented the integrated behaviour of Si, Al and Ti, and Si/Al is, as mentioned previously, linked to grain size  (Sj€ ostr€ om et al. , 2022). As shown above, the input of minerals during a given change can differ slightly in timing. Elementally based approaches average the mineralogical signal because it necessarily combines the presence of all minerals hosting, in the case of SMDS and Davidsmosse, Ti, Si and Al. In contrast, the data from Hyltemossen and Undarsmosse are based on the input of particles >200 mm and maximum observed grain sizes (Bj€ orck and  Clemmensen, 2004; de Jong et al. , 2006). The data from the potential source sediments in this study show that, although quartz clearly dominates the coarse fraction (>63 mm), the median grain sizes are somewhat lower than 200 mm.
Conclusions This study develops tools for reconstructing past storminess based on FTIR-ATR analyses of the inorganic fraction of the SMDS peat sequence, which spans the last ~5000 years. Reconstructed changes in quartz, whose coarse nature was confirmed by analyses of potential source sediments found locally, were used as a proxy for wind intensity. This was further supported by Si/Al as a grainsize proxy as well as K-feldspar and Cp2 XRF as indicators of mineral input to the bog. Changepoint modelling of these four indicators applied individually saw disparities in the timing of storm periods across the data suite and attest to differences in proxy response to the same windstorm conditions. Nonetheless, LSP could generally be dated to (cal yr BP): 4480e4425, 3320e3190, 2150e1920, 1450, 1005e850, 720e650 and 410e350.
These LSP agreed fairly well with data from the SMS 2008 sequence located ~5 km to the south, with offsets likely explained by the relative distance to mineral sources and shifting wind directions. Comparison with regionally relevant records along a ~100 km transect showed that while records are somewhat consistent, there are differences in site response which are potentially compounded by uncertainties in age modelling of the different datasets. To address this issue, we suggest the building of regional storm stacks. Changepoint modelling of multiple datasets together allowed us to define RSP from the S Swe-StSt to 4495e4290, 3880e3790, 2885e2855, 2300e2005, 1175e1065 and 715-425 cal yr BP. Comparison with a storm stack constructed from three sites in western Scotland and proximal dune records showed a great deal of heterogeneity and may reflect the changing position of the storm track over time.
Acknowledgements First and foremost, we would like to express our gratitude to Carina H€ arlin and the rest of the staff at  Store Mosse National Park  for their support in the field. The county of J€ onk€ oping is thanked for providing the opportunity for us to sample in the park. We thank  Mikaela Holm  and  Johan Ehrl  en for their help during fieldwork. Austin Stout helped with the collection and analysis of the AS samples. Daniel Ellerton  is gratefully acknowledged for performing the SM grain size analyses. Carina Johansson  is thanked for her constant support in the lab.
Sequencing and phylogeny reconstruction Amplicons were purified and sequenced by the Sanger method. The experimental sequences obtained from the mitochondrial cytB and cytC genes of Angiostrongylus cantonensis and the 16S rRNA gene of Achatina fulica were first examined for quality control and compared with published sequences in the National Center for Biotechnology Information (NCBI) GenBank database. The 71 new experimental sequences generated for this study are available in GenBank. Sequences were trimmed with BioEdit  [31] , and the subsequent steps of the analysis (presented below) were processed on the Galaxy KaruBioNet interface  [32]. Phylogenetic reconstruction based on the maximum likelihood method (1000 bootstrap) was performed with RAxML v8  [33]  from the multiple sequence alignments obtained with MAFFT software version 7  [34].
Phylogenetic analysis of A. cantonensis adult worms based on cytB sequences The phylogenetic tree (Fig. 2 ), based on partial sequences of cytB of A. cantonensis (668 bp), was constructed with the maximum likelihood method. Comparison of the 20 sequences from Guadeloupe with 137 sequences previously deposited in the NCBI GenBank database revealed the presence of six clusters, recorded as A, B, C, D, E and F (Fig. 2 ). Branch G was an outgroup composed of two sequences of Angiostrongylus malaysiensis. All the individuals from Guadeloupe were located on branch D, indicating a single monomorphic cytB pattern, specific to Guadeloupe. All the profiles described in Guadeloupe were distinct from those previously described in China, Thailand, and the USA, but the cluster from Guadeloupe (D) was close to cluster C.
Phylogenetic analysis of A. cantonensis adult worms based on cytC sequences The phylogenetic tree (Fig. 3 ) based on the partial sequence of cytC of A. cantonensis (351 pb) was constructed with the same technique as above. A total of 27 cytC sequences of individual adult A. cantonensis from Guadeloupe were compared with 72 cytC gene sequences in GenBank. One sequence of A. malaysiensis (KU532154) was included as an outgroup. Phylogenetic reconstruction showed the presence of 13 branches. Individual adult worms from Guadeloupe appeared to be monomorphic for this marker and their sequences were identical to 13 Japanese, two Hawaiian, and three Brazilian sequences. All of these individuals clustered on the same branch. At least two sequences were found for worms from all three areas, whereas only one sequence was seen in Guadeloupe.
Phylogeny of A. fulica by the 16S rRNA partial gene sequences The phylogenetic tree (Fig. 4 ) based on the partial sequence of the 16S rRNA gene of A. fulica (243 pb) was constructed with the same technique as above. A total of seventy-one 16S rRNA gene sequences of A. fulica from Guadeloupe were compared with the 37 sequences from 10 countries and areas in the NCBI GenBank database. Among the 71 sequences from 10 localities in Guadeloupe, two different yet closely related sequences were found, which differed in the position of one nucleotide in our alignment. The largest group consisted of 69 individuals, which shared a sequence with one individual from New Caledonia (JQ436758. 1). In the other group, two individuals shared a sequence that has not been described previously (Fig. 4 ). These sequences (GG_M32: OP962241 and GG_M53: OP962249) have a uracil while the others have a thymine at position 71 of the Fontanilla et al.
The second objective of our study was to better understand the role of Achatina fulica in the emergence of neuroangiostrongyliasis on Guadeloupe. Our results suggest that Guadeloupe was invaded by A. fulica from a single origin, belonging to the F haplotype described by Fontanilla et al. [28] , which is close to that of A. fulica from New Caledonia and Barbados  [28] , although we were unable to find sequences of A. fulica from Barbados in GenBank. Of the 71 individuals from Guadeloupe, only two had sequences that were slightly different from those of the others. On Mayotte, a French island in the Indian Ocean which is smaller than Guadeloupe, two closely related sequences were found in three individuals. As A. fulica has been reported in Mayotte over a long period of time  [41] , even with a single introduction, genetic drift is logical.
In Martinique, 100% of the snails studied belonged to haplotype C  [28] , which has been found in all countries and areas sampled except for Tanzania, Uganda, New Caledonia and Barbados  [28]. However, sequences of A. fulica from Martinique were not included in Fig. 4  since none were found in Genbank. It would be interesting to study more A. fulica from Martinique to confirm that they all differ from those from Guadeloupe. Because of its recent introduction into the Caribbean, Achatina fulica may have either introduced Angiostrongylus cantonensis into the area at the time of its arrival, or have amplified its presence if rats brought it in. If different geographic origins are clearly proven for Achatina fulica and Angiostrongylus cantonensis, A. fulica could not have been responsible for the introduction of the parasite.
Availability of data and materials The 20 partial Angiostrongylus cantonensis cytB sequences, 27 A. cantonensis sequences cytC and 71 Achatina fulica rRNA 16S sequences have been deposited in GenBank and are available under BioSample accession numbers OQ238771-OQ238790, OQ255893-OQ255919, and OP962225-OP962295, respectively.
Transcriptome Analysis Cells of strain ST-419 cultivated with different electron donors and acceptors under four types of catabolic conditions including S 0 oxidation, thiosulfate oxidation, S 0 reduction and nitrate reduction were harvested during the exponential growth phase, and centrifuged at 8000 rpm for 10 min at 4 • C. Resulting bacterial pellets obtained were rapidly frozen in liquid nitrogen and used for total RNA extraction. Transcriptome analysis was performed by Novogene (Tianjin, China). Detailed protocols of these procedures, including library preparation, clustering and sequencing, and the data analyses, are described in the Supple-mentary Materials. RNA-Seq reads were deposited in GenBank under accession numbers SRR22829317 to SRR22829328.