dataesr/scientific-paragraphs-categorization huggingface dataset:
Rates of morphological evolution To test the morphological evolution of the navicular, we estimated the branch-specific evolutionary rates and rate shifts using the variable rates model applied in BayesTraits v. 3 (http://www. evolution. rdg. ac. uk/). The shifts in the rate of continuous trait evolution, which was modeled using a Brownian motion model, were detected with a reversible-jump Monte Carlo algorithm. The first 10 PCs of the phylogenetic PCA on the speciesmean Procrustes shape coordinates, computed using the function 'phyl. pca' of the package phytools v. 0. 7e20  (Revell, 2012)  accounting for >99% of the overall variation in navicular shape, were used as an input. Ten independent chains were run for 200,000,000 iterations, sampling every 10,000 iterations, and the first 25,000,000 iterations were discarded as burn-in (SOM Table  S5 ).
Trace plots were examined, and the 10 independent chains that were stationary after burnin were kept (SOM Fig. S7 ). The effective sample size (ESS) of the posterior samples (ESS < 100) as well as the convergence of the chains were assessed with a Gelman and Rubin's convergence diagnostic (SOM Tables  S5  and S6 ), with the functions 'effectiveSize' and 'Gelman. diag' implemented in the package coda v. 0. 19e4  (Plummer et al. , 2005). Assessing the convergence of chains is critical to detect large deviations among the different chains (nonconvergence) and ensure similar between-chain variances  (Gelman and Rubin, 1992). The results were plotted on the tree using the function 'mytreebybranch' (https://github. com/anjgoswami/salam anders/blob/master/mytreerateplotter. R). Branch-specific average rates and the posterior probability of rate shifts (the mean of all ten independent chains) were summarized using the 'rjpp' and 'plot-Shift' functions of the package btrtools v.
Shape visualization To visualize shape differences in navicular shape between the extremes of the axes of the different analyses carried out, a thinplate spline deformation of the 3D model of a navicular bone was carried out. Shape visualization of the effects of PCs, PLS, and discriminant function values was performed using the functions 'shape. predictor' and 'plotRefToTarget' of the package geomorph v. 4. 0. 3 (Adams and Ot arola-Castillo, 2013) and 'shade3d' of the package rgl v. 0. 100. 47  (Adler and Murdoch, 2020). The specimen used as the reference was the navicular bone of Eulemur fulvus (USNM 542489), which is adequate to represent the mean shape of the sample.
Acknowledgments This work has been supported by  CERCA Programme/Generalitat de Catalunya , research projects  PID2020-116908GB-I00  and  PID2020-117289GB-I00  financed by  MCIN/AEI/10. 13039/501100011033 , and project  CLT_2022_EXP_ARQ001SOLC_00000197  financed by the  Culture Department of the Generalitat de Catalunya , as well as the consolidated research groups  2022 SGR 01188  and  2022 SGR 00620  ( Generalitat de Catalunya ), Ram on y Cajal grant ( RYC2021-034366-I  to J. M. ) funded by  MCIN/AEI/10. 13039/501100011033  and by the  European Union NextGenerationEU/ PRTR, FI AGAUR  fellowship ( 2021 FI_B 00524  and  2022 FI_B1 00131  to O. M. G. ) funded by the  Secretaria d'Universitats i Recerca de la Generalitat de Catalunya  and the  European Social Fund. We want to thank our colleagues  Doug M.
Transposable elements are an important source of genome variability. Here, we analyze their contribution to gene expression variability in crops by performing a TE insertion polymorphism (TIP)-eQTL mapping using expression data from rice varieties from the O. sativa ssp. indica and O. sativa ssp. japonica subspecies. Our data shows that TE insertions are associated with changes of expression of many genes known to be targets of rice domestication and breeding. An important fraction of these insertions were already present in the rice wild ancestors, and have been differentially selected in indica and japonica rice populations. Taken together, these data show that small changes of expression in signal transduction genes induced by TE insertions accompany the domestication and adaptation of rice populations.
TIPs are associated with gene expression variation in rice We used a recently published dataset containing genome resequencing and transcription data for rice (Oryza sativa) varieties to perform TIP-eQTL mapping analyses. Rice is one of the most important food crops in the world, and varieties are generally found in two subspecies -O. sativa ssp. japonica and ssp indica. Other minor variety groups include aus varieties, which appear closely related to indica, and basmati rice varieties that appear to be a hybrid between ssp. japonica and aus. The data we analyzed consists of an 'indica' dataset for 126 O. sativa ssp. indica and some circum-aus accessions, and 84 'japonica' accessions comprising of O. sativa ssp. japonica and some circum-basmati varieties, described in Groen et al. (2020)  (19).
We predicted TIPs using Popoolation TE2  (20)  , and identified 45,050 TIPs. Using the Minghui MH63 short read data and assembled draft reference genome  (21)  we estimated the performance of the TIP genotyping on this dataset to have 97. 1% sensitivity and 92. 2% precision (Supplementary text 1). Most of the TIPs detected are related to DNA transposons (84%), especially MITEs belonging to the Stowaway (24%) and Tourist (20%) superfamilies; LTR retrotransposons make up a minor fraction (9 %). The 45,050 TIPs are distributed along the 12 chromosomes and 81. 3 % of them are less than 5 kb away from an annotated gene (IRGSP RAP-DB gene models). This results in up to 81. 4% of rice genes having a TIP less than 5 Kb distant. In order to analyze the potential of TIPs to generate gene expression variability in rice, we used the 3' mRNA-seq expression data from leaves of adult plants grown in normally-watered soil ("wet" condition) and under intermittent drought-stress ("drought stress" condition)  (19).
We selected data from 15,549 genes showing expression in more than 10% of the samples. Due to the strong population structure present in rice we separately analyzed data in the indica (126 varieties) and japonica (82 varieties) datasets. We found a total of 563 significant associations between TIPs and gene expression levels in the indica population and 356 in the japonica population (TIP-eQTLs with MAF > 3%, FDR adjusted p value < 0. 05 in at least one replicate) (Figure  1A  and 1B, Supplementary Table  2 ). These involve 477 and 317 genes, in indica and japonica respectively, representing 3. 1% and 2% of the expressed genes. The TIP-eQTLs were related to the different TE types (orders  (22)  ), and the proportion of TE orders found in the significant TIPs did not differ significantly from their representation across the genome (Figure  1C ).
02, respectively, p < 0. 01, Wilcoxon test). Furthermore, TIPs with the most extreme PBS values (above 95 percentile) are enriched for TIP-eQTLs (Fisher's Exact test odds ratio = 4. 87, p < 0. 01), suggesting that a high fraction of TIP eQTLs have been positively selected in indica or japonica. Note that some of the TIPs not identified as eQTLs in this particular dataset may actually be linked to variation of gene expression in organs, developmental stages or environmental conditions different from the ones analyzed here. Interestingly, up to 35% of the TIP-eQTLs identified here, associated with 119 genes, fell within the top 10% of absolute PBS values for all TIPs (Fig 3A ), suggesting that TIPs associated with gene expression variation are likely under differential selection during rice evolution.
We see here that two independent TIPs are associated with changes of OsGAP expression in japonica but not in indica (Figure  5A , Supplementary Figure  2B ). Interestingly, there are clear and strong signs of selection for increased expression of OsGAP in japonica, wherein Hap A was under positive selection, and Hap B was selectively neutral, indicating positive selection acts on only those individuals carrying haplotype for high expression of OsGAP in japonica (Figure  5C ,D; Supplementary Table  6 ). In contrast, there is no evidence for selection in indica varieties despite the near fixation of one haplotype. This suggests that an increased level of OsGAP may be relevant for the control of dormancy in japonica but not in indica (despite its near-fixation in the latter), which would be in line with recent data suggesting that seed dormancy is regulated by different genes/alleles in indica and japonica  (41).
In these instances, as shown for the OsJAZ1 and OsGAP genes, the two haplotypes have strong signs of selection, although one positively and the other negatively, pointing to role these TE insertions may play in adaptive evolution of these crop subspecies. The results presented here show that most TIPs associated with variation of gene expression in indica and/or japonica are relatively old insertions that are also present in rufipogon/nivara, the wild ancestors of rice. This is in line with recent data showing that a high number of the structural variants associated with changes of expression in tomato were already present in its wild ancestor  (45)  and highlights the importance of the standing variation, already present in the wild ancestors, for crop adaptation. It is assumed that most TE insertions are selectively neutral or slightly deleterious  (46)  , and the presence of TIPs associated with expression variation of genes in the wild ancestors of rice, suggests that this variation can be well tolerated in wild rice.
TIP detection Re-sequencing data for 126 indica and 82 japonica rice accessions was obtained from Groen et al  (19)  (Bioproject accessions PRJNA557122, PRJNA422249 and PRJEB6180). BBDuK (https:// sourceforge. net/projects/bbmap/) was used for adaptor and quality trimming. Clean reads were aligned to the Nipponbare reference genome  (47)  using BWA  (48). PoPoolationTE2  (20)  was used to detect TIPs in the mode "joint" using Nipponbare TE annotation described by Ou et al  (47). TIPs with a zygosity lower than 0. 25 in all samples were excluded to avoid false positives. TIPs were further filtered using the parameters --min-count 5, --max-otherte-count 2, --max-structvar-count 2, and only those having MAF higher than 3% and no missing data were kept. Finally, the TIP matrix was transformed to binary form using zygosity cutoff of 0.
TIP and SNP-eQTL mapping Transcriptome data for the 208 rice accessions (wet and drought stress conditions; tree independent replicates per condition) was obtained from Groen el al  (19)  and separated into individual replicate matrices. Transcripts expressed in more than 10% of the samples on each replicate were extracted and counts were normalized using the yst function of DESeq2  (49). TIP and SNP-eQTL mapping were performed using Matrix eQTL software  (50)  , applying the simple linear regression model and including subpopulation groups as covariates. We used a cut-off distance of 5,000bp to identify cis-eQTLs and a 5% False Discovery Rate threshold for multiple testing corrections.
This was done using a custom script in a window of 25SNPs with a sliding window of 5. Further, we also estimated the H12 homozygosity statistic  (39)  that can detect the presence of both hard and soft sweeps associated with selection. Since we expect long runs of homozygosity in the regions around the selected loci, H12 was calcuted in windows of 50 SNPs, with a sliding window of 25. Both these statistics were estimated separately for indica and japonica, and for the samples with TIP insertions present and absent. This was for a range of 100kb (50kb up-and downstream the TIP insertion), using SNPs identified from Groen et al  (19)  but without the 1000bp linkage thinning. To test for the significance of the statistics, we performed a permutation test (N=1000) with random sampling with the same number of individuals that were used to estimate the selection statistics.
Acknowledgements We thank  Jae Yong Choi ,  Simon C. Groen  and  Sebastián Ramos-Onsins  for helpful discussions. Noemia Morales-Diaz  holds Predoctoral fellowship  PRE2020-095111- funded by  MCIN/AEI/ 10. 13039/501100011033  and by  "ESF Investing  in your future". Raúl Castanera  holds a  Juan de la Cierva Incorporación Postdoctoral  fellowship  IJC2020-045949- I funded by  MCIN/AEI/10. 13039/501100011033  and by the  "European Union NextGenerationEU/PRTR ". This work was funded by the  Spanish Ministerio de Ciencia e Innovación  ( PID2019-106374RB-I00/AEI/10. 13039/501100011033 ). We also acknowledge financial support from the  Spanish Ministerio de Economía y Competitividad  through the " Severo Ochoa Program for Centers of Excellence  in R&D"  2016-2019  ( SEV-2015-0533 ) and  CEX2019-000902-S  funded by  MCIN/AEI/10.
MATERIAL AND METHODS In this section, we first present the different species, the datasets and how we process and combine them to produce LPUE data in space and time. Second, we extend the model proposed by  Alglave et al. (2022)  to introduce a temporal dimension on a discrete monthly time step. In our applications, the models were fitted to data from 2010 to 2018 on a monthly time step (108 time steps). Then, we illustrate how the PS component modifies model predictions and can be interpreted, and how integrating several fleets in the analysis further improves model predictions. Last, we detail the method used to investigate spatio-temporal dynamics from model outputs and identify reproduction grounds based on the aggregation patterns of each of three species.
This was done by crossing the landings data with length class and maturity data. For most of the landings, information on the commercial size categories is available from the sales notes. These commercial categories are regularly sampled to derive length structure of each commercial category. This allows us to estimate the proportion of potentially mature fish in each commercial category by applying maturity ogives and in turn estimate the proportion of mature fish for each landing declaration. See SM1 for more detail. Note that this procedure was not possible for squids, as there are no data on maturity and size classes for this species group. Landing data were then combined with VMS data to finally obtained high spatial resolution LPUE data discretized on a 0. 05° x 0. 05° grid (i. e. 5.
5 km x 3 km) on a monthly time step (see SM2). This combination requires: (1) to identify the fishing locations within the VMS data. This is realized individually for each fishing vessel trajectory based on a speed threshold similarly as in common data processing methods  (Hintzen et al. 2012 ). (2) to reallocate the logbooks declaration on the related VMS fishing locations. This reallocation is realized individually for each fishing vessel trajectory by uniformly reallocating the landings on all fishing locations. The link between both data sources is realized through the combination 'vessel identifier x statistical rectangle x fishing trip x day'. LPUE are then computed by simply dividing the reallocated landings by the related fishing time.
Scientific data We also integrated scientific data in the analysis. For whiting and squids we used the survey data from the EVHOE survey. The Orhago survey was used for sole (ICES, 2020 -see SM3, Figure  S3 ). The data were extracted from the DATRAS database on the period 2010 -2018. Only the mature fraction of the survey catches were kept in the analysis to make it comparable with commercial data. Orhago is an annual beam trawl survey occurring in November and designed to assess sole stock status in the Bay of Biscay. Each year 50 stations are sampled within 4 strata all along the Bay of Biscay. Note that this survey is mainly coastal and does not sample offshore areas. EVHOE is an annual bottom trawl survey occurring in late October, November and early December with a stratified sampling plan.
Comparing the inferences with and without PS We first assessed the impact of PS on the distribution of biomass by comparing estimations obtained from integrated models (i. e. models fitted to all data sources) accounting for PS with those obtained when ignoring PS. We computed the log-likelihood related to each data source (commercial and scientific data) to assess if there is an improvement in model goodness-of-fit when accounting or not for PS. Note that fitting a model without PS is straightforward as it only requires to remove the sampling process component from the likelihood function.
Main findings In this paper, we develop a framework to infer fish spatio-temporal distribution on a monthly time step while combining scientific survey data and commercial catch declarations from several fleets. Commercial catch data constitute a valuable data source that complements scientific survey or onboard sampling programs by providing much higher spatio-temporal sampling density. Those complementary sources of data were integrated through a spatio-temporal hierarchical model taking into account spatiotemporal variation within the biomass field and PS on a monthly time step. We fitted the model to VMS-logbooks data filtered and processed over the period 2010-2018 for 3 demersal species (sole, squids and whiting) in the Bay of Biscay. We emphasize the benefit of integrating several spatially complementary fleets to infer fish distribution throughout the year.
Another exciting research avenue would consist in integrating population dynamics in the latent field of biomass  (Cao et al. 2020). This would require to refine further the demographic resolution of the VMS-logbooks data (see for instance Azevedo and Silva 2020), but once done, it would give access to huge data for inferring the space-time dynamics of fish populations. Finally, our model considers fisher preferentially sample areas where the biomass is higher (preferential sampling), but does not consider any other drivers and specifically the temporal and spatio-temporal relations that can affect fishers behavior. These can be highly complex and may depend on the distribution of the resource, tradition/habits, management regulations  (Abbott et al. , 2015; Girardin et al. , 2017; Salas and Gaertner, 2004; Hintzen, 2021).
Including discards would potentially improve our approach. Indeed, logbooks data are landings declarations data which means they inform on the landings and not on the true catch. Thus, by assuming the landings per unit effort are proportional to the biomass, we make the hypothesis that the discard rate is constant in space and time and does not affect model predictions. This should not be a problem for sole and squids as the discards are low and TAC have not been really binding during the studied period. However, the issue might be more stringent for whiting and/or other species with a high and nonstationary level of discards. Integrating discards data in the analysis could help solving this issue. Stock et al. (2019)  and  Yan et al. (2022)  used observer data to model bycatch in both space and time and  Breivik et al.
(2017)  used bycatch data from onboard surveys to predict the temporal evolution of bycatch realized in the full commercial data. Similarly, we could integrate into the same analysis the logbooks and the observer data by assuming that the catch of observer represents the sum of landings (which is also observed in the logbooks data) and discards (which is unobserved in the logbooks data). This way, the discards information available from observer data would be shared with the logbooks data and would allow correcting for the missing portion of catch declarations data while possibly accounting for possible space or time variation in the discard rate. Our analysis rely on the hypothesis that the spawning season is known a priori. Extending the approach to infer the spawning season based on the temporal dynamics of the aggregation patterns could improve our knowledge of species spatio-temporal distribution.
Hence, these methods would be more appropriate to identify phenological modifications in species life cycle in response to climate change. Applying those kind of methods to the huge amount of data available from mandatory declarations, would make possible to track the effect of climate change on fish phenology at a monthly/seasonal scale, while it is generally only possible at a yearly time step through scientific survey data  (Maureaud et al. 2020). Last, confidentiality remains a major limitation to the massive use of VMS data  (Hintzen 2021). Indeed, there are strong confidentiality constraints on these data due to the huge information available on fisher fishing grounds. Few countries are now giving free access to their data (e. g. Norway), but in most cases administrative procedures to get access to the data remain a burden and still constitute a limitation for the use of 'VMS x logbooks' data for routine operational use.
If, the model accounting for PS better fits the data than the model ignoring PS (no PS). Can. J. Fish. Aquat. Sci. Downloaded from cdnsciencepub. com by IFREMER BIBLIOTHEQUE LA PEROUSE on 01/23/23For personal use only. This Just-IN manuscript is the accepted manuscript prior to copy editing and page composition. It may differ from the final official version of record. Page 37 of 47 Canadian Journal of Fisheries and Aquatic Sciences (Author's Accepted Manuscript) Species Negative log-likelihood ratio Scientific data Commercial data Sole 0. 97 0. 92 Squids 1. 00 1. 01 Whiting 0. 99 1. 00 Note. The ratio between negative log-likelihoods ( -𝑙𝑜𝑔(𝑙𝑘𝑙) ) is given as: 𝑟 =. -𝑙𝑜𝑔 (𝑙𝑘𝑙 𝑛𝑜𝑃𝑆 ) -𝑙𝑜𝑔 (𝑙𝑘𝑙 𝑃𝑆 ) 𝑟 < 1 © The Author(s) or their Institution(s).
Observed patterns of trait integration We next sought to determine which of the modeled processes produced results consistent with observed patterns of trait integration. To do so we conducted a literature review ⁄ values for simulated (above solid line) and observed G matrices. Trunks (large points) are the medians for the specified group (e. g. Gaussian landscapes or Insecta), branches (thick lines) are interquartile ranges, twigs (thin lines) give the full range of values, and fruits (smaller points) are individual estimates within a simulation or taxonomic group. Rightmost letters correspond to statistical significance-or lack thereof-of comparisons of ratios among simulations. Datasets sharing letters did not significantly differ (Table  S3 ). Populations evolving due to drift alone had a significantly higher ratio than observed for either stabilizing selection or evolution on any of the holey landscapes.
Drift alone For populations evolving via drift alone phenotypes were generated as for Gaussian adaptive landscapes. Composition of subsequent generations was likewise generated based on the means and variances of the prior generation, without selection. The drift model therefore was simply a model of sampling error. Each of five modeling scenarios (simple landscapes, drift alone, three Holey landscapes with p = 0. 2, 0. 5, or 0. 8) was simulated 250 times for populations of 7500 individuals and for 100 generations for each population. All modeling code is available at https://github. com/DochtermannLab/Wright_vs_Holey.
Samples of the lithic material and animal bone assemblages collected from the site have already been the object of studies  (Celiberti et al. , 2018; Frère et al. , 2018) , but much of the material is still under study. In this paper, we will present zooarchaeological data collected on the herpetofaunal taxa bone remains recovered at the site until 2019. To collect this dataset, the entire archaeofaunal material has been observed to extract and study the reptile and amphibian bone remains. The material screened in this way corresponds to the material previously studied by S. Frère  (Frère et al. , 2018) , with the addition of the material collected following this first study. In the initial study, S. Frère analyzed 4256 animal remains, of which 2541 were attributed to vertebrate species. So far, no complete study of this faunal assemblage has been completed, and no Minimum Number of Individuals (MNI) data have been published.