dataesr/scientific-paragraphs-categorization huggingface dataset:
| Genome scans for outliers PCAdapt identified 27% of the 10,987 NZ-wide loci as 'outlier' markers, 3. 8% of the 12,012 loci in the NI-specific dataset, 4. 1% of the 7,476 loci in the SI-specific dataset and 9% of the 13,004 loci in the southern-specific dataset (Figure  2 ). In comparison, Bayescan identified 21% of the NZ-wide loci as 'outlier' markers, 0. 2% of the loci in the NI dataset, 0. 3% of the loci in the SI dataset and 1% in the southern dataset (Figure  2 ). There was a much smaller number of 'outlier' markers found to be in common (intersection) between the two genome scan methods (Figure  2 ). Combining the two methods using the geometric mean of q-values resulted in a larger number of 'outlier' markers NZ-wide than each of the methods separately, and in intermediate numbers in the other datasets (Figure  2 ).
Of the 3,979 combined 'outlier markers' in the NZ-wide dataset, 35 had observed heterozygosity above 0. 5. In addition, 73 were completely missing in a population and were removed from downstream analyses. In the southern dataset, out of the 795 combined 'outlier' markers, 290 were completely missing in one population, and 247 were missing from two or three populations, and all these were removed from downstream analyses. No 'outlier' marker was completely missing from populations of the NI-and the SI-specific datasets. The allele frequency spectra of the 'outlier' markers are broadly similar to that of the remaining loci for all populations (i. e. the set of loci after 'outlier' markers are removed), but often have a greater proportion of high-frequency alleles (Figure  S26 ). The stairway plots show a recent bottleneck (between one and five thousand years ago) in both 'outlier' markers and the remaining loci, but the former have a larger final effective population size (Figure  S30 ).
The general pattern for each North, Central and South clades does not differ among different sets of loci. The NZ-wide population structure derived from all SNP loci (Figure  1c ) is similar to the structure shown by combined 'outlier' markers and by the remaining loci (Figure  1d ,e), all strongly supporting the three genetic clades previously described  (Salloum et al. , 2020). The patterns of 'outlier' markers and the remaining loci are also broadly similar in all spatial scales (NI, SI and southern, Figures  S17-S23 ), but it is clear that the remaining loci exhibit relatively more differentiation among nearby populations, while 'outlier' markers may reflect higher F ST overall (Figures S17-S23 and S31). The 'outlier' markers show lower heterozygosity than the remaining loci in most populations of all datasets, except for populations of the South clade in the NZ-wide dataset (Table  S6 ).
| Genotype-environment association Many of the environmental variables tested are strongly correlated with latitude at all scales (NZ-wide, NI, SI and southern, Figure  S32 ). The RDA analyses identified no genotype-environment associations in the NZ-wide, NI and SI datasets, but 708 associations in the southern dataset (5% of the loci). In comparison, BayeScEnv identified 3,093 associations from the New Zealand-wide dataset (28% of the loci), 15 from the NI dataset (0. 13% of the loci), 10 from the SI dataset (0. 13% of the loci) and 135 from the southern dataset (1% of the loci) (Table  3 , Tables  S4  and S5 ). After combining qvalues between RDA and BayeScEnv, and removing loci associated with random variables, no high-confidence GEA loci remained at the NZ-wide, NI and SI scales, but 86 GEA loci remained at the southern scale, corresponding to 0.
05 and not associated with >5% of the random variables. The total number of loci in each dataset is shown within parentheses proportion of candidate loci detected NZ-wide is much larger than within the other scales. Considering that 2%-10% of the loci in a dataset are usually found to be significant in this type of analyses  (Bierne et al. , 2011) , our numbers of NZ-wide candidate loci are extremely high and are likely to include many false positives. F I G U R E 2 However, our GEA analyses revealed 86 high-confidence loci (with an increased probability of being true positives) within the southern region. Thus, despite greater gene flow in this region, there are candidate loci suggesting potential for local adaptation. Apart from population structure, there are other factors that can lead to an increase in false-positive rates of genome scans (GSO and GEA).
environmental adaptation, gene flow, genome scans for outliers, genotypeenvironment associations, outlier loci, population genetic structure, q-values geometric mean may lead to populations becoming adapted to local conditions, and expect less evidence of local adaptation among southern populations, which experience higher levels of gene flow and encompass only a subset of the entire environmental variation. Within this framework, we aimed to (a) compare the detection of candidate loci in low versus moderate gene flow scenarios and (b) determine F I G U R E 1 Geographical and genetic relationships among sampled populations of Onithochiton neglectus. (a) Geographical location of populations asnd clades. Populations included in the NI dataset are indicated with *, and in the SI dataset with +; (b) relative migration network (estimated using divMigrate).
zygosity and missing data across populations. Past demographic patterns were inferred for the combined 'outlier' markers and for the remaining loci (i. e. having removed combined 'outlier' markers from the dataset) in the NZ-wide dataset with the program Stairway Plot 2 (Liu & Fu, 2020) , as well as for each clade separately. A plot of allele frequencies, a PCA and a plot of the distribution of overall F ST were done with the combined 'outlier' markers and the remaining loci for each dataset. For comparison with the NI, SI and southern datasets (where SNPs were called independently within these groups), the genome scans, PCAs and allele frequency plots were also done for these groups by subsampling the NZ-wide SNP dataset into the North Island, South Island and southern regions (Table.
Figures S17-S23; assignment plots: FigureS24), and allele frequencies NZ-wide show loci differentially fixed between clades (FigureS17). In addition, the SNP datasets subsampled from the NZ-wide dataset (NI, SI and southern subsets) show very similar patterns to those datasets with SNPs called separately (NI, SI and southern specific), although the latter all have a larger number of loci (FiguresS18-S23, TableS2). Distributions of pairwise F ST between clades show strong differentiation between each of the three clades with high F ST (North-Central = 0. 713; North-South = 0. 639; Central-South = 0. 643, FigureS25). The allele frequency spectrum plots reveal a broadly different pattern for all populations in the South clade as compared to populations from other clades, as southern populations have a higher proportion of low-frequency alleles (FigureS26).
Note that the population allele frequency spectra are based on counts from the NZ-wide datasets, and the larger number of South populations means the south alleles are the majority, and thus taken as reference. The 2D allele frequency spectra between clades show no correlation pattern, with most of the highcount alleles in one clade missing or infrequent in the other, indicating no evidence of introgression (FigureS27). The distribution of observed heterozygosity per population NZ-wide shows most loci with low heterozygosity (below 0. 2, FigureS28, TableS6). Finally, relative migration.
(Catchen et al. , 2011;Catchen et al. , 2013)ributions to the associated New Zealand wide (NZ-wide), upper North Island (NI), South Island (SI) and south and Subantarctic Islands (southern) datasets to AgResearch Invermay, Mosgiel, New Zealand, for genotypingby-sequencing (GBS), as detailed in the Supplementary Material (Supporting Information 1). SNPs were called de-novo in Stacks v. 2. 4(Catchen et al. , 2011;Catchen et al. , 2013)for four datasets: New Zealand-wide across all populations (NZ-wide), within the North Island (NI) and South Island (SI), and within the South clade (southern). The Central clade was not called separately as it had only two populations, and the North clade is the same as the NI dataset. SNPs with minor allele frequency <0. 05 were filtered out. To exclude loci in strong linkage disequilibrium (LD), one SNP per rad-tag was chosen at random, decreasing the possibility of having SNPs across loci in LD.
Finally, individuals with more than 90% missing data (two individuals from the Christchurch population) and rad-tags with more than 30% missing data were removed (full details of software and parameter settings are provided in Supporting Information 1, Figures S1- Population Population abbreviation Individuals Clade Dataset Russell RU 14 North NZ-wide, NI Auckland TI 12 North NZ-wide, NI Coromandel NC 10 North NZ-wide, NI East Cape EC 9 North NZ-wide, NI Wellington WE 18 Central NZ-wide Cape Palliser CP 10 Central NZ-wide Christchurch CR 6 b South NZ-wide a Dunedin DU 20 South NZ-wide, southern, SI Akatore AK 9 South NZ-wide, southern, SI Curio Bay CU 5 South NZ-wide, southern, SI Stewart Is. ST 8 South NZ-wide, southern, SI Auckland Is. AU 10 South NZ-wide, southern Campbell Is. CA 10 South NZ-wide, southern Antipodes Is.
For this reason, it was important to test different parameter combinations and population maps when identifying SNPs, particularly in the NZ-wide scale. The resulting dataset for this broader scale might be more representative of conserved re- gions of the genome, as we aimed to reduce missing data among clades with the applied filters. By also analysing the North Island, South Island and southern populations separately, determining SNPs within each group (as opposed to simply splitting the original SNP dataset), we were able to reduce missing data, and identified a larger number of loci that are variable within clades, and not only between them (Figures S17-S23, Table.
MeRIP-seq data analyses Read quality was evaluated using FastQC (v. 0. 11. 7) and MultiQC (v. 1. 7) and adaptor sequences and low-quality reads were removed using Trimmommatic (v. 0. 38). The remaining reads were aligned to the oyster genome v. 9 (GCF_000297895. 1) and uniquely mapped reads were counted using STAR (v. 2. 7. 3a) with the parameter "-quantMode GeneCounts"  [88]. mRNA and ncRNA were identified from the gene annotation and TEs were identified using the RepeatMasker annotation output provided with assembly data. The expression level in all Input samples was expressed in transcripts per million (TPM)  [89]. The identification of m 6 A-enriched peaks was performed on uniquely mapped reads of the IP samples using SAMtools (v. 1. 9) and MeTPeak R package  [90]  [false discovery rate (FDR) < 5%] with the cognate Input.
GO analysis The RNA sequences identified as DM across oyster development were identified using BLASTN  [92] [93] [94]  against the GigaTON reference transcriptome database  [65]  with default settings. GO analyses were carried out with the GO annotations obtained from the GigaTON database gene universe  [65]. GO term enrichment tests were performed using ClueGO plugin (v. 2. 5. 7)  [95]  on Cytoscape (v. 3. 8. 0). The hypergeometric test with a FDR < 5% was used to consider significant GO term enrichment.
The correlation between methylation level and transcript content across oyster development, as well as methylation variation and transcript content variation between stages, were estimated using linear regression. A P value < 0. 05 was considered significant. All bioinformatic analyses (unless otherwise stated) were performed using R (v. 3. 6. 3) and RStudio (v. 1. 0. 153) softwares. The R packages eulerr  [96] , ComplexHeatmap  [97] , ggplot2  [98] , Guitar  [91] , PCAtools  [99] , and Prism v. 6 (GraphPad) software were used for figure production. Figure legends.
Acknowledgments This work was supported by the  French national program CNRS EC2CO ECODYN (Ecosphère Continentale et Côtière 'HERITAGe' , Grant No. 2019-200919362L ) awarded to  Guillaume Rivière  and the  Council of the Normandy Region (RIN ECUME  Grant No. 18E01643 -18P02383 ) awarded to  Pascal Favrel. ORCID 0000-0002-4051-4415  ( Lorane Le Franc )  0000-0002-4169-2811  ( Bruno Petton )  0000-0002-2504-9957  ( Pascal Favrel )  0000-0001-8372-8715  ( Guillaume Rivière ).
Data availability All data relative to this study are publicly available at the National Center for Biotechnology Information Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/) under the accession number GEO: GSE180388, and in the Genome Sequence Archive [100] at the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation (GSA: CRA007976), and are publicly accessible at https://ngdc. cncb. ac. cn/gsa.
Acknowledgments This research was supported by grants from the  CERCA  ( Centres de Recerca de Catalunya) Programme/Generalitat de Catalunya  for institutional support. It was additionally supported by grants from the  Ministerio de Economía y Competitividad  (grant  PSI2015-68701-R ) and from the  Ministerio de Ciencia, Innovación y Universidades  (grant  RTI2018-101837-B-100 ). The research was funded by the  Delegación del Gobierno para el Plan Nacional sobre Drogas  (grants  2019I47  and  2021I031 ),  Instituto de Salud Carlos III (ISCIII ; grant  PI17/01167 ), and cofunded by the  FEDER (Federacion Espanola de Enfermedades Raras)  funds/ European Regional Development Fund , a way to build  Europe. CIBERObn  ( Centro de Investigación Biomédica en Red-Fisiopatología de la Obesidad y la Nutrición ) and  CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)  are both initiatives of  ISCIII.
As such, allocating budgets to the management of populations that have not yet shown tangible, strong impacts may be difficult to justify for decision makers. For biological invasions, there is presently a lack of such justification to invest in earlystage management actions. Thus, the objective of this study is to provide quantitative support for early investment by showing that management becomes increasingly costly with implementation delay. We focus on a theoretical demonstration, but use both mathematical modelling and empirical data from the InvaCost databasethe most comprehensive and upto-date dataset of costs caused by IAS globally  (Diagne et al. , 2020a (Diagne et al. , , 2020b)). Our central hypothesis and model assumption is that the cumulative costs of both damage and management of IAS follow a logistic curve with time.
Datasets and logistic growth of invasion costs At the time of initiating the study, the InvaCost database includes 9,823 entries from systematic and opportunistic literature searches conducted primarily in English, and altogether in 15 languages  (Diagne et al. , 2020b; Angulo et al. , 2021). This database captures reported economic costs associated with IAS in their novel range (incurring costs from management, damage and losses), including species that may become established in the future (incurring costs from prevention and early detection and rapid response) in a specific area. These data are described with a number of key database descriptors (over 60 in the latest InvaCost version 3. 0, see https://doi. org/10. 6084/m9. figshare. 12668570 for complete details) and standardised against a single currency (2017 US$).
These descriptors include, among other things, the cost type ("Type of cost merged"), which groups costs into three distinct categories: (a) "Damage" referring to damages or losses incurred by the invasion (e. g. costs for damage repair, resource losses, medical care), (b) "Management" comprising any expenditures dedicated to prevent, limit and/or mitigate invasion impacts (e. g. monitoring, prevention, control, education, eradication) and (c) "Mixed" including indistinguishable damage and management costs (cases where reported costs were not clearly separable from the aforementioned cost types categories). We considered all types of damage costs, but only post-invasion management costs, in order to eliminate preventative management. This was done using the "Management_type" column of the database by selecting the "Post-invasion_management" category therein.
We further filtered our dataset to examine only costs incurred at larger scales, using only "Country" and "Site" spatial scales from the "Spatial_scale" column, and focussed on only costs deemed to be of "High" reliability in the "Method_reliability" column. We also removed any extrapolated (unobserved) costs by limiting our search to "Observed" costs in the "Implementation" column. For the consistency and comparison in data used, all costs in the original database were 'expanded' so that cost entries could be considered on an annual basis. This means that single cost entries spanning multiple years (e. g. $10 million between 2001 and 2010) were divided into distinct entries according to their duration (e. g. $1 million for each year between 2001 and 2010 corresponding to ten entries in the expanded database).
Expansion was done using the expandYearlyCosts function of the 'invacost' R package  (Leroy et al. , 2020) , which repeats the annual cost for each database entry according to the estimated time range of impacts provided with each reference in the InvaCost database. To illustrate the logistic growth of economic costs, we used species-specific datasets from the InvaCost database to model the cumulative yearly costs with time. Ideally, we would have shown the applicability of this approach for the majority of IAS. However, we were limited by the number of species with sufficient temporal cost data. Our species-specific approach was thus restricted to genera with both >10 independent post-colonial (after year 1500) cost estimates from InvaCost and a lack of large gaps in cost records over time (7 genera in total: 3 for damage costs: Ambrosia, Callosciurus, and Procyon, 3 for management costs: Anoplophora, Cenchrus, and Salvinia and 1 genus with adequate records for both damage and management costs: Aedes).
Damage and Management curves All of our logistic fits were very strong across genera ( 2 > 0. 9, Table  1 ). We found that raccoons (Procyon) and squirrels (Callosciurus) had the highest  values, while longhorn beetles (Anoplophora) and sandburs (Cenchrus) had the highest   values (Fig. 5 ). Aedes, Anoplophora and Ambrosia spp. reached very high damage and/or management costs. Anoplophora had both high   and   values. While all economic impact data are subject to time lags in invasion and detection  (Crooks, 2005) , we found greater support for lagged occurrence of damage costs compared to management costs when collating InvaCost data with the sTwist database for first records of invasions  (Seebens et al. , 2020 , Table  1 ). The initial damage cost is found to be significantly lower than the cost carrying capacity (approx.
10% or lower), and varies across genera. This does not hold for initial management costs, which can be relatively much higher, for e. g. Salvinia (floating fern). The "cost lag" column provides the time between the first known record of the IAS anywhere outside of its native range (from the sTwist Database,  Seebens et al. , 2020)  and the first record within InvaCost. Table  1. Logistic modelling results for (A) cumulative damage costs and (B) cumulative management costs. Note that these costs were modelled separately without assuming proportionate cost carrying capacities. Growth rates: low ,   < 0. 1 (yellow), intermediate 0. 1 < ,   < 0. 3 (orange), high ,   > 0. 3 (red). Cost carrying capacity: low ,   < 100 (yellow), intermediate 100 < ,   < 1000 (orange), high ,   > 1000 (red). The "# estimates" column provides the number of independent cost references provided in the InvaCost database with which the curves were derived.
Implications for management Our work highlights that failing to begin managing an invasion can quickly lead to immense costs. Under the assumptions of our model, in the absence of any management, the cost of inaction itself has a logistic shape (see red curve in Fig. 2d ), as has been shown for damage costs across a range of genera and habitat types considered in the InvaCost database  (Ahmed et al. , this special issue). Therefore, the cost of inaction increases exponentially prior to a certain threshold time, after which it eventually saturates at a high level. This means not only that IAS costs can quickly increase to unbearable amounts, but also that they may initially be deceitfully slow to accrue, therefore not signaling to policy makers the urgency to invest in management. Indeed, during this initial time period, the willingness to allocate funds to IAS management may be low due to the lack of perceived risk or impact detection  (Finnoff et al.
, 2020; Diagne et al. , 2020). However, we acknowledge that many IAS may not have any reported economic costs at all, let alone costs that conform to a logistic curve. IAS impacts are often hard to quantify and monetize  (Charles & Dukes, 2008) , with many economic losses therefore going unreported due to a suite of biases or limited capacity to capture them  (Bellard & Jeschke, 2016). The data we selected for this analysis were chosen based on the availability of consistent cost reporting through time by multiple independent sources. While this resulted in a limited sample of IAS suitable for our analysis, this approach was necessary given the lack of cost information for most species at sufficient temporal resolution. Although this work should serve primarily as a theoretical demonstration, where estimated, the costs of inaction were found to be striking.
The authors acknowledge the French National Research Agency (ANR-14-CE02-0021) and the BNP-Paribas Foundation Climate Initiative for funding the InvaCost project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the AXA Research Fund Chair of Invasion Biology and is part of the AlienScenarios project funded by BiodivERsA and Belmont-Forum call 2018 on biodiversity scenarios. DAA is funded by the Kuwait Foundation for the Advancement of Sciences (KFAS), grant no. PR1914SM-01 and the Gulf University for Science and Technology (GUST) internal seed fund, grant no. 187092. RNC acknowledges funding from the Alexander von Humboldt Foundation. EJH is supported by a Fonds de recherche du Québec -nature et téchnologies B3X fellowship.
Availability of data and material The data used in this work come from publicly available databases (sTwist:https://zenodo. org/record/3925164#. YDv-dS3b0Wo; InvaCost: https://doi. org/10. 6084/m9. figshare. 12668570). Derived data used for curve parameterization are provided at https://github. com/emmajhudgins/CostOfInaction.
The Southern Hemisphere Westerly Winds (SWW) control the amount and latitudinal distribution of rainfall in southwestern Patagonia. Recent studies have shown that SWW has intensified in the last decades, but their past behavior is not yet well understood. To understand this behavior, it is necessary to analyze climatic data from meteorological stations and reconstruct their variability through paleoclimatic evidence, such as lake cores. Nevertheless, Patagonia is an austral region characterized by its complex topography and quasi lack of a meteorological network. In this work, three reanalyses are studied (MERRA-2, ERA5, and GLDAS) and compared with the Cerro Castillo and Teniente Gallardo stations (~51 • S), with the aim of simulating the winds in the past. The results indicate that ERA5 and MERRA-2 simulate well the wind variability in the study region, while GLDAS is less reliable.