dataesr/scientific-paragraphs-categorization huggingface dataset:
Important variables and partial dependence The analysis presented here showed the dependence of all target P forms, especially total P, on total N (Fig  S8 ). Considering the theory about the fate of these elements in soils  (Walker and Syers, 1976; Lambers et al. , 2008) , one possible explanation to this observed link comes from the development stage of the soils in the region, that are originated from old lithologies (minimum age > 1. 5 million years) and characterizted by millions of years of geological stability and countinuous weathering  (Val et al. , 2021). Meaning that the younger soils in the fitting dataset are old if compared with volcanic soils in the common chronosequences studied in Hawaii, for example  (Crews et al. , 1995). Quesada et al. (2010)  already identified a positive correlation between total P and total N.
In general, higher values of total P concentration and total N were found in the younger reference soil groups in the fitting dataset, with the concentration of both N and P decreasing with soil age, indicating that the correlation between total N and total P could be explained by the gradient of soil particle specific surface area, surface charge densities, and organic matter adsorption  (Quesada et al. , 2010). Another concurrent explanation could be the possible control of P on N fixation  (Reed et al. , 2013 , but see Wong et al. , 2020). In one hand, for younger and less weathered soils the main source of N is biological fixation and for P it is weathering and/or deposition of primary minerals rich in P  (Lambers, 2022). On the other hand, older and weathered soils are characterized by the continuous loss of N and P and the accumulation of P as occluded forms  (Crews et al.
, 1995) , with the organic forms of both elements exerting a strong control on the nutrient availability to plants  (da Silva et al. , 2022). The predominant low nutritional status of the soils in the fitting dataset and the positive relation between total N and total P indicates that in the very old and weathered soils of the pan-Amazon region, the low availability of P may be a limiting factor for biological N fixation  (Liese et al. , 2017; Van Langenhove et al. , 2021; Zhang et al. , 2022). Besides total N, other variables also presented high values of MDA for the target P forms. The variable pH has high values of MDA for all target P forms with the exception of the inorganic P. The partial dependence of total P on pH (Fig. 8 ) indicates positive relation between pH and total P. This is expected because in the sampled sites, in general, a greater concentration of total P is associated with younger soils that have a higher sum of bases and less exchangeable Al, which in turn are associated with higher pH values  (Quesada et al.
The sand fraction presented high values of MDA for the prediction of the inorganic P form (Fig. 6 ), and the partial dependence analysis (Fig. S14 ) shows a negative relation between sand fraction and inorganic P. The reduced adsorption capacity in the sandy soils of the fitting dataset can explain the negative relationship with the inorganic P form  (Quesada et al. 2010; Osman, 2018). The relations between the target P forms (and the fractional components of the Hedley sequential extraction) and the most important features of the fitting dataset are analysed and discussed extensively in the study of  Quesada et al. (2010).
Uncertainties related to the fitting and predictive datasets The reference soil groups presented low importance values for predicting the target P forms (Fig. S15 ). This indicates that the spatial extrapolation of P forms made from Hedley fractionation data based on reference soil groups can lead to a wrong interpretation of the concentrations and proportions of P forms in soils. The low number of observations of Hedley fractionation in different soil types  (Yang and Post, 2011)  leads to a high coefficient of variation of Hedley P fractions for each reference soil group. Moreover, there are a few soil classes that are under-represented in the fitting dataset (Table  S4   Quesada et al. , 2011). In general, this is the case for sites with higher elevations characterized by the occurrence of younger soils like Leptosols and Andosols (Figs.
S16 and Table  S4 ). Important intervals in the upper and/or lower ranges of some features related to elevation are absent in the training data, as shown by the dissimilarity index analysis (Fig. S3 ), reducing the suitable area to predictions using the random forest models. Our random forest regression models presented a good level of accuracy during the training phase. Nonetheless, the results could be improved by a greater number of in situ observations of soil P fractions and other edaphic and climatic variables in the Amazon region, especially in high elevational areas where observations are lacking, with notable exeptions  (Wilcke et al. 2019). Finally, it is important to note that our P maps rely on raster datasets used to predict the P pools, which are subject to their own uncertainties and inconsistencies.
2018). Random forests are recognized for their power in prediction exercises; however, the methods to unveil the underlying drivers and mechanisms relating target variables and explanatory feaures are still under development  (Lucas, 2020; Simon et al. , 2023). Moreover, the dataset used to fit the random forest models is subject to a lack of observations in elevated regions and have their methodological limitations and uncertainties. Nonetheless, the method applied here to estimate the spatial distribution of P forms in the topsoil of the pan-Amazon region relies on the relationships that maximize the amount of information that each predictive variable (i. e. , feature related to the soil P content at the landscape scale) can contribute to the random forest regression models. Thus, our approach can partially overcome the lack of certainty in the Hedley fractionation P forms/soil classification relationship by applying a nonparametric method based on statistical learning to predict the P foms in the soils of the pan-Amazon region.
Acknowledgements We are thankful to  Andy Krause  for the insightful comments on the manuscript. TR would like to thank for the financial support of the  International Graduate School of Science and Engineering (IGSSE-TUM)  through the grant associated with the  PhosForest  project and the financial support of the  Bayerische Staatskanzlei (Bavarian State Chancellery)  through the grant associated with the  Amazon-FLUX  project. LFL would like to acknowledge the  Bavarian State Chancellery (Project Amazon-FLUX)  for financial support. DML would like to acknowledge the  Sao Paulo Research Foundation -FAPESP  (grant nº  2015/02537-7 ) and the  Brazil's National Council on Research and Technological Development -CNPq  (grant nº  309074/2021-5 ). JPDF would like to thank for the financial support of the  Sao Paulo Research Foundation -FAPESP  (grant nº  2017/00005-3  and grant  nº 2019/08194-5 ).
Hijmans, https://doi. org/10. 5194/essd-2023-272 Preprint. Discussion started: 16 August 2023 c Author(s) 2023. CC BY 4. 0 License. 2017) and refer to the climatological means for the 2001-2010 decade. Soil pH in water, soil texture (sand, silt, and clay, %), and total organic C (TOC, %) were extracted from the Harmonized World Soil Database -HWSD version 1. 2.
Fecundity was added as random factor in the model testing the impact of temperature on the number of surviving juveniles, to account for the variation in the number of eggs laid (Table  S3 ). In the analyses of daily fecundity and offspring number, the type of cross between males and females from different developmental temperatures (♀25 x ♂25, ♀33 x ♂25, ♀35 x ♂25, ♀36 x ♂25 or ♀37 x ♂25) was included in the model as fixed factor (Table  S3 ). To determine if the difference of each temperature to the control was significant, we used the function summary and took the value that reports the difference of the first temperature listed on the dataset to an intercept of zero (which in this case corresponds to the mean trait value at control temperature). This was repeated for all temperatures by reordering this variable on the dataset.
Acknowledgments We thank all  Mite Squad  members for unconditional support in the laboratory and elsewhere, particularly  Cátia Eira ,  Juan Lopez  and  Lucie Santos  for valuable help in the maintenance of the populations and rearing of the plants used in these experiments. We also thank  Élio Sucena  for useful discussions on the backcross procedure, and  Thomas Van Leeuwen  for providing the SB9 strain. This work was funded by an  European Research Council (ERC)  consolidating grant ( COMPCON GA 725419 ) to SM, and a  Fundação para Ciência e Tecnologia (FCT)  project ( EXPL/BIAEVL/0131/2021 ) to LRR. This is publication  ISEM-2023 -XXX of the  Institut des Sciences de l'Évolution.
Many human-introduced alien species economically impact essential industries worldwide. Management prioritization and coordination efforts towards biological invasions are hampered by a lack of comprehensive quanti cation of costs to key economic activity sectors. Here, we quantify and predict global invasion costs to seven major sectors and unravel the introduction pathways of species causing these costs -focusing mainly on resource-based agriculture, shery and forestry industries. From 1970 to 2020, costs reported in the InvaCost database as pertaining to Agriculture, Fisheries, and Forestry totaled $509 bn, $1. 3 bn, and $134 bn, respectively (in 2017 United States dollars). Pathways of costly species were diverse, arising predominantly from cultural and agricultural activities, through unintentional contaminants with trade, and often impacted different sectors than those for which species were initially introduced.
To address our aims, we rst used the 'invacost' R package  (Leroy et al. 2022) , which allows complete processing and investigation of the InvaCost database, to decipher the distribution and dynamics of recorded costs over a number of parameters (e. g. , time, space, taxa and sectors). Second, we examined the pathways of entry and establishment resulting in the greatest impacts to each sector based on  Turbelin et al. (2022). Third, we examined whether particular countries possessed a high burden of economic impact relative to the value of their resource-based industries, by visualizing each country's economic impact as a function of the amount of their GDP contributed from these industries. Finally, we extrapolated unrecorded costs of all invasive species for these resource-based sectors with a more comprehensive list of potential invasive species threats that are directly linked to the harvest of biological resources.
To create a more complete list of the total set of identi ed invasive species impacting Agriculture, Fisheries and Forestry, we used an independent pest database to extrapolate the potential cost of the entire set of invasive species known to impact a particular sector, both reported in InvaCost and unreported. Together, these approaches allowed us to examine the observed costs of biological invasions to primary sectors, unravel their introduction pathways, ll knowledge gaps and extrapolate risks among countries.
Observed economic losses We focused on a portion of the InvaCost database that contains only Observed costs, i. e. , those cost estimates that were actually realized due to an invasive species within the invaded region (Fig. 1 ). The costs estimated here ranged from $1 bn for Fishery (including aquaculture) to $509 bn for Agriculture between 1970 and 2020. Over $732 bn in losses from biological invasions were attributed to mixed or unspeci ed sectors. Of these, ~53% were a combination of an impact on Agriculture and one or more other sectors -the highest type of mixed-sector costs (inset in Fig. 1a ). When considering the type of cost incurred, Damage-loss costs accounted for over 50% of economic losses to all resource-based economic sectors (Agriculture, Fishery & Forestry), as well as to Environment, Public and social welfare and Mixed / Unspeci ed (Fig.
Both the high number of cost records for Agriculture and associated high observed losses can be explained by a combination of factors  (Vaissière et al. 2022) , including (i) costs being easily monetised, (ii) impacts being monitored consistently and (iii) the size of the sector -agriculture represents 4% of global GDP (FAO, 2022) (see SI Discussion). Pathways for damaging invasive species were diverse, with impacts frequently incurred by sectors disconnected to the initial introduction pathway (e. g. , cultural introductions damaging agriculture). Despite these widespread impacts of biological invasions across sectors, our extrapolations indicate that costs could be several times higher than currently reported. Further, our extrapolations should be considered conservative in that they assume all sectoral and geographic impacts of species present in InvaCost are fully reported, when inclusion in this database is subject to well-described underreporting  (Angulo et al.
Data Preparation Global costs to sectors Cost data were extracted from the InvaCost Database  (Diagne et al. , 2020)  using the R invacost package version 1. 1-4 (R Core Team 2020)  (Leroy et al. 2020; Leroy et al. 2022). We extracted entries for all species that were reported at the country level within any country at any point in time. We conservatively excluded low-reliability estimates (those from gray material sources lacking documented, repeatable or traceable methods) and potential costs (those not incurred but rather expected and/or predicted over time within or beyond the species' actual distribution area), as de ned within the InvaCost database  (Diagne et al. 2020). We extracted the years over which impacts were reported within each InvaCost entry ("Impact_year" column of the database extracted with the invacost R package).
Pathways of introduction We acquired pathway information for individual species listed in InvaCost (i. e. , where the cost was attributed to a single species as opposed to multi-species or genus-level) from  Turbelin et al. (2022). Data available were for InvaCost version 4. 0 so we completed pathway information for 48 additional species with highly reliable observed costs listed in version 4. 1 following the methods described in Turbelin et al. (2022). Pathway information for the species was mainly gathered from CABI ISC (www. cabi. org/isc/), the GISD (http://www. iucngisd. org/gisd) and other sources when information was not available in the aforementioned databases (e. g. , targeted searches of the published literature; national checklists). Pathway descriptions were recorded and matched to both the CABI ISC pathway description and the pathway mechanisms, categories, and subcategories of the CBD scheme using the published guidelines for the scheme (i.
External impact data While many economic sectors are reported within InvaCost, we focused our extrapolation on three major primary production sectors (resource-based sectors) that have a well de ned list of invasive species known to be impactful: Agriculture, Fisheries, and Forestry. Other sectors contain a more diverse set of actors (e. g. , Authorities-Stakeholders, Public and Social Welfare) and are less easily linked to impacts listed by databases such as CABI's Invasive Species Compendium (ISC) (https://www. cabi. org/ISC).
Funding This work was supported by  Open Program of International Joint Research Laboratory   for Recombinant Pharmaceutical Protein Expression System of Henan (Program  No. KFKTYB202201 ) and  National Natural Science Foundation of China  (No. 31500982 ). This work was also funded by  Key Scientific Research Project Plans of Higher Education Institutions in Henan Province  ( 24A350011 ,  24B310009 ).
Data analysis The raw reads were analyzed using the QIIME2 pipeline  (version 2021. 4 ). Due to the poor quality of the reverse reads, only the forward reads (which contain almost the entire V4 amplicon) were used for the subsequent analyses. DADA2 was used for quality filtering, denoising, and Amplicon Sequence Variant calling, using the plug-in q2-dada2 with the denoise-single method. Sequences were then clustered into operational taxonomic units (OTUs) at 97% identity, using the plug-in qiime vsearch cluster-features-de-novo. Taxonomic assignment was done against the SILVA database version 138  (Quast et al. 2012). OTUs identified as chloroplasts, mitochondria, archaea, and eukaryotes were filtered out along with rare OTUs (i. e. , singletons and OTUs represented by < 10 reads). The resulting OTU table was then used for the subsequent analyses.
Acknowledgements This study was funded by the  INTERREG Dolomiti Live  Projects  ITAT4132  and  ITAT4153 , to HS, MS, CS, MF and AB, the  province of Bolzano  and the  Hermann Rubner Privatstiftung Onlus  to HS, the  Amt der Tiroler Landesregierung Abteilung Waldschutz  to CS and MS. We thank  Alessandro Andriolo ,  Harald Lercher  and  Andrea Stradner  for assistance in insect collections and  Peter H. W. Biedermann  ( University of Freiburg, Germany ) for helpful comments and suggestions.
8. 4. Downstream analyses for aligning reads for assembly were performed with scripts within Trinity using Salmon. For flounder transcriptome assembly we added liver transcriptome data from   3  in the Methods section. Dashed ovals indicate spawning areas of flounders in the Baltic Sea, representing separate populations  (Nissling et al. , 2017) , no comparable data was available for dab. Base map from Esri. Pomianowski et al. (2021)  to increase assembly quality. We ran the analyses for flounder also without this additional data to control if the differences with dab might have arisen from assembly quality. Both flounder assembly versions (only our data vs our data + data from  Pomianowski et al. , 2021)  gave quantitatively (but not qualitatively) similar results (described in Supplementary Materials, part 2).
Differential expression analysis between groups was conducted using both edgeR and DESeq2. EdgeR method and DESeq2 method perform the differential expression analysis slightly differently, with edgeR giving more conservative results. In both approaches, p-values were adjusted for multiple testing with the Benjamini-Hochberg procedure. To annotate the obtained transcriptome, we used Dammit using orthologous genes database (OrthoDB) version 10. 1. Human orthologues for each transcript were retrieved through OrthoDB using the best match cluster ID. We matched against human gene database, as curated cancer gene lists exist for humans but not for other species. However, as cancer genes are among the oldest gene classes in vertebrates  (Makashov et al. , 2019) , using human gene databases for cancer gene annotation is a reasonable proxy.
In addition to OrthoDB we utilized Trinotate for the assignment of Gene Ontology (GO) terms and best SwissProt gene matches. Trinotate is a comprehensive functional annotation suite specifically designed for automatic functional annotation of transcriptomes, particularly de novo assembled transcriptomes, from model or non-model organisms  (Bryant et al. , 2017). Trinotate leverages a number of different wellknown bioinformatics tools and databases to assign various functional annotations to transcripts. These include homology search to known sequence data (BLAST+/SwissProt), protein domain identification (HMMER/PFAM), protein signal peptide and transmembrane domain prediction (signalP/tmHMM), and leveraging various annotation databases (eggNOG/GO/Kegg databases) to provide functional annotation information.
We followed the standard Trinotate protocol. After obtaining a set of assembled transcripts, we first identified the likely coding regions within these transcripts using TransDecoder. The resulting protein sequences were then subjected to homology search against the SwissProt database. The top matching entries were retrieved. The GO terms were assigned based on the homology search results. Trinotate uses both Pfam and BLAST databases for this purpose, which allows for a more comprehensive functional assignment compared to other tools that use only one database  (Das and Mykles, 2016). For the GO category analysis, we used the differentially expressed genes obtained from DeSeq2 instead of EdgeR. This allowed compiling larger number of genes into a reasonable number of categories for interpretation.
1. 3 (R Core Team, 2022). KEGG plots were produced using Pathview. Heatmaps were generated from log transformed individual expression levels. For heatmaps, we used the edgeR differential expression analysis, which is more conservative compared to the DeSeq2 method. Having less transcripts simplifies the visualization. The gene symbols for heatmaps are the SwisProt best matches (if available) or shortened transcript names corresponding to the respective field is supplementary tables. Other R packages used included several packages from the tidyverse and their dependencies. The raw sequencing data along with the assembled transcriptome is openly available in EMBL-EBI European Nucleotide Archive under the primary study accession number PRJEB53201. In order to generate specific hypotheses on the molecular pathways linked with cancer and modulated in cancerous liver tissue, or as compensatory adaptation to oncogenic pollution, we additionally used pathways defined in the KEGG database (Kyoto Encyclopedia of Genes and Genomes, http://www.
05), whereas using the EdgeR approach, there were 42 significant results (FDR <0. 05) (differentially expressed transcripts listed in Supplementary data file). We categorized the annotated transcripts according to GO biological processes (if available) or GO molecular functions (Fig. 4 , The Gene Ontology Consortium, 2021). We ran similar analysis with only females included to control for the potential confounding effect of sex (Fig. S4 ), ending up with qualitatively similar results. In dabs, we mostly observed the upregulation of genes in cancerous fish, but in flounders, we could also see downregulation of different categories of genes in cancerous fish, although upregulation was still more common.
05). In order to get the best possible matches for proteins, Table  3  shows the 12 gene ID's that were significantly different with both methods. In dabs, the DESeq2 approach found 67 gene ID's that were significantly different between polluted and reference sites (p-value adjusted <0. 05), while the EdgeR approach recovered only 12 gene ID's that were significantly different between polluted and reference sites (FDR <0. 05). Table  4  shows the 11 gene ID's that were significantly different in both methods in dabs. Using the OrthoDB we searched for the best protein match for each significant gene ID (See Tables  3  and 4 ) for both species, and also added GO categories for molecular processes from Trinotate, if available. There is greater diversity in the best protein matches for flounder than dab, and different molecular functions for transcripts are indicated.
, 2006). Mammalian studies have also suggested that some CYP enzymes are upregulated in cancer cells compared to adjacent non-diseased cells  (Willett et al. , 2006). CYP activity is higher in cancerous flounders compared to dabs (higher number of linked transcripts and higher transcript abundance), suggesting that compared to dabs, flounders show more active pollutant metabolism. This is usually linked to higher cancer risk, but we did not see this in flounders compared to dabs. We suggest two possible explanations for this. First, it is possible that the more active pollutant metabolism in flounders produces some signals in the liver that trigger cancer defence mechanisms. This could be described as a potential hormesis effect, which is defined as an adaptive response of biological systems to moderate environmental challenges through which the system improves its functionality and/or tolerance to more severe challenges  (Calabrese and Mattson, 2017).