dataesr/scientific-paragraphs-categorization huggingface dataset:
Global Transcriptomic Analysis of Early Kelp Responses Upon Endophyte Presence The cleaned RNA sequencing reads of S. latissima and L. digitata were de novo assembled by Trinity and the general features of these two de novo transcriptomes are presented in Supplementary Table  3. 90% of total expression was present in 24,733 and 34,251 transcripts, respectively. The results of the BUSCO analysis revealed a near-complete gene sequence information for two transcriptomes with 82. 4 and 84. 4% complete BUSCO matches with eukaryotic dataset in S. latissima and L. digitata, respectively. Overall, the distribution of GO terms for the three root categories "Molecular Function, " "Cellular Component, " and "Biological process" were similar for the assembled transcriptomes of S. latissima and L. digitata (Supplementary Figure  2 ).
51 to -14. 28 (Supplementary Table  4 ): 52 DEGs (47. 71%) showed moderate log2FC between 2 and -2 and 29 DEGs (26. 61%) were strongly upregulated (log2FC > 6) (Figure  3B , Supplementary Table  4 ). Among 262 DEGs identified in S. latissima and L. digitata in the 2-days cocultures with the endophyte, only 21 homologous genes were shared by both kelps. Similar to the whole transcriptome analysis, an important part of the differentially expressed genes of both kelp species (between 39. 0 to 45. 0%) did not have a match through Blastx search in the available protein databases, as represented on Figure  3. In the case of S. latissima, after 24 h of co-cultivation with the endophyte, only one gene had a match through Blastx search as a conserved unknown Ectocarpus protein (Figure  3A , Supplementary Table  4 ).
In L. digitata, among the 23 DEGs annotated based on at least one database after 24 h of the addition of L. elsbetiae, one putative respiratory burst oxidase homolog protein was significantly strongly upregulated (Table  2 ), and 9 genes corresponded to conserved unknown or hypothetical proteins in Ectocarpus genome (Supplementary Table  4 ). After 48 h of co-cultivation of S. latissima with the endophyte, 19 DEGs corresponded to conserved unknown or hypothetical Ectocarpus proteins. A functional putative annotation was retrieved for only 38 genes out of 101 DEG (see Supplementary Table  4 ). Among those DEGs, several genes related to cell wall metabolism were significantly up-regulated, including five mannuronan C-5-epimerases, an endo-1,3-beta-glucanase, and an alginate lyase (Table  3 ).
Therefore, both partners are underlying strong selective pressures which are driving and accelerating co-evolution. The comparison of physiological interactions between the brown algal endophyte L. elsbetiae and its different kelp hosts here provides an experimental system to study defense responses in kelps upon endophyte infection and further explore co-evolution of algal endophytes and hosts. to the Institut Français de Bioinformatique (ANR-11-INBS-0013), and BioGenouest for providing help and computational resources.
FUNDING This work has benefited from the support of the  European Union 's  Horizon 2020 Research and Innovation Program  under the grant agreement N • 727892,  GENIALG  ( GENetic diversity exploitation for Innovative macro-228 ALGal biorefinery , http: //genialgproject. eu/)for data analysis and under the  Marie Składowska-Curie grant agreement N • 642575  (MB's Ph. D. work), from the  Blue Granary company (QX's Ph. D. work) and from the  Centre National de la Recherche Scientifique (CNRS). It was also supported by the  French National Research Agency  via the ' Investment for the Future ' project  IDEALG  (no. ANR-10-BTBR-04 ).
This is the starting point of our analysis. We focus on floods, which have a clear impact on physical assets, and model the allocation of these impacts among domestic institutional sectors, i. e. private sector, public sector, and financial sector (distinguishing between insurance and the rest of the financial sector). The second issue is that the analysis of financial contagion requires detailed data on financial interdependencies. In this respect, we build on a new dataset of cross-border exposures  [20] , which allows us to reconstruct the global financial network at the level of institutional sectors. We then use the debtrank algorithm  [21]  to estimate the propagation and the amplification of shocks through this network. The third issue is that an assessment of climate-induced financial risks must address the attribution question  [22] , i.
Our analysis focuses on a set of 109 countries for which data about bilateral financial exposures are available (see subsection 2. 3. 1 in detailed methods below and subsection 1. 1. 1. in the SI). This set of countries accounts for 95 % of global GDP and can thus be considered as representative of the current global economy. For each country in the sample and for each scenario, we obtain estimates of the distribution of direct impacts, i. e. the yearly value of damages on physical capital, at the horizons 2030, 2050 and 2080, measured as a share of world GDP at the corresponding date. The estimates for coastal flood risk are derived by aggregating risks assessed at the level of about 12,000 coastline segments with the DIVA model by Hinkel et al. [6]  (see detailed methods 2. 2 below). River flood risk is simulated using the cascade of models of the GLOFRIS framework  [24, 34, 7] , combining inundation maps (30 x 30), land-use maps and depth-damage functions (see detailed methods 2.
3. 4 in detailed methods for details). In order to quantify this effect, we build on a new dataset  [20]  to reconstruct the global network of bilateral financial exposures at the institutional sector level (see subsection 2. 3. 4 in detailed methods below). This first allows us to estimate the transmission of direct damages from the public and private sectors of each country to their international creditors. More importantly, this allows us to simulate and estimate the propagation and the amplification of shocks through international exposures between financial sectors, i. e. through the global financial network. In this framework, the total impact induced by a climate shock depends on the exposure (assets potentially affected by the hazards) and vulnerability (the conditions that determine the losses for a given level of hazard and exposure) of the impacted country on the one hand and on its leverage and centrality in the global financial network on the other hand (see Figure  1  and Figure  9  in the SI).
The hydrological model is forced by meteorological data from the EU-WATCH project  [57]. For calculating economic damage, exposure is represented by land-use maps showing the percentage of built-up area per cell (30 x 30) from HYDE  [58]. For each cell that contains built-up area, we assume that 75% of the built-up area is residential, 15% commercial and 10% industrial respectively, following  [59]. Maximum damage values are assigned per country for each of these classes, using the values from  [59]. Vulnerability is represented through depth-damage functions, which show the percentage of the maximum damage that would actually occur for different inundation depths; these depth-damage functions are only applied for the urban damage risk indicator, and are also taken from  [59]. Economic impact damage per grid-cell is calculated per return period by combining these different datasets using the following equation: I θ (p) = θ r (p)M r + θ c (p)M c + θ i (p)M i (1) where I θ is the economic impact damage of a cell at inundation depth of the exceedance probability p, θ is the vulnerability and M the maximum damage assigned to residential (r), commercial (c) and industrial (i) occupancy types.
Estimates of current protection standard per subnational region are estimated using the FLOPROS model  [60]. For the future simulations, changes in hazard are simulated by forcing the hydrological model PCR-GLOBWB with biascorrected future climate data from five General Circulation Models (GCMs) taken from ISI-MIP  [61]. Changes in exposure are simulated by using the SSPs to project future changes in built-up area, using the methodology of Jongman et al  [62]. Future maximum damages are scaled according to the change in GDP per capita between the current and future conditions under the relevant SSP. In our simulations, the vulnerability curves are held constant in the future. Current and future gridded GDP values are taken from  [63] , which uses the national GDP per capita from the SSP database as input.
8  [67]. Third, we compute flood damages for flood events with return periods of 2, 5, 10, 25, 50, 100, 250, 500 and 1000 years by integrating over all elevation levels affected by the flood event and applying depth-damage functions. Extreme water levels are taken from the GTSR database  [68]  and are assumed to uniformly increase with sea-level rise, following 20th century observations  [69]. Sea-level rise scenarios are taken from  [6]. For each RCP used in this study (RCP2. 6 and RCP8. 5) sea-level rise has been computed for four global climate models (HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM, NorESM1-M) using the 50th percentile of ice sheet contributions. Finally, we average across the four climate models and aggregate damages assessed on the level of coast-line segments to national levels.
Characteristics of the model and data sources We focus on the set of N = 109 countries for which data on global financial interlinkages is available in the JRC Finflows database  [20]. This set contains in particular all OECD countries and the major low and middle income countries (see subsection 1. 1. 1 in SI for details). Each country is partitioned into four institutional sectors: the insurance sector, the governmental sector, the private sector and the financial sector (other than insurance). The insurance sector of country n is characterised by the share of insured losses for hydrological events σ n ∈ [0, 1], which is obtained from the Munich Re NatCat database  [71] , and by the size of its capital buffer I n ∈ R + , which is set equal, by default, to the 99. 5th percentile of the (insured) loss distribution, consistently with Solvency II requirements  [72].
The governmental sector of country n is characterized by the total level of public debt D n , obtained from the IMF global debt database  [73] , the maximum sustainable level of public debt D n (set by default to 200% of GDP consistently with the maximal values observed in  [73] ) and the fiscal space which is set equal to a share β ∈ [0, 1] of GDP ( set by default to β = 6% consistently with the largest fiscal costs incurred for natural hazards reported in  [74] ). Furthermore, as explicitly specified in the legislation of high-income countries on natural catastrophes and empirically observed in low and middle income countries  [75, 76] , the state has an insurance role that we represent by setting a target share τ ∈ [0, 1] of losses that ought to be covered by private insurance or governmental support, conditional on fiscal space (τ is set by default to 0.
5 in simulations). The private sector of country n is characterized by the value of its stock of physical assets K n ∈ R + , which is obtained from the World Bank 2018 wealth of nations database  [77]. The value of the equity of the private sector E n is equal to the difference between the capital and the total value of credit to the private sector C pr n (see below). The financial sector of country n is characterized on the one hand by the total bank capital B n and on the other hand by the total value of credit to the domestic private and public sectors, denoted respectively by C pr n and C pb n. All values are obtained from the World Bank global financial development database  [78]. In the following, unless otherwise specified, all values are expressed in terms of share of GDP and a generic country is denoted by the index n.
International propagation of shocks Domestic shocks propagate at the global scale according to three main channels: (i) from the domestic private sector to the international private sector via FDI and equity holdings, (ii) from the domestic private and governmental sector through international debt holdings (mainly bonds), (iii) from the domestic to the international financial sectors through claims and liabilities of internationally active banks. The JRC Finflows database  [20]  provides detailed statistics on these cross-border exposures, in particular for each pair of countries i, j : (i) the value A pr i,j of FDI and equity holdings of entities (private sector) of country i in entities (private sector) of country j, (ii) the value of portfolio debt A db i,j held by entities (financial sector) of country i on entities (private sector) of country j, (iii) the residual value of claims A f in i,j held by the financial sector of country i on the financial sector of country j, which is inferred from the BIS locational banking statistics  [82].
Furthermore, the World Bank global financial development database provides the relative shares α j ∈ [0, 1] and 1 -α j of public and private debt in the international debt liabilities of country j. In this context, the private sector of each country is further impacted by the shocks received through its equity holdings, leading to a total impact of S intpr n = j =n A pr n,j S pr j E j (3) Moreover, the financial sector of a country is impacted by its cross-border debt holdings on public and private sectors as well as through indirect exposure via FDI and equity holdings of the private sectors. Namely, the impact of international climate shocks on the financial sector of country n is given by S intf in n = j =n (α j S pb j D j -D + (1 -α j ) S pr j K j )A db n,j + S intpr n E n C pr n. (4) where the first term of the sum corresponds to the impact via international debt holdings and the second term to the impact mediated by international FDI and equity holdings.
Financial contagion Further to the international propagation of shocks through cross-border equity and debt holdings, a well-identified source of systemic risk is the endogenous amplification of shocks within financial networks  [21]. The BIS locational banking statistics, captured in the adjacency matrix (A f in i,j ) i,j=1,••• ,N provides a detailed representation of the network of financial interdependencies at the country-scale. Building on this dataset, we simulate the amplification of shocks through the global financial network by applying the debtrank algorithm  [21]. More precisely, we consider that: -The book value of the interbank claims of country n on country j is initially equal to its nominal value, that is A n,j (0) = A f in n,j -The "equity" of the financial sector of country n in period 0 equal to E n (0) = B n + j =N A f in n,j (0) - j =N A f in j,n -The financial sector of country n can be in either of three states: undistressed (U), distressed (D) or inactive (I).
Antoine Mandel  acknowledges the support from  EU   H2020  project grants  TIPPING+ 884565 ,  CLIMEX 851876  ,  EPOC 956107. Elke Koks  was supported by the  Netherlands Organisation for Scientific Research (NWO ; grant no. VI. Veni. 194. 033 ). Philip J. Ward  received funding from the  Dutch Research Council (NWO)  in the form of a  VIDI grant  (grant no. 016. 161. 324 ). Jochen Hinkel  and  Daniel Lincke  have been partially supported by the  European Unions Horizon 2020 Programme  under the  COACCH  project (grant  776479 ) and the  German Federal Ministry of Education and Research  through the  ERA4CS projects ISIPEDIA (grant   01LS1711C ) and  INSEAPTION  (grant  01LS1703A ).
These primers were expected to result in a single PCR product of 92 bp (http://spottedwingflybase. org/gene?gid=DS10_0000 3841). Quality of the qPCR result was validated by visual inspection of the PCR and melting curves. Raw measurements were converted to R values (linear proportional to cDNA concentration) using Data Analysis for Real-Time PCR (DART-PCR)  (Peirson et al. , 2003). We standardized RNA concentration used to synthesize cDNA and randomized all experimental steps rather than using a housekeeping gene for.
Accordingly, to test the effect of acclimation temperature combined with test temperature, we split the dataset according to sex so that slopes and intercepts from different acclimation groups were compared within each sex. Neither of the sexes displayed slopes that differed significantly with acclimation temperature (p-values with Tukey adjustment ranging from 0. 057 to 0. 904, for female 19-23 • C and male 15-19 • C, respectively). Since the slopes (thermal sensitivity) were not different, we tested if the intercept (Lt 50 at 0 • C) were different between acclimation groups. For both sexes we found that flies acclimated to 23 • C had a log 10 (Lt 50 ) at 0 • C that was significantly lower than that of flies acclimated to 15 and 19 • C (p < 0. 001 for both comparisons in the two sexes, Table  2 ).
2013 ). To de-convolute and identify co-eluting peaks, to detect and subtract the background noise from small peaks, and to verify the absence of missing peaks we used the AMDIS 32 software (National Institute of Standards and Technology, Gaithersburg, MD, U. S. A. ). We found that the deconvoluted peaks do not necessarily contribute equally to the different principal components (see below), and can even contribute in opposing directions (Supplementary Fig. S1 ,  Pulliainen et al. 2018).
4% of the total variance (original data in  Pulliainen et al. 2018). All statistical analyses were done in R, version 3. 5. 2. (https:// www. r-proje ct. org/). To assess whether cocoons, developing individuals, castes, and/or genders differed in their surface chemistry, we used pairwise permutational MANOVA on the PCs, with sample category (e. g. gyne cocoons, male cocoons etc. ) as a factor. In the analysis we used both sequential Bonferroni, and Holm-Bonferroni -adjusted P-values, the Euclidean simulation method set to 999 permutations, and the function pairwise. adonis in the R package pairwiseAdonis  (Arbizu 2019). Adonis truncates all P-values to three decimals and the permutation procedure produces minor random variation in P-values, which may be critical in the case of borderline significances.
Thus, we ran the analysis 100 times and report the mean and standard deviation for the P-values obtained. When P-values are below 0. 001 there is no variance since all P-values are then truncated to 0. 001. To verify our results further, we also performed a linear discriminant analysis (LDA) on the PCs for the different sample categories, using the function lda in the R package MASS  (Venables and Ripley 2002). Finally, given that n-alkenes have been shown to carry information on colony identity, whereas other compounds may have other functions  (Martin and Drijfhout 2009a)  we tested for chemical information on colony identity with a permutational MANOVA on all compounds, on n-alkenes only, and on all compounds except n-alkenes. In this analysis we did the peak standardizations and PCAs separately for each data set, and used the PCs obtained separately for each sample group (Table  1 ) as the response variable, and colony as a fixed effect.
exsecta  (Martin et al. 2008c (Martin et al. , 2012a (Martin et al. , 2013;; Martin and Drijfhout 2009b). This supports the earlier finding by  Martin et al. (2012a) , that colony-specific proportions of alkenes are stable across several years (c. f. Supplementary Fig. S3 ). All sample sets carried colony information, irrespective of whether the dataset contained all compounds, only alkenes, or all except alkenes. However, earlier behavioral studies have shown that only (Z)-9-alkenes elicit aggressive responses towards non-nest-mates in adult workers of F. exsecta  (Martin et al. 2008a) , and thus are principally responsible for nest-mate recognition in this species  (Martin et al. 2008a (Martin et al. , 2013;; Martin and Drijfhout 2009b). Hence, although the other classes of hydrocarbons do vary with colony, the ants do not appear to use this information for nestmate recognition.
For soil total nitrogen and phosphorus, we used the R function LMER from the LME4 package  (Bates et al. , 2015). For ammonium, we used an aligned rank test (equivalent to a nonparametric linear mixed effects model), using the R function art from the ARTOOL package  (Kay & Wobbrock, 2020) , because of nonnormally distributed residuals that were not remedied by data transformation. We used the emmeans function from the R package EMMEANS  (Lenth, 2020)  to conduct Tukey post hoc comparisons between estimated marginal means for all pairs of samples. Mean soil ammonium concentrations ranged between 0. 28 (27-yr-old site) and 0. 36 mg l À1 (32-yr-old site). These differences were marginally nonsignificant between sites of different ages (P = 0. 08). Mean soil total nitrogen per site ranged from 2.
In all three sites, we allowed the soil to settle around the tubes for more than 5 months (March to September 2018) between initial installation and first data collection. Doing so helped limit fine root production artefacts, such as abnormal fine root production occurring in response to the severing of roots or in response to nutrients released during soil tube installation  (Johnson et al. , 2001; Iversen et al. , 2012). We took monthly measurements from September to December 2018. Our measurement period encompasses the end of the monsoon season and the beginning of the dry season. All measurements were carried out at low tide and in the morning. In total, we measured root growth during three periods: September-October, October-November and November-December. The root production measurements were made over 3 d consecutively each month.
Indeed, the change of diameter of fine roots was difficult to measure accurately with ROOTFLY. More than 90% of all measured roots had a diameter < 1 mm. Although minirhizotrons sample roots up to 2-5 mm in diameter, their small sampling area might bias the relative proportion of fine roots toward the most common roots, the finest roots (< 1 mm,  Taylor et al. , 2013). We have not attempted to convert our data into mass of production per unit volume of soil because further methodological developments are required for such a conversion. In particular, there is a need to define the depth of field of the observation  (Iversen et al. , 2012) , and to establish a relationship between root length and biomass for different root types  (Iversen et al. , 2012; McCormack et al. , 2015). In total, the dataset we report here comprises 504 fine root production measurements, from 672 minirhizotron images of fine roots.
Statistical analyses To test our hypotheses (see the section 'Aim and hypotheses'), we developed a statistical model to describe our fine root production New Phytologist (2021) www. newphytologist. com measurements in terms of two categorical predictor variables, stand age (henceforth, age) and depth interval (depth), and their two-way interaction age 9 depth (Notes S1). Preliminary analyses showed our response variable, productivity, to be highly positively skewed, with a large proportion of zero measurements. Such a situation violates the assumptions of equal variance and normally distributed residuals required using standard ANOVA techniques. We used the glmmTMB function from the R package GLMMTMB  (Brooks et al. , 2017)  to fit a generalised linear mixed model (GLMM) to our data. To account for the zero-inflated response variable we specified a log link function, and we used a Tweedie distribution to represent the probability distribution of residuals.