dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis We first sought to identify the model structure and form that provided the best fit to the age-specific mortality data of each species. The structure and the form of age-dependent mortality were analyzed independently for each species. Then, we assessed whether the phylogenetic relatedness among species accounted for the distribution of the levels of model complexity among species. We used the method presented in  [30] , which was developed especially for discrete traits with more than 2 states to compute delta, which is a measure of the phylogenetic signal (i. e. , the tendency for more closely related species to have more similar trait values). The higher the delta was, the higher the phylogenetic signal. To assess the statistical significance of the phylogenetic signal, we simulated 1000 trait distributions with no phylogenetic signal (i.
As the demographic knowledge of mammalian species is steadily increasing  [40]  thanks to detailed long-term population studies covering an increasingly wider diversity of species, it is likely that we could find more diverse and complex forms, especially when the number of very old individuals increases or when looking at the mortality of very fast species such as small rodents, which are absent from our dataset (the fastest species from our dataset is Tupaia glis with a life expectancy at adulthood of 3. 25 years). Likewise, the diversity of the model structures to describe best the age-specific mortality changes in adult mammals supports that the age at which senescence starts is highly variable  [17]. Thus, we encourage researchers to fit consistently bathtub models (as we did here), threshold models  [25] , or non-parametric models  [41] , even when the analysis is restricted to adult individuals older than the age at first reproduction.
Supplementary Materials: The following are available online at http://www. mdpi. com/1424-2818/12/9/354/s1, Table  S1. DIC table associated with each model fitted to the mammalian species Author Contributions: V. R. , J. -F. L. and J. -M. G. designed the study; M. T. provided data; V. R. carried out the statistical analyses; V. R. , J. -F. L. and J. -M. G. drafted the manuscript with help from M. T. ; All authors have read and agreed to the published version of the manuscript. Funding: V. R. was supported by grants from the Agence Nationale de la Recherche (ANR-18-CE02-011).
MAGs were affiliated to Hokovirus (n=1) and Yasminevirus (n=1) within Klosneuvirinae, as well as to two lineages related to Mimiviridae dubbed MVGL55 (n=15) and MM15 (n=1) that were recently characterized from large metagenomic surveys  10, 11. These clades correspond to the newly revealed diversity of Mimiviridae relatives that have been recently included into the Imitervirales 5 order. The position of the viruses encoding viractin within the Imitervirales order was confirmed with a phylogenetic reconstruction of representative sequences using previously studied markers and datasets (Fig  Completion was estimated using HMMs targeting eight NCLDV gene markers 8. The identity is given for a coverage ranging from 99% to 100% to the human actin (* for viractin 04 the identity is given for a coverage of 73%).
Method: Viractin identification. The first viractin protein (VBB18706, 377 aa) was detected in the giant virus Yasminevirus by screening viruses in the NCBI nr database by BLAST search using Homo sapiens actin cytoplasmic 1 (NP_001092. 1, 375 aa) as query. In order to identify potential new viractin genes in giant viruses, we searched for additional viractins in NCLDV MAGs originating mostly from marine and freshwater systems  9, 10, 27  , using one HMM dedicated to the identification of actin. In screening for actin-related proteins in Archaea, we identified by BLASTP a new clade of ARPs in some Bathyarchaea, and we also extracted ARP encoded by the Asgard archaea  3, [28] [29] [30] [31].
Genome-resolved metagenomics. Two metagenome-assembled genomes (MAGs) of Mimiviridae containing a viractin were characterized from metagenomes of TARA Oceans (Pacific Ocean and Mediterranean Sea), by performing manual binning and curation on large size fractions of surface ocean plankton (0. 8-2,000 microns)  32. Briefly, we used the anvi'o platform  33  and a co-assembly strategy followed by binning using sequence composition and differential coverage, as previously applied to a small size fraction of surface ocean plankton (0. 2-3 microns)  34. We searched for NCLDV MAGs containing a viractin using eight HMMs of gene markers for NCLDV  8  and the HMM dedicated to identification of actin. Nucleocytoviricota phylogenetic tree and Imitervirales order schematic tree. The phylogenetic tree of Nucleocytoviricota in Fig S1 was performed on the concatenation of the two largest DNA-dependent RNA polymerase subunits with the protocol and dataset detailed in 8 : the model was estimated using ModelFinder Plus option in IQ-TREE version 1.
6. 12, and supports were computed from 1,000 replicates for the Shimodaira-Hasegawa (SH)-like approximation likelihood ratio test (aLRT)  35  and ultrafast bootstrap approximation (UFBoot)  36   Actin superfamily phylogeny: To position the viractin within the actin superfamily, we extracted representative sequences using reference datasets  1, 2  , and incorporated other actins and ARPs to expend the dataset. Finally, we added archaeal sequences corresponding to crenactin as an outgroup. The alignment was performed using MAFFT v7. 45  37. Aligned sequences were trimmed with BMGE, with the -m BLOSUM 30 and -b1 options  38. The maximum likelihood trees were constructed using IQ-TREE version 1. 6. 12 39 under the LG+R5 model of evolution according the MFP option for model selection. The branches support was calculated by the SHlike aLRT (10,000 replicates) and UFBoot (10,000 replicates)  35, 36.
Data availability. All data our study used was made publically available. This includes (1) the HMM to search for actin related genes in genomes and metagenomic assemblies (https://figshare. com/s/0cbbc12cce5bce2ed3a1), (  2 ) the anvi'o summary of 19 NCLDV genomes containing a viractin (https://figshare. com/s/23a8419492b3ae726f39), (3) raw phylogenetic trees for figures 1 and 2 (https://figshare. com/s/e40401fc13331992730a), (  4 ) the FASTA sequences of amino acid sequences used in the EL-actin phylogenetic analyses (https://figshare. com/s/c3af4f2cdce6b3bb5a11), (5)  and data related to predicted protein structures (https://figshare. com/s/052304ec83f13ed41411).
this volume). For Europe, the compilation of information on more than 12 000 alien species from a wide range of taxonomic groups, through the EU-funded project Delivering Alien Species Inventories for Europe  (DAISIE 2009;  hereafter called the DAISIE project; data now available on GBIF, www. gbif. org), has been the basis of many broad scale analyses. This also includes the accumulation rates of alien species over time  (Hulme et al. 2009 ) and the role of past and present environmental and economic factors in determining regional alien species richness  (Pyšek et al. 2010; Essl et al. 2010)  or interplay of invasions and extinctions leading to the homogenisation of regional floras  (Winter et al. 2009 ). The DAISIE project and its database, have subsequently contributed to assessments at the global scale, including analyses of trends of increase of naturalised species  (Seebens et al.
2008). This framework has been extensively used in various studies assessing variation in pathways of introduction across different environments, taxonomic groups and ecological impacts  (Katsanevakis et al. 2013; Essl et al. 2015; Roques et al. 2016; Pergl et al. 2017). Notably, the  Hulme et al. (2008)  classification formed the basis for the Convention on Biological Diversity (CBD) Pathway Classification framework (2014; https://www. cbd. int). The DAISIE database, including the records of impact, pathways and associated references, was added to the European Alien Species Information Network (EASIN;  Gatto et al. 2013). EASIN (https://easin. jrc. ec. europa. eu/easin) has been developed by the European Commission's Joint Research Centre (JRC;  Katsanevakis et al. 2012 ) and supports the implementation of Regulation (EU) no.
1143/2014 on Invasive Alien Species (European Union 2014;  Genovesi et al. 2015 , hereafter referred to as the EU IAS regulation). EASIN initially adopted the pathway classification framework proposed by  Hulme et al. (2008)  and classified the pathways of the alien species included in the EASIN catalogue through members of its Editorial Board  (Katsanevakis et al. 2015; Nunes et al. 2015; Tsiamis et al. 2016). Successively, a new unified system to categorise introduction pathways of alien species was proposed by the CBD (2014) through the document UNEP/CBD/SB-STTA/18/9/Add. 1 to improve the understanding of the most relevant vectors (agents that transport the alien species such as trains, containers, ships etc. ) and activities of introduction of alien species. The CBD Pathway Classification framework has since become a standard for pathway terminology, which is a key requirement for interoperability and harmonisation of databases  (Groom et al.
2016a). In this paper, we discuss issues arising from the implementation of the CBD Pathway Classification framework, based on an expert assessment within the EASIN database, to assigning pathway information for a large subset of alien species in Europe. We summarise the patterns and trends amongst the taxon groups in this dataset, which is based on the experience gained through the process. We also discuss the potential amendments which may be required to the CBD Pathway Classification framework to improve consistency in its application. We are aware that the set of taxa is not exhaustive and does not randomly cover the full alien species pool in Europe; however, no comparable dataset is currently available that uses the primary and secondary pathways in the detailed CBD Pathway Classification framework.
Pathway assignment For each species, 3-4 experts with knowledge of the specific taxonomic/environmental groups were selected. Each expert was assigned a subset of alien species and performed searches of the scientific literature (WoS), online repositories of information on alien species (e. g. CABI Invasive Species Compendium, CABI abstracts, DAISIE database, EPPO Global Database) and grey-literature to find information on primary introduction and secondary spread pathways. For each assessed alien species, these pathways were then assigned to one or more of the CBD pathways categories and subcategories and at least one supporting reference was given for each recorded pathway. This was based on the CBD Pathway Classification guidance document that was developed during the same period  (Harrower et al.
Discussion Our study highlights that the importance of different pathways differs amongst taxonomic/ environmental groups and for both primary introductions and secondary spread in Europe. However, it is apparent that some of the CBD Pathway Classification framework subcategories were not used at all or were relevant for only a few species (see Table  4 ; e. g. contaminated bait, tunnels and land bridges). This is, in part, because the names of these pathways have been kept unchanged amongst databases. Consequently there was a high probability of a direct match from the original EASIN pathway classification scheme to the CBD Pathway Classification framework (and, thus, these species did not appear in our assessment). The ease of assigning pathway information using the CBD Pathway Classification framework depends on the availability of information.
The dataset described in this study has a limited coverage of some large taxonomic groups (e. g. only about 400 species of plants were included from the 6600 species within the EASIN catalogue). Nevertheless, this pathway dataset covers about one third of the alien flora and fauna of Europe and so, we believe, the observed patterns of pathways have wide relevance. Furthermore, the analysis presented is limited by the fact that the pathways were not prioritised according to their relative importance, for example, in terms of rates of introduction or propagule number, because of lack of robust data. In addition, the importance of specific pathways can vary regionally and temporally  (Pyšek et al. 2011 , Roques et al. 2016). The discrepancy between the number of assessed species (Table  1 ) and presented primary and secondary pathways (Table  2 ) is caused by the evidence-based approach of this study.
We are not recommending an increase of the hierarchical levels of the CBD Pathway Classification framework, but to adjust the width of the subcategories and their direct link to vectors and possible legislation management. In many cases there is a residual subcategory "other" (e. g. "Other intentional release", "Other escape from confinement"), so that one possible approach would be to specify and split this residual subcategory, limiting the number of unclassified pathways. Adopting a nested structure in the pathway descriptions would need to be reflected in the database structures and most of the data would be available at a less detailed scale. Furthermore, we found that it is difficult to separate the pathways for "Horticulture" and "Ornamental purposes other than horticulture". The distinction is based on the risk or event of escape from a private garden compared to an escape from horticultural (commercial, industrial) facilities.
Lack of data leading to low confidence Assessments of presence and impact of IAS is always affected by the uncertainty in available data  (Probert et al. 2020). The lack of available information on introduction and secondary spread pathways for a high number of species is problematic. For example, a number of alien species have been recorded only a few times. It is often challenging to establish whether this pattern is the result of independent primary introduction events or of secondary spread after a single introduction. However, in some cases genetic analyses have provided evidence of independent introductions, for example, in insects or plants  (Bras et al. 2019; Neophytou et al. 2019). It is likely that the importance of the pathways within stowaways has been underestimated in terrestrial arthropods because a large number of the categorisations within our exercise were based on the biology of the transported species and their host organism, especially for those associated with plants, but only a few of these arthropods were actually intercepted along the putative pathways  (Eschen et al.
Acknowledgements The authors gratefully acknowledge funding from  European Commission  funded study ( ENV. B. 2/SER/2015/0037rl ) and the  COST Action   CA17122. JP and IP were partly supported by the  Czech Ministry of Environment , project no. 20-10349J  ( Czech Science Foundation ) and long-term research development project  RVO 67985939  ( Czech Academy of Sciences ). HER and SR were partly supported by the  Natural Environment Research Council  award number  NE/R016429/1  as part of the  UK-SCAPE programme delivering National Capability. WR was partly supported by the project " Capacity building Neobiota " ( Federal Ministry for Sustainability and Tourism ) and MV by the  Belmont Forum and BiodivERsA  joint call for research proposals, under the  BiodivScen ERA-Net COFUND  programme funded by the  Spanish Ministry of Science and Innovation  ( PCI2018-092939 ,  MCIU/AEI/FEDER, UE).
Global measurements of absorptive aerosol optical depth (AAOD) are scarce and mostly provided by the ground network AERONET (AErosol RObotic NETwork). In recent years, several satellite products of AAOD have appeared. This study's primary aim is to establish the usefulness of these datasets for AEROCOM (AEROsol Comparisons between Observations and Models) model evaluation with a focus on the years 2006, 2008 and 2010. The satellite products are super-observations con- This study consist of two parts: 1) an assessment of satellite datasets; 2) their application to the evaluation of AEROCOM models. The current paper describes the first part and details an evaluation with AERONET observations from the sparse AERONET network as well as a global intercomparison of satellite datasets, with a focus on how minimum AOD (Aerosol Optical Depth) thresholds and temporal averaging may improve agreement.
All satellite datasets are shown to have reasonable skill for AAOD (3 out of 4 datasets show correlations with AERONET in excess of 0. 6) but less skill for SSA (Single Scattering Albedo; only 1 out of 4 datasets shows correlations with AERONET in excess of 0. 6). In comparison, satellite AOD shows correlations from 0. 72 to 0. 88 against the same AERONET dataset. We do show that performance vs. AERONET and satellite agreements for SSA significantly improve at higher AOD. Temporal averaging also improves agreements between satellite datasets. Nevertheless multi-annual averages still show systematic differences, even at high AOD. In particular, we show that two POLDER products appear to have a systematic SSA difference 1.
grass-roots communities, the first organised around aerosol modellers, and the second around retrieval groups. They meet every year to discuss common issues in the field of aerosol studies. The observational model datasets used in this study are described in Sect. 2. The collocation and analysis methodology are described in Sect. 3. A first look at the satellite datasets is presented in Sect. 4. Evaluation of satellite AOD, AAOD and SSA with AERONET is performed in Sect 5 and a more detailed intercomparison of satellite data is shown in Sect. 6. A summary and conclusions can be found in Sect. 7.
Remote sensing data Original satellite L2 data were aggregated unto a regular spatio-temporal grid with spatio-temporal grid-boxes of 1 o × 1 o × 30 min. The resulting super-observations (1 o × 1 o × 30 min aggregates) are more representative of global model grid-boxes (∼ 1 o -3 o in size) while allowing accurate temporal collocation with other datasets. At the same time, the use of super-observations significantly reduces data amount without much loss of information (at the scale of global model grid-boxes). A list of products used in this paper is given in Table  1. A colour legend to the different products can be found in Fig. 1. More explanation of the aggregation procedure can be found in Appendix A. Super-observations of AOD and AAOD at the same location and time were derived from the same set of L2 data and therefor measure the exact same scene (note an exception for GRASP dataset described below).
It is an extension of the near UV TOMS method (see the OMAERUV product) to a wider wavelength range. The OMAERO algorithm is applied over all surface types, however its primary objective is to derive aerosol properties over the oceans due to the limited availablity of spectral surface reflectivity databases over land. https://doi. org/10. 5194/acp-2020-1207 Preprint. Discussion started: 7 December 2020 c Author(s) 2020. CC BY 4. 0 License. model and measurement and a regularization term using a priori estimates of values of some of the retrieved parameters. The algorithm, including an application to PARASOL measurements over ocean, is described in  Hasekamp et al. (2011). More recent refinements are described by  Stap et al. (2015) ;  Wu et al. (2015) ;  Lacagnina et al. (2015) ;  Fu and Hasekamp (2018) ;  Fu et al.
POLDER-GRASP For a description of the POLDER instrument, see the previous subsection. GRASP (Generalized Retrieval of Aerosol and Surface Properties) is a unified retrieval algorithm for atmosphere properties from diverse remote sensing observations  (Dubovik et al. , 2011 (Dubovik et al. , , 2014)) , based on earlier work by  Dubovik and King (2000) ;  Dubovik et al. (2002 Dubovik et al. ( , 2006) )  for AERONET Inversions. In the current paper, retrievals from the so-called "models" dataset (here: GRASP-M) are presented. Aerosol is assumed an external mixture of five different aerosol components and are retrieved together with spectral parameters of surface BRDF and BPDF. The aerosol is assumed a mixture of spherical and non-spherical particles. Each fraction is characterized by particle size distributions similarly to AERONET retrievals.
The non-spherical component is modeled as a mixture of randomly oriented spheroids with fixed shape distribution  (Dubovik et al. , 2006). The actual inversion uses multi-pixel retrieval  (Dubovik et al. , 2011)  where horizontal pixel-to-pixel variations of aerosol and day-to-day variations of surface reflectance are enforced to be smooth. The full archive of POLDER/PARASOL observations was retrieved using GRASP and can be found at https://www. graspopen. com. In addition to the "models" dataset, two other datasets are available ("improved" and "high-precision") that use slightly different assumptions in the retrieval. The dataset used in this paper is considered the most applicable for a wide range of circumstances. Another issue is that POLDER-GRASP-M provides aggregate AOD and SSA for slightly different samplings (there is an additional minimum AOD threshold for the calculation of the AAOD that will be aggregated and the resulting aggregated SSA).
We assumed that this SSA nevertheless represents the same scene as the AOD aggregate and recalculated an AAOD from that AOD and SSA. Consequently, The POLDER-GRASP-M AAOD presented in this paper is different from the AAOD found in the official L3 product. The latter shows a high bias vs. AERONET due to same aforementioned minimum AOD threshold. Note that in-situ measurements  (Delene and Ogren, 2002; Andrews et al. , 2011 Andrews et al. , , 2017;; Schmeisser et al. , 2018)  have suggested a change in SSA at lower AOD so our SSA assumption may introduce additional biases. For this study the L3 GRASP data were filtered based on the FittingResidual field which was required to be smaller than 0. 05 (over Land) or 0. 1 (over Ocean). This subset evaluates substantially better for AOD retrievals and somewhat better for AAOD retrievals than the full dataset.
AERONET AERONET  (Holben et al. , 1998)  DirectSun V3 L2. 0  (Giles et al. , 2019; Smirnov et al. , 2000)  and Inversion V3 L1. 5 & 2. 0 data were downloaded from https://aeronet. gsfc. nasa. gov, logarithmically interpolated to values at 550 nm and aggregated by averaging over 30 minutes. The DirectSun dataset contains only AOD (at multiple wavelengths). These observations are based on direct transmission measurements of solar light and have a low uncertainty of ±0. 01  (Eck et al. , 1999; Schmid et al. , 1999) , at 400nm and larger. The Inversion dataset contains both AOD and AAOD (at multiple wavelengths) and these observations are based on measurements of scattered solar light from multiple directions. This inversion uses radiative transfer calculations  (Dubovik and King, 2000)  and yields larger errors than the DirectSun measurements.
Collocation & analysis methodology To evaluate and intercompare the remote sensing datasets, they will need to be collocated in time and space to reduce representation errors  (Colarco et al. , 2014; Schutgens et al. , 2016b Schutgens et al. , , 2017)). In practice this collocation is another aggregation (performed for each dataset individually) to a spatio-temporal grid with slightly coarser temporal resolution (1 or 3 hours, the spatial gridbox size remains 1 o × 1 o ). This is followed by a masking operation that retains only aggregated data if it exists in the same grid-boxes for all involved datasets. More details can be found in Appendix A. We need to allow some flexibility in the time separation between data (here 3 hours) to ensure sufficient numbers of collocated data pairs for further analysis.