dataesr/scientific-paragraphs-categorization huggingface dataset:
2017). In contrast, environmental data show us that we still do not have a full understanding of the diversity of Holozoa  (del Campo et al. 2015; Arroyo et al. 2018). Therefore, current interpretations on the evolutionary transition toward animal multicellularity may be challenged by improving our knowledge about Holozoa diversity  (Ruiz-Trillo et al. 2007; del Campo et al. 2014). To fill this gap and provide a more accurate perspective on Holozoa diversity and their geographical distribution, we analyzed the longest and largest metabarcoding marine data set: the Tara Oceans expedition, which is based on the 18S ribosomal RNA gene (hereafter 18S or 18S rDNA)  (de Vargas et al. 2015; Pesant et al. 2015). Tara Oceans comprise thousands of reads from hundreds of sampling stations around the globe, with a third of those reads not matching any reference in databases  (de Vargas et al.
Initial Data Sets and Network Construction To look for potential new diversity of unicellular Holozoa and to address their geographical distribution, we combined two 18S rRNA data sets: an environmental data set of OTUs and a reference data set with known holozoan sequences. The environmental data set came from the worldwide Tara Oceans expedition  (de Vargas et al. 2015) , which included metabarcoding data from the V9 region of the 18S rRNA gene from a total of 1,086 samples from 210 oceanic stations, 3 water column layers, and 10 size fractions (further details about sampling procedures can be found in  Pesant et al. 2015). The reference data set was built by collecting sequences from both GenBank Nucleotide and PR2 databases (see Materials and Methods). The initial unicellular Holozoa network was built from 2,426 sequences (2,197 from Tara Oceans, 229 from the reference data set).
In the network, each node represented either an environmental OTU from Tara Oceans (hereafter ENV) or a sequence from the reference database (hereafter REF) (fig. 1 ). The basic structure of the network consisted of Connected components (CCs): subgraphs of the network in which there is always a path between all nodes (fig. 2 ). The initial network was subsequently partitioned using increasing percentages of sequence similarity thresholds (!85%, !87%, !90%, !95%, and !97%), resulting in more fragmented networks (fig. 2 ). In each of these networks, CCs could be classified in three types: CCs in which all nodes were environmental (CC ENV ), CC in which all nodes were reference (CC REF ), and CC in which there were both types of nodes (CC MIX ) (fig. 1 ). Networks produced at all thresholds displayed a similar trend: the number of CC ENV was always the largest, followed by a CC MIX and CC REF (supplementary fig.
Reference nodes from GenBank data set are depicted with circles that are colored according to the taxonomy (left panel). Connected components composed of only reference nodes are located in the top right corner. The novel Holozoa group described in this article, MASHOL (for MArine Small HOLozoa), is shown in red triangles and pointed in the network with a black circle. Raw network data can be found in supplementary material 4, Supplementary Material online. All these steps of graph-mining were used to detect ENV sequences that could potentially indicate novelty, for which phylogenetic placement could be finally computed.
The Structure of the Unicellular Holozoa Network Shows Potential Undiscovered Diversity The general structure of the network provided an overview of the unicellular Holozoa diversity and highlighted potential new diversity (fig. 1 ). First, we computed the closeness of all nodes (figs. 2 and 3 and supplementary material 1, Supplementary Material online) to test whether the distribution of closeness values for REF nodes was: 1) significantly different and 2) significantly higher than the distribution of closeness values for ENV nodes, using Wilcoxon signed-rank test. The results showed that ENV nodes were significantly more peripheral than REF nodes (Wilcoxon signed-rank test, P value < 0. 01**) (fig. 3A ) in all networks. This result indicates a high amount of potential new diversity in our unicellular Holozoa data set from Tara Oceans.
For every network, we computed 1) the distribution of null assortativity values by randomly shuffling the ENV and REF node labels, and we contrasted these values with 2) the assortativity values of all our real networks (see Materials and Methods). All networks were significantly assortative (one-sample t-test, P value < 0. 01**) (fig. 3B ). This tendency for intragroup preferential linkage suggests a lack of representation of oceanic Holozoa in the reference data set before the Tara Ocean expedition, stressing the high level of potential new diversity present in Tara Oceans data. Overall, these metrics (closeness and assortativity) indicated that our environmental data set of unicellular holozoans from Tara Oceans was different from the reference data set, expanding the current known diversity of this group.
New Molecular Diversity in Holozoa, Including a Potential Novel Clade To identify new groups of interest, we first performed network comparisons using BRIDES software (fig. 2  and supplementary fig. 2 , Supplementary Material online) (see  Materials and Methods and Lord 2016). This allowed us to contrast the topologies of networks built exclusively from REF nodes (original networks) with that in which ENV nodes had been included (augmented networks). BRIDES analysis showed that ENV sequences of unicellular Holozoa created numerous new paths in the augmented similarity networks (fig. 3C ), guiding the discovery of evolutionary relevant novel sequences. First, despite the enhanced molecular diversity provided by the Tara Oceans data set, some REF nodes remained disconnected from other REF nodes, indicating that the diversity of most ENV sequences was not close enough to fill the gaps between REF sequences.
This was especially noticeable for networks built at high similarity thresholds. At !97% threshold, the vast majority of paths were impasses (I), meaning that ENV sequences did not create bridges between REF sequences in the augmented network (supplementary fig. 2 , Supplementary Material online). This is logical because, given this high level of stringency, only sequences from the closest related holozoan lineages would connect in a given CC, confirming the general divergent nature of most ENV sequences with respect to sequences from sequenced holozoan taxa. Interestingly, when lowering the similarity threshold required to connect sequences in the networks, the proportion of impasses decreased, showing that some of these divergent ENV sequences started to connect some REF sequences. Still, at !85% identity, some Holozoa REF sequences remained disconnected, suggesting that the Tara Oceans data set did not provide evidence for ENV groups bridging phylogenetic gaps between some known Holozoan clades.
In particular, we analyzed the OTUs that created breakthroughs and shortcuts in the network at 85% similarity threshold (fig. 3D ). These OTUs unraveled novelty within Acanthoecida, one of the two subgroups of Choanoflagellatea. A group of 6 sequences or OTUs (with a total of 1,675 reads) branched off Choanoflagellate H, suggesting a potential novel environmental group of acanthoecids. Another group of 3 sequences (including one of the most abundant OTUs in the whole Tara Oceans data set: OTU 2703, with >28,000 reads) appeared to be the sister group of Choanoflagellate G. The importance of this result lies in the fact these OTUs did not cluster together with the already morphologically described Choanoflagellate G species (i. e. , Acanthocorbis unguiculata, Acanthoeca spectabilis, Savillea micropora, Helgoeca nana), but branched at an internal node, showing their divergent nature.
Unicellular Holozoans Are Globally Distributed, with Some Lineages Showing Specific Geographical Patterns There is no data on the geographical distribution of unicellular Holozoa. Thus, we decided to take the most of the Tara Oceans data set and evaluate the geographical distribution of the different unicellular holozoan lineages across oceans, layers of the water column, and size fractions. In general, all lineages of unicellular Holozoa were widely distributed across the world's oceans (fig. 5A ). Ichthyosporeans were the most homogeneously dispersed group across all oceans. There were, however, some exceptions. For example, Acanthoecida choanoflagellates were more abundant in the Arctic samples (60. 29% of total abundance), and in contrast to Craspedida (4. 5%) (fig. 5A ). These results are consistent with previous morphological studies of choanoflagellates in sea ice  (Thomsen et al.
Data Sets The initial environmental data set was provided by the Tara Oceans consortium, which contained a total of 474,303 OTUs from all eukaryotic clades. Note that this is the full data set generated in the expedition, not the one used in  de Vargas et al. (2015) , as the latter is a subsample of the former. The Tara Oceans consortium provided us with this data set already cleaned, filtered, and clustered. During the first steps of the bioinformatic pipeline, they merged, dereplicated, and quality filtered the original V9 barcodes. A chimera detection analysis was carried out using the usearch program  (Edgar et al. 2011). After a filtering process to discard possible spurious reads, barcodes were clustered using Swarm approach  (Mah e et al. 2017). For further details on the OTU table generation, see http://taraoceans.
sb-roscoff. fr/EukDiv/. Our reference database was obtained by merging three different databases: GenBank, PR2-Opistho, and PR2_V9. First, we downloaded two databases from GenBank: nucleotide (nt) and environmental nucleotide (env_nt) by January 25, 2018. We retrieved 18S rDNA sequences from these databases by searching them using the human 18S sequence as a query  (AC139250, positions 551,257-553,055). This sequence had been previously confirmed to contain the Tara Oceans V9 primer sequences. BlastN parameters were: Evalue <1E-10, percentage of identity !60%, and maximum target sequences of 9. 9Â10 7 (for nt) and 9. 9Â10 8 (for env_nt). From the BlastN output, we implemented two filtering processes. In the first one, we retrieved the sequences that contained both Tara Oceans V9 primer sequences.
We then trimmed the sequences to have only the V9 region. In the second step, we kept those sequences whose length was comprised between 80 and 120 bp to keep the most frequent length range of this region  (Amaral-Zettler et al. 2009 ). The second database, PR2-Opistho, was a well-curated and updated version of the original PR2 database for Opisthokonta clade. This database (PR2-Opistho) was also trimmed with the Tara Oceans primer sequences to keep only the V9 region. The third database, PR2_V9, was generated by the Tara Oceans consortium  (de Vargas et al. 2015). Because both PR2-Opistho and PR2_V9 were originally generated from PR2 database, we eliminated redundancies and kept the taxonomical annotation from the PR2-Opistho database. Finally, we combined all databases, producing a global reference database of 49,379 eukaryotic sequences.
To retrieve the unicellular Holozoa sequences, we performed a phylogenetic placement of both environmental and reference data sets against a eukaryotic reference tree and took those that branched within Holozoa and outside animals. A phylogenetic placement consists of mapping short amplicons (in this case, Tara Oceans OTUs) into a fixed reference tree made from full-length 18S rDNA sequences. This reference was constructed using 130 full 18S sequences that covered all eukaryotic groups. We performed the phylogenetic placement using the RAxML-EPA algorithm  (Berger et al. 2011 ) and we selected the sequences that were placed into unicellular Holozoa using the Cþþ script extract_clade_placements from Genesis software v0. 18. 1  (Czech and Stamatakis 2016). Therefore, the starting data set of unicellular Holozoa contained 2,426 sequences (2,197 were environmental from Tara Oceans, whereas 229 were reference sequences).
Similarity Network Construction We built the initial similarity network based on a BLAST allagainst-all of the unicellular Holozoa data set. We used BlastN v2. 7. 1þ  (Camacho et al. 2009) , with the following options: Evalue <1E-10, percentage of identity !85%, maximum number of HSPs 1, and maximum target sequences 3,000. We used the cleanblastp script from CompositeSearch software to filter the output in order to remove auto-loops and reciprocal connections (A-B would be the same as B-A)  (Pathmanathan et al. 2018). Final networks were obtained by setting up a mutual cover threshold of !95% and increasing sequence similarity thresholds: !85%, !87%, !90%, !95%, and !97% identity threshold, respectively. These networks can be found in supplementary material 4, Supplementary Material online.
Network Node Annotation In order to annotate taxonomically every node in the network, we performed a BLAST of the initial 2,426 holozoan sequences against the PR2-Opistho database, using the following parameters: E-value <1E-50 and !97% percentage of identity. Under these conditions, only 438 sequences could be Potential Novel Unicellular Group Related to Animals from the Tara Oceans Expedition GBE Genome Biol. Evol. 12(9):1664-1678 doi:10. 1093/gbe/evaa117 Advance Access publication 13 June 2020 annotated. Thus, we decided to use a phylogenetic method to taxonomically assign the rest of the unannotated OTUs: tax2tree algorithm  (McDonald et al. 2012). This software requires the structure of the phylogenetic tree of both reference and unannotated sequences. Then, it assigns the taxonomy to the unannotated tips, given a file with the taxonomical information of the annotated tips.
e. , a node composed of an environmental sequence) could turn out to be ENV or REF (i. e. , a node composed of a reference sequence) after the shuffling. For all these 100 random networks, we computed the assortativity, generating the distribution of assortativity values for random networks. We next computed the actual value of assortativity in the networks (fig. 3B   Novelty Assessment: BRIDES BRIDES software characterizes new paths that are created when extra nodes are added to an original network  (Lord et al. 2016). For every sequence similarity network, we first used only the REF nodes (original network), and then we added the ENV nodes of unicellular Holozoa (augmented networks) to compute BRIDES using the default parameters.
We performed the phylogenetic placement using the RAxML-EPA algorithm  (Berger et al. 2011). The final tree in figure 4B was enhanced using iTOL  (Letunic and Bork 2007). We validated the quality of the phylogenetic placement using the placement_histograms script from Genesis package v0. 18. 1  (Czech and Stamatakis 2016). The first parameter computed was the EDPL (Expected Distance between Placement Locations). For every OTU, it calculates the weighted distance between all placement positions. In other words, EDPL quantifies to which extent all placements from an OTU are scattered over the tree. In both groups, EDPL values were extremely small (<0. 05) (supplementary fig. 2A  and C, Supplementary Material online). Considering that most branches in the tree had <0. 05 nucleotide substitutions per site, it meant that the majority of the OTUs were located within the same branch.
Co-occurrence Patterns To test the association between unicellular Holozoa and animal OTUs, we carried out a co-occurrence analysis. First, we filtered the data set to keep those OTUs that were present in at least 3 samples (out of 1,086 total samples in Tara Oceans). Then, we summed up OTU abundances if these OTUs belonged to the same class in animals or the same genus/ species in unicellular Holozoa. We used "corrplot" and "Hmisc" libraries in  Rstudio v. 1. 1. 383 to perform the analyses (RStudio 2017;  Wei et al. 2017; Harrell 2019). These consist of building a correlation matrix among all pairwise comparisons and then extract the significant relationships (Spearman's significance < 0. 01**), which finally were plotted in a heatmap. There was a possibility, however, that some associations could be neither monotonic nor linear.
Acknowledgments We thank  Ram on Massana ,  Philippe Lopez , and  Ramiro Logares  for discussion on the article. This work was supported by grants ( BFU2014-57779-P  and  BFU2017-90114-P ) from  Ministerio de Econom ıa y Competitividad (MINECO) ,  Agencia Estatal de Investigaci on (AEI) , and  Fondo Europeo de Desarrollo Regional (FEDER)  to I. R. -T.
3. FIG. 2. -Unicellular Holozoa network at !85% similarity threshold. Environmental nodes from Tara Oceans are depicted with triangles that are colored according to the distance to their shortest reference sequences (right panel). Reference nodes from GenBank data set are depicted with circles that are colored according to the taxonomy (left panel). Connected components composed of only reference nodes are located in the top right corner. The novel Holozoa group described in this article, MASHOL (for MArine Small HOLozoa), is shown in red triangles and pointed in the network with a black circle. Raw network data can be found in supplementary material 4, Supplementary Material online.
interested in understanding human engagement with nature, iEcology methods focus on the ecological knowledge that can be gained from these human-nature interactions in the digital realm. iEcology data predominantly give rise to insights that are correlative in nature, similar to other large-scale ecological explorations such as much of macroecology  [11] , and should be viewed as such. Here, we present a broad overview and description of iEcology, including its scope, data types, sources and methods, as well as current major caveats and future prospects for the development of this emerging research approach.
iEcology: Harnessing Large Online Resources to Generate Ecological Insights Ivan Jarić, 1,2, * Ricardo A. Correia, 3,4,5,6 Barry W. Brook, 7,8 Jessie C. Buettel, 7,8 Franck Courchamp, 9  Enrico Di Minin, 3,4,10 Josh A. Firth, 11,12 Kevin J. Gaston, 13 Paul Jepson, 14 Gregor Kalinkat, 15 Richard Ladle, 6  Andrea Soriano-Redondo, 16,17 Allan T. Souza, 1 and Uri Roll 18   Digital data are accumulating at unprecedented rates. These contain a lot of information about the natural world, some of which can be used to answer key ecological questions. Here, we introduce iEcology (i. e. , internet ecology), an emerging research approach that uses diverse online data sources and methods to generate insights about species distribution over space and time, interactions and dynamics of organisms and their environment, and anthropogenic impacts.
As such, iEcology has the potential to provide new understandings of ecological dynamics and mechanisms, complementing more traditional methods of obtaining ecological data. While iEcology can be considered to fit within the wider scope of ecological informatics (see Glossary), it is distinct from other uses of Big Data sources in the biological sciences in that data are not specifically and intentionally generated to address ecological and environmental questions  [2] [3] [4]. Moreover, iEcology expands on the traditional scope of ecological informatics with new data sources and dedicated methods to analyze them. iEcology is predominantly focused on collecting, collating, and exploring data generated online by human society, either passively or unintentionally (e. g. , Internet search activity, social media interactions, and uploaded data and media), a process also referred to as passive crowdsourcing  [5].
iEcology uses digital methods to access, handle, and analyze these data, in a manner akin to techniques from other research fields such as sociology, culture and media studies, biomedical sciences, computer sciences, and economics  [6, 7]. iEcology also shares part of its toolbox with conservation culturomicsan emerging research area in conservation science  [8] [9] [10]  albeit with a different focus. Specifically, while conservation culturomics is Highlights iEcology is a new research approach that seeks to quantify patterns and processes in the natural world using data accumulated in digital sources collected for other purposes. iEcology studies have provided new insights into species occurrences, traits, phenology, functional roles, behavior, and abiotic environmental features. iEcology is expanding, and will be able to provide valuable support for ongoing research efforts, as comparatively lowcost research based on freely available data.
In the same way, behavioral changes in animals in response to anthropogenic impacts  [46] [47] [48]  can be tracked by such methods. While inherently varied in scope, other fields within ecology and environmental science could conceivably benefit from iEcology tools and methods, such as functional ecology, macroecology, landscape ecology, and urban ecology. iEcology Research Toolbox -Data Types, Sources, and Methods At their core, iEcology data sources fall into two categories: (i) new data uploaded by users for different purposes; and (ii) data on online activity, including data access and search engine usage. Types of data within the first category can comprise text, images, videos, and sounds (Figure  1 ). The second category is aggregated data and the exploration of frequencies (e. g. , the number of times a term was searched or a webpage visited, but could also include interactions on social media such as shares and likes).
Both categories have different types of associated metadata that are particularly important for iEcology, such as locality, timestamp, user identity, and links across data. iEcology data sources differ in their scope, availability, ease of access, associated metadata, and therefore utility for different types of research. Potential data sources range from various social media platforms (e. g. , Twitter and Flickr)  [49] , search engines (e. g. , Google, Baidu, and Bing), online encyclopedias (e. g. , Wikipedia and Encyclopedia Britannica online), and other online repositories (blogs, discussion forums, popular articles, books, etc. ). Many of these sources can also be accessed through search engines. The scope of sources differs based on spatiotemporal coverage, linguistic or cultural breadth, data resolution, and the degree of multimedia composition (e.
g. , text, images, and video) per source. Data also differ in availability: while many sources are freely available, some platforms may restrict availability by limiting data collection (i. e. , limits on Glossary Application programming interface (API): a set of protocols and tools that provide a communication interface between applications, commonly between a client and the server, to enable simplified construction of clientside software. Augmented reality: digital devices that electronically supplement elements of the physical world with portable, interactive, computer-generated attributes, and synthetic sensory inputs. Big Data: large volumes (petabyte scale upwards) of structured or unstructured data that require advanced tools for management and processing. Also denotes a unique field that explores methods to store, analyze, and generally deal with voluminous data that is too complex to be handled with traditional processing methods.
Trends Trends in in Ecology Ecology & Evolution Evolution subset of the worldone that is nonrandom in extent and depth. Indeed, as the data are not generated systematically, there is great variance in content generation among different users, regions, cultures, and time frames, with inherent risks of biases  [52]. Such individual and cultural subjectivity can further complicate data interpretation. Moreover, multiple entries of the same data by single or different users could cause biases related to nonindependence. Therefore, underlying data for iEcology research should neither be treated as randomly distributed, nor used in raw form without addressing these issues. Indeed, rather than ignoring such considerations, specific investigations into aspects of the data such as the nonrandom distribution and the level of nonindependence can actually provide further insights into data structure and any discovered patterns.