Philippa Kaur

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Discovery and historiography of USNM PAL V 11367With basic image enhancement tools (e.g., Adobe Photoshop), we were able to better resolve the original but faded specimen label in the collections associated with USNM PAL V 11367 (Fig. 1 and Related file 1). Specifically, we were able to make the now-faded handwritten notes legible (Fig. 1A,B), revealing critical information about the specimen. The widespread availability of image enhancement for faded fieldnotes and labels provides a new source of information for uncovering legacy issues in museum collections (e.g.21,22,23), especially in cases where locality data or collecting information cannot be well resolved.Accession files with this specimen (Related file 1) show that it was gifted from Arthur M. Ames to the United States National Museum (now the National Museum of Natural History, Smithsonian Institution) on 15 October 1925, and approved by George P. Merrill, head curator of geology from 1917 to 1929. Prior to its accession to the museum, an anonymous individual identified the tooth as belonging to Desmostylus hesperus. Forty years later, on 17 November 1965, Charles A. Repenning reidentified this specimen as Paleoparadoxia sp. (Fig. 1A,B), an assertion that was incorporated into its catalog information. According to the label, USNM PAL V 11367 was collected in the city of Corona, Riverside County, California, yet no precise information of its geological provenance was recorded. On the backside of the label, there are notes (Fig. 1B) referring to the US Geologic Survey Corona South 7.5′ quadrangle map for Riverside and Orange counties, California24. However, no geographic location, exact horizon, nor lithology was stated, and the specimen’s collector, A. M. Ames, lived in Santa Barbara, California but died on 25 August 193921,22,23.In nearly a century after its discovery, the only mention of USNM PAL V 11367 was by Panofsky25, who listed it in a catalog of desmostylian tooth specimens used as a comparative basis for a mandible restoration of the “Stanford specimen” N. repenningi. Panofsky25 identified USNM PAL V 11367 as a left m2 with six main cusps, with no additional cusps (Table 1 in25), while also stating that this specimen has “an open lake in the center of each of the seven cusps” (25: p. 103). The inconsistency of this description differs from our own, which we attribute to differences in morphological criteria or a typographic error.Geological horizon and age of USNM PAL V 11367In this paper, we refer to the “Topanga” Formation following recent studies20,26,27 of this geologic unit. This formation was originally based on a sequence of marine sandstones exposed in an anticline just west of Old Topanga Canyon in the central Santa Monica Mountains of Los Angeles County, California28. After its initial description, the name of the formation was applied to a much thicker and heterogeneous sequence of sedimentary and volcanic rocks29. Campbell et al.30 compiled the history and chronology of changes in usage of “Topanga” in the Miocene stratigraphic nomenclature in Southern California, showing that the criteria of continuous deposition and shared provenance were not demonstrated in every instance. Campbell et al.30 argued that strata assigned to the Topanga Formation in the Los Angeles Basin and eastern Ventura Basin areas are different from other units that have also been referred to the Topanga Formation in Orange County or in the Santa Monica Mountains of Los Angeles and Ventura counties. To distinguish these units, here we follow recent studies20,26,27 and use the name of “Topanga” Formation for the early to middle Miocene rocks bearing fossil marine mammals20,26,31,32,33 in Southern California.According to the collections records (Fig. 1), USNM PAL V 11367 was collected in the city of Corona, Riverside County, California, USA. This city is in the western part of Riverside County, comprising an approximate area of 100 km234. Previously, Panofsky25 suggested that USNM PAL V 11367 would have derived from the Temblor Formation (14.8 to 15.8 Ma35), likely as a guess based on the prevalence of desmostylian teeth recovered from this unit in central California, yet today there are no Temblor Formation outcrops mapped near Corona24,36; the closest Temblor outcrops are located in Fresno and Kern counties37, approximately 200 km away.The geologic maps of Riverside County24,36,38 indicate that the city limits of Corona encompass a wide variety of sedimentary rocks from the Jurassic to the Holocene in age, but only a few marine deposits, such as the Jurassic Bedford Canyon Formation and the middle Miocene “Topanga” Formation are exposed24,39. Specifically, the marine sandstones of the “Topanga” Formation occur within the fault zone at the southeast and northwest of Corona.Outside of Riverside County, the “Topanga” Formation has yielded a diverse assemblage of fossil marine vertebrates in Southern California20,26,31, including desmostylians referred to Desmostylus hesperus and Paleoparadoxia sp. in Orange County (Supplementary 1). USNM PAL V 11367 represents the second reported fossil marine mammal from Riverside County. Previously, an isolated record of “Cetacea indet.” was mentioned from the Zanclean stage Imperial Formation40 and Supplementary Data 2), which is exposed far east of Corona’s city limits.In assessing the age of the “Topanga” Formation in Southern California, Boessenecker and Churchill26,31 argued that the land mammals (late Hemingfordian North American Land Mammal Age, represented by Aepycamelus, Copemys and Merychippus; 17.5–15.9 Ma35,41), benthic foraminifera, fossil mollusks, and K/Ar dating all placed the age range between 17.5 and 15 Ma for this geological unit41 in Orange County. More recently, Velez-Juarbe20 revised the age of “Topanga” Formation in this county to 16.5–14.5 Ma based on new foraminiferal zones presented in Ogg et al.42.We propose that USNM PAL V 11367 derives from exposures of the “Topanga” Formation in Riverside County. If this mapped unit in Riverside can be correlated with “Topanga” Formation units in Orange County, it would imply a middle Miocene age, likely 16.5–14.5 Ma20, and given the morphological similarities of this isolated tooth with more complete paleoparadoxiid material in Orange County with stronger age constraints, we think a middle Miocene age for USNM PAL V 11367 is warranted. Given the reduced distribution of outcrops of the “Topanga” Formation24,36 in Corona, we identify two potential localities for USNM PAL V 11367 (Fig. 3). These two localities are situated in urbanized areas, less than 21 km apart, in the northwest and the southeast corners of Corona’s city limits (see Fig. 3B). Both are notably less than 40 km apart from the type locality of N. cecilialina in Orange County, but we urge skepticism for a direct correlation as the marine units of Riverside County requires detailed stratigraphic revision to determine their age constraints; they likely belong to a different depositional basin than “Topanga” Formation exposures in westward Southern California counties.Morphological variation and potential diversity of PaleoparadoxiidaeOur comparisons reveal considerable morphological variation in the arrangement and number of dental cusps across Paleoparadoxiidae (Fig. 4). The cusps arrangement for the m2-3 of Archaeoparadoxia and Paleoparadoxia were previously reported by Inuzuka et al.43 (Fig. 4B), but the addition of another specimen (USNM PAL V 11367) reveals larger morphological variability than previously known for the genus Neoparadoxia (Fig. 4C). Specifically, the holotype of N. cecilialina displays slightly different configurations between its right and left m2, driven mainly by the position of the hypoconulid in occlusal view (Fig. 4C). USNM PAL V 11367, the second known Neoparadoxia m2 (or the first m3), is comparable in size and shape with the same teeth in the type specimen of N. cecilialina, especially the right m2. Both the Smithsonian and LACM specimens display a horizontal alignment of the extra cusp, the hypoconulid, and the entoconid; nevertheless, USNM PAL V 11367 shows a tighter configuration, lacking a wide internal spacing between cusps characteristic of the type specimen of N. cecilialina (Fig. 4C). Given the known ontogenetic changes that affect the dental nomenclature in desmostylians32,44, the addition of more comparative material should help discriminate between competing statements of homology45. The identification of USNM PAL V 11367 from the “Topanga” Formation of Corona represents a second diagnostic record of Neoparadoxia from three separate Middle Miocene units in Southern California, reaffirming its presence as a Middle Miocene taxon: USNM PAL V 11367 from the “Topanga” Formation of Riverside County; Neoparapdoxia (LACM 6920) from the Altamira Shale46; Neoparadoxia from the Topanga Formation of Orange County46,47; and the holotype of N. cecilialina from the lower part of Monterey Formation in the Capistrano syncline, Orange County46. It is possible that other records of Palaeoparadoxiidae from Orange County (e.g.47) and elsewhere in California may represent Neoparadoxia. For example, Awalt et al.32 noted that a palaeoparadoxiid from Orange County identified by Panofsky as Paleoparadoxia sp. (LACM 131889)25 is better referred to Paleoparadoxidae sp., pending a more detailed evaluation of this material, which differs in clear ways from N. ceciliana. One of the benefits of continued descriptive work on desmostylian material from well-constrained stratigraphic contexts in Southern California will be the biostratigraphic opportunities for cross-basin comparisons, especially for exposures of the “Topanga” Formation.Parham et al.46 emphasized that Neoparadoxia occurs widely in middle Miocene units across California: besides the aforementioned ones, Parham et al.46 noted records of this genus from the Sharktooth Hill Bonebed (LACM 120023), the Altamira Shale (LACM 6920), and the Ladera Sandstone15 (UCMP 81302). To date, Neoparadoxia is only known from California, yet it is likely that other paleoparadoxiid material tentatively assigned to other genera may expand the geographic range of this taxon. Interestingly, on the west side of the Pacific (Russia–Japan) and some parts of the east side of the Pacific (Oregon–Washington), Desmostylus spp. and paleoparadoxiids rarely co-occurred from the same formation48,49, yet there are many geological units in South California where desmostylids and paleoparadoxiids co-occurred (e.g., Santa Margarita Formation50,51, Rosarito Beach Formation52, Tortugas Formation51, and Temblor Formation3,4). The abundance of new material from the “Topanga” Formation from Orange and Riverside counties should contribute to the discussion of desmostylian environmental preferences48,53.Lastly, like other marine mammal lineages, desmostylian body sizes reached their maximum body size late in their evolutionary history54. By the middle to late Miocene, desmostylians were the largest herbivorous marine mammals along the North Pacific coastlines54, although they likely competed ecologically with co-occurring sirenians, which later eclipsed desmostylians in body size and survived until historical times in the North Pacific Ocean55. Specifically, in the “Topanga” Formation of Orange County, desmostylians co-occurred with sirenians such as Metaxytherium arctodites56, an ecological association that likely was repeated elsewhere in the mid-Miocene of California (e.g., coeval deposits of the Round Mountain Silt). Given the improving stratigraphic picture of Southern California marine mammal-bearing localities, future work on desmostylian paleoecology could test hypotheses of competition with taxonomic co-occurrence data grounded in strong comparative taphonomic and sedimentological frameworks. More

Site descriptionThe field experiment was initiated in 2013 at the Yongchun County, Fujian Province, China (25°12′37″ N, 118°10′24″ E), using the two rotations of vegetables and rice (Fig. 1). The site is in the north of the Tropic of Cancer, with a typical subtropical marine monsoon climate, sufficient sunshine, and average annual solar radiation 462.26 kJ/cm2. The climate is mild and humid, with average annual temperature 16–21 °C and average annual rainfall about 1400 mm. Agricultural production allows for the cultivation of three crops annually. The soil of the test field was lateritic red soil.Figure 1Location of the field experiment site.Full size imageExperiment designThe experiment was conducted over 9 years from 2013 to 2021. Soil samples were collected before the experiment began to determine the main physical and chemical properties of the soil in the test plot, which were: organic matter content 19.96 g/kg, total nitrogen 2.25 g/kg, total phosphorus 1.31 g/kg, total potassium 27.86 g/kg, alkaline hydrolyzable nitrogen 107.73 mg/kg, available phosphorus 60.35 mg/kg, available potassium 116 mg/kg and soil pH 5.54. The test site was a rectangular field, 26 m long and 9 m wide, divided into 15 test blocks, each 5 m long and 2.8 m wide. Cement ridges were used to separate the test blocks, and irrigation drainage ditches were set outside the blocks. A protective isolation strip 1 m wide was formed around the test site. The experiment included two crop rotations: (I) rotation P–B–O: P, kidney bean (Phaseolus vulgaris L.), B, mustard (Brassica juncea L.), O, rice (Oryza sativa L.); and II) rotation P–B–V: P, kidney bean (P. vulgaris L.), B, mustard (B. juncea L.), V, cowpea (Vigna unguiculata L.). Four fertilizer treatments were selected: (1) recommended fertilization (RF) used with rotation P–B–O; (2) recommended fertilization (RF) used with P–B–V; (3) conventional fertilization (CF) used with P–B–O; (4) conventional fertilization (CF) used with P–B–V. A randomized complete block experimental design with three replications was used in the field study. The fertilization amounts used for treatments RF and CF are shown in Table 1. Under the CF, the amount of fertilizer applied to crops in each season is determined according to the years of fertilization habits of local farmers. The fertilization amount of crops in each season under the RF was calculated according to the measured basic soil fertility combined with the fertilization model of previous studies. The fertilization amount of crops in each season under the CF in this study is obtained by investigating the local farmers. The data on the fertilization amount of crops in each season under the RF is cited from the research report of Zhang et al.23. Urea (N 46%) was the nitrogen fertilizer, calcium superphosphate (P2O5 12%) was the phosphorus fertilizer, and potassium chloride (K2O 60%) was the potassium fertilizer. All phosphorus fertilizer applied to crops in each season was used as base fertilizer, and nitrogen and potassium fertilizer were applied separately as base fertilizer (40% of the total fertilization) and topdressing (60% of the total fertilization). The topdressing method was that nitrogen and potassium fertilizer for kidney bean and cowpea were applied twice, 30% of the fertilization amount each time; nitrogen and potassium fertilizer for mustard was applied three times, 20% of the fertilization amount each time; nitrogen fertilizer for rice was applied at two different growing stages, 50% of the fertilization amount at the tillering stage and 10% of the fertilization amount at the panicle stage; potassium fertilizer was applied once, using 60% of the fertilization amount. The first crop, kidney bean, was sown in early September and harvested in November. The second crop mustard, was sown in early December and harvested in February of the following year. The third crop, rice or cowpea, was sown in early April and harvested in July.Table 1 Fertilization rate of each treatment in the long term crop rotation experiment (kg/hm2).Full size tableData analysis and methodsYield stability analysis was conducted for the 9 years period using three different approaches. First, the coefficient of variation (CV) was calculated to give a measure of the temporal variability of yield for each treatment:$$CV=frac{upsigma }{Y}*100 {%}$$
(1)
where σ is the standard deviation of average crop yield in each year, and Y is the average crop yield in each year. A low value of CV indicates little variation, which implies that interannual difference in crop yield in the experimental plot is small and the yield is relatively stable over the years of the experimental period.A second yield stability indicator is the sustainable yield index (SYI), which is calculated by Singh et al.25:$$SYI=frac{mathrm{Y}-upsigma }{{Y}_{max}}$$
(2)
where Y is the average annual crop yield, σ is the standard deviation of the average annual crop yield, and YMax is the maximum annual crop yield. A high value of SYI indicates a greater capacity of the soil to sustain a particular crop yield over time.The third stability measure is Wricke’s ecovalence index (Wi2), which was calculated individually for each crop management system by Wricke26:$${Wi}^{2}={sum }_{j=1}^{q}({x}_{ij}-{{m}_{i}-{m}_{j}+m)}^{2}$$
(3)
where xij is the yield for treatment i in year j, mi is the yield for treatment i across all years, mj is the yield for year j across all treatments, and m is the average yield for all treatments across all years. When Wi2 is close to 0, the yield for treatment i is very stable.Analysis of crop yield trendsA simple linear regression analysis of grain yield (slopes and P values) over the years was performed to identify the yield trend (Choudhary et al.27):$$Y=a+bt$$
(4)
where Y is the crop yield (t/ha), a is a constant, t is the time in years, and b is the slope, or magnitude of the yield trend (annual rate of change in yield).Analysis of variance (ANOVA) was performed using MATLAB R2019b in order to compare crop yields in the long term experiment. Yield stability and univariate linear regression equations were created and statistically analyzed using the software toolbox. The coefficients of variation for yields, yield sustainability indexes, and graphs presented in this paper were calculated and drawn using MATLAB; differences were considered to be significant when P  More

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Departamento de Biología, Escuela Politécnica Nacional del Ecuador, Ladrón de Guevara E11-253 y Andalucía, Quito, EcuadorSelene BáezBiology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Calle Tulipán s/n, Móstoles, Madrid, SpainLuis Cayuela & Guillermo Bañares de DiosDepartamento de Biología, Área de Botánica, Universidad Autónoma de Madrid, Madrid, Calle Darwin 2, ES–28049, Madrid, SpainManuel J. Macía, Celina Ben Saadi, Julia G. de Aledo & Laura Matas-GranadosCentro de Investigación en Biodiversidad y Cambio Global (CIBC-UAM), Universidad Autónoma de Madrid, Calle Darwin 2, ES–28049, Madrid, SpainManuel J. MacíaEscuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente, Universidad Nacional Abierta a Distancia de Colombia, Sede José Celestino Mutis, Cl. 14 Sur 14-23, Bogotá, ColombiaEsteban Álvarez-DávilaInstituto Experimental de Biología Luis Adam Briancon, Universidad Mayor Real y Pontificia San Francisco Xavier de Chuquisaca, Dalence 235, Sucre, BoliviaAmira Apaza-QuevedoDepartamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, Ecuador. San Cayetano Alto s/n. Paris y Marcelino Chamagnat, 1101608, Loja, EcuadorItziar Arnelas & Carlos Iván EspinosaDepartamento de Biología. Grupo de Biología de Páramos y Ecosistemas Andinos, Universidad de Nariño, Calle 18 # 50-02 Ciudadela Universitaria Torobajo, Pasto, ColombiaNatalia Baca-Cortes, Marian Cabrera & María Elena Solarte-CruzDepartment of Environment, CAVElab – Computational and Applied Vegetation Ecology, Ghent University, Coupure links 653, B-9000, Gent, BelgiumMarijn Bauters & Hans VerbeeckInstituto de Ecología Regional, Universidad Nacional de Tucumán, CONICET, Residencia Universitaria Horco Molle, Edificio Las Cúpulas, 4107, Tucumán, ArgentinaCecilia BlundoHerbario UIS, Escuela de Biología, Universidad Industrial de Santander, Carrera. 27, calle 9a, Bucaramanga, ColombiaFelipe CastañoHerbario Nacional de Bolivia, Instituto de Ecología, Universidad Mayor de San Andrés, Calle 27 s/n, La Paz, BoliviaLeslie Cayola, Alfredo Fuentes, M. Isabel Loza & Carla MaldonadoCenter for Conservation and Sustainable Development, Missouri Botanical Garden, 4344 Shaw Blvd., St. Louis, MO, 63110, USALeslie Cayola, William Farfán-Rios, Alfredo Fuentes, M. Isabel Loza & J. Sebastián TelloSchool of Geography, University of Leeds, Leeds, LS2 9JT, UKBelén FadriqueLiving Earth Collaborative, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USAWilliam Farfán-RiosDepartment of Biology, University of Florida, 876 Newell Drive, ZIP 32611, Gainesville, Florida, USAClaudia Garnica-DíazInstituto de Investigación de Recursos Biológicos Alexander von Humboldt, Calle 28 A # 15-09, Bogotá, ColombiaMailyn González, Ana Belén Hurtado & Natalia NordenConservación Internacional, Colombia, Carrea 13 # 71-41, Bogotá, ColombiaDiego GonzálezInstitute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, D-06108, Halle, GermanyIsabell Hensen & Denis LippokEscuela de Ingeniería Agronómica, Universidad de Cuenca, Av. 12 de Abril y Av. Loja s/n, Cuenca, EcuadorOswaldo JadánGlobal Tree Conservation Program and the Center for Tree Science, The Morton Arboretum, Lisle, IL, 60532-1293, USAM. Isabel LozaFacultad de Ciencias Agrarias, Universidad Nacional de Jujuy, Alberdi 47, San Salvador de Jujuy, CP 4600, Jujuy, ArgentinaLucio MaliziaDepartment of Biology, Washington University, 1 Brookings Drive, St. Louis, MO, 63130, USAJonathan A. MyersAMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université  de Montpellier, TA-A51/PS, Boulevard de la Lironde, 34398 cedex 5, Montpellier, FranceImma Oliveras MenorEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, UKImma Oliveras Menor & Greta WeithmannPlant Ecology and Ecosystems Research, University of Goettingen, Untere Karspüle 2, 37073, Goettingen, GermanyKerstin Pierick & Jürgen HomeierInstituto de Investigaciones para el Desarrollo Forestal (Indefor), Vía los Chorros de Milla, Mérida, VenezuelaHirma Ramírez-AnguloDepartamento de Biología, Universidad Nacional de Colombia, Cra 45 #26-85, Bogotá, ColombiaBeatriz Salgado-NegretSenckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325, Frankfurt, GermanyMatthias SchleuningDepartment of Biology, Wake Forest University, Winston-Salem, NC, 27109, USAMiles SilmanWildlife Conservation Society (WCS), 2300 Southern Boulevard Bronx, New York, 10460, USAEmilio VilanovaFaculty of Resource Management, HAWK University of Applied Sciences and Arts, Büsgenweg 1 A, 37077, Goettingen, GermanyJürgen HomeierCentre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen, GermanyJürgen HomeierL.C., J.H., M.J.M. and S.B. conceived the idea. S.B., L.C., M.J.M., J.A.M. and J.S.T. obtained funding and coordinated the L.E.C. and iDiv workshops. L.C., S.B., J.H. and K.P. compiled the data sets and performed data quality checks. L.C., S.B., J.H. and K.P. conceived and developed the figures. S.B., J.H. and L.C. wrote the manuscript. The rest of authors (ordered alphabetically) contributed data, revised and agreed on the final version of the manuscript. More

Data collectionVirus-host data was collated from various sources. Major sources for the association databases included data shared by Olival et al4., Pandit et al.3, and Johnson et al.13. In data provided by Olival et al (assessed September 2019), host-virus associations have been assigned a score, based on detection methods and tests that are specific and more reliable. We used associations that have been identified as the most reliable (stringent data) from Olival et al4. In addition, a query in GenBank was run to parse out hosts reported for each GenBank submission for viruses presented in each of these three databases. Initially, for each virus name, taxonomic ID was identified using entrez.esearch function in biopython package. The taxonomic ID helped linked to the GenBank databases, identify the ICTV lineage and associated data in PubMed20,21. NCBI TaxID closely follows the ICTV database, but some recent changes in ICTV might not always be reflected in NCBI, so we manually checked names to ensure matching. This included virus genus and family information along with a standard virus name. Host data were aggregated based on the taxonomic ID and associated standard name. Finally, for each virus, a search was completed in PubMed to compile the number of hits related to the virus and their vertebrate hosts using the search terms below. The number of PubMed hits (PMH1) were used as a proxy for sampling bias3,13. The virus-host association data source is presented in supplementary code and data files (https://zenodo.org/record/5899054).$$ searchterm= (+virus_name+,[Title/Abstract])\ ANDleft(host,OR,hosts,OR,reservoir,OR,reservoirs,OR right.\ wild,OR,wildlife,OR,domestic,OR,animal,OR,animals,OR\ mammal,OR,bird,OR,birds,OR,aves,OR,avian,OR,avians\ left. OR,vertebrate,OR,vertebrates,OR,surveillance,OR,sylvaticright)$$Along with the PubMed terms we also queried the nucleotide database on PubMed using the taxonomic ID to find the number of GenBank entries for these viruses (PMH2). A correlation analysis between the PMH1 and PMH2 of well-recognized known viruses showed a high correlation with each other for us to safely use GenBank hits for novel viruses during the prediction stage of the model (Fig. S32).Development of ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})
a. Centrality measures of observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}}))To test if centrality measures (degree centrality, betweenness centrality, eigenvector centrality, clustering coefficient) for viral nodes in the observed network (({G}_{c})) vary significantly between viral families, we firstly used the Kolmogorov-Smirnov (KS) test. KS test is routinely used to identify distances between cumulative distribution functions of two probability distributions and is largely used to compare degree distributions of networks22,23. For each viral family, distributions of centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) and clustering coefficient within the observed network (({G}_{c})) were compared with the distribution of all nodes in the network using the two-tailed KS test. Secondly, a linear regression model with virus family as a categorical variable and the number of PubMed hits as a covariate to adjust for sampling bias were fitted to understand associations of viral families with centrality measures.$${centrality},{measure}={beta }_{0}{intercept}+{{beta }_{1}{Viral}{family}}_{{categorical}}+{beta }_{2}{PubMed},{hits}$$After fitting the model, node-level permutations were implemented. For each random permutation, the output variable was randomly assigned to covariate values and the model was re-fitted. Finally, a p-value was calculated by comparing the distribution of coefficients from permutations with the original model coefficient.Network topology feature selectionUsing the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})), multiple network topological features for all node (virus) pairs were calculated. The following are topological network features calculated. Features data type, definition and methods to calculate these features are presented in Table S3.1. The Jaccard coefficient: a commonly used similarity metric between nodes in information retrieval, is also called an intersection of over the union for two nodes in the network. In the unipartite network generated here, it represents the proportion of common neighbor viruses from the union of neighbor viruses for two nodes. Neighbor viruses are defined as viruses with which the virus shares at least a single host.2. Adamic/Adar (Frequency-Weighted Common Neighbors): Is the sum of inverse logarithmic degree centrality of the neighbors shared by two nodes in the network24. The concept of Adamic Adar index is a weighted common neighbors for viruses in the network. Within network prediction, the index assumes that viruses with large neighborhoods have a less significant impact while predicting a connection between two viruses compared with smaller neighborhoods.Both Jaccard and Adamic Adar coefficients have been routinely used for generalized network prediction and have shown high accuracy in predicting missing links in networks, specifically bipartite networks25, the information flowing through neighborhoods formed by two nodes might not always be enough to have similar predictive power in an unipartite network. This warrants use of other topology features along with neighborhood-based features.3. Resource allocation: Similarity score of two nodes defined by the weights of common neighbors of two nodes. Resource allocation is another measure to quantify the closeness of two nodes in the network and hence to understand the similarity of hosts they infect.4. Preferential attachment coefficients: The mechanism of preferential attachment can be used to generate evolving scale-free networks, where the probability that a new link is connected to node x is proportional to k26.5. Betweenness centrality: For a node in the network betweenness centrality is the sum of the fraction of all-pairs shortest paths that pass through it. The feature that we used for training the supervised learning model was the absolute difference between of betweenness centralities of two nodes. The difference between the betweenness centrality represents the difference in the sharing observed by two viruses in the pair.6. Degree centrality: The degree centrality for a node v is the fraction of nodes it is connected to. The feature that we used for training the supervised learning model was the absolute difference between degree centralities of two nodes. Unlike the difference in the betweenness centrality, the difference in degree centrality only looks at the difference in the number of observed host sharing.7. Network clustering: All nodes were classified into community clusters using Louvain methods27. A binary feature variable was generated to describe if both the nodes in the pair were part of the same cluster or not. If both viruses are from the same cluster, it represents a similar host predilection than when both viruses are not from the same cluster hence accounting for the evolutionary predilection of viruses (or virus families) to infect a certain type of host.These topological network characteristics come with certain limitations when it comes to the unipartite network of viruses with links formed due to shared hosts and might not truly represent the flow of information between nodes as compared to a bipartite network. Therefore, to account for these limitations, we use multiple network features as weak learners in our model building characteristics summarizing the network through the use of several quantitative metrics. In addition to this, we estimated the feature importance of these metrics in predicting missing links between viruses to quantify the information pasting through these links.Pearson’s correlation coefficients were calculated to identify highly correlated features and for choosing features for model training (Fig. S33). Virological features included in model training were categorical variables describing the virus family of both the nodes in the pair, followed by a binary variable if both the viruses belong to the same virus family. During the model development, PubMed hits generated three predictive features for each pair of viruses on which model training and predictions were conducted. These included two features representing PubMed hits for the two viruses in the pair (PubMedV1, PubMedV2) and the absolute difference between PubMedV1 and PubMedV2 to account for differences in sampling bias between the two viruses.Cross-validation and fitting generalized boosting machine (GBMs) modelsA nested-cross-validation was implemented for the binary model while simple cross-validation was implemented for the multiclass model (multiple output categories). The parameters of the binary model were first hyper-tuned using a cross-validated grid-search method. Values were tested using a grid search to find the best-performing model parameters that showed the highest sensitivity (recall). The parameters tested for hypertuning and their performance are provided in the supplementary material (supplementary results and Table S5). For further cross-validation of the overall binary model, all the viruses were randomly assigned to five groups. For each fold, the viruses assigned to a group were dropped from the data, and a temporary training network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}{{{{{boldsymbol{)}}}}}}) was constructed, assuming that this represented the current observed status of the virus-host community. For all possible pairs in ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}) (both that sharing and not sharing any hosts) ten topological and viral characteristics were calculated as training features (Table S4). Categorical features were one-hot-encoded and numeric features were scaled. An XGBClassifier model with binary: logistic family was trained using the feature dataset to predict if virus pairs share hosts (1,0 encoded output). The cross-validation was also used to determine the optimum decision threshold for determining binary classification (Fig. S6) and a precision-recall curve was used to identify positive predictive value and sensitivity at the optimum threshold (Fig. S8).The multiclass model was implemented in the same way, creating an observed network (({G}_{c})) based on species-level sharing of hosts and randomly dropping viruses to generate a training network (({G}_{t})) to train the XGboost model. The output variables were generated based on the taxonomical orders of shared hosts. A pair of viruses can share multiple hosts, hence we trained a multioutput-multiclass model. Humans were considered an independent category of taxonomical order (label) and were given a separate label from primates. For fine-tuning the multiclass model, we started with the best performing parameters of the binary model and manually tested 5 combinations of model parameters by adjusting values of the learning rate, number of estimators, maximum depth, and minimum child weight (Supplementary code and results).We used three methods to estimate the importance of features for our binary model. Specifically, improvement in accuracy brought by branching based on the feature (gain), the percentage of times the feature appears in the XGboost tree model (weight), and the relative number of observations related to the specific feature (cover). Results for feature importance are shown in supplementary results (Fig. S10).Missing links for novel viruses, binary and multiclass predictionThe wildlife surveillance data represented a sampling of 99,379 animals (94,723 wildlife, 4656 domesticated animals) conducted in 34 countries around the world between 2009–2019 (Table S6)1. Specimens were tested using conventional Rt-PCR, Quantitative PCR, Sanger sequencing, and Next Generation Sequencing protocols to detect viruses from 28 virus families or taxonomic groups (Table S7). Testing resulted in 951 novel monophyletic clusters of virus sequences (referred to as novel viruses henceforth). Within 951 novel viruses, 944 novel viruses had vertebrate hosts that were identified with certainty based on barcoding methods and field identification. Host species identification was confirmed by cytochrome b (cytb) DNA barcoding using DNA extracted from the samples28. We predicted the shared host links between novel viruses and known viruses using binary and multiclass models in the following steps. Out of 944 novel viruses discovered in the last ten years, we were able to generate predictions for 531 novel viruses that were detected in species already classified as hosts within the network. The remaining 413 viruses were the first detection of any virus in that species and thus host associations could not be informed by the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{C}}}}}}})) data.1. A new node representing the novel virus was inserted in the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})). Using the list of species in which the novel virus was detected, new edges were created with known viruses that are also known to be found in those hosts. This generated a temporary network for the novel virus (({{{{{{boldsymbol{G}}}}}}}_{{temp}})). If the novel virus was not able to generate any edges with known viruses, meaning the host in which they have been found was never found positive for any known virus, predictions were not performed.2. Using ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) feature values were calculated for the novel virus (betweenness centrality, clustering, and degree). For all possible pairs of the novel virus with known viruses that are not yet connected with each other through an edge in ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) a feature dataset was generated (Jaccard coefficient(novel virus, known virus), the difference in betweenness centrality of the novel virus and known virus, if the novel virus and known virus were in the same cluster, the difference in degree centrality(novel virus, known virus), if the novel virus and known virus were from same virus family, the difference in PubMed hits(novel virus, known virus), PubMed hits for the novel virus, PubMed hits for the known virus). Studies and nucleotide sequences for novel viruses are expected to be published and shared on PubMed’s Nucleotide database and in various peer-reviewed publications. Data associated with GenBank accession numbers and nucleotide sequences for novel viruses are presented in Supplementary Data 3 and Supplementary Data 4 respectively. At the time of development of the model, data for all viruses was not shared in a format that would reflect on PubMed’s database, we decided to use the number of unique species the virus was detected in the last ten years of wildlife surveillance conducted by the USAID PREDICT project. These detections will be reflected in PubMed’s Nucleotide database and search term eventually, hence we considered them as a proxy for search terms conducted for known viruses. Currently, evaluation of the effects of this substitution of PubMed hits with the number of detections for novel viruses is not possible with limited data on novel viruses but needs to be reevaluated as more studies are published on these novel viruses. To further evaluate the association between PubMed hits through search term and Genbank hits, we ran a generalized linear regression model with PubMed hits as dependent variable and Genbank hits as intendent variable, accounting for virus families.$${{PubMed}}_{{Search}}left({log }right)={beta }_{0}{intercept}+{{beta }_{1}{Virus}{family}}_{{categorical}}+{beta }_{2}{Genbank},{hits},({log })$$The results indicated that Genbank hits had statistically significant predictive value in predicting PubMed hits (β = 0.72, p  More

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