rOpenSci | Taxonomy

Taxonomy

Handle and Transform Taxonomic Information
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Taxonomic Information from Around the Web

Scott Chamberlain
Description

Interacts with a suite of web APIs for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more.

Scientific use cases
  1. Baden, H. M., Särkinen, T., Conde, D. A., Matthews, A. C., Vandrot, H., Chicas, S., Harris, D. J. (2015). A botanical inventory of forest on karstic limestone and metamorphic substrate in the Chiquibul Forest, Belize, with focus on woody taxa. Edinburgh Journal of Botany, 73(01), 39–81. https://doi.org/10.1017/s0960428615000256
  2. Vanden Berghe, E., Coro, G., Bailly, N., Fiorellato, F., Aldemita, C., Ellenbroek, A., & Pagano, P. (2015). Retrieving taxa names from large biodiversity data collections using a flexible matching workflow. Ecological Informatics, 28, 29–41. https://doi.org/10.1016/j.ecoinf.2015.05.004
  3. Bocci, G. (2015). TR8: an R package for easily retrieving plant species traits. Methods in Ecology and Evolution, 6(3), 347–350. https://doi.org/10.1111/2041-210x.12327
  4. Bradie, J., Pietrobon, A., & Leung, B. (2015). Beyond species-specific assessments: an analysis and validation of environmental distance metrics for non-indigenous species risk assessment. Biological Invasions, 17(12), 3455–3465. https://doi.org/10.1007/s10530-015-0970-8
  5. Dodd, A. J., Burgman, M. A., McCarthy, M. A., & Ainsworth, N. (2015). The changing patterns of plant naturalization in Australia. Diversity Distrib., 21(9), 1038–1050. https://doi.org/10.1111/ddi.12351
  6. Drozd, P., & Šipoš, J. (2013). R for all (I): Introduction to the new age of biological analyses. Casopis Slezskeho Zemskeho Muzea A, 62(1). https://doi.org/10.2478/cszma-2013-0004
  7. Chamberlain, S. A., & Szöcs, E. (2013). taxize: taxonomic search and retrieval in R. F1000Research, 2, 191. https://doi.org/10.12688/f1000research.2-191.v1
  8. Hodgins, K. A., Bock, D. G., Hahn, M. A., Heredia, S. M., Turner, K. G., & Rieseberg, L. H. (2015). Comparative genomics in the Asteraceae reveals little evidence for parallel evolutionary change in invasive taxa. Mol Ecol, 24(9), 2226–2240. https://doi.org/10.1111/mec.13026
  9. Lapatas, V., Stefanidakis, M., Jimenez, R. C., Via, A., & Schneider, M. V. (2015). Data integration in biological research: an overview. J of Biol Res-Thessaloniki, 22(1). https://doi.org/10.1186/s40709-015-0032-5
  10. Niedballa, J., Sollmann, R., Courtiol, A., & Wilting, A. (2016). camtrapR: an R package for efficient camera trap data management. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12600
  11. Ningthoujam, S. S., Choudhury, M. D., Potsangbam, K. S., Chetia, P., Nahar, L., Sarker, S. D., … Talukdar, A. D. (2014). NoSQL Data Model for Semi-automatic Integration of Ethnomedicinal Plant Data from Multiple Sources. Phytochemical Analysis, 25(6), 495–507. https://doi.org/10.1002/pca.2520
  12. Pérez-Luque, A. J., Barea-Azcón, J. M., Álvarez-Ruiz, L., Bonet-García, F. J., & Zamora, R. (2016). Dataset of Passerine bird communities in a Mediterranean high mountain (Sierra Nevada, Spain). ZK, 552, 137–154. https://doi.org/10.3897/zookeys.552.6934
  13. Poisot, T. (2015). Best publishing practices to improve user confidence in scientific software. IEE, 8. https://doi.org/10.4033/iee.2015.8.8.f
  14. Pos, E., Guevara Andino, J. E., Sabatier, D., Molino, J.-F., Pitman, N., Mogollón, H., … ter Steege, H. (2014). Are all species necessary to reveal ecologically important patterns? Ecology and Evolution, 4(24), 4626–4636. https://doi.org/10.1002/ece3.1246
  15. Bachelot, B., Uriarte, M., Zimmerman, J. K., Thompson, J., Leff, J. W., Asiaii, A., … McGuire, K. (2016). Long-lasting effects of land use history on soil fungal communities in second-growth tropical rain forests. Ecol Appl. https://doi.org/10.1890/15-1397.1
  16. Pérez-Luque, A. J., Sánchez-Rojas, C. P., Zamora, R., Pérez-Pérez, R., & Bonet, F. J. (2015). Dataset of Phenology of Mediterranean high-mountain meadows flora (Sierra Nevada, Spain). PhytoKeys, 46, 89–107. https://doi.org/10.3897/phytokeys.46.9116
  17. Poisot, T., Gravel, D., Leroux, S., Wood, S. A., Fortin, M.-J., Baiser, B., … Stouffer, D. B. (2015). Synthetic datasets and community tools for the rapid testing of ecological hypotheses. Ecography, 39(4), 402–408. https://doi.org/10.1111/ecog.01941
  18. Wagner, F. H., Hérault, B., Bonal, D., Stahl, C., Anderson, L. O., Baker, T. R., … Botosso, P. C. (2016). Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests. Biogeosciences, 13(8), 2537–2562. https://doi.org/10.5194/bg-13-2537-2016
  19. Schwery, O., & O’Meara, B. C. (2016). MonoPhy : a simple R package to find and visualize monophyly issues . PeerJ Computer Science, 2, e56. https://doi.org/10.7717/peerj-cs.56
  20. Bradie, J., & Leung, B. (2016). A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography. https://doi.org/10.1111/jbi.12894
  21. Bufford, J. L., Hulme, P. E., Sikes, B. A., Cooper, J. A., Johnston, P. R., & Duncan, R. P. (2016). Taxonomic similarity, more than contact opportunity, explains novel plant-pathogen associations between native and alien taxa. New Phytol. https://doi.org/10.1111/nph.14077
  22. Cramer, M. D., & Verboom, G. A. (2016). Measures of biologically relevant environmental heterogeneity improve prediction of regional plant species richness. Journal of Biogeography. https://doi.org/10.1111/jbi.12911
  23. Foster, Z. S. L., Sharpton, T., & Grunwald, N. J. (2016). MetacodeR: An R package for manipulation and heat tree visualization of community taxonomic data from metabarcoding. https://doi.org/10.1101/071019
  24. Halse-Gramkow, M., Ernst, M., Rønsted, N., Dunn, R. R., & Saslis-Lagoudakis, C. H. (2016). Using evolutionary tools to search for novel psychoactive plants. Plant Genetic Resources, 1–10. https://doi.org/10.1017/s1479262116000344
  25. Liang, J., Crowther, T. W., Picard, N., Wiser, S., Zhou, M., Alberti, G., et al. (2016). Positive biodiversity-productivity relationship predominant in global forests. Science, 354(6309), aaf8957–aaf8957. https://doi.org/10.1126/science.aaf8957
  26. Nath, C. D., Munoz, F., Pélissier, R., Burslem, D. F. R. P., & Muthusankar, G. (2016). Growth rings in tropical trees: role of functional traits, environment, and phylogeny. Trees. https://doi.org/10.1007/s00468-016-1442-1
  27. Sclavi, B., & Herrick, J. (2016). Genome size variation and species diversity in salamander families. https://doi.org/10.1101/065425
  28. Vincze, O. (2016). Light enough to travel or wise enough to stay? Brain size evolution and migratory behaviour in birds. Evolution. https://doi.org/10.1111/evo.13012
  29. Wagner, V. (2016). A review of software tools for spell-checking taxon names in vegetation databases. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12432
  30. Weber, M. G., Porturas, L. D., & Taylor, S. A. (2016). Foliar nectar enhances plant–mite mutualisms: the effect of leaf sugar on the control of powdery mildew by domatia-inhabiting mites. Annals of Botany, mcw118. https://doi.org/10.1093/aob/mcw118
  31. Wiser, S. K. (2016). Achievements and challenges in the integration, reuse and synthesis of vegetation plot data. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12419
  32. Galata, V., Backes, C., Laczny, C. C., Hemmrich-Stanisak, G., Li, H., Smoot, L., et al. (2016). Comparing genome versus proteome-based identification of clinical bacterial isolates. Briefings in Bioinformatics, bbw122. https://doi.org/10.1093/bib/bbw122
  33. Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J., & Hérault, B. (2017). BIOMASS: An R Package for estimating aboveground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.12753
  34. O’Donnell JL, Kelly RP, Shelton AO, Samhouri JF, Lowell NC, Williams GD. (2017) Spatial distribution of environmental DNA in a nearshore marine habitat. PeerJ 5:e3044 https://doi.org/10.7717/peerj.3044
  35. Mohiuddin, M. M., Salama, Y., Schellhorn, H. E., & Golding, G. B. (2017). Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Research. https://doi.org/10.1016/j.watres.2017.02.057
  36. Andruszkiewicz, E. A., Starks, H. A., Chavez, F. P., Sassoubre, L. M., Block, B. A., & Boehm, A. B. (2017). Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLOS ONE, 12(4), e0176343. https://doi.org/10.1371/journal.pone.0176343
  37. Olson, N. D., Zook, J. M., Morrow, J. B., & Lin, N. J. (2017). Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data. PeerJ, 5, e3729. https://doi.org/10.7717/peerj.3729
  38. Ordano, M., Blendinger, P. G., Lomáscolo, S. B., Chacoff, N. P., Sánchez, M. S., Núñez Montellano, M. G., … Valoy, M. (2017). The role of trait combination in the conspicuousness of fruit display among bird-dispersed plants. Functional Ecology. https://doi.org/10.1111/1365-2435.12899
  39. Bartomeus, I., Cariveau, D. P., Harrison, T., & Winfree, R. (2017). On the inconsistency of pollinator species traits for predicting either response to land-use change or functional contribution. Oikos. https://doi.org/10.1111/oik.04507
  40. Bartomeus, I., Cariveau, D., Harrison, T., & Winfree, R. (2016). On the inconsistency of pollinator species traits for predicting either response to agricultural intensification or functional contribution. https://doi.org/10.1101/072132
  41. Leung, W. T. M., Thomas-Walters, L., Garner, T. W. J., Balloux, F., Durrant, C., & Price, S. J. (2017). A quantitative-PCR based method to estimate ranavirus viral load following normalisation by reference to an ultraconserved vertebrate target. Journal of Virological Methods. https://doi.org/10.1016/j.jviromet.2017.08.016
  42. Malcolm F. Rosenthal, Matthew Gertler, Angela D. Hamilton, Sonal Prasad, Maydianne C.B. Andrade, Taxonomic bias in animal behaviour publications. Animal Behaviour, Volume 127, 2017, pgs. 83-89. https://doi.org/10.1016/j.anbehav.2017.02.017
  43. Reznik, E., Christodoulou, D., Goldford, J. E., Briars, E., Sauer, U., Segrè, D., & Noor, E. (2017). Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity. Cell Reports, 20(11), 2666–2677. https://doi.org/10.1016/j.celrep.2017.08.066
  44. Power, S. C., Anthony Verboom, G., Bond, W. J., & Cramer, M. D. (2017). Environmental correlates of biome-level floristic turnover in South Africa. Journal of Biogeography. https://doi.org/10.1111/jbi.12971
  45. Branoff, B. L. (2017). Quantifying the influence of urban land use on mangrove biology and ecology: A meta-analysis. Global Ecology and Biogeography. https://doi.org/10.1111/geb.12638
  46. Berlemont, R. (2017). Distribution and diversity of enzymes for polysaccharide degradation in fungi. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-00258-w
  47. Dallas, T., Decker, R. R., & Hastings, A. (2017). Species are not most abundant in the centre of their geographic range or climatic niche. Ecology Letters. https://doi.org/10.1111/ele.12860
  48. Hutchinson, M. C., Cagua, E. F., & Stouffer, D. B. (2017). Cophylogenetic signal is detectable in pollination interactions across ecological scales. Ecology. https://doi.org/10.1002/ecy.1955
  49. Chalmandrier, L., Albouy, C., & Pellissier, L. (2017). Species pool distributions along functional trade-offs shape plant productivity–diversity relationships. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-15334-4
  50. Drost, H.-G., Gabel, A., Liu, J., Quint, M., & Grosse, I. (2017). myTAI: evolutionary transcriptomics with R. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx835
  51. Emer, C., Galetti, M., Pizo, M. A., Guimarães, P. R., Moraes, S., Piratelli, A., & Jordano, P. (2018). Seed-dispersal interactions in fragmented landscapes - a metanetwork approach. Ecology Letters. https://doi.org/10.1111/ele.12909
  52. Surabhi, S., Avvaru, A. K., Sowpati, D. T., & Mishra, R. K. (2018). Patterns of microsatellite distribution reflect the evolution of biological complexity. https://doi.org/10.1101/253930
  53. Khorramdelazad, M., Bar, I., Whatmore, P., Smetham, G., Bhaaskaria, V., Yang, Y., … Ford, R. (2018). Transcriptome profiling of lentil (Lens culinaris) through the first 24 hours of Ascochyta lentis infection reveals key defence response genes. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-4488-1
  54. Vieilledent, G., Fischer, F. J., Chave, J., Guibal, D., Langbour, P., & Gérard, J. (2018). New formula and conversion factor to compute tree species basic wood density from a global wood technology database. bioRxiv, 274068. https://doi.org/10.1101/274068
  55. Foster, Z. S. L., Chamberlain, S., & Grünwald, N. J. (2018). Taxa: An R package implementing data standards and methods for taxonomic data. F1000Research, 7, 272. https://doi.org/10.12688/f1000research.14013.1
  56. Bennett, J. M., Calosi, P., Clusella-Trullas, S., Martínez, B., Sunday, J., Algar, A. C., … Morales-Castilla, I. (2018). GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Scientific Data, 5, 180022. https://doi.org/10.1038/sdata.2018.22
  57. Correia, R. A., Jarić, I., Jepson, P., Malhado, A. C. M., Alves, J. A., & Ladle, R. J. (2018). Nomenclature instability in species culturomic assessments: Why synonyms matter. Ecological Indicators, 90, 74–78. https://doi.org/10.1016/j.ecolind.2018.02.059
  58. Holmes, I., & Davis Rabosky, A. R. (2018). Natural history bycatch: a pipeline for identifying metagenomic sequences in RADseq data. PeerJ, 6, e4662. https://doi.org/10.7717/peerj.4662
  59. Ondei, S., Brook, B. W., & Buettel, J. C. (2018). Nature’s untold stories: an overview on the availability and type of on-line data on long-term biodiversity monitoring. Biodiversity and Conservation. https://doi.org/10.1007/s10531-018-1582-2
  60. Tsuboi, M., van der Bijl, W., Kopperud, B. T., Erritzøe, J., Voje, K. L., Kotrschal, A., … Kolm, N. (2018). Breakdown of brain–body allometry and the encephalization of birds and mammals. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-018-0632-1
  61. Grenié, M., Mouillot, D., Villéger, S., Denelle, P., Tucker, C. M., Munoz, F., & Violle, C. (2018). Functional rarity of coral reef fishes at the global scale: Hotspots and challenges for conservation. Biological Conservation, 226, 288–299. https://doi.org/10.1016/j.biocon.2018.08.011
  62. Morzaria-Luna, H. N., Cruz-Piñón, G., Brusca, R. C., López-Ortiz, A. M., Moreno-Báez, M., Reyes-Bonilla, H., & Turk-Boyer, P. (2018). Biodiversity hotspots are not congruent with conservation areas in the Gulf of California. Biodiversity and Conservation. https://doi.org/10.1007/s10531-018-1631-x
  63. Vieilledent, G., Fischer, F. J., Chave, J., Guibal, D., Langbour, P., & Gérard, J. (2018). New formula and conversion factor to compute basic wood density of tree species using a global wood technology database. American Journal of Botany. https://doi.org/10.1002/ajb2.1175
  64. Milla, R., Bastida, J. M., Turcotte, M. M., Jones, G., Violle, C., Osborne, C. P., … Byun, C. (2018). Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nature Ecology & Evolution, 2(11), 1808–1817. https://doi.org/10.1038/s41559-018-0690-4
  65. Kandlikar, G. S., Gold, Z. J., Cowen, M. C., Meyer, R. S., Freise, A. C., Kraft, N. J. B., … Curd, E. E. (2018). ranacapa: An R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations. F1000Research, 7, 1734. https://doi.org/10.12688/f1000research.16680.1
  66. Bartomeus, I., Stavert, J. R., Ward, D., & Aguado, O. (2018). Historical collections as a tool for assessing the global pollination crisis. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1763), 20170389. https://doi.org/10.1098/rstb.2017.0389
  67. Pelletier, T. A., Carstens, B. C., Tank, D. C., Sullivan, J., & Espíndola, A. (2018). Predicting plant conservation priorities on a global scale. Proceedings of the National Academy of Sciences, 201804098. https://doi.org/10.1073/pnas.1804098115
  68. Da Silva, R., Pearce Kelly, P., Zimmerman, B., Knott, M., Foden, W., & Conde, D. A. (2018). Assessing the Conservation Potential of Fish and Corals in Aquariums Globally. Journal for Nature Conservation. https://doi.org/10.1016/j.jnc.2018.12.001
  69. Da Silva, R., & Conde, D. A. (2018). Data on the conservation potential of fish and coral populations in aquariums. Data in Brief. https://doi.org/10.1016/j.dib.2018.12.083
  70. Sclavi, B., & Herrick, J. (2018). Genome size variation and species diversity in salamanders. Journal of Evolutionary Biology. https://doi.org/10.1111/jeb.13412
  71. Muñoz, G., Trøjelsgaard, K., & Kissling, W. D. (2019). A synthesis of animal-mediated seed dispersal of palms reveals distinct biogeographical differences in species interactions. Journal of Biogeography. https://doi.org/10.1111/jbi.13493
  72. Muñoz, G., Kissling, W. D., & van Loon, E. E. (2019). Biodiversity Observations Miner: A web application to unlock primary biodiversity data from published literature. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e28737
  73. Smith, T. P., Thomas, T. J., Garcia-Carreras, B., Sal, S., Yvon-Durocher, G., Bell, T., & Pawar, S. (2019). Metabolic rates of prokaryotic microbes may inevitably rise with global warming. bioRxiv, 524264. https://doi.org/10.1101/524264
  74. Srivastava, S., Avvaru, A. K., Sowpati, D. T., & Mishra, R. K. (2019). Patterns of microsatellite distribution across eukaryotic genomes. BMC Genomics, 20(1). https://doi.org/10.1186/s12864-019-5516-5
  75. Thomsen, P. F., & Sigsgaard, E. E. (2019). Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods. Ecology and Evolution. https://doi.org/10.1002/ece3.4809
  76. König, C., Weigelt, P., Schrader, J., Taylor, A., Kattge, J., & Kreft, H. (2019). Biodiversity data integration–The significance of data resolution and domain. PLOS Biology, 17(3), e3000183. https://doi.org/10.1371/journal.pbio.3000183
  77. Higino, G., & Vital, M. V. C. (2019). Mapping and understanding the digital biodiversity knowledge about vertebrates in the Atlantic Rainforest. https://doi.org/10.32942/osf.io/c63vj
  78. Jo, J., Lee, H.-G., Kim, K. Y., & Park, C. (2019). SoEM: a novel PCR-free biodiversity assessment method based on small-organelles enriched metagenomics. ALGAE, 34(1), 57–70. https://doi.org/10.4490/algae.2019.34.2.26
  79. Axtner, J., Crampton-Platt, A., Hörig, L. A., Mohamed, A., Xu, C. C. Y., Yu, D. W., & Wilting, A. (2019). An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies. GigaScience, 8(4). https://doi.org/10.1093/gigascience/giz029
  80. Lin, B. Y., Chan, P. P., & Lowe, T. M. (2019). tRNAviz: explore and visualize tRNA sequence features. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz438
  81. Sporbert, M., Bruelheide, H., Seidler, G., Keil, P., Jandt, U., Austrheim, G., … Welk, E. (2019). Assessing sampling coverage of species distribution in biodiversity databases. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12763
  82. Steidinger, B. S., Crowther, T. W., Liang, J., Van Nuland, M. E., Werner, G. D. A., … Peay, K. G. (2019). Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature, 569(7756), 404–408. https://doi.org/10.1038/s41586-019-1128-0
  83. Bagley, M., Pilgrim, E., Knapp, M., Yoder, C., Santo Domingo, J., & Banerji, A. (2019). High-throughput environmental DNA analysis informs a biological assessment of an urban stream. Ecological Indicators, 104, 378–389. https://doi.org/10.1016/j.ecolind.2019.04.088
  84. Foisy, M. R., Albert, L. P., Hughes, D. W. W., & Weber, M. G. (2019). Do latex and resin canals spur plant diversification? Re‐examining a classic example of escape and radiate coevolution. Journal of Ecology. https://doi.org/10.1111/1365-2745.13203
  85. Boggs, Scheible, Machado, & Meiklejohn. (2019). Single Fragment or Bulk Soil DNA Metabarcoding: Which is Better for Characterizing Biological Taxa Found in Surface Soils for Sample Separation? Genes, 10(6), 431. https://doi.org/10.3390/genes10060431
  86. Palacios-Abrantes, J., Cisneros-Montemayor, A. M., Cisneros-Mata, M. A., Rodríguez, L., Arreguín-Sánchez, F., Aguilar, V., … Cheung, W. W. L. (2019). A metadata approach to evaluate the state of ocean knowledge: Strengths, limitations, and application to Mexico. PLOS ONE, 14(6), e0216723. https://doi.org/10.1371/journal.pone.0216723
  87. Grattarola, F., Botto, G., da Rosa, I., Gobel, N., González, E., González, J., … Pincheira-Donoso, D. (2019). Biodiversidata: An Open-Access Biodiversity Database for Uruguay. Biodiversity Data Journal, 7. https://doi.org/10.3897/bdj.7.e36226
  88. Danella Figo, D., De Amicis, K., Neiva Santos de Aquino, D., Pomiecinski, F., Gadermaier, G., Briza, P., … Souza Santos, K. (2019). Cashew Tree Pollen: An Unknown Source of IgE-Reactive Molecules. International Journal of Molecular Sciences, 20(10), 2397. https://doi.org/10.3390/ijms20102397
  89. Hagen, O., Vaterlaus, L., Albouy, C., Brown, A., Leugger, F., Onstein, R. E., … Pellissier, L. (2019). Mountain building, climate cooling and the richness of cold‐adapted plants in the Northern Hemisphere. Journal of Biogeography. https://doi.org/10.1111/jbi.13653
  90. Alhajeri, B. H., Porto, L., & Maestri, R. (2019). Habitat productivity is a poor predictor of body size in rodents. Current Zoology. https://doi.org/10.1093/cz/zoz037
  91. Lennox, R. J., Veríssimo, D., Twardek, W. M., Davis, C. R., & Jarić, I. (2019). Sentiment analysis as a measure of conservation culture in scientific literature. Conservation Biology. https://doi.org/10.1111/cobi.13404
  92. Esperon‐Rodriguez, M., Power, S. A., Tjoelker, M. G., Beaumont, L. J., Burley, H., Caballero‐Rodriguez, D., & Rymer, P. D. (2019). Assessing the vulnerability of Australia’s urban forests to climate extremes. Plants, People, Planet. https://doi.org/10.1002/ppp3.10064
  93. Cazelles, K., Bartley, T., Guzzo, M. M., Brice, M., MacDougall, A. S., Bennett, J. R., … McCann, K. S. (2019). Homogenization of freshwater lakes: recent compositional shifts in fish communities are explained by gamefish movement and not climate change. Global Change Biology. https://doi.org/10.1111/gcb.14829
  94. Bufford, J. L., Hulme, P. E., Sikes, B. A., Cooper, J. A., Johnston, P. R., & Duncan, R. P. (2019). Novel interactions between alien pathogens and native plants increase plant‐pathogen network connectance and decrease specialization. Journal of Ecology. https://doi.org/10.1111/1365-2745.13293
  95. Sydenham, M. A. K., Moe, S. R., & Eldegard, K. (2020). When context matters: Spatial prediction models of environmental conditions can identify target areas for wild bee habitat management interventions. Landscape and Urban Planning, 193, 103673. https://doi.org/10.1016/j.landurbplan.2019.103673
  96. Bottin, M., Peyre, G., Vargas, C., Raz, L., Richardson, J. E., & Sanchez, A. (2019). Phytosociological data and herbarium collections show congruent large scale patterns but differ in their local descriptions of community composition. Journal of Vegetation Science. https://doi.org/10.1111/jvs.12825
  97. Millard, J. W., Freeman, R., & Newbold, T. (2019). Text‐analysis reveals taxonomic and geographic disparities in animal pollination literature. Ecography. https://doi.org/10.1111/ecog.04532
  98. Hung, T., Rosales, M., Kurobe, T., Stevenson, T., Ellison, L., Tigan, G., … Teh, S. (2019). A pilot study of the performance of captive‐reared delta smelt Hypomesus transpacificus in a semi‐natural environment. Journal of Fish Biology. https://doi.org/10.1111/jfb.14162
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Parse Scientific Names

Scott Chamberlain
Description

Parse scientific names using gnparser (https://gitlab.com/gogna/gnparser), written in Go. gnparser parses scientific names into their component parts; it utilizes a Parsing Expression Grammar specifically for scientific names.

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taxadb
CRAN

A High-Performance Local Taxonomic Database Interface

Carl Boettiger
Description

Creates a local database of many commonly used taxonomic authorities and provides functions that can quickly query this data.

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Extract Scientific Names from Text

Scott Chamberlain
Description

Extract scientific names from text using the Golang tool gnfinder https://github.com/gnames/gnfinder.

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Integrated Taxonomic Information System Client

Scott Chamberlain
Description

An interface to the Integrated Taxonomic Information System (ITIS) (https://www.itis.gov). Includes functions to work with the ITIS REST API methods (https://www.itis.gov/ws_description.html), as well as the Solr web service (https://www.itis.gov/solr_documentation.html).

Scientific use cases
  1. Goring, S., Lacourse, T., Pellatt, M. G., & Mathewes, R. W. (2013). Pollen assemblage richness does not reflect regional plant species richness: a cautionary tale. Journal of Ecology, 101(5), 1137–1145. https://doi.org/10.1111/1365-2745.12135
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Tools for Vizualizing Data Taxonomically

Scott Chamberlain
Description

Tools for vizualizing data taxonomically.

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ZooBank API Client

Scott Chamberlain
Description

Interface to the ZooBank API (http://zoobank.org/Api) client. ZooBank (http://zoobank.org/) is the official registry of zoological nomenclature. Methods are provided for using each of the API endpoints, including for querying by author, querying for publications, get statistics on ZooBank activity, and more.

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Tools for Working with Taxonomic Databases

Scott Chamberlain
Description

Tools for working with taxonomic databases, including utilities for downloading databases, loading them into various SQL databases, cleaning up files, and providing a SQL connection that can be used to do SQL queries directly or used in dplyr.

Scientific use cases
  1. Jin, J., & Yang, J. (2020). BDcleaner: A workflow for cleaning taxonomic and geographic errors in occurrence data archived in biodiversity databases. Global Ecology and Conservation, 21, e00852. https://doi.org/10.1016/j.gecco.2019.e00852
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World Register of Marine Species (WoRMS) Client

Scott Chamberlain
Description

Client for World Register of Marine Species (http://www.marinespecies.org/). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification.

Scientific use cases
  1. O’Hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K., & Halpern, B. S. (2017). Aligning marine species range data to better serve science and conservation. PLOS ONE, 12(5), e0175739. https://doi.org/10.1371/journal.pone.0175739
  2. Clegg, T., Ali, M., & Beckerman, A. P. (2018). The impact of intraspecific variation on food web structure. Ecology. https://doi.org./10.1002/ecy.2523
  3. Webb, T. J., Lines, A., & Howarth, L. M. (2020). Occupancy‐derived thermal affinities reflect known physiological thermal limits of marine species. Ecology and Evolution, 10(14), 7050–7061. https://doi.org/10.1002/ece3.6407
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Taxonomic Information from Wikipedia

Scott Chamberlain
Description

Taxonomic information from Wikipedia, Wikicommons, Wikispecies, and Wikidata. Functions included for getting taxonomic information from each of the sources just listed, as well performing taxonomic search.

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taxa
CRAN

Taxonomic Classes

Zachary Foster
Description

Provides taxonomic classes for groupings of taxonomic names without data, and those with data. Methods provided are “taxonomically aware”, in that they know about ordering of ranks, and methods that filter based on taxonomy also filter associated data. This package is described in the publication: “Taxa: An R package implementing data standards and methods for taxonomic data”, Zachary S.L. Foster, Scott Chamberlain,
Niklaus J. Grünwald (2018) doi:10.12688/f1000research.14013.2.

Scientific use cases
  1. Foster, Z. S. L., Chamberlain, S., & Grünwald, N. J. (2018). Taxa: An R package implementing data standards and methods for taxonomic data. F1000Research, 7, 272. https://doi.org/10.12688/f1000research.14013.1
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