rOpenSci | Taxonomy


Handle and Transform Taxonomic Information
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Staff maintained

Parse Scientific Names

Scott Chamberlain

Parse scientific names using 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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Taxonomic Information from Around the Web

Scott Chamberlain

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.
  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.
  3. Bocci, G. (2015). TR8: an R package for easily retrieving plant species traits. Methods in Ecology and Evolution, 6(3), 347–350.
  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.
  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.
  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).
  7. Chamberlain, S. A., & Szöcs, E. (2013). taxize: taxonomic search and retrieval in R. F1000Research, 2, 191.
  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.
  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).
  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.
  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.
  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.
  13. Poisot, T. (2015). Best publishing practices to improve user confidence in scientific software. IEE, 8.
  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.
  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.
  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.
  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.
  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.
  19. Schwery, O., & O’Meara, B. C. (2016). MonoPhy : a simple R package to find and visualize monophyly issues . PeerJ Computer Science, 2, e56.
  20. Bradie, J., & Leung, B. (2016). A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. Journal of Biogeography.
  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.
  22. Cramer, M. D., & Verboom, G. A. (2016). Measures of biologically relevant environmental heterogeneity improve prediction of regional plant species richness. Journal of Biogeography.
  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.
  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.
  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.
  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.
  27. Sclavi, B., & Herrick, J. (2016). Genome size variation and species diversity in salamander families.
  28. Vincze, O. (2016). Light enough to travel or wise enough to stay? Brain size evolution and migratory behaviour in birds. Evolution.
  29. Wagner, V. (2016). A review of software tools for spell-checking taxon names in vegetation databases. Journal of Vegetation Science.
  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.
  31. Wiser, S. K. (2016). Achievements and challenges in the integration, reuse and synthesis of vegetation plot data. Journal of Vegetation Science.
  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.
  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.
  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
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  45. Branoff, B. L. (2017). Quantifying the influence of urban land use on mangrove biology and ecology: A meta-analysis. Global Ecology and Biogeography.
  46. Berlemont, R. (2017). Distribution and diversity of enzymes for polysaccharide degradation in fungi. Scientific Reports, 7(1).
  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.
  48. Hutchinson, M. C., Cagua, E. F., & Stouffer, D. B. (2017). Cophylogenetic signal is detectable in pollination interactions across ecological scales. Ecology.
  49. Chalmandrier, L., Albouy, C., & Pellissier, L. (2017). Species pool distributions along functional trade-offs shape plant productivity–diversity relationships. Scientific Reports, 7(1).
  50. Drost, H.-G., Gabel, A., Liu, J., Quint, M., & Grosse, I. (2017). myTAI: evolutionary transcriptomics with R. Bioinformatics.
  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.
  52. Surabhi, S., Avvaru, A. K., Sowpati, D. T., & Mishra, R. K. (2018). Patterns of microsatellite distribution reflect the evolution of biological complexity.
  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).
  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.
  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.
  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.
  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.
  58. Holmes, I., & Davis Rabosky, A. R. (2018). Natural history bycatch: a pipeline for identifying metagenomic sequences in RADseq data. PeerJ, 6, e4662.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  69. Da Silva, R., & Conde, D. A. (2018). Data on the conservation potential of fish and coral populations in aquariums. Data in Brief.
  70. Sclavi, B., & Herrick, J. (2018). Genome size variation and species diversity in salamanders. Journal of Evolutionary Biology.
  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.
  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.
  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.
  74. Srivastava, S., Avvaru, A. K., Sowpati, D. T., & Mishra, R. K. (2019). Patterns of microsatellite distribution across eukaryotic genomes. BMC Genomics, 20(1).
  75. Thomsen, P. F., & Sigsgaard, E. E. (2019). Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods. Ecology and Evolution.
  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.
  77. Higino, G., & Vital, M. V. C. (2019). Mapping and understanding the digital biodiversity knowledge about vertebrates in the Atlantic Rainforest.
  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.
  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).
  80. Lin, B. Y., Chan, P. P., & Lowe, T. M. (2019). tRNAviz: explore and visualize tRNA sequence features. Nucleic Acids Research.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  90. Alhajeri, B. H., Porto, L., & Maestri, R. (2019). Habitat productivity is a poor predictor of body size in rodents. Current Zoology.
  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.
  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.
  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.
  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.
  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.
  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.
  97. Millard, J. W., Freeman, R., & Newbold, T. (2019). Text‐analysis reveals taxonomic and geographic disparities in animal pollination literature. Ecography.
  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.
  99. Chalmandrier, L., Pansu, J., Zinger, L., Boyer, F., Coissac, E., Génin, A., … Thuiller, W. (2019). Environmental and biotic drivers of soil microbial β‐diversity across spatial and phylogenetic scales. Ecography.
  100. Gryseels, S., Watts, T. D., Kabongo, J.-M. M., Larsen, B. B., Lemey, P., Muyembe-Tamfum, J.-J., … Worobey, M. (2019). A near-full-length HIV-1 genome from 1966 recovered from formalin-fixed paraffin-embedded tissue.
  101. Zheleznova, G., Shubina, T., Degteva, S., Chadin, I., & Rubtsov, M. (2019). Moss occurrences in Yugyd Va National Park, Subpolar and Northern Urals, European North-East Russia. Biodiversity Data Journal, 7.
  102. Outhwaite, C. L., Powney, G. D., August, T. A., Chandler, R. E., Rorke, S., Pescott, O. L., … Isaac, N. J. B. (2019). Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK, 1970–2015. Scientific Data, 6(1).
  103. Smith, T. P., Thomas, T. J. H., García-Carreras, B., Sal, S., Yvon-Durocher, G., Bell, T., & Pawar, S. (2019). Community-level respiration of prokaryotic microbes may rise with global warming. Nature Communications, 10(1).
  104. Mancinelli, G., Mali, S., & Belmonte, G. (2019). Species Richness and Taxonomic Distinctness of Zooplankton in Ponds and Small Lakes from Albania and North Macedonia: The Role of Bioclimatic Factors. Water, 11(11), 2384.
  105. Sigsgaard, E. E., Torquato, F., Frøslev, T. G., Moore, A. B. M., Sørensen, J. M., Range, P., … Thomsen, P. F. (2019). Using vertebrate environmental DNA from seawater in biomonitoring of marine habitats. Conservation Biology.
  106. Toussaint, A., Bueno, G., Davison, J., Moora, M., Tedersoo, L., Zobel, M., … Pärtel, M. (2019). Asymmetric patterns of global diversity among plants and mycorrhizal fungi. Journal of Vegetation Science.
  107. 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.
  108. Geary, W. L., Doherty, T. S., Nimmo, D. G., Tulloch, A. I. T., & Ritchie, E. G. (2020). Predator responses to fire: A global systematic review and meta‐analysis. Journal of Animal Ecology.
  109. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059.
  110. Champagne, E., Royo, A. A., Tremblay, J.-P., & Raymond, P. (2019). Phytochemicals Involved in Plant Resistance to Leporids and Cervids: a Systematic Review. Journal of Chemical Ecology, 46(1), 84–98.
  111. Burrows, M. T., Hawkins, S. J., Moore, J. J., Adams, L., Sugden, H., Firth, L., & Mieszkowska, N. (2020). Global‐scale species distributions predict temperature‐related changes in species composition of rocky shore communities in Britain. Global Change Biology, 26(4), 2093–2105.
  112. Kim, H. M., Jo, J., Park, C., Choi, B.-J., Lee, H.-G., & Kim, K. Y. (2019). Epibionts associated with floating Sargassum horneri in the Korea Strait. ALGAE, 34(4), 303–313.
  113. Hansen, O. L. P., Svenning, J., Olsen, K., Dupont, S., Garner, B. H., Iosifidis, A., … Høye, T. T. (2019). Species‐level image classification with convolutional neural network enables insect identification from habitus images. Ecology and Evolution, 10(2), 737–747.
  114. Quintero, E., Pizo, M. A., & Jordano, P. (2020). Fruit resource provisioning for avian frugivores: The overlooked side of effectiveness in seed dispersal mutualisms. Journal of Ecology.
  115. Cirtwill, A. R., Dalla Riva, G. V., Baker, N. J., Ohlsson, M., Norström, I., Wohlfarth, I., … Stouffer, D. B. (2020). Related plants tend to share pollinators and herbivores, but strength of phylogenetic signal varies among plant families. New Phytologist.
  116. Akpınar, B. A., Carlson, P. O., Paavilainen, V. O., & Dunn, C. D. (2020). Pathogenicity of human mtDNA variants is revealed by combining a novel phylogenetic analysis with machine learning.
  117. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8.
  118. Mooney, A., Conde, D. A., Healy, K., & Buckley, Y. M. (2020). A system wide approach to managing zoo collections for visitor attendance and in situ conservation. Nature Communications, 11(1).
  119. Gagné, T. O., Reygondeau, G., Jenkins, C. N., Sexton, J. O., Bograd, S. J., Hazen, E. L., & Van Houtan, K. S. (2020). Towards a global understanding of the drivers of marine and terrestrial biodiversity. PLOS ONE, 15(2), e0228065.
  120. Cederwall, J., Black, T. A., Blais, J. M., Hanson, M. L., Hollebone, B. P., Palace, V. P., … Orihel, D. M. (2020). Life under an oil slick: response of a freshwater food web to simulated spills of diluted bitumen in field mesocosms. Canadian Journal of Fisheries and Aquatic Sciences, 77(5), 779–788.
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A High-Performance Local Taxonomic Database Interface

Carl Boettiger

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

Extract scientific names from text using the Golang tool gnfinder

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Integrated Taxonomic Information System Client

Scott Chamberlain

An interface to the Integrated Taxonomic Information System (ITIS) ( Includes functions to work with the ITIS REST API methods (, as well as the Solr web service (

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.
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Tools for Vizualizing Data Taxonomically

Scott Chamberlain

Tools for vizualizing data taxonomically.

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ZooBank API Client

Scott Chamberlain

Interface to the ZooBank API ( client. ZooBank ( 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

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.
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World Register of Marine Species (WoRMS) Client

Scott Chamberlain

Client for World Register of Marine Species ( 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.
  2. Clegg, T., Ali, M., & Beckerman, A. P. (2018). The impact of intraspecific variation on food web structure. Ecology.
  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.
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Taxonomic Information from Wikipedia

Scott Chamberlain

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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Taxonomic Classes

Zachary Foster

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.
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