rOpenSci | Taxonomy
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Taxonomy

Handle and Transform Taxonomic Information
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Handling Taxonomic Lists

Miguel Alvarez
Description

Handling taxonomic lists through objects of class taxlist. This package provides functions to import species lists from Turboveg (https://www.synbiosys.alterra.nl/turboveg/) and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods.

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taxize
CRAN

Taxonomic Information from Around the Web

Zachary Foster
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.

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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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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  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
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Edit and Validate Darwin Core Taxon Data

Joel H. Nitta
Description

Edit and validate taxonomic data in compliance with Darwin Core standards (Darwin Core Taxon class https://dwc.tdwg.org/terms/#taxon).

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taxizedb

Tools for Working with Taxonomic Databases

Tamás Stirling
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.

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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
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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taxa
CRAN

Classes for Storing and Manipulating Taxonomic Data

Zachary Foster
Description

Provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them.

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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
  2. Harvey, B. P., Kerfahi, D., Jung, Y., Shin, J.-H., Adams, J. M., & Hall-Spencer, J. M. (2020). Ocean acidification alters bacterial communities on marine plastic debris. Marine Pollution Bulletin, 161, 111749. https://doi.org/10.1016/j.marpolbul.2020.111749
worrms
CRAN

World Register of Marine Species (WoRMS) Client

Bart Vanhoorne.
Description

Client for World Register of Marine Species (https://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.

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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
  4. Webb, T. J., & Vanhoorne, B. (2020). Linking dimensions of data on global marine animal diversity. Philosophical Transactions of the Royal Society B: Biological Sciences, 375(1814), 20190445. https://doi.org/10.1098/rstb.2019.0445
rgnparser
CRAN

Parse Scientific Names

Joel H. Nitta
Description

Parse scientific names using gnparser (https://github.com/gnames/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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wikitaxa
CRAN

Taxonomic Information from Wikipedia

Zachary Foster
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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ritis
CRAN

Integrated Taxonomic Information System Client

Julia Blum
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).

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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