Taxonomy

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.

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

Tools for vizualizing data taxonomically.

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.

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
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. https://doi.org/10.1111/ecog.04492
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. https://doi.org/10.1101/687863
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. https://doi.org/10.3897/bdj.7.e32307
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). https://doi.org/10.1038/s41597-019-0269-1
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). https://doi.org/10.1038/s41467-019-13109-1
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. https://doi.org/10.3390/w11112384
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. https://doi.org/10.1111/cobi.13437
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. https://doi.org/10.1111/jvs.12837
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. https://doi.org/10.1016/j.gecco.2019.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. https://doi.org/10.1111/1365-2656.13153
109. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
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. https://doi.org/10.1007/s10886-019-01130-z
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. https://doi.org/10.1111/gcb.14968
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. https://doi.org/10.4490/algae.2019.34.12.10
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. https://doi.org/10.1002/ece3.5921
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. https://doi.org/10.1111/1365-2745.13352
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. https://doi.org/10.1111/nph.16420
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. https://doi.org/10.1101/2020.01.10.902239
117. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e47018
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). https://doi.org/10.1038/s41467-020-14303-2
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. https://doi.org/10.1371/journal.pone.0228065
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. https://doi.org/10.1139/cjfas-2019-0224
121. Mossion, V., Dauphin, B., Grant, J., Zemp, N., & Croll, D. (2020). A reference transcriptome for the early-branching fern Botrychium lunaria enables fine-grained resolution of population structure. https://doi.org/10.1101/2020.02.17.952283
122. Verde Arregoitia, L. D., Teta, P., & D’Elía, G. (2020). Patterns in research and data sharing for the study of form and function in caviomorph rodents. Journal of Mammalogy. https://doi.org/10.1093/jmammal/gyaa002
123. Rodrigues, B. N., & Boscolo, D. (2020). Do bipartite binary antagonistic and mutualistic networks have different responses to the taxonomic resolution of nodes? Ecological Entomology. https://doi.org/10.1111/een.12844
124. Thompson, K. A. (2020). Experimental hybridization studies suggest that pleiotropic alleles commonly underlie adaptive divergence between natural populations. The American Naturalist. https://doi.org/10.1086/708722
125. Kaczvinsky, C., & Hardy, N. B. (2020). Do major host shifts spark diversification in butterflies? Ecology and Evolution, 10(8), 3636–3646. https://doi.org/10.1002/ece3.6116
126. Zizka, A., Carvalho‐Sobrinho, J. G., Pennington, R. T., Queiroz, L. P., Alcantara, S., Baum, D. A., … Antonelli, A. (2020). Transitions between biomes are common and directional in Bombacoideae (Malvaceae). Journal of Biogeography. https://doi.org/10.1111/jbi.13815
127. Young, N. E., Jarnevich, C. S., Sofaer, H. R., Pearse, I., Sullivan, J., Engelstad, P., & Stohlgren, T. J. (2020). A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales. PLOS ONE, 15(3), e0229253. https://doi.org/10.1371/journal.pone.0229253
128. Martins, P. T., & Boeckx, C. (2020). Vocal learning: Beyond the continuum. PLOS Biology, 18(3), e3000672. https://doi.org/10.1371/journal.pbio.3000672
129. Timpano, E. K., Scheible, M. K. R., & Meiklejohn, K. A. (2020). Optimization of the second internal transcribed spacer (ITS2) for characterizing land plants from soil. PLOS ONE, 15(4), e0231436. https://doi.org/10.1371/journal.pone.0231436
130. Mishler, B. D., Guralnick, R., Soltis, P. S., Smith, S. A., Soltis, D. E., Barve, N., … Laffan, S. W. (2020). Spatial phylogenetics of the North American flora. Journal of Systematics and Evolution. https://doi.org/10.1111/jse.12590
131. Chandler, J. O., Haas, F. B., Khan, S., Bowden, L., Ignatz, M., Enfissi, E. M. A., … Leubner-Metzger, G. (2020). Rocket Science: The Effect of Spaceflight on Germination Physiology, Ageing, and Transcriptome of Eruca sativa Seeds. Life, 10(4), 49. https://doi.org/10.3390/life10040049
132. Verhoeven, M. R., Glisson, W. J., & Larkin, D. J. (2020). Niche Models Differentiate Potential Impacts of Two Aquatic Invasive Plant Species on Native Macrophytes. Diversity, 12(4), 162. https://doi.org/10.3390/d12040162
133. Ladwig, L. M., Zirbel, C. R., Sorenson, Q. M., & Damschen, E. I. (2020). A taxonomic, phylogenetic, and functional comparison of restoration seed mixes and historical plant communities in Midwestern oak savannas. Forest Ecology and Management, 466, 118122. https://doi.org/10.1016/j.foreco.2020.118122
134. Van den Berg, S. J. P., Rendal, C., Focks, A., Butler, E., Peeters, E. T. H. M., De Laender, F., & Van den Brink, P. J. (2020). Potential impact of chemical stress on freshwater invertebrates: A sensitivity assessment on continental and national scale based on distribution patterns, biological traits, and relatedness. Science of The Total Environment, 731, 139150. https://doi.org/10.1016/j.scitotenv.2020.139150
135. Scharmüller, A., Schreiner, V. C., & Schäfer, R. B. (2020). Standartox: Standardizing Toxicity Data. Data, 5(2), 46. https://doi.org/10.3390/data5020046
136. Crowley, D., Becker, D., Washburne, A., & Plowright, R. (2020). Identifying Suspect Bat Reservoirs of Emerging Infections. Vaccines, 8(2), 228. https://doi.org/10.3390/vaccines8020228
137. Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne, J., & Grenouillet, G. (2020). Species better track climate warming in the oceans than on land. Nature Ecology & Evolution. https://doi.org/10.1038/s41559-020-1198-2
138. Stringham, O., Toomes, A., Kanishka, A. M., Mitchell, L., Heinrich, S., Ross, J. V., & Cassey, P. (2020). A guide to using the Internet to monitor and quantify the wildlife trade. https://ecoevorxiv.org/5yzw9/download?format=pdf
139. Szöcs, E., Stirling, T., Scott, E. R., Scharmüller, A., & Schäfer, R. B. (2020). webchem: An R Package to Retrieve Chemical Information from the Web. Journal of Statistical Software, 93(1), 1-17. https://www.jstatsoft.org/article/view/v093i13/v93i13.pdf
140. Monaco, C. J., Bradshaw, C. J. A., Booth, D. J., Gillanders, B. M., Schoeman, D. S., & Nagelkerken, I. (2020). Dietary generalism accelerates arrival and persistence of coral‐reef fishes in their novel ranges under climate change. Global Change Biology. https://doi.org/10.1111/gcb.15221
141. Pal Negi, A., Singh, R., Sharma, A., & Negi, V. S. (2020). Insights into high mobility group A (HMGA) proteins from Poaceae family: An in silico approach for studying homologs. Computational Biology and Chemistry, 87, 107306. https://doi.org/10.1016/j.compbiolchem.2020.107306
142. Loewen, C. J. G., Strecker, A. L., Gilbert, B., & Jackson, D. A. (2020). Climate warming moderates the impacts of introduced sportfish on multiple dimensions of prey biodiversity. Global Change Biology, 26(9), 4937–4951. https://doi.org/10.1111/gcb.15225
143. Li, D., Olden, J. D., Lockwood, J. L., Record, S., McKinney, M. L., & Baiser, B. (2020). Changes in taxonomic and phylogenetic diversity in the Anthropocene. Proceedings of the Royal Society B: Biological Sciences, 287(1929), 20200777. https://doi.org/10.1098/rspb.2020.0777
144. Arranz, V., Pearman, W. S., Aguirre, J. D., & Liggins, L. (2020). MARES, a replicable pipeline and curated reference database for marine eukaryote metabarcoding. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0549-9
145. Carraro, L., Mächler, E., Wüthrich, R., & Altermatt, F. (2020). Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17337-8
146. Grattarola, F., & Rodríguez-Tricot, L. (2020). Mammals of Paso Centurión, an area with relicts of Atlantic Forest in Uruguay. Neotropical Biology and Conservation, 15(3), 267–283. https://doi.org/10.3897/neotropical.15.e53062
147. Calderón Franco, D. (2020). Hi-C pipeline development for linking resistome and mobilome to the microbiome of wastewater samples http://openaccess.uoc.edu/webapps/o2/bitstream/10609/120166/1/MEP_BioinformaticsBiostatistics_DavidCalderonFranco_UB_UOC_good.pdf
148. Walton, S., Livermore, L., Bánki, O., Cubey, R., Drinkwater, R., Englund, M., … Wu, Z. (2020). Landscape Analysis for the Specimen Data Refinery. Research Ideas and Outcomes, 6. https://doi.org/10.3897/rio.6.e57602
149. Ossola, A., Hoeppner, M. J., Burley, H. M., Gallagher, R. V., Beaumont, L. J., & Leishman, M. R. (2020). The Global Urban Tree Inventory: A database of the diverse tree flora that inhabits the world’s cities. Global Ecology and Biogeography, 29(11), 1907–1914. https://doi.org/10.1111/geb.13169
150. Atwood, T. B., Valentine, S. A., Hammill, E., McCauley, D. J., Madin, E. M. P., Beard, K. H., & Pearse, W. D. (2020). Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Science Advances, 6(32), eabb8458. https://doi.org/10.1126/sciadv.abb8458
151. Etard, A., Morrill, S., & Newbold, T. (2020). Global gaps in trait data for terrestrial vertebrates. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13184
152. Oegelund Nielsen, R., da Silva, R., Juergens, J., Staerk, J., Lindholm Sørensen, L., Jackson, J., … Conde, D. A. (2020). Standardized data to support conservation prioritization for sharks and batoids (Elasmobranchii). Data in Brief, 33, 106337. https://doi.org/10.1016/j.dib.2020.106337
153. Grattarola, F., González, A., Mai, P., Cappuccio, L., Fagúndez-Pachón, C., Rossi, F., … Pincheira-Donoso, D. (2020). Biodiversidata: A novel dataset for the vascular plant species diversity in Uruguay. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e56850
154. Fredston, A., Pinsky, M., Selden, R., Szuwalski, C., Thorson, J., Halpern, B., & Gaines, S. (2020). Range edges of North American marine species are tracking temperature over decades. Authorea Preprints. https://www.authorea.com/doi/pdf/10.22541/au.160331933.33155622
155. Benesh, D. P., Parker, G., Chubb, J. C., & Lafferty, K. D. (2020). Tradeoffs with growth limit host range in complex life cycle helminths. The American Naturalist. https://doi.org/10.1086/712249
156. Loiseau, N., Mouquet, N., Casajus, N., Grenié, M., Guéguen, M., Maitner, B., … Violle, C. (2020). Global distribution and conservation status of ecologically rare mammal and bird species. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-18779-w
157. Esenkulova, S., Sutherland, B. J. G., Tabata, A., Haigh, N., Pearce, C. M., & Miller, K. M. (2020). Comparing metabarcoding and morphological approaches to identify phytoplankton taxa associated with harmful algal blooms. FACETS, 5(1), 784–811. https://doi.org/10.1139/facets-2020-0025
158. Guerrero‐Ramírez, N. R., Mommer, L., Freschet, G. T., Iversen, C. M., McCormack, M. L., Kattge, J., … Weigelt, A. (2020). Global root traits (GRooT) database. Global Ecology and Biogeography, 30(1), 25–37. https://doi.org/10.1111/geb.13179
159. Niittynen, P., Heikkinen, R. K., Aalto, J., Guisan, A., Kemppinen, J., & Luoto, M. (2020). Fine-scale tundra vegetation patterns are strongly related to winter thermal conditions. Nature Climate Change, 10(12), 1143–1148. https://doi.org/10.1038/s41558-020-00916-4
160. Sigsgaard, E. E., Olsen, K., Hansen, M. D. D., Hansen, O. L. P., Høye, T. T., Svenning, J., & Thomsen, P. F. (2020). Environmental DNA metabarcoding of cow dung reveals taxonomic and functional diversity of invertebrate assemblages. Molecular Ecology. https://doi.org/10.1111/mec.15734
161. Freiberg, M., Winter, M., Gentile, A., Zizka, A., Muellner-Riehl, A. N., Weigelt, A., & Wirth, C. (2020). LCVP, The Leipzig catalogue of vascular plants, a new taxonomic reference list for all known vascular plants. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-00702-z

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).

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

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.

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.

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

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.

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.

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

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.

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

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

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.