Can ChatGPT be leveraged for taxonomic investigations? Potential and limitations of a new technology

References

1. Allken V., Handegard, N.O., Rosen, S., Schreyeck, T., Mahiout, T. & Malde, K. (2018) Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science, 76 (1), 342–349. https://doi.org/10.1093/icesjms/fsy147
2. Arif T.B., Munaf, U. & Ul-Haque, I. (2023) The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Medical education online, 28 (1), 2181052. https://doi.org/10.1080/10872981.2023.2181052
3. Armbrust, E.V. & Palumbi, S.R. (2015) Uncovering hidden worlds of ocean biodiversity. Science, 348 (6237), 865–867. https://doi.org/10.1126/science.aaa7378
4. Drew, L.W. (2011) Are we losing the science of taxonomy? As need grows, numbers and training are failing to keep up. BioScience, 61 (12), 942–946. https://doi.org/10.1525/bio.2011.61.12.4
5. Hochkirch, A., Samways, M.J., Gerlach, J., Bohm, M., Williams, P., Cardoso, P., Cumberlidge, N., Stephenson, P.J., Seddon, M.B., Clausnitzer, V., Borges, P.A.V., Mueller, G.M., Pearce-Kelly, P., Raimondo, D.C., Danielczak, A. & Dijkstra, K-D.B. (2021) A strategy for the next decade to address data deficiency in neglected biodiversity. Conservation Biology, 35 (2), 502–509. https://doi.org/10.1111/cobi.13589
6. Khuroo, A.A., Dar, G.H., Khan, Z.S. & Malik, A.H. (2007) Exploring an inherent interface between taxonomy and biodiversity: current problems and future challenges. Journal for Nature Conservation, 15, 256–261. https://doi.org/10.1016/j.jnc.2007.07.003
7. Malan, A., Williams, J.D., Abe, H., Sato-Okoshi, W., Matthee, C.A. & Simon, C.A. (2020) Clarifying the cryptogenic species Polydora neocaeca Williams & Radashevsky, 1999 (Annelida: Spionidae): a shell-boring invasive pest of molluscs from locations worldwide. Marine Biodiversity, 50, 51. https://doi.org/10.1007/s12526-020-01066-8
8. Martinelli, J.C., Lopes, H.M., Hauser, L., Jimenez-Hidalgo, I., King, T.L., Padilla-Gamino, J.L., Rawson, P., Spencer, L.H., Williams, J.D. & Wood, C.L. (2020) Confirmation of the shell-boring oyster parasite Polydora websteri (Polychaeta: Spionidae) in Washington State, USA. Scientific Reports, 10, 3961. https://doi.org/10.1038/s41598-020-60805-w
9. Pauchard, A., Meyerson, L.A., Bacher, S., Blackburn, T.M., Brundu, G., Cadotte, M.W., Courchamp, F., Essl, F., Genovesi, P., Haider, S., Holmes, N.D., Hulme, P.E., Jeschke, J.M., Lockwood, J.L., Novoa, A., Nunez, M.A., Peltzer, D.A., Pysek, P., Richardson, D.M., Simberloff, D., Smith, K., van Wilgen, B.W., Vila, M., Wilson, J.R.U., Winter, M. & Zenni, R.D. (2018) Biodiversity assessments: origin matters. PLoS Biology, 16 (11), e2006686. https://doi.org/10.1371/journal.pbio.2006686
10. Pederson, J., Carlton, J.T., Bastidas, C., David, A., Grady, S., Green-Gavrielidis, L., Hobbs, N-V., Kennedy, C., Knack, J., McCuller, M., O’Brien, B., Osborne, K., Pankey, S. & Trott, T. (2021) BioInvasions Records, 10 (2), 227 – 237. https://doi.org/10.3391/bir.2021.10.2.01
11. Pysek, P., Hulme, P.E., Meyerson, L.A., Smith, G.F., Boatwright, J.S., Crouch, N.R., Figueiredo, E., Foxcroft, L.C., Jarosik, V., Richardson, D.M., Suda, J. & Wilson, J.R.U. (2013) Hitting the right target: taxonomic challenges for, and of, plant invasions. AoB PLANTS, 5. https://doi.org/10.1093/aobpla/plt042
12. Rohde, S., Schupp, P.J., Markert, A. & Wehrmann, A. (2017) Only half of the truth: Managing invasive alien species by rapid assessment. Ocean & Coastal Management, 146, 26–35. https://doi.org/10.1016/j.ocecoaman.2017.05.013
13. Thenmozhi, K., Dakshayani, S. & Srinivasulu, R.U. (2021) Insect classification and detection in field crops using modern machine learning techniques. Information Processing in Agriculture, 8, 446–457.
14. https://doi.org/10.1016/j.inpa.2020.09.006
15. Tan, J.W., Chang, S.W., Abdul-Kareem, S., Yap, H.J. & Yong, K.T. (2020) Deep learning for plant species classification using leaf vein morphometric. IEE-ACM Transactions on Computational Biology and Bioinformatics, 17 (1), 82–90. https://doi.org/10.1109/TCBB.2018.2848653
16. Tan, H.Y., Goh, Z.Y., Loh, K., Then, A.Y., Omar, H. & Chang, S. (2021) Cephalopod species identification using integrated analysis of machine learning and deep learning approaches. PeerJ, 9, e11825. https://doi.org/10.7717/peerj.11825
17. Thomson, S.A., Pyle, R.L., Ahyong, S.T., Alonso-Zarazaga, M., Ammirati, J., Araya, J.F., Ascher J.S., Audisio, T.L., Azevedo-Santos, V.M., Bailly, N., Baker, W.J., Balke, M., Barclay, M.V.L., Barrett, R.L., Benine, R.C., Bickerstaff, J.R.M., Bouchard, P., Bour, R., Bourgoin, T., Boyko, C.B., Breure, A.S.H., Brothers, D.J., Byng, J.W., Campbell, D., Ceríaco, L.M.P., Cernák, I., Cerretti, P., Chang, C.-H., Cho, S., Copus, J.M., Costello, M.J., Cseh, A., Csuzdi, C., Culham, A., D’Elía, G., d’Udekem d’Acoz, C., Daneliya, M.E., Dekker, R., Dickinson, E.C., Dickinson, T.A., van Dijk, P.P., Dijkstra, K.-D.B., Dima, B., Dmitriev, D.A., Duistermaat, L., Dumbacher, J.P., Eiserhardt, W.L., Ekrem, T., Evenhuis, N.L., Faille, A., Fernández-Triana, J.L., Fiesler, E., Fishbein, M., Fordham, B.G., Freitas, A.V.L., Friol, N.R., Fritz, U., Frøslev, T., Funk, V.A., Gaimari, S.D., Garbino, G.S.T., Garraffoni, A.R.S., Geml, J., Gill, A.C., Gray, A., Grazziotin, F.G., Greenslade, P., Gutiérrez, E.E., Harvey, M.S., Hazevoet, C.J., He, K., He, X., Helfer, S., Helgen, K.M., van Heteren, A.H., Hita Garcia, F., Holstein, N., Horváth, M.K., Hovenkamp, P.H., Hwang W.S., Hyvönen, J., Islam, M.B., Iverson, J.B., Ivie, M.A., Jaafar Z., Jackson, M.D., Jayat, J.P., Johnson, N.F., Kaiser, H., Klitgård, B.B., Knapp, D.G., Kojima, J.-I., Kõljalg, U., Kontschán, J., Krell, F.-T., Krisai-Greilhuber, I., Kullander, S., Latella, L., Lattke, J.E., Lencioni, V., Lewis, G.P., Lhano, M.G., Lujan, N.K., Luksenburg, J.A., Mariaux, J., Marinho-Filho, J., Marshall, C.J., Mate, J.F., McDonough, M.M., Michel, E., Miranda, V.F.O., Mitroiu, M.-D., Molinari, J., Monks, S., Moore, A.J., Moratelli, R., Murányi, D., Nakano, T., Nikolaeva, S., Noyes, J., Ohl, M., Oleas, N.H., Orrell, T., Páll-Gergely, B., Pape, T., Papp, V., Parenti, L.R., Patterson, D., Pavlinov, I.Y., Pine, R.H., Poczai, P., Prado, J., Prathapan, D., Rabeler, R.K., Randall, J.E., Rheindt F.E., Rhodin, A.G.J., Rodríguez, S.M., Rogers, D.C., Roque, F.D.O., Rowe, K.C., Ruedas, L.A., Salazar-Bravo, J., Salvador, R.B., Sangster, G., Sarmiento, C.E., Schigel, D.S., Schmidt, S., Schueler, F.W., Segers, H., Snow, N., Souza-Dias, P.G.B., Stals, R., Stenroos, S., Stone, R.D., Sturm, C.F., Štys, P., Teta, P., Thomas, D.C., Timm, R.M., Tindall, B.J., Todd, J.A., Triebel, D., Valdecasas, A.G., Vizzini, A., Vorontsova, M.S., de Vos, J.M., Wagner, P., Watling, L., Weakley, A., Welter-Schultes, F., Whitmore, D., Wilding, N., Will, K., Williams, J., Wilson, K., Winston, J.E., Wüster, W., Yanega, D., Yeates, D.K., Zaher, H., Zhang, G., Zhang, Z.-Q. & Zhou, H.-Z. (2018) Taxonomy based on science is necessary for global conservation. PLoS Biology, 16, e2005075. https://doi.org/10.1371/journal.pbio.2005075
18. van Dis, E.A.M., Bollen, J., Zuidema, W., van Rooij, R. & Bockting, C.L. (2023) ChatGPT: five priorities for research. Nature, 614, 224–226. https://doi.org/10.1038/d41586-023-00288-7
19. Zhu, J.J., Jiang, J., Yang, M. & Ren, Z.J. (2023) ChatGPT and environmental research. Environmental Science & Technology. https://doi.org/10.1021/acs.est.3c01818
20. Zhu, Y., Han, D., Chen, S., Zeng, F. & Wang, C. (2023) How can ChatGPT benefit pharmacy: A case report on review writing. Preprints, Mar. 20, 2023. https://doi.org/10.20944/preprints202302.0324.v1