New method for taxonomic descriptions with coded notation, producing dynamic and interchangeable output

Author:

Zeppelini Douglas12ORCID,de Oliveira‐Neto Misael Augusto12ORCID,de Oliveira João Victor Lemos Cavalcante1ORCID,Ferreira Aila Soares1ORCID,de Brito Roniere Andrade1ORCID,Lopes Bruna Carolline Honório12ORCID,Brito Nathan Paiva12ORCID,Stievano Luis Carlos1ORCID,de Lima Estevam Cipriano Araujo1ORCID

Affiliation:

1. Laboratório de Sistemática de Collembola e Conservação, Instituto de Biologia de Solo Universidade Estadual da Paraíba João Pessoa PB Brazil

2. Programa de Pós‐Graduação em Ciências Biológicas – Zoologia Universidade Federal da Paraíba João Pessoa PB Brazil

Abstract

AbstractA proposal for taxonomic species description notation is presented to replace the traditional descriptive texts for a coded matrix, avoiding redundant adjectives and subjective descriptions. This is an attempt to enhance the species description rate and to make the descriptions output available to other scientific disciplines, machine learning, interactive and computer‐assisted identification keys, metadata analysis and its applications. The method consists of presenting the description of the overall morphology in a coded matrix, following a character list with detailed observed conditions for each character. The method is dynamic and open to amendments and new data addition as they become available. We test the new method describing five new species of Collembola Symphypleona of the genus Pararrhopalites as a generalized model and made the coded output available. We conclude that a coded taxonomic description is an advance to the traditional taxonomic text, with potential to enhance the global descriptions rate. The generated descriptions are dynamic, expandable and can be easily used in other fields of science, allowing non‐experts to access the data for phylogenetic, biogeographic, ecological studies and metadata analysis. Even though an experienced taxonomist will always be necessary to make a detailed taxonomic description, it is a step forward to a general template to semi‐automated taxon recognition and to future development of auxiliary tools for species description using machine learning and templates to speed up the time‐consuming phase of schematic figures preparation, after the expert interpretations are done.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Apoio à Pesquisa do Estado da Paraíba

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Wiley

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