HELIX: A data-driven characterization of Brazilian land snails

  • Marcelo N. Almeida Universidade Federal Fluminense (UFF)
  • Rodolfo Alves de Oliveira Universidade Federal Fluminense (UFF)
  • Luiz Olmes Universidade Federal de Itajubá (UNIFEI)
  • Gustavo S. Semaan Universidade Federal Fluminense (UFF) http://orcid.org/0000-0003-2873-2628
  • Daniel de Oliveira Universidade Federal Fluminense (UFF) http://orcid.org/0000-0001-9346-7651
  • Lúcio Santos Instituto Federal do Norte de Minas Gerais (IFNMG)
  • Marcos Bedo Universidade Federal Fluminense (UFF)


Decision-support systems benefit from hidden patterns extracted from digital information. In the specific domain of gastropod characterization, morphometrical measurements support biologists in the identification of land snail specimens. Although snails can be easily identified by their excretory and reproductive systems, the after-death mollusk body is commonly inaccessible because of either soft material deterioration or fossilization. This study aims at characterizing Brazilian land snails by morphometrical data features manually taken from the shells. In particular, we examined a dataset of shells by using different learning models that labeled snail specimens with a precision up to 97.5% (F1-Score = .975, CKC = .967 and ROC Area = .998). The extracted patterns describe similarities and trends among land snail species and indicates possible outliers physiologies due to climate traits and breeding. Finally, we show some morphometrical characteristics dominate others according to different feature selection biases. Those data-based patterns can be applied to fast land snail identification whenever their bodies are unavailable, as in the recurrent cases of lost shells in nature or private and museum collections.
Palavras-chave: Quantitative Methods, Data analysis


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ALMEIDA, Marcelo N.; DE OLIVEIRA, Rodolfo Alves; OLMES, Luiz; SEMAAN, Gustavo S.; DE OLIVEIRA, Daniel; SANTOS, Lúcio; BEDO, Marcos. HELIX: A data-driven characterization of Brazilian land snails. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 319-324. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2021.17892.