AntClassify
Functional Guilds, Invasion Status, Endemism, and Rarity of Ants
Provides functions for the analysis of ant communities, aiming to
standardize workflows in myrmecology. The package automates the assignment
of species to functional guilds based on trophic strategies, feeding
habits, and foraging behavior, using established classification frameworks
(Silva et al., 2015
AntClassify
AntClassify is an R package designed to standardize ant community analyses, particularly for Neotropical and Brazilian Atlantic Forest assemblages. It automates:
- Classification of species into functional guilds based on trophic strategies and foraging behavior. The package offers two approaches: (1) classification using established criteria from the literature (Delabie et al., 2000; Silvestre et al., 2003; Silva et al., 2015), and (2) a built-in classification derived from urban ant communities.
- Identification of exotic species recorded in Brazil (Vieira, 2025).
- Identification of endemic species of the Atlantic Forest (Silva et al., 2025).
- Classification of rarity based on geographic distribution and local abundance (Silva et al., 2024).
By automating these tasks, AntClassify reduces manual effort and increases reproducibility, making it a practical tool for researchers working with ant assemblages.
Installation
You can install the development version of AntClassify from GitHub:
# install.packages("remotes")
remotes::install_github("cogdebora/AntClassify")
Once the package is accepted on CRAN, you will also be able to install it with:
install.packages("AntClassify")
Example
Below is a reproducible example using a standardized test dataset. To keep the demonstration fast, we set validate = FALSE; you can enable GBIF validation by using validate = TRUE.
library(AntClassify)
# Create example dataset (35 species)
data <- data.frame(
Atta_sexdens = 50,
Camponotus_atriceps = 40,
Crematogaster_sp = 35,
Cyphomyrmex_minutus = 30,
Cyphomyrmex_rimosus = 28,
Ectatomma_edentatum = 25,
Heteroponera_mayri = 22,
Holcoponera_striatula = 20,
Monomorium_floricola = 18,
Monomorium_pharaonis = 17,
Pheidole_megacephala = 16,
Strumigenys_emmae = 15,
Strumigenys_rogeri = 14,
Nylanderia_fulva = 13,
Odontomachus_chelifer = 12,
Oxyepoecus_reticulatus = 11,
Pachycondyla_striata = 10,
Apterostigma_serratum = 9,
Brachymyrmex_delabiei = 8,
Brachymyrmex_feitosai = 7,
Camponotus_fallatus = 6,
Camponotus_hermanni = 5,
Camponotus_xanthogaster = 4,
Pheidole_aberrans = 3,
Pheidole_fimbriata = 3,
Pheidole_obscurithorax = 2,
Pheidole_subarmata = 2,
Strumigenys_fridericimuelleri = 2,
Heteroponera_inermis = 2,
Oxyepoecus_browni = 2,
Sphinctomyrmex_stali = 1,
Strumigenys_sanctipauli = 1,
Brachymyrmex_micromegas = 1,
Camponotus_tripartitus = 1,
Diaphoromyrma_sofiae = 1
)
# Convert underscores to spaces in species names
colnames(data) <- gsub("_", " ", colnames(data))
# Run full pipeline (validation disabled for speed)
results <- antclassify(data, validate = FALSE, plot = FALSE)
# View outputs
names(results)
head(results$guilds$table)
results$exotic$table
results$endemic$table
results$rarity$table
Generating plots
To generate plots, set plot = TRUE in the individual functions or in antclassify().
# Distribution of rarity forms
check_rarity_atlantic_ants(data, validate = FALSE, plot = TRUE, plot_type = "status")
# Rare species abundance by rarity form
check_rarity_atlantic_ants(data, validate = FALSE, plot = TRUE, plot_type = "species")
For more detailed examples and function documentation, see the package vignettes:
vignette("antclassify_workflow", package = "AntClassify")
Citation
If you use AntClassify in your research, please cite the following references:
Silva, N. S., Maciel, E. A., Prado, L. P., Silva, O. G., Barbosa, D. A., Andrade-Silva, J., ... & Morini, M. S. (2024). Ant rarity and vulnerability in Brazilian Atlantic Forest fragments. Biological Conservation, 296, 110640. DOI: https://doi.org/10.1016/j.biocon.2024.110640
Silva, N. S., Gonçalves, D. C. de O., Wazema, C. T., Barbosa, D. A., Prado, L. P. do, Andrade-Silva, J., Fernandes, T. T., Silva, R. R., & Morini, M. S. de C. (2025). Endemism and vulnerability of ants in the phytophysiognomies of the Brazilian Atlantic Forest. In Brazilian Myrmecology: Exploring the World’s Richest Ant Fauna (Cap. 16, pp. 371–394). Editora Científica Digital. DOI: https://doi.org/10.37885/250920259
Vieira, V. B. (2025). Quem são e onde estão as formigas exóticas do Brasil? [Dissertação de Mestrado, Universidade Federal do Paraná]. Curitiba, PR, Brasil.
Silvestre, R., Brandão, C. R. F., & Silva, R. R. (2003). Grupos funcionales de hormigas: el caso de los gremios del Cerrado. In F. Fernández (Ed.), Introducción a las Hormigas de la Región Neotropical (pp. 113–148). Instituto Alexander Von Humboldt.
Silva, R. R., Silvestre, R., Brandão, C. R. F., Morini, M. S. C., & Delabie, J. H. C. (2015). Grupos tróficos e guildas em formigas poneromorfas. In: Delabie, Jacques H. C. et al. As formigas poneromorfas do Brasil. Ilhéus: Editus, 2015. p. 163-179.
Delabie, J. H. C., Agosti, D., & Nascimento, I. C. (2000). Litter ant communities of the Brazilian Atlantic rain forest region. Sampling Ground-dwelling Ants: case studies from the world’s rain forests. Curtin University of Technology School of Environmental Biology Bulletin, v. 18.
Additionally, if you use the package itself, please cite:
Gonçalves, D. C. O., et al. (2026). AntClassify: An R package for ant community analysis (Version 0.1.0) [Computer software]. https://github.com/cogdebora/AntClassify