Diversity of the Ant Genus Neoponera Emery, 1901 (Formicidae: Ponerinae) in the north of the Brazilian Atlantic Forest, with new Records of Occurrence

Abstract

Composed of two main forest formations, Ombrophilous Forest and Seasonal Forest, the Brazilian Atlantic Forest biome is constituted currently by a mosaic of forest remnants and secondary vegetation. Representatives of the Ponerinae ant genus Neoponera are observed mainly in both wet and seasonally dry forests. The aim of this study was to approach the diversity of the genus Neoponera in the north of the Atlantic Forest of Brazil (from the extreme north of its distribution to the Doce River hydrographic basin in the south), associating the occurrence of ant species with the types of vegetation. We have compiled occurrence data from the collection of the Myrmecology Laboratory of the Cocoa Research Center, on internet, or available in literature. We found information on 23 species of Neoponera, including a new record for the Atlantic Forest, Neoponera globularia (Mackay & Mackay, 2010), and a new record for Brazil, Neoponera fiebrigi Forel, 1912. The relative composition of the Neoponera assemblages was evaluated according to the types of vegetation. We found that the occurrence of the genus Neoponera is mainly related to the types of vegetation of the focus region, principally dense forests where a higher diversity was observed.

Review of Distribution Modeling in Ant (Hymenoptera: Formicidae) Biogeographic Studies

“…In recent years, great advances have been made in this kind of study, allowing inferring the history of distribution based on phylogenetic analyzes, such as on Myrmicinae (Ward et al, 2014). A range of taxonomic studies by biogeographical regions (Ladino & Feitosa, 2020), new occurrences, and records (Dias & Lattke, 2019;Fernandes & Delabie, 2019;Franco, et al, 2019), invasive species (Chen & Adams, 2018), and diversity (Koch et al, 2020;Silva et al, 2020) can also be found. In addition, modeling distribution studies on Formicidae have gained space, under the following names and techniques: potential distribution modeling (Murphy & Breed, 2007;Koch et al, 2018), niche modeling (Peterson & Nakazawa, 2007), paleodistribution (Cristiano et al, 2016), and projections of future scenarios (Jung et al, 2017).…”