Gnaphalieae (Asteraceae): diversity and distribution in Rio de Janeiro - Brazil.

Abstract

Background: As an area of great diversity and suffering from many threats, the Brazilian Mata Atlântica and its floristic zones, such as Restingas, Rock Outcrops and Grasslands, call for attention and recurrent studies on plant diversity as a means of helping conservation efforts. In this context, acquiring, curating and using herbaria data is crucial to filling gaps in plant distribution and biogeography, as well as confirming or denying species incidence in the area of interest. This study provides a solid dataset with information regarding diversity and distribution of species from the tribe Gnaphalieae (Asteraceae) in the State of Rio de Janeiro, Brazil, with significant updates since the publication of Flora Fluminensis by Vellozo almost 200 years ago, the Rio de Janeiro checklist one decade ago and the milestone of the Flora e Funga do Brasil continuously updated dynamic dataset.

New information: We recorded 31 species of Gnaphalieae (Cass.) Lecoq. & Juill. for Rio de Janeiro State of which nine are newly recorded in the area and five are refuted occurrences, compared to the listing in Flora e Funga do Brasil 2023. In our compilation, we confirmed the occurrence of eight genera in the State flora: Achyrocline (Less.) DC., Chevreulia Cass., Chionolaena DC., Facelis Cass., Gamochaeta Wedd., Gnaphalium L., Lucilia Cass. and Pseudognaphalium Kirp. Moreover, we compared the cost-benefit on using municipality centroids versus original/curated coordinates on 5 km² quadrants. We found that, on this geographical scale, there is no significant difference between the two methods. We advocate for using the less time-consuming centroids, which are less prone to human error, for expediting presence/absence checklist data curation and if the main goals are to quantify records, map species richness and evaluate sampling effort. Nevertheless, precise coordinates are essential for ecological niche modelling, conservation assessments and other data usage, focusing on habitat level mapping.