RESUMO
Background: Species from the "cacao group" are traditionally allocated into two genera, Theobroma and Herrania (Malvaceae, Byttnerioideae), both groups of Neotropical species economically relevant, such as the cacao tree (Theobromacacao), which forms the source of chocolate. This study aimed at compiling and describing a dataset of preserved specimen collections available in the Global Biodiversity Information Facility repository (GBIF) for Tropical Americas. Data were exhaustively revisited and analysed in terms of taxonomic identity, conditions of collection and georeferencing, all of which should enable downstream taxonomic, geographic and evolutionary analyses. New information: Our dataset compiles 7975 records of preserved specimen collections found at herbaria. Records are from 18 species of Theobroma and 14 of Herrania, occurring in 60 countries or major territories, with two species endemic to a single country (H.kofanorum from Ecuador and H.laciniifolium from Colombia). Occurrence records are mostly restricted to the Amazon rainforest and species with more occurrence records are cupuí, T.subincanum (1535 records), followed by the cacao tree, T.cacao (1500 records), the latter having cultivated specimens in Africa, Asia and Oceania. In the case of the genus Herrania, H.nitida and H.purpurea are the species with the majority of occurrences (respectively, 431 and 273 records). Most of the botanical samples from these genera are found in American, Brazilian and Colombian collections, with a particular strength for American herbaria. We describe how occurrence records are spread spatially and temporally and highlight key field expeditions responsible for enhancing most of the knowledge of cacao and its wild relatives, especially in countries where they prevail, such as Colombia (with 29 species), Ecuador (23 species), Brazil (18 species) and Peru (15 species). Specifically, expeditions in these countries were led by American and European initiatives in conjunction with local funding in the mid-20th century. We emphasise how initiatives of such kind seems to have weakened in the 21st century and most of the collections of Theobroma and Herrania made afterwards are from various collectors that seek to resample specimens in already explored sites.
RESUMO
Species occurrence records provide the basis for many biodiversity studies. They derive from georeferenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic filtering is a scalable and reproducible means to identify potentially problematic records and tailor datasets from public databases such as the Global Biodiversity Information Facility (GBIF; http://www.gbif.org), for biodiversity analyses. However, it is unclear how much data may be lost by filtering, whether the same filters should be applied across all taxonomic groups, and what the effect of filtering is on common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa, including terrestrial and marine animals, fungi, and plants downloaded from GBIF. We find that a total of 44.3% of the records are potentially problematic, with large variation across taxonomic groups (25-90%). A small fraction of records was identified as erroneous in the strict sense (4.2%), and a much larger proportion as unfit for most downstream analyses (41.7%). Filters of duplicated information, collection year, and basis of record, as well as coordinates in urban areas, or for terrestrial taxa in the sea or marine taxa on land, have the greatest effect. Automated filtering can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough recording and exploration of the meta-data associated with species records for biodiversity research.
