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.

Summary: More than 15% of all vascular plant species may remain scientifically undescribed, and many of the > 350 000 described species have no or few geographic records documenting their distribution. Identifying and understanding taxonomic and geographic knowledge shortfalls is key to prioritising future collection and conservation efforts. Using extensive data for 343 523 vascular plant species and time‐to‐event analyses, we conducted multiple tests related to plant taxonomic and geographic data shortfalls, and identified 33 global diversity darkspots (those ‘botanical countries’ predicted to contain most undescribed and not yet recorded species). We defined priority regions for future collection according to several socio‐economic and environmental scenarios. Most plant diversity darkspots are found within global biodiversity hotspots, with the exception of New Guinea. We identify Colombia, Myanmar, New Guinea, Peru, Philippines and Turkey as global collection priorities under all environmental and socio‐economic conditions considered. Our study provides a flexible framework to help accelerate the documentation of global plant diversity for the implementation of conservation actions. As digitisation of the world's herbaria progresses, collection and conservation priorities may soon be identifiable at finer scales.

Malvales is a diverse order of flowering plants, economically and ecologically relevant, and it is known for its broad morphological variability. Recent phylogenomic studies have revealed a complex evolutionary history for the order, including localised phylogenetic discordances among nuclear loci. However, since the late 1990s, Malvales classification has largely been neglected. This study aims to address this gap by revisiting the classification of Malvales, with a focus on its largest family, Malvaceae. By integrating phylogenomic and morphological datasets, our primary goals are to provide an updated phylogeny for the order and to map key traits supporting a revised suprageneric classification, while accounting for gene and species tree conflicts. Our molecular dataset included 194 genera and 309 nuclear genes, obtained through target sequence capture using Angiosperms353 probes. This dataset covers approximately half of the known genera in the order, representing all families and subfamilies, as well as nearly all tribes, and all subtribes. A coalescent approach utilising nuclear gene trees was used to infer phylogenetic relationships. A morphological matrix with 50 characters relevant for suprageneric classification was compiled, and character‐state distributions for selected traits were mapped against the phylogenetic tree to identify and discuss diagnostic features for clades. Phylogenetic relationships and the monophyly of most groups aligned closely with previous studies. Morphological traits that define key clades varied significantly across Malvales, with differences observed in growth habit, leaf structure, pollen type, floral features, and fruit/seed morphology. In Malvaceae, we resolved uncertainties in the early Malvoideae lineages, and we describe a new tribe, Pentaplareae tr. nov., which clarifies the taxonomic placement of a previously uncertain genus. Additionally, we propose the recognition of a new subfamily, Matisioideae subfam. nov., elevated from its former status as a tribe, and positioned as the sister group to Malvoideae. This study highlights how taxonomic frameworks can be refined even in the face of conflicting phylogenomic data, demonstrating the importance of integrating molecular and morphological evidence in revising classifications.