1.Species occurrence records from online databases are an indispensable resource in ecological, biogeographic and palaeontological research. However, issues with data quality, especially incorrect geo‐referencing or dating, can diminish their usefulness. Manual cleaning is time‐consuming, error prone, difficult to reproduce, and limited to known geographic areas and taxonomic groups, making it impractical for data sets with thousands or millions of records. 2.Here, we present CoordinateCleaner, an R‐package to scan data sets of species occurrence records for geo‐referencing and dating imprecisions and data‐entry errors in a standardized and reproducible way. CoordinateCleaner is tailored to problems common in biological and palaeontological databases and can handle data sets with millions of records. The software includes I) functions to flag potentially problematic coordinate records based on geographic gazetteers, II) a global database of 9,691 geo‐referenced biodiversity institutions to identify records that are likely from horticulture or captivity, III) novel algorithms to identify data sets with rasterized data, conversion errors and strong decimal rounding, and IV) spatio‐temporal tests for fossils. 3.We describe the individual functions available in CoordinateCleaner and demonstrate them on more than 90 million occurrences of flowering plants from the Global Biodiversity Information Facility (GBIF) and 19,000 fossil occurrences from the Paleobiology Database (PBDB). We find that in GBIF more than 3.4 million records (3.7%) are potentially problematic and that 179 of the tested contributing data sets (18.5%) might be biased by rasterized coordinates. In PBDB, 1205 records (6,3%) are potentially problematic. 4.All cleaning functions and the biodiversity institution database are open‐source and available within the CoordinateCleaner R‐package.

Significance: Amazonia is not only the world’s most diverse rainforest but is also the region in tropical America that has contributed most to its total biodiversity. We show this by estimating and comparing the evolutionary history of a large number of animal and plant species. We find that there has been extensive interchange of evolutionary lineages among different regions and biomes, over the course of tens of millions of years. Amazonia stands out as the primary source of diversity, which can be mainly explained by the total amount of time Amazonian lineages have occupied the region. The exceedingly rich and heterogeneous diversity of the American tropics could only be achieved by high rates of dispersal events across the continent.

Aim: Massive digitalization of natural history collections is now leading to a steep accumulation of publicly available species distribution data. However, taxonomic errors and geographical uncertainty of species occurrence records are now acknowledged by the scientific community – putting into question to what extent such data can be used to unveil correct patterns of biodiversity and distribution. We explore this question through quantitative and qualitative analyses of uncleaned versus manually verified datasets of species distribution records across different spatial scales.Location: The American tropics.Methods: As test case we used the plant tribe Cinchoneae (Rubiaceae). We compiled four datasets of species occurrences: one created manually and verified through classical taxonomic work, and the rest derived from GBIF under different cleaning and filling schemes. We used new bioinformatic tools to code species into grids, ecoregions, and biomes following WWF's classification. We analysed species richness and altitudinal ranges of the species.Results: Altitudinal ranges for species and genera were correctly inferred even without manual data cleaning and filling. However, erroneous records affected spatial patterns of species richness. They led to an overestimation of species richness in certain areas outside the centres of diversity in the clade. The location of many of these areas comprised the geographical midpoint of countries and political subdivisions, assigned long after the specimens had been collected.Main conclusion: Open databases and integrative bioinformatic tools allow a rapid approximation of large‐scale patterns of biodiversity across space and altitudinal ranges. We found that geographic inaccuracy affects diversity patterns more than taxonomic uncertainties, often leading to false positives, i.e. overestimating species richness in relatively species poor regions. Public databases for species distribution are valuable and should be more explored, but under scrutiny and validation by taxonomic experts. We suggest that database managers implement easy ways of community feedback on data quality.

The Andes are the world's most biodiverse mountain chain, encompassing a complex array of ecosystems from tropical rainforests to alpine habitats. We provide a synthesis of Andean vascular plant diversity by estimating a list of all species with publicly available records, which we integrate with a phylogenetic dataset of 14 501 Neotropical plant species in 194 clades. We find that (i) the Andean flora comprises at least 28 691 georeferenced species documented to date, (ii) Northern Andean mid-elevation cloud forests are the most species-rich Andean ecosystems, (iii) the Andes are a key source and sink of Neotropical plant diversity, and (iv) the Andes, Amazonia, and other Neotropical biomes have had a considerable amount of biotic interchange through time.

Madagascar’s biota is hyperdiverse and includes exceptional levels of endemicity. We review the current state of knowledge on Madagascar’s past and current terrestrial and freshwater biodiversity by compiling and presenting comprehensive data on species diversity, endemism, and rates of species description and human uses, in addition to presenting an updated and simplified map of vegetation types. We report a substantial increase of records and species new to science in recent years; however, the diversity and evolution of many groups remain practically unknown (e.g., fungi and most invertebrates). Digitization efforts are increasing the resolution of species richness patterns and we highlight the crucial role of field- and collections-based research for advancing biodiversity knowledge and identifying gaps in our understanding, particularly as species richness corresponds closely to collection effort. Phylogenetic diversity patterns mirror that of species richness and endemism in most of the analyzed groups. We highlight humid forests as centers of diversity and endemism because of their role as refugia and centers of recent and rapid radiations. However, the distinct endemism of other areas, such as the grassland-woodland mosaic of the Central Highlands and the spiny forest of the southwest, is also biologically important despite lower species richness. The documented uses of Malagasy biodiversity are manifold, with much potential for the uncovering of new useful traits for food, medicine, and climate mitigation. The data presented here showcase Madagascar as a unique “living laboratory” for our understanding of evolution and the complex interactions between people and nature. The gathering and analysis of biodiversity data must continue and accelerate if we are to fully understand and safeguard this unique subset of Earth’s biodiversity.

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.