1. Species occurrence records from online databases are an indispensable resource in ecological, biogeographical 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 geographical areas and taxonomic groups, making it impractical for datasets with thousands or millions of records.2. Here, we present CoordinateCleaner, an r-package to scan datasets 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 datasets with millions of records. The software includes (a) functions to flag potentially problematic coordinate records based on geographical gazetteers, (b) a global database of 9,691 geo-referenced biodiversity institutions to identify records that are likely from horticulture or captivity, (c) novel algorithms to identify datasets with rasterized data, conversion errors and strong decimal rounding and (d) 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 Palaeobiology 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 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Amazonia is an environmentally heterogeneous and biologically megadiverse region, and its biodiversity varies considerably over space. However, existing knowledge on Amazonian biodiversity and its environmental determinants stems almost exclusively from studies of macroscopic above‐ground organisms, notably vertebrates and trees. In contrast, diversity patterns of most other organisms remain elusive, although some of them, for instance microorganisms, constitute the overwhelming majority of taxa in any given location, both in terms of diversity and abundance. Here, we use DNA metabarcoding to estimate prokaryote and eukaryote diversity in environmental soil and litter samples from 39 survey plots in a longitudinal transect across Brazilian Amazonia using 16S and 18S gene sequences, respectively. We characterize richness and community composition based on operational taxonomic units (OTUs) and test their correlation with longitude and habitat. We find that prokaryote and eukaryote OTU richness and community composition differ significantly among localities and habitats, and that prokaryotes are more strongly structured by locality and habitat type than eukaryotes. Our results 1) provide a first large‐scale mapping of Amazonian soil biodiversity, suggesting that OTU richness patterns might follow substantially different patterns from those observed for macro‐organisms; and 2) indicate that locality and habitat factors interact in determining OTU richness patterns and community composition. This study shows the potential of DNA metabarcoding in unveiling Amazonia's outstanding diversity, despite the lack of complete reference sequence databases for the organisms sequenced.

The unparalleled biodiversity found in the American tropics (the Neotropics) has attracted the attention of naturalists for centuries. Despite major advances in recent years in our understanding of the origin and diversification of many Neotropical taxa and biotic regions, many questions remain to be answered. Additional biological and geological data are still needed, as well as methodological advances that are capable of bridging these research fields. In this review, aimed primarily at advanced students and early-career scientists, we introduce the concept of “trans-disciplinary biogeography,” which refers to the integration of data from multiple areas of research in biology (e.g., community ecology, phylogeography, systematics, historical biogeography) and Earth and the physical sciences (e.g., geology, climatology, palaeontology), as a means to reconstruct the giant puzzle of Neotropical biodiversity and evolution in space and time. We caution against extrapolating results derived from the study of one or a few taxa to convey general scenarios of Neotropical evolution and landscape formation. We urge more coordination and integration of data and ideas among disciplines, transcending their traditional boundaries, as a basis for advancing tomorrow’s ground-breaking research. Our review highlights the great opportunities for studying the Neotropical biota to understand the evolution of life.

BackgroundKnowledge on the globally outstanding Amazonian biodiversity and its environmental determinants stems almost exclusively from aboveground organisms, notably plants. In contrast, the environmental factors and habitat preferences that drive diversity patterns for micro-organisms in the ground remain elusive, despite the fact that micro-organisms constitute the overwhelming majority of life forms in any given location, in terms of both diversity and abundance. Here we address how the diversity and community turnover of operational taxonomic units (OTU) of organisms in soil and litter respond to soil physicochemical properties; whether OTU diversities and community composition in soil and litter are correlated with each other; and whether they respond in a similar way to soil properties.MethodsWe used recently inferred OTUs from high-throughput metabarcoding of the 16S (prokaryotes) and 18S (eukaryotes) genes to estimate OTU diversity (OTU richness and effective number of OTUs) and community composition for prokaryotes and eukaryotes in soil and litter across four localities in Brazilian Amazonia. All analyses were run separately for prokaryote and eukaryote OTUs, and for each group using both presence-absence and abundance data. Combining these with novel data on soil chemical and physical properties, we identify abiotic correlates of soil and litter organism diversity and community structure using regression, ordination, and variance partitioning analysis.ResultsSoil organic carbon content was the strongest factor explaining OTU diversity (negative correlation) and pH was the strongest factor explaining community turnover for prokaryotes and eukaryotes in both soil and litter. We found significant effects also for other soil variables, including both chemical and physical properties. The correlation between OTU diversity in litter and in soil was non-significant for eukaryotes and weak for prokaryotes. The community compositions of both prokaryotes and eukaryotes were more separated among habitat types (terra-firme, várzea, igapó and campina) than between substrates (soil and litter).DiscussionIn spite of the limited sampling (four localities, 39 plots), our results provide a broad-scale view of the physical and chemical correlations of soil and litter biodiversity in a longitudinal transect across the world’s largest rainforest. Our methods help to understand links between soil properties, OTU diversity patterns, and community composition and turnover. The lack of strong correlation between OTU diversity in litter and in soil suggests independence of diversity drives of these substrates and highlights the importance of including both measures in biodiversity assessments. Massive sequencing of soil and litter samples holds the potential to complement traditional biological inventories in advancing our understanding of the factors affecting tropical diversity.

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The rapid loss of biodiversity, coupled with difficulties in species identification, call for innovative approaches to assess biodiversity. Insects make up a substantial proportion of extant diversity and play fundamental roles in any given ecosystem. To complement morphological species identification, new techniques such as metabarcoding make it possible to quantify insect diversity and insect–ecosystem interactions through DNA sequencing. Here we examine the potential of bulk insect samples (i.e., containing many non-sorted specimens) to assess prokaryote and eukaryote biodiversity and to complement the taxonomic coverage of soil samples. We sampled 25 sites on three continents and in various ecosystems, collecting insects with SLAM traps (Brazil) and Malaise traps (South Africa and Sweden). We then compared our diversity estimates with the results obtained with biodiversity data from soil samples from the same localities. We found a largely different taxonomic composition between the soil and insect samples, testifying to the potential of bulk insect samples to complement soil samples. Finally, we found that non-destructive DNA extraction protocols, which preserve insect specimens for morphological studies, constitute a promising choice for cost-effective biodiversity assessments. We propose that the sampling and sequencing of insect samples should become a standard complement for biodiversity studies based on environmental DNA.

Most knowledge on biodiversity derives from the study of charismatic macro-organisms, such as birds and trees. However, the diversity of micro-organisms constitutes the majority of all life forms on Earth. Here, we ask if the patterns of richness inferred for macro-organisms are similar for micro-organisms. For this, we barcoded samples of soil, litter and insects from four localities on a west-to-east transect across Amazonia. We quantified richness as Operational Taxonomic Units (OTUs) in those samples using three molecular markers. We then compared OTU richness with species richness of two relatively well-studied organism groups in Amazonia: trees and birds. We find that OTU richness shows a declining west-to-east diversity gradient that is in agreement with the species richness patterns documented here and previously for birds and trees. These results suggest that most taxonomic groups respond to the same overall diversity gradients at large spatial scales. However, our results show a different pattern of richness in relation to habitat types, suggesting that the idiosyncrasies of each taxonomic group and peculiarities of the local environment frequently override large-scale diversity gradients. Our findings caution against using the diversity distribution of one taxonomic group as an indication of patterns of richness across all groups.

A peer-reviewed open-access journal MycoKeysLaunched to accelerate biodiversity research RESEARCH ARTICLEKessy Abarenkov et al. / MycoKeys 16: 1-15 (2016) Abstract Recent molecular studies have identified substantial fungal diversity in indoor environments. Fungi and fungal particles have been linked to a range of potentially unwanted effects in the built environment, including asthma, decay of building materials, and food spoilage. The study of the built mycobiome is hampered by a number of constraints, one of which is the poor state of the metadata annotation of fungal DNA sequences from the built environment in public databases. In order to enable precise interrogation of such data -for example, "retrieve all fungal sequences recovered from bathrooms" -a workshop was organized at the University of Gothenburg (May 23-24, 2016) to annotate public fungal barcode (ITS) sequences according to the MIxS-Built Environment annotation standard (http://gensc.org/mixs/). The 36 participants assembled a total of 45,488 data points from the published literature, including the addition of 8,430 instances of countries of collection from a total of 83 countries, 5,801 instances of building types, and 3,876 instances of surface-air contaminants. The results were implemented in the UNITE database for molecular identification of fungi (http://unite.ut.ee) and were shared with other online resources. Data obtained from human/animal pathogenic fungi will furthermore be verified on culture based metadata for subsequent inclusion in the ISHAM-ITS database (http://its.mycologylab.org).