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Hardwick, B.

Publications and source records attributed to Hardwick, B..

2 recordsLinked to original sources

Is there a fly in my soup? To what extent do metabarcoding and individual barcoding tell the same story?

O_LIMetabarcoding has become the method of choice for characterising complex arthropod communities. The extent to which metabarcoded bulk samples will recover the same community composition as individual sequencing of all individuals in the sample remains poorly quantified. Biases such as unequal extraction of DNA from different taxa, primer mismatches and non-random PCR may cause the selective drop-out of species from metabarcoding data. At the same time, DNA metabarcoding may reveal arthropod taxa present not as individuals, but as DNA residues on the surface or in the gut of insects. C_LIO_LITo quantify the consistency in sample contents established by different means, we metabarcoded 45 bulk insect samples, then extracted all arthropods and sequenced them individually. Metabarcoding targeted 418 bp at the 3 end of the Folmer barcoding region, while individual barcodes captured the entire 658 bp Folmer region. The metabarcoding workflow, including PCR amplification, sequencing, and bioinformatics, was performed in three replicates from three separate lysate aliquots per sample. Sequences were assigned to Barcode Index Numbers (BINs) as identical taxonomic categories across data types. C_LIO_LIWe found that metabarcoding is replicable, as different replicates of the same sample recover similar species richness and composition. Individual barcoding and metabarcoding provide the same impression of relative differences in community structure: estimates of relative species richness and relative dissimilarity between sample pairs are congruent among data types. Dissimilarity between data types varies with BIN richness in the sample, but this relationship reflects nestedness rather than turnover: metabarcoding recovers the same set of core species as individual barcoding but adds hundreds of species on top. Any BIN recovered as an individual occurred with high probability in the metabarcoding data, and any BIN found in high read abundances by metabarcoding was likely found as an individual. C_LIO_LIOur analysis suggests that metabarcoding data will closely mimic the sample contents in terms of arthropod species richness and composition. Taxa recovered in low copy numbers in metabarcoding sequence data will typically represent DNA left as residues from past biotic interactions. Barring sequencing errors, both types of data yield biologically relevant insights into the taxa present in the source community. C_LI

ecology↗

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

Modern DNA-based biodiversity surveys result in massive-scale data, including up to millions of species - of which most are rare. Making the most of such data for inference and prediction requires modelling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a novel "common to rare transfer learning" approach (CORAL), based on borrowing information from the common species to enable statistically and computationally efficient modelling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2874 samples.

ecology↗