Biology subjects

Granqvist, E.

Publications and source records attributed to Granqvist, E..

6 recordsLinked to original sources

bioRxiv2026

Global homeostatic forces shape the composition of a hyperdiverse tropical insect fauna

Despite comprising the bulk of terrestrial biodiversity, we know surprisingly little about insect faunas, particularly in the tropics. Here, we characterize the insect fauna of Madagascar, a global biodiversity hotspot, based on extensive DNA metabarcoding of Malaise trap and leaf litter samples. We estimate that the fauna comprises at least 250,000 species, only 5% of which are currently recorded. The ecological and taxonomic composition is surprisingly similar to that of temperate forests. We show that this is likely caused by global homeostatic forces shaping net diversification rates after colonization of Madagascar rather than by the composition of colonizing lineages. Features differing from temperate forests include the dearth of parasitoids and richness of beetles. Understanding the forces shaping these patterns is key in sustainable management of tropical insect faunas.

evolutionary biology↗

bioRxiv2026

Accuracy of occurrence and abundance estimates from insect metabarcoding

1. DNA metabarcoding--high-throughput sequencing of barcode regions from bulk samples--has become a key tool for insect biodiversity assessment. Yet, how methodological choices affect the accuracy of metabarcoding data remains insufficiently explored. In this paper, we ask: (1) How does the lysis method (non-destructive lysis vs. destructive homogenization) affect community recovery? (2) How comprehensively does metabarcoding capture species richness? (3) To what extent can spike-ins improve abundance estimates? (4) How accurately can species abundances be estimated? 2. We evaluated the accuracy of insect metabarcoding using 4,749 bulk samples from a large-scale biodiversity survey subjected to mild lysis. Of these samples, 856 were also homogenized, allowing a systematic comparison of the effect of alternative treatments. To potentially improve abundance estimates, we added six biological spike-ins (i.e., foreign insects) to all samples, and two synthetic spike-ins (artificial DNA fragments) to the homogenization treatment. In addition, we established the contents of 15 samples by individually barcoding all specimens, enabling direct assessment of occurrence and abundance estimates. 3. Our results revealed consistent differences between destructive and non-destructive treatments. While both methods reliably detected the majority of species, small and soft-bodied taxa were more often recovered after mild lysis than after homogenization, while the reverse was true for heavily sclerotized, hairy, and large taxa. Using biological spike-ins for calibration reduced the variance in read numbers per specimen considerably, especially in homogenized samples, while synthetic spike-ins were less effective. In a Bayesian analysis, where species data were matched to the best-fitting spike-in calibration curve, accurate abundance estimates (+/-1 individual) were obtained for 72.9% of species occurrences. 4. Our results show that it is possible to obtain reasonably accurate abundance estimates from metabarcoding data, and that mild lysis and homogenization result in different taxon-specific biases in terms of occurrence data, with neither method outperforming the other. Accuracy is improved by homogenization rather than mild lysis of samples, and by the use of biological rather than synthetic spike-ins. Together, these findings provide a major step towards robust, quantitative biodiversity monitoring using DNA-metabarcoding.

zoology↗

bioRxiv2024

HAPP: High-Accuracy Pipeline for Processing deep metabarcoding data

We introduce HAPP, a high-accuracy pipeline for processing deep metabarcoding data, leveraging data richness to enhance the signal-to-noise-ratio. Starting with denoised amplicon sequence variants, the pipeline consists of four steps: (1) additional chimera removal, using UCHIME and a strict sample-based approach; (2) taxonomic annotation, combining k-mer matching (SINTAX) to a reference library with phylogenetic placement (EPA-NG) on a reference tree; (3) OTU clustering using SWARM, an open-source algorithm with precision and recall comparable to RESL used in circumscribing BOLD BINs; and (4) noise filtering (NUMTs and sequencing errors), using a new algorithm introduced here, NEEAT, which combines "echo" signals across samples with detection of unusual evolutionary signatures among clusters with similar DNA sequences. HAPP computations are parallelized across taxa, making analyses tractable on very large datasets. The performance of HAPP was validated through extensive benchmarks, involving CO1 data from BOLD and Malaise trap data, demonstrating significant improvements over the state of the art.

bioinformatics↗

bioRxiv2024

Data of the Insect Biome Atlas: a metabarcoding survey of the terrestrial arthropods of Sweden and Madagascar

We present the data from the Insect Biome Atlas project (IBA), characterizing the terrestrial arthropod faunas of Sweden and Madagascar. Over 12 months, weekly Malaise trap samples were collected at 203 locations within 100 sites in Sweden and at 50 locations within 33 sites in Madagascar; this was complemented by soil and litter samples from each site. The field samples comprise 4,749 Malaise trap, 192 soil and 192 litter samples from Sweden and 2,566 Malaise trap and 190 litter samples from Madagascar. Samples were processed using mild lysis or homogenization, followed by DNA metabarcoding of COI (418 bp). The data comprise 698,378 non-chimeric sequence variants from Sweden and 687,866 from Madagascar, representing 33,989 (33,046 Arthropoda) and 77,599 (77,380 Arthropoda) operational taxonomic units, respectively. These are the most comprehensive data presented on these faunas so far, allowing unique analyses of the size, composition, spatial turnover and seasonal dynamics of the sampled communities. They also provide an invaluable baseline against which to gauge future changes.

ecology↗

bioRxiv2023

TreePPL: A Universal Probabilistic Programming Language for Phylogenetics

We present TreePPL, a universal probabilistic programming language (PPL) designed for probabilistic modeling and inference in phylogenetics. In TreePPL, the model is expressed as a computer program, which can generate simulations from the model conditioned on some input data. Specialized inference machinery then uses this program to estimate the posterior probability distribution. The aim is to allow the user to focus on describing the model, and provide the inference machinery for free. The TreePPL modeling language is meant to be familiar to users of R or Python, and utilizes a functional programming style that facilitates the application of generic inference algorithms. The model program can be conveniently compiled and run from a Python or R environment, which can be used for pre-processing, feeding the model with the observed data, controlling and running the inference, and receiving and post-processing the output data. The inference machinery is generated by a compiler framework developed specifically for supporting domain-specific modeling and inference, the Miking CorePPL framework. It currently supports a range of inference strategies--including sequential Monte Carlo, Markov chain Monte Carlo, and combinations thereof--and is based on several recent innovations that are important for efficient PPL inference on phylogenetic models. It also allows advanced users to implement novel inference strategies for models described using TreePPL or other domain-specific modeling languages. We briefly describe the TreePPL modeling language and the Python environment, and give some examples of modeling and inference with TreePPL. The examples illustrate how TreePPL can be used to address a range of common problem types considered in statistical phylogenetics, from diversification and tree inference to complex trait evolution. A few major challenges remain to be addressed before the phylogenetic model space is adequately covered by efficient automatic inference techniques, but several of them are being addressed in ongoing work on TreePPL. We end the paper by discussing how probabilistic programming can facilitate further use of machine learning in addressing important challenges in statistical phylogenetics.

bioinformatics↗

bioRxiv2022

Optimizing insect metabarcoding using replicated mock communities

Metabarcoding (high-throughput sequencing of marker gene amplicons) has emerged as a promising and cost-effective method for characterizing insect community samples. Yet, the methodology varies greatly among studies and its performance has not been systematically evaluated to date. In particular, it is unclear how accurately metabarcoding can resolve species communities in terms of presence-absence, abundances, and biomass. Here we use mock community experiments and a simple probabilistic model to evaluate the performance of different metabarcoding protocols. Specifically, we ask four questions: (Q1) How consistent are the recovered community profiles across replicate mock communities?; (Q2) How does the choice of lysis buffer affect the recovery of the original community?; (Q3) How are community estimates affected by differing lysis times and homogenization?; and (Q4) Is it possible to obtain adequate species abundance estimates through the use of biological spike-ins? We show that estimates are quite variable across community replicates. In general, a mild lysis protocol is better at reconstructing species lists and approximate counts, while homogenization is better at retrieving biomass composition. Tiny insects are more likely to be detected in lysates, while some tough species require homogenization to be detected. Results are less consistent across biological replicates for lysates than for homogenates. Some species are associated with strong PCR amplification bias, which complicates the reconstruction of species counts. Yet, with adequate spike-in data, species abundance can be determined with roughly 40% standard error for homogenates, and with roughly 50% standard error for lysates, under ideal conditions. In the latter case, however, this often requires species-specific reference data, while spike-in data generalizes better across species for homogenates. We conclude that a non-destructive, mild lysis approach shows the highest promise for presence/absence description of the community, while also allowing future morphological or molecular work on the material. However, homogenization protocols perform better for characterizing community composition, in particular in terms of biomass.

ecology↗