Biology subjects

Andermann, T.

Publications and source records attributed to Andermann, T..

8 recordsLinked to original sources

bioRxiv2026

Environmental DNA substrates capture complementary dimensions of insect biodiversity

Environmental DNA (eDNA) metabarcoding has become a powerful approach for biodiversity assessment, yet whether different environmental substrates provide equivalent or complementary representations of terrestrial biodiversity remains unresolved. Here we addressed this question by comparing insect assemblages recovered from Malaise traps and six environmental substrates (water, sediment, soil, spiderwebs, deadwood, and anthills) across 45 forest locations using a standardized COI metabarcoding workflow. Substrate identity consistently shaped insect detection, diversity, taxonomic representation, and community composition, with each substrate recovering distinct components of insect biodiversity rather than a common biodiversity signal. Malaise traps recovered the greatest overall richness, while water and spiderwebs contributed the largest proportion of additional taxa, demonstrating that no single substrate represented the full spectrum of insect biodiversity. These findings show that environmental substrates capture complementary representations of biodiversity through distinct ecological processes of eDNA deposition, transport, accumulation, and persistence. Different substrates should therefore be interpreted as complementary sources of biodiversity observations instead of interchangeable sampling media, and their integration can support more comprehensive ecological inference. Our study identifies substrate complementarity as a key consideration for interpreting terrestrial eDNA observations and for designing future biodiversity surveys.

ecology↗

bioRxiv2022

Estimating alpha, beta, and gamma diversity through deep learning

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.

evolutionary biology↗

bioRxiv2021

The evolution of open habitats in North America revealed by deep learning models

Open vegetation today constitutes one of the most extensive biomes on earth, including temperate grasslands and tropical savannas. Yet these biomes originated relatively recently in earth history, likely replacing forested habitats as recently as the second half of the Cenozoic, although the timing of their origination and the dynamics of their expansion remain uncertain. Here, we present a new hypothesis of paleovegetation change in North America, showing that open habitats originated between 25 and 20 Ma in the center of the continent, and expanded rapidly starting 8 Ma to eventually become the most prominent vegetation type today. To obtain space-time predictions of paleovegetation, we developed a new Bayesian deep learning model that utilizes available information from fossil evidence, geologic models, and paleoclimate proxies. We compiled a large dataset of paleovegetation reconstructions from the peer-reviewed literature, which we used in combination with current vegetation data to train the model. The model learns to predict vegetation based on the learned associations between the vegetation at a given site and multiple biotic and abiotic predictors: fossil mammal occurrences, plant macrofossils, estimates of temperature and precipitation, latitude, and the effects of spatial and temporal autocorrelation. Our results provide a new, spatially detailed reconstruction of habitat evolution in North America and our deep learning model paves the way for a new quantitative approach to estimating paleovegetation changes.

evolutionary biology↗

bioRxiv2021

IUCNN - deep learning approaches to approximate species' extinction risk

AimThe global Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time- consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for understudied areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here we present IUCNN, an R- package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other traits if available). InnovationWe implement a user-friendly workflow to train and validate neural network models, and subsequently use them to predict species RL status. IUCNN contains functions to address specific issues related to the RL framework, including a regression-based approach to account for the ordinal nature of RL categories and class imbalance in the training data, a Bayesian approach for improved uncertainty quantification, and a target accuracy threshold approach that limits predictions to only those species whose RL status can be predicted with high confidence. Most analyses can be run with few lines of code, without prior knowledge of neural network models. We demonstrate the use of IUCNN on an empirical dataset of [~]14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods. Main conclusionsIUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large-scale analyses on the extinction risk of species so far not well represented on the official RL.

ecology↗

bioRxiv2021

Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults

Bacterial bloodstream infections are a major cause of morbidity and mortality among patients undergoing hematopoietic cell transplantation (HCT). Although previous research has demonstrated that pathogenic organisms may translocate from the gut microbiome into the bloodstream to cause infections, the mechanisms by which HCT patients acquire pathogens in their microbiome have not yet been described. We hypothesized that patient-patient transmission may be responsible for pathogens colonizing the microbiome of HCT patients, and that patients who share time and space in the hospital are more likely to share bacterial strains. Here, we used linked-read and short-read metagenomic sequencing to analyze 401 stool samples collected from 149 adults undergoing HCT and hospitalized in the same unit over five years. We used metagenomic assembly and strain-specific comparison methods to investigate transmission of gut microbiota between individuals. While patients who shared time and space in the hospital did not converge in overall microbiome composition, we did observe four pairs of patients who harbor identical or nearly identical E. faecium strains in their microbiome. These strains may be the result of transmission between patients who shared a room and bathroom, acquisition from a common source in the hospital or transmission from an unsampled source. We also observed identical Akkermansia muciniphila and Hungatella hathewayi strains in two pairs of patients. In both cases, the patients were roommates for at least one day, the strain was absent in the putative recipients microbiome prior to the period of roommate overlap and the putative recipient had a microbiome perturbed by antibiotic treatment for a bloodstream infection. Finally, we identified multiple patients who harbored identical strains of several species commonly found in commercial probiotics and dairy products, including Lactobacillus rhamnosus, Lactobacillus gasseri and Streptococcus thermophilus. Overall, our findings indicate that pathogenic organisms from a single source are not frequently colonizing the gut microbiome of multiple patients. However, the potential transmission of commensal microbes with immunomodulatory properties raises questions about the recovery of microbiome diversity after HCT, and indicates that patients in this setting may acquire new microbes by sharing space with others.

microbiology↗

bioRxiv2019

iucn_sim - Improved predictions of future extinctions using IUCN status assessments

The ongoing environmental crisis poses an urgent need to forecast the who, where, and when of future species extinctions, as such information is crucial for targeting conservation efforts. Commonly, such forecasts are made based on conservation status assessments produced by the International Union for Conservation of Nature (IUCN). However, when researchers apply these IUCN conservation status data for predicting future extinctions, important information is often omitted, which can impact the accuracy of these predictions. Here we present a new approach and a software for simulating future extinctions based on IUCN conservation status information, which incorporates generation length information of individual species when modeling extinction risks. Additionally, we explicitly model future changes in conservation status for each species, based on status transition rates that we estimate from the IUCN assessment history of the last decades. Finally, we apply a Markov chain Monte Carlo algorithm to estimate extinction rates for each species, based on the simulated future extinctions. These estimates inherently incorporate the chances of conservation status changes and the generation length for each given species and are specific to the simulated time frame. We demonstrate the utility of our approach by estimating extinction rates for all bird species. Our average extinction risk estimate for the next 100 years across all birds is 6.98 x 10-4 extinctions per species-year, and we predict an expected biodiversity loss of between 669 to 738 bird species within that time frame. Further, the rate estimates between species sharing the same IUCN status show larger variation than the rates estimated with alternative approaches, which reflects expected differences in extinction risk among taxa of the same conservation status. Our method demonstrates the utility of applying species-specific information to the estimation of extinction rates, rather than assuming equal extinction risks for species assigned to the same conservation status.

evolutionary biology↗

bioRxiv2019

Combined use of metagenomic sequencing and host response profiling for the diagnosis of suspected sepsis.

BackgroundCurrent diagnostic techniques are inadequate for rapid microbial diagnosis and optimal management of patients with suspected sepsis. We assessed the clinical impact of three powerful molecular diagnostic methods. MethodsWith blood samples from 200 consecutive patients with suspected sepsis, we evaluated 1) metagenomic shotgun sequencing together with a Bayesian inference approach for contaminant sequence removal, for detecting bacterial DNA; 2) viral capture sequencing; and 3) transcript-based host response profiling for classifying patients as infected or not, and if infected, with bacteria or viruses. We then evaluated changes in diagnostic decision-making among three expert physicians by unblinding the results of these methods in a staged fashion. ResultsMetagenomic shotgun sequencing confirmed positive blood culture results in 14 of 26 patients. In 17 of 200 patients, metagenomic sequencing and viral capture sequencing revealed organisms that were 1) not detected by conventional hospital tests within 5 days after presentation, and 2) classified as of probable clinical relevance by physician consensus. Host response profiling led at least two of three physicians to change their diagnostic decisions in 46 of 100 patients. The data suggested possible bacterial DNA translocation in 8 patients who were originally classified by physicians as noninfected and illustrate how host response profiling can guide interpretation of metagenomic shotgun sequencing results. ConclusionsThe integration of host response profiling, metagenomic shotgun sequencing, and viral capture sequencing enhances the utility of each, and may improve the diagnosis and management of patients with suspected sepsis.

microbiology↗

bioRxiv2018

Allele Phasing Greatly Improves the Phylogenetic Utility of Ultraconserved Elements

Advances in high-throughput sequencing techniques now allow relatively easy and affordable sequencing of large portions of the genome, even for non-model organisms. Many phylogenetic studies reduce costs by focusing their sequencing efforts on a selected set of targeted loci, commonly enriched using sequence capture. The advantage of this approach is that it recovers a consistent set of loci, each with high sequencing depth, which leads to more confidence in the assembly of target sequences. High sequencing depth can also be used to identify phylogenetically informative allelic variation within sequenced individuals, but allele sequences are infrequently assembled in phylogenetic studies.\n\nInstead, many scientists perform their phylogenetic analyses using contig sequences which result from the de novo assembly of sequencing reads into contigs containing only canonical nucleobases, and this may reduce both statistical power and phylogenetic accuracy. Here, we develop an easy-to-use pipeline to recover allele sequences from sequence capture data, and we use simulated and empirical data to demonstrate the utility of integrating these allele sequences to analyses performed under the Multispecies Coalescent (MSC) model. Our empirical analyses of Ultraconserved Element (UCE) locus data collected from the South American hummingbird genus Topaza demonstrate that phased allele sequences carry sufficient phylogenetic information to infer the genetic structure, lineage divergence, and biogeographic history of a genus that diversified during the last three million years. The phylogenetic results support the recognition of two species, and suggest a high rate of gene flow across large distances of rainforest habitats but rare admixture across the Amazon River. Our simulations provide evidence that analyzing allele sequences leads to more accurate estimates of tree topology and divergence times than the more common approach of using contig sequences.

evolutionary biology↗