Biology subjects

Goldmann, K.

Publications and source records attributed to Goldmann, K..

8 recordsLinked to original sources

bioRxiv2026

The core microbiome as a reproducible, data-driven abstraction, not a biological entity

Microbial communities are highly diverse, making it difficult to distinguish stable ecological patterns from stochastic variation. The core microbiome offers a widely used framework for simplification, but prevalence-based definitions are often criticized for relying on arbitrary thresholds. We propose an alternative view in which the core microbiome is not a biologically complete or functionally exhaustive subset, but a data-driven abstraction that preserves dominant ecological information under strong dimensional reduction. Using bacterial (16S rRNA) and fungal (ITS2) communities from leaves, roots, and rhizosphere of clonally replicated pedunculate oak (Quercus robur) across a continental environmental gradient, we defined cores using a non-arbitrary threshold derived from the prevalence distribution. Despite retaining fewer than 6% of bacterial and 3% of fungal OTUs, cores preserved composition patterns, reproduced site differentiation, retained most predicted functional information, maintained network structure, and were robust across a wide range of sampling efforts. Prevalence and abundance captured distinct ecological dimensions: consistently occurring taxa were not necessarily the most abundant, and vice versa. Our results show that core value therefore lies not in identifying the most important microorganisms, but in providing a reproducible, information -preserving representation of complex communities - though core taxa nonetheless retained fundamental ecological and functional roles.

ecology↗

bioRxiv2026

The illusion of diversity: sampling design drives conflicting estimates of soil bacterial richness

Soil microbial diversity is shaped by the spatial scale at which communities are sampled, yet standard sampling practices often homogenize samples, obscuring fine-scale spatial structure and diversity patterns. To better understand how sampling effort, spatial extent, and physical homogenization influence plot-level microbial richness estimates, we sampled 57 forest and grassland sites across three regions in Germany using a 14-core cross-transect design, and performed 16S rRNA gene metabarcoding. We simulated sampling efforts of 1-14 cores and a range of spatial extents, and compared diversity estimates to those from physically homogenized composite samples. Plot-level richness increased continuously with sampling effort and spatial extent, with no evidence of saturation. However, when sequencing depth was held constant, sampling completeness declined with increasing sampling effort, meaning that more diversity is not captured. Composite samples substantially underestimated plot-level richness and altered apparent diversity relationships between ecosystems; individual cores identified forests as richer than grasslands, whereas homogenized samples suggested the opposite relationship. These results demonstrate that sampling effort, spatial extent, and homogenization fundamentally shape soil microbial diversity estimates. Homogenized composite samples cannot substitute for individual cores when the goal is to reliably quantify plot-level richness or compare diversity across ecosystems.

ecology↗

bioRxiv2025

Resource economics of tree communities control soil food web multifunctionality in European forests

Plants affect terrestrial ecosystem functioning by performing the primary production that energetically sustains heterotrophic organisms1, and by shaping the microenvironment2. However, the influence of plant diversity and community composition on ecosystem functioning through their effects on energy flow into food webs has been little studied3,4, especially for soil food webs that channel most of the plant-derived energy1,5. Applying a food web energetics approach6,7, we show that the resource economics of dominant tree species control soil food web multifunctionality across European forests. Specifically, tree communities dominated by resource acquisitive species promoted faster rates of multiple soil trophic functions simultaneously than did those dominated by resource conservative species. This was primarily driven by their production of plant litter with higher nutritional quality and their warmer forest microclimate, leading to a higher metabolic activity of soil organisms8. Tree species mixing had rather weak and negative effects on soil food web multifunctionality, mostly due to a shift in the resource-based energy channeling from living plant fine roots to litter and a cooling effect on the forest microclimate. Tree diversity effects were largely outweighed by community compositional effects, which were of similar magnitude to the effects of biogeographic differences among locations. Our findings emphasize the importance of plant functional traits related to resource economics as drivers of plant community effects on soil food web functioning5,9 and highlight the consequences that climate-driven shifts in tree community composition could have for forest soil functioning.

ecology↗

bioRxiv2024

Old, deciduous stands support both high biodiversity and carbon storage in German forests

Ecosystems worldwide face threats related to human-driven degradation, climate change, and biodiversity loss. Addressing these challenges requires management strategies that combine biodiversity conservation with climate change mitigation. Here, we aimed to identify local-scale management actions that promote biodiversity at multiple trophic levels while also promoting carbon storage and sequestration. We combined data on the diversity of nine taxonomic groups (plants, birds, moths, Mollusca, soil fungi, active soil bacteria, Cercozoan and Endomyxan soil protists, Oomycotan soil protists, and nematodes), with above- and belowground carbon storage in 150 temperate forest plots in three regions of Germany. These were dominated by European beech, pine, spruce and oak. We investigated the relationships between multiple forest structure and management variables, biodiversity and carbon storage and sequestration in forest plots with different management types. Carbon storage was 32% higher in uneven-aged than even-aged forests and increased with mean tree diameter, while carbon sequestration in trees was 15% higher in even-aged than uneven-aged stands. Mean tree diameter was positively related to overall biodiversity, especially bird species richness and the richness of forest specialist birds. Oak and beech-dominated stands harboured higher biodiversity than stands dominated by conifers (especially Pine). One exception to this was the richness of plant species and forest specialist plants, which were highest in spruce plantations. Surprisingly, deadwood input did not significantly affect the diversity of any taxonomic group in this study. By showing that older forests with a high proportion of uneven-aged broad-leafed trees, or oak-dominated forests, can promote both biodiversity and carbon storage, our results could help inform sustainable local-scale forest management in Central Europe that promotes both biodiversity conservation and carbon storage. These findings can form the basis of further larger-scale studies investigating such relations at larger spatial scales to inform landscape-level recommendations for sustainable multifunctional forest management.

ecology↗

bioRxiv2023

A fast-slow trait continuum at the level of entire communities

Across the tree of life, organismal functional strategies form a continuum from slow-to fast-growing organisms, in response to common drivers such as resource availability and disturbance. However, the synchronization of these strategies at the entire community level is untested. We combine trait data for >2800 above-and belowground taxa from 14 trophic guilds spanning a disturbance and resource availability gradient in German grasslands. Most guilds consistently respond to these drivers through both direct and trophically-mediated effects, resulting in a slow-fast axis at the level of the entire community. Fast trait communities were also associated with faster rates of whole ecosystem functioning. These findings demonstrate that slow and fast strategies can be manifested at the level of whole ecosystems, opening new avenues of ecosystem-level functional classification.

ecology↗

bioRxiv2021

Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

genomics↗

bioRxiv2021

Distinct effects of host and neighbour tree identity on arbuscular and ectomycorrhizal fungi along a tree diversity gradient

Plant diversity and plant-related ecosystem functions have been in focus in biodiversity-ecosystem functioning studies. However, in this context, biotic interactions with mycorrhizal fungi have been understudied although they are crucial for plant-resource acquisition. We investigated the effects of tree species richness, tree mycorrhizal type on arbuscular (AMF) and ectomycorrhizal fungal (EMF) communities. We aimed to understand how dissimilarities in taxa composition and beta-diversity are related to target trees and neighbours of the same/different mycorrhizal type. We sampled a tree experiment with saplings ([~]7 years old), where tree species richness (monocultures, 2-species, and 4-species mixtures) and mycorrhizal type were manipulated. AMF and EMF richness significantly increased with increasing tree species richness. AMF richness of mixture plots resembled that of the sum of the respective monocultures, whereas EMF richness of mixture plots was lower compared to the sum of the respective monocultures. Specialisation scores revealed significantly more specialised AMF than EMF suggesting that, in contrast to previous studies, AMF were more specialised, whereas EMF were not. We further found that AMF communities were little driven by the surrounding trees, whereas EMF communities were. Our study revealed the drivers of mycorrhizal fungal communities and further highlights the distinct strategies of AMF and EMF.

ecology↗

bioRxiv2018

M3C: A Monte Carlo reference-based consensus clustering algorithm

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.

bioinformatics↗