Verena Rubel



Building 14, Room 262
67663 Kaiserslautern

Postbox 3049 
67663 Kaiserslautern


Tel.: +49 631 205 3253
E-Mail: verena.rubel(at)

Curriculum Vitae

2022Ph.D., University of Kaiserslautern
2019M. Sc., University of Kaiserslautern
2017B. Sc., University of Kaiserslautern



Leontidou K, Abad-Recio IL, Rubel V, Filker S, Däumer M, Thielen A, Lanzén A & Stoeck T

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection. 

Environmental Microbiology, doi: 10.1111/1462-2920.16530


Leontidou C, Rubel V & Stoeck T

Comparing quantile regression spline analyses and supervised machine learning for environmental quality assessment at coastal marine aquaculture installations.

PeerJ 11:e15425, doi: 10.7717/peerj.15425


Dully V, Rech G, Wilding TA, Lanzén A, MacKichan K, Berrill I & Stoeck T

Comparing sediment preservation methods for genomic biomonitoring of coastal marine ecosystems.

Marine Pollution Bulletin 173: 113129, doi: 10.1016/j.marpolbul.2021.113129


Dully V, Wilding TA, Mühlhaus T & Stoeck T

Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning

Computational and Structural Biotechnolgy Journal 19: 2256-2268, doi: 10.1016/j.csbj.2021.04.005


Frühe L, Dully V, Forster D, Keeley NB, Laroche O, Pochon X, Robinson SMC, Wilding TA & Stoeck T

Global trends of benthic bacterial diversity and community composition along organic enrichment gradients of salmon farms

Frontiers in Microbiology (section Aquatic Microbiology) 12: 637811, doi: 10.3389/fmicb.2021.637811




Dully V, Balliet H, Frühe L, Däumer M, Thielen A, Gallie S, Berril I & Stoeck T

Robustness, sensitivity and reproducibility of eDNA metabarcoding as an environmental biomonitoring tool in coastal salmon aquaculture - An inter-laboratory study.

Ecological Indicators, doi: 10.1016/j.ecolind.2020.107049


Frühe L, Cordier T, Dully V, Breiner HW, Lentendu G, Pawlowski J, Martins C, Wilding TA & Stoeck T

Supervised machine learning is superior to indicator value inference in monitoring the environmental impacts of salmon aquaculture using eDNA metabarcodes.

Molecular Ecology, doi: 10.1111/mec15434



Stoeck T, Pan H, Dully V, Forster D & Jung T

Towards an eDNA metabarcode-based performance indicator for full-scale municipal wastewater treatment plants.

Water Research, doi: 10.1016/j.watres.2019.07.051


The infancy of MANIDE: Machine learning driven Assessment of polymetallic Nodule mining Impacts on Deep-sea Ecosystems (poster)

Helmholtz Data Science Symposium 2024, Bremen, Germany


Current state-of-the-art in eDNA-based benthic biomonitoring of salmon aquaculture installations (poster & flash talk)

Aquaculture Europe 2023, Vienna, Austria


Translating Illumina®-derived massive sequence data into biologically meaningful information for biomonitoring via supervised machine learning (invited talk)

INTECOL (INTECOL CONFERENCE - Frontiers in Ecology: Nature and Society), Workshop on environmental DNA for biodiversity monitoring and conservation, Geneva, Switzerland


Predicting classifications in marine biomonitoring with supervised machine learning: how much data is required?

1. DNAqua International Conference, Evian, France


Towards a standard protocol in coastal aquaculture biomonitoring: an interlaboratory study to assess reproducibility of the wet lab protocol and of Illumina sequencing (poster)

1. DNAqua International Conference, Evian, France


Inter-laboratory reproducibility of machine learning predictions in applied environmental coastal monitoring

Faculty meeting, University of Kaiserslautern


Award for the best poster

Aquaculture Europe 2023, Vienna, Austria


Award for the best student oral presentation

1. DNAqua International Conference, Evian, France


Award for an outstanding Master thesis

Kreissparkassen-Stiftung Kaiserslautern