Fachgebiet Ecology

Verena Dully

Ph. D. student


Building 14, Room 262
67663 Kaiserslautern

Postbox 3049 
67663 Kaiserslautern


Tel.: +49 631 205 3253
E-Mail: vdully(at)rhrk.uni-kl.de

Curriculum Vitae

2019M. Sc., University of Kaiserslautern
2017B. Sc., University of Kaiserslautern



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


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 student oral presentation

1. DNAqua International Conference, Evian, France


Award for an outstanding Master thesis

Kreissparkassen-Stiftung Kaiserslautern

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