Author

M. Grung

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Ecology· 2 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Approach:Bayesian inference· 3 Research Focus:ecology & biodiversity· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

K. E. Tollefsen· 3 shared S. Mentzel· 3 shared J. Moe· 2 shared M. Stenrod· 2 shared A. Rico· 1 shared C. Martinez-Megias· 1 shared K. Petersen· 1 shared P. Van den Brink· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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3 shared papersEcology

K. E. Tollefsen

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3 papers

Recent·Most cited

Using a Bayesian network model to predict effects of pesticides on aquatic community endpoints in a rice field - A southern European case study

S. Mentzel, C. Martinez-Megias, M. Grung, A. Rico, K. E. Tollefsen, P. Van den Brink, S. J. Moe

This study uses a Bayesian network model to predict pesticide effects on aquatic ecosystems in rice fields, finding azoxystrobin and MCPA pose higher community risk than acetamiprid.

bioRxiv · Oct 19, 2022 · PreprintEcology

Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using Bayesian network

S. Mentzel, M. Grung, R. Holten, K. E. Tollefsen, M. Stenrod, J. Moe

This paper uses Bayesian networks to assess pesticide risk under future climate and agricultural scenarios, finding that increased pesticide use, not climate variables, drives higher risk.

bioRxiv · May 30, 2022Ecology

Development of a Bayesian network for probabilistic risk assessment of pesticides

S. Mentzel, M. Grung, K. E. Tollefsen, M. Stenrod, K. Petersen, J. Moe

This paper develops a Bayesian network for probabilistic risk assessment of pesticides, demonstrating its application to azoxystrobin, metribuzin, and imidacloprid.

bioRxiv · May 20, 2021Scientific Communication & Education