Author

T. Opitz

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· 3 Bioengineering· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Research Focus:ecology & biodiversity· 2 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

E. Gabriel· 2 shared J. Papaix· 2 shared P. Zamberletti· 2 shared C. Duchamp· 1 shared E. Rigolot· 1 shared F. Pimont· 1 shared H. Fargeon· 1 shared J. Ruffault· 1 shared

Activity by Year

2020-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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2 shared papersEcology

E. Gabriel

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Author profile →

4 papers

Recent·Most cited

Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France

O. Grente, T. Opitz, C. Duchamp, N. Drouet-Hoguet, S. Chamaille-Jammes, O. Gimenez

This study assesses the effectiveness of wolf lethal control in reducing livestock attacks in France using a spatio-temporal analysis framework.

bioRxiv · Jul 18, 2024Ecology

Spatio-temporal point processes as meta-models for population dynamics in heterogeneous landscapes

P. Zamberletti, J. Papaix, E. Gabriel, T. Opitz

The paper develops a meta-modelling approach using spatio-temporal point processes to analyze population dynamics in heterogeneous agricultural landscapes, revealing local and global driver effects on pest-predator systems.

bioRxiv · Jun 4, 2021 · PreprintEcology

More pests but less treatments: ambivalent effect of landscape complexity on Conservation Biological Control

P. Zamberletti, K. Sabir, T. Opitz, O. Bonnefon, E. Gabriel, J. Papaix

The paper models how landscape complexity affects predator-prey dynamics and pesticide use in agricultural systems, finding that habitat structure influences pest control effectiveness.

bioRxiv · Mar 19, 2021Ecology

Prediction of regional wildfire activity with a probabilistic Bayesian framework

F. Pimont, H. Fargeon, T. Opitz, J. Ruffault, R. Barbero, N. Martin-StPaul, E. Rigolot, M. Riviere, J.-L. Dupuy

This paper develops a Bayesian probabilistic model to predict regional wildfire activity using fire weather index and forest area data, showing improved accuracy at weekly scales but limitations in predicting large fires during extreme events.

bioRxiv · May 20, 2020Bioengineering