Author

D. N. Karger

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· 6

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Research Focus:ecology & biodiversity· 6 Organism:plant (other)· 4 Assay:metagenomics· 3 Approach:statistical model· 3 Approach:machine learning (classical)· 2 Approach:deep learning· 1 Organism:multiple species· 1

Frequent Co-Authors

N. E. Zimmermann· 4 shared D. Zurell· 3 shared P. Brun· 2 shared P. Descombes· 2 shared A. Kuprijanov· 1 shared A. Nowak· 1 shared A. Patzelt· 1 shared A. Stein· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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4 shared papersEcology

N. E. Zimmermann

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6 papers

Recent·Most cited

Megafauna diversity and functional declines in Europe from the Last Interglacial (LIG) to the present

M. Davoli, S. Monsarrat, R. Pedersen, P. Scussolini, D. N. Karger, S. Normand, J.-C. Svenning

This study estimates European megafauna diversity and functional declines from the Last Interglacial to the present, finding severe defaunation driven by human impact rather than climate change.

bioRxiv · Jun 12, 2023Ecology

Rank-based deep learning from citizen-science data to model plant communities

P. Brun, D. N. Karger, D. Zurell, P. Descombes, L. de Witte, R. de Lutio, J. D. Wegner, N. E. Zimmermann

The paper uses deep learning to model plant species distributions and community composition from citizen-science data, showing improved accuracy over traditional methods.

bioRxiv · May 30, 2023 · PreprintEcology

Machine learning improves global models of plant diversity

L. Cai, H. Kreft, A. Taylor, P. Denelle, J. Schrader, F. Essl, M. v. Kleunen, J. Pergl, P. Pysek, A. Stein, M. Winter, J. F. Barcelona, N. Fuentes, I. Inderjit, D. N. Karger, , P. Weigelt

The paper uses machine learning and statistical models to predict global plant diversity patterns based on environmental data, achieving high accuracy for species and phylogenetic richness.

bioRxiv · Apr 8, 2022Ecology

Strategies for sampling pseudo-absences for species distribution models in complex mountainous terrain

P. Descombes, Y. Chauvier, P. Brun, D. Righetti, R. O. Wuest, D. N. Karger, D. Zurell, N. E. Zimmermann

This study evaluates pseudo-absence strategies for species distribution models in complex mountainous terrain, finding that generic and species-specific approaches can produce robust predictions, with environmental stratification improving performance in biased datasets.

bioRxiv · Mar 24, 2022 · PreprintEcology

Interannual climate variability data improves niche estimates in species distribution models

D. N. Karger, B. Saladin, R. O. Wueest, C. H. Graham, D. Zurell, L. Mo, N. E. Zimmermann

This study shows that incorporating interannual climate variability improves species distribution model predictions, especially for ectotherms.

bioRxiv · Aug 30, 2021 · PreprintEcology

Mapping tree species for restoration potential resilient to climate change

N. van Tiel, L. Lyu, F. Fopp, J. van den Hoogen, D. N. Karger, N. E. Zimmermann, T. W. Crowther, L. Pellissier

This paper maps tree species' ecological suitability globally using machine learning to guide climate-resilient forest restoration.

bioRxiv · Jun 4, 2021 · PreprintEcology