Author

N. E. Zimmermann

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· 7 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:ecology & biodiversity· 7 Approach:statistical model· 6 Assay:metagenomics· 5 Organism:plant (other)· 4 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

D. N. Karger· 4 shared C. H. Graham· 3 shared D. Zurell· 3 shared L. Pellissier· 3 shared D. Righetti· 2 shared P. Brun· 2 shared P. Descombes· 2 shared A. Aebli· 1 shared

Activity by Year

2021-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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4 shared papersEcology

D. N. Karger

Mutual co-authors in cluster

Author profile →

8 papers

Recent·Most cited

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

Mapping global marine biodiversity under sparse data conditions

D. Righetti, M. Vogt, N. Gruber, N. E. Zimmermann

The paper uses species distribution models to predict marine biodiversity under sparse data conditions, finding that optimized models can explain up to 91% of species richness variations.

bioRxiv · Feb 28, 2023 · PreprintEcology

Precipitation is the main axis of tropical phylogenetic turnover across space and time

J. J. Ringelberg, E. J. M. Koenen, B. Sauter, A. Aebli, J. G. Rando, J. R. Iganci, L. P. de Queiroz, D. J. Murphy, M. Gaudeul, A. Bruneau, M. Luckow, G. P. Lewis, J. T. Miller, M. F. Simon, L. S. B. Jordao, , C. E. Hughes

The paper shows that precipitation gradients drive phylogenetic turnover in Mimosoid legumes across tropical regions, with 95% of speciation occurring within a precipitation niche.

bioRxiv · May 27, 2022Evolutionary Biology

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

Blue and green food webs respond differently to elevation and land use

H.-C. Ho, J. Brodersen, M. M. Gossner, C. H. Graham, S. Kaeser, M. Reji Chacko, O. Seehausen, N. E. Zimmermann, L. Pellissier, F. Altermatt

The paper compares structural and ecological differences between blue and green food webs across elevation and land use in Switzerland, finding divergent responses to environmental gradients.

bioRxiv · Dec 22, 2021Ecology

Global plant-frugivore trait matching is shaped by climate and biogeographic history

I. R. McFadden, S. A. Fritz, N. E. Zimmermann, L. Pellissier, W. D. Kissling, J. A. Tobias, M. Schleuning, C. H. Graham

The paper examines global trait matching between frugivorous birds and palms, finding it influenced by climate, biogeographic history, and species richness.

bioRxiv · Dec 17, 2021Ecology

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