Author

C. M. Tonra

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 Zoology· 1

Signature Assays, Organisms & Methods

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Organism:multiple species· 3 Approach:statistical model· 3 Research Focus:ecology & biodiversity· 1 Research Focus:host-pathogen & immune response· 1 Assay:metagenomics· 1

Frequent Co-Authors

A. A. Skinner· 1 shared A. H. Moeller· 1 shared A. Hedenström· 1 shared A. K. Scharf· 1 shared A. M. Korpach· 1 shared A. Roulin· 1 shared A. Vitz· 1 shared A.-L. Harrison· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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1 shared paperZoology

A. A. Skinner

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Assessing an age-old ecogeographical rule in nightjars across the full annual cycle

A. A. Skinner, A. M. Korpach, S. Akesson, M. Bakermans, T. J. Benson, R. M. Brigham, G. J. Conway, C. M. Davy, R. Evens, K. C. Fraser, A. Hedenström, I. G. Henderson, J. Honkala, L. B. Jacobsen, G. Norevik, , E. Knight

This study assesses Bergmanns rule in nightjars across their annual cycle, finding some species follow the rule while others do not, with geographic variables strongly influencing body size.

bioRxiv · Aug 30, 2023Zoology

Convergent remodeling of the gut microbiome is associated with host energetic condition over long-distance migration

B. K. Trevelline, D. Sprockett, W. V. DeLuca, C. R. Andreadis, A. H. Moeller, C. M. Tonra

This study shows that the gut microbiome of Blackpoll Warblers changes during migration, with increased Proteobacteria and reduced diversity, linked to host energy needs.

bioRxiv · Nov 30, 2022Ecology

A comprehensive framework for handling location error in animal tracking data

C. H. Fleming, J. Drescher-Lehman, M. J. Noonan, T. S. B. Akre, D. J. Brown, M. M. Cochrane, N. Dejid, V. DeNicola, C. S. DePerno, J. N. Dunlop, N. P. Gould, A.-L. Harrison, J. Hollins, H. Ishii, Y. Kaneko, , J. M. Calabrese

This paper introduces a framework for handling location error in animal tracking data, showing that error-model selection improves analysis accuracy across various tracking devices and species.

bioRxiv · Jun 12, 2020 · PreprintEcology