Author

M. J. Silk

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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Animal Behavior & Cognition· 4 Ecology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Research Focus:cell type annotation· 2 Research Focus:host-pathogen & immune response· 2 Research Focus:clustering· 1 Assay:metagenomics· 1 Organism:mouse· 1 Organism:multiple species· 1 Approach:simulation· 1

Frequent Co-Authors

D. P. Croft· 2 shared D. W. Franks· 2 shared J. C. Evans· 2 shared M. N. Weiss· 2 shared A. Highlands· 1 shared A. McCubbin· 1 shared A. McDevitt· 1 shared B. König· 1 shared

Activity by Year

2020-2021 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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2 shared papersAnimal Behavior & Cognition

D. P. Croft

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Author profile →

5 papers

Recent·Most cited

Group size and modularity interact to shape the spread of infection and information through animal societies

J. C. Evans, D. J. Hodgson, N. J. Boogert, M. J. Silk

This study uses simulations to show that modular social networks can promote information spread over disease spread, especially in small groups and fragmented networks.

bioRxiv · May 1, 2021Animal Behavior & Cognition

Quantifying the influence of space on social group structure

J. C. Evans, J. I. Liechti, M. J. Silk, S. Bonhoeffer, B. König

The paper introduces a method to quantify the relationship between social and spatial structure in animal groups and applies it to both simulated and empirical data.

bioRxiv · Dec 10, 2020 · PreprintAnimal Behavior & Cognition

Host-associated fungal communities are determined by host phylogeny and exhibit widespread associations with the bacterial microbiome

X. A. Harrison, A. McDevitt, J. C. Dunn, S. Griffiths, C. Benvenuto, R. Birtles, J. P. Boubli, K. Bown, C. Bridson, D. Brooks, S. S. Browett, R. F. Carden, J. Chantrey, F. Clever, I. Coscia, , R. E. Antwis

The paper shows fungal communities in hosts are shaped by host phylogeny and interact with bacterial microbiomes across 49 species.

bioRxiv · Jul 7, 2020 · PreprintEcology

Calculating effect sizes in animal social network analysis

D. W. Franks, M. N. Weiss, M. J. Silk, R. Perryman, D. P. Croft

The paper demonstrates that relying solely on permutations to control for confounding variables in social network analysis can lead to biased and uninterpretable effect sizes, and recommends controlling for confounders in statistical models.

bioRxiv · May 8, 2020Animal Behavior & Cognition

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

M. N. Weiss, D. W. Franks, L. J. N. Brent, S. Ellis, M. J. Silk, D. P. Croft

The paper shows that common datastream permutations are not suitable for testing regression models in animal social networks due to high false-positive rates.

bioRxiv · Apr 29, 2020Animal Behavior & Cognition