Matthew Symonds

Nature and Landscape Conservation · 2009–2014

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Nature and Landscape Conservation — 34 citations into it; #601 of 6,941 by trust here, #2,763 by citations
• Statistics and Probability — 24 citations into it
• Ecology — 22 citations into it
• Ecological Modeling — 21 citations into it; #2,421 of 2,530 by trust here, #1,715 by citations
• Ecology, Evolution, Behavior and Systematics — 16 citations into it; #430 of 6,648 by trust here, #2,363 by citations
• Genetics — 16 citations into it
• Paleontology — 16 citations into it
• Artificial Intelligence — 11 citations into it

Whom Matthew Symonds trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. S. Richards
2. L. Garamszegi
3. K. Burnham
4. Hirotogu Akaike
5. Jerald B. Johnson
6. Shinichi Nakagawa
7. David R. Anderson
8. P. Lukacs
9. R. Freckleton
10. Mark J. Whittingham

Who trusts Matthew Symonds overall

1. Felix D. Schönbrodt
2. M. Galipaud
3. Roger Mundry
4. Shinichi Nakagawa
5. L. Garamszegi
6. Xingli Giam
7. April L. Hamblin
8. Mark A. F. Gillingham
9. C. Grueber
10. Zhiyong Liu

Papers from Matthew Symonds's perspective

Ordered by how strongly the researchers Matthew Symonds trusts endorse each paper. Same corpus, a different order for every researcher.

1. Why do we still use stepwise modelling in ecology and behaviour? (Journal of Animal Ecology)
2. Null Hypothesis Testing: Problems, Prevalence, and an Alternative
3. Effect size, confidence interval and statistical significance: a practical guide for biologists (Biological Reviews of The Cambridge Philosophical Society)
4. Missing inaction: the dangers of ignoring missing data. (Trends in Ecology & Evolution)
5. Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. (Ecology Letters)
6. Ecological Models and Data in R
7. Model selection and multimodel inference : a practical information-theoretic approach
8. Kullback-Leibler information as a basis for strong inference in ecological studies
9. Information Theory and an Extension of the Maximum Likelihood Principle
10. Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (Behavioral Ecology and Sociobiology)

Their papers

• A Primer on Phylogenetic Generalised Least Squares (2014) — 391 citations
• A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion (2010) — 2,414 citations
• Changing philosophies and tools for statistical inferences in behavioral ecology (2009) — 131 citations