M. Galipaud

Nature and Landscape Conservation · 2014–2017

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Nature and Landscape Conservation — 23 citations into it; #5,314 of 6,941 by trust here, #5,720 by citations
• Ecological Modeling — 17 citations into it; #1,904 of 2,530 by trust here, #2,101 by citations
• Ecology, Evolution, Behavior and Systematics — 16 citations into it
• Statistics and Probability — 15 citations into it
• Artificial Intelligence — 7 citations into it
• Ecology — 7 citations into it

Whom M. Galipaud trusts overall

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

1. K. Burnham
2. S. Richards
3. David R. Anderson
4. Shinichi Nakagawa
5. Matthew Symonds
6. H. Schielzeth
7. Brian S. Cade
8. Edward I. George
9. N. Dochtermann
10. Philip Stephens

Who trusts M. Galipaud overall

1. Xingli Giam
2. Carsten F. Dormann
3. Julian D. Olden
4. Mark A. F. Gillingham
5. Jochen Fründ
6. X. Harrison

Papers from M. Galipaud's perspective

Ordered by how strongly the researchers M. Galipaud 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. Model selection and multimodel inference : a practical information-theoretic approach
3. Information Theory and an Extension of the Maximum Likelihood Principle
4. Information theory and hypothesis testing: a call for pluralism
5. Model selection in ecology and evolution. (Trends in Ecology & Evolution)
6. Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error (Behavioral Ecology and Sociobiology)
7. Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (Behavioral Ecology and Sociobiology)
8. TESTING ECOLOGICAL THEORY USING THE INFORMATION‐THEORETIC APPROACH: EXAMPLES AND CAUTIONARY RESULTS
9. Effect size, confidence interval and statistical significance: a practical guide for biologists (Biological Reviews of The Cambridge Philosophical Society)
10. Understanding AIC and BIC in Model Selection

Their papers

• A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection (2017) — 81 citations
• Ecologists overestimate the importance of predictor variables in model averaging: a plea for cautious interpretations (2014) — 147 citations