Lynda Donaldson

Nature and Landscape Conservation · 2018

A researcher in the Science corpus: 1 paper; trusts 11 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Nature and Landscape Conservation — 34 citations into it; #6,811 of 6,941 by trust here, #5,573 by citations
• Statistics and Probability — 26 citations into it
• Ecological Modeling — 24 citations into it; #2,444 of 2,530 by trust here, #2,047 by citations
• Ecology, Evolution, Behavior and Systematics — 16 citations into it
• Artificial Intelligence — 15 citations into it
• Ecology — 11 citations into it; #10,493 of 10,717 by trust here, #8,686 by citations

Whom Lynda Donaldson trusts overall

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

1. Shinichi Nakagawa
2. X. Harrison
3. Andrew Gelman
4. R. Freckleton
5. A. Zuur
6. H. Schielzeth
7. Joseph Hilbe
8. Aaron M. Christ
9. Anthony R. Ives
10. K. Burnham

Who trusts Lynda Donaldson overall

1. Harlan Campbell

Papers from Lynda Donaldson's perspective

Ordered by how strongly the researchers Lynda Donaldson 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. Experimental Design and Data Analysis for Biologists
3. Conditional Akaike information for mixed-effects models
4. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion (Behavioral Ecology and Sociobiology)
5. Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error (Behavioral Ecology and Sociobiology)
6. Generalized linear mixed models: a practical guide for ecology and evolution. (Trends in Ecology & Evolution)
7. R: A language and environment for statistical computing.
8. Model selection in ecology and evolution. (Trends in Ecology & Evolution)
9. Missing inaction: the dangers of ignoring missing data. (Trends in Ecology & Evolution)
10. Simple means to improve the interpretability of regression coefficients

Their papers

• A brief introduction to mixed effects modelling and multi-model inference in ecology (2018) — 1,831 citations