Changing philosophies and tools for statistical inferences in behavioral ecology

By L. Garamszegi, S. Calhim, N. Dochtermann, G. Hegyi, P. Hurd, C. Jørgensen, Nobuyuki Kutsukake, M. Lajeunesse, Kimberly A. Pollard, H. Schielzeth and 2 more. 2009

131 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/1d54faed4bfa18687fac3ed58c1dc38e0de13ae6

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Sexing Birds Using Discriminant Function Analysis: A Critical Appraisal (2011, background): “In addition, all the 141 studies that we reviewed failed to report 95% CIs around discriminant rates, thus hampering comparisons among studies (Nakagawa and Cuthill 2007, Garamszegi et al. 2009), even when conducted on the same species.”
Quantifying variable importance in a multimodel inference framework (2016, unknown): “In recent years, an information-theoretic (IT) approach to model-based inference (Burnham & Anderson 2002) has become increasingly popular across the field of ecology (e.g. Johnson & Omland 2004; Whittingham et al. 2006; Garamszegi et al. 2009; Bradshaw & Brook 2010; Grueber et al. 2011).”
Issues in information theory-based statistical inference—a commentary from a frequentist’s perspective (2010, background): “2001) but regularly complement them with measures of effect sizes, point estimates and their standard errors or confidence intervals (e.g. Stoehr 1999; Anderson et al. 2001; Nakagawa and Cuthill 2007; Garamszegi et al. 2009).”
Repeatability for Gaussian and non‐Gaussian data: a practical guide for biologists (2010, background): “Since these comparisons rely on empirical data, the accuracy of measurements and hence the quality of the data should be of central concern to researchers (Garamszegi et al., 2009).”
Simple means to improve the interpretability of regression coefficients (2010, methodology): “Such models can be used to test null hypotheses about the significance of individual predictors and to estimate effect sizes and their standard errors (Nakagawa & Cuthill 2007; Stephens, Buskirk, & del Rio 2007; Garamszegi et al. 2009).”
Model averaging, missing data and multiple imputation: a case study for behavioural ecology (2010, background): “…Anderson 2002) have had far-reaching effects on the way that statistical analysis is conducted in ecology and evolutionary biology (Strimmer and Rambaut 2002; Rushton et al. 2004; Johnson and Omland 2004; Stephens et al. 2005, 2007; Lukacs et al. 2007; Garamszegi et al. 2009a, b; Garamszegi 2010).”
Ecologists overestimate the importance of predictor variables in model averaging: a plea for cautious interpretations (2014, background): “…(Richards 2005, Richards, Whittingham & Stephens 2011), others believe that, for sound interpretations, they should always be preferred to parameter estimates of the first-ranked model (Burnham & Anderson 2002, Garamszegi et al. 2009, Burnham, Anderson & Huyvaert 2011, Symonds & Moussalli 2011).”
Negative impact of traffic noise on avian reproductive success (2011, methodology): “We calculated for each explanatory factor the probability that it would be in the best approximating model using Akaike weights (see e.g. Whittingham et al. 2005; Garamszegi et al. 2009).”
A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection (2017, background): “…a number of methodological articles and books have been published to ease their application by empiricists (Burnham & Anderson, 2002; Garamszegi et al., 2009; Grueber, Nakagawa, Laws, & Jamieson, 2011; Behavioural Ecology and Sociobiology special issue “Model selection, multimodel…”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, methodology): “Model selection should be expected to be a widespread task in behavioural ecology because multiple hypotheses might be necessary to envisage for the interpretation of confounding effects and alternative mechanisms that can also mediate an association between the investigated traits (Towner and Luttb”
A meta-analysis of correlated behaviours with implications for behavioural syndromes: mean effect size, publication bias, phylogenetic effects and the role of mediator variables (2012, methodology): “In the collared flycatcher, Ficedula albicollis, the strength of certain behavioural correlations was different in magnitude in different samples (captured, non-captured) of individuals (Garamszegi et al. 2009b).”