Model selection in ecology and evolution.

By Jerald B. Johnson, Kristian S. Omland. Trends in Ecology & Evolution, 2004

3,836 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/e8722d1ccaeb862a9c893a8c46802fe77ea0697b

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Citations in the corpus

The Ecological Niche: History and Recent Controversies (2015, positive): “…also invites us to prefer, rather than a test of null hypothesis, an approach of model selection, in which competing hypotheses are confronted simultaneously with data and classifi ed according to criteria such as likelihood, parsimony, etc. (see Johnson and Omland 2004 ; Clark et al. 2007 : 656).”
Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California (2015, methodology): “and used likelihood-ratio tests to compare nested models to identify the best model (Johnson and Omland, 2004).”
A guide to Bayesian model selection for ecologists (2015, methodology): “Model selection and Bayesian statistics have become increasingly important tools in the field of ecology (Johnson and Omland 2004, Clark 2005, Cressie et al. 2009, Hobbs 2009).”
Estimation of Age-Depth Relationships (2012, background): “Adding the error associated with model selection (Johnson and Omland 2004) to other errors would be a major step forward.”
Body size variation in insects: a macroecological perspective (2010, methodology): “Moreover, a strong inference approach, or one using model selection based on information theory (see e.g. Johnson & Omland, 2004; Angilletta, 2009) sorely needs to be applied to the question of the mechanism underlying intraspecific clines in body size.”
Melanin-based colorations signal strategies to cope with poor and rich environments (2008, methodology): “We performed preliminary analyses with the coloration of biological father, biological mother, and mean coloration of both parents as dependent variables in separate models, and we present the best model based on AIC criterion (Johnson and Omland 2004).”
Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, methodology): “We built two GAM models: one with independent variables and the other incorporating interactions between variables; the AIC value of each model was compared, and the model with the smaller AIC was used as the final model (Johnson and Omland, 2004).”
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014, background): “Finding the appropriate model complexity across this so-called bias-variance trade-off is a crucial topic in spatial statistics (Burnham & Anderson, 2002; Johnson & Omland, 2004).”
Seed dispersal of the non-native invasive tree Ailanthus altissima into contrasting environments (2007, methodology): “We used AICc which applies a correction that adjusts for small sample size (Sugiura 1978; Akaike 1992; Johnson and Omland 2004).”
Partial least squares regression as an alternative to current regression methods used in ecology (2009, background): “‘Best subsets’ is another way to select the most relevant variables in our set of predictors (Johnson and Omland 2004, Whittingham et al. 2006).”
Feeding complementarity versus redundancy among herbivorous fishes on a Caribbean reef (2011, positive): “…Akaike weights were used as a measure of relative importance to evaluate the strength of correlation between each algal group and herbivore feeding (i.e., benthic groups with higher summed Akaike weights provided more information to explain herbivore feeding patterns) (Johnson and Omland 2004).”