Mixed Effects Models and Extensions in Ecology with R

By Aaron M. Christ. 2009

6,583 citations recorded by the source corpus.

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Subfields: Management Science and Operations Research

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In Management Science and Operations Research

Citations in the corpus

A statistical framework to explore ontogenetic growth variation among individuals and populations: a marine fish example (2015, methodology): “Best models were then reanalyzed using REML to produce unbiased parameter estimates (Zuur et al. 2009).”
Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats (2013, result): “As we sampled biomass several times from the same locations, the results we obtained could have been biased due to autocorrelation (Zuur et al. 2009).”
Using observation-level random effects to model overdispersion in count data in ecology and evolution (2014, methodology): “Following Zuur et al. (2009), the zero-inflated Poisson (ZIP) probability function is given by: f yi = 0 = πi + (1 − πi) × e −µi (9) f yi|yi > 0 = πi + (1 − πi) × µyi × e−µi yi!”
Responses of common and rare aliens and natives to nutrient availability and fluctuations (2017, methodology): “As the homoscedasticity assumption was violated, we also included variance structures modelling different variances per species in all models using the ‘varIdent’ function in the R package NLME (Pinheiro et al. 2015; see also Zuur et al. 2009).”
Continental-scale decreases in shorebird populations in Australia (2016, methodology): “Models were assessed by inspecting residual vs fitted value plots, and random effects plots (Zuur et al. 2009).”
Nitrogen deposition is negatively related to species richness and species composition of vascular plants and bryophytes in Swiss mountain grassland (2013, methodology): “…mean species ndicator values was calculated of all recorded plant species using he recalibrated values for the Swiss Flora (Landolt et al., 2010). ariance inflation factors (VIF) were used to assess which explanaory variables are collinear and should not be used in the analyses Zuur et al., 2009).”
Rapid ecological replacement of a native bumble bee by invasive species. (2013, methodology): “The value of the ACF at different time intervals gives an indication of whether there is any autocorrelation in the data (Zuur et al. 2009).”
Quantifying variation among garden plants in attractiveness to bees and other flower‐visiting insects (2014, methodology): “Further, to account for temporal autocorrelation within the data recorded at approximately weekly intervals across the season, the AR-1 correlation structure was added to the models (Zuur et al. 2009).”
Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists (2014, methodology): “Using thresholds of 3 for the variance inflation factors and 0.65 for the Pearson correlation coefficients, none of the explanatory variables were found to covary as strongly with other variables to prohibit their parallel use in models (Zuur et al., 2010, 2009).”
Population-specific home ranges and migration timing of Pacific Arctic beluga whales (Delphinapterus leucas) (2014, methodology): “In each case, model specification followed procedures outlined in Zuur et al. (2009) for model selection with fixed (month and sex, in this case) and random effects (individual whales).”
Scale‐dependent relationships between tree species richness and ecosystem function in forests (2013, methodology): “We used a maximum likelihood method rather than a restricted maximum 281 likelihood method because we wanted to compare the separate models with Akaike 282 Information Criterion (AIC) and because we did not need to estimate variance 283 components (Zuur et al. 2009).”
Impacts of agricultural intensification on bird communities: New insights from a multi-level and multi-facet approach of biodiversity (2016, methodology): “Then, to determine the importance of fixed effects while limiting uncertainty from model selection, a model averaging procedure was applied (Wood, 2006) (R package {MuMIn}; Barton, 2015) on the models fitted with the Maximum Likelihood method (which allows comparing fixed-effects structure of mixed ”