A guide to Bayesian model selection for ecologists

By Mevin B. Hooten, N. Hobbs. 2015

805 citations recorded by the source corpus.

Subfields: Ecological Modeling, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/250356ebcc521ba77bd97dd39539077618a9620a

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In Ecological Modeling

In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

A statistical framework to explore ontogenetic growth variation among individuals and populations: a marine fish example (2015, methodology): “Other random-effect model selection options include standard likelihood-based hypothesis tests (conservative; Bolker et al. 2009) and out of sample or crossvalidation techniques (Hooten and Hobbs 2015).”
The priority of prediction in ecological understanding (2017, background): “These two assertions should guide all scientific disciplines including ecology, but there is little evidence in the recent ecological literature that prediction is playing anything but a peripheral role (Hooten and Hobbs 2015).”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, methodology): “…have been interpreted as Bayesian model probabilities (Burnham and Anderson 2002:75, Link and Barker 2006), assuming a specific, model complexity and sample size-dependent, “savvy prior” (Burnham and Anderson 2002:302, see also Hooten and Hobbs 2015:16, for reformulation as regularization prior).”
Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities (2017, methodology): “Books by MacKenzie et al. (2006) and Royle & Dorazio (2008) provide statistical description of the models, and Hooten & Hobbs (2015) review Bayesian model selection methods using an occupancy-detection model as example.”
Synthesizing multiple data types for biological conservation using integrated population models (2018, background): “Model fit is notoriously difficult to assess in hierarchical models and remains an area of active research (Hooten and Hobbs, 2015).”
A multispecies occupancy model for two or more interacting species (2016, methodology): “Finding a parsimonious model in such situations can prove challenging, but can be aided with model-based variable selec- tion techniques such as indicator variable selection and reversible jump MCMC (reviewed in O’Hara & Sillanp€a€a 2009 and Hooten & Hobbs 2015).”
MMI: Multimodel inference or models with management implications? (2015, methodology): “A variety of other shrinkage methods (e.g., penalized likelihood, ridge regression, LASSO) have been developed to improve model performance (e.g., reviews by van Houwelingen 2001, Hooten and Hobbs 2015).”
Thermal biology of mosquito-borne disease (2019, methodology): “Connecting mechanistic model predictions to independent field data – model validation – is critical for comparing different models and for assessing their applicability in the field (Hooten & Hobbs 2015).”