Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference <s> Averaged prediction should be accompanied by uncertainty estimates <s> We combine data from separate ring recovery and survey studies to provide indices of estimated abundance for the UK lapwing "Vanellus vanellus" population. Using a descriptive state space model, we demonstrate the observed decline in population size in relation to directly interpretable parameters describing the demographic characteristics of the population. The Bayesian approach readily provides information that is directly relevant to the conservation of this important bird species. The method proposed extends previous work in this area in several ways. Restrictive normality assumptions that have traditionally been imposed are removed, in addition to the assumption of constant measurement error by using information relating to the index variability across time to account fully for this source of uncertainty within the model. We also provide model-averaged inference to help to inform management policy and uses. Copyright (c) 2008 Royal Statistical Society. <s> BIB001 </s> Model averaging in ecology: a review of Bayesian, information-theoretic and tactical approaches for predictive inference <s> Averaged prediction should be accompanied by uncertainty estimates <s> Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal— for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate ‘‘good’’ and ‘‘bad’’ models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models. <s> BIB002
Just like any other statistical approach, model averaging can be used wrongly. Focusing entirely on the predictions, rather than their uncertainty, can be misleading, BIB002 showed this for combining precipitation predictions: spatial heterogeneity cancelled out across models, giving the erroneous impression of little change when in fact all models predict large changes (albeit in different regions). Similarly, BIB001 found that averaging parameters from two competing models led to no effect of two hypothesized impacts, although in both models a (different) driver was very influential. We thus strongly encourage including at least model-averaged confidence intervals alongside any prediction, possibly in addition to the individual model predictions, to prevent erroneous interpretation of averaged predictions. Also, more attention should be paid to the full model. It has many desirable properties (unbiased parameter estimates, very good coverage), but suffers from violation of the parsimony principle ("Occam's razor") and requires more consideration in which form covariates should be fit. Its larger prediction error, compared to the over-optimistic single-best partial model, is the reason for correct confidence intervals.