This review discusses the interface between two of the most important types of interactions between species, interspecific competition and predation. Predation has been claimed to increase, decrease, or have little effect on, the strength, impact or importance of interspecific competition. There is confusion about both the meaning of these terms and the likelihood of, and conditions required for, each of these outcomes. In this article we distinguish among three measures of the influence of predation on competitive outcomes: short-term per capita consumption or growth rates, long-term changes in density, and the probability of competitive coexistence. We then outline various theoretical mechanisms that can lead to qualitatively distinct effects of predators. The qualitative effect of predators can depend both on the mechanism of competition and on the definition of competitive strength/impact. In assessing the empirical literature, we ask: (1) What definitions of competitive strength/impact have been assumed? (2) Does strong evidence exist to support one or more of the possible mechanisms that can produce a given outcome? (3) Do biases in the choice of organism or manipulation exist, and are they likely to have influenced the conclusions reached? We conclude by discussing several unanswered questions, and espouse a stronger interchange between empirical and theoretical approaches to this important question.

1. The ability to identify key ecological processes is important when solving applied problems. Increasingly, ecologists are adopting Akaike's information criterion (AIC) as a metric to help them assess and select among multiple process-based ecological models. Surprisingly, however, it is still unclear how best to incorporate AIC into the selection process in order to address the trade-off between maximizing the probability of retaining the most parsimonious model while minimizing the number of models retained. 2. Ecological count data are often observed to be overdispersed with respect to best-fitting models. Overdispersion is problematic when performing an AIC analysis, as it can result in selection of overly complex models which can lead to poor ecological inference. This paper describes and illustrates two approaches that deal effectively with overdispersion. The first approach involves modelling the causes of overdispersion implicitly using compound probability distributions. The second approach ignores the causes of overdispersion and uses quasi-AIC (QAIC) as a metric for model parsimony. 3.Simulations and a novel method that identifies the most parsimonious model are used to demonstrate the utility of the two overdispersion approaches within the context of two ecological examples. The first example addresses binomial data obtained from a study of fish survival (as related to habitat structure) and the second example addresses Poisson data obtained from a study of flower visitation by nectarivores. 4. Applying either overdispersion approach reduces the chance of selecting overly complex models, and both approaches result in very similar ecological inference. In addition, inference can be made more reliable by incorporating model nesting into the selection process (i.e. identifying which models are special cases of others), as it reduces the number of models selected without significantly reducing the probability of retaining the most parsimonious models. 5. Synthesis and applications . When data are overdispersed, inference can be improved by either modelling the causes of overdispersion or applying QAIC as a metric for model parsimony. Inference can also be improved by adopting a model filtering procedure based on how models are nested. The general simulation approach presented in this paper for identifying the most parsimonious model, as defined by information theory, should help to improve our understanding of the reliability of model selection when using AIC, and help the development of better selection rules.

Ecologists are increasingly applying model selection to their data analyses, primarily to compare regression models. Model selection can also be used to compare mechanistic models derived from ecological theory, thereby providing a formal framework for testing the theory. The Akaike Information Criterion (AIC) is the most commonly adopted criterion used to compare models; however, its performance in general is not very well known. The best model according to AIC has the smallest expected Kullback‐Leibler (K‐L) distance, which is an information‐theoretic measure of the difference between a model and the truth. I review the theory behind AIC and demonstrate how it can be used to test ecological theory by considering two example studies of foraging, motivated by simple foraging theory. I present plausible truths for the two studies, and models that can be fit to the foraging data. K‐L distances are calculated for simulated studies, which provide an appropriate test of AIC. Results support the use of a commonly adopted rule of thumb for selecting models based on AIC differences. However, AICc, a corrected version of AIC commonly used to reduce model selection bias, showed no clear improvement, and model averaging, a technique to reduce model prediction bias, gave mixed results.

No abstract

These data support a model in which Rsk1 transduces the mammalian MEK-MAP kinase signal in part by phosphorylating Bad.