Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

Sexual Risk Recognition Deficits: The Role of Prior Victimization and Emotion Dysregulation

Recent theoretical writings suggest that the ineffective regulation of negative emotional states may reduce the ability of women to detect and respond effectively to situational and interpersonal factors that increase risk for sexual assault. However, little empirical research has explored this hypothesis. In the present study, it was hypothesized that prior sexual victimization and negative mood state would each independently predict poor risk recognition and less effective defensive actions in response to an analogue sexual assault vignette. Further, these variables were expected to interact to produce particularly impaired risk responses. Finally, that the in vivo emotion regulation strategy of suppression and corresponding cognitive resource usage (operationalized as memory impairment for the vignette) were hypothesized to mediate these associations. Participants were 668 female undergraduate students who were randomly assigned to receive a negative or neutral film mood induction followed by an audiotaped dating interaction during which they were instructed to indicate when the man had “gone too far” and describe an adaptive response to the situation. Approximately 33.5% of the sample reported a single victimization and 10% reported revictimization. Hypotheses were largely unsupported as sexual victimization history, mood condition, and their interaction did not impact risk recognition or adaptive responding. However, in vivo emotional suppression and cognitive resource usage were shown to predict delayed risk recognition only. Findings suggest that contrary to hypotheses, negative mood (as induced here) may not relate to risk recognition and response impairments. However, it may be important for victimization prevention programs that focus on risk perception to address possible underlying issues with emotional suppression and limited cognitive resources to improve risk perception abilities. Limitations and future directions are discussed.