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.

BEHAVIORAL RESPONSE OF CATTLE EGRETS TO POPULATION CONTROL MEASURES IN HAWAII

We monitored behavior of cattle egrets (Bubulcus ibis) during a population control program to reduce egret-aircraft strike hazards from a small heronry near the Hilo, Hawaii, airport. Results verified that attempts to move egrets from undesirable roost sites should be undertaken before nesting begins. Although possibly compounded by previous treatments, our observations also indicate that 1) egrets may abandon a new roost in response to a few dead egrets placed in clear view around the roost, and 2) shooting at egrets as they attempt to land at a traditional feeding site causes long-term avoidance of the area. Rapid repopulation after control indicates that techniques to move roosts and prevent congregations are more likely than population control to resolve problems.

Unifying Representations and Responses: Perseverative Biases Arise From a Single Behavioral System

A dominant account of perseverative errors in early development contends that such errors reflect a failure to inhibit a prepotent response. This study investigated whether perseveration might also arise from a failure to inhibit a prepotent representation. Children watched as a toy was hidden at an A location, waited during a delay, and then watched the experimenter find the toy. After six observation-only A trials, the toy was hidden at a B location, and children were allowed to search for the toy. Two- and 4-year-olds’ responses on the B trials were significantly biased toward A even though they had never overtly responded to this location. Thus, perseverative biases in early development can arise as a result of prepotent representations, demonstrating that the prepotent-response account is incomplete. We discuss three alternative interpretations of these results, including the possibility that representational and response-based biases reflect the operation of a single, integrated behavioral system.