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.

A Tool Box Half Full: How Social Science can Help Solve Human–Wildlife Conflict

There is a growing recognition among wildlife managers that focusing management on wildlife often provides a temporary fix to human–wildlife conflicts, whereas changing human behavior can provide long-term solutions. Human dimensions research of wildlife conflicts frequently focuses on stakeholders’ characteristics, problem identification, and acceptability of management, and less frequently on human behavior and evaluation of management actions to change that behavior. Consequently, little information exists to assess overall success of management. We draw on our experience studying human–bear conflicts, and argue for more human dimensions studies that focus on change in human behavior to measure management success. We call for help from social scientists to conduct applied experiments utilizing two methods, direct observation and self-reported data, to measure change in behavior. We are optimistic these approaches will help fill the managers’ tool box and lead to better integration of human dimensions into human–wildlife conflict management.