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.

Energy Restriction During Breeding Gilt Development: An Economic Analysis

Swine production has increasingly become a lowmargin business. As costs of production have increased, producers are continuing to increase efficiency in both market pig production and gilt development. Restricting energy during gilt development reduces feeding costs and can enhance some productivity measures, but can also negatively impact other areas of production. Thus, the net economic returns from a restricted energy gilt development program are unclear. This study utilized gilt development and market pig production data for two genetic lines of hogs, LWxLR (a cross between industry Large White and Landrace) and L45X (a Nebraska line selected 23 generations for increased litter size) from Johnson and Miller and Johnson et al., to estimate the returns to finishing market hogs using conventional and restricted energy gilt development programs.