Saving the Vaquita: Immediate Action, Not More Data

The recent likely extinction of the baiji (Chinese river dolphin [Lipotes vexillifer]) (Turvey et al. 2007) makes the vaquita (Gulf of California porpoise [Phocoena sinus]) the most endangered cetacean. The vaquita has the smallest range of any porpoise, dolphin, or whale and, like the baiji, has long been threatened primarily by accidental deaths in fishing gear (bycatch) (Rojas-Bracho et al. 2006). Despite repeated recommendations from scientific bodies and conservation organizations, no effective actions have been taken to remove nets from the vaquita’s environment. Here, we address three questions that are important to vaquita conservation: (1) How many vaquitas remain? (2) How much time is left to find a solution to the bycatch problem? and (3) Are further abundance surveys or bycatch estimates needed to justify the immediate removal of all entangling nets from the range of the vaquita? Our answers are, in short: (1) there are about 150 vaquitas left, (2) there are at most 2 years within which to find a solution, and (3) further abundance surveys or bycatch estimates are not needed. The answers to the first two questions make clear that action is needed now, whereas the answer to the last question removes the excuse of uncertainty as a delay tactic. Herein we explain our reasoning.

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.