The Phylogenetic Relationships and Biogeography of True Porpoises (Mammalia: Phocoenidae) Based On Morphological Data

Prior studies of phylogenetic relationships among phocoenids based on morphology and molecular sequence data conflict and yield unresolved relationships among species. This study evaluates a comprehensive set of cranial, postcranial, and soft anatomical characters to infer interrelationships among extant species and several well-known fossil phocoenids, using two different methods to analyze polymorphic data: polymorphic coding and frequency step matrix. Our phylogenetic results confirmed phocoenid monophyly. The division of Phocoenidae into two subfamilies previously proposed was rejected, as well as the alliance of the two extinct genera Salumiphocaena and Piscolithax with Phocoena dioptrica and Phocoenoides dalli. Extinct phocoenids are basal to all extant species. We also examined the origin and distribution of porpoises within the context of this phylogenetic framework. Phocoenid phylogeny together with available geologic evidence suggests that the early history of phocoenids was centered in the North Pacific during the middle Miocene, with subsequent dispersal into the southern hemisphere in the middle Pliocene. A cooling period in the Pleistocene allowed dispersal of the southern ancestor of Phocoena sinusinto the North Pacific (Gulf of California).

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.

Seeking a Second Opinion: Uncertainty in Disease Ecology

Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.