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).

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.