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.

Dynamic Routing in Translucent WDM Optical Networks

Translucent WDM optical networks use sparse placement of regenerators to overcome the impairments and wavelength contention introduced by fully transparent networks, and achieve a performance close to fully opaque networks with much less cost. Our previous study proved the feasibility of translucent networks using sparse regeneration technique. We addressed the placement of regenerators based on static schemes allowing only fixed number of regenerators at fixed locations. This paper furthers the study by proposing a suite of dynamical routing schemes. Dynamic allocation, advertisement and discovery of regeneration resources are proposed to support sharing transmitters and receivers between regeneration and access functions. This study follows the current trend in optical networking industry by utilizing extension of IP control protocols. Dynamic routing algorithms, aware of current regeneration resources and link states, are designed to smartly route the connection requests under quality constraints. A hierarchical network model, supported by the MPLS-based control plane, is also proposed to provide scalability. Experiments show that network performance is improved without placement of extra regenerators.

Cetaceans of Southeast Alaska: Distribution and Seasonal Occurrence

Aim To assess the distribution, group size, seasonal occurrence and annual trends of cetaceans. Location The study area included all major inland waters of Southeast Alaska. Methods Between 1991 and 2007, cetacean surveys were conducted by observers who kept a constant watch when the vessel was underway and recorded all cetaceans encountered. For each species, we examined distributional patterns, group size, seasonal occurrence and annual trends. Analysis of variance (anova F) was used to test for differences in group sizes between multiple means, and Student’s t-test was used to detect differences between pairwise means. Cetacean seasonal occurrence and annual trends were investigated using a generalized linear model framework. Results Humpback whales (Megaptera novaeangliae) were seen throughout the region, with numbers lowest in spring and highest in the fall. Fin whale (Balaenoptera physalus) and minke whale (Balaenoptera acutorostrata) distributions were more restricted than that reported for humpback whales, and the low number of sightings precluded evaluating seasonal trends. Three killer whale (Orcinus orca) eco-types were documented with distributions occurring throughout inland waters. Seasonal patterns were not detected or could not be evaluated for resident and offshore killer whales, respectively; however, the transient eco-type was more abundant in the summer. Dall’s porpoise (Phocoenoides dalli) were distributed throughout the region, with more sightings in spring and summer than in fall. Harbour porpoise (Phocoena phocoena) distribution was clumped, with concentrations occurring in the Icy Strait/Glacier Bay and Wrangell areas and with no evidence of seasonality. Pacific white-sided dolphins (Lagenorhynchus obliquidens) were observed only occasionally, with more sightings in the spring. For most species, group size varied on both an annual and seasonal basis. Main conclusions Seven cetacean species occupy the inland waters of Southeast Alaska, with distribution, group size, seasonal occurrence and annual trends varying by species. Future studies that compare spatial and temporal patterns with other features (e.g. oceanography, prey resources) may help in identifying the key factors that support the high density and biodiversity of cetaceans found in this region. An increased understanding of the region’s marine ecology is an essential step towards ensuring the long-term conservation of cetaceans in Southeast Alaska.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.