A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly auto-correlated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

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.

Risk-based Modeling to Develop Zoning Criteria for Land-use Near Canadian Airports

Land development in the vicinity of airports often leads to land-use that can attract birds that are hazardous to aviation operations. For this reason, certain forms of land-use have traditionally been discouraged within prescribed distances of Canadian airports. However, this often leads to an unrealistic prohibition of land-use in the vicinity of airports located in urban settings. Furthermore, it is often unclear that the desired safety goals have been achieved. This paper describes a model that was created to assist in the development of zoning regulations for a future airport site in Canada. The framework links land-use to bird-related safety-risks and aircraft operations by categorizing the predictable relationships between: (i) different land uses found in urbanized and urbanizing settings near airports; (ii) bird species; and (iii) the different safety-risks to aircraft during various phases of flight. The latter is assessed relative to the runway approach and departure paths. Bird species are ranked to reflect the potential severity of an impact with an aircraft (using bird weight, flocking characteristics, and flight behaviours). These criteria are then employed to chart bird-related safety-risks relative to runway reference points. Each form of land-use is categorized to reflect the degree to which it attracts hazardous bird species. From this information, hazard and risk matrices have been developed and applied to the future airport setting, thereby providing risk-based guidance on appropriate land-uses that range from prohibited to acceptable. The framework has subsequently been applied to an existing Canadian airport, and is currently being adapted for national application. The framework provides a risk-based and science-based approach that offers municipalities and property owner’s flexibility in managing the risks to aviation related to their land use.