| Summary: | The study of geographical ecology is about how species populations are distributed in space and time. Using a model-based approach to study ecological processes controlling species distributions provides us with the means to compare different ecological hypothesis, predict a distribution of a species population in locations we have not sampled, and assess uncertainty of the predictions. Such models are practical tools for environmental management as well. For this doctoral thesis, I studied statistical modeling methodologies to reveal and accommodate model uncertainties, which originate from natural variation of the environment, inconstant surveying, and lack of important ecological covariate data from a model. I apply the methods to improve decision making process in conservation planning. As a case study I examined how Arctic marine environment has changed during the recent decades and how severely the Arctic marine mammals are exposed to stress from environmental change and disturbance from marine traffic in the Siberian Shelf Sea area. First, to assess the magnitude of change of the hydrographic conditions, I applied spatially-explicit prediction method, which accounts for uncertainties especially from strong spatio-temporal variation of the environment. Second, I developed a method for jointly analyzing different types of species observations, generated by heterogeneous sampling methods. Third, I combined species distribution predictions with the locations of marine traffic routes to define the mortality risk marine oil spill accidents pose to different species. Lastly, I developed approaches for further utilizing prior information in ecological models to improve the identifiability of the different processes that control distributions of species populations. I demonstrated this approach on vegetation data in northern Norway. This thesis shows, that the western part of the Siberian Shelf Area has become warmer and less saline during 1980-2000, but the magnitudes of changes are highly uncertain. The Arctic ...
|
|---|