New models and methods to fuse spatial information from complementary sources

A wide range of marine managers and users require access to information on the spatial distribution of species and environmental properties in order to plan strategies and activities on a range of spatial scales. Application areas include, for example, marine spatial planning, conservation planning, and area prioritization in case of environmental disasters. Current development in the Geographic Information System (GIS) technology provides us increasing amount of data and efficient tools to collect and visualize them on thematic maps. However, in many situations of practical interest, especially in marine areas, the available useful data are patchy, sparse or totally missing. In such situations, we need to be able to fuse complementary sources of information, including, e.g., scientific survey and voluntarily collected citizen science data as well as expert information, in order to build useful knowledge for management. To succeed in this, we need novel probabilistic modeling techniques. In this project, we will concentrate on methods i) to fuse information from different kinds of data, including, e.g., survey, voluntarily collected observational and censored data, ii) to fuse expert knowledge with various data sets, and iii) to plan future surveys cost
efficiently.