Part II - The data needed for modeling species distributions

Summary

This section addresses the data model – the second part of the framework presented by Austin (2002), outlined in Chapter 1 and used as an organizing principle for this book. Austin's data model encompasses theory and decisions about how the data are sampled, and measured or estimated. When making spatial prediction of species distributions, spatial aspects of both the species (Chapter 4) and environmental (Chapter 5) data are important.

Because species data (Chapter 4) are linked, in species distribution modeling, to digital maps of environmental variables (Chapter 5) used for spatial prediction, a conceptual model of geographical data will help to frame the material discussed in this section. Goodchild (1992, 1994) described two mental models of real geography relevant to species distribution mapping: the field and the entity. In the “field” view of the world, geographical variables can be categorical or continuous but they can be measured (have a value) at every location, so, mathematically, geography is a multivariate vector field. Examples are elevation (a continuous variable) and vegetation type (a categorical variable), which can be defined exhaustively for every point on the earth's surface. In the “entity” view, there are discrete geographic objects scattered in geographical space that is otherwise empty. Examples are records of species presence, or maps of trees, roads or fire perimeters.