ABSTRACT
Many ecological data sets include not only a spatial but also a temporal component. Data may be collected in an agricultural –eld trial over a period of years in the same –eld. An uncultivated ecosystem may be monitored over an extended period of time. Both of these processes lead to spatiotemporal data sets. In this chapter, we discuss methods for dealing with data that contain both spatial and temporal components. The theory for spatiotemporal data is much less well developed than that for dealing with purely spatial or purely temporal data, and the contents of this chapter are somewhat ad hoc in nature. Section 15.2 deals with a method that develops clusters across time based on attribute values and then tests these clusters for validity based on their spatial relationships. Section 15.3 describes the use of classi–cation trees to explore the biophysical processes that in£uence these clusters. Section 15.4 describes the use of Bayesian updating to study temporal sequences of spatial data. Section 15.5 brie£y describes two methods used in modeling data with a temporal component: partial differential equation models for dispersing organisms and state and transition models for ecosystems.
