A Bayesian test of hierarchy theory: scaling up variability in plant cover from field to remotely sensed data

Hierarchy theory predicts that a hierarchy of process rates should be reflected in a hierarchy of spatial and temporal scales observable on the landscape. We will show that multiple scales of pattern for total plant cover measured in the field at 1-m resolution are correlated with scales of vegetative pattern obtained from remotely sensed data with resolutions of 25 m² and 30 ². Second, using four models based on postulates of hierarchy theory, we will combine the scales of pattern of each individual species within a community to estimate the remotely sensed community scales of pattern. Finally, we will compare the four models using a Bayesian analysis to determine which model best portrays how vegetative patterns of individual species combine to produce remotely observed community patterns. The results of the model comparisons provide an example of how postulates of hierarchy theory can be tested and how individual species patterns can be scaled-up to estimate remotely observed scales of pattern.