Identifying landscape scale patterns from individual scale processes

Abstract

Extrapolating across scales is a critical problem in ecology. Explicit mechanistic models of ecological systems provide a bridge from measurements of processes at small and short scales to larger scales; spatial patterns at large scales can be used to test the outcomes of these models. However, it is necessary to identify patterns that are not dependent on initial conditions, because small scale initial conditions will not normally be measured at large scales. We examined one possible pattern that could meet these conditions, the relationship between mean and variance in abundance of a parasitic tick in an individual based model of a lizard tick interaction. We scaled discrepancies between the observed and simulated patterns with a transformation of the variance–covariance matrix of the observed pattern to objectively identify patterns that are “close”. The results indicate that it is possible to generate patterns that are independent of initial conditions, verifying that the small scale processes in the model are able to reproduce the large scale patterns observed in real data. The pattern analysis also indicates that we have a poor understanding of the density dependent effect of larval engorgement success and host refuge use.