PhD Thesis. The Australian National University, Canberra.

Scaling Up Population Dynamics: Spatial Variance and the Scale Transition for Stream Biofilm and Grazers

Supervisors: Dr Peter Chesson (Australian National University & University of California, Davis), Dr Julian Ash (Australian National University), Prof. P. Sam Lake (Monash University).

Examiners: Prof. Scott Cooper (University of California, Santa Barbara), Prof. Hugh Possingham (University of Queensland), Prof. Mary Power (University of California, Berkeley). What they said about my thesis.


Short abstract

How to scale up from local scale interactions to regional scale dynamics is a critical issue in ecology. Using scale-transition theory as a framework for field studies, we examined the dynamics of grazers and periphyton in a freshwater stream. Scale-transition theory shows that dynamics change by scaling up because of an interaction between (1) nonlinearity in population dynamics at the local scale and (2) variation over the whole population. Thus, the change in dynamics can be estimated from field data by quantifying nonlinearities and spatial variation. To quantify nonlinearities in local dynamics, we conducted grazer exclusion experiments and used these data to fit alternative models of periphyton growth and grazer foraging. At the same time, using a hierarchical design, we estimated variance components for grazer and periphyton biomass at six spatial scales, from samples-within-rocks to catchments. We estimated that the equilibrium biomass of periphyton was reduced by up to 32 percent by scaling up. This change was driven mostly by an interaction between nonlinearity in periphyton growth and spatial variance of periphyton biomass. The effect of scaling up was greatest under the assumption that the scales of density dependence for periphyton growth and grazer foraging were very different. The approach provides a powerful way to investigate the mechanisms and consequences of changes in population dynamics with spatial scale using a relatively small amount of field data.

Long abstract