Anatomy of a plot between spatial and regional temporal variability.

<p>A stochastic null model of a three patch mosaic illustrated several features of a plot between log mean spatial CV and log regional temporal CV. (<b>A</b>) Three regions exist in which a variable (point) can fall - an “independent dynamics region” when values are independent between patches <i>i</i> and <i>j</i> and time points <i>k</i> and <i>l</i>, a “synchrony region” when inter-patch synchrony boosts temporal CV, and a “persistence region” when spatial gradients are retained over time; (<b>B</b>) Weak linear relationship when variables share similar spatiotemporal variability, leading to scatter from small variations in synchrony or persistence; (<b>C</b>) Strong linear relationships when variables differ in spatiotemporal variabililty and occupy the “independent dynamics region” (black circles), but also when all variables are equally dispaced by synchrony (blue circles) or by persistence (red circles); (<b>D</b>) Deviation of regression slope from ∼1 (black circles) when variables change in synchrony or persistence as a function of variability. Here, a gradient exists from variables with low variability and synchrony to variables with high variability and persistence. Spatial CV values are means of spatial CV measured at time point <i>k</i>. Each point represents a variable and is a mean of ten replicates.