Multivariate recurrence plots for visualizing and quantifying the dynamics of spatially extended ecosystems

Few methods for quantifying the dynamics of temporal processes are readily applicable to spatially extended systems when equations governing the motion are unknown. The objective of this paper is to illustrate how the MRP-RQA (multivariate recurrence plot-recurrence quantification analysis) approach may serve to characterize ecosystems driven by both deterministic and stochastic forces. The strength of the MRP-RQA approach resides in its independence from constraining assumptions regarding outliers, noise, stationarity and transients. Its utility is demonstrated by means of two spatiotemporal series (summer and spring datasets) of light intensity variations in an old growth forest ecosystem. Results revealed qualitative differences in homogeneity, transiency, and drift typologies between the MRPs derived from each dataset. RQA estimates of determinism and Kolmogorov entropy supported the idea that mixed chaotic-stochastic dynamics may be common in mesoscale forest habitats. Advantages and inconveniences of the MRP-RQA approach are also discussed in the more general context of monitoring ecosystems.