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Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

June 16, 2010

A number of important questions in ecology involve the possibility of interactions or ?coupling? among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator?prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Citation Information

Publication Year 2005
Title Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics
DOI 10.1016/j.tpb.2004.08.004
Authors J.M. Nichols, L. Moniz, J. D. Nichols, L.M. Pecora, E. Cooch
Publication Type Article
Publication Subtype Journal Article
Series Title Theoretical Population Biology
Series Number
Index ID 5224398
Record Source USGS Publications Warehouse
USGS Organization Patuxent Wildlife Research Center