TY - JOUR
T1 - Anatomy of a Chaotic Attractor
T2 - Subtle Model-predicted Patterns Revealed in Population Data
AU - King, Aaron A.
AU - Costantino, R. F.
AU - Cushing, J. M.
AU - Henson, Shandelle M.
AU - Desharnais, Robert A.
AU - Dennis, Brian
PY - 2004/1
Y1 - 2004/1
N2 - Mathematically, chaotic dynamics are not devoid of order but display episodes of near-cyclic temporal patterns. This is illustrated, in interesting ways, in the case of chaotic biological populations. Despite the individual nature of organisms and the noisy nature of biological time series, subtle temporal patterns have been detected. By using data drawn from chaotic insect populations, we show quantitatively that chaos manifests itself as a tapestry of identifiable and predictable patterns woven together by stochasticity. We show too that the mixture of patterns an experimentalist can expect to see depends on the scale of the system under study.
AB - Mathematically, chaotic dynamics are not devoid of order but display episodes of near-cyclic temporal patterns. This is illustrated, in interesting ways, in the case of chaotic biological populations. Despite the individual nature of organisms and the noisy nature of biological time series, subtle temporal patterns have been detected. By using data drawn from chaotic insect populations, we show quantitatively that chaos manifests itself as a tapestry of identifiable and predictable patterns woven together by stochasticity. We show too that the mixture of patterns an experimentalist can expect to see depends on the scale of the system under study.
UR - https://digitalcommons.andrews.edu/pubs/2223
U2 - 10.1073/pnas.2237266100
DO - 10.1073/pnas.2237266100
M3 - Article
SN - 1091-6490
VL - 101
SP - 408
EP - 413
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 1
ER -