Proceedings of the PICES/CoML/IPRC Workshop on Impact of Climate Variability on Observation and Prediction of Ecosystem and Biodiversity Changes in the North Pacific

Table of Contents [pdf, 0.09 Mb] Section I - Presentations and Discussions at Plenary Sessions Introduction and Overview of Workshop Objectives [pdf, 0.07 Mb] Plenary Session Presentations [pdf, 2.23 Mb] Reports of the Breakout Group Discussions [pdf, 0.43 Mb] Closing Plenary Discussion and Recommendations [pdf, 0.11 Mb] Section II - Extended Abstracts of Individual Presentations at Breakout Group Sessions Breakout Group 1: Physical/Chemical Oceanography and Climate [pdf, 6.14 Mb] Breakout Group 2: Phytoplankton, Zooplankton, Micronekton and Benthos [pdf, 28.14 Mb] Breakout Group 3: Fish, Squid, Crabs and Shrimps [pdf, 4.30 Mb] Breakout Group 4: Highly Migratory Fishes, Seabirds and Marine Mammals [pdf, 6.27 Mb] Appendix 1. Workshop agenda [pdf, 0.15 Mb] Appendix 2. List of participants [pdf, 0.13 Mb] (Document pdf contains 216 pages)

Prediction, Chaos and Ecological Perspective.

As defined, the modeling procedure is quite broad. For example, the chosen compartments may contain a single organism, a population of organisms, or an ensemble of populations. A population compartment, in turn, could be homogeneous or possess structure in size or age. Likewise, the mathematical statements may be deterministic or probabilistic in nature, linear or nonlinear, autonomous or able to possess memory. Examples of all types appear in the literature. In practice, however, ecosystem modelers have focused upon particular types of model constructions. Most analyses seem to treat compartments which are nonsegregated (populations or trophic levels) and homogeneous. The accompanying mathematics is, for the most part, deterministic and autonomous. Despite the enormous effort which has gone into such ecosystem modeling, there remains a paucity of models which meets the rigorous &! validation criteria which might be applied to a model of a mechanical system. Most ecosystem models are short on prediction ability. Even some classical examples, such as the Lotka-Volterra predator-prey scheme, have not spawned validated examples.