Stochastic Dynamics and Deterministic Skeletons: Population Behavior of Dungeness Crab

• Published in: Science (American Association for the Advancement of Science) Vol. 276; no. 5317; pp. 1431 - 1435
• Main Authors: Higgins, Kevin, Hastings, Alan, Sarvela, Jacob N., Botsford, Louis W.
• Format: Journal Article
• Language: English
• Published: Washington, DC American Society for the Advancement of Science 30-05-1997; American Association for the Advancement of Science; The American Association for the Advancement of Science
• Subjects: Age structure; Animal and plant ecology; Animal populations; Animal, plant and microbial ecology; Animals; Biological and medical sciences; Cancer magister; Crabs; Decapoda; Demecology; Departments; Dungeness crab; Dynamic modeling; Ecology; Eggs; Environmental aspects; Field Studies; Fundamental and applied biological sciences. Psychology; Game fishes; Marine; Modeling; Mollusks; Population biology; Population dynamics; Protozoa. Invertebrata; River ports; Stochastic models; Time series; INE, USA; Stochastic model; Population regulation; Fluctuations; Brachyura; Environmental factor; Crustacea; Cancer magister; Arthropoda; Deterministic model; Density dependence; Population dynamics; Decapoda; Invertebrata; Population density
• ISSN: 0036-8075; 1095-9203
• DOI: 10.1126/science.276.5317.1431

Ecologists have fiercely debated for many decades whether populations are self-regulated by density-dependent biological mechanisms or are controlled by exogenous environmental forces. Here, a stochastic mechanistic model is used to show that the interaction of these two forces can explain observed large fluctuations in Dungeness crab (Cancer magister) numbers. Relatively small environmental perturbations interact with realistic nonlinear (density dependent) biological mechanisms, to produce dynamics that are similar to observations. This finding has implications throughout population biology, suggesting both that the study of deterministic density-dependent models is highly problematic and that stochastic models must include biologically relevant nonlinear mechanisms.