A generalized perturbation approach for exploring stock recruitment relationships

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• 作者：Justin D. Yeakel (1) (2)
  Marc Mangel (3) (4)
  
  1. Center for Stock Assessment Research and Department of Ecology and Evolutionary Biology ; University of California Santa Cruz ; Santa Cruz ; CA ; 95064 ; USA
  2. Santa Fe Institute ; Santa Fe ; NM ; 87501 ; USA
  3. Center for Stock Assessment Research and Department of Applied Mathematics and Statistics ; University of California Santa Cruz ; Santa Cruz ; CA ; 95064 ; USA
  4. Department of Biology ; University of Bergen ; Bergen ; 5020 ; Norway
  
• 关键词：Compensatory dynamics ; Generalized modeling ; Stock ; recruitment relationships ; Shepherd function ; Neimark ; Sacker bifurcation
• 刊名：Theoretical Ecology
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：8
• 期：1
• 页码：1-13
• 全文大小：949 KB
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• 刊物主题：Theoretical Ecology/Statistics; Plant Sciences; Zoology;
• 出版者：Springer Netherlands
• ISSN：1874-1746

文摘

Models of stock-recruitment relationships (SRRs) are often used to predict fish population dynamics. Commonly used SRRs include the Ricker, Beverton-Holt, and Cushing functional forms, which differ primarily by the degree of density-dependent effects (compensation). The degree of compensation determines whether recruitment respectively decreases, saturates, or increases at high levels of spawning stock biomass. In 1982, J.G. Shepherd united these dynamics into a single model, where the degree of compensation is determined by a single parameter. However, the difficulty in relating this parameter to biological data has limited its usefulness. Here, we use a generalized modeling framework to show that the degree of compensation can be related directly to the functional elasticity of growth, which is a general quantity that measures the change in recruitment relative to a change in biomass. We show that the elasticity of growth can be calculated from perturbations in fish biomass, is robust to observation error, and can be used to determine general attributes of the SRR in both continuous time production models, as well as discrete time age-structured models.