Modeling the seasonal and interannual variability of Peruvian anchovy (Engraulis ringens) population dynamics: Linking environmental conditions with fish.

摘要

The coastal waters of Peru support the world's largest single-species fishery, the Peruvian anchovy. The Peruvian anchovy catch vary dramatically year-to-year in response to the El Nino Southern Oscillation (ENSO). This dissertation uses a coupled physical-biological modeling approach to investigate the response of anchovy growth, survival, and distribution to seasonal and ENSO climatic variability.;A bioenergetics model was developed for the Peruvian anchovy that simulated growth and survival from egg to age-3. The model used constant temperature and concentrations of multiple plankton groups as input. The model simulated weight and length over time generally agreed with available field and laboratory observations. Sensitivity analyses showed that the model was robust in response to variation in temperature and prey.;The modeled monthly output from the Regional Ocean Model System (ROMS), coupled with the Carbon Silicate Nitrogen Ecosystem model (CoSINE), was used as input to the bioenergetics model to study growth and survival to recruitment length (5 cm). Anchovy cohorts showed faster growth in the early and the late parts of the year, and sensitivity analysis showed the importance of prey and that realistic distribution of monthly spawning yielded high recruitment. Simulation of monthly cohorts for the 1991-2007 period showed that anchovy grew extremely slowly and had low recruitment during 1997-1998 El Nino. Sensitivity analysis showed that both temperature and prey concentrations regulated anchovy growth during normal years, while temperature was critical during El Nino conditions.;Similar analyses were performed using the three-dimensional (3D) model output from the ROMS-CoSINE simulation and adding currents and behavioral movement to individuals in the bioenergetics model. Simulated growth and survival showed similar interannual variation as with the Box Model analysis. The more detail in the 3D case allowed anchovy to locate better conditions, which explained most situations when Box Model and 3D results differed. During El Nino events, however, both Box Model and 3D analyses predict slow growth and low recruitment because of harsh environmental conditions.;Coupling physical and ecological models offers a promising method for studying the complex responses of fish populations to environmental variation, which can be potentially used for ecological forecasting and fishery management.;Keywords. Peruvian anchovy, Engraulis ringens , Individual Based Model (IBM), seasonal cycle, interannual variation, population dynamics