Abstract

Black Sea anchovy (Engraulis encrasicolus) undertake extensive overwintering migrations every fall from nursery grounds to warmer overwintering areas located on the south-eastern coast of the Black Sea. During migration and particularly upon arrival at the Anatolian coast, they support an important fishery and valuable source of income for regional communities. Black Sea anchovy have undergone significant stock fluctuations partly related to climatic conditions; for example, migrating anchovy schools arrived late or failed to arrive at the Anatolian coast when fall temperatures increased. This study invokes a Lagrangian modeling approach applied to satellite derived circulation and temperature data as a first attempt to model anchovy migration dynamics in the Black Sea. This modeling approach takes the influence of the physical environment into account, while the quality of overwintering grounds, schooling and homing behavior is neglected. The model is used to investigate the possible influence of interannual and seasonal variability of temperature and surface currents, as well as the influence of migration behavior on the success of anchovy overwintering migration for both the Black Sea and Azov Sea anchovy. The results of the present work show the possibility that overwintering anchovy fished along the southeastern Anatolian coast may not exclusively originate from the northwestern shelf, but mainly from the eastern Black Sea basin. Migration pathways are identified for both Black Sea and Azov Sea anchovy, which are of importance for the national fisheries efforts of riparian countries. Modeling results are in agreement with general patterns of anchovy migration given in the literature indicating that the physical environment may be a major factor in shaping general migration patterns. Simulation results are used to hypothesize about alternatives to previously determined migration routes and provide potential reasons that explain the inability of the Bulgarian anchovy fishery to recover. Results show that the intensity and timing of autumnal cooling, coupled with current strength, can be of significant importance in determining annual and seasonal variability of migration success. Considering the need for fisheries management to account for the variability in fishable overwintering anchovy stocks a modeling approach as developed in the current study may provide such tool.

Highlights

  • The key to estimating the available species in an ecosystem requires an understanding of their geographical distribution (Harte, 2002)

  • Black Sea anchovy When drifters representing Black Sea anchovy were released at the end of October on the northwestern shelf (NWS) and tracked for 2 months in the advection only simulation, the results showed that the transport of anchovy from the NWS to the overwintering grounds is not possible given the variability by currents alone and migration fails in years 2001, 2002, and 2003 (Figures 7A–F)

  • We demonstrate that the Lagrangian modeling approach including fish behavior applied to satellite derived circulation and temperature data can be used to explore the migration of complex organisms such as fish in the marine environment

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Summary

Introduction

The key to estimating the available species in an ecosystem requires an understanding of their geographical distribution (Harte, 2002). Movement can affect fish populations through changes in population density, modifying interspecific interactions or through genetic reorganizations (Turchin and Omland, 1999). In fisheries assessment and management, the patterns of fish movement can be used to explain stock fluctuations (Pelletier and Parma, 1994). Considering the long-distance migration that anchovy in the Black Sea undergo every year, studying movement of populations can provide valuable insights for fisheries science (Goodwin et al, 2006). Simulation of movement of individual particles in a dynamic environment is an important tool for investigating ecological processes in the marine environment (Heath and Gallego, 1998; Miller et al, 1998; Hare et al, 1999)

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