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A combination of hydrodynamical and statistical modelling reveals non-stationary climate effects on fish larvae distributions
Author(s) -
Manuel Hidalgo,
Yvonne Gusdal,
Gjert Endre Dingsør,
Dag Ø. Hjermann,
Geir Ottersen,
Leif Christian Stige,
Arne Melsom,
Nils Chr. Stenseth
Publication year - 2011
Publication title -
proceedings of the royal society b biological sciences
Language(s) - English
Resource type - Journals
eISSN - 1471-2954
pISSN - 0962-8452
DOI - 10.1098/rspb.2011.0750
Subject(s) - advection , environmental science , north atlantic oscillation , spatial distribution , hydrography , ichthyoplankton , climate change , climatology , oceanography , climate model , sea surface temperature , fish <actinopterygii> , fishery , geography , biology , geology , physics , remote sensing , thermodynamics
Biological processes and physical oceanography are often integrated in numerical modelling of marine fish larvae, but rarely in statistical analyses of spatio-temporal observation data. Here, we examine the relative contribution of inter-annual variability in spawner distribution, advection by ocean currents, hydrography and climate in modifying observed distribution patterns of cod larvae in the Lofoten-Barents Sea. By integrating predictions from a particle-tracking model into a spatially explicit statistical analysis, the effects of advection and the timing and locations of spawning are accounted for. The analysis also includes other environmental factors: temperature, salinity, a convergence index and a climate threshold determined by the North Atlantic Oscillation (NAO). We found that the spatial pattern of larvae changed over the two climate periods, being more upstream in low NAO years. We also demonstrate that spawning distribution and ocean circulation are the main factors shaping this distribution, while temperature effects are different between climate periods, probably due to a different spatial overlap of the fish larvae and their prey, and the consequent effect on the spatial pattern of larval survival. Our new methodological approach combines numerical and statistical modelling to draw robust inferences from observed distributions and will be of general interest for studies of many marine fish species.

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