Estimating effects of ocean environmental conditions on summer flounder (Paralichthys dentatus) distribution

Abstract The relative abundance of summer flounder ( Paralichthys dentatus ) differs over space and time with changes in environmental factors, such as depth, bottom temperature, sea surface temperature (SST) and bottom salinity. We use the integrated nested Laplace approximation (INLA) approach to account for the random effects arising from either over-dispersion, or spatial and temporal autocorrelation. We explore how the different assumptions in the spatial temporal models result in varying model predictions. The results indicate that the distribution of summer flounder is correlated with depth, regional increases in bottom temperature, SST and bottom salinity. We find that in the Fall relative abundance increased 10–15% with a $$1^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>1</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> C increase in SST, by 12% with each $$1^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>1</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> C increase in bottom temperature and 3–4% with each meter increase in depth across all models. In the spring, relative abundance increased by about 30% with each $$1^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>1</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> C increase in SST with an upper preferred temperature between $$10-20^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10</mml:mn> <mml:mo>-</mml:mo> <mml:msup> <mml:mn>20</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> C. Our study also shows that models that include spatio-temporally correlated variables can inadvertently be over parameterized when including higher order interaction terms between spatial and temporal random effects. This can lead to inflated variances in the estimates and predictions as well as lengthening model convergence times. Therefore, care should be taken in identifying the level of model complexity given the indirect implications of these results on fisheries management and marine ecology.