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Explanatory Power of Human and Environmental Pressures on the Fish Community of the Grand Bank before and after the Biomass Collapse

Ecosystem based fisheries management will benefit from assessment of how various pressures affect the fish community, including delayed responses. The objective of this study was to identify which pressures are most directly related to changes in the fish community of the Grand Bank, Northwest Atlantic. These changes are characterized by a collapse and partial recovery of fish biomass and shifting trophic structure over the past three decades. All possible subsets of nine fishing and environmental pressure indicators were evaluated as predictors of the fish community structure (represented by the biomasses of six fish functional-feeding groups), for periods Before (1985 – 1995) and After (1996 – 2013) the collapse, and the Full time series. We modelled these relationships using redundancy analysis, an extension of multiple linear regression that simultaneously evaluates the effect of one or more predictors on several response variables. The analysis was repeated with different lengths (0 to 5 years) and types (moving average vs. lags) of time delays imposed on the predictors. Both fishing and environmental indicators were included in the best models for all types and length of time delays, reinforcing that there is no single type of pressure impacting the fish community in this region. Results show notable differences in the most influential pressures Before and After the collapse, which reflects the changes in harvester behavior in response to the groundfish moratoria in the mid-1990s. The best models for Before the collapse had strikingly high explanatory power when compared to the other periods, which we speculate is because of changes in the relationships among and within the pressures and responses. Moving average predictor sets generally had higher explanatory power than lagged sets, implying that trends in pressures are important for predicting changes in the fish community. Assigning a carefully chosen delay to each predictor further improved the explanatory power, which is indicative of the complexity of interactions between pressures and responses. Here we add to the current understanding of this ecosystem while demonstrating a method for selecting pressures that could be useful to scientists and managers in other ecosystems.