Reassessing regime shifts in the North Pacific: incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability

• Published in: Global Change Biology
• Main Authors: Litzow, MA, Mueter, FJ, Hobday, AJ
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2014
• Subjects: Alaska;Arctic Oscillation;climate change;fisheries;groundfish;North Pacific Gyre Oscillation;North Pacific Ocean;Pacific Decadal Oscillation;regime shift;salmon; Arctic; Bering Sea; Gulf of Alaska; Pacific; Climate change; Alaska
• Online Access: https://eprints.utas.edu.au/18646/; https://eprints.utas.edu.au/18646/1/gcb12373.pdf; https://doi.org/10.1111/gcb.12373

n areas of the North Pacific that are largely free of overfishing, climate regime shifts – abrupt changes in modes of low-frequency climate variability – are seen as the dominant drivers of decadal-scale ecological variability. We assessed the ability of leading modes of climate variability [Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), Arctic Oscillation (AO), Pacific-North American Pattern (PNA), North Pacific Index (NPI), El Niño-Southern Oscillation (ENSO)] to explain decadal-scale (1965–2008) patterns of climatic and biological variability across two North Pacific ecosystems (Gulf of Alaska and Bering Sea). Our response variables were the first principle component (PC1) of four regional climate parameters [sea surface temperature (SST), sea level pressure (SLP), freshwater input, ice cover], and PCs 1–2 of 36 biological time series [production or abundance for populations of salmon (Oncorhynchus spp.), groundfish, herring (Clupea pallasii), shrimp, and jellyfish]. We found that the climate modes alone could not explain ecological variability in the study region. Both linear models (for climate PC1) and generalized additive models (for biology PC1–2) invoking only the climate modes produced residuals with significant temporal trends, indicating that the models failed to capture coherent patterns of ecological variability. However, when the residual climate trend and a time series of commercial fishery catches were used as additional candidate variables, resulting models of biology PC1–2 satisfied assumptions of independent residuals and out-performed models constructed from the climate modes alone in terms of predictive power. As measured by effect size and Akaike weights, the residual climate trend was the most important variable for explaining biology PC1 variability, and commercial catch the most important variable for biology PC2. Patterns of climate sensitivity and exploitation history for taxa strongly associated with biology PC1–2 suggest plausible mechanistic explanations for these modeling results. Our findings suggest that, even in the absence of overfishing and in areas strongly influenced by internal climate variability, climate regime shift effects can only be understood in the context of other ecosystem perturbations.