Tracking and forecasting community responses to climate perturbations in the California Current Ecosystem

Abstract

Ocean ecosystems are vulnerable to climate-driven perturbations, which are increasing in frequency and can have profound effects on marine social-ecological systems. Thus, there is an urgency to develop tools that can detect the response of ecosystem components to these perturbations as early as possible. We used Bayesian Dynamic Factor Analysis (DFA) to develop a community state indicator for the California Current Ecosystem (CCE) to track the system’s response to climate perturbations, and to forecast future changes in community state. Our key objectives were to (1) summarize environmental and biological variability in the southern and central regions of the CCE during a recent and unprecedented marine heatwave in the northeast Pacific Ocean (2014–2016) and compare these patterns to past variability, (2) examine whether there is evidence of a shift in the community to a new state in response to the heatwave, (3) identify relationships between community variability and climate variables; and (4) test our ability to create one-year ahead forecasts of individual species responses and the broader community response based on ocean conditions. Our analysis detected a clear community response to the marine heatwave, although it did not exceed normal variability over the past six decades (1951–2017), and we did not find evidence of a shift to a new community state. We found that nitrate flux through the base of the mixed layer exhibited the strongest relationship with species and community-level responses. Furthermore, we demonstrated skill in creating forecasts of species responses and community state based on estimates of nitrate flux. Our indicator and forecasts of community state show promise as tools for informing ecosystem-based and climate-ready fisheries management in the CCE. Our modeling framework is also widely applicable to other ecosystems where scientists and managers are faced with the challenge of managing and protecting living marine resources in a rapidly changing climate.

Environment-driven trends in fish larval abundance predict fishery recruitment in two temperate reef congeners: Mechanisms and implications for fishery recovery under a changing ocean

“…We used a mean Bakun upwelling index (m 3 /sec) for June/July at latitude 33°N, where positive values indicate wind-driven offshore transport (Bakun 1973). For both species, we considered another candidate Model 2 where the Bakun index was substituted for both the Biologically Effective Upwelled Transport Index (BEUTI) and Coastal Upwelling Transport Index (CUTI) specific to the southern California region, which provide a measure of nitrate flux through the mixed layer and coastal vertical transport, respectively (Jacox et al 2018, data from Hunsicker et al 2022). Positive BEUTI values indicate nitrate flux into the mixed layer and positive CUTI values indicate coastal upwelling.…”

“…For both species, we considered another candidate Model 2 where we replaced the Bakun index with the BEUTI and CUTI specific to the southern California region, which provide a measure of nitrate flux through the mixed layer and coastal vertical transport, respectively (Jacox et al 2018a, data from Hunsicker et al 2022). Positive BEUTI values indicate nitrate flux into the mixed layer and positive CUTI values indicate coastal upwelling.…”

“…To explore changes in larval abundance through time, we plotted spatiotemporal trends in larval abundance of both species using predicted annual abundances defined by a 2 km x 2 km resolution grid (exclusive of land and within the southern California CalCOFI survey area) and the parameter estimates generated from the index standardization model; missing years not included (Anderson et al 2022). These trends included both fixed and random effects.…”

“…To detect potential latitudinal shifts in bass larval abundance over time, we used larval abundance predictions from the standardized index of abundance models using only years with observed larvae and a species distribution function estimator within sdmTMB to calculate temporal trends in the center of gravity (COG) for both species (Thorson et al 2016, Anderson et al 2022. For larvae of both species, we plotted changes in the mean COG, grouped by decade and the mean July SST for each decade.…”

Long‐term variability in the fish assemblage around Japan over the last century and early warning signals of regime shifts

“…Other methods for multivariate time series such as dynamic factor analysis (DFA) and its Bayesian extension had been used in the Northeastern Pacific to explore responses of marine communities and ecosystems to changing climatic conditions with great novelty in identifying non-stationary biology-climate relationships and ecosystem forecasting, suggesting their promising prospect in relevant studies (e.g. Hunsicker et al, 2022;Litzow et al, 2019Litzow et al, , 2020Ward et al, 2022). However, in this study we still use the PCA to be consistent with our previous studies targeting this study area (e.g.…”