Michael G. Jacox

Impacts of the 2015–2016 El Niño on the California Current System: Early assessment and comparison to past events

The 2015–2016 El Nino is by some measures one of the strongest on record, comparable to the 1982–1983 and 1997–1998 events that triggered widespread ecosystem change in the northeast Pacific. Here we describe impacts of the 2015–2016 El Nino on the California Current System (CCS) and place them in historical context using a regional ocean model and underwater glider observations. Impacts on the physical state of the CCS are weaker than expected based on tropical sea surface temperature anomalies; temperature and density fields reflect persistence of multiyear anomalies more than El Nino. While we anticipate El Nino-related impacts on spring/summer 2016 productivity to be similarly weak, their combination with preexisting anomalous conditions likely means continued low phytoplankton biomass. This study highlights the need for regional metrics of El Ninos effects and demonstrates the potential to assess these effects before the upwelling season, when altered ecosystem functioning is most apparent.

Spatially resolved upwelling in the California Current System and its connections to climate variability

A historical analysis of California Current System (CCS) circulation, performed using the Regional Ocean Modeling System with four-dimensional variational data assimilation, was used to study upwelling variability during the 1988-2010 period. We examined upwelling directly from the vertical velocity field, which elucidates important temporal and spatial variability not captured by traditional coastal upwelling indices. Through much of the CCS, upwelling within 50 km of the coast has increased, as reported elsewhere. However, from 50 to 200 km offshore, upwelling trends are negative and interannual variability is 180 ◦ out of phase with the nearshore signal. This cross-shore pattern shows up as the primary mode of variability in central and northern CCS vertical velocity anomalies, accounting for ∼40% of the total variance. Corresponding time series of the dominant modes in the central and northern CCS are strongly correlated with large-scale climate indices, suggesting that climate fluctuations may alternately favor different biological communities.

ENSO and the California Current coastal upwelling response

A 31 year (1980–2010) sequence of historical analyses of the California Current System (CCS) is used to describe the central CCS (35–43˚N) coastal upwelling response to El Nino-Southern Oscillation (ENSO) variability. The analysis period captures 10 El Nino and 10 La Nina events, including the extreme El Ninos of 1982–1983 and 1997–1998. Data-assimilative model runs and backward trajectory calculations of passive tracers are used to elucidate physical conditions and source water characteristics during the upwelling season of each year. In general, El Nino events produce anomalously weak upwelling and source waters that are unusually shallow, warm, and fresh, while La Nina conditions produce the opposite. Maximum vertical transport anomalies in the CCS occur ∼1 month after El Nino peaks in midwinter, and before the onset of the upwelling season. Source density anomalies peak later than transport anomalies and persist more strongly through the spring and summer, causing the former to impact the upwelling season more directly. As nitrate concentration covaries with density in the central CCS, El Nino may exert more influence over the nitrate concentration of upwelled waters than it does over vertical transport, although both factors are expected to reduce nitrate supply during El Nino events. Interannual comparison of individual diagnostics highlights their relative impacts during different ENSO events, as well as years deviating from the canonical response to ENSO variability. The net impact of ENSO on upwelling is explored through an “Upwelling Efficacy Index”, which may be a useful indicator of bottom-up control on primary productivity.

Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data

&NA; Understanding and predicting the responses of wide‐ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat‐based models that can sufficiently capture their environmental preferences. Marine ecosystems are inherently dynamic, and animal–environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of environmental data layers are therefore key considerations in modelling the environmental determinants of habitat selection. The utility of environmental data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade‐offs between near real‐time, high resolution and composite (i.e. synoptic, cloud‐free) data fields. Using movement simulations with built‐in environmental preferences in combination with both modelled and remotely‐sensed (ROMS, MODIS‐Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3–111 km, daily–climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological data fields can introduce bias in presence‐availability designs based upon animal movement datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely‐sensed data fields. We found that model accuracy decreased substantially above 70% data loss. In cloudy regions, weekly or monthly environmental data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate‐mediated ecosystem changes.

Optimal Environmental Conditions and Anomalous Ecosystem Responses: Constraining Bottom-up Controls of Phytoplankton Biomass in the California Current System

In Eastern Boundary Current systems, wind-driven upwelling drives nutrient-rich water to the ocean surface, making these regions among the most productive on Earth. Regulation of productivity by changing wind and/or nutrient conditions can dramatically impact ecosystem functioning, though the mechanisms are not well understood beyond broad-scale relationships. Here, we explore bottom-up controls during the California Current System (CCS) upwelling season by quantifying the dependence of phytoplankton biomass (as indicated by satellite chlorophyll estimates) on two key environmental parameters: subsurface nitrate concentration and surface wind stress. In general, moderate winds and high nitrate concentrations yield maximal biomass near shore, while offshore biomass is positively correlated with subsurface nitrate concentration. However, due to nonlinear interactions between the influences of wind and nitrate, bottom-up control of phytoplankton cannot be described by either one alone, nor by a combined metric such as nitrate flux. We quantify optimal environmental conditions for phytoplankton, defined as the wind/nitrate space that maximizes chlorophyll concentration, and present a framework for evaluating ecosystem change relative to environmental drivers. The utility of this framework is demonstrated by (i) elucidating anomalous CCS responses in 1998–1999, 2002, and 2005, and (ii) providing a basis for assessing potential biological impacts of projected climate change.

Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modelling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks Isurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (<100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models.

Observing System Impacts on Estimates of California Current Transport

Abstract The impact of different observing platforms and control vector elements on 4D-Var analyses of California Current transport are explored using the adjoint Kalman gain matrix to map a transport metric into observation space. The contribution of control vector elements on the metric provide a useful measure for tracking performance characteristics of the 4D-Var algorithm, and they can be used to detect potential inconsistencies within the analysis system. Observation impact calculations provide detailed information about the contribution of each observation or observing platform on the transport. A novel aspect of this work is that it provides a direct quantitative measure of the observing system impact on ocean state estimates spanning three decades, and it reveals the complex interplay between different observing platforms within the 4D-Var analyses as different observing systems become available. The method employed here requires considerably less computational effort than more traditional ones.

Fisheries bycatch risk to marine megafauna is intensified in Lagrangian coherent structures

Significance Marine capture fisheries provide a valuable source of protein and are economically important in coastal communities. However, fisheries sustainability is impacted by incidental capture of nontarget species (bycatch), which remains a major global threat to marine megafauna such as sharks, sea turtles, seals, cetaceans, and seabirds. Understanding where and when bycatch events take place can guide fisheries sustainability solutions. Here, we model how dynamic structures in the ocean such as fronts and eddies influence fisheries effort, catch, and bycatch likelihood. We find that bycatch of a diverse range of species is more likely in attracting Lagrangian coherent structures, in which water masses meet and aggregate prey, predators, and fishers into hotspots of risk. Incidental catch of nontarget species (bycatch) is a major barrier to ecological and economic sustainability in marine capture fisheries. Key to mitigating bycatch is an understanding of the habitat requirements of target and nontarget species and the influence of heterogeneity and variability in the dynamic marine environment. While patterns of overlap among marine capture fisheries and habitats of a taxonomically diverse range of marine vertebrates have been reported, a mechanistic understanding of the real-time physical drivers of bycatch events is lacking. Moving from describing patterns toward understanding processes, we apply a Lagrangian analysis to a high-resolution ocean model output to elucidate the fundamental mechanisms that drive fisheries interactions. We find that the likelihood of marine megafauna bycatch is intensified in attracting Lagrangian coherent structures associated with submesoscale and mesoscale filaments, fronts, and eddies. These results highlight how the real-time tracking of dynamic structures in the oceans can support fisheries sustainability and advance ecosystem-based management.

Practical considerations for operationalizing dynamic management tools

Dynamic management (DM) is a novel approach to spatial management that aligns scales of environmental variability, animal movement and human uses. While static approaches to spatial management rely on one‐time assessments of biological, environmental, economic, and/or social conditions, dynamic approaches repeatedly assess conditions to produce regularly updated management recommendations. Owing to this complexity, particularly regarding operational challenges, examples of applied DM are rare. To implement DM, scientific methodologies are operationalized into tools, i.e., self‐contained workflows that run automatically at a prescribed temporal frequency (e.g., daily, weekly, monthly). Here we present a start‐to‐finish framework for operationalizing DM tools, consisting of four stages: Acquisition, Prediction, Dissemination, and Automation. We illustrate this operationalization framework using an applied DM tool as a case study. Our DM tool operates in near real‐time and was designed to maximize target catch and minimize bycatch of non‐target and protected species in a US‐based commercial fishery. It is important to quantify the sensitivity of DM tools to missing data, because dissemination streams for observed (i.e., remotely sensed or directly sampled) data can experience delays or gaps. To address this issue, we perform a detailed example sensitivity analysis using our case study tool. Synthesis and applications. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives.;

Relative catchability (probability of presence) of broadbill swordfish Xiphias gladius in the California Current System. Hindcast predictions for November 2014 using UC Santa Cruz Regional Ocean Modelling System

Model-derived relative probability of swordfish presence, hind-cast on Regional Ocean Model outputs for the period 1st-31st November 2014.