In pacific-hake/hake-assessment: Stock Assessment for Pacific Hake

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\rfoot{Ecosystem report}

Hake-specific ecosystem summary {#sec:app-ecosystem-report}

Contributed by Kristin N. Marshall, Mary E. Hunsicker, and Andrew M. Edwards

The base model for this stock assessment includes year-specific maturity-at-age that explicitly accounts for changes in temperature, and empirical weight-at-age data that may be due to ecosystem effects. As such, the assessment is consistent with an `Ecosystem Approach to Fisheries Management'.

Section~\@ref(sec:recruitment-variables) discusses relevant ecosystem information that has been previously found to influence r sp recruitment [@VestfalsEtAl2023]. We focus on easily-available and updatable information, and what is presented here is not exhaustive. As such it should be considered a first step in providing an operational summary (updatable and expanded upon every year) of relevant ecosystem information for r sp. Section~\@ref(sec:ecosystem-conditions) then describes broader ecosystem and environmental conditions relevant to r sp. A risk table (Table~\@ref(tab:app-ecosystem-report-risk-tab)) summarizes conditions, uncertainty, and concerns related to r sp.

Ecosystem variables relevant for recruitment {#sec:recruitment-variables}

We present some of the indicators that @VestfalsEtAl2023 and subsequent analyses (Marshall et al., pers. comm.) found to correlate with higher r sp recruitment deviations (Table \@ref(tab:app-ecosystem-report-indicators-tab)). @VestfalsEtAl2023 used estimated hake recruitment deviations from 1980 to 2010, based on results in the @JTC2020 hake assessment and ocean model variables from the University of California, Santa Cruz Regional Ocean Modeling System (ROMS) hindcast. We also conducted a preliminary update using a newer ocean modeling product (GLORYS, described below) that extends the analysis to a more recent time period (1993 to 2023), and used extended statistical methods that focus on within-sample and out-of-sample prediction for variable selection (K.~Marshall, pers.~comm.).

\begin{table}[hbp] \centering \caption{\label{tab:app-ecosystem-report-indicators-tab}Descriptions of easily available ecosystem variables that were found to influence r sp recruitment either by Vestfals et al. (2023) or our updated analyses (unpublished). Variables are in order of life-history stage, following Table~A1 of Vestfals et al. (2023); see that reference for full details. Shelf break is considered between 100 and 2,000~m isobaths, defining the longitudinal extent for several variables. The first subscripts refer to life-history stage (pre is preconditioning, while JA is Jan-Apr, in the egg to late larvae stage).} \centering \fontsize{10}{12}\selectfont \begin{tabular}[t]{p{3cm}p{6cm}p{6cm}} \toprule Abbreviation~of variable & Definition & Effect on recruitment; expectation for an increase in variable\ \midrule

PREY$_{\mbox{pre,her}}$ & Pacific Herring spawning biomass off the west coast of Vancouver Island & Increased competition with herring on summer feeding grounds leads to poorer feeding conditions and reduced adult condition; $\downarrow$ recruitment the following year\

BI$_{\mbox{pre}}$ & North Pacific Current Bifurcation Index & Northward-shifted bifurcation leads to increased advection of prey southwards, leading to poorer feeding conditions off British Columbia, Washington, and Oregon, and reduced adult condition; $\downarrow$ recruitment the following year\

PDO$_{\mbox{pre}}$ & Pacific Decadal Oscillation during preconditioning (Apr-Sep) & Indicator of basin-scale processes, negative phase linked to higher productivity and improved adult condition (Apr-Sep); $\downarrow$ recruitment the following year\

NPGO$_{\mbox{pre}}$ & North Pacific Gyre Oscillation during preconditioning (Apr-Sep) & Indicator of basin-scale processes, positive phase linked to higher nutrient concentrations and productivity, and improved adult condition; $\uparrow$ recruitment the following year\

TEMP$_{\mbox{spawn}}$ & Mean temperature during spawning (shelf break, Jan-Mar, 130-500~m depth, 31-36~\textdegree~N) & At higher temperatures, fish are less likely to spawn but growth rate of larvae increases; $\cup$-shaped relationship with recruitment that year\

AST$_{\mbox{eggs}}$ & Net along-shore transport (shelf break, Jan-Mar, 40-60~m depth, 31-36~\textdegree~N) & Increased northward advection away from juvenile nursery areas decreases recruitment; $\downarrow$ recruitment that year\

MLD$_{\mbox{yolk}}$ & Mean mixed layer depth (shelf break Jan-Apr, 31-36~\textdegree~N) & Larvae aggregate at base of mixed layer so mixed layer depth may limit how far they rise in the water column affecting later transport; $\downarrow$ recruitment that year\

SSH$_{\mbox{JA.c}}$ & Average sea-surface height off California as an indicator of basin-scale processes (from coast to 30~km offshore, Jan-Apr, 34.5-42.5~\textdegree~N) & Higher sea surface is indicative of higher productivity and better conditions for copepods; $\uparrow$ recruitment that year\

MLD$_{\mbox{latelarv}}$ & Mean mixed layer depth (shelf break Mar-Jun, 31-37~\textdegree~N) & Larvae aggregate at base of mixed layer so mixed layer depth may limit how far they rise in the water column affecting later transport, and possibly less competition and predation when mixed layer shallower; % Kristin's guess $\uparrow$ recruitment that year\

PU$_{\mbox{latelarv}}$ & Strength of poleward undercurrent (from coast to 275~m isobath, Mar-Jun, 75-275~m depth, 33.5-34.5~\textdegree~N) & Increased northward advection away from juvenile nursery areas decreases recruitment; $\downarrow$ recruitment that year \

PRED$_{\mbox{age0.age1hake}}$ & Biomass of age-1 Pacific Hake (from this stock assessment) & Age-1 hake predate on pelagic juveniles (roughly Apr-Sep); $\downarrow$ recruitment that year \

\bottomrule \end{tabular} \end{table}

Overall, this resulted in 11 ecosystem indices described in Table~\@ref(tab:app-ecosystem-report-indicators-tab) that correlate with recruitment deviations of r sp. These include three of the five indices included in the best-fitting model of @VestfalsEtAl2023 (see their Figure~7), and three of the further nine indices found in their top 16 candidate models (their Table~2). Time series for five indices are based on values amalgamated in the pacea R package [@EdwardsEtAl2024]. The other six indices were calculated from outputs of the Global Ocean Physics Reanalysis (GLORYS12V1) product, which is a global ocean eddy-resolving oceanographic model covering 1993 onwards. Details of spatial, depth, and temporal ranges are given in Table~\@ref(tab:app-ecosystem-report-indicators-tab).

The following variables are taken from pacea. Pacific Herring (\emph{Clupea pallasii}) spawning biomass index was calculated from latest results [@DFO2024] of the stock assessment, which is conducted using a statistical catch-at-age model [@ClearyEtAl2019]. The North Pacific Current Bifurcation Index (BI) was developed by @MalickEtAl2017, who found that a northward-shifted bifurcation was associated with increased salmon productivity in British Columbia and Washington State waters. The index is updated annually (Michael Malick, pers.~comm.). The Pacific Decadal Oscillation (PDO) is a monthly index which is a long-lived El Niño-like pattern of Pacific climate variability [@MantuaEtAl1997]. The North Pacific Gyre Oscillation (NPGO) is a climate pattern that is significantly correlated with fluctuations of salinity, nutrients, and chlorophyll-a in long-term observations in the California Current and Gulf of Alaska [@DiLorenzoEtAl2024]. The estimates of age-1 r sp are taken from this current assessment model (and will be updated in pacea). The six further indices were calculated from GLORYS outputs by K.~Marshall.

Estimates of r sp recruitment from this assessment are presented alongside the five indices calculated using outputs from pacea (Figure~\@ref(fig:app-ecosystem-summary-from-pacea-fig)) and the six indices calculated from GLORYS output (Figure~\@ref(fig:app-ecosystem-summary-from-glorys-fig)). All indices are standardised over the ranges of years shown (different for each figure), such that each index has a mean of zero and standard deviation of one. The x-axis corresponds to the year for which recruitment is expected to be influenced by the ecosystem variable represented by the index. For example, PREY${\mbox{pre,her}}$ is an index representing Pacific Herring spawning biomass off the west coast of Vancouver Island (Table~\@ref(tab:app-ecosystem-report-indicators-tab)). @VestfalsEtAl2023 found that increased herring biomass led to increased competition with hake on summer feeding grounds, leading to poorer feeding conditions and reduced adult condition. r sp recruitment would then be affected the following year. Thus, the PREY${\mbox{pre,her}}$ index for year 2020 in Figure~\@ref(fig:app-ecosystem-summary-from-pacea-fig) represents the herring spawning biomass in 2019, because that is the biomass expected to influence hake recruitment in 2020. A similar shift is done for the other three variables that influence the adult preconditioning stage of hake.

The notation PREY$_{\mbox{pre,her}}$ represents herring being a prey item for adult hake in the preconditioning stage, although @VestfalsEtAl2023 found a competition effect (not a predator-prey effect as originally hypothesised).

Some variables are plotted with a flipped y-axis, so that upwards in all plots consistently corresponds to conditions supposedly good for hake recruitment. For example, a lower herring biomass is expected to correspond to an increase in hake recruitment, and so the y-axis for the herring index in Figure~\@ref(fig:app-ecosystem-summary-from-pacea-fig) is flipped, so that lower-than-average herring biomass is upwards on the plot. It is coloured blue to represent a negative herring biomass anomaly. Similarly, red represents a positive herring biomass anomaly but values point downwards to indicate a negative potential effect on hake recruitment. A similar flip is done for all variables for which a negative index was found to correspond to positive hake recruitment, to make it easier to visualise potential effects. A similar approach is taken for Figure~\@ref(fig:app-ecosystem-summary-from-glorys-fig).

We have not conducted further analyses on these time series, but present them as a first attempt to compile varied ecosystem information into single figures that can stimulate thinking about ecosystem effects, and that can be updated and expanded upon for future assessments. Note that the strongest driver of hake recruitment found by @VestfalsEtAl2023 was eddy kinetic energy between May and September (during the female spawning preconditioning stage) from point Conception to Cape Blanco, but an up-to-date index of this was not easily available.

(ref:app-ecosystem-summary-from-pacea-cap) Estimated hake recruitment from this assessment (top), plus five ecosystem indices based on the pacea R package. The x-axis corresponds to the expected influenced year of r sp recruitment (see text). Red (blue) bars represent a positive (negative) index, with the y-axis flipped for four indices so that upwards represents a positive expected influence on recruitment.

(ref:app-ecosystem-summary-from-pacea-alt) This figures shows time series of hake recruitment plus five ecosystem indices.

include_graphics(file.path(figures_dir,
                          "ecosystem_summary_from_pacea.png"),
                 error = FALSE)

(ref:app-ecosystem-summary-from-glorys-cap) Estimated r sp recruitment from the current assessment model (top), plus six ecosystem calculated from GLORYS output. Axes are defined as in Figure~\@ref(fig:app-ecosystem-summary-from-pacea-fig) and in the text. *Recruitment has a $\cup$-shaped relationship with mean temperature during spawning and for simplicity we do not flip the axis.

(ref:app-ecosystem-summary-from-glorys-alt) This figures shows time series of hake recruitment plus six further ecosystem indices.

include_graphics(file.path(figures_dir,
                          "ecosystem_summary_from_glorys.png"),
                 error = FALSE) #

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\begin{table}[tbp] \centering \caption{\label{tab:app-ecosystem-report-risk-tab} `Risk table' for Pacific Hake, to document ecosystem and climate factors potentially affecting stock productivity and uncertainty or other concerns arising from the stock assessment (see text). Level~1 is a favourable ranking, Level~2 neutral, and Level~3 is unfavourable. CVA is the Climate Variability Assessment approach (McClure et al., 2023).} \centering \fontsize{10}{12}\selectfont \begin{tabular}[t]{p{5cm}p{5cm}p{5cm}} \toprule Ecosystem and environmental conditions & Assessment data inputs & Assessment model fits and structural uncertainty\ \midrule \begin{itemize} \itemindent = -8pt \item Recruitment: 2021-2024 recruitment indicators neutral to favorable \item Prey: favorable (krill, juvenile hake) \item Predators: unfavorable (increasing) \item CVA rank: low \end{itemize} & \begin{itemize} \itemindent = -8pt \item Very reliable catch reporting \item Generally well-sampled fishery-dependent and \protect{-independent} age compositions \item Informative age-2+ fishery-independent survey biomass index every other year \item Informative age-1 recruitment index every other year \item Includes externally estimated time-varying weight-at-age (growth) and time-varying and temperature-dependent maturity (fecundity) as data inputs \end{itemize} & \begin{itemize} \itemindent = -8pt \item Fully Bayesian stock assessment, integrating over multiple sources of parametric uncertainty \item High recruitment variability and no information on recent recruitment in assessment model from 2023 onwards, given no survey in 2024 \item Key demographics estimated using priors (natural mortality and steepness) \item Clear identity of large cohorts after at least age-3 \item Model fits well to age compositions, though occasionally at the expense of the survey index of abundance \item Uncertain how changes in the distribution of fishing relate to migration patterns and stock abundance \end{itemize} \ \hline Level~2 (medium agreement, robust evidence) & Level~1 & Level 2\ \bottomrule \end{tabular} \end{table}

Ecosystem and environmental conditions {#sec:ecosystem-conditions}

To start the process of discussing the vulnerability of r sp to climate change, we evaluated recent trends in environmental drivers of hake recruitment and growth, predators, and prey, along with the climate vulnerability assessment (CVA) rank assigned to hake by @McClureEtAl2023. We did not consider competitors, habitat, or non-fisheries human activities (such as offshore wind development) during this evaluation. Overall, we consider ecosystem and environmental conditions to be neutral (Level 2) for hake, with medium to high confidence, based on medium agreement among indicators and robust evidence. We use this, plus information related to this stock assessment, to fill out the `risk table' in Table~\@ref(tab:app-ecosystem-report-risk-tab), based on the framework outlined by the California Current Integrated Ecosystem Assessment (CCIEA) team.

Recruitment

A strong El Niño in 2023-24 caused warmer than average ocean temperatures in winter and spring. As expected during an El Niño, the biomass of lipid-rich northern copepods was generally lower during this period and indicators of krill abundance were below average. However, these conditions rapidly subsided in late spring. A delayed but strong spring upwelling ushered in cool and productive conditions, and the biomass of northern copepods and krill rebounded to near average levels for the remainder of the year. Overall, environmental conditions in winter and spring were likely less favorable for age-0 hake in 2024 [@VestfalsEtAl2023, Table A.1] but conditions transitioned to those more favorable for hake productivity in summer and fall. Observations of larval hake off Southern California were above average during 2022-2024 and juvenile hake from Central California were at or above average 2021-2024.

In 2025, the ecosystem is transitioning back to La Niña conditions, however large areas of warmer than average temperatures are still prevalent. In addition, marine heatwaves are forecasted to occur in offshore waters with the possibility of moving into coastal regions in summer and fall. It’s uncertain whether these conditions will negatively impact hake recruitment.

Hake distribution

Seasonal forecasts of hake distribution in the northern region of the California Current Ecosystem [@MalickHunsickerEtAl2020] were not available at the time of writing this summary, however these temperature-driven forecasts will be presented at the 2025 SRG meeting. Given what we know about the influence of temperature on hake distribution and the role of temperature as a proxy for prey availability [@MalickHunsickerEtAl2020, @Phillips2022], we suspect that the delayed upwelling observed in 2024 may have delayed the extent of the northern migration of hake in the spring. Return to average conditions later in summer may have supported a later northern shift as feeding conditions for hake improved.

Predators and prey

r sp are common in many predator's diets (e.g., Bluefin Tuna, Swordfish, sharks, marine mammals, Sablefish, Arrowtooth Flounder, and adult hake). Recent trends (i.e. over the last five years) in many of these populations of hake predators are stable or increasing, suggesting mild concern about increased predation. For example, the consumption of hake by Bluefin Tuna and Swordfish has been well above average in recent years and the spawning biomass of these species has increased over this period as well (Leising et al., in prep). However, recent indices of prey abundance are favorable. Krill is a dominant diet item for juvenile and adult hake [@WippelEtAl2017, @Phillips2022, @BizzarroEtAl2023]. The CCIEA krill indicator shows an increasing trend in the central CCE over the last five years.

Climate vulnerability assessment results

Hake are highly exposed with low sensitivity to climate change, with an overall rank of low [@McClureEtAl2023]. However, we note that the CVA work pre-dates recent studies on the relationships between hake distribution and recruitment and ocean conditions [@MalickHunsickerEtAl2020, @VestfalsEtAl2023].

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pacific-hake/hake-assessment documentation built on Feb. 17, 2025, 1:58 p.m.