Trends in Pacific Canadian groundfish stock status

We assembled estimated biomass (B) time series from stock assessments for 24 Pacific Canadian groundfish stocks and modelled average and stock status through 2020 based on biomass relative to each stock's (1) Limit Reference Point (B/LRP), (2) Upper Stock Reference (B/USR), and (3) biomass at maximum sustainable yield (B/BMSY). The overall mean B/LRP in 2020 was 3.2 (95% credible interval [CI]: 2.6-3.9). The overall mean B/USR and B/BMSY in 2020 was 1.5 (95% CI: 1.3-1.9) and 1.4 (95% CI: 1.1-1.7), respectively. Average stock status declined from 1950 to around 2000 and has remained relatively stable since then. The change around 2000 followed the implementation of ITQs (individual transferable quotas) for the trawl fleet and the commencement of the synoptic trawl surveys. As of their last assessment, four stocks (Strait of Georgia Lingcod [Area 4B], coastwide Bocaccio, and inside and outside Quillback Rockfish) had a greater than 5% probability of being below their LRP (i.e., in the "critical zone"); Pacific Cod in Area 3CD had a 4.6% probability. Roughly one-third of stocks had a greater than 1 in 4 chance of being below their USR (i.e., in the "cautious zone"). Conversely, two-thirds of assessed groundfish stocks had a high (>75%) probability of being above the USR (i.e., in the "healthy zone").

Harmful Algae and Oceanographic Conditions in the Strait of Georgia, Canada Based on Citizen Science Monitoring

In British Columbia (BC), harmful algal blooms (HABs) regularly cause severe economic losses through finfish mortalities and shellfish harvest closures due to toxin accumulation, gill damage, or hypoxia. As there is no routine governmental monitoring of HAB phenomena in BC, HAB variability, and its potential links to environmental drivers are not well understood. Here we present results from a well-managed citizen science program which collected an unprecedented 4 year, high-resolution (∼bi-monthly, ∼80 stations) dataset of harmful algae (HA) concentrations and corresponding physical and chemical properties of seawater throughout the Strait of Georgia (SoG), BC. Analysis of this dataset revealed statistically significant interannual and seasonal relationships between environmental drivers and the most common HA taxa: Rhizosolenia setigera, Dictyocha spp., Alexandrium spp., Heterosigma akashiwo, Chaetoceros convolutus, and C. concavicornis. HABs exhibited significant interannual variations; specifically, no HABs were found during the summer of 2015, blooms of Dictyocha occurred in 2016 and 2017, and dense blooms of Heterosigma and Noctiluca occurred in 2018. In addition, HA prevalence corresponded with negative effects observed in local aquaculture facilities where higher toxins concentrations (causing Paralytic and Diarrhetic Shellfish Poisonings) in shellfish flesh were detected during years with greater abundance of Alexandrium and Dinophysis. Furthermore, salmon mass mortality at fish farms corresponded to years with high concentrations of Heterosigma and Dictyocha. As such, these results highlight the need for long-term data to evaluate the potential role of HA as a stressor on the SoG ecosystem.

A clustering approach to determine biophysical provinces and physical drivers of productivity dynamics in a complex coastal sea

The balance between ocean mixing and stratification influences primary productivity through light limitation and nutrient supply in the euphotic ocean. Here, we apply a hierarchical clustering algorithm (Ward's method) to four factors relating to stratification and depth-integrated phytoplankton biomass extracted from a biophysical regional ocean model of the Salish Sea to assess spatial co-occurrence. Running the clustering algorithm on four years of model output, we identify distinct regions of the model domain that exhibit contrasting wind and freshwater input dynamics, as well as regions of varying watercolumn-averaged vertical eddy diffusivity and halocline depth regimes. The spatial regionalizations in physical variables are similar in all four analyzed years. We also find distinct interannually consistent biological zones. In the Northern Strait of Georgia and Juan de Fuca Strait, a deeper winter halocline and episodic summer mixing coincide with higher summer diatom abundance, while in the Fraser River stratified Central Strait of Georgia, shallower haloclines and stronger summer stratification coincide with summer flagellate abundance. Cluster based model results and evaluation suggest that the Juan de Fuca Strait supports more biomass than previously thought. Our approach elucidates probable physical mechanisms controlling phytoplankton abundance and composition. It also demonstrates a simple, powerful technique for finding structure in large datasets and determining boundaries of biophysical provinces.

A study of intermediate water circulation in the Strait of Georgia using tracer-based, Eulerian, and Lagrangian methods

The intermediate circulation of the Strait of Georgia, British Columbia, Canada, plays a key role in dispersing contaminants throughout the Salish Sea, yet little is known about its dynamics. Here, we use hydrographic observations and hindcast fields from a regional 3D model to approach the intermediate circulation from three perspectives. Firstly, we derive and model a “seasonality” tracer from temperature observations to age the water, estimate mixing, and infer circulation. Secondly, we analyze modeled velocity fields to create mean current maps and examine the advective and diffusive components of the mean flow field. Lastly, we calculate Lagrangian trajectories to derive Transit Time Distributions and Lagrangian statistics. In combination, these analyses provide an overview of the mean intermediate circulation that can be summarized as follows: subducting water in Haro Strait ventilates the intermediate water primarily via an up-strait boundary current that flows along the eastern shores of the southernmost basin in 1–2 months. This inflowing water is either incorporated into the interior of the basin, recirculated southwards, or transported into the northernmost basin, mixing steadily with adjacent water masses during its transit. A second, shallower ventilating jet emanates southwards from Discovery Passage, locally modifying the Haro Strait inflow signal. Outside of these well-defined advective features, diffusive transport dominates in the majority of the region. The intermediate renewal signal fully ventilates the region in 100–140 days, which serves as a benchmark for contaminant dispersal timescale estimates.

Clustering coupled biochemical-physical model results formalizes regional provinces in a coastal region

<p>Coastal regions by their very nature are dynamically diverse.  Within one geographical region there are often multiple areas dominated by substantially different dynamics that shape not only the physical characteristics but also the ecosystem.  The Salish Sea, in the northeast Pacific, is an excellent example with strongly tidally mixed regions, freshwater-dominated regions, and regions directly influenced by the open ocean.  These regions are generally well known and multiple disciplines refer to them with various boundaries and under various names.  Here we use unsupervised clustering on numerical model results to formalize these regional provinces.  The model is SalishSeaCast,  a three-dimensional real-time coupled bio-chem-physical model based on the NEMO framework.  We find that the regions clustered on ecosystem variables (phytoplankton biomass) spatially coincide with those clustered on physical variables, particularly the stratification as diagnosed by the halocline depth.  The clusters are robust across years with interannual variability manifesting mostly in changes in the size of the clusters.  As the clusters are dynamically distinct, they provide a natural framework on which to evaluate the model against observations.  We find that the model accurately simulates each of the major clusters.  The spatial and temporal resolution of the model can then characterize these different clusters more systematically than the observations, revealing biases associated with sparse sampling in the observations. Two examples will be given, one addressing a long-standing issue of the productivity gradient in the stratified main basin, the Strait of Georgia, and another concerning the seasonal cycle of productivity in the ocean-influenced Juan de Fuca Strait.</p>

Observations Inform Improvements in Model Silicon Cycling in a Semi-enclosed Coastal Sea

<p>We have developed a coupled physical-biological model representing plankton and nutrient dynamics of the Strait of Georgia, a fjord-like semi-enclosed coastal sea on the west coast of Canada. The nutrient-phytoplankton-zooplankton-detritus (NPZD)-type biological model is based on nitrogen uptake and remineralization with a coupled silicon cycle and includes both diatom and non-siliceous phytoplankton functional groups. The Strait of Georgia exhibits an estuarine circulation driven by input from the Fraser River as well as many smaller rivers and streams. It has high levels of dissolved silica (can be >50 μM even at the surface). Silicon-replete conditions shape key characteristics of the local ecosystem, which include heavily silicified glass sponge reefs as well as frequent diatom and occasional silicoflagellate blooms. We therefore consider the ability of the model to match observed silicon levels an indicator of the fidelity of its representation of local biogeochemistry. Silicon in the model may be in the form of dissolved silica, living diatoms, or particulate biogenic silica, and model diatom growth may be limited by nitrogen, light, or dissolved silica availability. We will discuss the challenges involved in accurately representing important drivers of the regional silicon cycle. These include accurately capturing the division of primary productivity between diatoms and non-siliceous phytoplankton functional groups, as well as uncertainties in the magnitude of terrestrial inputs and sediment fluxes. We will show how evaluating the model functional groups by comparison with phytoplankton community composition determined by high performance liquid chromatography (HPLC) has informed our interpretation of model results and provided direction for efforts at improving model performance. We will discuss the impact of targeted adjustments to model parameters on the model silicon cycle in light of comparisons to observations.</p>