Krill hotspots in the California Current

Oceanic processes that concentrate zooplankton and forage fish in so-called hotspots (areas of enhanced species abundance, diversity and/or trophic interactions) have remained elusive. Zooplankton including euphausiids (krill) and copepods are important grazers of phytoplankton and prey species for a diverse array of predators; therefore, they represent a key link in marine food webs. The distribution of zooplankton is patchy and often decoupled from phytoplankton in space and time. Consequently, it has been difficult to predict the abundance and distribution of fish, seabirds and marine mammals, which depend directly on zooplankton for growth and reproduction, from remotely-sensed variables such as chlorophyll or primary production. A NASA-funded project (80NSSC17K0574) combined remote sensing products, ecosystem models and in situ data to investigate zooplankton hotspots along the U.S. West Coast and their relationship with environmental forcing, lower and higher trophic levels. We simulated the distribution of hotspots using two different, complementary approaches: 1) a high-resolution coupled biophysical model (Fiechter et al., 2020), and 2) a simple combination of satellite-based winds and currents with plankton growth and grazing equations (Messié et al., in prep). Our simulations were evaluated against in situ observations of krill from fisheries surveys and distributions of krill predators (e.g., seabirds and marine mammals). Our results highlight the importance of the upwelling process and oceanic circulation in shaping the mesoscale distribution of biological hotspots. Here we present routine products for the prediction of zooplankton hotspots along the U.S. West Coast from remotely-sensed variables.

Team: Monique Messié & Francisco Chavez (MBARI), Jerome Fiechter (UCSC), Jarrod Santora (NOAA).

Data available for download: a monthly retrospective and near real-time modeled zooplankton concentrations is available as a NetCDF file (1993-present). These were obtained using the growth-advection method described in Messié et al. (in prep), using nitrate supply computed from CCMP winds and AVISO geostrophic currents (available for download on the Nsupply webpage) and GlobCurrent 15 m depth currents. Recent data (exact date given in the file summary attribute) are based on near real-time (NRT) satellite products and are subject to caution.

Reference: Please refer to Messié et al. (in prep, available upon request) when using this product.

Contact: monique@mbari.org.

Krill spatio-temporal patterns

Current conditions

Fig. 1: Latest modeled zooplankton concentrations in the California Current (left) and corresponding anomaly relative to the 1993-2018 seasonal cycle (right).

Average patterns (1993-2018)

Fig. 2: Modeled zooplankton hotspot spatial distribution and timing (1993-2018). Left: mean concentration, middle: seasonal maximum concentration, right: timing of the seasonal maximum. Red bars indicate the location of major hotspots and the black contour is 150 km from shore. Reproduced from Messié et al. (in prep, Fig. 7).

Hotspot temporal variability

Fig. 3: Time series zooplankton concentrations within the 4 hotspots identified with red bars in Fig. 4, averaged each year over their peak months. Horizontal lines display the mean and standard deviation over the 1993-2018 time period.

Method

Monthly zooplankton hotspot maps were obtained by modeling plankton concentration as a function of satellite winds and currents using the “growth-advection” approach (Messié and Chavez, 2017; Messié et al., 2020). The method considers the evolution of plankton communities within the surface mixed layer of a water mass advected by surface currents, following an input of nutrients by a given process, here coastal upwelling. A detailed description of the method can be found in Messié et al. (in prep) and the Matlab functions used to run the full growth-advection method are available at https://bitbucket.org/messiem/toolbox_GrowthAdvection.

Fig. 4: Growth-advection method schematic. Step 1: a simple plankton model calculates zooplankton concentration over time (Zbig, black) following an upwelling event. Step 2: the model is initialized at each latitude and the result mapped on oceanic currents (example for one daily run). Step 3: daily runs are combined into monthly maps. Reproduced from Messié et al. (in prep, Fig. 1).

The plankton model was tuned to represent krill similarly to NEMURO in Fiechter et al. (2020). The result matches in situ krill data measured during the yearly Rockfish Recruitment and Ecosystem Analysis Survey (RREAS) fairly well. Relative to in situ surveys, the model has the advantage to provide krill estimates for the full year and the entire California Current. More extensive validation can be found in Messié et al. (in prep).

Fig. 5: Comparison of modeled zooplankton from the growth-advection method with yearly in situ krill surveys in May-June (RREAS) for the central hotspot. Reproduced from Messié et al. (in prep, Fig. 6b).

References

Fiechter, J., J.A. Santora, F.P. Chavez, D. Northcott and M. Messié, 2020. Krill hotspot formation and phenology in the California Current Ecosystem. Geophysical Research Letters, 47(13), e2020GL088039, doi:10.1029/2020GL088039

Messié, M. and F.P. Chavez, 2017. Nutrient supply, surface currents and plankton dynamics predict zooplankton hotspots in coastal upwelling systems. Geophysical Research Letters, 44(17), 8979-8986, doi:10.1002/2017GL074322 (press release)

Messié, M., A. Petrenko, A.M. Doglioli, C. Aldebert, E. Martinez, G. Koenig, S. Bonnet and T. Moutin, 2020. The delayed island mass effect: How islands can remotely trigger blooms in the oligotrophic ocean. Geophysical Research Letters, 47(2), e2019GL085282, doi:10.1029/2019GL085282