Interannual variability in both local environmental conditions and basin-scale oceanographic modes of variability is thought to drive larval fish abundance by affecting the timing and location of spawning. We used an extensive compiled dataset of larval fish density from 1995-2018 from continental shelf regions off central California to the Gulf of Alaska to model how sablefish (Anoplopoma fimbria) shifted larval seasonality and geography in response to environmental variability and oceanographic regime shifts. The most predictive spatiotemporal model was the simplest and did not include seasonal or geographic variability, with bottom depth and sea surface temperature as the most predictive environmental covariates. Sablefish larval distributions were centered in the shelf region of the eastern Gulf of Alaska and exhibited correlated shifts among latitudinal, longitudinal, and onshore-offshore center of gravities, but not with seasonal changes. There were few links between geographic and seasonal center of gravities and average environmental conditions, though modeled larval abundance peaked earlier in the year during warmer, spicier average ocean conditions and positive Pacific Decadal Oscillation values. Phase-specific conditional effects models for the Pacific Decadal Oscillation and Oceanic Niño Index showed that sablefish larvae were uniformly associated with water temperatures between 7-8°C while having a more variable relationship with bottom depth. Our results indicate that sablefish larvae demonstrate seasonal shifts in order to maintain a consistent thermal habitat.

Sablefish larval data

We collated data from five long-term ichthyoplankton sampling programs covering the region between the Southern California Bight (35° latitude) to the Alaska Peninsula in the western Gulf of Alaska. From north to south, these surveys were the National Oceanic and Atmospheric Administration (NOAA) Ecosystems and Fisheries Oceanography Coordinated Investigations (EcoFOCI), multiple compiled surveys organized by Department of Fisheries & Oceans Canada (DFO), the NOAA Pre-recruit Survey, Newport Hydrographic Line (NH-Line), and the California Cooperative Oceanic Fisheries Investigations (CalCOFI). Larval data includes samples taken by oblique and paired bongo tows with 236-505 µm mesh from throughout the study area, along with neuston tows from Alaska and southern California. Neuston tows had 505 µm mesh and net openings between 0.13-15 m². Tows were subset to 1995 to 2018 within the 200 nmi Exclusive Economic Zone within ocean or estuarine waters (salinity ≥ 30 ppt). The northern geographic extent of the area examined was bounded by the latitudinal range of positive tows (59.8°N). We restricted the southern limit to be ≥ 35°N latitude. From north to south, these surveys were the National Oceanic and Atmospheric Administration (NOAA) Ecosystems and Fisheries Oceanography Coordinated Investigations (EcoFOCI), multiple compiled surveys organized by Department of Fisheries & Oceans Canada (DFO), the NOAA Pre-recruit Survey, Newport Hydrographic Line (NH-Line), and the California Cooperative Oceanic Fisheries Investigations (CalCOFI).

The full datasets of the surveys compiled are available through the following locations/contacts:

- EcoFOCI: By request: https://apps-afsc.fisheries.noaa.gov/ichthyo/index.php 

- Department of Fisheries & Oceans Canada: By request (current contacts are R. Ian Perry or Moira Galbraith)

- NH-Line: By request: https://www.integratedecosystemassessment.noaa.gov/regions/california-current/newport-hydrographic-line

- Pre-Recruit Survey: By request: https://www.fisheries.noaa.gov/inport/item/18603

- CalCOFI: ERDDAP, https://coastwatch.pfeg.noaa.gov/erddap/tabledap/erdCalCOFIlrvcnt.html (Dataset ID:  erdCalCOFIlrvcnt)

 Environmental data

Environmental data were extracted from a 10-km Regional Ocean Modeling System (ROMS) configured for the eastern Pacific at a 10-km resolution from 1995-2020 (Pozo Buil et al. in prep). The model was forced with daily atmospheric fields from the ERA5 reanalysis produced by European Centre for Medium-Range Weather Forecasts (Hersbach et al. 2020) and with open-ocean boundary conditions from the 1/12° Global Ocean Reanalysis and Simulation (Lellouche et al. 2021) with a five-day resolution. We extracted sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and spiciness from ROMS. Spiciness was averaged over the upper 250 m of the water column, corresponding to the maximum depth sampled across the five programs. Empirical catch data collected from field sampling were matched to ROMS data using bilinear interpolation. Bottom depth (i.e., water depth at each sampling location) was extracted for each data point using R package ‘marmap’ (Pante and Simon-Bouhet 2013) which uses bathymetry 15 arc-second data from the 2022 NOAA Earth TOPOgraphy dataset (National Centers for Environmental Information [NCEI] 2022). Pacifical Decadal Oscillation values are available at https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/index/ersst.v5.pdo.dat and Oceanic Nino Index values are available at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php.

 References:

Hersbach H, Bell B, Berrisford P et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 2020;146(730):1999–2049. https://doi.org/10.1002/QJ.3803.

Lellouche JM, Eric G, Romain BB et al. The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. Front Earth Sci (Lausanne) 2021;9:698876. https://doi.org/10.3389/FEART.2021.698876.

National Centers for Environmental Information (NCEI). ETOPO 2022 15 Arc-Second Global Relief Model. 2022. https://doi.org/10.25921/fd45-gt74.

Pante E, Simon-Bouhet B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS One 2013;8(9). https://doi.org/10.1371/journal.pone.0073051.