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.

Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Curation Notes
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

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).

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). 

We used R version 4.4.1 to collate and analyze data.

Package | General purpose | Version | Citation

• blockCV | Analysis | 3.2-0 | Valavi et al. (2019)
• cmocean | Visualization | 0.3-2 | Thyng et al. (2016)
• corrplot | Visualization | 0.95 | Wei & Simko (2024)
• cowplot | Visualization | 1.2.0 | Wilke (2025)
• dplyr | Data management | 1.2.1 | Wickham et al. (2014)
• fmesher | Analysis | 0.7.0 | Lindgren (2023)
• ggeffects | Analysis | 2.3.2 | Lüdecke (2018)
• ggplot2 | Visualization | 4.0.2 | Wickham (2016)
• ggpubr | Visualization | 0.6.3 | Kassambara (2026)
• httr | Data management | 1.4.8 | Wickham (2026)
• lubridate | Data management | 1.9.5 | Grolemund & Wickham (2011)
• marmap | Analysis | 1.0.12 | Pante & Simon-Bouhet (2013)
• mgcv | Analysis | 1.9-4 | Wood (2011)
• mgcViz | Visualization | 0.2.1 | Fasiolo et al. (2020)
• ncdf4 | Data management | 1.24 | Pierce (2025)
• nlme | Analysis | 3.1-169 | Pinheiro et al (2019)
• raster | Visualization | 3.5-32 | Fasiolo et al. (2020)
• readxl | Data management | 1.4.5 | Wickham & Bryan (2026)
• rerddap | Data management | 1.2.3 | Chamberlain (2026)
• rerddapXtracto | Data management | 1.2.5 | Mendelssohn (2026)
• sdmTMB | Analysis | 1.0.0 | Anderson et al. (2022)
• sdmTMBextra | Analysis | 0.0.5 | Anderson et al. (2022)
• sf | Data management | 1.1-0 | Pebesma & Bivand (2023)
• stringr | Data management | 1.6.0 | Wickham (2025)
• tidyr | Data management | 1.3.2 | Wickham & Bryan (2026)
• writexl | Data management | 1.5.4 | Ooms (2025)
• xlsx | Data management | 0.6.5 | Dragulescu & Arendt (2020)

This section documents curation actions performed prior to publication review with the submitter, and additional information relevant to understanding and reusing this dataset. It distinguishes changes made to the submitted (meta)data from unresolved issues and/or enhancements that improve future reuse and interoperability.

CURATION ACTIONS PERFORMED ON DATA

-

CURATION ACTIONS PERFORMED ON METADATA

-

ISSUES POTENTIALLY IMPACTING REUSE

NA

Coverage: California Current System and Gulf of Alaska

NSF Award Abstract:
The investigators are using statistical and biophysical modeling techniques to examine the characteristics and drivers of timing (phenology) and distribution (geography) of fish eggs, larvae, and juveniles. They are focusing on the California Current and western Gulf of Alaska regions of the Northeast Pacific. Changes in phenology and geography are anticipated responses of marine organisms to global change in climate, with consequences for food web interactions, species abundance and productivity, and local extinctions. These responses, in turn, will have important implications for marine conservation, resource management, and the economies of coastal communities that depend on marine resources for sustenance and livelihoods. Data analysis is in collaboration with fisheries scientists at National Oceanic and Atmospheric Administration, and results will be of interest to resource managers. One of the investigators is an early-career female scientist, and the study provides training for REU students, other undergraduates, graduate students, and a postdoctoral scientist. Educational outreach includes development of online classroom and lab modules.

The overarching objective of this project is to examine the drivers and characteristics of phenology and geography of fish early life history stages (ELS) in the Northeast Pacific Ocean. Most research on how climate change impacts marine organisms has focused on adult life history stages, even though existing literature indicates that early life history stages may be more vulnerable to climate stressors. During ELS, marine fish and invertebrates can be limited in their ability to move in space and time due to strong ecological, physiological, physical, and evolutionary constraints. For these species, their ability to adapt to global change in climate through shifts in distribution and phenology depends on the events that occur during the early portion of their life cycle. Similarly, climate change impacts on organismal distribution and phenology have principally been studied separately, without any consideration of interactions between these two types of responses to changing oceanic conditions. The investigators are conducting modeling analyses based on ELS collected since the mid-1950s to test the null hypotheses that: 1) fish ELS will experience similar spatio-temporal variability in relation to ocean climate regardless of the life history strategy; 2) changes will be greater at the leading edge of a species' distribution and that the start of the spawning season will exhibit the greatest change in phenology; 3) species with large distribution changes will have smaller changes in phenology, and vice versa; and 4) historical and future projections of fish distribution that include life history information will be more skillful than those that do not include life history.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.