http://lod.bco-dmo.org/id/dataset/1010420
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2026-10-21
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Larval fish observations, environmental data, and associated analysis code from 1995-2018 from Mexico, USA, and Canada
2026-10-21
publication
2026-10-21
revision
Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)
2026-10-22
publication
https://doi.org/10.26008/1912/bco-dmo.1010420.1
Rebecca G. Asch
East Carolina University
principalInvestigator
Lorenzo Ciannelli
Oregon State University
principalInvestigator
Katherine Dale
East Carolina University
principalInvestigator
Jerome Fiechter
University of California-Santa Cruz
principalInvestigator
Richard D Brodeur
Oregon State University
principalInvestigator
John Field
National Oceanic and Atmospheric Administration - Southwest Fisheries Science Center
principalInvestigator
Moira Galbraith
Department of Fisheries and Oceans Canada
principalInvestigator
Rubén Esteban García Gómez
Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas
principalInvestigator
Rebecca Howard
Oregon State University
principalInvestigator
Mercedes Pozo Buil
University of California-Santa Cruz
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Dale, K., Ciannelli, L., Fiechter, J., Asch, R. G., Pozo Buil, M., Brodeur, R. D., García Gómez, R., Field, J., Galbraith, M., Howard, R. (2026) Larval fish observations, environmental data, and associated analysis code from 1995-2018 from Mexico, USA, and Canada. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-10-21 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.1010420.1 [access date]
Spatiotemporal tradeoffs manuscript Dataset Description: Methods and Sampling: <p>We collated larval fish density data in larvae/m<sup>3</sup> from six long-term ichthyoplankton sampling programs covering the region between the southern part of the Baja California Peninsula to the Alaska Peninsula in the western Gulf of Alaska. These surveys were the National Oceanic and Atmospheric Administration (NOAA) Ecosystems and Fisheries Oceanography Coordinated Investigations (EcoFOCI), multiple compiled surveys organized by Fisheries &amp; Oceans Canada, the NOAA Pre-recruit Survey, Newport Hydrographic Line, California Cooperative Oceanic Fisheries Investigations and the Investigaciones Mexicanas de la Corriente de California. All surveys sampled to 100-200 m depth (or bottom depth, if shallower) with net meshes ranging between 200-505 μm. Only bongo nets or ring net tows within the 200 nm Exclusive Economic Zone were retained within this study. We examined years 1995-2018. Larvae from sixteen species from three adult habitat categories (coastal pelagics, groundfishes, and mesopelagics) were retained. All species were of interest for their importance in commercial or recreational fisheries, as forage fish, or due to high biomass. Species varied in their geographic spread, from regional to coastwide. For instance, of selected mesopelagics, Panama lightfish (<em>Vinciguerria lucetia</em>) and Mexican lampfish (<em>Triphoturus mexicanus</em>) are both restricted to southern California and the Baja Peninsula, while Northern lampfish (<em>Stenobrachius leucopsarus</em>) and blue lanternfish (<em>Tarletonbeania crenularis</em>) are abundant throughout the entire study domain.</p>
<p>&nbsp;</p>
<p>The full datasets of the surveys compiled are available through the following locations/contacts:</p>
<p>- EcoFOCI:&nbsp;By request: https://apps-afsc.fisheries.noaa.gov/ichthyo/index.php&nbsp;</p>
<p>- Department of Fisheries &amp; Oceans Canada: By request (current contacts are R. Ian Perry or Moira Galbraith)</p>
<p>- NH-Line:&nbsp;By request: https://www.integratedecosystemassessment.noaa.gov/regions/california-current/newport-hydrographic-line</p>
<p>- Pre-Recruit Survey:&nbsp;By request: https://www.fisheries.noaa.gov/inport/item/18603</p>
<p>- CalCOFI: ERDDAP, https://coastwatch.pfeg.noaa.gov/erddap/tabledap/erdCalCOFIlrvcnt.html (Dataset ID: &nbsp;erdCalCOFIlrvcnt)</p>
<p>- IMECOCAL: By request: https://imecocal.cicese.mx/wp/base-de-datos/</p>
<p>&nbsp;</p>
<p><strong>Environmental data</strong></p>
<p>For environmental variables, we used a hindcast developed with the Regional Ocean Modeling System (ROMS), configured for the eastern North Pacific at 10-km resolution and encompassing the period from 1995-2020. 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 open ocean boundary conditions from the 1/12° Global Ocean Reanalysis and Simulation (Lellouche et al. 2021) provided every five days. We extracted sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and “spiciness” from ROMS data. Spiciness combines salinity and temperature and is an indicator of water mass changes. We additionally extracted bottom depth using R package <em>marmap</em> (Flament 2002). SST, SSS, and SSH were surface values, which is appropriate given the near-surface distribution for most fish larvae. Spiciness was averaged over the upper 250 m of the water column, approximately corresponding to the deepest depth that nets reached across all programs. Each empirical tow was matched to ROMS data using bilinear interpolation. ROMS data is available by request from Jerome Fiechter (fiechter@ucsc.edu).</p>
<p>&nbsp;</p>
<p><strong>References:</strong></p>
<p>Flament P. 2002. A state variable for characterizing water masses and their diffusive stability: spiciness. Prog Oceanogr. 54(1–4):493–501. https://doi.org/10.1016/S0079-6611(02)00065-4</p>
<p>Hersbach H, Bell B, Berrisford P <em>et al.</em> The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em> 2020;<strong>146</strong>(730):1999–2049. https://doi.org/10.1002/QJ.3803.</p>
<p>Lellouche JM, Eric G, Romain BB <em>et al.</em> The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. <em>Front Earth Sci (Lausanne)</em> 2021;<strong>9</strong>:698876. https://doi.org/10.3389/FEART.2021.698876.</p>
<p>&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-2049626 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2049626
onGoing
Rebecca G. Asch
East Carolina University
252-328-6307
209 East 5th Street, Mail Stop 211
Greenville
NC
27858-1821
USA
ASCHR16@ECU.EDU
pointOfContact
Lorenzo Ciannelli
Oregon State University
541-737-3142
College of Earth, Ocean, and Atmospheric Sciences (CEOAS) Oregon State University 104 CEOAS Admin. Bldg.
Corvallis
OR
97331-5503
USA
lorenzo.ciannelli@oregonstate.edu
pointOfContact
Katherine Dale
East Carolina University
6109842710
kdale@ucsc.edu
pointOfContact
Jerome Fiechter
University of California-Santa Cruz
831-459-4736
1156 High Street EMS Building A450
Santa Cruz
CA
95064
USA
fiechter@ucsc.edu
pointOfContact
Richard D Brodeur
Oregon State University
541-867-0336
Northwest Fisheries Science Center National Oceanographic and Atmospheric Administration
Newport
OR
97365
USA
ricbrodeur1@gmail.com
pointOfContact
John Field
National Oceanic and Atmospheric Administration - Southwest Fisheries Science Center
john.field@noaa.gov
pointOfContact
Moira Galbraith
Department of Fisheries and Oceans Canada
moira.galbraith@dfo-mpo.gc.ca
pointOfContact
Rubén Esteban García Gómez
Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas
regarcia@ipn.mx
pointOfContact
Rebecca Howard
Oregon State University
rebecca.howard@noaa.gov
pointOfContact
Mercedes Pozo Buil
University of California-Santa Cruz
mpozobui@ucsc.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Collaborative Research: Tradeoffs between phenology and geography constraints in response to climate change across species life cycles
https://osprey.bco-dmo.org/project/879511
Collaborative Research: Tradeoffs between phenology and geography constraints in response to climate change across species life cycles
<p><em>NSF Award Abstract:</em><br />
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.</p>
<p>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.</p>
<p>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.</p>
Pheno Geo Tradeoffs
largerWorkCitation
project
eng; USA
oceans
2026-10-21
California Current System and Gulf of Alaska
0
BCO-DMO catalogue of parameters from Larval fish observations, environmental data, and associated analysis code from 1995-2018 from Mexico, USA, and Canada
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
https://osprey.bco-dmo.org/dataset/1010420/data/download
download
onLine
dataset
<p>We collated larval fish density data in larvae/m<sup>3</sup> from six long-term ichthyoplankton sampling programs covering the region between the southern part of the Baja California Peninsula to the Alaska Peninsula in the western Gulf of Alaska. These surveys were the National Oceanic and Atmospheric Administration (NOAA) Ecosystems and Fisheries Oceanography Coordinated Investigations (EcoFOCI), multiple compiled surveys organized by Fisheries &amp; Oceans Canada, the NOAA Pre-recruit Survey, Newport Hydrographic Line, California Cooperative Oceanic Fisheries Investigations and the Investigaciones Mexicanas de la Corriente de California. All surveys sampled to 100-200 m depth (or bottom depth, if shallower) with net meshes ranging between 200-505 μm. Only bongo nets or ring net tows within the 200 nm Exclusive Economic Zone were retained within this study. We examined years 1995-2018. Larvae from sixteen species from three adult habitat categories (coastal pelagics, groundfishes, and mesopelagics) were retained. All species were of interest for their importance in commercial or recreational fisheries, as forage fish, or due to high biomass. Species varied in their geographic spread, from regional to coastwide. For instance, of selected mesopelagics, Panama lightfish (<em>Vinciguerria lucetia</em>) and Mexican lampfish (<em>Triphoturus mexicanus</em>) are both restricted to southern California and the Baja Peninsula, while Northern lampfish (<em>Stenobrachius leucopsarus</em>) and blue lanternfish (<em>Tarletonbeania crenularis</em>) are abundant throughout the entire study domain.</p>
<p>&nbsp;</p>
<p>The full datasets of the surveys compiled are available through the following locations/contacts:</p>
<p>- EcoFOCI:&nbsp;By request: https://apps-afsc.fisheries.noaa.gov/ichthyo/index.php&nbsp;</p>
<p>- Department of Fisheries &amp; Oceans Canada: By request (current contacts are R. Ian Perry or Moira Galbraith)</p>
<p>- NH-Line:&nbsp;By request: https://www.integratedecosystemassessment.noaa.gov/regions/california-current/newport-hydrographic-line</p>
<p>- Pre-Recruit Survey:&nbsp;By request: https://www.fisheries.noaa.gov/inport/item/18603</p>
<p>- CalCOFI: ERDDAP, https://coastwatch.pfeg.noaa.gov/erddap/tabledap/erdCalCOFIlrvcnt.html (Dataset ID: &nbsp;erdCalCOFIlrvcnt)</p>
<p>- IMECOCAL: By request: https://imecocal.cicese.mx/wp/base-de-datos/</p>
<p>&nbsp;</p>
<p><strong>Environmental data</strong></p>
<p>For environmental variables, we used a hindcast developed with the Regional Ocean Modeling System (ROMS), configured for the eastern North Pacific at 10-km resolution and encompassing the period from 1995-2020. 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 open ocean boundary conditions from the 1/12° Global Ocean Reanalysis and Simulation (Lellouche et al. 2021) provided every five days. We extracted sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and “spiciness” from ROMS data. Spiciness combines salinity and temperature and is an indicator of water mass changes. We additionally extracted bottom depth using R package <em>marmap</em> (Flament 2002). SST, SSS, and SSH were surface values, which is appropriate given the near-surface distribution for most fish larvae. Spiciness was averaged over the upper 250 m of the water column, approximately corresponding to the deepest depth that nets reached across all programs. Each empirical tow was matched to ROMS data using bilinear interpolation. ROMS data is available by request from Jerome Fiechter (fiechter@ucsc.edu).</p>
<p>&nbsp;</p>
<p><strong>References:</strong></p>
<p>Flament P. 2002. A state variable for characterizing water masses and their diffusive stability: spiciness. Prog Oceanogr. 54(1–4):493–501. https://doi.org/10.1016/S0079-6611(02)00065-4</p>
<p>Hersbach H, Bell B, Berrisford P <em>et al.</em> The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em> 2020;<strong>146</strong>(730):1999–2049. https://doi.org/10.1002/QJ.3803.</p>
<p>Lellouche JM, Eric G, Romain BB <em>et al.</em> The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. <em>Front Earth Sci (Lausanne)</em> 2021;<strong>9</strong>:698876. https://doi.org/10.3389/FEART.2021.698876.</p>
<p>&nbsp;</p>
Specified by the Principal Investigator(s)
<p><strong>R scripts provided:</strong></p>
<p>The modeling procedure generally began with modelPrediction.R, which iteratively compares models with different environmental covariates for each species (<em>runEnviroCovariatesComparison() </em>and<em> evaluateEnviroCovariates()</em>). Once the most predictive covariates are identified for each species, we then compare four spatiotemporal models through a similar process (<em>runTradeoffModelComparison()</em> and <em>evaluateTradeoffs()</em>). This script uses getMesh.R and getSpeciesData.R, as well as the covariates.xlsx and timeblocks.xlsx files.</p>
<p>modelPrediction.R contains all remaining analyses, beginning with generating a new prediction grid and predicting larval densities across this grid (<em>createPredictionObjects()</em>).&nbsp;</p>
<p>&nbsp;</p>
<p><strong>Phenology.Rproj - </strong>R project shell, for running code without the need to redefine working directory variable.</p>
<p><strong>modelComparison.R</strong> - To run model comparison loops for environmental covariates and for tradeoff models.</p>
<p><strong>getMesh.R</strong> - For creating sdmTMB meshes. Called by modelComparison.R.</p>
<p><strong>getSpeciesData.R</strong> - For extracting both positive tows and zero stations for a specific species. Called by modelComparison.R and createPredictionGrid.R.</p>
<p><strong>createPredictionGrid.R</strong> - Creates prediction grid of points and links this to ROMS outputs. ROMS data available by request from Jerome Fiechter (fiechter@ucsc.edu)</p>
<p><strong>modelPrediction.R</strong> - For creating prediction objects based on stacked spatiotemporal models, then complete analyses on subsequent modeled output (calculate and analyze tradeoff metrics; calculate niche hypervolume; create figures; run ridge regression).&nbsp;</p>
<p><strong>linkRoms.R </strong>- For linking ROMS output with points in new prediction grids. Called by createPredictionGrid.R. ROMS data available by request from Jerome Fiechter (fiechter@ucsc.edu).</p>
<p>&nbsp;</p>
<p><strong>Non-data spreadsheets:</strong></p>
<p><strong>Covariates.xlsx</strong> - Used in modelComparison.R to compare models with different sets of environmental covariates. Also called by modelPrediction.R.</p>
<p><strong>Timeblocks.xlsx</strong> - Timeblock designations for 1995-2018, used to link year in datasets to a character timeblock string. Required in modelPrediction.R, getSpeciesData.R, and modelComparison.R.<br />
&nbsp;</p>
Package | Version | Files | General purpose | Citation
• blockCV | 3.2-0 | modelComparison | Analysis | Valavi et al. (2019)
• boot | 1.3-32 | modelPrediction | Analysis | Canty & Ripley (1999)
• Cairo | 1.7-0 | modelPrediction, modelComparison | Visualization | Urbanek & Horner (2025)
• cmocean | 0.3-2 | modelPrediction | Visualization | Thyng et al. (2016)
• colorspace | 2.1-2 | modelPrediction | Visualization | Zeileis et al. (2020)
• corrplot | 0.95 | modelPrediction, modelComparison | Analysis | Wei & Simko (2024)
• cowplot | 1.2.0 | modelPrediction | Visualization | Wilke (2025)
• dplyr | 1.2.1 | modelPrediction, modelComparison, linkRoms, getSpeciesData | Data management | Wickham et al. (2014)
• fmesher | 2.3.2 | getMesh | Analysis | Lindgren (2023)
• ggeffects | 0.5.2 | modelPrediction | Analysis | Lüdecke (2018)
• ggnewscale | 4.0.2 | modelPrediction | Visualization | Campitelli (2025)
• ggplot2 | 0.6.3 | modelPrediction, modelComparison, createPredictionGrid | Visualization | Hadley Wickham (2016)
• ggpubr | 0.1.2 | modelPrediction | Visualization | Kassambara (2026)
• ggtext | 4.1-10 | modelPrediction | Visualization | Wilke & Wiernik (2022)
• glmnet | 1.9.5 | modelPrediction | Analysis | Friedman et al. (2010)
• lubridate | 1.0.12 | linkRoms | Data management | Grolemund & Wickham (2011
• marmap | 1.9-4 | createPredictionGrid | Data access | Pante & Simon-Bouhet (2013)
• mgcv | 0.2.1 | modelPrediction | Analysis | Wood (2011)
• mgcViz | 1.24 | modelPrediction | Analysis | Fasiolo et al. (2020)
• ncdf4 | 1.2.2 | linkRoms | Data access | Pierce (2025)
• purrr | 1.4.5 | modelComparison | Data management | Wickham & Henry (2025)
• readxl | 1.4 | modelPrediction, modelComparison, getSpeciesData | Data management | Wickham & Bryan (2026)
• scales | 1.0.0 | modelPrediction | Visualization | Wickham et al. (2025)
• sdmTMB | 0.0.5 | modelPrediction, modelComparison, getMesh | Analysis | Anderson et al. (2022)
• sdmTMBextra | 1.1-0 | getMesh | Analysis | Anderson et al. (2022)
• sf | 1.6.0 | modelPrediction, modelComparison, getSpeciesData, getMesh, createPredictionGrid | Mapping | Pebesma & Bivand (2023)
• stringr | 1.9-11 | modelPrediction | Data management | Wickham (2025)
• terra | 1.3.2 | linkRoms | Data management | Hijmans et al. (2026)
• tidyr | 0.6.5 | modelPrediction, linkRoms, getSpeciesData, createPredictionGrid | Data management | Wickham & Bryan (2026)
• viridis | 1.5.4 | modelPrediction | Visualization | Garnier et al. (2024)
• writexl | 0.6.5 | modelPrediction, modelComparison | Data management | Ooms (2025)
• xlsx | 3.2-0 | modelPrediction, modelComparison | Data management | Dragulescu & Arendt (2020)
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact