Direct effects and prey-mediated effects of global change in projections of early life stages of pelagic predators
Data files
Aug 27, 2025 version files 1.20 MB
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(01)_ESM_Projections_and_Maps.R
25.80 KB
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(02)_Predictive_Skill_of_Candidate_GAMs.R
14.90 KB
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(03)_Plankton_SDMs.R
74.22 KB
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(04)_Partial_DE_and_Effects_Plots_of_GAMs.R
24.76 KB
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(05)_Plankton_Summary_Plots.R
12.68 KB
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btESM.nc
30.61 KB
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chlorESM2.nc
34.93 KB
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GCOB_Output.xlsx
10.38 KB
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historicalclimatedata.xlsx
302.75 KB
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README.md
12.39 KB
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salESM.nc
41.62 KB
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springplankton4.xlsx
573.02 KB
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sstESM.nc
40.77 KB
Abstract
Global change will impact the distribution and abundance of predators through a combination of abiotic variables, such as temperature, and biotic variables, such as prey availability. However, there is a poor understanding of how distribution projections with biotic variables differ from those with abiotic variables, particularly in resource limited and marine systems. We address this knowledge gap using the planktonic larvae of iconic and economically important pelagic fish predators. We leverage a multidecadal, long-term sampling program from the western Atlantic Ocean to assess the efficacy of using zooplankton prey (copepods, larvaceans and cladocerans) and climate variables to predict the distribution of larvae of seven pelagic fish species, including tunas, billfishes and mahi-mahi. These data (2 excel files, plus an additional excel file), as well as earth system model data (4 netcdf files) that inform projections are included here. We also include 5 R scripts that processed data, configured models, produced plots, etc. Results revealed that zooplankton prey, particularly larvaceans, showed high importance for predicting the distribution of smaller tunas. Temperature showed high importance for true tuna (Thunnus spp.), billfish and mahi-mahi. Statistical models linking predator, prey and abiotic variables were forced with climate projections from an ensemble of earth system models to assess zooplankton and fish larvae distribution changes. Redistributions and declines of zooplankton prey led to minimal changes in abundance and distribution for most larval taxa. However, direct climate change effects, driven partially by ocean warming, led to increases in abundance and northward distribution shifts for multiple larval taxa. These climate change-zooplankton–fish larvae relationships highlight that future distribution and abundance changes of predators can be dampened when assessing impacts of prey availability changes. We also show that in a resource-limited system, key pelagic predators, many of which produce lucrative fisheries, are spatiotemporally linked with their preferred zooplankton prey.
This repository contains model inputs and model processing (R) files. Specifically, the repo contains:
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Five R scripts for model processing (eg model creation, model selection, creating model output and creating figures).
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Three excel files that also serve as input for R files (biotic variables, additional abiotic variables, and figure input).
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Four (for each abiotic variable) netcdf files of earth system model (ESM) output (surface temperature, bottom temperature, surface salinity and surface chlorophyll) that serve as input for R files.
-Netcdf files can be viewed in R via the ncdf4 package (other options include the netCDF4 library in Python). We also suggest Panoply as an open software that does not require programmatic skills.
Description of five R scripts (run R scripts in numerical order)
(01)_ESM_Projections_and_Maps.R
- Description: This R file processes all of the data from the ESM projections.
It also uses the Excel files to perform bias corrections so that abiotic variables can be used in the proceeding R scritps where models are further output are created.
It also create maps of the different variables (supplemental material).
(02)_Predictive_Skill_of_Candidate_GAMs.R
- Description: This R file uses the abiotic data processed from the previous R file as well as the abundances of the different zooplankton (springplankton4) and ichthyoplankton to create candidate generalized addivitve models (GAMs) for each species.
Predictive skill of each candidate gam for each species is assessed, and the highest performing gam is then chosen for further analysis in the upcoming scripts.
Outputs are included in Table 1, and figures are included in the supplemental material.
(03)_Plankton_SDMs.R
- Description: This R file creates distribution models (from the highest performing GAM per the previous script) and associated maps for each model type and each plankton species.
It creates Figures 2 and 5.
(04)_Partial_DE_and_Effects_Plots_of_GAMs.R
- Description: This R file produces partial deviance explained values for each variable in each final model.
It also produced partial effects plots for each variable in each final model.
Outputs are included in tables and figures in the supplemental material.
(05)_Plankton_Summary_Plots.R
- Description: This R file produces the vector plot displaying changes in displacement and the percent change in abundances plot.
The GCOB_Output.xlsx file can be used to produce the vector plot, or, output from the previous R script can be used.
It creates Figures 3 and 4.
Description of three excel files
springplankton4.xlsx
- Description: A microsoft excel file containing zooplankton and ichthyoplankton abundance data, as well as associated geographic and abiotic variables, through time and space.
- Format:
.xlsx - Size: 666 KB
- Dimensions: 700 rows × 126 columns
- Variables:
SAMPLE_NO: 5-characer code designating the SEAMAP plankton sample number. This is a unique identifier for each sample.TEMPSURF: Water temperature in degrees celsius for the sea surfaceTEMPMAX: Water temperature in degrees celsius for DEPTH_IMAX.SALSURF: Salinity in ppt or practical salinity units (PSU) for the sea surfaceCHLORSURF: "Chlorophyll a in in milligrams per cubic meter for the sea surface.SEASON: Season that data was collected: Spring, Summer, Fall, Winter.YEAR: Year that data was collected.MO_DAY_YR: Date station was taken in MM/DD/YYYY format.VESSEL: 2-character vessel code.CRUISE_NO: 3-character cruise code.P_STA_NO: 5-character code designating the Pascagoula station number.S_STA_NO: 5-character SEAMAP or alternate station number.TIME_ZN: 1-character time zone code.TIME_MIL: Starting time (HHMM) in 24 hour military time.DEPTH_SSTA: Station depth in fathoms for the position occupied at the start time of a station.VOL_FILT: Volume of water in cubic meters filtered through the plankton net.DEPTH_IMAX: Maximum tow depth in meters.lat: Degree of latitude for the position occupied at the start time of a station.lon: Degree of longitude for the position occupied at the start time of a station.Anomurans-THUNNUSTHYNNUS(column 20 - column 112): Abundances (individuals under 1 m^2 of sea surface) of 92 plankton taxa. Zooplankton contain lowercase letter. Ichthyoplankton are in all caps.ISTIOPHORIDAE_PA-SCOMBEROMORUS_PA(column 113 - column 126): Presence - absence (1 = presence, 0 = absence) of 13 ichthyoplankton taxa.
historicalclimatedata.xlsx
- Description: A microsoft excel file containing historical climate data from included (SEAMAP) surveys used for ESM bias corrections.
- Format:
.xlsx - Size: 303 KB
- Dimensions: 4748 rows × 10 columns
- Variables:
VESSEL: 2-character vessel code.CRUISE_NO: 3-character cruise code.P_STA_NO: 5-character code designating the Pascagoula station number.MO_DAY_YR: Date station was taken in MM/DD/YYYY format.lat: Degree of latitude for the position occupied at the start time of a station.lon: Degree of longitude for the position occupied at the start time of a station.TEMPSURF: Water temperature in degrees celsius for the sea surfaceTEMPMAX: Water temperature in degrees celsius for DEPTH_IMAX.SALSURF: Salinity in ppt or practical salinity units (PSU) for the sea surfaceCHLORSURF: "Chlorophyll a in in milligrams per cubic meter for the sea surface.
- Other Notes: This file contains empty cells (not filled in with 'n/a's to maintain R script function and output) in the last 4 columns.
Such empty cells (ie missing values) occur due to gear/equipment malfunctioning and/or errors (eg seawater container leaks, chlorophyll extraction contamination, etc., prevent obtaining accurate values).
GCOB_Output.xlsx
- Description: A microsoft excel file containing changes in geographic center of abundance (GCOA) and displacement distances for each taxa and model.
- Format:
.xlsx - Size: 10 KB
- Dimensions: 19 rows × 8 columns
- Variables:
Taxa: Plankton taxa and model type (abiotic vs biotic) for ichthyoplankton taxaDisplacement Direction (degrees): Degree direction change between historical and future GCOAsDisplacement Cardinal Direction: Cardinal direction change (eg north, east) between historical and future GCOAsDisplacement Distance (km): Distance (km) between historical and future GCOAsHistorical GCOA Longitude: Historical center of abundance longitude (degrees)Historical GCOA Latitude: Historical center of abundance latitude (degrees)Projected GCOA Longitude: Future center of abundance longitude (degrees)Projected GCOA Latitude: Future center of abundance latitude (degrees)
Description of four netcdf files
sstESM.nc
- Description: A NetCDF file containing spatial surface temperature predictions from an ensemble of 4 Earth System Models (ESMs) in the Gulf of Mexico.
- Format:
.nc - Size: 193584 B
- Dimensions: 11 rows (latitude) × 10 columns (longitude)
- Coordinate system: Unprojected geographic coordinates on a sphere (EPSG:4326 – WGS84) (
proj=longlat) - Resolution: 1.0 × 1.0 degrees
- Extent: 261.5, 280.5, 20.5, 30.5 (xmin, xmax, ymin, ymax)
- Abiotic 'Variable': Sea Surface Temperature (the below data variables pertain to this abiotic variable)
- Units: degrees celcius
- Data Variables:
histclim1-4: Mean historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.anomaly1-4: Differences between mean historical (1985-2014) and future (2070-2099) values, Numbers (1-4) denote one of four ESMs in the ensemble.histstddv1-4: Standard deviation of historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.varratio1-4: Variance ration between historical (1985-2014) and future (2070-2099) values. Numbers (1-4) denote one of four ESMs in the ensemble.
btESM.nc
- Description: A NetCDF file containing spatial temperature predictions (at a depth of 200 m) from an ensemble of 4 Earth System Models (ESMs) in the Gulf of Mexico.
- Format:
.nc - Size: 148144 B
- Dimensions: 11 rows (latitude) × 10 columns (longitude)
- Coordinate system: Unprojected geographic coordinates on a sphere (EPSG:4326 – WGS84) (
proj=longlat) - Resolution: 1.0 × 1.0 degrees
- Extent: 261.5, 280.5, 20.5, 30.5 (xmin, xmax, ymin, ymax)
- Abiotic 'Variable': "Sea Water Temperature at 200m" (the below data variables pertain to this abiotic variable)
- Units: degrees celcius
- Data Variables:
histclim1-4: Mean historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.anomaly1-4: Differences between mean historical (1985-2014) and future (2070-2099) values, Numbers (1-4) denote one of four ESMs in the ensemble.histstddv1-4: Standard deviation of historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.varratio1-4: Variance ration between historical (1985-2014) and future (2070-2099) values. Numbers (1-4) denote one of four ESMs in the ensemble.
salESM.nc
- Description: A NetCDF file containing spatial surface salinity predictions from an ensemble of 4 Earth System Models (ESMs) in the Gulf of Mexico.
- Format:
.nc - Size: 193584 B
- Dimensions: 11 rows (latitude) × 10 columns (longitude)
- Coordinate system: Unprojected geographic coordinates on a sphere (EPSG:4326 – WGS84) (
proj=longlat) - Resolution: 1.0 × 1.0 degrees
- Extent: 261.5, 280.5, 20.5, 30.5 (xmin, xmax, ymin, ymax)
- Abiotic 'Variable': Sea Surface Salinity (the below data variables pertain to this abiotic variable)
- Units: psu (Partial Salinity Units)
- Data Variables:
histclim1-4: Mean historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.anomaly1-4: Differences between mean historical (1985-2014) and future (2070-2099) values, Numbers (1-4) denote one of four ESMs in the ensemble.histstddv1-4: Standard deviation of historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.varratio1-4: Variance ration between historical (1985-2014) and future (2070-2099) values. Numbers (1-4) denote one of four ESMs in the ensemble.
chlorESM2.nc
- Description: A NetCDF file containing spatial surface chlorophyll predictions from an ensemble of 3 Earth System Models (ESMs) in the Gulf of Mexico.
- Format:
.nc - Size: 149368 B
- Dimensions: 11 rows (latitude) × 10 columns (longitude)
- Coordinate system: Unprojected geographic coordinates on a sphere (EPSG:4326 – WGS84) (
proj=longlat) - Resolution: 1.0 × 1.0 degrees
- Extent: 261.5, 280.5, 20.5, 30.5 (xmin, xmax, ymin, ymax)
- Abiotic 'Variable': Mass Concentration of Total Phytoplankton Expressed as Chlorophyll in Sea Water (the below data variables pertain to this abiotic variable)
- Units: 1.E-9 kg m-3
- Data Variables:
histclim1-3: Mean historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.anomaly1-3: Differences between mean historical (1985-2014) and future (2070-2099) values, Numbers (1-4) denote one of four ESMs in the ensemble.histstddv1-3: Standard deviation of historical (1985-2014) values. Numbers (1-4) denote one of four ESMs in the ensemble.varratio1-3: Variance ration between historical (1985-2014) and future (2070-2099) values. Numbers (1-4) denote one of four ESMs in the ensemble.
All analyses were conducted using R Statistical Software (v4.3.0; R Core Team 2023)
Access Information
SEAMAP data is available upon request from the Southeast Fisheries Science Center.
ESM data is available at https://psl.noaa.gov/ipcc/cmip6/ccwp6.html
