DOI: 10.5061/dryad.tx95x6b8x
title: Direct effects and prey-mediated effects of global change in projections of early life stages of pelagic predators

description: 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.
description: # Direct effects and prey-mediated effects of global change in projections
 of early life stages of pelagic predators This repository contains model
 inputs and model processing (R) files. Specifically, the repo contains: 1.
 Five R scripts for model processing (eg model creation, model selection,
 creating model output and creating figures). 2. Three excel files that
 also serve as input for R files (biotic variables, additional abiotic
 variables, and figure input). 3. 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
 surface * `TEMPMAX`: Water temperature in degrees celsius for DEPTH_IMAX.
 * `SALSURF`: Salinity in ppt or practical salinity units (PSU) for the sea
 surface * `CHLORSURF`: "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
 surface * `TEMPMAX`: Water temperature in degrees celsius for DEPTH_IMAX.
 * `SALSURF`: Salinity in ppt or practical salinity units (PSU) for the sea
 surface * `CHLORSURF`: "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 taxa * `Displacement Direction (degrees)`: Degree
 direction change between historical and future GCOAs * `Displacement
 Cardinal Direction`: Cardinal direction change (eg north, east) between
 historical and future GCOAs * `Displacement Distance (km)`: Distance (km)
 between historical and future GCOAs * `Historical 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](https://psl.noaa.gov/ipcc/cmip6/ccwp6.html)