DOI: 10.5061/dryad.3n5tb2rs7
title: Large differences in the distribution of pelagic biomass as a result of sonar frequency choice

description: Recent efforts to understand the global distribution of pelagic fauna have
 primarily relied on 38 kHz sonar observations, using water-column
 backscatter as a proxy of biomass. However, backscatter gradients across
 ocean provinces are not always consistent with biomass observations from
 net sampling. This mismatch is particularly evident in temperate to polar
 transition zones due to changes in resonance of pelagic fauna, which
 depends on sonar frequency but also on the size of resonant organs, such
 as fish swimbladders or zooplankton gas inclusions. Here, we investigate
 how sonar frequency choice changes our vision of pelagic ecosystems across
 latitudes. We analyse sonar observations at 38 and 18 kHz along with size
 distributions of swimbladdered fish species across the Indian Ocean
 Subantarctic Front. Our results show a shift from 38 to 18 kHz dominance
 towards the poles. More interestingly, backscatter differences across the
 Subantarctic Front are four times larger at 38 kHz compared to 18 kHz. The
 size distribution of fish suggests an increase in swimbladder volumes in
 subantarctic waters, which may explain the observed shift in frequency
 response. This study highlights the need to address swimbladder resonance
 variability across latitudes, with the aim of harmonising large-scale
 sonar observations of pelagic fauna.
description: # **Large differences in the distribution of pelagic biomass as a result
 of sonar frequency choice** **Dataset DOI:** 10.5061/dryad.3n5tb2rs7 ###
 Description of the Data and File Structure This dataset supports the
 analyses presented in our paper and its supplementary material. The latter
 provides detailed information on the methodology and intermediate results
 used to describe and model the vertical distribution of acoustic
 backscatter across the Indian sector of the Southern Ocean. Data were
 collected as part of the *Toward Hydroacoustics and Ecology of Mid-trophic
 levels in Indian and Southern Ocean* (THEMISTO) observation programme
 (DOI: 10.18142/288). We recommend consulting the supplementary material
 alongside this dataset and the provided scripts. Data were collected
 during austral summer cruises in January, February, and March of 2021,
 2022, and 2023. Firstly, we detail the analysis of *in situ* acoustic
 observations, focusing on the application of functional data analysis and
 clustering methods. Secondly, the backscatter modelling process using
 Random Forest with principal components from environmental profiles as
 predictors is explained. Finally, we describe net sampling methods to
 validate our acoustic observations, including fishing operations conducted
 during the 2022 acoustic surveys, as well as historical data from museum
 collections and literature. **Dataset structure and data formats** **File:
 CC0_DRYAD_deposit.zip** The dataset includes: * Raw acoustic and
 environmental data * Scripts for each step of the analysis * Output
 folders (mostly empty) to preserve reproducibility structure ### Folder
 Structure and Contents The dataset is structured to facilitate
 reproducibility and transparency. The file organisation follows the
 standard R project format, and the folder hierarchy reflects the different
 stages of the analysis. │ Analysis_Roy_Soc.Rproj # Main R project file
 ├───00_data # All input datasets │ │ polyW75.RDS │ │ zone_bathy.Rdata │ │
 │ ├───acous_data_averaged_by_period # Averaged acoustic data │ │
 days.Rdata │ │ days_metadata.Rdata │ │ depths.Rdata │ │ nights.Rdata │ │
 nights_and_days.Rdata │ │ nights_and_days_metadata.Rdata │ │
 nights_metadata.Rdata │ │ │ ├───acous_resonance_model # Excel and CSV
 files for resonance modelling │ │
 model_resonance_and_dwba_at_multiple_depths.xlsx │ │
 model_resonance_dwba_500m.csv │ │ │ ├───copernicus-data #
 Satellite-derived environmental profiles and metadata │ │ │ depth.rds │ │
 │ DN_metadata.rds │ │ │ final_CHL_mat.rds │ │ │ final_OX_mat.rds │ │ │
 final_S_mat.rds │ │ │ final_T_mat.rds │ │ │ lat.rds │ │ │ lon.rds │ │ │
 metadata_themisto.rds │ │ │ NA_to_rem.rds │ │ │
 smooth_daily_chl_profiles.rds │ │ │ smooth_daily_ox_profiles.rds │ │ │
 smooth_daily_sal_profiles.rds │ │ │ smooth_daily_temp_profiles.rds │ │ │ │
 │ ├───2021 │ │ │ ├───daily # Daily NetCDF data (2021) │ │ │ └───monthly #
 Monthly NetCDF data (2021) │ │ ├───2022 │ │ │ ├───daily │ │ │ └───monthly
 │ │ └───2023 │ │ ├───daily │ │ └───monthly │ │ │ └───Fronts_bioregions #
 Oceanic fronts and biogeographic regions │ DSTF_Graham.csv │
 fronts_Park_2019.nc │ Kim_fronts_pf.csv │ Kim_fronts_saccf.csv │
 Kim_fronts_saf.csv │ Kim_fronts_sbdy.csv │ MBGCP_reygondeau_18.csv │
 ├───01_scripts # All processing scripts │ │
 Step1_acous_MFDA_averaged_18_38.R # Acoustic smoothing + fPCA │ │
 Step2_classif_mfPCA_scores.R # Clustering of acoustic fPCA scores │ │
 Step3A_env_get_satellite_data_21_22_23.R # Download Copernicus data │ │
 Step3B_get_daily_profiles.R # Create daily environmental profiles │ │
 Step3C_fPCA_and_associate_cps_to_cps_21_22_23.R# fPCA on env. profiles and
 associations │ │ Step4_predict_acous_pcs_.R # Predict acoustic PCs from
 env. PCs │ │ Step5_plot_var_importance.R # Plot variable importance │ │
 Step6_reconstruction_from_acous_pred_pcs.R # Reconstruct acoustic profiles
 │ │ Step7_pred_profiles_Sv_to_NASC.R # Convert predictions to NASC │ │
 Step8A_make_figure1.R # Make figure 1 │ │ Step8A_make_figure2.R # Make
 figure 2 │ │ Step8A_make_figure3.R # Make figure 3 │ │
 Step8A_make_figure4.R # Make figure 4 │ │ │ └───functions_and_config #
 Custom functions and config files │ jj2date_f.R │ Step0a_CONFIG_FILE.R │
 Step0b_MAP_FILE.R │ Step0_to_make_and_save_map_GEBCO.R │ └───02_outputs #
 All outputs from the analysis pipeline ├───Article_figures # Final figures
 for publication ├───Data # Intermediate and processed data │
 ├───mfpca_objects_for_reco # fPCA objects used in reconstructions │ │
 ├───days │ │ ├───env │ │ └───nights │ ├───model_data_after_proj #
 Predicted profiles │ │ ├───days_pres │ │ │ ├───NASC_computation │ │ │
 ├───predicted_data │ │ │ ├───pred_1jan_21 to pred_9mar_23 │ │
 └───nights_pres │ │ ├───NASC_computation │ │ ├───predicted_data │ │
 ├───pred_1jan_21 to pred_9mar_23 │ ├───model_data_before_proj # Input data
 before projection │ ├───model_results # Model evaluation and summary
 results │ └───res_classif # Clustering results │ ├───days │ └───nights
 └───Figures # Organised output plots ``` ├───days │ ├───mfPCA │ │
 └───eigenfunctions │ │ └───FIG_VMs_and_scores │ ├───res_classif │
 └───res_pred ├───fPCA_env │ └───eigenfunctions ├───model_results
 ├───NASC_predit │ ├───days │ └───nights └───nights ├───mfPCA │
 └───eigenfunctions │ └───FIG_VMs_and_scores ├───res_classif └───res_pred
 ``` ### Description of the Data by Folder --- **Root**\
 `Analysis_Roy_Soc.Rproj` – This R Project file should be opened first. It
 defines the working environment and organises all elements of the analysis
 workflow. --- **00_data** – Contains all raw data used in the analysis,
 divided into several thematic folders: * `polyW75.RDS` – R object defining
 the 75th percentile weighted polygon of an Area of Ecological Significance
 (AES) in the Indian sector of the Southern Ocean (after Hindell et al.,
 2020). * `zone_bathy.Rdata` – Matrix of bathymetry (in metres) for the
 study region. --- **Folder: acous_data_averaged_by_period** – Averaged
 vertical acoustic backscatter data: * `depths.Rdata` – Vector of standard
 depths (in metres) corresponding to acoustic measurements for each
 frequency. * `days.Rdata` – Median vertical acoustic backscatter profiles
 (Sv) for daytime, per station and frequency (18 & 38 kHz). *
 `days_metadata.Rdata` – Metadata for daytime stations (e.g. coordinates,
 date, sampling period). * `nights.Rdata` – Median vertical acoustic
 backscatter profiles (Sv) for nighttime, per station and frequency (18
 & 38 kHz). * `nights_metadata.Rdata` – Metadata for nighttime
 stations. * `nights_and_days.Rdata` – Combined day and night backscatter
 profiles, per station and frequency. * `nights_and_days_metadata.Rdata` –
 Metadata for stations with both day and night observations. --- **Folder:
 acous_resonance_model** – Files for theoretical scattering model
 calculations: * `model_resonance_and_dwba_at_multiple_depths.xlsx` – Excel
 file containing outputs of a resonance and DWBA (Distorted Wave Born
 Approximation) scattering model for key fish families across depths and
 frequencies. * `model_resonance_dwba_500m.csv` – Same model outputs,
 limited to 500 m depth. --- **Folder: copernicus-data** – Environmental
 data from the Copernicus Marine Environment Monitoring Service:
 **Subfolders:** * `2021/`, `2022/`, `2023/` – Contain NetCDF (`.nc`) files
 of raw chlorophyll and oxygen profiles: * `daily/` – Daily resolution
 NetCDF files (0–1060 m). * `monthly/` – Monthly resolution NetCDF files.
 **Processed files (in the root of `copernicus-data/`):** * `depth.rds` –
 Depths of environmental observations. * `DN_metadata.rds` – Metadata
 linking Copernicus profiles with acoustic observations. * `lat.rds`,
 `lon.rds` – Latitude and longitude of environmental observations. *
 `metadata_themisto.rds` – General metadata for THEMISTO Copernicus
 extractions. * `NA_to_rem.rds` – Indices of profiles to be removed for
 NASC computations and plots. **Processed environmental matrices (monthly
 and smoothed daily):** * `final_CHL_mat.rds` – Processed chlorophyll
 profiles (monthly). * `final_OX_mat.rds` – Processed oxygen profiles
 (monthly). * `final_S_mat.rds` – Processed salinity profiles (monthly). *
 `final_T_mat.rds` – Processed temperature profiles (monthly). *
 `smooth_daily_chl_profiles.rds` – Smoothed daily chlorophyll profiles. *
 `smooth_daily_ox_profiles.rds` – Smoothed daily oxygen profiles. *
 `smooth_daily_sal_profiles.rds` – Smoothed daily salinity profiles. *
 `smooth_daily_temp_profiles.rds` – Smoothed daily temperature profiles.
 --- **Folder: Fronts_bioregions** – Oceanic fronts and large-scale
 bioregions: * `DSTF_Graham.csv` – Position of the Dynamical Subtropical
 Front (DSTF). * `fronts_Park_2019.nc` – NetCDF file with multiple frontal
 positions from Park et al. (2019). * `Kim_fronts_pf.csv` – Polar Front
 (PF) positions from Kim & Orsi (1995). * `Kim_fronts_saccf.csv` –
 Southern ACC Front (SACCF) positions. * `Kim_fronts_saf.csv` –
 Subantarctic Front (SAF) positions. * `Kim_fronts_sbdy.csv` – Southern
 Boundary (SBDY) of the ACC. * `MBGCP_reygondeau_18.csv` – Marine
 biogeographic classification polygons from Reygondeau et al. (2018). ###
 01\_scripts – Analysis Code The analysis is written entirely in **R** and
 structured as an RStudio project (`Analysis_Roy_Soc.Rproj`). The pipeline
 is composed of a sequence of scripts located in the `01_scripts/` folder.
 These scripts should be run in order, as intermediate outputs are saved
 and reused in subsequent steps. Before starting, make sure all required R
 packages are installed. These are listed and loaded in the files within
 `functions_and_config/`, which also contains custom functions and mapping
 tools used across the pipeline. Each script is modular but follows a
 logical progression. Below is a description of the main analysis steps:
 --- **1. `Step1_acous_MFDA_averaged_18_38.R`** Processes raw vertical
 acoustic backscatter data (Sv) recorded at 18 and 38 kHz.\ The script
 smooths these profiles using Functional Data Analysis (FDA) and performs
 Functional Principal Component Analysis (fPCA) to extract dominant modes
 of vertical variability. The resulting principal components (PCs) are
 saved for use in clustering and modelling steps. --- **2.
 `Step2_classif_mfPCA_scores.R`** Applies unsupervised clustering on the
 acoustic PCs to identify distinct *echobiomes*—groups of stations with
 similar vertical acoustic structure. It also computes mean Sv profiles and
 NASC (Nautical Area Scattering Coefficient) values for each echobiome. ---
 **3. `Step3A_env_get_satellite_data_21_22_23.R`** Process
 satellite-derived environmental profiles (temperature, chlorophyll,
 oxygen) from the Copernicus Marine Service for the years 2021 to 2023. The
 data are filtered to the 25–1000 m depth range and formatted for further
 processing. --- **4. `Step3B_get_daily_profiles.R`** Process
 satellite-derived environmental profiles (temperature, chlorophyll,
 oxygen) from the Copernicus Marine Service for the years 2021 to 2023,
 from daily profiles matching acoustic observations. The data are filtered
 to the 25–1000 m depth range and formatted for further processing. ---
 **5. `Step3C_fPCA_and_associate_cps_to_cps_21_22_23.R`** Performs fPCA on
 the environmental profiles to extract the main modes of environmental
 variability. Daily profiles are projected into this environmental PC space
 and associated with the corresponding acoustic PCs from Step 1. Then links
 daily environmental profiles with the corresponding acoustic observations.
 This allows for a day-by-day comparison between environmental structure
 and acoustic features at each station. This matched dataset is used to
 train predictive models. --- **6. `Step4_predict_CP.R`** Trains Random
 Forest models (via the `h2o` package) to predict acoustic PC scores from
 environmental PC scores. The trained models are then applied to monthly
 environmental climatologies to generate predicted acoustic PCs across the
 study region. --- **7. `Step5_plot_var_importance.R`** Evaluates the
 relative importance of each environmental variable (e.g., temperature,
 chlorophyll, oxygen) in predicting acoustic PCs. Produces visualisations
 of variable importance derived from the Random Forest models. --- **8.
 `Step6_reconstruction_from_pred_cp.R`** Reconstructs full vertical Sv
 profiles from the predicted acoustic PCs by summing weighted
 eigenfunctions obtained in Step 1. This yields spatially continuous
 estimates of vertical acoustic structure across the study region. --- **9.
 `Step7_pred_profiles_Sv_to_NASC.R`** Converts the reconstructed Sv
 profiles into NASC values—standard acoustic biomass metrics. These are
 integrated over specified depth ranges to produce spatial predictions of
 acoustic energy. --- **10. `Step8_make_figureX.R`** Scripts for generating
 Figures 1 to 4 of the article. These include visual summaries of the
 identified echobiomes, spatial distributions of NASC, and reconstructions
 of acoustic structure. ### 02\_outputs – Results and Figures This folder
 is mostly empty in the `.zip` archive to preserve structure and facilitate
 reproducibility. It will be populated automatically as the scripts are
 run. └───02_outputs # All outputs from the workflow ├───Article_figures #
 Final figures for publication ├───Data # Intermediate and final data
 products │ ├───mfpca_objects_for_reco # fPCA objects used to reconstruct
 observed profiles │ │ ├───days # fPCA objects for the daytime period │ │
 ├───env # fPCA objects for environmental profiles │ │ └───nights # fPCA
 objects for nighttime period │ ├───model_data_after_proj # Data after
 projection of environmental PCs │ │ ├───days_pres # For daytime │ │ │
 ├───NASC_computation # Integrated backscatter (NASC) values │ │ │
 ├───predicted_data # predicted profiles │ │ │ ├───pred_1jan_21 to
 pred_9mar_23 # Predicted acoustic PCs for each day │ │ └───nights_pres #
 Same for night │ │ ├───NASC_computation │ │ ├───predicted_data │ │
 ├───pred_1jan_21 to pred_9mar_23 │ ├───model_data_before_proj # Training
 data for the Random Forest models │ ├───model_results # Model outputs and
 performance metrics │ └───res_classif # Clustering results (echobiome
 assignments) │ ├───days # for daytime │ └───nights # for nightime
 └───Figures # Diagnostic and exploratory plots ``` ├───days # Figures for
 daytime data │ ├───mfPCA # result of the multivariate functional principal
 component analysis │ │ └───eigenfunctions # plot the eigenfunction │ │
 └───FIG_VMs_and_scores # Formatted figures for LaTeX export │
 ├───res_classif # Clustering result figures │ └───res_pred # Prediction
 visualisations ├───fPCA_env # fPCA results for environmental data │
 └───eigenfunctions # plot the eigenfunction ├───model_results # Random
 Forest model result figures ├───NASC_predit # NASC prediction figures │
 ├───days # for daytime │ └───nights # for nightime └───nights # Figures
 for nighttime data ├───mfPCA # result of mfpca │ └───eigenfunctions # plot
 the eigenfunctions │ └───FIG_VMs_and_scores ├───res_classif # result of
 the clustering └───res_pred # result of the prediction ```