Abstract
Pelagic fauna is expected to be impacted under climate change according to ecosystem simulations. However, the direction and magnitude of the impact is still uncertain and still not corroborated by observation-based statistical studies. Here we compile a global underwater sonar database and 20 ocean climate projections to predict the future distribution of sound-scattering fauna around the world’s oceans. We show that global pelagic fauna will be seriously compromised by the end of the twenty-first century if we continue under the current greenhouse emission scenario. Low and mid latitudes are expected to lose from 3% to 22% of animal biomass due to the expansion of low-productive systems, while higher latitudes would be populated by present-day temperate fauna, supporting results from ecosystem simulations. We further show that strong mitigation measures to contain global warming below 2 °C would reduce these impacts to less than half.
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Data availability
All data used in the present study are publicly available. Acoustic data can be accessed through diverse internet repositories indicated in Supplementary Table 1. Observations of sea surface temperature and dissolved oxygen are available in the 2018 World Ocean Atlas (https://www.ncei.noaa.gov/products/world-ocean-atlas), and satellite chlorophyll observations can be accessed through the Ocean Climate Change Initiative data portal (http://www.esa-oceancolour-cci.org). CMIP5 and CMIP6 simulations are publicly available in the Earth System Grid Federation data portal (https://esgf-node.llnl.gov). We also provide in supplementary data the global acoustic atlases elaborated for the present study.
Code availability
Raw acoustic data from the Malaspina circumnavigation expedition were processed using the open-source software Matecho v.6.7 following the standard procedures detailed in Methods. The rest of the acoustic repositories were already available as processed data. Data analysis was conducted with custom-made analysis routines in Python v.3.8 and diverse open-source Python packages indicated in Methods. All analysis routines used in the present study are available upon request.
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Acknowledgements
We acknowledge the Australian Integrated Marine Observing System (IMOS), the French National Research Institute for Sustainable Development (IRD), the British Antarctic Survey (BAS), the Peruvian Marine Institute (IMARPE), the Pierre and Marie Curie University (UPMC) and the Spanish National Research Council (CSIC) for their generous and invaluable contributions to the public acoustic databases used in the present study. A.A. was funded by a post-doctoral IRD fellowship. This work is a contribution to and was supported by the International Joint Laboratory TAPIOCA and the Horizon 2020 UE projects PADDLE (grant agreement no. 73427) and TRIATLAS (grant agreement no. 817578).
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A.A., A.B., M.L., C.M. and A.L.-D. designed the study. M.L. and T.G. processed environmental data. J.H. and M.G. processed acoustic data. A.A. analysed environmental and acoustic data and wrote the manuscript with contribution from M.L, C.M., A.L.-D., A.R., T.G., J.H., M.G., O.M. and A.B.
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Extended data
Extended Data Fig. 1 Dataset description.
Acoustic tracks (a) and acoustic data gridded into 4×4 degrees cells, with indication of the most sampled season (b), and the total surveyed distance averaged at each cell (c). Season colours refer to the most sampled month number in reference to the season. For example, month one in spring (orange) represent March or September, depending on the hemisphere where data were collected. The distance of acoustic transects averaged within cells ranged from 10 to 8000 km.
Extended Data Fig. 2 Acoustic profiles decomposition in a function basis system.
Example of averaged day and night profiles from a 4×4 degrees cell (a) decomposed in two function basis systems (b, c). 18 basis functions were fitted to the mean profiles at 40 m depth intervals, from 20 to 750 m depth (red-blue lines). Function coefficients were used to describe the day and night shape profiles at each location (d). Functional PCA and clustering was performed on the coefficients matrix to classify the acoustic seascape. Dots and horizontal bars in acoustic profiles indicate the median and the interquartile range, as Volume backscattering strength (Sv, dB re 1 m−1).
Extended Data Fig. 3 PCA and clustering.
Percentage of the dataset variance explained by each PC (a), acoustic observations projected in the PC space and classified through hierarchical clustering (b, c), and main modes of day and night profile variance corresponding to each principal component (d-s). The central line shows the mean while plus and minus symbols represent the deformation with respect to the mean, for positive and negative weights along the principal components axes. Eight PCs were included in the classification which explained a 90% of the total dataset variance. Cumulated and PC-specific variance is indicated with grey and black bars.
Extended Data Fig. 4 Acoustic seascape predictive model.
A Random Forest learning algorithm was used to predict the acoustic seascape classification from environmental variables. The model was trained with sea surface temperature (a), subsurface dissolved oxygen (b) and chlorophyll (c) as inputs, and with the acoustic seascape classification as the output (d). An example of predicted classification is shown (e) with the average importance of predictors (f) and the rate of success (F1 score, from 0 to 1) to predict the subpolar (SP), Gyre (G), subtropical (ST), tropical (T), upwelling (UW), and low-oxygen (LO) seascape classes (g). Environmental inputs were extracted as monthly-weighted averages from the period 2000–2020 based on the monthly acoustic data coverage at each 4×4 degrees cell (see Supplementary Fig. 1 and Methods). The model accuracy was evaluated by comparing 25% subsets of the observed classification against predictions trained with the remaining 75% of data. The operation was repeated 100 times with random data subsets for testing and training. On average, the prediction accuracy of the model was 0.78 ± 0.04%.
Extended Data Fig. 5 Alternative seascape classifications and predictions.
The results of 4 classification approaches are shown: K-means, Agglomerative, Spectral, and Gaussian mixture clustering. Seascape classes from agglomerative clustering are then projected globally, using 10 supervised learning algorithms from the scikit-learn Python module: AdaBoost, Decision Tree, Gaussian Process, Naive Bayes, Nearest Neighbors, Neural Network, Quadratic Discriminant Analysis (QDA), Support Vector Classification with linear kernel (SVM-linear), Support Vector Classification with Radial-basis function kernel (SVM-RBF), and Random Forest. The overall predictive accuracy of algorithms was calculated as in Extended Data Fig. 4 and the results are shown in the table above (F1 score). Examples of acoustic seascape projections are shown for the most accurate algorithms: AdaBoost, Decission Tree, and Random Forest. The method chosen for the present study was Agglomerative clustering and Random Forest projection.
Extended Data Fig. 6 Acoustic seascape projections with spatially truncated training datasets.
Seascape classification is predicted at global scale using Random Forest with different training datasets, in order to evaluate how sampling bias affects model’s performance. The following training datasets are used: Complete (a), or after removing the South Indian (b), North Pacific (c), South Pacific (d), North Atlantic (e), and South Atlantic (f) ocean basins.
Extended Data Fig. 7 Biogeography and biomass change based on CMIP6-SSP5-8.5 ensemble projections (median average).
Distribution of echobiomes and biomass based on day and night water-column backscatter. Projections are shown for 2000–2020 and 2080–2100, and as present-to-future changes due to different combinations of future environmental conditions: if only chlorophyll changes (CHL), only subsurface dissolved oxygen changes (SDO), only sea surface temperature changes (SST), or the three environmental variables change (CHL + SDO + SST). In biogeographical changes, light and bold colour shades indicate current and future expansions of echobiomes, respectively. Colour-scales are consistent along Extended Data Figs. 7–10.
Extended Data Fig. 8 Biogeography and biomass change based on CMIP5-RCP8.5 ensemble projections (median average).
Distribution of echobiomes and biomass based on day and night water-column backscatter. Projections are shown for 2000–2020 and 2080–2100, and as present-to-future changes due to different combinations of future environmental conditions: if only chlorophyll changes (CHL), only subsurface dissolved oxygen changes (SDO), only sea surface temperature changes (SST), or the three environmental variables change (CHL + SDO + SST). In biogeographical changes, light and bold colour shades indicate current and future expansions of echobiomes, respectively. Colour-scales are consistent along Extended Data Figs. 7–10.
Extended Data Fig. 9 Biogeography and biomass change based on CMIP6-SSP1-2.6 ensemble projections (median average).
Distribution of echobiomes and biomass based on day and night water-column backscatter. Projections are shown for 2000–2020 and 2080–2100, and as present-to-future changes due to different combinations of future environmental conditions: if only chlorophyll changes (CHL), only subsurface dissolved oxygen changes (SDO), only sea surface temperature changes (SST), or the three environmental variables change (CHL + SDO + SST). In biogeographical changes, light and bold colour shades indicate current and future expansions of echobiomes, respectively. Colour-scales are consistent along Extended Data Figs. 7–10.
Extended Data Fig. 10 Biogeography and biomass change based on CMIP5-RCP2.6 ensemble projections (median average).
Distribution of echobiomes and biomass based on day and night water-column backscatter. Projections are shown for 2000–2020 and 2080–2100, and as present-to-future changes due to different combinations of future environmental conditions: if only chlorophyll changes (CHL), only subsurface dissolved oxygen changes (SDO), only sea surface temperature changes (SST), or the three environmental variables change (CHL + SDO + SST). In biogeographical changes, light and bold colour shades indicate current and future expansions of echobiomes, respectively. Colour-scales are consistent along Extended Data Figs. 7–10.
Supplementary information
Supplementary Information
Supplementary Tables 1–3, Figs. 1–10 and references.
Supplementary Data
Acoustic seascape regionalization and vertically integrated backscatter from 20 to 750 m depth. Computed from environmental observations in 2000–2020 and CMIP6 projections in 2080–2100. Average projections from 13 CMIP6 models are considered under SSP5–8.5 IPCC greenhouse emissions scenarios. Further details in the main text.
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Ariza, A., Lengaigne, M., Menkes, C. et al. Global decline of pelagic fauna in a warmer ocean. Nat. Clim. Chang. 12, 928–934 (2022). https://doi.org/10.1038/s41558-022-01479-2
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DOI: https://doi.org/10.1038/s41558-022-01479-2
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