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Global decline of pelagic fauna in a warmer ocean

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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Fig. 1: Acoustic seascape classification.
Fig. 2: Biogeography and acoustic-based biomass.
Fig. 3: Biomass changes by 2080–2100 under variable climate forcing.

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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.

References

  1. Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).

    Article  CAS  Google Scholar 

  2. Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).

    Article  CAS  Google Scholar 

  3. Choy, C., Wabnitz, C., Weijerman, M., Woodworth-Jefcoats, P. & Polovina, J. Finding the way to the top: how the composition of oceanic mid-trophic micronekton groups determines apex predator biomass in the central North Pacific. Mar. Ecol. Prog. Ser. 549, 9–25 (2016).

    Article  Google Scholar 

  4. Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).

  5. Bertrand, A. et al. Broad impacts of fine-scale dynamics on seascape structure from zooplankton to seabirds. Nat. Commun. 5, 5239 (2014).

    Article  CAS  Google Scholar 

  6. Brierley, A. S. Diel vertical migration. Curr. Biol. 24, R1074–R1076 (2014).

    Article  CAS  Google Scholar 

  7. Behrenfeld, M. J. et al. Global satellite-observed daily vertical migrations of ocean animals. Nature 576, 257–261 (2019).

    Article  CAS  Google Scholar 

  8. Angel, M. V. & de C. Baker, A. Vertical distribution of the standing crop of plankton and micronekton at three stations in the northeast Atlantic. Biol. Oceanogr. 2, 1–30 (1982).

    Google Scholar 

  9. Cook, A. B., Sutton, T. T., Galbraith, J. K. & Vecchione, M. Deep-pelagic (0–3000 m) fish assemblage structure over the Mid-Atlantic Ridge in the area of the Charlie-Gibbs Fracture Zone. Deep Sea Res. 2 98, 279–291 (2013).

    Article  Google Scholar 

  10. Hidaka, K., Kawaguchi, K., Murakami, M. & Takahashi, M. Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep Sea Res. 1 48, 1923–1939 (2001).

  11. Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342 (2015).

    Article  Google Scholar 

  12. Saba, G. K. et al. Toward a better understanding of fish-based contribution to ocean carbon flux. Limnol. Oceanogr. 66, 1639–1664 (2021).

    Article  CAS  Google Scholar 

  13. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Article  Google Scholar 

  14. Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).

    Article  CAS  Google Scholar 

  15. Tittensor, D. P. et al. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geosci. Model Dev. 11, 1421–1442 (2018).

    Article  Google Scholar 

  16. Bryndum-Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).

    Article  Google Scholar 

  17. Kwiatkowski, L., Aumont, O. & Bopp, L. Consistent trophic amplification of marine biomass declines under climate change. Glob. Change Biol. 25, 218–229 (2019).

    Article  Google Scholar 

  18. Lotze, H. K. et al. Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proc. Natl Acad. Sci. USA 116, 12907–12912 (2019).

    Article  CAS  Google Scholar 

  19. Tittensor, D. P. et al. Next-generation ensemble projections reveal higher climate risks for marine ecosystems. Nat. Clim. Change 11, 973–981 (2021).

    Article  Google Scholar 

  20. Heneghan, R. F. et al. Disentangling diverse responses to climate change among global marine ecosystem models. Prog. Oceanogr. 198, 102659 (2021).

    Article  Google Scholar 

  21. Reid, S. B., Hirota, J., Young, R. E. & Hallacher, L. E. Mesopelagic-boundary community in Hawaii: micronekton at the interface between neritic and oceanic ecosystems. Mar. Biol. 109, 427–440 (1991).

    Article  Google Scholar 

  22. Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).

    Article  Google Scholar 

  23. Pakhomov, E. & Yamamura, O. Report of the Advisory Panel on Micronekton Sampling Inter-calibration Experiment. PICES Scientific Report 38 (North Pacific Marine Science Organization, 2010).

  24. Kaartvedt, S., Staby, A. & Aksnes, D. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6 (2012).

    Article  Google Scholar 

  25. Gjøsaeter, J. & Kawaguchi, K. A Review of the World Resources of Mesopelagic Fish Fisheries Technical Paper 193 (FAO, 1980).

  26. Catul, V., Gauns, M. & Karuppasamy, P. K. A review on mesopelagic fishes belonging to family Myctophidae. Rev. Fish Biol. Fish. 21, 339–354 (2011).

    Article  Google Scholar 

  27. Benoit-Bird, K. J. & Lawson, G. L. Ecological insights from pelagic habitats acquired using active acoustic techniques. Annu. Rev. Mar. Sci. 8, 463–490 (2016).

    Article  Google Scholar 

  28. Annasawmy, P. et al. Micronekton diel migration, community composition and trophic position within two biogeochemical provinces of the south west Indian Ocean: insight from acoustics and stable isotopes. Deep Sea Res. 1 138, 85–97 (2018).

    Article  CAS  Google Scholar 

  29. Haris, K. et al. Sounding out life in the deep using acoustic data from ships of opportunity. Sci. Data 8, 23 (2021).

    Article  CAS  Google Scholar 

  30. Irigoien, X. et al. The Simrad EK60 echosounder dataset from the Malaspina circumnavigation. Sci. Data 8, 259 (2021).

    Article  Google Scholar 

  31. Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).

    Article  Google Scholar 

  32. Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873 (2016).

    Article  CAS  Google Scholar 

  33. Proud, R., Cox, M., Le Guen, C. & Brierley, A. Fine-scale depth structure of pelagic communities throughout the global ocean based on acoustic sound scattering layers. Mar. Ecol. Prog. Ser. 598, 35–48 (2018).

    Article  Google Scholar 

  34. Proud, R., Cox, M. J. & Brierley, A. S. Biogeography of the global ocean’s mesopelagic zone. Curr. Biol. 27, 113–119 (2017).

    Article  CAS  Google Scholar 

  35. Ramsay, J. O. & Silverman, B. W. Functional Data Analysis (Springer, 2005).

  36. Moriarty, R. & O’Brien, T. D. Distribution of mesozooplankton biomass in the global ocean. Earth Syst. Sci. Data 5, 45–55 (2013).

    Article  Google Scholar 

  37. Aksnes, D. L. et al. Light penetration structures the deep acoustic scattering layers in the global ocean. Sci. Adv. 3, e1602468 (2017).

    Article  Google Scholar 

  38. Bertrand, A., Ballón, M. & Chaigneau, A. Acoustic observation of living organisms reveals the upper limit of the oxygen minimum zone. PLoS ONE 5, e10330 (2010).

    Article  Google Scholar 

  39. Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).

    Article  CAS  Google Scholar 

  40. Godø, O. R., Patel, R. & Pedersen, G. Diel migration and swimbladder resonance of small fish: some implications for analyses of multifrequency echo data. ICES J. Mar. Sci. 66, 1143–1148 (2009).

    Article  Google Scholar 

  41. Agersted, M. D. et al. Mass estimates of individual gas-bearing mesopelagic fish from in situ wideband acoustic measurements ground-truthed by biological net sampling. ICES J. Mar. Sci. 78, 3658–3673 (2021).

    Article  Google Scholar 

  42. Backus, R. & Craddock, J. in Oceanic Sound Scattering Prediction (eds Anderson, N. R. & Zahuranec, B. J.) 529–547 (Springer, 1977).

  43. Longhurst, A. Ecological Geography of the Sea (Elsevier, 2010).

  44. Spalding, M. D., Agostini, V. N., Rice, J. & Grant, S. M. Pelagic provinces of the world: A biogeographic classification of the world’s surface pelagic waters. Ocean Coast. Manage. 60, 19–30 (2012).

    Article  Google Scholar 

  45. Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. 1 126, 85–102 (2017).

    Article  Google Scholar 

  46. IPCC Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  47. Kooijman, B. & Kooijman, S. A. L. M. Dynamic Energy Budget Theory for Metabolic Organisation (Cambridge Univ. Press, 2010).

  48. Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).

    Article  CAS  Google Scholar 

  49. Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).

    Article  Google Scholar 

  50. Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J. & Brierley, A. S. From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES J. Mar. Sci. 76, 718–733 (2019).

    Article  Google Scholar 

  51. Chapman, R. P., Bluy, O. Z., Adlington, R. H. & Robison, A. E. Deep scattering layer spectra in the Atlantic and Pacific oceans and adjacent seas. J. Acoust. Soc. Am. 56, 1722–1734 (1974).

    Article  Google Scholar 

  52. Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Swimbladder morphology masks Southern Ocean mesopelagic fish biomass. Proc. R. Soc. B 286, 20190353 (2019).

    Article  Google Scholar 

  53. Escobar-Flores, P. C., O’Driscoll, R. L., Montgomery, J. C., Ladroit, Y. & Jendersie, S. Estimates of density of mesopelagic fish in the Southern Ocean derived from bulk acoustic data collected by ships of opportunity. Polar Biol. 43, 43–61 (2020).

    Article  Google Scholar 

  54. Dornan, T., Fielding, S., Saunders, R. A. & Genner, M. J. Large mesopelagic fish biomass in the Southern Ocean resolved by acoustic properties. Proc. R. Soc. B 289, 20211781 (2022).

    Article  Google Scholar 

  55. Reygondeau, G. et al. Climate change-induced emergence of novel biogeochemical provinces. Front. Mar. Sci. 7, 657 (2020).

    Article  Google Scholar 

  56. Blanchard, J. L. et al. Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol. 1, 1240–1249 (2017).

    Article  Google Scholar 

  57. Bianchi, D., Carozza, D. A., Galbraith, E. D., Guiet, J. & DeVries, T. Estimating global biomass and biogeochemical cycling of marine fish with and without fishing. Sci. Adv. 7, eabd7554 (2021).

    Article  Google Scholar 

  58. Grimaldo, E. et al. Investigating the potential for a commercial fishery in the northeast Atlantic utilizing mesopelagic species. ICES J. Mar. Sci. 77, 2541–2556 (2020).

    Article  Google Scholar 

  59. Olsen, R. E. et al. Can mesopelagic mixed layers be used as feed sources for salmon aquaculture? Deep Sea Res. 2 180, 104722 (2020).

    Article  CAS  Google Scholar 

  60. De Robertis, A. & Higginbottom, I. A post-processing technique to estimate the signal-to-noise ratio and remove echosounder background noise. ICES J. Mar. Sci. 64, 1282–1291 (2007).

    Article  Google Scholar 

  61. Ryan, T. E., Downie, R. A., Kloser, R. J. & Keith, G. Reducing bias due to noise and attenuation in open-ocean echo integration data. ICES J. Mar. Sci. 72, 2482–2493 (2015).

    Article  Google Scholar 

  62. Perrot, Y. et al. Matecho: an open-source tool for processing fisheries acoustics data. Acoust. Aust. 46, 241–248 (2018).

    Article  Google Scholar 

  63. Stanton, T. Review and recommendations for the modelling of acoustic scattering by fluid-like elongated zooplankton: euphausiids and copepods. ICES J. Mar. Sci. 57, 793–807 (2000).

    Article  Google Scholar 

  64. GEBCO: A Continuous Terrain Model of the Global Oceans and Land (British Oceanographic Data Centre, 2019).

  65. EchoPY v.1.1: Fisheries Acoustic Data Processing in Python (Python, 2020); https://pypi.org/project/echopy

  66. de Boor, C. A Practical Guide to Splines (Springer, 1978).

  67. Clustering (SciKit Learn, 2021); https://scikit-learn.org/stable/modules/clustering

  68. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  69. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  70. Sonnewald, M., Dutkiewicz, S., Hill, C. & Forget, G. Elucidating ecological complexity: unsupervised learning determines global marine eco-provinces. Sci. Adv. 6, eaay4740 (2020).

    Article  Google Scholar 

  71. Sonnewald, M. & Lguensat, R. Revealing the impact of global heating on North Atlantic circulation using transparent machine learning. J. Adv. Model. Earth Syst. 13, e2021MS002496 (2021).

    Article  Google Scholar 

  72. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  73. Locarnini, R. et al. World Ocean Atlas 2018, Volume 1: Temperature NOAA Atlas NESDIS 81 (NOAA, 2018).

  74. García, H. et al. World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation NOAA Atlas NESDIS 83 (NOAA, 2018).

  75. Sathyendranath, S. et al. ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 5.0 Data. NERC EDS Centre for Environmental Data Analysis, 19 May 2021; http://www.esa-oceancolour-cci.org

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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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Authors

Contributions

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.

Corresponding author

Correspondence to Alejandro Ariza.

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Nature Climate Change thanks Redouane Lguensat and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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. 710.

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. 710.

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. 710.

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. 710.

Supplementary information

Supplementary Information

Supplementary Tables 1–3, Figs. 1–10 and references.

Reporting Summary

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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