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Marine heatwaves are not a dominant driver of change in demersal fishes

Nature volume 621, pages 324–329 (2023)Cite this article

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Abstract

Marine heatwaves have been linked to negative ecological effects in recent decades1,2. If marine heatwaves regularly induce community reorganization and biomass collapses in fishes, the consequences could be catastrophic for ecosystems, fisheries and human communities3,4. However, the extent to which marine heatwaves have negative impacts on fish biomass or community composition, or even whether their effects can be distinguished from natural and sampling variability, remains unclear. We investigated the effects of 248 sea-bottom heatwaves from 1993 to 2019 on marine fishes by analysing 82,322 hauls (samples) from long-term scientific surveys of continental shelf ecosystems in North America and Europe spanning the subtropics to the Arctic. Here we show that the effects of marine heatwaves on fish biomass were often minimal and could not be distinguished from natural and sampling variability. Furthermore, marine heatwaves were not consistently associated with tropicalization (gain of warm-affiliated species) or deborealization (loss of cold-affiliated species) in these ecosystems. Although steep declines in biomass occasionally occurred after marine heatwaves, these were the exception, not the rule. Against the highly variable backdrop of ocean ecosystems, marine heatwaves have not driven biomass change or community turnover in fish communities that support many of the world’s largest and most productive fisheries.

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Fig. 1: Of 18 regions studied from the Atlantic and Pacific Oceans, all experienced MHWs during the available scientific fish survey time series.
Fig. 2: More intense MHWs were not associated with a decline in fish biomass or an increase in biomass variability, and biomass was approximately as likely to increase as it was to decrease from one year to the next, regardless of whether a MHW occurred.
Fig. 3: Example of divergent responses to a large MHW.
Fig. 4: Scientific bottom trawl surveys during the year following MHWs were as likely to exhibit tropicalization and/or deborealization as those that did not follow MHWs.

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Data availability

The data used in this project are available at https://doi.org/10.17605/OSF.IO/H6UKT.

Code availability

The code for this study is publicly available on GitHub at https://github.com/afredston/marine_heatwaves_trawl and archived at https://doi.org/10.17605/OSF.IO/H6UKT.

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Acknowledgements

This research was performed as part of the FISHGLOB working group, ‘Fish biodiversity under global change: a worldwide assessment from scientific trawl surveys’, co-funded by the Centre for the Synthesis and Analysis of Biodiversity (CESAB) of the French Foundation for Research on Biodiversity (FRB), the Canadian Institute of Ecology and Evolution (CIEE) and the French embassy in Canada. We also acknowledge funding by the Lenfest Ocean Program grant no. 00032755 (A.L.F. and M.L.P.) and by US National Science Foundation grant nos. DEB-1616821 and CBET-2137701 (M.L.P.). T.L.F. acknowledges funding from the Swiss National Science Foundation (grant no. PP00P2_198897) and the European Union’s Horizon 2020 research and innovation programme under grant no. 820989 (project COMFORT, Our common future ocean in the Earth system—quantifying coupled cycles of carbon, oxygen and nutrients for determining and achieving safe operating spaces with respect to tipping points). W.W.L.C. and J.P.-A. acknowledge funding support from a NSERC discovery grant (RGPIN-2018-03864) and a SSHRC partnership grant (Solving-FCB). The work reflects only the authors’ views; the European Commission and its executive agency are not responsible for any use that may be made of the information the work contains.

Author information

Authors and Affiliations

  1. Department of Ocean Sciences, University of California, Santa Cruz, Santa Cruz, CA, USA

    Alexa L. Fredston

  2. Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, British Columbia, Canada

    William W. L. Cheung & Juliano Palacios-Abrantes

  3. Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland

    Thomas L. Frölicher

  4. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    Thomas L. Frölicher

  5. Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, USA

    Zoë J. Kitchel, Aurore A. Maureaud & Malin L. Pinsky

  6. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Aurore A. Maureaud

  7. Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, USA

    James T. Thorson

  8. Institut Français de Recherche pour l’Exploitation de la MER (Ifremer), Unité Halieutique Manche Mer du Nord, Laboratoire Ressources Halieutiques, Boulogne-sur-Mer, France

    Arnaud Auber

  9. MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France

    Bastien Mérigot

  10. Sea Around Us, Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, British Columbia, Canada

    Maria Lourdes D. Palomares

  11. The Arctic University of Norway, Tromsø, Norway

    Laurène Pecuchet

  12. Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada

    Nancy L. Shackell

  13. Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA

    Malin L. Pinsky

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  1. Alexa L. Fredston
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  2. William W. L. Cheung
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Contributions

All authors contributed to writing and revising the manuscript. A.L.F., L.P., W.W.L.C., M.L.P., A.A.M., Z.J.K., M.L.D.P., J.T.T., A.A., B.M., J.P.-A. and N.L.S. contributed to the study conception and design. A.L.F., L.P., M.L.P., A.A.M., Z.J.K., T.L.F., M.L.D.P., J.T.T., B.M. and J.P.-A. contributed to data acquisition and analysis. All authors approved the submitted manuscript and subsequent revisions.

Corresponding author

Correspondence to Alexa L. Fredston.

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Nature thanks Graham Edgar, Andrea Havron, Michael Jacox and Mark Payne for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Alternate version of Fig. 2 from the main text, showing results by region.

MHWs were calculated from the detrended GLORYS sea bottom temperature data with a five-day minimum duration threshold for MHWs, as used in the main text. Points represent log ratios of mean biomass in a survey from one year to the next. The fitted lines are linear regressions. The shaded areas are 95% confidence intervals. Survey names and sample sizes per survey are listed in Supp. Tab. 1.

Extended Data Fig. 2 Results did not change when alternative methods were used to quantify marine heatwaves.

Results were robust to (a) removing the five-day threshold for MHWs, (b) using SST from OISST instead of SBT from GLORYS (detrended), (c) using non-detrended data, (d) using a MHW metric of duration (days), (e) using a MHW metric of intensity (°C), (f) calculating degree heating days instead of MHW anomalies, and (g) using only summer MHWs (see Methods). The fitted lines are linear regressions. The shaded areas are 95% confidence intervals. For all panels n = 369 except in (b) n = 441.

Extended Data Fig. 3 Marine heatwave cumulative intensity (total anomaly in °C-days) in each survey region with and without detrending the temperature data to remove the signal of secular warming.

The main text results are detrended. Here, we plot MHW cumulative intensity based on all SBT anomalies from GLORYS, rather than applying the five-day threshold that was used the main text, to more clearly show the differences between the two methods.

Extended Data Fig. 4 Daily 95th percentile anomalies in the two marine heatwave data sources: sea surface temperature from OISST and sea bottom temperature from GLORYS (both detrended).

To simplify comparison we plot all anomalies, not just those MHWs that exceeded a five-day threshold. Note that the OISST time-series began in 1982 and GLORYS began in 1993. Region names are listed in Supp. Tab. 1.

Extended Data Fig. 5 Results are consistent across different metrics of the fish community.

We calculated mean abundance (a), mean biomass (b, used in the main text), median abundance (c), and median biomass (d). MHWs were calculated from the detrended GLORYS sea bottom temperature data with a five-day minimum duration threshold for MHWs, as used in the main text. Points represent log ratios of each metric in a survey from one year to the next (n = 343). The fitted lines are linear regressions. The shaded areas are 95% confidence intervals. The Northeast US survey was omitted because it did not have abundance data recorded.

Extended Data Fig. 6 Depth changes in the fish assemblage in response to marine heatwaves.

Fish assemblage depth change (log ratio) was not predicted by (a) the presence or absence of a MHW or (b) MHW cumulative intensity (total anomaly in °C-days; n = 369). MHWs were calculated from the detrended GLORYS sea bottom temperature data with a five-day minimum duration threshold for MHWs, as used in the main text. The fitted line in (b) is a linear regression and the shaded area is its 95% confidence interval.

Extended Data Fig. 7 Marine heatwave effect on taxon-specific biomass log ratios grouped by traits.

Biomass log ratio and MHW cumulative intensity (total anomaly in °C-days) grouped by (a) feeding mode (n = 29,628), (b) trophic level (n = 29,909), and (c) habitat preference (n = 29,681) of each taxon. Trait data were extracted from Beukhof et al75. (see Methods). MHWs were calculated from the detrended GLORYS sea bottom temperature data with a five-day minimum duration threshold for MHWs, as used in the main text. Fitted lines are linear regressions. Shaded areas are 95% confidence intervals.

Extended Data Fig. 8 The presence or absence of a MHW did not affect temporal community dissimilarity.

We measured community dissimilarity as partitioned occurrence-based beta diversity metrics of substitution and subset (Jaccard turnover (a) and nestedness (b)) and partitioned biomass-based beta diversity metrics of substitution and subset (Bray-Curtis balanced variation (c) and biomass gradient (d)). Community dissimilarity metrics were calculated within each region from one year to the next (n = 369). MHWs were calculated from the detrended GLORYS sea bottom temperature data with a five-day minimum duration threshold for MHWs, as used in the main text.

Extended Data Fig. 9 Results from a power analysis simulating how much data would be required to detect a range of MHW-induced biomass losses.

Approximately 600 survey-years in total (summed across all regions) would be required to find a significant effect if MHWs reduced biomass by 6% using either the GLORYS (a) or OISST (b) datasets; the dashed vertical line shows the sample size of our actual datasets. Given the true size of our datasets (n = 369 survey-years for GLORYS and 441 for OISST), our analysis had the power to detect a MHW-induced biomass decline of ~9% with GLORYS (c) and ~8% with OISST (d). The dashed horizontal line denotes one conventionally accepted threshold for power (0.8).

Extended Data Fig. 10 Biomass trends over time in each survey.

The top five taxa by biomass are highlighted. Shaded grey rectangles denote when any MHWs occurred in the preceding survey-year. MHWs were calculated from the detrended GLORYS sea bottom temperature data with a five-day minimum duration threshold for MHWs, as used in the main text. Note that x- and y-axes vary depending on time-series length and overall survey catch.

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Fredston, A.L., Cheung, W.W.L., Frölicher, T.L. et al. Marine heatwaves are not a dominant driver of change in demersal fishes. Nature 621, 324–329 (2023). https://doi.org/10.1038/s41586-023-06449-y

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