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Circumpolar projections of Antarctic krill growth potential

An Author Correction to this article was published on 10 December 2020

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Antarctic krill is a key species of important Southern Ocean food webs, yet how changes in ocean temperature and primary production may impact their habitat quality remains poorly understood. We provide a circumpolar assessment of the robustness of krill growth habitat to climate change by coupling an empirical krill growth model with projections from a weighted subset of IPCC Earth system models. We find that 85% of the study area experienced only a moderate change in relative gross growth potential (± 20%) by 2100. However, a temporal shift in seasonal timings of habitat quality may cause disjunctions between krill’s biological timings and the future environment. Regions likely to experience habitat quality decline or retreat are concentrated near the northern limits of krill distribution and in the Amundsen–Bellingshausen seas region during autumn, meaning habitat will likely shift to higher latitudes in these areas.

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Fig. 1: Taylor diagram assessing individual model performance in projecting growth potential against observation-based values for selection and weighting scheme.
Fig. 2: Changes in future environmental drivers projected by the weighted subset for RCP scenarios 4.5 and 8.5.
Fig. 3: Evaluating the performance of the weighted subset.
Fig. 4: The projected change in krill growth potential between each RCP scenario and the historical scenario.
Fig. 5: The projected RGGP in CCAMLR management areas.

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Data availability

The data that support the findings of this study are publicly available at the Australian Antarctic Data Centre, (ref. 85). The CMIP5 output is available from the Earth System Grid Federation ( In addition to being retrievable using the raadtools package in R (, the satellite data can be found at for sea surface temperature, for SeaWiFS75 chlorophyll a and for the Johnson et al.76 chlorophyll a data.

Code availability

The code relating to this study is available from the corresponding author on request.

Change history

  • 10 December 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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We thank A. Constable, R. Johnson and A. Lenton for their advice on the project, as well as M. Sumner and J. Berkhout for their technical support.

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 2) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

This research was supported by the Australian Government through Antarctic Science Project 4408 and the Cooperative Research Centres Programme through the Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC). D.V. is the recipient of a Tasmania Graduate Research Scholarship provided by the University of Tasmania. S.B. is the recipient of an Australian Research Council Australian Discovery Early Career Award (project DE180100828). E.M. was supported by the BAS ALI-Science Ecosystems project (developing krill life-cycle models and future projections) and acknowledges BAS colleagues for discussions on the development of analyses, models and projections of Antarctic krill population processes. The study is also a contribution to the international Integrating Climate and Ecosystems Dynamics Programme (ICED).

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The study was developed by all authors. All authors supervised D.V. in conducting the analyses, designing figures and preparing the manuscript, as well as provided feedback and ideas in the manuscript development.

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Correspondence to Devi Veytia.

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Peer review information Nature Climate Change thanks Jilda Caccavo, Bettina Fach and Elisa Ravagnan for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Map of study area.

Highlights commonly referenced locations throughout the text. White text with a large font size denotes ocean basins, white text with a small font size denotes seas, and yellow text denotes commonly-referenced topographic features.

Extended Data Fig. 2 Assessing model outputs of explanatory variables a. SST and b. chlorophyll with a Taylor diagram.

Statistics for the Taylor diagram were calculated from the area-weighted seasonal surface averages of SST and chlorophyll. Seasonal surface averages of observations were calculated from OISST v2 for SST, and the mean of the SeaWiFS75 and Johnson et al.76 datasets for chlorophyll.

Extended Data Fig. 3 Comparing chlorophyll observation datasets.

These values represent a climatology taken from Dec 1997-2010. a, is the difference of seasonal surface averages of the Johnson et al.76 dataset minus the SeaWiFS75 dataset. b, The climatologies of the two datasets using a monthly timeseries. c, Seasonal growth potential calculated using each chlorophyll dataset and OISST v2 SST. Black meridional lines on maps delineate ocean basin sectors9.

Extended Data Fig. 4 Different model weighting schemes trialled.

To compare the shapes of these weighting schemes together, weighting schemes #2-5 were multiplied by a constant of 30.

Extended Data Fig. 5 Observation-based seasonal growth potential compared to different model weighting schemes.

The five weighting schemes pictured correspond to the weighting schemes picture in Supplementary Fig. 3. Observation-based growth potential here represents the mean calculated from the Johnson et al.76 and SeaWiFS75 datasets pictured in Supplementary Fig. 2c. Growth potential for the model weighting schemes was calculated over the CMIP5 “historical” period of 1960-1989. Black meridional lines on maps delineate ocean basin sectors9.

Extended Data Fig. 6 Taylor diagram assessing modelled growth potential of different weighting schemes trialled.

The weighting schemes correspond to the weighting schemes picture in Supplementary Fig. 3, except that weighting schemes #2-5 were not multiplied by a constant of 30. Statistics for the Taylor diagram were calculated from the area-weighted seasonal modelled growth potential, averaged over 1960-1989. Observation-based growth potential was calculated using the average of the SeaWiFS75 and Johnson et al.76 chlorophyll datasets and the OISST v2 dataset, over the climatology of 1997-2010.

Extended Data Fig. 7 Comparing the density of growth potential values between chlorophyll datasets, the weighted subset and full ensemble.

The growth potential values represented were taken from surface averages, for a. spring, b. summer, c. autumn and d. winter. Averages were taken over the period of Dec 1997-2010 for the chlorophyll datasets and 1960-1989 for models. See reference for definitions of the sectors and zones used9. The distribution of growth potential values from the weighted subset falls largely within the range of observed variability obtained using the two different Southern Ocean chlorophyll-a algorithms, particularly during spring and summer, which are the seasons with the greatest observation coverage.

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Supplementary Appendices A–C, Figs. 1–5 and Tables 1–4.

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Veytia, D., Corney, S., Meiners, K.M. et al. Circumpolar projections of Antarctic krill growth potential. Nat. Clim. Chang. 10, 568–575 (2020).

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