Southern Ocean ecosystems are under pressure from resource exploitation and climate change1,2. Mitigation requires the identification and protection of Areas of Ecological Significance (AESs), which have so far not been determined at the ocean-basin scale. Here, using assemblage-level tracking of marine predators, we identify AESs for this globally important region and assess current threats and protection levels. Integration of more than 4,000 tracks from 17 bird and mammal species reveals AESs around sub-Antarctic islands in the Atlantic and Indian Oceans and over the Antarctic continental shelf. Fishing pressure is disproportionately concentrated inside AESs, and climate change over the next century is predicted to impose pressure on these areas, particularly around the Antarctic continent. At present, 7.1% of the ocean south of 40°S is under formal protection, including 29% of the total AESs. The establishment and regular revision of networks of protection that encompass AESs are needed to provide long-term mitigation of growing pressures on Southern Ocean ecosystems.
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The tracking data are available through our companion paper16.
Computer code is available at https://github.com/SCAR/RAATD.
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This contribution is part of the Retrospective Analysis of Antarctic Tracking Data (RAATD), a project of the Expert Group on Birds and Marine Mammals of the Scientific Committee on Antarctic Research (SCAR; www.scar.org). The RAATD project would not have been possible without the many scientists, students and field assistants who helped collect data in the field or process them, including S. Adlard, A. Agüera, M. Biuw, M.-A. Blanchet, J. Clarke, P. Cock, H. Cox, M. Connan, A. R. Carlini, S. Corsolini, M. Cottin, J. D. Le Croquant, G. A. Danieri, D. Davies, B. Dilley, R. Downie, M. Dunn, B. M. Dyer, S. Focardi, H. O. Gillett, S. Haaland, L. Jonsen-Humble, H. Kane, B. A. Krafft, C. Kroeger, C. A. E. Lemon, G. Mabille, M. Marczak, T. McIntyre, S. McKooy, J. A. Mennucci, T. Nordstad, C. Oosthuizen, R. Orben, T. Photopoulou, B. Picard, O. Prud’homme, T. Raclot, S. Ramdohr, D. H. Raymond, L. Riekkola, G. Richard, G. Robertson, T. Rogers, K. Ropert-Kato, S. Schoombie, T. N. Snakes, E. Soininen, A. Specht, K. Stevens, J. N. Swærd, C. Tosh, S. G. Trivelpiece, O. S. G. Trolli, T. Truly, L. Upfold, M. Le Vaillant, Y. Watanabe, M. Wege, C. Wheeler, T. O. Whitehead, M. Widmann, A. G. Wood, N. Youdjou and I. Zimmer. We also thank the large number of fieldworkers without whom these data would not have been collected. D. G. Ainley and A. Constable commented on an earlier version of the manuscript. Support and funding were provided by supranational committees and organizations including SCAR, BirdLife International and the European Commission (IUCN BEST program), as well as various national institutions (see also author affiliations) and foundations, including but not limited to: Argentina (Dirección Nacional del Antártico); Australia (Australian Antarctic program; Australian Research Council; Sea World Research and Rescue Foundation; Australian Integrated Marine Observing System (IMOS) (IMOS is a national collaborative research infrastructure, supported by the Australian Government and operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as Lead Agent)); Belgium (Belgian Science Policy Office/Lifewatch), Brazil (Brazilian Antarctic Programme; National Council for Scientific and Technological Development (CNPq); Ministry of Science, Technology, Innovation and Communications (MCTIC); Ministry of the Environment; CAPES); France (Agence Nationale de la Recherche; Centre National d’Etudes Spatiales; Centre National de la Recherche Scientifique; the French Foundation for Research on Biodiversity (FRB; www.fondationbiodiversite.fr) in the context of the CESAB project ‘RAATD’; Fondation Total; Institut Paul-Emile Victor; Programme Zone Atelier de Recherches sur l’Environnement Antarctique et Subantarctique; Terres Australes et Antarctiques Françaises); Germany (Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Deutsche Forschungsgemeinschaft, Hanse-Wissenschaftskolleg (Institute for Advanced Study)); Italy (Programma Nazionale di Ricerche in Antartide (PNRA)); Japan (Japanese Antarctic Research Expedition; JSPS Kakenhi grant 20310016; NIPR visiting professor fellowship for M.A.H.); Monaco (Fondation Prince Albert II de Monaco); New Zealand (Ministry for Primary Industries BRAG; Pew Charitable Trusts); Norway (Norwegian Antarctic Research Expeditions; Norwegian Research Council); Portugal (Foundation for Science and Technology); South Africa (Department of Environmental Affairs; National Research Foundation; South African National Antarctic Programme); UK (Darwin Plus; Ecosystems Programme at the British Antarctic Survey; Natural Environment Research Council; WWF); and USA (US AMLR Program of NOAA Fisheries; National Science Foundation Office of Polar Programs).
H.B., J.-B.C., D.P.C., B.D., M.A.H., L.A.H., I.D.J., M.-A.L., M.M., B.R., R.R.R., Y.R.-C., P.G.R., A. Takahashi, D.T., L.G.T., P.N.T., A.P.V. S.W. and J.C.X. are members of the SCAR Expert Group on Birds and Marine Mammals. S.L.C. is President of SCAR.
Peer review information Nature thanks Tiago Marques and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Habitat importance for a given life-history stage (for example, chick-rearing) of a given species (for example, king penguin (A. patagonicus)) is calculated using two models (grey boxes): the habitat-selection model (box 1) and the habitat accessibility model (box 2). b, These stage-specific, species-specific predictions of habitat importance are combined to calculate the mean habitat importance for multiple species (for example, king penguin and Antarctic fur seal (Arctocephalus gazella)). In the habitat accessibility model (box 2 in a) the distance to colony can be weighted by relative colony size or not. The unweighted version is shown here.
Extended Data Fig. 2 Maps showing the 19 environmental covariates that were used to model the habitat selection of marine predators in the Southern Ocean.
Grey lines indicate major oceanographic fronts. CHLA, chlorophyll a concentration; CURR, geostrophic current velocity; DEPTH, depth; DEPTHg, depth gradient; dSHELF, distance to shelf; EKE, eddy kinetic energy; ICE, sea-ice concentration; ICEA, accessibility through sea ice; ICEsd, standard deviation of sea-ice concentration; SAL, salinity difference; SHFLUX, surface heat flux; SHFLUXsd, standard deviation of surface heat flux; SSHa, sea surface height anomaly; SSHsd, sea surface height standard deviation; SST, sea surface temperature; SSTg, sea surface temperature gradient; VMIX, vertical velocity; VMIXsd, standard deviation of vertical velocity; WIND, surface wind speed. Sources and units of measurement are defined in Supplementary Table 2.
Extended Data Fig. 3 Habitat-importance scores for 16 marine predator species in the Southern Ocean.
The maps show predicted habitat importance for each species. Predictions for macaroni penguins (Eudyptes chrysocome) and royal penguins (Eudyptes schlegeli) are combined. Black circles show all known colony locations for the 14 colony-breeding species, which we used to predict the models across the whole Southern Ocean.
Relative importance of 19 environmental variables that were used as predictors in 40 boosted regression tree models of the habitat selection of Southern Ocean marine predators. Higher values of variable relative importance indicate that the variable has higher predictive power. Points show the values for each model and box plots (in grey, behind) show the distribution of values. Variables are ordered (top to bottom) by decreasing median importance. The three panels show the results for three different groups of species that were identified by hierarchical cluster analysis (see ‘Species grouping’ in Methods, and Extended Data Fig. 7). Full covariate names are provided in Supplementary Table 2. Box plots as in Fig. 4.
Scatter plot smoothed curves (black lines) of the relationship between predictions of the species habitat-selection models (boosted regression trees) (vertical axis) and the values of covariates used as predictors in our boosted regression tree models (horizontal axis). The smoothed curves were drawn by fitting generalized additive models for large datasets with a thin plate regression spline basis, as LOESS (locally estimated scatter plot) smoothing was not computationally feasible. Full covariate names and units are provided in Supplementary Table 2. Higher habitat-selection values indicate higher probabilities of use, irrespective of availability in this case. A smooth curve is shown for each species. Because each species had one to five predictions, for different life-history stages, we took the mean habitat-selection estimate per cell for each species. Rug marks on the horizontal axis indicate the distributions of the data points.
a–c, Maps showing the change (mean in 1987–1998 compared to mean in 2007–2017) in sea-ice duration (days) (a), SST (°C) (b) and wind speed (m s−1) (c). Contour lines (black) indicate AESs. d–f, Kernel density plots show the distribution of values of each of a–c inside (red) and outside (grey) AESs. Horizontal lines represent zero change. Two-tailed permutation tests indicate significant differences in each case, and the number of grid cells included in the test is given in each case (n).
a, Cells that were AES in the original results are shown in blue (remain as AES) or orange (become non-AES in the future). The gradation from orange to blue shows the proportion of climate models that indicate loss (orange) or retention (blue) of AESs. Similarly, the gradation from white to green shows the proportion of models that indicate that non-AES cells will remain as non-AES (white) or become AES (green). Orange and magenta outlines show current and proposed MPAs, respectively. b, Percentage change in the area of AESs according to the eight different climate models (black points), and the mean of these (red points). Box plots as in Fig. 4.
a, Overall habitat importance, calculated without accounting for colony sizes. b, Overall habitat importance if colony sizes are taken into account. Black points indicate colony locations for the 14 colony-breeding species; white contours indicate AESs. See Methods and Supplementary Information for details.
Extended Data Fig. 9 Dendrogram of hierarchical cluster analysis showing species groups in the dataset.
We performed UPGMA hierarchical cluster analysis on the Manhattan distance among species, calculated from the habitat-importance scores. The results show two clear species groups: Antarctic (blue) and sub-Antarctic (magenta). Humpback whales and southern elephant seals (orange) did not fall into either group and we assigned them to both groups for subsequent analyses. The cophenetic correlation coefficient between the distance matrix and the dendrogram was 0.86, which means that the dendrogram is a good representation of the Manhattan distance values among the species. Values can range from 0 (no correlation) to 1 (perfect correlation).
a, b, To account for regional differences in species richness we defined two species groups (Methods and Extended Data Fig. 5) and calculated the mean habitat importance for these two groups separately. These two mean habitat-importance layers (a and b) were then combined into a single overall habitat-importance layer by choosing the maximum value in each cell. Black points indicate the colony locations of colony-breeding species in each species group.
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Hindell, M.A., Reisinger, R.R., Ropert-Coudert, Y. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020). https://doi.org/10.1038/s41586-020-2126-y
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