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Antarctica’s wilderness fails to capture continent’s biodiversity

Nature volume 583, pages 567–571 (2020)Cite this article

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Abstract

Recent assessments of Earth’s dwindling wilderness have emphasized that Antarctica is a crucial wilderness in need of protection1,2. Yet human impacts on the continent are widespread3,4,5, the extent of its wilderness unquantified2 and the importance thereof for biodiversity conservation unknown. Here we assemble a comprehensive record of human activity (approximately 2.7 million records, spanning 200 years) and use it to quantify the extent of Antarctica’s wilderness and its representation of biodiversity. We show that 99.6% of the continent’s area can still be considered wilderness, but this area captures few biodiversity features. Pristine areas, free from human interference, cover a much smaller area (less than 32% of Antarctica) and are declining as human activity escalates6. Urgent expansion of Antarctica’s network of specially protected areas7 can both reverse this trend and secure the continent’s biodiversity8,9,10.

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Fig. 1: Antarctic wilderness areas.
Fig. 2: Inviolate Antarctic Wilderness areas (definition 5).
Fig. 3: Biodiversity Relevant Antarctic Wilderness.

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Data availability

The wilderness areas and historical human activity data are available at https://doi.org/10.26180/5c32bf1b041ea. The other spatial data can be obtained from their creators18,28,53,68.

Code availability

Computer code is available at https://doi.org/10.26180/5c32bf1b041ea.

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Acknowledgements

We thank K. Close, G. A. Duffy, R. Harvey, K. A. Hughes, T. Robertson and D. Smith for their assistance in identifying activity data and M. A. McGeoch, H. P. Baird, J. R. Lee, L. Chapman and A. Clarke for reading a previous version of this manuscript. This research was supported by Australian Antarctic Science (AAS) grant 4482 and a Sir James McNeill Foundation Postgraduate Research Scholarship to R.I.L. F.M. was supported by a New Zealand Ministry of Business, Innovation and Employment grant (CO9X1413). A.T. was supported by AAS grant 4296.

Author information

Authors and Affiliations

  1. School of Biological Sciences, Monash University, Melbourne, Victoria, Australia

    Rachel I. Leihy & Steven L. Chown

  2. Global Change Institute, University of the Witwatersrand, Johannesburg, South Africa

    Bernard W. T. Coetzee

  3. Organization for Tropical Studies, Scientific Services, South African National Parks, Skukuza, South Africa

    Bernard W. T. Coetzee

  4. Manaaki Whenua Landcare Research, Auckland, New Zealand

    Fraser Morgan

  5. Te Pūnaha Matatini, University of Auckland, Auckland, New Zealand

    Fraser Morgan

  6. Australian Antarctic Division, Department of Agriculture, Water and the Environment, Kingston, Tasmania, Australia

    Ben Raymond & Aleks Terauds

  7. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

    Ben Raymond

  8. School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia

    Justine D. Shaw

  9. Tilburg Law School, Tilburg University, Tilburg, The Netherlands

    Kees Bastmeijer

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  1. Rachel I. Leihy
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Contributions

S.L.C., B.W.T.C. and R.I.L. conceived the study. R.I.L., B.W.T.C., F.M., B.R., J.D.S. and A.T. collected the human activity data. R.I.L. conducted the analyses. R.I.L., K.B. and S.L.C. drafted the initial manuscript. All authors contributed to the final manuscript.

Corresponding author

Correspondence to Steven L. Chown.

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Competing interests

S.L.C. is the President of the Scientific Committee on Antarctic Research.

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Peer review information Nature thanks Andrew Clarke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Cumulative impact of human visitation across Antarctica.

a, Weighted relative impact (WRI) scores of the number of independent historical human visitation records per 25-km2 grid cell, weighted by the proportion of ice-free area in each cell and the eight adjacent cells. Visitation to sites with WRI scores ≤20 is likely to have had a negligible impact. Cells without WRI scores (grey) have no historical visitation record in the data set and are considered here as unvisited. b, WRI scores for the McMurdo Dry Valleys region. c, Location of 18 field camps (points) in the McMurdo Dry Valleys, used to validate the WRI scores (Extended Data Table 2). d, Frequency distribution of 1,999 bootstrapped binomial generalized linear model regression coefficients for the relationship between WRI scores and the presence/absence of field camps in the McMurdo Dry Valleys (b, c; n = 252 grid cells). The dashed line indicates the regression coefficient (0.0023) for the model fit to the original sample.

Extended Data Fig. 2 Historical human visitation record density.

a, b, Number of historical human activity records (a) and number of independent historical human activity records (b) per 50 × 50 km cell across Antarctica from 1819 to 2018 (n = 2,698,429 records). Dark purple lines (a) indicate the routes of recent overland traverses (for example, 2007–2008 Norwegian–US Scientific Traverse of East Antarctica), where geo-positioning data were collected automatically at high temporal resolutions (~10 min), resulting in many records for relatively transitory site visits. To standardize sampling frequencies across different data sources, independent records (b) count only one record per cell per data set for data sets describing a single event (for example, a traverse).

Extended Data Fig. 3 Sampling completeness and scale dependency of visitation records.

a, Relationship between the number of visitation records and percentage of land area visited across Antarctica at a 25-km2 grid resolution, modelled using a power-law model (f(x) = 0.78x0.39; r2 = 0.99). The visitation accumulation curve is extrapolated (dashed line) to predict the percentage land area expected to be identified as visited if twice the number of visitation records (5.4 million records) were available in the data set. Points indicate the percentage of visited land, calculated using 20 random subsamples of the complete visitation records for each interval (n = 240 subsamples); shaded blue area indicates 95% confidence interval. b, Relationship between grid cell resolution and the percentage of land area across Antarctica with no visitation records (that is, unvisited areas), modelled using an exponential model (f(x) = 91.78e(–0.01x); r2 = 0.95; n = 15 grids). In this study, visitation was modelled at a 5-km (25-km2) resolution (square).

Extended Data Table 1 Place-name record categories
Full size table
Extended Data Table 2 Weighted Relative Impact score validation
Full size table
Extended Data Table 3 Inviolate and Negligibly Impacted Antarctic Wilderness areas
Full size table
Extended Data Table 4 Biodiversity representation in wilderness areas
Full size table

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Leihy, R.I., Coetzee, B.W.T., Morgan, F. et al. Antarctica’s wilderness fails to capture continent’s biodiversity. Nature 583, 567–571 (2020). https://doi.org/10.1038/s41586-020-2506-3

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