Advertisement

From ecological records to big data: the invention of global biodiversity

  • Vincent DevictorEmail author
  • Bernadette Bensaude-Vincent
Original Paper

Abstract

This paper is a critical assessment of the epistemological impact of the systematic quantification of nature with the accumulation of big datasets on the practice and orientation of ecological science. We examine the contents of big databases and argue that it is not just accumulated information; records are translated into digital data in a process that changes their meanings. In order to better understand what is at stake in the ‘datafication’ process, we explore the context for the emergence and quantification of biodiversity in the 1980s, along with the concept of the global environment. In tracing the origin and development of the global biodiversity information facility (GBIF) we describe big data biodiversity projects as a techno-political construction dedicated to monitoring a new object: the global diversity. We argue that, biodiversity big data became a powerful driver behind the invention of the concept of the global environment, and a way to embed ecological science in the political agenda.

Keywords

Big data Biodiversity Ecology Foucault Politics 

Notes

Acknowledgments

We would like to thank three anaymous reviewers and Staffan Müeller-Wille for their very constructive comments and suggestions on earlier version of this paper.

References

  1. Andersson, J., & Rindzevičiūtė, E. (2012). The political life of prediction. The future as a space of scientific world governance in the Cold War era. Les cahiers européens de Sciences-Po, 4, 2–25.Google Scholar
  2. Aronova, E. (2015). Environmental monitoring in the making: From surveying nature’s resources to monitoring nature’s change. Historical Social Research, 40, 222–245.Google Scholar
  3. Aronova, E., Baker, K. S., & Oreskes, N. (2010). Big Science and Big Data in Biology: From the international geophysical year through the international biological program to the Long Term Ecological Research (LTER) Network, 1957–Present. Historical Studies in the Natural Sciences, 40, 183–224.CrossRefGoogle Scholar
  4. Balmford, A., Bennun, L., Brink, B., Cooper, D., Côté, I. M., Crane, P., et al. (2005). The Convention on Biological Diversity’s 2010 Target. Science, 307, 212–213.CrossRefGoogle Scholar
  5. Barnosky, A. D., Hadly, E. A., Bascompte, J., Berlow, J., Brown, J. H., Fortelius, M., et al. (2012). Approaching a state shift in Earth’s biosphere. Nature, 486, 52–58.CrossRefGoogle Scholar
  6. Beck, J., Böller, M., Erhardt, A., & Schwanghart, W. (2014). Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics, 19, 10–15.CrossRefGoogle Scholar
  7. Bensaude-Vincent, B. (2009). Les vertiges de la technoscience: Façonner le monde atome par atome. Paris: La Découverte.Google Scholar
  8. Bensaude-Vincent, B., Loeve, S., Nordmann, A., & Schwarz, A. (2011). Matters of interest: The objects of research in science and technoscience. Journal for General Philosophy of Science, 42, 365–383.CrossRefGoogle Scholar
  9. Bisby, F. A. (2000). The quiet revolution: Biodiversity informatics and the internet. Science, 289, 2309–2312.CrossRefGoogle Scholar
  10. Bocking, S. (2013). The ecosystem: Research and practice in North America. Web Ecology, 13, 43–47.CrossRefGoogle Scholar
  11. Bowker, G. C. (2000a). Biodiversity datadiversity. Social Studies of Science, 30, 643–683.CrossRefGoogle Scholar
  12. Bowker, G. C. (2000b). Mapping biodiversity. International Journal of Geographical Information Science, 14, 739–754.CrossRefGoogle Scholar
  13. Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15, 662–679.CrossRefGoogle Scholar
  14. Bromley, D. A. (2002). Science, technology, and politics. Technology in Society, 24, 9–26.CrossRefGoogle Scholar
  15. Butchart, S. H. M., Walpole, M., Collen, B., van Strien, A., Scharlemann, J. P. W., Almond, R. E. A., et al. (2010). Global biodiversity: Indicators of recent declines. Science, 328, 1164–1168.CrossRefGoogle Scholar
  16. Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 69–80.CrossRefGoogle Scholar
  17. Calude, C. S., & Longo, G. (2015). The deluge of spurious correlations in big data. In CDMTCS Research Report Series (pp. 1–13).Google Scholar
  18. Chase, J. M., & Leibold, M. (2003). Ecological Niches. Linking classical and contemporary approaches: University of Chicago Press.CrossRefGoogle Scholar
  19. Chase, J. M., & Myers, J. A. (2011). Disentangling the importance of ecological niches from stochastic processes across scales. Philosophical Transactions of the Royal Society of Londonc B, Biological Sciences, 366, 2351–2363.CrossRefGoogle Scholar
  20. Clarke, G. (1954). Elements of Ecology. New Jersey: John Wiley & Sons INC, Chapman & Hall LTD.CrossRefGoogle Scholar
  21. Curry, G. B., & Humphries, C. J. (2007). Biodiversity databases: Techniques, politics and applications (Vol. 485). Abingdon: Taylor & Francis.CrossRefGoogle Scholar
  22. Deans, A. R., Yoder, M. J., & Balhoff, J. P. (2012). Time to change how we describe biodiversity. Trends in Ecology & Evolution, 27, 78–84.CrossRefGoogle Scholar
  23. Devictor, V., Clavel, J., Julliard, R., Lavergne, S., Mouillot, D., Thuiller, W., et al. (2010). Defining and measuring ecological specialization. Journal of Applied Ecology, 47, 15–25.CrossRefGoogle Scholar
  24. Edwards, J. L. (2000). Interoperability of biodiversity databases: Biodiversity information on every desktop. Science, 289, 2312–2314.CrossRefGoogle Scholar
  25. Edwards, P. (2010). A vast machine. Cambridge MA: The MIT Press.Google Scholar
  26. Elith, J., & Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology Evolution and Systematics, 40, 677–697.CrossRefGoogle Scholar
  27. Ellis, R., Pacha, M., & Waterton, C. (2007). Assembling nature: The social and political lives of biodiversity softwares. Lancaster.Google Scholar
  28. Elton, C. (1927). Animal ecology. London: Sidgwick and Jackson.Google Scholar
  29. Elton, C. S. (1966). The pattern of animal communities. London: Methuen and Co Ltd.Google Scholar
  30. Foucault, M. (1980). The confession of the flesh. in power/knowledge: Select interviews and other writings 1972–1977 (p. 193). New York: Pantheon Books Edition.Google Scholar
  31. Greiner, W., & Lane, N. (2009). David Allan Bromley 1926—2005. National Academy of Sciences, 1–49.Google Scholar
  32. Grinnell, J. (1917). The niche relationship of the California Thrasher. The Auk, 34, 427–433.CrossRefGoogle Scholar
  33. Grundmann, R., & Stehr, N. (2012). The power of scientific knowledge. From research to public policy. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  34. Guisan, A., & Thuiller, W. (2005). Predicting species distribution: Offering more than simple habitat models. Ecology Letters, 8, 993–1009.CrossRefGoogle Scholar
  35. Güttler, N. R. (2011). Scaling the period eye: Oscar drude and the cartographical practice of plant geography, 1870s–1910s. Science in Context, 24, 1–41.CrossRefGoogle Scholar
  36. Hamblin, J. H. (2013). Arming mother nature: The birth of catastrophic environmentalism. Oxford: Oxford University Press.Google Scholar
  37. Hutchinson, G. E. (1957). Cold spring harbor symposium. Quantitative biology. Concluding remarks, 22, 415–427.Google Scholar
  38. Jax, K., Jones, C. G., & Pickett, S. T. A. (1998). The Self-Identity of Ecological Units. Oikos, 82, 253–264.CrossRefGoogle Scholar
  39. Jiménez-Valverde, A., Lobo, J. M., & Hortal, J. (2008). Not as good as they seem: The importance of concepts in species distribution modelling. Diversity and Distributions, 14, 885–890.CrossRefGoogle Scholar
  40. Kelmelis, J. A., & Snow, M. (1991). Proceedings of the U.S. Geological Survey Global Change Research Forum. Circular 1086.Google Scholar
  41. Kingsland, P. S. E. (2005). The Evolution of American Ecology, 1890–2000. Baltimore: The Johns Hopkins University Press.Google Scholar
  42. Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1, 1–12.CrossRefGoogle Scholar
  43. Kwa, C. (2005). Local ecologies and global science: Discourses and strategies of the international geosphere-biosphere programme. Social Studies of Science, 35, 923–950.CrossRefGoogle Scholar
  44. Laney, D. (2001). 3D data management: controlling data volume, velocity, and variety. META Group Research Note 6.Google Scholar
  45. Lawrence, A. (2006). ‘No personal motive?’ Volunteers, biodiversity, and the false dichotomies of participation. Ethics, Place & Environment, 9, 279–298.CrossRefGoogle Scholar
  46. Leonelli, S. (2011). Packaging small facts for re-use: Databases in model organism biology. In P. Howlett & M. Morgan (Eds.), How well do facts travel? The dissemination of reliable knwoledge (pp. 325–348). Cambridge: Cambridge University Press.Google Scholar
  47. Leonelli, S. (2014). What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society, 1(1), 2053951714534395.CrossRefGoogle Scholar
  48. Levin, S. A. (1992). The problem of pattern and scale in ecology: The Robert H. MacArthur Award Lecture. Ecology, 73, 1943.Google Scholar
  49. Loh, J., Green, R. E., Ricketts, T., Lamoreux, J., Jenkins, M., Kapos, V., et al. (2005). The Living Planet Index: Using species population time series to track trends in biodiversity. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 360, 289–295.CrossRefGoogle Scholar
  50. Maldonado, C., Molina, C. I., Zizka, A., Persson, C., Taylor, C. M., Albán, J., et al. (2015). Estimating species diversity and distribution in the era of Big Data: To what extent can we trust public databases? Global Ecology and Biogeography, 24, 973–984.CrossRefGoogle Scholar
  51. McAfee, A., & Brynjolfsson, E. (2012). Big Data. Harvard Business Review, (October), 60–68.Google Scholar
  52. Michener, W. K., & Jones, M. B. (2012). Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology & Evolution, 27, 85–93.CrossRefGoogle Scholar
  53. Müller-Wille, S. (2015). How the great chain of being fell apart: Diversity in natural history 1758–1859. THEMA La Revue Des Musées de La Civilisation, 2, 85–95.Google Scholar
  54. OECD. (1993). Megascience and its background. Paris: OECD.Google Scholar
  55. OECD. (1999). Final report of the OECD megascience forum.Working group on biological informatics. OECD: Paris.Google Scholar
  56. OECD. (2003). OECD Environmental Indicators. Development, Measurement and Use. OECD Reference paper (Vol. 51).Google Scholar
  57. Pielke, R., & Klein, R. A. (2010). Presidential Science Advisors: perspectives and reflections on science, policy and politics. New York: Springer.CrossRefGoogle Scholar
  58. Ratchford, J. T., & Colombo, U. (1996). Megascience. UNESCO World science report.Google Scholar
  59. Sarkar, I. N. (2009). Biodiversity informatics: The emergence of a field. BMC Bioinformatics, 10(Suppl 1), 1–2.CrossRefGoogle Scholar
  60. Sarrazin, F., & Lecomte, J. (2016). Evolution in the Anthropocene. Science, 351, 922–923.CrossRefGoogle Scholar
  61. Schulp, C. J. E., Thuiller, W., & Verburg, P. H. (2014). Wild food in Europe: A synthesis of knowledge and data of terrestrial wild food as an ecosystem service. Ecological Economics, 105, 292–305.CrossRefGoogle Scholar
  62. Shavit, A., & Griesemer, J. (2011). Transforming objects into data: how minute technicalities of recording ‘species location’ entrench a basic challenge for biodiversity. In M. Carrier & A. Nordmann (Eds.), Science in the context of application (pp. 169–193). New York: Springer.CrossRefGoogle Scholar
  63. Slota, S., & Bowker, G. C. (2015). On the value of ‘useless data’: Infrastructures, biodiversity, and policy. iConference 2015 Proceedings. http://hdl.handle.net/2142/73663. Accessed 5 Sep 2016.
  64. Soberón, J., & Peterson, A. T. (2004). Biodiversity informatics: managing and applying primary biodiversity data. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 359, 689–698.CrossRefGoogle Scholar
  65. Stevens, H. (2013). Life out of sequence—A data-driven history of bioinformatics. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  66. Strasser, B. J. (2012). Data-driven sciences: From wonder cabinets to electronic databases. Studies in History and Philosophy of Biological and Biomedical Sciences, 43, 85–87.CrossRefGoogle Scholar
  67. Takacs, D. (1996). The Idea of Biodiversity: Philosophies of Paradise. Baltimore: Johns Hopkins University Press.Google Scholar
  68. Turnhout, E., & Boonman-berson, S. (2011). Databases, scaling practices, and the globalization of biodiversity. Ecology and Society, 16(1), 35.CrossRefGoogle Scholar
  69. Turnhout, E., Dewulf, A., & Hulme, M. (2016). What does policy-relevant global environmental knowledge do? The cases of climate and biodiversity. Current Opinion in Environmental Sustainability, 18, 65–72.CrossRefGoogle Scholar
  70. Turnhout, E., Neves, K., & De Lijster, E. (2014). ‘Measurementality’ in biodiversity governance: Knowledge, transparency, and the intergovernmental science-policy platform on biodiversity and ecosystem services (ipbes). Environment and Planning A, 46, 581–597.CrossRefGoogle Scholar
  71. Watson, R. T. (2005). Turning science into policy: Challenges and experiences from the science-policy interface. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 360, 471–477.CrossRefGoogle Scholar
  72. Wilson, E. O. (1985). The biological diversity crisis. BioScience, 35, 700–706.CrossRefGoogle Scholar
  73. Wilson, E. O. (1988). Biodiversity. (N. A. of Science, Ed.).Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Vincent Devictor
    • 1
    • 2
    Email author
  • Bernadette Bensaude-Vincent
    • 1
  1. 1.CETCOPRA (Centre d’Etudes des Techniques, des Connaissances et des Pratiques)Université Paris 1 Panthèon SorbonneParisFrance
  2. 2.Institut des Sciences de l’Evolution, Université Montpellier, CNRS, IRDMontpellierFrance

Personalised recommendations