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Front. Ecol. Evol. | doi: 10.3389/fevo.2018.00239

Do big unstructured biodiversity data mean more knowledge?

 Elisa Bayraktarov1*, Glenn Ehmke1, 2,  James O'Connor2,  Emma L. Burns3,  Hoang A. Nguyen1,  Louise McRae4, Hugh P. Possingham5 and David B. Lindenmayer3
  • 1The University of Queensland, Australia
  • 2BirdLife Australia, Ashmore and Cartier Islands
  • 3Australian National University, Australia
  • 4Zoological Society of London, United Kingdom
  • 5The Nature Conservancy (United States), United States

Conserving species biodiversity demands decisive and effective action. Effective action requires an understanding of species population dynamics. Therefore, robust measures which track temporal changes in species populations are needed. This need, however, must be balanced against the scale at which population change is being assessed. Advances in citizen science and remote sensing technology have heralded an era of “big unstructured data” for biodiversity conservation. However, the value of big unstructured data for assessing changes in species populations, and effectively guiding conservation management has not been rigorously assessed. This can be achieved only by benchmarking big unstructured data against high-quality structured datasets, and ensuring the latter are not lost through an over-emphasis on “big data”. Here, we illustrate the current trend to disproportionately prioritise data quantity over data quality and highlight the discrepancy in global availability between both data types. We propose a research agenda to test whether this trend will result in a net decrease of useful knowledge for biodiversity conservation. We exemplify this by examining the availability of big unstructured data vs standardised data using data from global repositories on birds as an example. We share experiences from the data collation exercise needed to develop the Australian Threatened Species Index. We argue there is an urgent need to validate and enhance the utility of big unstructured data by: 1) maintaining existing well-designed, standardised long-term species population studies; 2) strengthening data quality control, management, and curation of any type of dataset; and 3) developing purpose-specific rankings to assess data quality.

Keywords: Environmental policies, sound decision-making, Species monitoring, species population trends, structured long-term monitoring data, Threatened species, value of big data for conservation

Received: 21 Sep 2018; Accepted: 27 Dec 2018.

Edited by:

Laurentiu Rozylowicz, University of Bucharest, Romania

Reviewed by:

William E. Magnusson, National Institute of Amazonian Research, Brazil
Luca Santini, Radboud University Nijmegen, Netherlands  

Copyright: © 2018 Bayraktarov, Ehmke, O'Connor, Burns, Nguyen, McRae, Possingham and Lindenmayer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Elisa Bayraktarov, The University of Queensland, Brisbane, Australia, e.bayraktarov@uq.edu.au