DOI QR코드

DOI QR Code

Benford's Law and its Potential for Data Verification in Ecological Monitoring

  • Tae-Jun Choi (Department of Biological Sciences, Kongju National University) ;
  • Woong-Bae Park (Department of Biological Sciences, Kongju National University) ;
  • Dae-Hee Kim (Department of Biological Sciences, Kongju National University) ;
  • Dohee Lee (Department of Biological Sciences, Kongju National University) ;
  • Yuno Do (Department of Biological Sciences, Kongju National University)
  • Received : 2023.12.14
  • Accepted : 2024.02.07
  • Published : 2024.05.01

Abstract

Ecological monitoring provides indispensable data for biodiversity conservation and sustainable resource management. However, the complexity and variability inherent in ecological monitoring data necessitate robust verification processes to ensure data integrity. This study employed Benford's Law, a statistical principle traditionally used in fields such as finance and health sciences, to evaluate the authenticity of ecological monitoring data related to the abundance of migratory bird species across various locations in South Korea. Benford's Law anticipates a specific logarithmic distribution of leading digits in naturally occurring numerical datasets. Our investigation involved two stages of analysis: a first-order analysis considering the leading digit and a second-order analysis examining the first two digits of bird population counts. While the first-order analysis displayed moderate conformity to Benford's Law that suggested overall data integrity, the second-order analysis revealed more pronounced deviations, indicating potential inconsistencies or inaccuracies in certain subsets of the data. Although our data did not perfectly align with Benford's Law, these deviations underscore the complex nature of ecological research, which is influenced by a multitude of environmental, methodological, and human factors.

Keywords

References

  1. Beck, J., and Schwanghart, W. (2010). Comparing measures of species diversity from incomplete inventories: an update. Methods in Ecology and Evolution, 1, 38-44. https://doi.org/10.1111/J.2041-210X.2009.00003.X
  2. Bhole, G., Shukla, A., and Mahesh, T.S. (2015). Benford analysis: A useful paradigm for spectroscopic analysis. Chemical Physics Letters, 639, 36-40. https://doi.org/10.1016/J.CPLETT.2015.08.061
  3. Biber, E. (2013) The challenge of collecting and using environmental monitoring data. Ecology and Society, 18, 68. http://dx.doi.org/10.5751/ES-06117-180468
  4. Burgess, H.K., DeBey, L.B., Froehlich, H.E., Schmidt, N., Theobald, E.J., Ettinger, A.K., et al. (2017). The science of citizen science: Exploring barriers to use as a primary research tool. Biological Conservation, 208, 113-120. https://doi.org/10.1016/J.BIOCON.2016.05.014
  5. Campos, L., Salvo, A.E., and Flores-Moya, A. (2016). Natural taxonomic categories of angiosperms obey Benford's law, but artificial ones do not. Systematics and Biodiversity, 14, 431-440. https://doi.org/10.1080/14772000.2016.1181 683
  6. Chao, A., and Jost, L. (2012). Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology, 93, 2533-2547. https://doi.org/10.1890/11-1952.1
  7. Cinelli, C. (2022). Benford Analysis for Data Validation and Forensic Analytics. Retrieved July 02 2023, from https://cran.r-project.org/web/packages/benford.analysis/benford.analysis.pdf
  8. Costas, E., Lopez-Rodas, V., Toro, F.J., and Flores-Moya, A. (2008). The number of cells in colonies of the cyanobacterium Microcystis aeruginosa satisfies Benford's law. Aquatic Botany, 89, 341-343. https://doi.org/10.1016/J.AQUABOT.2008.03.011
  9. Diekmann, A. (2007). Not the First Digit! Using Benford's Law to Detect Fraudulent Scientific Data. Journal of Applied Statistics, 34, 321-329. https://doi.org/10.1080/02664760601004940
  10. Dixon, P. (2003). VEGAN, a package of R functions for community ecology. Journal of Vegetation Science, 14, 927-930. https://doi.org/10.1111/J.1654-1103.2003.TB022 28.X
  11. Docampo, S., Del Mar Trigo, M., Aira, M.J., Cabezudo, B., and Flores-Moya, A. (2009). Benford's law applied to aerobiological data and its potential as a quality control tool. Aerobiologia, 25, 275-283. https://doi.org/10.1007/S10453-009-9132-8
  12. Gorenc, M. (2019). Benford's Law As a Useful Tool to Determine Fraud in Financial Statements. Management, 14. https://doi.org/10.26493/1854-4231.14.19-31
  13. Kvam, P., Vidakovic, B., and Kim, S. J. (2022). Nonparametric statistics with applications to science and engineering with R (p. 158). John Wiley & Sons.
  14. Magurran, A.E. (1988). Ecological Diversity and Its Measurements. Chapman & Hall. https://doi.org/10.1007/978-94-015-7358-0
  15. Nigrini, M.J. (2012). Benford's Law: Applications for forensic accounting, auditing, and fraud detection. John Wiley & Sons.
  16. National Institute of Biological Resources (NIBR). (2021). 2020-2021 Winter Waterbird Census of Korea. NIBR.
  17. Ozkundakci, D., and Pingram, M.A. (2019). Nature favours "one" as the leading digit in phytoplankton abundance data. Limnologica, 78, 125707. https://doi.org/10.1016/J.LIMNO.2019.125707
  18. Proger, L., Griesberger, P., Hacklander, K., Brunner, N., and Kuhleitner, M. (2021). Benford's Law for Telemetry Data of Wildlife. Stats, 4, 943-949. https://doi.org/10.3390/STATS4040055
  19. Shikano, S., and Mack, V. (2016). When does the second-digit benforďs law-test signal an election fraud? Journal of Economics and Statistics, 231, 719-732. https://doi.org/10.1515/JBNST-2011-5-610
  20. Szabo, J.K., Forti, L.R., and Callaghan, C.T. (2023). Large biodiversity datasets conform to Benford's law: Implications for assessing sampling heterogeneity. Biological Conservation, 280, 109982. https://doi.org/10.1016/J.BIOCON.2023.1099 82
  21. Turner, M.G., Donato, D.C., and Romme, W.H. (2013). Consequences of spatial heterogeneity for ecosystem services in changing forest landscapes: priorities for future research. Landscape Ecology, 28, 1081-1097. https://doi.org/10.1007/s10980-012-9741-4
  22. Yoccoz, N.G., Nichols, J.D., and Boulinier, T. (2001). Monitoring of biological diversity in space and time. Trends in Ecology & Evolution, 16, 446-453. https://doi.org/10.1016/S0169-5347(01)02205-4