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Non-detection errors in a survey of persistent, highly-detectable vegetation species

Abstract

Rare, small or annual vegetation species are widely known to be imperfectly detected with single site surveys by most conventional vegetation survey methods. However, the detectability of common, persistent vegetation species is assumed to be high, but without supporting research. In this study, we evaluate the extent of false-negative errors of perennial vegetation species in a systematic vegetation survey in arid South Australia. Analysis was limited to the seven most easily detected persistent vegetation species and controlled for observer skill. By comparison of methodologies, we then predict the magnitude of non-detection error rates in a second survey. The analysis revealed that all but one highly detectable perennial vegetation species was imperfectly detected (detection probabilities ranged from 0.22 to 0.83). While focussed in the Australian rangelands, the implications of this study are far reaching. Inferences drawn from systematic vegetation surveys that fail to identify and account for non-detection errors should be considered potentially flawed. The identification of this problem in vegetation surveying is long overdue. By comparison, non-detection has been a widely acknowledged, and dealt with, problem in fauna surveying for decades. We recommend that, where necessary, vegetation survey methodology adopt the methods developed in fauna surveying to cope with non-detection errors.

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References

  • Alexander, H. M., Slade, N. A., et al. (1997). Application of mark-recapture models to estimation of the population size of plants. Ecology, 78(4), 1230–1237.

    Google Scholar 

  • Black, J. M. (1986). Flora of South Australia. Adelaide: Government Printer.

    Google Scholar 

  • Boudreau, S. A., & Yan, N. D. (2004). Auditing the accuracy of a volunteer-based surveillance program for an aquatic invader bythotrephes. Environmental Monitoring and Assessment, 91, 17–26.

    Article  Google Scholar 

  • Brandle, R. (1998). A biological survey of the Stony Deserts South Australia, 1994–1997. Heritage and Biodiversity Section, Department for Environment, Heritage and Aboriginal Affairs, South Australia.

  • Brandle, R., & Moseby, K. E. (1999). Comparative ecology of two populations of Pseudomys australis in northern South Australia. Wildlife Research, 26, 541–564.

    Article  Google Scholar 

  • Bunge, J., & Fitzpatrick, M. (1993). Estimating the number of species: A review. Journal of the American Statistical Association, 88, 364–373.

    Article  Google Scholar 

  • Chaudhari, P. R., Gajghate, D. G., et al. (2007). Monitoring of environmental parameters for CO2 sequestration: A case study of Nagpur City, India. Environmental Monitoring and Assessment, 135, 281–290.

    Article  CAS  Google Scholar 

  • Colwell, R. K., & Coddington, J. A. (1994). Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society of London, 345, 101–118.

    Article  CAS  Google Scholar 

  • Cunningham, G. M., Mulham, W. E., Milthorpe, P. L., & Leigh, J. H. (1981). Plants of Western New South Wales: NSW Government Printing Office.

  • Dorazio, R. M., & Royle, J. A. (2005). Estimating size and composition of biological communities by modeling the occurence of species. Journal of the American Statistical Association, 100(470), 389–398.

    Article  CAS  Google Scholar 

  • Field, S. A., Tyre, A. J., et al. (2005). Optimizing allocation of monitoring effort under economic and observational constraints. Journal of Wildlife Management, 69(2), 473–482.

    Article  Google Scholar 

  • Gu, W., & Swihart, R. K. (2004). Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation, 116(2), 195–203.

    Article  Google Scholar 

  • Guoke, C., Kéry, M., et al. (2009). Factors affecting detection probability in plant distribution studies. Journal of Ecology, 97(6), 1383–1389.

    Article  Google Scholar 

  • Kéry, M., & Schmidt, B. R. (2008). Imperfect detection and its consequences for monitoring for conservation. Community Ecology, 9(2), 207–216.

    Article  Google Scholar 

  • Kéry, M., Spillmann, J. H., et al. (2006). How biased are estimates of extinction probability in revisitation studies? Journal of Ecology, 94, 980–986.

    Article  Google Scholar 

  • Kutsche, F., & Lay, B. (2003). Field guide to the plants of Outback South Australia. Openbook.

  • Laut, P., Keig, G., et al. (1977). Environments of South Australia: Province 8, Northern Arid (pp. 116–168). Canberra, Division of Land Use Research, CSIRO.

  • MacKenzie, D. I. (2005). Improving inferences in popoulation studies of rare species that are detected imperfectly. Ecology, 86(5), 1101–1113.

    Article  Google Scholar 

  • MacKenzie, D. I., Nichols, J. D., et al. (2002). Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83(8), 2248–2255.

    Article  Google Scholar 

  • Mao, C. X., & Colwell, R. K. (2005). Estimation of species richness: Mixture models, the role of rare species, and inferential challenges. Ecology, 86(5), 1143–1153.

    Article  Google Scholar 

  • Milberg, P., Bergstedt, J., et al. (2008). Observer bias and random variation in vegetation monitoring data. Journal of Vegetation Science, 19(5), 633–644.

    Article  Google Scholar 

  • Myers, N., Mittermeier, R. A., et al. (2000). Biodiversity hotspots for conservation priorities. Nature, 403(6772), 853(856).

    Article  Google Scholar 

  • Regan, T. J., McCarthy, M. A., et al. (2006). Optimatl eradication: When to stop looking for an invasive plant. Ecology Letters, 9, 759–766.

    Article  Google Scholar 

  • Slade, N. A., Alexander, H. M., et al. (2003). Estimation of population size and probabilities of survival and detection in Mead’s Milkweed. Ecology, 84(3), 791–797.

    Article  Google Scholar 

  • Tynan, R. (1993). Lease assessment overview report: Kingoonya soil conservation district, Pastoral Management Branch. South Australian Department of Environment and Natural Resources.

  • Tyre, A. J., Tenhumberg, B., et al. (2003). Improving precision and reducing bias in biological surveys: Estimating false-negative error rates. Ecological Applications, 13(6), 1790–1801.

    Article  Google Scholar 

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Correspondence to Kenneth D. Clarke.

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Clarke, K.D., Lewis, M., Brandle, R. et al. Non-detection errors in a survey of persistent, highly-detectable vegetation species. Environ Monit Assess 184, 625–635 (2012). https://doi.org/10.1007/s10661-011-1991-0

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  • DOI: https://doi.org/10.1007/s10661-011-1991-0

Keywords

  • Non-detection
  • Vegetation survey