Abstract
Riparian condition is commonly measured as part of stream health monitoring programs as riparian vegetation provides an intricate linkage between the terrestrial and aquatic ecosystems. Field surveys of a riparian zone provide comprehensive riparian attribute data but can be considerably intensive and onerous on resources and workers. Our objective was to assess the impact of reducing the sampling effort on the variation in key riparian health indicators. Subsequently, we developed a non-parametric approach to calculate an information retained (IR) statistic for comparing several constrained systematic sampling schemes to the original survey. The IR statistic is used to select a scheme that reduces the time taken to undertake riparian surveys (and thus potentially the costs) whilst maximising the IR from the original survey. Approximate bootstrap confidence intervals were calculated to improve the inferential capability of the IR statistic. The approach is demonstrated using riparian vegetation indicators collected as part of an aquatic ecosystem health monitoring program in Queensland, Australia. Of the nine alternative sampling designs considered, the sampling design that reduced the sampling intensity per site by sixfold without significantly comprising the quality of the IR, results in halving the time taken to complete a riparian survey at a site. This approach could also be applied to reducing sampling effort involved in monitoring other ecosystem health indicators, where an intensive systematic sampling scheme was initially employed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Davison, A. C., & Hinkley, D. (2006). Bootstrap methods and their application (8th edn). Cambridge: Cambridge Series in Statistical and Probabilistic Mathematics
EHMP. (2008). Ecosystem health monitoring program 2006–07 annual technical report. Brisbane: South East Queensland Healthy Waterways Partnership.
Fairweather, P. G. (1991). Statistical power and design requirements for environmental monitoring. Australian Journal of Marine & Freshwater Research, 42, 555–567.
Keough, M. J., & Mapstone, B. D. (1997). Designing environmental monitoring for pulp mills in Australia. Water Science and Technology, 35, 397–404.
Lynch, R. J., Bunn, S. E., & Catterall, C. P. (2002). Adult aquatic insects: potential contributors to riparian food webs in Australia’s wet–dry tropics. Austral Ecology, 27, 515–526.
Marshall, J., Steward, A., McGregor, G., Marshall, C., Negus, P., Johnson, D., Lobegeiger, J., Davies, P., Harch, B., Winning, M., Choy, S., & Cochayne, B. (2003). Condamine-balonne integrated monitoring pilot project: methods. Aquatic ecosystems technical report no. 41. Queensland: Queensland Department of Natural Resources and Mines.
Marshall, C., Negus, P., Winning, M., Johnson, D,. & Harch, B. (2004). Fitzroy basin resource operation plan: Ecological monitoring and assessment program: Part 2 research project methods: Using a gradient of impact approach to identify indicators for inclusion in the design of an on-going monitoring program. Freshwater Biological Monitoring Report, Department of Natural Resources and Mines, Queensland Government
Muenz, T. L., Golladay, S. W., Smith, L. L., & Jones, J. W. (2006). Stream buffer effectiveness in an agriculturally influenced area, Southwestern Georgia: responses of water quality, macroinvertebrates, and amphibians. Journal of Environmental Quality, 5, 1924–1938.
Naiman, R. J., Decamps, H., & McClain, M. E. (2005). Riparia: ecology, conservation, and management of streamside communities. London: Elsevier Academic Press.
Negus, P., Moller, G., Blessing, J., Davis, L., Marshall, J., & Dobbie, M. (2010). Stream and estuary assessment program: An assessment framework for riverine ecosystems. Water Planning Sciences, Department of Environment and Resource Management, The State of Queensland. ISBN 9311662183293
Osenberg, C. W., Schmitt, R. J., Holbrook, S. J., Abu-Saba, K. E., & Flegal, A. R. (1994). Detection of environmental impacts: natural variability, effect size, and power analysis. Ecological Applications, 4, 16–30.
Pusey, B. J., & Arthington, A. H. (2003). Importance of the riparian zone to the conservation and management of freshwater fish: a review. Marine and Freshwater Research, 54, 1–16.
R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org
Stoddard, J. L., Peck, D. V., Paulsen, S. G., Van Sickle, J., Hawkins, C. P., Herlihy, A. T., Hughes, R. M., Kaufmann, P. R., Larsen, D. P., Lomnicky, G., Olsen, A. R., Peterson, S. A., Ringold, P. L., & Whittier, T. R. (2005). An ecological assessment of Western streams and rivers. EPA 620/R-05/005. Washington, DC: U.S. Environmental Protection Agency.
Zaimes, G. N., Schultz, R. C., & Isenhart, T. N. (2006). Riparian land use and precipitation influences on stream bank erosion in Central Iowa. Journal of the American Water Resources Association, 42, 83–87.
Acknowledgements
We thank Louisa Davis, formerly of the Queensland Department of Environment and Resource Management, for supplying the data and for her constructive and thoughtful input throughout the analyses. We also appreciate feedback from the referees and CSIRO colleague Erin Peterson, which helped improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dobbie, M.J., Wang, YG., Zammit, R. et al. Optimising the sampling effort in riparian surveys. Environ Monit Assess 185, 3721–3733 (2013). https://doi.org/10.1007/s10661-012-2823-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10661-012-2823-6