TY - JOUR
T1 - Evaluating the efficiency of environmental monitoring programs
AU - Levine, Carrie R.
AU - Yanai, Ruth D.
AU - Lampman, Gregory G.
AU - Burns, Douglas A.
AU - Driscoll, Charles T.
AU - Lawrence, Gregory B.
AU - Lynch, Jason A.
AU - Schoch, Nina
N1 - Funding Information:
Support for this study was provided by the New York State Energy Research and Development Authority . We appreciate the support of the Adirondack Lakes Survey Corporation and the New York State Department of Environmental Conservation in providing lake chemistry data from the Adirondack Long Term Monitoring program. We thank Kevin Civerolo, Alan Domaracki, and Gary Lovett for providing valuable feedback on an earlier version of this work and Eddie Bevilacqua for advising us on mixed model analyses. Thanks also to the Journal of Forestry for permission to reprint the map of Hubbard Brook W6. The Hubbard Brook Experimental Forest is operated and maintained by the USDA Forest Service, Northern Research Station, Newtown Square, PA. Hubbard Brook is part of the Long-Term Ecological Research (LTER) network, which is supported by the National Science Foundation. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
PY - 2014/4
Y1 - 2014/4
N2 - Statistical uncertainty analyses can be used to improve the efficiency of environmental monitoring, allowing sampling designs to maximize information gained relative to resources required for data collection and analysis. In this paper, we illustrate four methods of data analysis appropriate to four types of environmental monitoring designs. To analyze a long-term record from a single site, we applied a general linear model to weekly stream chemistry data at Biscuit Brook, NY, to simulate the effects of reducing sampling effort and to evaluate statistical confidence in the detection of change over time. To illustrate a detectable difference analysis, we analyzed a one-time survey of mercury concentrations in loon tissues in lakes in the Adirondack Park, NY, demonstrating the effects of sampling intensity on statistical power and the selection of a resampling interval. To illustrate a bootstrapping method, we analyzed the plot-level sampling intensity of forest inventory at the Hubbard Brook Experimental Forest, NH, to quantify the sampling regime needed to achieve a desired confidence interval. Finally, to analyze time-series data from multiple sites, we assessed the number of lakes and the number of samples per year needed to monitor change over time in Adirondack lake chemistry using a repeated-measures mixed-effects model. Evaluations of time series and synoptic long-term monitoring data can help determine whether sampling should be re-allocated in space or time to optimize the use of financial and human resources.
AB - Statistical uncertainty analyses can be used to improve the efficiency of environmental monitoring, allowing sampling designs to maximize information gained relative to resources required for data collection and analysis. In this paper, we illustrate four methods of data analysis appropriate to four types of environmental monitoring designs. To analyze a long-term record from a single site, we applied a general linear model to weekly stream chemistry data at Biscuit Brook, NY, to simulate the effects of reducing sampling effort and to evaluate statistical confidence in the detection of change over time. To illustrate a detectable difference analysis, we analyzed a one-time survey of mercury concentrations in loon tissues in lakes in the Adirondack Park, NY, demonstrating the effects of sampling intensity on statistical power and the selection of a resampling interval. To illustrate a bootstrapping method, we analyzed the plot-level sampling intensity of forest inventory at the Hubbard Brook Experimental Forest, NH, to quantify the sampling regime needed to achieve a desired confidence interval. Finally, to analyze time-series data from multiple sites, we assessed the number of lakes and the number of samples per year needed to monitor change over time in Adirondack lake chemistry using a repeated-measures mixed-effects model. Evaluations of time series and synoptic long-term monitoring data can help determine whether sampling should be re-allocated in space or time to optimize the use of financial and human resources.
KW - Biomass
KW - Lakes
KW - Long-term
KW - Loons
KW - Monitoring
KW - Streams
KW - Uncertainty
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U2 - 10.1016/j.ecolind.2013.12.010
DO - 10.1016/j.ecolind.2013.12.010
M3 - Article
AN - SCOPUS:84891679163
SN - 1470-160X
VL - 39
SP - 94
EP - 101
JO - Ecological Indicators
JF - Ecological Indicators
ER -