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Spatially balanced designs that incorporate legacy sites
Author(s) -
Foster Scott D.,
Hosack Geoffrey R.,
Lawrence Emma,
Przeslawski Rachel,
Hedge Paul,
Caley M. Julian,
Barrett Neville S.,
Williams Alan,
Li Jin,
Lynch Tim,
Dambacher Jeffrey M.,
Sweatman Hugh P.A.,
Hayes Keith R.
Publication year - 2017
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12782
Subject(s) - sample (material) , process (computing) , computer science , sampling design , sampling (signal processing) , population , chemistry , demography , filter (signal processing) , chromatography , sociology , computer vision , operating system
Summary A robust scientific conclusion is the result of a rigorous scientific process. In observational ecology, this process involves making inferences about a population from a sample. The sample is crucial, and is the result of implementing a survey design . A good survey design ensures that the data from the survey are capable of answering the research question. Better designs, such as spatially balanced designs, will also be as precise as possible given the constraints of the budget. Many study areas will have previously sampled ‘legacy sites’ that already have accumulated a time series of observations. For estimating trent, it is often beneficial to include these sites within a new survey. In this paper, we propose a method to incorporate the locations of legacy sites into new spatially balanced survey designs to ensure spatial balance among all sample locations. Simulation experiments indicate that incorporating the spatial location of legacy sites increases spatial balance and decreases uncertainty in inferences (smaller standard errors in mean estimates) when compared to designs that ignore legacy site locations. We illustrate the process of incorporating legacy sites using a proposed survey of a large marine reserve in South‐Eastern Australia, although the method is applicable to all environments. Our approach allows for integration of legacy sites into a new spatially balanced design, increasing efficiency. Scientists, managers and funders alike will benefit from this methodology – it provides a tool to provide efficient survey designs around established ones, including in‐the‐field adjustments. In this way, it can aid integrated monitoring programmes. An R ‐package that implements these methods, called MBHdesign , is available from CRAN.