2014
DOI: 10.1890/13-2042.1
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On tests of spatial pattern based on simulation envelopes

Abstract: In the analysis of spatial point patterns, an important role is played by statistical tests based on simulation envelopes, such as the envelope of simulations of Ripley's K function. Recent ecological literature has correctly pointed out a common error in the interpretation of simulation envelopes. However, this has led to a widespread belief that the tests themselves are invalid. On the contrary, envelope‐based statistical tests are correct statistical procedures, under appropriate conditions. In this paper, … Show more

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Cited by 177 publications
(187 citation statements)
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“…We used these functions (in R statistical software) to compare measured distances between drumlins to expectations of CSR, producing a metric indicative of dispersion (regularity), randomness or clustering. To further this approach it is common to use simulation envelopes to compare the observed result to Monte-Carlo simulations of CSR derived from a Poisson (or other) process (Baddeley et al, 2014). Defining the bounding box is an important consideration, as it forms the spatial domain over which simulations of CSR are implemented and Output from the L-function for the complete data set (black) and the patch (blue).…”
Section: Spatial Statistical Methods For Assessing Regularitymentioning
confidence: 99%
“…We used these functions (in R statistical software) to compare measured distances between drumlins to expectations of CSR, producing a metric indicative of dispersion (regularity), randomness or clustering. To further this approach it is common to use simulation envelopes to compare the observed result to Monte-Carlo simulations of CSR derived from a Poisson (or other) process (Baddeley et al, 2014). Defining the bounding box is an important consideration, as it forms the spatial domain over which simulations of CSR are implemented and Output from the L-function for the complete data set (black) and the patch (blue).…”
Section: Spatial Statistical Methods For Assessing Regularitymentioning
confidence: 99%
“…The K function estimates spatial dependence between points of the same type (e.g., residual trees) across spatial scales by determining the expected number of points within a distance (r) from any randomly sampled point. For each plot, we evaluate deviations from complete spatial randomness (CSR) by comparing observed data with an inhomogeneous Poisson null model [49][50][51]. We used an inhomogeneous rather than a homogeneous Poisson process to account for non-constant density gradients in the data [52].…”
Section: Residual Forest Structurementioning
confidence: 99%
“…At each site both L and O functions were used in the analysis. O-rings width was influenced by the distance of biological interactions [46], which was restricted by tree spacing. Rings were 10 m in width at densely-planted sites (Rookery wood, Sandpit wood, Sheen wood, Winding wood) and 20 m at the remaining, more open, sites.…”
Section: Spatial Relationship Between Emergence Holes and Stem Bleedsmentioning
confidence: 99%