A fractal‐based sampling design for ecological surveys quantifying β‐diversity. Issue 1 (16th October 2012) - Marsh, Charles J.; Ewers, Robert M.; Murrell, David
Summary Species turnover, β‐diversity, underpins a number of ecological processes that define patterns of diversity. Estimates of β‐diversity are dependent upon the spatial scale investigated, and patterns may vary across spatial scales. This presents us with a logistical problem of how to sample sufficiently at fine, local scales through to broad, landscape scales to provide accurate estimates of β‐diversity at all spatial scales. Here, we present a scalable sampling design based on fractal geometry that is designed to explicitly address questions about β‐diversity. Using simulated communities, we assessed the efficacy of the fractal design, along with two further designs representing subsamples of the fractal design and several classical ecological sampling designs (grids and transects) to estimate β‐diversity across multiple spatial scales using two measures of β‐diversity: community dissimilarity modelled against geographic distance and additive partitioning. All designs successfully modelled dissimilarity against distance, with the exception of grid sets and transects that were found to be unsuitable. When diversity was partitioned into multiple spatial scales, all sampling designs overestimated large‐scale β‐diversity. The accuracy of distance‐decay estimates were primarily determined by the spatial configuration of sampling points. By contrast, the accuracy of diversity partitioning estimates was also influenced by sampling effort, with insufficient sampling effort and unsuitable sampling point configuration causing overestimates of β‐diversity at larger spatial scales. We recommend that studies investigating β‐diversity use a cluster‐based configuration of sampling points, such as the fractal‐based design presented here, to ensure accurate and comparable estimates at multiple spatial scales. Furthermore, when comparing results between studies, care should be taken to account for differences in sampling grain, sampling effort and the configuration of sampling points.