Optimizing dispersal study design by Monte Carlo simulation.
Abstract
The distribution of dispersal distances (the dispersal kernel) is a major determinant of spatial population dynamics, yet little is known about the shape of the dispersal kernel for most species. This is partly because of the relative difficulty of measuring dispersal, exacerbated by a lack of standardized protocols. We suggest that this problem can be addressed by using modelling approaches to aid the design of studies to quantify dispersal. In this study we present such an approach by optimizing seed trap sampling design using stochastic simulations. A number of alternative sampling designs (random placements, grid arrays, transects, sectors and annuli arrangements) for a point source were tested against a common kernel to assess the best methods for estimating the dispersal kernel. For a given source strength and total trap area, transects and sectors of traps usually provided better data for kernel estimation than random placement, grid arrays and annuli. Kernel estimation was improved by increasing the source strength (the number of dispersing propagules) and the trap area, as expected. When the 'true' kernel was unknown, transects were slightly better for identifying the thin-tailed exponential distribution, whereas sectors were better for detecting the fat-tailed half-Cauchy. In the case of anisotropic dispersal (here, dispersal biased in one direction), annuli and grid arrays performed better than transects and sectors when the anisotropy was unknown. However, when the anisotropy was anticipated, and the trap arrangements were adjusted accordingly, transects and sectors were better. This was true regardless of source strength and total trap area. Synthesis and applications. This study presents a simulation approach to the design of dispersal experiments. While the general results of our simulations can be used by those designing field studies for plant point sources, the simulation approach itself can be modified for a wide range of organisms, dispersal mechanisms and dispersal measurement approaches. Thus, the approach presented here facilitates improvements of dispersal study designs, which in turn will increase the precision of dispersal kernel estimates and predictions of spatial population dynamics, including modelling of rates of spread or metapopulations. This is invaluable in a range of ecological applications, such as the management of rare or invasive species, predicting species' response to climate change, or planning species reintroductions.