Data from: A synthesis of empirical plant dispersal kernels

• Main Authors: Bullock, James M., Mallada González, Laura, Tamme, Riin, Götzenberger, Lars, White, Steven M., Pärtel, Meelis, Hooftman, Danny A. P.
• Format: Dataset
• Language: unknown
• Published: 2017
• Subjects: exponential; exponential power; Dispersal distance; dispersal location kernel; log-sech; Seed mass; Plantae; dispersal syndrome; Gaussian; plant height; Holocene; probability density function; Antarc*; Antarctica
• Online Access: https://zenodo.org/record/4967986; https://doi.org/10.5061/dryad.mq2ff

Dispersal is fundamental to ecological processes at all scales and levels of organization, but progress is limited by a lack of information about the general shape and form of plant dispersal kernels. We addressed this gap by synthesizing empirical data describing seed dispersal and fitting general dispersal kernels representing major plant types and dispersal modes. A comprehensive literature search resulted in 107 papers describing 168 dispersal kernels for 144 vascular plant species. The data covered 63 families, all the continents except Antarctica, and the broad vegetation types of forest, grassland, shrubland and more open habitats (e.g. deserts). We classified kernels in terms of dispersal mode (ant, ballistic, rodent, vertebrates other than rodents, vehicle or wind), plant growth form (climber, graminoid, herb, shrub or tree), seed mass and plant height. We fitted 11 widely used probability density functions to each of the 168 data sets to provide a statistical description of the dispersal kernel. The exponential power (ExP) and log-sech (LogS) functions performed best. Other 2-parameter functions varied in performance. For example, the log-normal and Weibull performed poorly, while the 2Dt and power law performed moderately well. Of the single-parameter functions, the Gaussian performed very poorly, while the exponential performed better. No function was among the best-fitting for all data sets. For 10 plant growth form/dispersal mode combinations for which we had >3 data sets, we fitted ExP and LogS functions across multiple data sets to provide generalized dispersal kernels. We also fitted these functions to subdivisions of these growth form/dispersal mode combinations in terms of seed mass (for animal-dispersed seeds) or plant height (wind-dispersed) classes. These functions provided generally good fits to the grouped data sets, despite variation in empirical methods, local conditions, vegetation type and the exact dispersal process. Synthesis. We synthesize the rich empirical information on seed ...