Mechanistic analytical models for long-distance seed dispersal by wind.

Published

Journal Article

We introduce an analytical model, the Wald analytical long-distance dispersal (WALD) model, for estimating dispersal kernels of wind-dispersed seeds and their escape probability from the canopy. The model is based on simplifications to well-established three-dimensional Lagrangian stochastic approaches for turbulent scalar transport resulting in a two-parameter Wald (or inverse Gaussian) distribution. Unlike commonly used phenomenological models, WALD's parameters can be estimated from the key factors affecting wind dispersal--wind statistics, seed release height, and seed terminal velocity--determined independently of dispersal data. WALD's asymptotic power-law tail has an exponent of -3/2, a limiting value verified by a meta-analysis for a wide variety of measured dispersal kernels and larger than the exponent of the bivariate Student t-test (2Dt). We tested WALD using three dispersal data sets on forest trees, heathland shrubs, and grassland forbs and compared WALD's performance with that of other analytical mechanistic models (revised versions of the tilted Gaussian Plume model and the advection-diffusion equation), revealing fairest agreement between WALD predictions and measurements. Analytical mechanistic models, such as WALD, combine the advantages of simplicity and mechanistic understanding and are valuable tools for modeling large-scale, long-term plant population dynamics.

Full Text

Duke Authors

Cited Authors

  • Katul, GG; Porporato, A; Nathan, R; Siqueira, M; Soons, MB; Poggi, D; Horn, HS; Levin, SA

Published Date

  • September 2005

Published In

Volume / Issue

  • 166 / 3

Start / End Page

  • 368 - 381

PubMed ID

  • 16224691

Pubmed Central ID

  • 16224691

Electronic International Standard Serial Number (EISSN)

  • 1537-5323

International Standard Serial Number (ISSN)

  • 0003-0147

Digital Object Identifier (DOI)

  • 10.1086/432589

Language

  • eng