Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Mechanistic Analytical Models for Long‐Distance Seed Dispersal by Wind

G. G. Katul, A. Porporato, R. Nathan, M. Siqueira, M. B. Soons, D. Poggi, H. S. Horn and S. A. Levin
The American Naturalist
Vol. 166, No. 3 (September 2005), pp. 368-381
DOI: 10.1086/432589
Stable URL: http://www.jstor.org/stable/10.1086/432589
Page Count: 14
  • Read Online (Free)
  • Download ($19.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Mechanistic Analytical Models for Long‐Distance Seed Dispersal by Wind
Preview not available

Abstract

Abstract: 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.

Page Thumbnails

  • Thumbnail: Page 
1
    1
  • Thumbnail: Page 
2
    2
  • Thumbnail: Page 
3
    3
  • Thumbnail: Page 
4
    4
  • Thumbnail: Page 
5
    5
  • Thumbnail: Page 
6
    6
  • Thumbnail: Page 
7
    7
  • Thumbnail: Page 
8
    8
  • Thumbnail: Page 
9
    9
  • Thumbnail: Page 
10
    10
  • Thumbnail: Page 
11
    11
  • Thumbnail: Page 
12
    12
  • Thumbnail: Page 
13
    13
  • Thumbnail: Page 
14
    14