A mechanistic model for secondary seed dispersal by wind and its experimental validation

Authors

  • FRANK M. SCHURR,

    Corresponding author
    1. Department of Ecological Modelling, UFZ – Centre for Environmental Research Leipzig-Halle, PO Box 500135, 04301 Leipzig, Germany,
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  • WILLIAM J. BOND,

    1. Botany Department, University of Cape Town, Rondebosch 7700, South Africa,
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  • GUY F. MIDGLEY,

    1. Climate Change Research Group, South African National Biodiversity Institute, Private Bag x7, Claremont 7735, South Africa, and
    2. Center for Applied Biodiversity Science, Conservation International, 1919 M Street, Washington, DC 20036, USA
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  • STEVEN I. HIGGINS

    1. Department of Ecological Modelling, UFZ – Centre for Environmental Research Leipzig-Halle, PO Box 500135, 04301 Leipzig, Germany,
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    • Present address: Chair of Vegetation Ecology, Technical University of Munich, 85350 Freising-Weihenstephan, Germany.


*Present address and correspondence: F. M. Schurr, Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, 14469 Potsdam, Germany (fax + 49 331 9771948; e-mail frank.schurr@ufz.de).

Summary

  • 1Secondary seed dispersal by wind, the wind-driven movement of seeds along the ground surface, is an important dispersal mechanism for plant species in a range of environments.
  • 2We formulate a mechanistic model that describes how secondary dispersal by wind is affected by seed traits, wind conditions and obstacles to seed movement. The model simulates the movement paths of individual seeds and can be fully specified using independently measured parameters.
  • 3We develop an explicit version of the model that uses a spatially explicit representation of obstacle patterns, and also an aggregated version that uses probability distributions to model seed retention at obstacles and seed movement between obstacles. The aggregated version is computationally efficient and therefore suited to large-scale simulations. It provides a very good approximation of the explicit version (R2 > 0.99) if initial seed positions vary randomly relative to the obstacle pattern.
  • 4To validate the model, we conducted a field experiment in which we released seeds of seven South African Proteaceae species that differ in seed size and morphology into an arena in which we systematically varied obstacle patterns. When parameterized with maximum likelihood estimates obtained from independent measurements, the explicit model version explained 70–77% of the observed variation in the proportion of seeds dispersed over 25 m and 67–69% of the observed variation in the direction of seed dispersal.
  • 5The model tended to underestimate dispersal rates, possibly due to the omission of turbulence from the model, although this could also be explained by imprecise estimation of one model parameter (the aerodynamic roughness length).
  • 6Our analysis of the aggregated model predicts a unimodal relationship between the distance of secondary dispersal by wind and seed size. The model can also be used to identify species with the potential for long-distance seed transport by secondary wind dispersal.
  • 7The validated model expands the domain of mechanistic dispersal models, contributes to a functional understanding of seed dispersal, and provides a tool for predicting the distances that seeds move.

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