Mechanistic Models for the Seed Shadows of Wind-Dispersed Plants

Andersen, Mark C.

doi:10.1086/285178

Cited by 106 publications

(87 citation statements)

References 27 publications

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“…We have not used dispersal models to simulate dispersal patterns because all of the models have a weak fit with field data (Verkaar et al 1983, Okubo & Levin 1989, Andersen 1991.…”

Section: Methodsmentioning

confidence: 99%

“…For example, in Enneapogon cenchroides release height varied from 30 to 80 cm; in addition the length of the infructescences within a plant varied from 7.0 to 11.3 cm. Even the improved stochastic-differential-equation model (Andersen 1991), which considers release height, has a low (50~o) predictive value. For our purpose, we have used minimum and maximum release height.…”

Section: And the Colonizationmentioning

confidence: 99%

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Possibilities for dispersal in annual and perennial grasses in a savanna in Botswana

1992

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Dispersal of 11 dominant grass species in the savanna of southern Botswana was investigated. The dispersal is autochorous, anemochorous and epizoochorous independent of the life-cycle (annuals, perennials). Dispersal distances were estimated experimentally. Anemochorous species with a plume-like spikelet such as Chloris virgata and Enneapogon cenchroides have a low rate of descent ( < 1 m s 1) and a low Reynold number (100-110). Nevertheless they can only be transported up to 13 m from the originating infructescence at a wind velocity of 10 m s-x. Therefore, the majority of the disseminules remained near the parent plant. By analysing the seed pool under the canopy of trees of Dichrostachys cinerea epizoochorous species such as Tragus berteronianus were dominant at the cattle resting sites under trees. The results are discussed in relation to the three seed dispersal hypotheses and the model of selective interaction of dispersal, dormancy, and seed site as adaptions to variable environments.

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“…We have not used dispersal models to simulate dispersal patterns because all of the models have a weak fit with field data (Verkaar et al 1983, Okubo & Levin 1989, Andersen 1991.…”

Section: Methodsmentioning

confidence: 99%

Section: And the Colonizationmentioning

confidence: 99%

Possibilities for dispersal in annual and perennial grasses in a savanna in Botswana

1992

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“…The most frequently proposed phenomenological models, being functions of distance from the seed source, are simple to apply but difficult to generalise because their parameters bear no direct relation to particular characteristics of the plant or disperser. Mechanistic models, on the other hand, are more general and can predict seed dispersion patterns directly from the characteristics of plants and their dispersal agents (Andersen 1991;Kuparinen 2006); however, they are complicated to develop, parameterise and implement (Nathan et al 2001;Schippers and Jongejans 2005). Likewise, information on seed appearance and seed dispersal has not yet been satisfactorily consolidated.…”

Section: Introductionmentioning

confidence: 99%

Ground seed density patterns under conditions of strongly overlapping seed shadows in Abies alba Mill. stands

Paluch¹

2011

Eur J Forest Res

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In three subsequent years, seeds were collected from 66 to 75 seed traps in three Abies alba stands that differed in vertical structure and the number of potentially reproducing trees. The objective was to compare empirical patterns of seed density with a random (Poisson) model, which assumes that seed density would be the same in every location, and a binomial negative model that is appropriate for over-dispersed data. The seed density patterns were tested for spatio-temporal independence. The effect of some dispersal variables (number of mother trees and seed fall seasons, dispersal distance, fecundity rate) on seed density pattern was studied in a simple simulation experiment. Several local stand density measures (stem density, basal area, distance-weighted indices, canopy openness) were also tested for their ability to predict local seed density. The Poisson model was rejected in all the stands studied, and a much better fit achieved using the binomial negative model. The simulation study revealed that random seed distribution may occur only at considerable dispersal distances, concave seed shadows, and at very low fecundity rates. The seed density patterns identified exhibited both spatial and temporal correlation, which can probably be linked to the arrangement of mother trees and variation in their seed output. To a minor degree, this seed density pattern also correlated with local stand density.

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“…However, the refinement of either spatial or temporal resolution would exponentially increase the amount of data and time to simulate the process. Over the years, complex anemochory models have been proposed based on wind physics that take into consideration such variables as tree height, vertical wind velocity, and species-dependent seed terminal velocity (Andersen, 1991;Nathan et al, 2001;Waldron, 2002). In this prototype model, we simply did not include all these variables, due to the lack of the necessary data to account for them.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

A GIS based spatially explicit model of dispersal agent behavior

Qiu¹,

Li²,

Chastain³

et al. 2008

Forest Ecology and Management

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Spatial simulation models of seed dispersal have been constructed at the landscape level under the assumption of ubiquitous or uniform dispersibility. The anisotropic nature of vegetation distribution caused by different dispersal agents such as wind, gravity, water and animals were ignored. We propose a prototype of a GIS-based spatially explicit model of dispersal agent behavior (SEMODAR) to simulate the seed dispersal process by considering the unique behavioral characteristics of each seed dispersal agent. As a result, the influence of dispersal agent behavior on the species coexistence in competitive communities with and without habitat destruction could be explored. The model consists of four module components: dispersal rules, species competition, species colonization, and habitat destruction. An experimental simulation was conducted using three hypothetical species with differing competitive and migration abilities in both intact and disturbed conditions for 250 years. The findings of this study support the theoretical expectation that inferior competitors can coexist with superior competitors given that the inferior competitors have efficient colonization ability. The simulation also reveals the important role of agent behavior in the seed dispersal process and the biased impact of environment fragmentation on superior competitors that are not superior dispersers. #

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Mechanistic Models for the Seed Shadows of Wind-Dispersed Plants

Cited by 106 publications

References 27 publications

Possibilities for dispersal in annual and perennial grasses in a savanna in Botswana

Possibilities for dispersal in annual and perennial grasses in a savanna in Botswana

Ground seed density patterns under conditions of strongly overlapping seed shadows in Abies alba Mill. stands

A GIS based spatially explicit model of dispersal agent behavior

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