Seed terminal velocity, wind turbulence, and demography drive the spread of an invasive tree in an analytical model
(2012) In Ecology 93(2). p.368-377- Abstract
Little is known about the relative importance of mechanistic drivers of plant spread, particularly when long-distance dispersal (LDD) events occur. Most methods to date approach LDD phenomenologically, and all mechanistic models, with one exception, have been implemented through simulation. Furthermore, the few recent mechanistically derived spread models have examined the relative role of different dispersal parameters using simulations, and a formal analytical approach has not yet been implemented. Here we incorporate an analytical mechanistic wind dispersal model (WALD) into a demographic matrix model within an analytical integrodifference equation spread model. We carry out analytical perturbation analysis on the combined model to... (More)
Little is known about the relative importance of mechanistic drivers of plant spread, particularly when long-distance dispersal (LDD) events occur. Most methods to date approach LDD phenomenologically, and all mechanistic models, with one exception, have been implemented through simulation. Furthermore, the few recent mechanistically derived spread models have examined the relative role of different dispersal parameters using simulations, and a formal analytical approach has not yet been implemented. Here we incorporate an analytical mechanistic wind dispersal model (WALD) into a demographic matrix model within an analytical integrodifference equation spread model. We carry out analytical perturbation analysis on the combined model to determine the relative effects of dispersal and demographic traits and wind statistics on the spread of an invasive tree. Models are parameterized using data collected in situ and tested using independent data on historical spread. Predicted spread rates and direction match well the two historical phases of observed spread. Seed terminal velocity has the greatest potential influence on spread rate, and three wind properties (turbulence coefficient, mean horizontal wind speed, and standard deviation of vertical wind speed) are also important. Fecundity has marginal importance for spread rate, but juvenile survival and establishment are consistently important. This coupled empirical/ theoretical framework enables prediction of plant spread rate and direction using fundamental dispersal and demographic parameters and identifies the traits and environmental conditions that facilitate spread. The development of an analytical perturbation analysis for a mechanistic spread model will enable multispecies comparative studies to be easily implemented in the future.
(Less)
- author
- Caplat, Paul LU ; Nathan, Ran and Buckley, Yvonne M.
- publishing date
- 2012-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Integrodifference equation, Invasion biology, Long-distance dispersal, Matrix model, Mechanistic model, Mount Barker, New Zealand, Pinus nigra, Plant traits, Population biology, Seed terminal velocity, Wald analytical long-distance dispersal kernel (WALD)
- in
- Ecology
- volume
- 93
- issue
- 2
- pages
- 10 pages
- publisher
- Ecological Society of America
- external identifiers
-
- pmid:22624318
- scopus:84860252658
- ISSN
- 0012-9658
- DOI
- 10.1890/11-0820.1
- language
- English
- LU publication?
- no
- id
- 12a6202f-3052-4695-967b-0f02b83d0443
- date added to LUP
- 2016-04-22 08:37:18
- date last changed
- 2024-04-18 23:41:59
@article{12a6202f-3052-4695-967b-0f02b83d0443,
abstract = {{<p>Little is known about the relative importance of mechanistic drivers of plant spread, particularly when long-distance dispersal (LDD) events occur. Most methods to date approach LDD phenomenologically, and all mechanistic models, with one exception, have been implemented through simulation. Furthermore, the few recent mechanistically derived spread models have examined the relative role of different dispersal parameters using simulations, and a formal analytical approach has not yet been implemented. Here we incorporate an analytical mechanistic wind dispersal model (WALD) into a demographic matrix model within an analytical integrodifference equation spread model. We carry out analytical perturbation analysis on the combined model to determine the relative effects of dispersal and demographic traits and wind statistics on the spread of an invasive tree. Models are parameterized using data collected in situ and tested using independent data on historical spread. Predicted spread rates and direction match well the two historical phases of observed spread. Seed terminal velocity has the greatest potential influence on spread rate, and three wind properties (turbulence coefficient, mean horizontal wind speed, and standard deviation of vertical wind speed) are also important. Fecundity has marginal importance for spread rate, but juvenile survival and establishment are consistently important. This coupled empirical/ theoretical framework enables prediction of plant spread rate and direction using fundamental dispersal and demographic parameters and identifies the traits and environmental conditions that facilitate spread. The development of an analytical perturbation analysis for a mechanistic spread model will enable multispecies comparative studies to be easily implemented in the future.</p>}},
author = {{Caplat, Paul and Nathan, Ran and Buckley, Yvonne M.}},
issn = {{0012-9658}},
keywords = {{Integrodifference equation; Invasion biology; Long-distance dispersal; Matrix model; Mechanistic model; Mount Barker, New Zealand; Pinus nigra; Plant traits; Population biology; Seed terminal velocity; Wald analytical long-distance dispersal kernel (WALD)}},
language = {{eng}},
number = {{2}},
pages = {{368--377}},
publisher = {{Ecological Society of America}},
series = {{Ecology}},
title = {{Seed terminal velocity, wind turbulence, and demography drive the spread of an invasive tree in an analytical model}},
url = {{http://dx.doi.org/10.1890/11-0820.1}},
doi = {{10.1890/11-0820.1}},
volume = {{93}},
year = {{2012}},
}