Modelling the Persistence of Weed Populations

B - Book chapters etc edited externally

Storkey, J., Gardarin, A., Colbach, N., Metcalfe, H. and Milne, A. E. 2022. Modelling the Persistence of Weed Populations. in: Upadhyaya, M. K., Clements, D. R. and Shrestha, A. (ed.) Persistence Strategies of Weeds Wiley. pp. Chapter 9

AuthorsStorkey, J., Gardarin, A., Colbach, N., Metcalfe, H. and Milne, A. E.
EditorsUpadhyaya, M. K., Clements, D. R. and Shrestha, A.
Abstract

One of the adaptations of weeds to highly disturbed environments is a persistent seedbank; meaning there can often be a lag of several years in the response of a weed flora to a change in management. This buffering effect of the seedbank is a challenge to understanding weed dynamics through empirical field experiments that tend to be short-lived. One solution for this effect is to simulate the response of weed populations to management in a computer model. Two approaches to modelling are discussed. The first draws on fundamental theory of plant ecological strategies to predict the shift in the distribution of weed functional traits under different management scenarios, and the second is a detailed, process-based model of weed population dynamics that is described with a worked case study example. This second approach also uses plant traits to parameterize the model and predict the function of emergent weed communities but has a more intensive requirement for parameterization. Models have a crucial role to play in predicting the persistence of weed populations in a field, but care should be taken in investing too much confidence in predictions of absolute weed numbers. Rather, models are a powerful tool for testing the relative efficacy of contrasting weed management scenarios.

Page rangeChapter 9
Year of Publication2022
Book titlePersistence Strategies of Weeds
PublisherWiley
Edition1st
ISBN978-1119525608
Digital Object Identifier (DOI)https://doi.org/10.1002/9781119525622.ch9
Web address (URL)https://dx.doi.org/10.1002/9781119525622.ch9
FunderBiotechnology and Biological Sciences Research Council
Open accessPublished as non-open access
Output statusPublished
Publication dates
Online25 Feb 2022

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