Literature DB >> 18337356

Control of Pteridium aquilinum: meta-analysis of a multi-site study in the UK.

Gavin Stewart1, Emma Cox, Mike Le Duc, Robin Pakeman, Andrew Pullin, Rob Marrs.   

Abstract

BACKGROUND AND AIMS: A great deal of money is spent controlling invasive weeds as part of international and national policies. It is essential that the funded treatments work across the region in which the policies operate. We argue that experiments across multiple sites are required to validate these programs as results from single sites may be misleading. Here, the control of Pteridium aquilinum (bracken) is used as a test example to address the following four questions. (1) Does the effectiveness of P. aquilinum-control treatments vary across sites? (2) Is the best treatment identified in previous research (cutting twice per year) consistent at all sites, and if not why not? (3) Is treatment performance related to P. aquilinum rhizome mass, litter cover or litter depth at the various sites? (4) Does successful P. aquilinum control influence species richness?
METHODS: Pteridium aquilinum-control treatments were monitored for 10 years using six replicated experiments and analysed using meta-analysis. Meta-regressions were used to explore heterogeneity between sites. KEY
RESULTS: The effectiveness of treatments varied between sites depending on the measure used to assess P. aquilinum performance. In general, cutting twice per year was the most successful treatment but on some sites other, less expensive treatments were as good. The effectiveness of treatments at different sites was not related to rhizome mass, but the effectiveness of most applied treatments were inversely related to post-control litter. Effective treatment was also associated with high species richness.
CONCLUSIONS: It is concluded that successful development of national weed control programs requires multi-site experimental approaches. Here, meta-analyses demonstrate that variation in effectiveness between sites could be explained in part by pre-specified variables. Reliance on data from a single site for policy formulation is therefore clearly dangerous.

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Year:  2008        PMID: 18337356      PMCID: PMC2710229          DOI: 10.1093/aob/mcn020

Source DB:  PubMed          Journal:  Ann Bot        ISSN: 0305-7364            Impact factor:   4.357


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