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Protocols for Robust Herbicide Resistance Testing in Different Weed Species
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Published on: July 2, 2015

Can global weed assemblages be used to predict future weeds?

Louise Morin1, Dean R Paini, Roderick P Randall

  • 1Commonwealth Scientific and Industrial Research Organisation-CSIRO, Ecosystem Sciences, Biosecurity Flagship, Canberra, Australian Capital Territory, Australia. louise.morin@csiro.au

Plos One
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PubMed
Summary

Predicting invasive plant taxa is challenging. A quantitative self-organising map (SOM) approach shows potential for analyzing weed assemblages, but data quality, based on perception rather than impact, limits accuracy.

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Area of Science:

  • Ecology
  • Invasive Species Management
  • Computational Biology

Background:

  • Predicting weed potential of plant taxa is complex, often relying on qualitative assessments.
  • Existing methods struggle to quantitatively estimate a taxon's likelihood of becoming a weed in new regions.

Purpose of the Study:

  • To explore the utility of a quantitative self-organising map (SOM) approach for analyzing global weed assemblages.
  • To estimate the likelihood of plant taxa becoming weeds before and after introduction to new regions.

Main Methods:

  • Utilized a global database of 6690 plant taxa across 187 regions.
  • Applied the self-organising map (SOM) to analyze taxon associations within weed assemblages.
  • Assessed the SOM approach using Australia as a case study.

Main Results:

  • The SOM approach offers a quantitative method for analyzing weed associations.
  • A significant limitation is the dataset's reliance on human perception rather than objective impact data.
  • Current weed classifications often reflect perceived troublesome status, not documented impacts.

Conclusions:

  • Standardized, objective criteria for plant taxon impact assessment are crucial for reliable predictive databases.
  • Developing predictive models for weediness requires improved, data-driven datasets.
  • Focusing on inherent invasive characteristics, separate from perceived impacts, may offer a more objective classification system.