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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
|February 9, 2013
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.
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.
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