Commonwealth Scientific and Industrial Research Organisation-CSIRO, Ecosystem Sciences, Biosecurity Flagship, Canberra, Australian Capital Territory, Australia.
PLoS One. 2013;8(2):e55547. doi: 10.1371/journal.pone.0055547. Epub 2013 Feb 5.
Predicting which plant taxa are more likely to become weeds in a region presents significant challenges to both researchers and government agencies. Often it is done in a qualitative or semi-quantitative way. In this study, we explored the potential of using the quantitative self-organising map (SOM) approach to analyse global weed assemblages and estimate likelihoods of plant taxa becoming weeds before and after they have been moved to a new region. The SOM approach examines plant taxa associations by analysing where a taxon is recorded as a weed and what other taxa are recorded as weeds in those regions. The dataset analysed was extracted from a pre-existing, extensive worldwide database of plant taxa recorded as weeds or other related status and, following reformatting, included 187 regions and 6690 plant taxa. To assess the value of the SOM approach we selected Australia as a case study. We found that the key and most important limitation in using such analytical approach lies with the dataset used. The classification of a taxon as a weed in the literature is not often based on actual data that document the economic, environmental and/or social impact of the taxon, but mostly based on human perceptions that the taxon is troublesome or simply not wanted in a particular situation. The adoption of consistent and objective criteria that incorporate a standardized approach for impact assessment of plant taxa will be necessary to develop a new global database suitable to make predictions regarding weediness using methods like SOM. It may however, be more realistic to opt for a classification system that focuses on the invasive characteristics of plant taxa without any inference to impacts, which to be defined would require some level of research to avoid bias from human perceptions and value systems.
预测哪些植物类群更有可能成为一个地区的杂草，这对研究人员和政府机构来说都是一个巨大的挑战。通常，这是通过定性或半定量的方法来完成的。在本研究中，我们探索了使用定量自组织映射（SOM）方法来分析全球杂草组合，并在植物类群被转移到一个新的地区之前和之后，估计它们成为杂草的可能性。SOM 方法通过分析一个类群被记录为杂草的地区以及在这些地区还有哪些其他类群被记录为杂草来检查植物类群的关联。分析的数据来自于一个预先存在的、广泛的全球植物类群数据库，这些类群被记录为杂草或其他相关状态，经过重新格式化后，包括 187 个地区和 6690 种植物类群。为了评估 SOM 方法的价值，我们选择澳大利亚作为一个案例研究。我们发现，使用这种分析方法的关键和最重要的限制在于所使用的数据集。在文献中，一个类群被分类为杂草通常不是基于实际数据，这些数据记录了该类群的经济、环境和/或社会影响，而是主要基于人类的认知，即该类群在特定情况下是麻烦的或根本不想要的。采用一致和客观的标准，纳入一种标准化的方法来评估植物类群的影响，对于开发一个新的全球数据库是必要的，以便使用 SOM 等方法对杂草性进行预测。然而，选择一个不涉及影响的分类系统，而专注于植物类群的入侵特征可能更现实，因为要定义影响，需要进行一些研究，以避免人类认知和价值体系的偏见。
