The Environmental Impact Classification for Alien Taxa (EICAT) and the Socio-Economic Impact Classification of Alien Taxa (SEICAT) have been proposed to provide unified methods for classifying alien species according to their magnitude of impacts. EICAT and SEICAT (herein “ICAT” when refered together) were designed to facilitate the comparison between taxa and invasion contexts by using a standardised, semi-quantitative scoring scheme. The ICAT scores are assigned after conducting a literature review to evaluate all impact observations against the protocols’ criteria. EICAT classifies impacts on the native biota of the recipient environments, whereas SEICAT classifies impacts on human activities. A key component of the process is to assign a level of confidence (high, medium or low) to account for uncertainty. Assessors assign confidence scores to each impact record depending on how confident they are that the assigned impact magnitude reflects the true situation. All possible sources of epistemic uncertainty are expected to be captured by one overall confidence score, neglecting linguistic uncertainties that assessors should be aware of. The current way of handling uncertainty is prone to subjectivity and therefore might lead to inconsistencies amongst assessors. This paper identifies the major sources of uncertainty for impacts classified under the ICAT frameworks, where they emerge in the assessment process and how they are likely to be contributing to biases and inconsistency in assessments. In addition, as the current procedures only capture uncertainty at the individual impact report, interspecific comparisons may be limited by various factors, including data availability. Therefore, ranking species, based on impact magnitude under the present systems, does not account for such uncertainty. We identify three types of biases occurring beyond the individual impact report level (and not captured by the confidence score): biases in the existing data, data collection and data assessment. These biases should be recognised when comparing alien species based on their impacts. Clarifying uncertainty concepts relevant to the ICAT frameworks will lead to more consistent impact assessments and more robust intra- and inter-specific comparisons of impact magnitudes.

中文翻译：

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了解外来分类群（ICAT）评估的影响分类中的不确定性

已提出了《外来生物分类环境影响分类》（EICAT）和《外来生物分类社会经济影响分类》（SEICAT），以根据外来物种的影响程度提供统一的分类方法。EICAT和SEICAT（在统称为“ ICAT”时）旨在通过使用标准化的半定量评分方案来促进分类单元和入侵环境之间的比较。在进行文献审查以根据方案标准评估所有影响观察结果后，将分配ICAT分数。EICAT对对接收者环境的原生生物群的影响进行分类，而SEICAT对对人类活动的影响进行分类。该过程的关键组成部分是分配置信度（高，中或低）以解决不确定性。评估人员根据对每个影响记录的信心程度，对他们对分配的影响程度反映真实情况的信心程度。可以通过一个整体置信度得分来捕获所有可能的认知不确定性来源，而忽略评估人员应该意识到的语言不确定性。当前处理不确定性的方法倾向于主观性，因此可能导致评估者之间的不一致。本文确定了在ICAT框架下归类的影响的不确定性的主要来源，这些不确定性在评估过程中出现，以及它们如何可能导致评估中的偏差和不一致。此外，由于当前程序仅在单个影响报告中捕获不确定性，因此种间比较可能会受到各种因素的限制，包括数据可用性。因此，基于本系统下的影响程度对物种进行排名并不能解决这种不确定性。我们确定了超出个人影响报告级别（但未由置信度得分捕获）发生的三种类型的偏差：现有数据，数据收集和数据评估中的偏差。在根据外来物种的影响比较外来物种时，应认识到这些偏见。澄清与ICAT框架相关的不确定性概念将导致更一致的影响评估以及更强大的种内和种间影响量级比较。我们确定了超出个人影响报告级别（但未由置信度得分捕获）发生的三种类型的偏差：现有数据，数据收集和数据评估中的偏差。在根据外来物种的影响比较外来物种时，应认识到这些偏见。澄清与ICAT框架相关的不确定性概念将导致更一致的影响评估，以及更强大的种内和种间影响量级比较。我们确定了超出个人影响报告级别（但未由置信度得分捕获）发生的三种类型的偏差：现有数据，数据收集和数据评估中的偏差。在根据外来物种的影响比较外来物种时，应认识到这些偏见。澄清与ICAT框架相关的不确定性概念将导致更一致的影响评估，以及更强大的种内和种间影响量级比较。