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Measurement and meaningfulness in conservation science

机译:保护科学中的测量和意义

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Incomplete databases often require conservation scientists to estimate data either through expert judgment or other scoring, rating, and ranking procedures. At the same time, ecosystem complexity has led to the use of increasingly sophisticated algorithms and mathematical models to aid in conservation theorizing, planning, and decision making. Understanding the limitations imposed by the scales of measurement of conservation data is importantfor the development of sound conservation theory and policy. In particular, biodiversity valuation methods, systematic conservation planning algorithms, geographic information systems (GIS), and other conservation metrics and decision-support tools, when improperly applied to estimated data, may lead to conclusions based on numerical artifact rather than empirical evidence. The representational theory of measurement is described here, and the description includes definitions of the key concepts of scale, scale type, and meaningfulness. Representational measurement is the view that measurement entails the faithful assignment of numbers to empirical entities. These assignments form scales that are organized into a hierarchy of scale types. A statement involving scales is meaningful if its truth value is invariant under changes of scale within scale type. I apply these concepts to three examples of measurement practice in the conservation literature. The results of my analysis suggest that conservation scientists do not always investigate the scale type of estimated data and hence may derive results that are not meaningful. Recognizing the complexity of observation and measurement in conservation biology, and the constraints that measurement theory imposes, the examples are accompanied by suggestions for informal estimation of the scale type of conservation data and for conducting meaningful analysis and synthesis of this information.
机译:不完整的数据库通常要求保护科学家通过专家判断或其他评分,评级和排名程序来估计数据。同时,生态系统的复杂性导致使用越来越复杂的算法和数学模型来帮助进行保护理论,规划和决策。理解保护数据量表所带来的局限性对于健全的保护理论和政策的发展很重要。特别是,如果将生物多样性评估方法,系统的保护规划算法,地理信息系统(GIS)以及其他保护指标和决策支持工具不适当地应用于估计数据,则可能会得出基于数字假象而不是经验证据的结论。这里描述了度量的代表性理论,并且描述包括尺度,尺度类型和意义的关键概念的定义。代表性度量是一种观点,认为度量需要将数字忠实地分配给经验实体。这些分配构成了按比例尺类型层次结构组织的比例尺。如果在量表类型内量表的变化下其真值不变,则涉及量表的陈述就有意义。我将这些概念应用于保护文献中的三个测量实践示例。我的分析结果表明,保护科学家并不总是调查估计数据的比例类型,因此可能得出没有意义的结果。认识到保护生物学中观测和测量的复杂性以及测量理论的局限性,这些示例伴随着一些建议,可以非正式地估算保护数据的规模类型,并对这些信息进行有意义的分析和综合。

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