Interobserver error in grassland vegetation surveys: sources and implications

人类观测误差是植被测量中不可避免的一个问题。我们量化了与高草草原植被长期监测相关的观测者间误差的四个组成部分：忽略误 差、误识别误差、谨慎误差和估计误差。由于观察者会产生误差，我们还评估了地块大小与伪周转率的关系，以及对比了物种组成和丰度的伪变化与四年间植被变化之间的关系。这项研究是在美国堪萨斯州的高草草原国家保护区进行的。监测点包括10个地块，每个地块由一系列的四个嵌套框架(0.01, 0.1, 1和10 m²)组成。在每个嵌套框架中记录了所有的草本物种，并且在10 m²的空间尺度下，视觉估计了7个覆盖类别内的叶面覆盖。总共调查了300个地块(30个地点)，并随机选择28个地块重新进行测量以评估观测者的误差。所有的调查由四名观测者分两组完成。研究结果表明，在10 m²空间尺度上，由忽略误差引起的伪周转率平均为18.6%，而由误识别误差和谨慎误差引起的伪周转率平均值分别为1.4%和0.6%。尽管由重新定位引起的误差可能也起一定的作用，由忽略误差导致的伪周转率随样地面积的减小而增 加。物种组成在四年期间的变化(排除潜在的误识别误差和谨慎误差)为30.7%，其中包括由忽略误差和实际变化引起的伪周转率。18.6%的忽略误差表明四年期间的实际变化只有12.1%。对于估计误差，26.2%会记录为不同的覆盖等级。在四年的时间内，46.9%的记录显示了不同的覆盖等级，这表明两个时间段间覆盖率变化的56%是由于观测者误差造成的。     

Observer error in vegetation surveys: a review

Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error. Three types of errors are common: (i) overlooking error (i.e. not observing species actually present), (ii) misidentification error (i.e. not correctly identifying species) and (iii) estimation error (i.e. not accurately estimating abundance). I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies.Important findings Almost all studies (92%) that tested for a statistically significant effect of observer error found at least one significant comparison. In surveys of species composition, mean pseudoturnover (the percentage of species overlooked by one observer but not another) was 10–30%. Species misidentification rates were on the order of 5–10%. The mean coefficient of variation (CV) among observers in surveys of vegetation cover was often several hundred % for species with low cover, although CVs of 25–50% were more representative of species with mean covers of>50%. A variety of metrics and indices (including commonly used diversity indices) and multivariate data analysis techniques (including ordinations and classifications) were found to be sensitive to observer error. Sources of error commonly include both characteristics of the vegetation (e.g. small size of populations, rarity, morphology, phenology) and attributes of the observers (e.g. mental fatigue, personal biases, differences in experience, physical stress). The use of multiple observers, additional training including active feedback approaches, and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys.