Understanding of causes for recent changes in vegetation structure and species richness of natural habitats is crucial for their maintaining for future generations. However, to avoid misinterpretation of vegetation changes in time, we should be aware of limits and errors of methods used for vegetation sampling. In a specific vegetation type, i.e. species rich wet meadow, we quantified sampling error in vegetation sampling at four different sampling levels (visual cover estimation, detailed recording in a grid of small cells, detailed assessment during clipping for biomass, biomass sorting), compared differences among three abundance estimates (frequency, cover and biomass), and assessed the effect of data transformation, and rapid change in vegetation structure caused by experimental species removal. At the 1 m² scale the captured proportion of species missed by classical relevé sampling was on average 16%. Subsequent detailed subquadrat sampling captured the majority of previously overlooked species. The chance of a species being overlooked increased both with rarity, and the species richness of the area sampled. Where abundance was measured using metrics of cover and biomass, common species were overvalued, but when abundance was measured using frequency, common species were undervalued. In this study, logarithmic transformation of values provided a more reliable characterization of vegetation, than binarized or untransformed values. With the exception of species abundance, the number of species overlooked, quadrat species richness, and vegetation characterization were all affected by the experimental treatment. Our findings highlight the potential effects of error when conducting vegetation sampling and analyses of community dynamics. Due to these effects, we need to consider the reliability of conclusions drawn when assessing temporal changes in plant dynamics. Data transformation modifies the effect of sampling error in analyses of vegetation data.

了解造成最近的植被结构变化和自然栖息地物种丰富度的原因，对于维持其后代至关重要。但是，为了避免对植被变化的误解，我们应该意识到植被采样方法的局限性和错误。在特定的植被类型（即物种丰富的湿地草甸）中，我们比较了四种不同采样水平下的植被采样中的采样误差（目测覆盖估计，小细胞网格中的详细记录，生物量修剪期间的详细评估，生物量分类）三种丰度估算值（频率，覆盖率和生物量）之间的差异，并评估了数据转换的效果以及实验物种去除引起的植被结构快速变化。在1 m ²估计，经典相关采样所遗漏的物种捕获比例平均为16％。随后的详细的二次quadrat采样捕获了大多数先前被忽略的物种。一个物种被忽视的机会随着稀有性以及所采样区域物种丰富度的增加而增加。使用覆盖率和生物量衡量丰度时，常见物种被高估，但是使用频率衡量丰度时，常见物种被低估。在这项研究中，值的对数变换比二值化或未变换的值提供了更可靠的植被特征。除物种丰富度外，被忽略的物种数量，四倍体物种丰富度和植被特征都受到实验处理的影响。我们的发现突出了进行植被采样和社区动态分析时错误的潜在影响。由于这些影响，我们需要在评估植物动态的时间变化时考虑得出结论的可靠性。数据转换修改了采样误差对植被数据分析的影响。