Under ongoing climate and land-use change, biodiversity is continuously decreasing and monitoring biodiversity is becoming increasingly important. National Forest Inventory (NFI) programmes provide valuable time-series data on biodiversity and thus contribute to assessments of the state and trends in biodiversity, as well as ecosystem functioning. Data quality in this context is of paramount relevance, particularly for ensuring a meaningful interpretation of changes. The Swiss NFI revisits about 8%–10% of its sample plots regularly in repeat surveys to supervise the quality of fieldwork. We analysed the relevance of observer bias with equivalence tests, examined data quality objectives defined by the Swiss NFI instructors, and calculated the pseudo-turnover (PT) of species composition, that is, the percentage of species not observed by both teams. Three attributes of woody species richness from the latest Swiss NFI cycles (3 and 4) were analysed: occurrence of small tree and shrub species (1) on the sample plot and (2) at the forest edge, and (3) main shrub and trees species in the upper storey. We found equivalent results between regular and repeat surveys for all attributes. Data quality, however, was significantly below expectations in all cases, that is, as much as 20%–30% below the expected data quality limit of 70%–80% (proportion of observations that should not deviate from a predefined threshold). PT values were about 10%–20%, and the PT of two out of three attributes decreased significantly in NFI4. This type of uncertainty – typically caused by a mixture of overlooking and misidentifying species – should be considered carefully when interpreting change figures on species richness estimates from NFI data. Our results provide important information on the data quality achieved in Swiss NFIs in terms of the reproducibility of the collected data. The three applied approaches proved to be effective for evaluating the quality of plot-level species richness and composition data in forest inventories and other biodiversity monitoring programmes. As such, they could also be recommended for assessing the quality of biodiversity indices derived from monitoring data.

中文翻译：

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分析瑞士国家森林清查木本物种丰富度测量的质量。


在持续的气候和土地利用变化下，生物多样性不断减少，监测生物多样性变得越来越重要。国家森林清查（NFI）计划提供了有关生物多样性的宝贵时间序列数据，从而有助于评估生物多样性的状况和趋势以及生态系统功能。在这种情况下，数据质量至关重要，特别是对于确保对变化进行有意义的解释。瑞士 NFI 在重复调查中定期重新访问约 8%–10% 的样地，以监督实地工作的质量。我们通过等价性检验分析了观察者偏差的相关性，检查了瑞士 NFI 讲师定义的数据质量目标，并计算了物种组成的伪更替 (PT)，即两个团队均未观察到的物种的百分比。分析了最新瑞士 NFI 周期（3 和 4）中木本物种丰富度的三个属性：小乔木和灌木物种的出现情况（1）样地和（2）森林边缘，以及（3）主要灌木和上层的树种。我们发现所有属性的定期调查和重复调查的结果相同。然而，在所有情况下，数据质量均显着低于预期，即比预期数据质量限制 70%–80%（不应偏离预定义阈值的观测值比例）低 20%–30%。 PT值约为10%~20%，NFI4中三分之二的PT显着下降。在解释 NFI 数据中物种丰富度估计值的变化数据时，应仔细考虑这种类型的不确定性（通常是由忽视和错误识别物种造成的）。 我们的结果提供了有关瑞士 NFI 在所收集数据的可重复性方面所实现的数据质量的重要信息。事实证明，这三种应用方法对于评估森林清单和其他生物多样性监测计划中地块级物种丰富度和组成数据的质量是有效的。因此，还可以建议它们用于评估从监测数据得出的生物多样性指数的质量。