AIM: Estimating species richness from a series of samples is an important and widely debated issue in ecology and biodiversity conservation. Numerous tests of respective richness estimators gave insights into the precision, the limitations and the pitfalls of richness forecasting. However, few benchmark tests used almost complete empiric census data obtained at those spatial scales where richness estimation is most useful for conservation management. LOCATION: Japan. METHODS: We use an extraordinary dataset on the spatial distribution of Japanese plants containing complete information on the occurrence of each Japanese plant species at the 10 × 10 km² grid cell level. We link the estimates of four estimators representing different theoretical approaches, Chao2, rarefaction, species–area relationships (SAR) and species abundance distributions (SAD), to environmental data using a fully nested sampling design. RESULTS: Chao2 and rarefaction behaved very similar in all tests and significantly underestimated true richness below 40% sampling fraction. SAR and SAD were less precise than Chao2 and rarefaction at higher sampling fraction but also less affected by low sample size. In general, SAD provided robust estimates over the whole range of sampling fraction and 67.4% of estimates ranged within the 10% error level. Higher species spatial turnover increased and high evenness in occurrence decreased the precision of the SAD estimator. Precision of the four estimators was largely unaffected by environmental variability but increased with increasing latitude. MAIN CONCLUSIONS: Our results strongly indicate that the pattern of Japanese plant species spatial distribution is sufficiently scale invariant for richness estimators to provide precise forecasting results at the country level. The simplest process to generate such a spatial distribution is ecological drift.

中文翻译：

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影响日本维管植物物种丰富度估计精度的因素

目的：从一系列样本中估计物种丰富度是生态学和生物多样性保护中一个重要且广泛争论的问题。对各自丰富度估计器的大量测试深入了解了丰富度预测的精度、局限性和陷阱。然而，很少有基准测试使用在丰富度估计对保护管理最有用的空间尺度上获得的几乎完整的经验普查数据。地点：日本。方法：我们使用了一个关于日本植物空间分布的非凡数据集，其中包含关于每个日本植物物种在 10 × 10 平方公里网格单元级别出现的完整信息。我们将代表不同理论方法的四个估计量的估计值联系起来，Chao2、稀疏、物种-面积关系 (SAR) 和物种丰度分布 (SAD)，使用完全嵌套的抽样设计来分析环境数据。结果：Chao2 和稀疏在所有测试中的表现非常相似，并且在低于 40% 的采样率时显着低估了真正的丰富度。SAR 和 SAD 不如 Chao2 精确，并且在较高的采样率下稀疏，但受低样本量的影响也较小。一般而言，SAD 在整个采样率范围内提供了稳健的估计，并且 67.4% 的估计值在 10% 的误差水平内。较高的物种空间周转率增加，出现的高度均匀性降低了 SAD 估计器的精度。四个估算器的精度在很大程度上不受环境变化的影响，但随着纬度的增加而增加。主要结论：我们的结果有力地表明，日本植物物种空间分布的模式对于丰富度估计器具有足够的尺度不变性，可以在国家层面提供精确的预测结果。产生这种空间分布的最简单的过程是生态漂移。