Data from: Informative plot sizes in presence-absence sampling of forest floor vegetation

1. Plant communities are attracting increased interest in connection with forest and landscape inventories due to society’s interest in ecosystem services. However, the acquisition of accurate information about plant communities poses several methodological challenges. Here we investigate the use of presence-absence sampling with the aim to monitor state and change of plant density. We study what plot sizes are informative, i.e. the estimators should have as high precision as possible. 2. Plant occurrences were modeled through different Poisson processes and tests were developed for assessing the plausibility of the model assumptions. Optimum plot sizes were determined by minimizing the variance of the estimators. While state estimators of similar kind as ours have been proposed in previous studies, our tests and change estimation procedures are new. 3. We found that the most informative plot size for state estimation is 1.6 divided by the plant density, i.e. if the true density is 1 plant per square meter the optimum plot size is 1.6 square meters. This is in accordance with previous findings. More importantly, the most informative plot size for change estimation was smaller and depended on the change patterns. We provide theoretical results as well as some empirical results based on data from the Swedish National Forest Inventory. 4. Use of too small or too large plots resulted in poor precision of the density (and density change) estimators. As a consequence, a range of different plot sizes would be required for jointly monitoring both common and rare plants using presence-absence sampling in monitoring programmes.

1. 由于社会对生态系统服务（ecosystem services）的关注度持续提升，植物群落在森林与景观清查相关研究中愈发受到关注。然而，获取植物群落的精准信息仍面临诸多方法学挑战。本研究探讨采用出现-缺失抽样（presence-absence sampling）的方法，以监测植物密度的现状与变化，并探究具备最优信息量的样地面积（plot sizes），即要求估计量（estimators）具备尽可能高的精度。
2. 本研究通过多种泊松过程（Poisson processes）对植物出现情况进行建模，并构建了用于评估模型假设合理性的检验方法。通过最小化估计量的方差（variance）确定最优样地面积。尽管既往研究已提出与本研究同类的现状估计量，但本研究的检验方法与变化估计流程均为原创性工作。
3. 研究结果显示，用于现状估计的信息量最优样地面积为1.6除以植物密度，即当真实密度为1株/平方米时，最优样地面积为1.6平方米，这与既往研究结论一致。更为关键的是，用于变化估计的信息量最优样地面积更小，且具体数值取决于变化模式。本研究既提供了理论推导结果，也基于瑞典国家森林清查（Swedish National Forest Inventory）的数据给出了实证结果。
4. 若使用过小或过大的样地，会导致密度（及密度变化）估计量的精度表现不佳。因此，在采用出现-缺失抽样的监测方案中，若需同时监测常见与稀有植物，则需要使用一系列不同规格的样地面积。