Environmental monitoring is a key component of understanding and managing ecosystems. Given that most monitoring efforts are still expensive and time-consuming, it is essential that monitoring programs are designed to be efficient and effective. In many situations, the expensive part of monitoring is not sample collection, but instead sample processing, which leads to only a subset of the samples being processed. For example, sediment or ice cores can be quickly obtained in the field, but they require weeks or months of processing in a laboratory setting. Standard sub-sampling approaches often involve equally-spaced sampling on depth. We use simulations to show how many samples, and which types of sampling approaches, are the most effective in detecting ecosystem change. We test these ideas with a case study of Cladocera community assemblage indicators reconstructed from a sediment core. We demonstrate that standard approaches to sample processing are less efficient than an iterative approach. For our case study, using an optimal sampling approach would have resulted in savings of 195 person–hours—thousands of dollars in labor costs. We also show that, compared with these standard approaches, fewer samples are typically needed to achieve high statistical power. We explain how our approach can be applied to monitoring programs that rely on video records, eDNA, remote sensing, and other common tools that allow re-sampling.

环境监测是了解和管理生态系统的关键组成部分。鉴于大多数监视工作仍是昂贵且费时的，因此必须将监视程序设计为高效有效。在许多情况下，监视的昂贵部分不是样本收集，而是样本处理，这仅导致样本的一部分被处理。例如，可以在野外快速获得沉积物或冰芯，但在实验室环境中需要数周或数月的加工时间。标准子采样方法通常涉及深度上等距采样。我们使用模拟来显示在检测生态系统变化方面最有效的样本数量和类型。我们以从沉积物核心重建的克拉多菌群落组合指标为例，对这些想法进行了测试。我们证明标准的样本处理方法比迭代方法效率低。在我们的案例研究中，使用最佳抽样方法将节省195人小时-数千美元的人工成本。我们还表明，与这些标准方法相比，实现高统计能力通常需要较少的样本。我们将说明如何将我们的方法应用于依赖视频记录，eDNA，遥感和其他允许重采样的常用工具的监视程序。使用最佳采样方法将节省195个人工时-数千美元的人工成本。我们还表明，与这些标准方法相比，实现高统计能力通常需要更少的样本。我们将说明如何将我们的方法应用于依赖视频记录，eDNA，遥感和其他允许重采样的常用工具的监视程序。使用最佳采样方法将节省195个人工时-数千美元的人工成本。我们还表明，与这些标准方法相比，实现高统计功效通常需要较少的样本。我们解释了如何将我们的方法应用于依赖视频记录，eDNA，遥感和其他允许重采样的常用工具的监视程序。