Institute for Future Environments, Queensland University of Technology, Brisbane, QLD, Australia.
Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, QLD, Australia.
PLoS One. 2020 Sep 22;15(9):e0238422. doi: 10.1371/journal.pone.0238422. eCollection 2020.
Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.
溪流和河流具有丰富的生物多样性，并提供有价值的生态系统服务。维护这些生态系统是一项重要任务，因此组织通常使用现场采样以及最近的自主原位传感器来监测溪流状况和生物多样性的现状和趋势。然而，数据收集通常成本高昂，因此有效和高效的调查设计对于在最小化成本的同时最大限度地获取信息至关重要。地统计学和最优及自适应设计理论可用于优化淡水研究和水生监测计划中采样点的位置。由于网络的分支结构、水流连通性和方向性以及流量差异，溪流网络上的地统计学建模和实验设计带来了统计挑战。现已有用于溪流网络数据的地统计学模型及其独特特征。先前也已经描述了一些用于溪流环境的实验设计的基础理论。然而，目前还没有为水生科学家提供这些设计方法的开源软件。为了解决这一需求，我们提出了 SSNdesign，这是一个用于解决溪流网络上最优和自适应设计问题的 R 包，它与现有的开源软件集成。我们展示了我们方法的数学基础，并使用来自澳大利亚昆士兰州的两个真实数据案例研究来说明 SSNdesign 的功能。在这两个案例研究中，我们都证明了最优或自适应设计优于现有的开源软件包中实施的随机和空间平衡调查设计。SSNdesign 包有可能提高淡水监测工作的效率，并为淡水保护和管理提供急需的信息。
