Indicators of large-scale ecological change are typically derived from long-term monitoring networks. As such, it is important to assess how well monitoring networks provide evidence for ecological trends in the regions they are monitoring. In part, this depends on the network’s representativeness of the full range of environmental conditions occurring in the monitored region. In addition, the statistical power to detect trends and ecological changes using the network depends on its structure, size and the intensity and accuracy of monitoring. This paper addresses the optimisation of representativeness and statistical power when re-designing existing large-scale ecological monitoring networks, for example due to financial constraints on monitoring programmes. It uses a real world example of a well-established river monitoring network of 254 sites distributed across Scotland. We first present a novel approach for assessing a monitoring network’s representativeness of national habitat and pressure gradients using the multivariate two-sample Cramér’s T statistic. This compares multivariate gradient distributions among sites inside and outside of the network. Using this test, the existing network was found to over-represent larger and more heavily polluted sites, reflecting earlier research priorities when it was originally designed. Network re-design was addressed through stepwise selection of individual sites to remove from or add to the network to maximise multivariate representativeness. This showed that combinations of selective site retention and addition can be used to modify existing monitoring networks, changing the number of sites and improving representativeness. We then investigated the effect of network re-design on the statistical power to detect long-term trends across the whole network. The power analysis was based on linear mixed effects models for long-term trends in three ecological indicators (ecological quality ratios for diatoms, invertebrates and macrophytes) over a ten-year period. This revealed a clear loss of power in smaller networks with less accurate sampling, but sampling schedule had a smaller effect on power. Interestingly, more representative networks had slightly lower trend detection power than the current unrepresentative network, though they should give a less biased estimate of national trends. Our analyses of representativeness and statistical power provide a general framework for designing and adapting large-scale ecological monitoring networks. Wider use of such methods would improve the quality of indicators derived from them and improve the evidence base for detecting and managing ecological change.

大规模生态变化的指标通常来自长期监测网络。因此，重要的是评估监测网络如何为监测区域的生态趋势提供证据。在某种程度上，这取决于网络对受监控区域中发生的所有环境条件的代表性。此外，使用网络检测趋势和生态变化的统计能力取决于其结构，规模以及监测的强度和准确性。本文在重新设计现有的大型生态监测网络时解决了代表性和统计能力的优化问题，例如，由于监测计划的资金限制。它以一个遍布苏格兰的254个站点的完善的河流监控网络为例。我们首先提出一种使用多元两样本Cramér's方法评估监测网络对国家栖息地和压力梯度的代表性的新颖方法Ť统计。这比较了网络内部和外部站点之间的多元梯度分布。使用该测试，发现现有网络过多地代表了更大和污染更严重的站点，反映了最初设计时的早期研究重点。通过逐步选择单个站点以从网络中删除或添加到网络中以最大程度地发挥多变量代表性来解决网络重新设计问题。这表明选择性站点保留和添加的组合可用于修改现有监视网络，更改站点数量并提高代表性。然后，我们调查了网络重新设计对统计能力的影响，以检测整个网络的长期趋势。功效分析基于线性混合效应模型，该模型对十年内三个生态指标（硅藻，无脊椎动物和大型植物的生态质量比）的长期趋势进行了分析。这表明在较小的网络中，由于采样精度较差，功率明显损失，但采样计划对功率的影响较小。有趣的是，尽管具有代表性的网络应该给出对国家趋势的偏向估计，但其趋势检测能力比当前的非代表性网络略低。我们对代表性和统计能力的分析为设计和调整大规模生态监测网络提供了一个通用框架。此类方法的广泛使用将提高从这些方法得出的指标的质量，并改善检测和管理生态变化的证据基础。