Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500‐m ROV transects) and temporal replication (every 1–3 yr) to detect expected post‐MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.

中文翻译：

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量化海洋保护区监测计划的统计能力。

海洋保护区（MPA）在全球范围内日益确立为一种空间管理工具，有助于实现保护和渔业管理目标。评估海洋保护区是否对人口产生了预期的效果，需要有效的监测计划。有效监控程序的基础是对抽样设计的统计能力进行评估，以检测变化的发生。我们提出了一种新颖的功率评估方法，该方法结合了空间点过程模型，积分投影模型（IPM）和采样模拟，以评估整个MPA网络中不同样本设计的功率。尽管可以将结果扩展到其他采样方法，但我们专注于使用远程驾驶（ROV）摄像机作为采样方法。我们使用来自美国加利福尼亚州三个MPA的示例指示鱼类物种的基线调查的经验数据作为案例研究。空间模型模拟了各个地点空间分布的时间序列，这些时间序列说明了环境协变量的影响，而IPM模拟了鱼类的丰富度和大小随时间的预期趋势。我们测试了不同级别的采样工作量（即500-m ROV样点的数量）和时间复制（每1-3年）的能力，以检测出MPA后预期的鱼类丰度和生物量变化。我们发现，生物量的变化比丰度的变化更早被检测到。我们还发现，在具有较高初始密度的位点中，MPA效应的可检测性较高。与增加采样频率对获得高功率所花费的时间相比，增加采样工作量的影响更大。通过组合来自多个站点的数据，可以最好地实现高功率。我们的方法提供了一个强大的工具，可以探索采样工作，空间分布，种群动态以及检测先前捕捞种群变化的指标之间的相互作用。