Distinguishing between human impacts and natural variation in abundance remains difficult because most species exhibit complex patterns of variation in space and time. When ecological monitoring data are available, a before-after-control-impact (BACI) analysis can control natural spatial and temporal variation to better identify an impact and estimate its magnitude. However, populations with limited distributions and confounding spatial-temporal dynamics can violate core assumptions of BACI-type designs. In this study, we assessed how such properties affect the potential to identify impacts. Specifically, we quantified the conditions under which BACI analyses correctly (or incorrectly) identified simulated anthropogenic impacts in a spatially and temporally replicated data set of fish, macroalgal, and invertebrate species found on nearshore subtidal reefs in southern California, USA. We found BACI failed to assess very localized impacts, and had low power but high precision when assessing region-wide impacts. Power was highest for severe impacts of moderate spatial scale, and impacts were most easily detected in species with stable, widely distributed populations. Serial autocorrelation in the data greatly inflated false impact detection rates, and could be partly controlled for statistically, while spatial synchrony in dynamics had no consistent effect on power or false detection rates. Unfortunately, species that offer high power to detect real impacts were also more likely to detect impacts where none had occurred. However, considering power and false detection rates together can identify promising indicator species, and collectively analyzing data for similar species improved the net ability to assess impacts. These insights set expectations for the sizes and severities of impacts that BACI analyses can detect in real systems, point to the importance of serial autocorrelation (but not of spatial synchrony), and indicate how to choose the species, and groups of species, that can best identify impacts.

中文翻译：

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提高 BACI 设计检测海带森林群落影响的能力


区分人类影响和自然丰度变化仍然很困难，因为大多数物种在空间和时间上表现出复杂的变化模式。当生态监测数据可用时，控制影响前后（BACI）分析可以控制自然空间和时间变化，以更好地识别影响并估计其严重程度。然而，分布有限的群体和混杂的时空动态可能违反 BACI 类型设计的核心假设。在这项研究中，我们评估了这些属性如何影响识别影响的潜力。具体来说，我们量化了 BACI 分析在美国南加州近岸潮下礁石上发现的鱼类、大型藻类和无脊椎动物物种的空间和时间复制数据集中正确（或错误）识别模拟人为影响的条件。我们发现 BACI 未能评估非常局部的影响，并且在评估区域范围的影响时功效较低但精度较高。对于中等空间尺度的严重影响，功率最高，并且在种群稳定、分布广泛的物种中最容易检测到影响。数据中的串行自相关极大地提高了错误碰撞检测率，并且可以在统计上部分控制，而动态中的空间同步对功率或错误检测率没有一致的影响。不幸的是，能够提供高能力来检测真实影响的物种也更有可能检测到未发生过的影响。然而，同时考虑功效和错误检测率可以识别有希望的指示物种，并且共同分析类似物种的数据可以提高评估影响的净能力。 这些见解设定了 BACI 分析在实际系统中可以检测到的影响的规模和严重程度的期望，指出了序列自相关（但不是空间同步）的重要性，并指出了如何选择可以影响的物种和物种组。最好确定影响。