Department of Environmental Science, Policy and Management, University of California Berkeley, 130 Mulford Hall #3114, Berkeley, California 94720-3114, USA.
Ecol Appl. 2012 Jun;22(4):1389-404. doi: 10.1890/11-1669.1.
Estimating environmental impacts on populations is one of the main goals of wildlife monitoring programs, which are often conducted in conjunction with management actions or following natural disturbances. In this study we investigate the statistical power of dynamic occupancy models to detect changes in local survival and colonization from detection-nondetection data, while accounting for imperfect detection probability, in a Before-After Control-Impact (BACI) framework. We simulated impacts on local survival and/or detection probabilities, and asked questions related to: (1) costs and benefits of different analysis models, (2) confounding changes in detection with changes in local survival, (3) sampling design trade-offs, and (4) species with low vs. high rates of turnover. Estimating seasonal effects on local survival and colonization, as opposed to estimating Before-After effects, had little effect on the power to detect changes in local survival. Estimating a parameter that accounted for pretreatment differences in local survival between Control and Impact sites decreased power by 50%, but it was critical to include when such differences existed. When the experimental treatment had a negative impact on species detectability but analysis assumed constant detection, the Type I error rates were dramatically inflated (0.20 0.33). In general, there was low power (< 0.5) to detect a 50% decrease in local survival for all combinations of sites (N = 50 vs. 100), seasons sampled (8 vs. 12), and visits per site per season (4 vs. 6). Unbalanced designs performed worse than balanced designs, with the exception of the case of treatments being implemented in different seasons at different sites. Adding more control sites improved the ability to detect changes in local survival. Surveying more seasons after impact resulted in modest power gains, but at least three seasons before impact were required to successfully implement BACI occupancy studies. Turnover rates had a low impact on power. Occupancy studies conducted in a BACI design offer the opportunity to detect environmental impacts on wildlife populations without the costs of intensive studies. However, given the low power to detect small changes (20%) in local survival, these studies should be used when researchers are confident that major treatment impacts will occur or very large sample sizes are obtainable.
估算人口对环境的影响是野生动物监测计划的主要目标之一，这些计划通常与管理行动相结合，或在自然干扰后进行。在这项研究中，我们调查了动态占有模型在检测-非检测数据中检测局部生存和定居变化的统计能力，同时考虑了不完全检测概率，在前后对照影响（BACI）框架中。我们模拟了对局部生存和/或检测概率的影响，并提出了以下问题：（1）不同分析模型的成本和收益；（2）将局部生存的变化与检测的变化混淆；（3）采样设计的权衡；（4）周转率低与周转率高的物种。与估计前后影响相比，估计季节性效应对局部生存和定居的影响对检测局部生存变化的能力影响不大。估计一个参数来解释对照和影响点之间局部生存的预处理差异，会降低 50%的能力，但当存在这种差异时，这是至关重要的。当实验处理对物种可检测性产生负面影响，但分析假设检测不变时，I 型错误率会大幅膨胀（0.20 0.33）。一般来说，对于所有站点组合（N = 50 与 100）、采样季节（8 与 12）和每个站点每个季节的访问次数（4 与 6），检测局部生存下降 50%的能力都很低（<0.5）。不平衡设计的表现不如平衡设计，除了在不同站点的不同季节实施治疗的情况。增加更多的对照点可以提高检测局部生存变化的能力。在影响后增加更多的季节调查会带来适度的能力提升，但至少需要三个影响前的季节才能成功实施 BACI 占有研究。周转率对能力的影响很小。在 BACI 设计中进行的占有研究为检测野生动物种群的环境影响提供了机会，而无需进行密集研究的成本。然而，由于检测局部生存小变化（20%）的能力较低，当研究人员确信会发生重大治疗影响或可获得非常大的样本量时，应使用这些研究。
