Båmstedt Ulf, Brugel Sonia
Umeå Marine Sciences Centre, Umeå University, Norrbyn, SE-905 71, Hörnefors, Sweden.
Department of Ecology and Environmental Science, Umeå University, SE-901 87, Umeå, Sweden.
Environ Monit Assess. 2017 Jul;189(7):354. doi: 10.1007/s10661-017-6064-6. Epub 2017 Jun 25.
Ongoing marine monitoring programs are seldom designed to detect changes in the environment between different years, mainly due to the high number of samples required for a sufficient statistical precision. We here show that pooling over time (time integration) of seasonal measurements provides an efficient method of reducing variability, thereby improving the precision and power in detecting inter-annual differences. Such data from weekly environmental sensor profiles at 21 stations in the northern Bothnian Sea was used in a cost-precision spatio-temporal allocation model. Time-integrated averages for six different variables over 6 months from a rather heterogeneous area showed low variability between stations (coefficient of variation, CV, range of 0.6-12.4%) compared to variability between stations in a single day (CV range 2.4-88.6%), or variability over time for a single station (CV range 0.4-110.7%). Reduced sampling frequency from weekly to approximately monthly sampling did not change the results markedly, whereas lower frequency differed more from results with weekly sampling. With monthly sampling, high precision and power of estimates could therefore be achieved with a low number of stations. With input of cost factors like ship time, labor, and analyses, the model can predict the cost for a given required precision in the time-integrated average of each variable by optimizing sampling allocation. A following power analysis can provide information on minimum sample size to detect differences between years with a required power. Alternatively, the model can predict the precision of annual means for the included variables when the program has a pre-defined budget. Use of time-integrated results from sampling stations with different areal coverage and environmental heterogeneity can thus be an efficient strategy to detect environmental differences between single years, as well as a long-term temporal trend. Use of the presented allocation model will then help to minimize the cost and effort of a monitoring program.
正在进行的海洋监测项目很少被设计用于检测不同年份之间的环境变化，主要是因为要达到足够的统计精度需要大量样本。我们在此表明，对季节性测量进行时间上的汇总（时间整合）提供了一种有效降低变异性的方法，从而提高检测年际差异的精度和效能。来自波的尼亚湾北部21个站点的每周环境传感器剖面数据被用于一个成本 - 精度时空分配模型。来自一个相当异质区域的6个月内6个不同变量的时间整合平均值显示，站点间的变异性较低（变异系数，CV，范围为0.6 - 12.4%），相比之下，单日站点间的变异性（CV范围为2.4 - 88.6%），或单个站点随时间的变异性（CV范围为0.4 - 110.7%）。采样频率从每周降至大约每月一次，结果没有明显变化，而更低的频率与每周采样的结果差异更大。因此，通过每月采样，只需少量站点就能实现高精度和高估计效能。输入诸如船舶时间、劳动力和分析等成本因素后，该模型可以通过优化采样分配来预测每个变量时间整合平均值达到给定所需精度的成本。随后的效能分析可以提供关于检测年份间差异所需最小样本量的信息，所需效能已知。或者，当项目有预定义预算时，该模型可以预测所包含变量年度平均值的精度。因此，使用来自不同区域覆盖和环境异质性的采样站点的时间整合结果，可以成为检测单年之间环境差异以及长期时间趋势的有效策略。使用所提出的分配模型将有助于最小化监测项目的成本和工作量。
