Beyond Baci: Optimization of environmental sampling designs through monitoring and simulation

The detection of anthropogenic disturbances requires appropriate sampling d esign and powerful statistical tests. This study illustrates how simulation s of environmental disturbances may be used to improve the capability of an y given sampling design to detect impacts of a specified magnitude. Here, r eal data on the abundance of algae and invertebrates are used to simulate t he effects of environmental disturbances on two rocky shores in the northwe st Mediterranean. Natural populations were sampled eight times between 1995 and 1996 in mid-shore habitats on-each of two shores. Four sites were samp led at each of three different levels on the shore each time. Three replica te quadrats were sampled in each site. This design was rearranged to create two "Impacted" and two "Control" sites at each level on each shore. The fi rst four times were used as "Before" data; the latter four times represente d a series of "After" data. Two different approaches were used to simulate environmental impacts. The first was based on the methodology developed in the context of Beyond BACI designs, and consisted in altering the mean abun dance of organisms of known amounts at the "Impacted" sites for the "After" series of data. Simulated data were generated by multiplying the real valu es by random variates obtained from binomial distributions of means 0.8, 0. 5, and 0.2, to simulate reductions in mean abundance to 0.8, 0.5, and 0.2, respectively. These data were analyzed using analysis of variance following the logic of Beyond BACI designs. A Monte Carlo procedure was also develop ed based on the linear model of the Beyond BACI design, using real estimate s of spatial and temporal variance of the red alga Rissoella verruculosa. P ower of the Before/After vs. Control/Impact (B X I) interaction was calcula ted from sets of 1000 simulations under the alternative hypothesis of an im pact causing a reduction in mean abundance of the alga to 0.5. Power curves were generated for the B X I interaction using different combinations of n umber of sites, number of times, and number of replicate plots, thereby pro viding a direct way of optimizing the sampling design. In most cases impacts were detected as significant changes from before to a fter the simulated disturbances in the differences between the impacted and control sites. The probability of detecting an impact was not consistent b etween the two shores, and it also changed in relation to level on the shor e. Statistical power was large for several of the tests involved in the det ection of the simulated impacts. Sample size was also calculated for differ ent combinations of Type I and Type II errors, indicating that the sample s ize required to design powerful environmental sampling programs can be main tained within a range of acceptable and logistically feasible sampling effo rts for these rocky shores. In some cases it was not possible to proceed wi th calculation of power and sample size using classical procedures based on non-central F distributions, due to the impossibility of providing an erro r term with a single component of variation. The Monte Carlo procedure deve loped here solved this problem, providing a tool of potential wide applicat ion for the design and optimization of environmental sampling programs.