## 2 Methods
### Statistical Analysis  
A statistical analysis was conducted to assess whether \"front\" labeled data were statistically different than \"rear\" labeled data associated with varied UV treatment. Due to the low number of replicate measurements (\(n=3\)), two methods of data aggregation were used to prepare datasets for statistical analysis. In the first \"pooled\" approach, untreated sample data were pooled across all treatment days to inform a \"control\" distribution of values. Comparison of treatment values to this common control distribution will establish the meaning of statistical difference. Thus, we made two major assumptions regarding the data collected in the field. Our first assumption is that control measurements are free of UV treatment at the time of data collection. Our second assumption regards null treatment data as identical and independently distributed random variables. Under this pooling procedure, a deviation in rear measurements from those of the pooled front control samples would provide fidelity of the statistical significance generally associated with UV treatment. Aside from these front \"control\" data, treatment measurements were analyzed on a per-day basis; that is, assays allowed to mature over time were analyzed individually and independently of data reported for other durations. For each such duration, front-labeled data were pooled across UV treatment intensity for each assay type (e.g., Chlorophyll), resulting in 12 samples. These 12 samples were compared against 3 replicates reported for each UV treatment intensity per assay type using methods of statistical significance described below. This pooled approach should be contrasted against the alternative \"unpooled\" approach, wherein front and rear labeled measurements are jointly conditioned on each treatment day and no others. Although this unpooled approach avoids bias from groupings with correlation spanning treatment days, it otherwise reflects a low number of replicate measurements, and statistical confidence is therefore limited. There is a risk that, for data collected in the field on each day, the front (UV = 0 mJ/cm\({}^{2}\)) data will be correlated across UV treatment conditions, potentially due to imperfect sampling, drift in water currents, mixing from the boat wake, etc. If so, then the pooling methodology described above would be invalidated. Analysis of Variance (ANOVA) was used to test for these potential, albeit linear correlations. Briefly, we use ANOVA to test whether, for each observable (Chlorophyll, Phycocyanin, Temperature, etc. ), the front data will be linearly correlated with the magnitude of UV treatment, and the algorithm tests whether the means of each treatment grouping are statistically equal to all others by assuming the data are independent and identically distributed (iid) random variables with equal population variances. Given the low number of replicate samples per UV treatment group (\(n=3\)), a rejection of the null hypothesis is not a strong indicator that front treatments are correlated with UV treatment intensity, and so would not necessarily require that results from a pooled methodology be invalidated. It is nevertheless useful to assess these statistics because they contextualize the degree of confidence we can assign to these pooled results. The results from this analysis are illustrated in Table 1. The entries of this table quantify the probability, \(p\), that the means of all front-collected data are statistically identical. Thus, for values\(p<0.05\), all front-collected data cannot be considered as statistically identical for each condition (i.e., at least 1 UV treatment front-collected grouping differs from the others), and thus the non-pooled methodology would be the best choice for data comparison. Given control data derived from either the pooled and unpooled approaches, we chose two common methods for which to quantify statistical significance: the nonparametric Kolmogorov-Smirnov (KS) and the parametric Student's \(t\)-test (T).