## 2 Materials and Methods
### Data Analysis
Differences between nutrient enrichment treatments (medium vs. high) were tested using two-sample _t_-tests and between sampling periods (for submerged data only) (T1 vs. T3) using paired two-sample _t_-tests. Statistical analyses for the control normalised data were performed using the R stats package (v3.6.2) in R Studio. No significant treatment effects on primary production were detected in the PERMANOVA (Figure 1, Table 10) or control normalised analyses (see Results) and therefore raw (i.e., not control normalised) data were pooled for subsequent analysis. To investigate if site turbidity modified the relative importance of submerged and emerged primary production and to see if this was consistent through time, two repeated measures PERMANOVA's were conducted. The first using T3 (November 2018) data with site and tidal state as fixed factors (6 and 2 levels, respectively) and replicate plotnested within site. The second considered a temporal element by including site (fixed factor, 3 levels: TAU-O, TAU-T and RAG), time (fixed factor, 2 levels: T2 and T3) and tidal state (fixed factor, 2 levels: submerged and emerged) with replicate plots nested within the site. All similarity matrices were based on Euclidian distances, and primary production measures tested were NPP, GPP, GPP\({}_{\rm i}\), GPP\({}_{\rm Sed}\) + chla-Main effects were not considered if interaction terms were significant, and instead, posthoc tests identified differences between sites, tidal state and sampling date. Distance-based Linear Models (DistLM) were then used to identify if any environmental (sediment characteristics, light etc.) or univariate macrofaunal variables were significant drivers of primary production measures across all nutrient enrichment treatments, sites and sampling dates. First, predictor variables were normalised to allow for comparisons despite differing units and scales. Significant individual predictors (marginal tests) could then be identified and the best combination of predictor variables (stepwise procedure) of NPP and GPP during submerged and emerged tidal periods calculated. The corrected Akaike information criterion (AICc) was used for all models as AICc is suggested to be most appropriate when there are a large number of predictor variables relative to the sample size (sample number/n. explanatory variable < 40) [44]. Where co-linearity occurred among predictor variables (r > 0.7), the variable explaining the least amount of variability was omitted from the full model. For June 2018 (T2), macrofauna data between the two November samples were averaged so this sample date could be included. Variance partitioning was used to determine how much of the observed variability was attributed to significant environment variables (organic content, mud content, median grain size, sediment chl \(a\) content, phaeophytin content, PAR\({}_{\rm i}\), PAR\({}_{\rm sed}\) (submerged data only), PAR\({}_{\rm site}\) and temperature (sediment surface)), community variables (total abundance, taxa richness, _A. stutchburyi_ abundance and _M. liliana_ abundance) and porewater variables (NH\({}_{4}\)\({}^{+}\) concentration at 0-2 cm and 5-7 cm sediment depth and porosity at 0-2 cm (emerged data only)). The two bivalve species were included based on their known influence on benthic processes within these systems [36]. All PERMANOVA and DistLM analysis were performed using PRIMER 7 with the PERMANOVA+ package.