# Effects of Invasive Watermilfoil on Primary Production in Littoral Zones of North-Temperate Lakes
## Materials and Methods
### Statistical Analyses  
To assess the integrity of our paired plot selection, plot and water characteristics, and IWM and native macrophyte standing crops, comparisons were tested using two-sided paired \(t\)-tests in R 3.4.4. Diversity of the macrophyte assemblage was characterized as species richness and evenness, [PERSON]'s diversity, and [PERSON]'s diversity index (Table 10). These diversity metrics were calculated from a matrix composed of standing crop values of macrophytes for 12 sites \(\times\) 25 species (Supplementary Materials, Table S1) in PC-ORD v6.30 ([PERSON]; [PERSON] PC-ORD, Version 7.08, 2018). To describe species structure of the macrophyte assemblages across lakes and study plots we also used a non-metric multidimensional scaling (NMS) in PC-ORD v6.30, which is described in the Supplementary Materials (Tables S1-S3, Figure S1).  
Comparisons of +IWM and -IWM plots were performed using paired \(t\)-tests in R 3.4.4. When testing hypotheses that macrophyte and epiphyte standing crops would be higher, plot GPP and ER rates would be higher, and macrophytes and epiphytes would make larger contributions to plot GPP in +IWM vs. -IWM plots, we used one-sided paired \(t\)-tests with the significance level set at alpha = 0.05. All other paired comparisons between +IWM and -IWM plots were performed using two-sided paired \(t\)-tests.  
Stepwise multiple linear regression performed in R 3.4.4 was used to identify significant predictors of phytoplankton, epiphyte, and benthic periphyton standing crops and primary production, and to plot GPP and ER rates. Predictors initially included were light extinction, water temperature, conductivity, TDN, DOC, NH\({}_{4}\)\({}^{+}\), SRP, NO\({}_{3}\)\({}^{-}\) + NO\({}_{2}\)\({}^{-}\), and macrophyte standing crop. Additionally, phytoplankton Chla, epiphyte Chla, and benthic periphyton Chla were included as predictors of plot GPP. Phytoplankton AFDM, epiphyte AFDM, and benthic periphyton AFDM were included as predictors of plot ER. All variables were examined for normality and homoscedasticity; when needed variables were transformed to meet the assumptions of multiple linear regression, or variables were removed if a suitable transformation was not possible. As a result, NH\({}_{4}\)\({}^{+}\), NO\({}_{3}\)\({}^{-}\) + NO\({}_{2}\)\({}^{-}\), benthic periphyton Chla, epiphyte Chla, phytoplankton Chla, and benthic periphyton AFDM were logarithmically transformed for analyses. SRP was removed due to a right skewed distribution. Prior to performing regression analysis, we conducted Pearson correlation analysis to identify significant correlations (\(p\leq 0.05\)) among predictor variables for each analysis (Supplementary Materials, Table S4). For all stepwise multiple linear regression analyses, conductivity, DOC, NH\({}_{4}\)\({}^{+}\), and NO\({}_{3}\)\({}^{-}\) + NO\({}_{2}\)\({}^{-}\) were removed due to significant correlations with other predictor variables (Table S4). For stepwise multiple linear regression analysis of plot GPP, benthic periphyton Chla was removed due to correlation with macrophyte standing crop, and phytoplankton Chla was removed due to correlation with TDN (Table S4). Additionally, for stepwise multiple linear regression analysis of plot ER, epiphyte AFDM was removed due to correlation with phytoplankton AFDM, and benthic periphyton AFDM was removed due to correlation with macrophyte standing crop (Table S4). We identified best regression models based on the smallest Akaike's information criteria (AIC) [(60)].