A non-metric multidimensional scaling (NMDS) was performed in R, in order to investigate the importance of environmental variables related to geographical location and management for the composition of species in treatment plots. NMDS was chosen to deal with skewed species distributions and categorical nominal variables (Shipley, 2021). A RxC matrix of species occurences was created, where the rows were constituted of the samples and the columns were the number of intercepts per registered species. Using the function “metaMDS” from the “vegan” package, a two-dimensional NMDS ordination was conducted. metaMDS is a wrapper function, meaning that it performs several functions in one command (Oksanen, 2021b). In this case, the default setting was used, which resulted in the function performing a Wisconsin double standardization on the data whereby each element is divided by its column maximum and then by the row total. Subsequently, the function applied a square root transformation. The Bray-Curtis dissimilarity measure was used for its ability to deal with count data and general popularity when analyzing data on species occurrences (Shipley, 2021). Two dimensions yielded a stress value of 0.146, where increasing the number of dimensions would yield only small additional reductions in stress.
A second RxC matrix of environmental variables was uploaded to R, containing the same samples as rows and with location, block and treatment as factor variables. The environmental factors were fitted to the ordination using the “envfit” function from the “vegan” package and the output gave the R2 and p values associated with the variables. The centroid value coordinates of the environmental variables were extracted and plotted along with the sample coordinates using the “ggplot” function from the “ggplot2” package.
The above process was repeated for another dataset where the intercept values were transformed into biomass estimations using the calibration models previously constructed.