Levene’s test was carried out, using the function “leveneTest” from the “car” package, to test whether variances were significantly different between variable groups, which is a requirement for e.g. analysis of variance. Only the variable “location” showed significant between-group differences and only for the parameters Simpsons diversity and evenness and Ellenberg N. There were no instances of significant differences in variance for treatment.
Linear regression models on the parameter values revealed that the errors were in many cases not normally distributed.
therefore constructed for each parameter, using the “glm” function from basic r, with the treatment, block and location as independent variables. GLM’s were chosen for their ability to deal with count data, and irregular error distributions (Crawley, 2007). Poisson’s error was used when modelling the species richness, withered intercepts and sensitive species, as it is able to deal with count data (Crawley, 2007).
The process was repeated for a second dataset where intercept values were transformed into biomass estimates, using the calibration models previously constructed. Since the parameters of species richness, sensitive species and unicity were not based on the number intercepts, but rather the presence of species in the plot, these were not repeated.