# What are the ways to normalize the vegetation data when the plot sizes of samples are different?

I have collected data on mangrove tree species density and composition from two habitat categories (sample groups) by using belt transects of various lengths between 100 x 10 M to 500 x 10 m. (3 & 2 samples at each sample group; n=5). data is similar to the attached image. The length of transects was varied because of the limited habitat availability in each sample group. Replicate data were collected from the same transects after 8 years. Now I want to compare the species richness and composition between the temporal replicates of each sample groups, but I'm a bit skeptical that due to the various sizes of samples I can't directly use the data for running some analysis like Mann-Whitney U test or Multi-response permutation procedure to compare the tree densities and species composition between the temporal replicates of sample groups. Kindly suggest a better way to analyze the data and how can I normalize the data for the sample area before running my analysis? Thanks in advance. - Nehru

Rarefaction is conservative, but it does result in you losing quite a bit of your data. An alternative method is to use species accumulation curves - these use the results of your sampling to estimate the 'unseen' diversity of each site, so that then you can then compare the "total" richness. The R function specpool includes methods for carrying this out.