I have 9 sites. Within each site, plant life was sampled to represent 70% of the basal area. Of the sampled plants, I know the corresponding family, genus, and species. For this project, I extracted leaf waxes and determined the total wax loading (TWL), average chain length (ACL), and the carbon preference index (CPI). My goal is to determine how much of the observed variation in TWL, ACL, and CPI, is explained by either the site, family, genus, or species. This would allow me to determine the major factors affecting the chemical composition of plant leaf waxes. Please note that most species, genera, and families occur several times within the data. The data look like this:
SITE FAM GEN SPEC TWL CPI ACL 1 TAM05 Fabaceae Tachigali Tachigali polyphylla 34.65 6.89 30.06 2 TAM05 Caryocaraceae Anthodiscus Anthodiscus peruanus 68.68 13.00 30.57 3 TAM05 Moraceae Clarisia Clarisia racemosa 46.85 12.22 29.68 4 TAM05 Lecythidaceae Bertholletia Bertholletia excelsa 69.64 6.27 30.52 5 TAM05 Moraceae Pseudolmedia Pseudolmedia laevigata 126.33 17.34 29.61 6 TAM05 Moraceae Pseudolmedia Pseudolmedia laevis 83.58 13.50 30.07 7 TAM05 Linaceae Roucheria Roucheria punctata 160.62 13.98 29.71 8 TAM05 Fabaceae Cedrelinga Cedrelinga cateniformis 151.12 10.82 30.17 9 TAM05 Urticaceae Pourouma Pourouma minor 61.47 1.41 29.64 10 TAM05 Fabaceae Sclerolobium Sclerolobium bracteosum 163.28 12.22 29.53 ...
By reviewing similar studies, I eventually ended up with the use of a nested linear mixed-effects model in R. My analysis here would likely consist of running the same analysis three times, once for every variable (TWL, CPI, ACL).
I am, however, unsure of how to proceed. I am currently at the point where I believe the formulas to use would be:
TWL1 <- lmer(TWL ~ SITE + (1|FAM/GEN/SPEC)) CPI1 <- lmer(CPI ~ SITE + (1|FAM/GEN/SPEC)) ACL1 <- lmer(ACL ~ SITE + (1|FAM/GEN/SPEC))
Is this correct? And is it correct that I would have to run reduced models as well, and compare them with a chi-squared test?