PCA is a good first step for showing relatedness of your data. You would then need a general linear model to determine if there are significant effects of elevation on species richness and type of flora. Most statistics cope with zeros, but you can add 1 to all values and do a log transformation

Please avoid using PCA for abundance or incidence data without the properly transformation:

"... species abundance data should be treated in a special way, avoiding the use of double zeros as indications of resemblance among sites; linear methods, which explicitly or implicitly use the Euclidean distance among sites or the covariance or correlation among variables, are therefore not appropriate for such data. Unfortunately, many of the most powerful statistical tools available to ecologists, like ANOVA, k-means partitioning ..., principal component analysis (PCA ...) and redundancy analysis (RDA ...) are linear. Consequently, these methods were more or less “forbidden” to species data until Legendre and Gallagher (2001) showed than several asymmetrical association measures (i.e. measures that are appropriate to species data) can be obtained by two computational steps: a transformation of the raw data followed by the calculation of the Euclidean distance. These two steps preserve the asymmetrical distance among sites, therefore allowing the use of all linear methods of analysis with species data."

Borcard, D., Gillet, F., & Legendre, P. (2011). Numerical Ecology with R. https://doi.org/10.1007/978-0-387-78171-6

I recomend you to read more about how to deal with ecological data before trying to use a linear method (ANOVA, PCA, RDA, etc.). If you use the raw data, you will violate several assumptions and your analisis will be invalid.