Can PC1 explain more than 90% of variance? I've been running some analysis using morphological data. However, when I run a PCA, PC1 explains more than 90% of variance. I'm not sure if this is possible. If not, what should I need to do?


Certainly that can happen. Below is a simulated example in R with just two original variables. As long as they are strongly enough correlated, they can very well be summarized by the projection to a single line, which means that the first principal component explains an arbitrarily large proportion of total variance:
library(MASS)
set.seed(1)
dataset <- mvrnorm(1000,c(0,0),cbind(c(1,0.99),c(0.99,1)))
summary(prcomp(dataset))
plot(dataset,las=1,pch=19,cex=0.6)

Output:
Importance of components:
                         PC1     PC2
Standard deviation     1.460 0.10400
Proportion of Variance 0.995 0.00505
Cumulative Proportion  0.995 1.00000


I very much recommend Making sense of principal component analysis, eigenvectors & eigenvalues.
