I have data for a test on three groups. The measured variable is ratio scaled. The R code is

g1a<-c(7, 3, 40)
g2a<-c(1,1,2)
g3a<-c(0,0,0)

Since the sample is small and normality cannot be guaranteed, I run a Kruskal Wallis test to check for significance:

l<-list(g1a,g2a,g3a)
kruskal.test(l)

The p-value is 0.02336, which is nice.

Now I run a post-hoc test, using the Mann-Whitney U:

wilcox.test(g1a,g2a,paired=FALSE,exact=TRUE)
wilcox.test(g2a,g3a,paired=FALSE,exact=TRUE)
wilcox.test(g1a,g3a,paired=FALSE,exact=TRUE)

All the resulting p-values are above 0.05 (0.07652, 0.0636, 0.05935). This is very strange. Shouldn't one of these tests give a much lower p-value? Especially since I'd have to use some sort of correction to account for the multiple comparisons in the post-hoc test. In other words: how can I interpret this result?