lme() vs aov()

The gut reaction is that you shouldn't trust lme() in low-df cases, but 
in this particular case the issue is different:

 > summary(mod.aov)

Error: source
         Df Sum Sq Mean Sq F value   Pr(>F)  drug       2 61.167  
30.583  61.167 0.003703 **
Residuals  3  1.500   0.500                   ---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Error: Within
         Df Sum Sq Mean Sq F value Pr(>F)
Residuals  6    9.0     1.5             
Notice that the Residuals Mean Sq is larger in the Within stratum than 
in the source stratum. In terms of a mixed-effects model, this implies a 
negative estimate for the variance of the source effect. lme() will have 
nothing of that and sets it to zero instead. If you drop the 
Error(source) you get the same F as in lme() although the df differ.

(The  "negative variance" can be interpreted as negative within-source 
correlation, but that only works properly for balanced designs. Long 
story...)