Random-effects-model: different random effects for lme and lmer?

Question

I’m very new to hierarchical/multilevel analysis, so please excuse me, if my question is basic.

I’m wondering why I get different results if I analyze my data with lme (of nlme) or lmer (of lme4) even though (to my knowledge) I’m using the same design-formula:

I have a repeated measures design with Condition being the repeated-measures factor. Gender is a between-subjects factor and additionally I’m interested in the interaction between Condition and Gender.

I’m analyzing the data of several studies, therefore I included Study as a random-effect factor.

I have to account for the repeated measures structure as well (right?) and therefore I include Person as a random effect, too. Because Person is nested in Study, I include the random effects term:

+ (1|Study/Person)

for lmer and random

= ~ 1|Study/Person

for lme. (I took care that Person is a truly unique number/factor).

However, in the output for lme only the random effects for Study are printed:

Random effects:     
Formula: ~1 | Study

Whereas in the output for lmer the random effects for Study and the interaction between Person and Study are printed

Random effects:
 Groups          Name        Variance Std.Dev.
 Person:Study (Intercept)  5.261   2.294   
 Study          (Intercept)  2.242   1.497   
 Residual                    66.342   8.145   
 Number of obs: 784, groups:  Person:Study, 392; Study, 7

What is correct? Should I use lme or lmer?

For reference the complete formulas:

nlme::lme(Outcome ~ Condition + Gender + Condition:Gender, 
                data = nestedData, random = ~ 1|Study/Person, 
                method="ML", na.action=na.exclude)
lme4::lmer(Outcome ~ Condition + Gender + Condition:Gender + 
       (1|Study/Person), data = nestedData, REML = FALSE)