How to interpret a negative intercept with summary lmer? I found a negative intercept in the fixed effect of the summary of my model and I do not know how to interpret the results of the variables.
Here is my model : 
model_velocity_HL <- lmer(PC1~HL*TIME_RECORDING + TIME_LAG + TARSUS + BODY_MASS + (1|RING), data = table_pca, REML = T)
The summary show a negative intercept for the t-value (PC1) and a positive intercept for the time recording.

Can I say that the higher the time recording, the higher the PC1 (if significant) ? Or because the intercept is negative I should say the opposite ?
Thank you for your help !
Mathilda
 A: The intercept is only the predicted value of your dependent variable when all the continuous covariate are set to 0, assuming there are only continuous predictors (as it seem to be the case given the output you copied). It has, therefore, no bearing on the direction of the effect of the predictors, which depends only on the signs of their coefficient. So in your case is correct to say that PC1 (whatever that is) increases with TIME_RECORDING, because the coefficient is positive (recall that the regression coefficient indicate by how much the dependent variable changes for a unitary increase in the continuous predictor, while keeping all the other variables constant).
Clearly, it can happen that the intercept takes on "impossible" values. In you case for example consider that the intercept is the predicted values of PC1 for BODY_MASS = 0, which (if I interpreted correctly the labels of your variables) should not be physically possible. To have more meaningful intercept you could for example center you predictor values (i.e. subtract their mean value), in which case the intercept represents the predicted value of the dependent variable when the continuous covariates takes on their mean values.
