[R-sig-ME] Scaling the response of a linear model to different factor groups

[Daniel Rubi]

I hope this question is general enough to be of broad interest.

Here's the abstract explanation of my problem:
I have two groups for which I measured a certain feature. Specifically, this feature can be divided into sub-categories, where I am aware that in each group there are inherent differences among the sub-categories. I'm interested to test if the two groups differ WRT to this feature, and if so what is the contribution of each sub-category to this feature.


Now, here's the actual data I'm working with:

My groups are two species of rodents. The feature is their whisker lengths. The whiskers are organized in four rows on the faces of each of the species (they are compatible betwen the two species).In both species the whiskers at different rows have different lengths (e.g., row 1 has the longest among all other rows whiskers in both species). 

What would be the correct linear model to test this? 

The simple mixed-effects model I can think of would be: whisker_length ~ species + whisker_row + (1|animal)

where animal is a random effect, since I measure whiske lengths for several animals of each species.


Is this model sufficient?
My concerns are:
1. Interpretation - if the result of the model is that both species and whisker_row (one or more of the four rows) are significant, does this model inform me whether the significant whisker rows are different between the two species? My impression is that the only interpreation is that whisker_row significantly determines whisker length, regardless of the species. Hence, should I add an interaction term between species and whisker_row to capture that?
2. Should I standardize all whisker lengths relative to their rows, so that they are on a common scale according to their row?


Thanks a lot,
rubi