I'm wondering if a treatment has an effect on my mite population. Therefore I've got a dataset with repeated measurements, some data is missing.

data:
<br>
<pre>
<b>ID   Treatment    Mites   Time   Location    StartPopulation    otherFactor</b>
ID1  Control      7       1      Loc1        5                  10000
ID1  Control      8       2      Loc1        5                  10000
ID1  Control      10      3      Loc1        5                  10000
ID2  Control      12      1      Loc2        11                 13000
ID2  Control              2      Loc2        11                 13000
ID2  Control      14      3      Loc2        11                 13000
ID3  Treatment    20      1      Loc1        20                 12000
ID3  Treatment    22      2      Loc1        20                 12000
ID3  Treatment            3      Loc1        20                 12000
and so on..
totally: 110<em>ID</em>s; 7 different measurement <em>Time</em>stamps
</pre>

variables:
<pre>
ID:              factor, unique ID for each population
Treatment:       factor ("Treatment" or "Control")
Mites:           numeric, the variable I'm interested in
Time:            factor with total 7 levels
Location:        factor with total 11 levels
StartPopulation: numeric (mean of Mites for t=-3, -2, -1 -> before Treatment started)
otherFactor:     numeric
</pre>

I'm interested if my <em>Treatment</em> changed the <em>Mites</em> count - and if yes if there's an increase in it's effect over time. <em>StartPopulation</em> sure had an influence on <em>Mites</em>, <em>otherFactor</em> and <em>Location</em> could've had also.

As I use a mixed model I'd like to use <b>lmer in R</b>. My syntax looks anything like that:
<code>
PPP <- lmer(Mites ~ Treatment + StartPopulation + Location + otherFactor + (1|Time) + (1|ID), data=vat_database)
</code>
But as I don't really know what I typed exactly I'm glad if you can help me understanding how this works.

Thank you so much, kind regards