Randy said:

> Every section of that graph has exactly the same curve-
> -just depends upon the scale you use on the Y axis.

I'm not sure where Randy thinks the scale on the Y axis
changes in he graph cited several posts ago, but I assure 
everyone that looking at a few graphs of "natural mite 
drop" (or better yet, make some yourself) will confirm 
that the general curve depicted is very consistent, and
a single (linear) scale can be used.

While one MIGHT say that it would be possible to see that
the growth is "exponential" from the start, the inherent
sloppy nature of the measurements tend to hide this.
Even ether rolls tend to show "linear" increases in 
varroa counts in early stages, and I've yet to hear
anyone claim to have a sampling technique better than
ether rolls.

So, the data shows, in early stages, what appears to
be "straight-line linear growth", and only "goes 
exponential" after a while.  Not surprisingly, this 
correlates to mite population growth very well.

> Mite treatment can be effective at any point, and gets
> more bang for the buck earlier.

This statement flies in the face of the ENTIRE BASIC 
CONCEPT OF IPM itself, where the goal is to only 
treat when and where it is necessary to do so, 
making conscious decisions based upon objective 
metrics and criteria.

Randy seems to have thought that a discussion of IPM
data collection would not be complete without discussing
his approach of using mite fall as a result of treatment
with powdered sugar as if it were a reliable number.

While there are such protocols for the short-term use
of Apistan strips (and even CheckMite strips) in
prompting mite fall, the gold standard remains the 
"natural mite fall" from a colony not subjected to 
any outside influence or overt attempt to dislodge 
mites.  So it is impossible to say if the numbers 
generated by treatment with powdered sugar are 
consistent, or of any value at all.

Now, in the case of the powdered sugar treatment itself, 
the specific methodology that has been proven in 
controlled studies to actually be an effective control 
for varroa was described in this paper:
http://ethesis.helsinki.fi/julkaisut/maa/selai/vk/fakhimzadeh/detectio.p
df

Other approaches to application methods, such as Randy's
practice of dumping the sugar on the top bars, and using
a bee brush to spread it around, so that it falls between
the top bars, may or may not be as effective.  On the
other hand, they may be more effective - we just don't
know, do we?

An understanding of the mechanism by which powdered sugar 
works can be gained by reading the paper (or the ABJ article
from 2000 that presented much of the same information) reveals
that one wants lots of little tiny particles only a few microns
in diameter.  The use of great massive globs of sugar may have
no effect at all, or may be a hindrance, we just don't know.

But this thread was about IPM, and I merely dared to, 
yet again, do nothing more than state the blindingly 
obvious about data collection as applied to a 
single-variable measurement (such as varroa population)
in the context of making a treatment decision.

I'm not sure why there is any argument on this, as
the term "threshold" inherently means a pest/prey
threshold by definition, and without an accurate
count of both pests and prey, one cannot claim
to have a "threshold".

I'm not sure how we got off "IPM" and onto Randy's
weekly powdered sugar dusting techniques, but the 
astute observer will recall that we have seen a number 
of attempts to come up with a better control for varroa, 
and the success rate has been less than spectacular for 
those attempts not supported by rigorous statistical proof
that compares the treatment to some known treatment
approach (for example, one of the miticides).

Regardless, the difference between a single data point
as a result of a single measurement, and multiple data
points as a result of multiple measurements should 
need no further explanation, as there are no IPM
seminars that will advise anyone to make any decision
based upon a single context-free number as a result
of a single measurement of a single variable.



 

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