You analyze the data including all data points.
Then, you examine the influential outliers.
If the influential outliers are influential due to a known bias that
was external to the conditions in the experiment, you can remove them.
You may need to change your statistical test due to changes in your
dataset after their removal.
Report EVERYTHING you did in your paper.
Malcolm

On Tue, Jun 5, 2012 at 5:54 PM, Timothy Wertin <[email protected]> wrote:
> We are trying to calculate relative growth rate of perennial grass
> species.  We have a large number of tagged individuals with which we
> estimate biomass each year.  Naturally, every year we experience at least
> a few mortality events, which are evenly distributed across our treatments
> (warming, watering and warming x watering).
>
> Could someone(s) please advise on how you would calculate RGR when you
> have a mortality that does not appear driven by treatment?  Do you remove
> the sample from the population the year it dies?  Do you remove completely
> from the analysis?
>
> A hypothetical example: Population RGR from 2009 - 2010 was -0.12.  In
> 2011 an individual died resulting in that individual's RGR from 2010 to
> 2011 of -20, and a population RGR from 2010 to 2011 of -3.  The individual
> is removed from the population 2010 - 2011 RGR calcuation and now the
> population RGR goes to 0.1.  It is likely that the population RGR from
> 2009-2010 was negative because the plant was dying.  Note: the plant was
> not damaged, subjected to an insect event and in general appeared the same
> as other plants in the experiment.
>
> Thanks!
> Tim Wertin
> USGS - Canyonlands Research Station



-- 
Malcolm L. McCallum
Department of Molecular Biology and Biochemistry
School of Biological Sciences
University of Missouri at Kansas City

Managing Editor,
Herpetological Conservation and Biology

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