On Thu, Jan 26, 2017 at 9:38 AM, Joaqu?n Aldabe
<joaquin.aldabe at gmail.com> wrote:
(I don't mind if you cc: me, but this is really a question to the
list. Probably better to frame it as "I sent this to the list and Ben
Bolker said ...")
with this subject about invertebrate biomass and its
possible effect on shorebird density. I have a couple of extra doubts
You suggested this:
(1) The relationship between bird density and invert biomass, as well
as the intercept (i.e., expected bird density at invert_biomass=0, or
better invert_biomass=<some sensible reference quantity>).
I tried a quantity considering the lowest value of the invertebrate
variable, but the model did not converge. So I wonder if I should pick
value or a different one?
Hmm, is this with lme or lmer? Can you give more detail? If it's
lmer, it's quite likely a false positive. I USED LME. I DON'T REALLY KNOW
WHAT DO YOU EXACTLY MEAN WITH SENSIBLE REFERENCE QUANTITY. I THOUGHT IT WAS
THE MINIMUM VALUE OF MY X VARIABLE (AS CERO IS NOT A POSSIBILITY).
(2) The relationship might be changing over time?
lme(Bird.density~Invertebrate biomass+sample_time,
random=~invert_biomass|Plot_identity, data=)
How should I treat sample time? Is it a ordered categorical variable?
Depends on a number of things. Your original message suggested
that you sampled over the course of 30 days (maybe at different times
in different plots?) If this is the case (e.g. you sampled plot 1 on
days 1, 5, 9, ... and plot 2 on days 2, 6, 10 ...) then it is probably
most sensible to treat time as a numeric variable (i.e., assume a
linear trend over days) and possibly a random effect with days as a
grouping variable (in which case you might have to switch from lme to
lmer). If you have a small number of distinct sample days then a
categorical variable makes sense. Whether you specify it as ordered or
(default) unordered doesn't affect the overall fit of the model, just
the particular contrasts that get tested with respect to the time
variable.
Thankyou very much.
Joaquin
2017-01-16 19:49 GMT-03:00 Ben Bolker <bbolker at gmail.com>:
Center your biomass variable on this value: either create a
mydata$invert_biomass_c <- mydata$invert_biomass-ref_value
or include it directly in your formula:
bird_dens ~ I(invert_biomass-ref_value), ...
On 17-01-16 05:44 PM, Joaqu?n Aldabe wrote:
Thankyou very much Ben. Can you please suggest a way of fixing some
sensible reference quantity for Invertebrate biomass?
All the best,
Joaqu?n
2017-01-16 18:59 GMT-03:00 Ben Bolker <bbolker at gmail.com
<mailto:bbolker at gmail.com>>:
That seems perfectly reasonable. There are a couple of things to
consider, although you may or may not find that your data supports
that much complexity.
(1) The relationship between bird density and invert biomass, as
well
as the intercept (i.e., expected bird density at invert_biomass=0,
or
better invert_biomass=<some sensible reference quantity>)
lme(Bird.density~Invertebrate biomass,
random=~invert_biomass|Plot_identity, data=)
(2) The relationship might be changing over time?
lme(Bird.density~Invertebrate biomass+sample_time,
random=~invert_biomass|Plot_identity, data=)
(3) In principle you could consider random effects of both time
invert biomass, but that will almost certainly overwhelm your
Don't forget to do the standard post-fitting checks: are your
residuals *approximately* equal-variance and (even more
approximately)
Normally distributed? Is the relationship between bird density
invert biomass *approximately* linear? (See ?plot.lme)
On Mon, Jan 16, 2017 at 2:06 PM, Joaqu?n Aldabe
<joaquin.aldabe at gmail.com <mailto:joaquin.aldabe at gmail.com>>
> Dear all, I'm interested in modeling the effect of invertebrate
> the density of a grassland shorebird (they eat invertebrates).
> picked 8 plots and sampled invertebrates and birds 6 times in
> about 30 days. This is, I went to each plot and did repeated
> invertebrates biomass and shorebird density separated in time by
> five days, as invertebrates biomass may change over time and it
> that birds density change accordingly.
>
> So, I'm trying to see a general pattern of the effect of changes
> on the density of this shorebird species at a plot scale. Plot
> not important; I consider them as particular events of a random
>
> Is this model correct:
>
> lme(Bird.density~Invertebrate biomass, random=~1|Plot_identity,
>
> Thank you very much,
>
> Joaquin.
>
>
> --
> *Joaqu?n Aldabe*
>
> *Grupo Biodiversidad, Ambiente y Sociedad*
> Centro Universitario de la Regi?n Este, Universidad de la
>
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>
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Joaqu?n Aldabe
Grupo Biodiversidad, Ambiente y Sociedad
Centro Universitario de la Regi?n Este, Universidad de la Rep?blica
Ruta 15 (y Ruta 9), Km 28.500, Departamento de Rocha
Departamento de Conservaci?n
Aves Uruguay
BirdLife International
Canelones 1164, Montevideo
https://sites.google.com/site/joaquin.aldabe