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On 10-09-24 07:26 AM, Peter Jørgensen wrote:
> Dear R-eco'ers, I am about to analyze a dataset with population
> trends from two decades (1990's and 2000's) for about 500
> populations.
>
> I would like to incorporate both trends in the same model (as the
> response variable) to get an idea of how the populations respond
> to different environmental conditions (the explanatory variables
> being the environment experienced in each decade).
>
> So far the best design I can come up with is to do a mixed effects
> linear model with the population ID as a random effect. In this I
> would also use the inverse error term of the slope to weight each
> observation. So something like:


lme(trend ~ winter_ndvi * summer_ndvi * land_use_change, random =
1|popID, weights =
(1/trend_SE))

> I guess and other option is to do a more explicit repeated
> measures model. Any suggestions and comments on improvement of the
> model design are very welcome.
 
    I don't see where 'decade' enters in the model.  (I'm assuming
that for the analysis listed above you've collapsed the population
trends down to a single value per population per decade.)  Did you mean to
have decade on the RHS of the formula as well?
   Since you only have two 'treatment groups' (decades), you could also
analyze this as a paired design -- i.e., analyze the *difference*
between trends
for each population.  Then you don't need mixed effect models at all
(formally,
paired testing is analogous to REML estimation in this case).  (This would
break down if you wanted to compare three decades.)
   You *could* do a more explicit repeated measures model (i.e.,
lme(pop ~ decade*years_within_decade*...) but, unless you are eventually
going to try to make the model more complex (e.g. allow for
autocorrelation,
nonlinear trends, etc.), I don't know that it would be worth it except
for the
purposes of learning how to do it.
 
   Ben Bolker



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