mixed model?

Joaquin,
It looks like you could use Year and Field as random effects, since there
might be variation in bird abundance across years, and similarly, variation
across fields.

So in this case your model is
Birdmodel<- glmer(Presence~ GrassHeight * ForestCover + (1|Year) +
(1|Field), data=BirdData, family =                  "binomial")

Alternatively you could use Year as a fixed effect, if you are interested
in particular years.
Another option is to include interaction terms as random effects, eg
(1|Field:GrassHeight), to allow the effect of GrassHeight to vary across
fields.


On Fri, Apr 28, 2017 at 9:32 AM, Joaqu?n Aldabe <joaquin.aldabe at gmail.com>
wrote:

Dear all, I'm analysing bird presence/absence in 16 grassland fields over
4
seasons (different years) and want to know the effect of grass height and
forest cover on presence/absence of the species. Grass height varied among
season but not forest cover in each field. So we have a spatial dimension
and a time dimension. I tried a binomial glm but wonder if I should use
generalized linear mixed models with field identity as the random as I
have
repeated measures (bird counts) in each field.

I appreciate your opinion.

Thanks in advanced,

Joaquin Aldabe.

--
*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
<https://sites.google.com/site/perfilprofesionaljoaquinaldabe>

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--
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PhD candidate
Department of Biology
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