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> [[alternative HTML version deleted]]
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