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I have a roughly exponential relationship between insect counts and distance along a growing tunnels, and need to examine the effects of different tunnel types as they relate to the abundance and distributions of insects along the tunnels, also considering microclimate factors. I have run GLMM models with lme4 and glmmadmb in R (poisson and negative binomial), and this seems okay. However, I wonder if transforming the dependant variable "distance to edge of tunnel (m)" by log10 transformation to create a more linear relationship with the response variable (insect counts) is statistically problematic in the context of GLMMs? I realise this make interpretation of the coefficients more difficult. The other option I see is to use GAMMs, but as I have little experience with them I would prefer not to use them. My count data is also over-dispersed, possibly zero-inflated, and I need to use offsets to account for differences in floral abundance and random effects to account for resampling, which in GAMMs is causing me some problems. An example of the GLMM models and the relationship between distance and insect abundance (image is here is graphed as a ratio per plant but the models use the raw count data ie integers) I am evaluating are:

glmmadmb(Pollinators_count ~ Distance_from_edge * Wind_speed * temperature * Treatment + offset(log(Flower_count)) + (1|ROW) + (1|DATE), data = transects, family ="nbinom")

I have a roughly exponential relationship between insect counts and distance along a growing tunnels, and need to examine the effects of different tunnel types as they relate to the abundance and distributions of insects along the tunnels, also considering microclimate factors. I have run GLMM models with lme4 and glmmadmb in R (poisson and negative binomial), and this seems okay. However, I wonder if transforming the predictor variable "distance to edge of tunnel (m)" by log10 transformation to create a more linear relationship with the response variable (insect counts) is statistically problematic in the context of GLMMs? I realise this make interpretation of the coefficients more difficult. The other option I see is to use GAMMs, but as I have little experience with them I would prefer not to use them. My count data is also over-dispersed, possibly zero-inflated, and I need to use offsets to account for differences in floral abundance and random effects to account for resampling, which in GAMMs is causing me some problems. An example of the GLMM models and the relationship between distance and insect abundance (image is here is graphed as a ratio per plant but the models use the raw count data ie integers) I am evaluating are:

glmmadmb(Pollinators_count ~ Distance_from_edge * Wind_speed * temperature * Treatment + offset(log(Flower_count)) + (1|ROW) + (1|DATE), data = transects, family ="nbinom")

I have a roughly exponential relationship between insect counts and distance along a growing tunnels, and need to examine the effects of different tunnel types as they relate to the abundance and distributions of insects along the tunnels, also considering microclimate factors. I have run GLMM models with lme4 and glmmadmb in R (poisson and negative binomial), and this seems okay. However, I wonder if transforming the dependant variable "distance to edge of tunnel (m)" by log10 transformation to create a more linear relationship with the response variable (insect counts) is statistically problematic in the context of GLMMs? I realise this make interpretation of the coefficients more difficult. The other option I see is to use GAMMs, but as I have little experience with them I would prefer not to use them. My count data is also over-dispersed, possibly zero-inflated, and I need to use offsets to account for differences in floral abundance and random effects to account for resampling, which in GAMMs is causing me some problems. An example of the GLMM models I am evaluating are:

glmmadmb(Pollinators_count ~ Distance_from_edge * Wind_speed * temperature * Treatment + offset(log(Flower_count)) + (1|ROW) + (1|DATE), data = transects, family ="nbinom")

I have a roughly exponential relationship between insect counts and distance along a growing tunnels, and need to examine the effects of different tunnel types as they relate to the abundance and distributions of insects along the tunnels, also considering microclimate factors. I have run GLMM models with lme4 and glmmadmb in R (poisson and negative binomial), and this seems okay. However, I wonder if transforming the dependant variable "distance to edge of tunnel (m)" by log10 transformation to create a more linear relationship with the response variable (insect counts) is statistically problematic in the context of GLMMs? I realise this make interpretation of the coefficients more difficult. The other option I see is to use GAMMs, but as I have little experience with them I would prefer not to use them. My count data is also over-dispersed, possibly zero-inflated, and I need to use offsets to account for differences in floral abundance and random effects to account for resampling, which in GAMMs is causing me some problems. An example of the GLMM models and the relationship between distance and insect abundance (image is here is graphed as a ratio per plant but the models use the raw count data ie integers) I am evaluating are:

glmmadmb(Pollinators_count ~ Distance_from_edge * Wind_speed * temperature * Treatment + offset(log(Flower_count)) + (1|ROW) + (1|DATE), data = transects, family ="nbinom")

I have a roughly exponential relationship between insect counts and distance along a growing tunnels, and need to examine the effects of different tunnel types as they relate to the abundance and distributions of insects along the tunnels, also considering microclimate factors. I have run GLMM models with lme4 and glmmadmb in R (poisson and negative binomial), and this seems okay. However, I wonder if transforming the dependant variable "distance to edge of tunnel (m)" by log10 transformation to create a more linear relationship with the response variable (insect counts) is statistically problematic in the context of GLMMs? I realise this make interpretation of the coefficients more difficult. The other option I see is to use GAMMs, but as I have little experience with them I would prefer not to use them. My count data is also over-dispersed, and possibly zero-inflated, and I need to use offsets to account for differences in floral abundance and random effects to account for resampling, which in GAMMs is causing me some problems. Thanks

I have a roughly exponential relationship between insect counts and distance along a growing tunnels, and need to examine the effects of different tunnel types as they relate to the abundance and distributions of insects along the tunnels, also considering microclimate factors. I have run GLMM models with lme4 and glmmadmb in R (poisson and negative binomial), and this seems okay. However, I wonder if transforming the dependant variable "distance to edge of tunnel (m)" by log10 transformation to create a more linear relationship with the response variable (insect counts) is statistically problematic in the context of GLMMs? I realise this make interpretation of the coefficients more difficult. The other option I see is to use GAMMs, but as I have little experience with them I would prefer not to use them. My count data is also over-dispersed, possibly zero-inflated, and I need to use offsets to account for differences in floral abundance and random effects to account for resampling, which in GAMMs is causing me some problems. An example of the GLMM models I am evaluating are:

glmmadmb(Pollinators_count ~ Distance_from_edge * Wind_speed * temperature * Treatment + offset(log(Flower_count)) + (1|ROW) + (1|DATE), data = transects, family ="nbinom")