I am running binomial GLMMs in R to determine whether species presence (binary) on a hydrophone is different between seasons (i.e. spring, summer, fall, and winter) and photoperiods (i.e. day, night, dawn, and dusk). My models include a temporal autocorrelation structure with Group given as a single value (single hydrohpone). I understand that I can use the multcomp or emmeans packages to conduct pairwise comparisons on my model(s), but am unsure whether I should run separate GLMMs with a single predictor:

M1 <- glmmTMB(Presence ~ Photoperiod + ou(Time - 1|Group), data = df, family = binomial(link="logit"))
M2 <- glmmTMB(Presence ~ Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

or a single model with both predictors:

M3 <- glmmTMB(Presence ~ Photoperiod + Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

Using car::Anova(), both photoperiod and season have significant effects on presence in all three models. However, my pairs(emmeans()) results are different enough to effect significance depending on whether I model the predictors together (M3) or separately (M1 and M2). M3 has a slightly lower AIC value than M1 or M2.

Are there any ways to justify using two models with a single predictor vs a single model with multiple predictors if my goal is determining whether species presence differs between photoperiods and seasons? I also have a few environmental covariates (i.e. sea-surface temperature, chlorophyll concentration, and sea level) I was planning on putting in a separate model, but am now wondering if I should model them alongside season and photoperiod?

As you may be able to tell from this post, I am a bit of a modelling novice and so am partial to simpler methods so long as they don't lead to incorrect/misleading results.

I am running binomial GLMMs in R to determine whether species presence (binary) on a hydrophone is different between seasons (i.e. spring, summer, fall, and winter) and photoperiods (i.e. day, night, dawn, and dusk). My models include a temporal autocorrelation structure with Group given as a single value since I am using a single hydrophone. I understand that I can use the multcomp or emmeans packages to conduct pairwise comparisons on my model(s), but am unsure whether I should run separate GLMMs with a single predictor:

M1 <- glmmTMB(Presence ~ Photoperiod + ou(Time - 1|Group), data = df, family = binomial(link="logit"))
M2 <- glmmTMB(Presence ~ Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

or a single model with both predictors:

M3 <- glmmTMB(Presence ~ Photoperiod + Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

Using car::Anova(), both photoperiod and season have significant effects on presence in all three models. However, my pairs(emmeans()) results are different enough to effect significance depending on whether I model the predictors together (M3) or separately (M1 and M2). M3 has a slightly lower AIC value than M1 or M2.

Are there any ways to justify using two models with a single predictor vs a single model with multiple predictors if my goal is determining whether species presence differs between photoperiods and seasons? I also have a few environmental covariates (i.e. sea-surface temperature, chlorophyll concentration, and sea level) I was planning on putting in a separate model, but am now wondering if I should model them alongside season and photoperiod?

As you may be able to tell from this post, I am a bit of a modelling novice and so am partial to simpler methods so long as they don't lead to incorrect/misleading results.

I am running binomial GLMMs in R to determine whether species presence (binary) on a hydrophone is different between seasons (i.e. spring, summer, fall, and winter) and photoperiods (i.e. day, night, dawn, and dusk). My models include a temporal autocorrelation structure with Group given as a single value (single hydrohpone). I understand that I can use the multcomp or emmeans packages to conduct pairwise comparisons on my model(s), but am unsure whether I should run separate GLMMs with a single predictor:

M1 <- glmmTMB(Presence ~ Photoperiod + ou(Time - 1|Group), data = df, family = binomial(link="logit"))
M2 <- glmmTMB(Presence ~ Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

or a single model with both predictors:

M3 <- glmmTMB(Presence ~ Photoperiod + Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

Using car::Anova(), both photoperiod and season have significant effects on presence in all three models. However, my pairs(emmeans()) results are different enough to effect significance depending on whether I model the predictors together (M3) or separately (M1 and M2). M3 has a slightly lower AIC value than M1 or M2.

Are there any ways to justify using two models with a single predictor vs a single model with multiple predictors if my goal is determining whether species presence differs between photoperiods and seasons? I also have a few environmental covariates (i.e. sea-surface temperature, chlorophyll concentration, and sea level) I was planning on putting in a separate model, but am now wondering if I should model them alongside season and photoperiod?

I am running binomial GLMMs in R to determine whether species presence (binary) on a hydrophone is different between seasons (i.e. spring, summer, fall, and winter) and photoperiods (i.e. day, night, dawn, and dusk). My models include a temporal autocorrelation structure with Group given as a single value (single hydrohpone). I understand that I can use the multcomp or emmeans packages to conduct pairwise comparisons on my model(s), but am unsure whether I should run separate GLMMs with a single predictor:

M1 <- glmmTMB(Presence ~ Photoperiod + ou(Time - 1|Group), data = df, family = binomial(link="logit"))
M2 <- glmmTMB(Presence ~ Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

or a single model with both predictors:

M3 <- glmmTMB(Presence ~ Photoperiod + Season + ou(Time - 1|Group), data = df, family = binomial(link="logit"))

Using car::Anova(), both photoperiod and season have significant effects on presence in all three models. However, my pairs(emmeans()) results are different enough to effect significance depending on whether I model the predictors together (M3) or separately (M1 and M2). M3 has a slightly lower AIC value than M1 or M2.

Are there any ways to justify using two models with a single predictor vs a single model with multiple predictors if my goal is determining whether species presence differs between photoperiods and seasons? I also have a few environmental covariates (i.e. sea-surface temperature, chlorophyll concentration, and sea level) I was planning on putting in a separate model, but am now wondering if I should model them alongside season and photoperiod?

As you may be able to tell from this post, I am a bit of a modelling novice and so am partial to simpler methods so long as they don't lead to incorrect/misleading results.