I have run a linear mixed-effects model using lmer package. I'm using year sampled (1990s or 2017), field(1967 or 1984), and treatment (open or closed) to determine differences in native herbaceous percent cover. I am using this model since I have two data points (year.sampled: 90s and 2017).
model2<-lmer(Native.Herbaceous.Percent.Cover~TreatmentYear.SampledField+(1|HerbPC$Plot),data = HerbPC)
I have set my plot names as my random effect.
As you can see the interaction between the variables treatment and year sampled (Treatment:Year.Sampled) is not significant at 0.566.
As you can see this emmeans tukey is showing 4 different significant pairwise interactions.
I am wondering why my anova of the model shows no significance of treatment and year sampled while my tukey shows multiple significant pairwise interactions of these variables?
I see that the emmeans tukey results are averaged over the levels "Fields" variable. Is the difference because the anova of treatment:year.sampled interactions is independent of fields, while the emmeans tukey is not?
Thanks for the help.