I've used a negative binomial GLM to look at how invertebrate abundance (over dispersed) is affected by seagrass density.

There is a negative interaction between seagrass density and epiphyte biomass (the little plants growing on seagrass blades that invertebrates eat).

My model implies that when epiphyte biomass is low, seagrass density increases the number of invertebrates. When epiphyte biomass is high, increasing the number of shoots decreases the number of invertebrates exponentially. This interaction does not make biological sense to me any way I think about it.

When I remove the top and bottom 15% of the dataset, the interaction term is no longer significant. This leads me to believe that there are some outliers in the dataset that are creating this interaction. But I'm not sure where to go from here. Can I justify not including the interaction without being bias?

With interaction included:

Null deviance: 136.532 on 74 degrees of freedom

Residual deviance: 90.627 on 70 degrees of freedom

AIC: 1043.1

With no interaction:

Null deviance: 122.169 on 74 degrees of freedom

Residual deviance: 90.996 on 71 degrees of freedom

AIC: 1051.2