Regression coefficient overestimated in full model I have constructed a linear regression model in R using the lm function.  I was hoping someone could explain to me why when I run my full model I get an overestimate of the categorical variables.  For instance, the intercept - I understand that this means the Biomass when all continuous variables = 0 but also when it is compared to the reference reef (Admiral Reef) and the reference season (Fall) and the reference species (O. annularis). 

However, when I run a model with just the categorical variables I get a totally different estimate (and one that is closer to the actual values).

Here are the means for the whole dataset

 A: It's difficult to compare raw group means against the intercept or coefficients in this type of regression with an unbalanced design and multiple predictors. To illustrate, compare your model with only the categorical variables against the overall means for your 3 species. The intercept of 7.42 is already somewhat below the group mean of 8.05 for the reference O.annularis. The difference of 3.17 in raw group means between O.faveolata and O.annularis is very close to the coefficient of 3.29 for O.faveolata, but the difference of -1.52 between group means of O.franksi and O.annularis is quite different from the O.franksi coefficient of -0.09. These discrepancies presumably represent different associations of the 3 species with the other categorical predictors.
This problem will be exacerbated in your model that includes the continuous predictors, as small differences among the species in values of the continuous predictors, near their observed non-zero values, will be magnified in the extrapolation down to values of 0 for the continuous predictors that are required to provide the intercept in this formulation of the problem. You could presumably examine the relations of all continuous predictors to the individual species to understand the detailed reasons why the intercept seems to be so high to you, but that might not be worth the effort. Try instead expressing each value of a continuous predictor as its difference from its overall mean value; I suspect that your distress over the apparent discrepancies will be greatly alleviated.
Finally, please get further expert guidance on how best to deal with your Time variable. A 6th-order non-orthogonal polynomial is probably not the best way to proceed, and it's not clear from the limited data description here how Time and Season would best be considered together for your model.
