ORIGINAL: Andreas
"The reason we use multivariate methods isn't necessarily to predict population density but to explain population trends which we can then use to predict with some small amount of statistical power. The problem with a single variable model is that is for instance you have a discrete disease event that causes your population to crash and your only looking at your precip data to explain the trend your seeing...then your model isn't even a model it's just arbitrary application of data. "
I disagree; First of all, precipitation is only one variable but it is strongly correlated with many other variables that are harder to measure. This can be called a "proxy variable". Including this type of variable with other strongly correlated variables creates many problems in statistical analysis (auto correlation etc). Therefore, using a few carefully selected variables is far superior than including all possible variables you potentially can include. Basically, including many related variables (vegetation mass AND precipitation for instance) would produce erronous results unless controlled for.
No I agree with that. I'm not saying in any way that we should use every conceivable variable we can get our hands on. This is of course due to diminishing returns and increased complexity. There is no reason for sacrificing accuracy for precision or vice versa just to get your equation to hit every data point. I understand what your saying and I agree with your statement above. That still doesn't explain why you think you can predict anything with nothing but precip data. I tire of this. Your speaking as if you know something the rest of the wildlife management field does not and if your methods were a) accurateand b) precise enough tomeetthe standards of an agency that manages public resources than our jobs would be much much easier. Maybe in Texas precip is really that important but I doubt it. Also, your model is not able to explain other large population fluctuations from other sources so reallly it's inconclusive at the least and arbitrary at the most.