Hello Ecolog - I was wondering if anyone had any advice on the following problem.
I have a data set that is infested by a plague of zeros that is causing me to violate all assumptions of classic parametric testing. These are true zeros in that the organisms in question did not occur in my randomly sampled quadrats. They are not "missing data" I have a fully nested Hierarchical design My response variable is density obtained from quadrat counts. my explanatory variables are as follows Region (3 levels-fixed) Location(Region) (4 levels - random Site(Location(Region)) (4 levels - random) My plan was to analyze the data with a nested anova and then proceed to calculate variance components to allow me to parse out the variance that could be attributed to each spatial scale in my design. Since it is known that violations of assumptions severely distort variance components in random factors, i would really like to clean up my data set to meet the assumptions but as of yet i have found no acceptable remedial measure. Has anyone else run into this problem when analysing abundance data. I am aware of conditional models, but i have no practical experience with them and i am not even sure how to proceed with analysis in that case. I have been using the R program to tackle this problem and i have also found no advice on the r-help mailing list. Thanks for any help that can be provided Stephen Cole Marine Ecology Lab Saint Francis Xavier University
