Hi all, I have both a how-to question and I would appreciate opinions from other quantitative ecologists. I'm working on a grant project to examine trends in water quality for three metrics (Chlorophyll a, Dissolved Oxygen, and Kd (light attenuation)). For these particular data, there are 30 permanent adjacent hexagons the comprise the estuary of interest. During each sampling year, a random location is identified within each hexagon and the metrics of interest are measured there.
I developed a straightforward linear model (using lme() in the nlme package) for each metric with year as a categorical variable. There are only four years of data at this point, and my collaborators want to compare year to year. I also included a random effect for each hexagon, using the continuous form of the year variable. Lastly, I verified that there was no discernible temporal autocorrelation in the data. My problem is that within each year, the data show substantial spatial autocorrelation, but the spatial autocorrelation differs substantially from year to year in both the shape and magnitude of the variogram. Consequently, to account for that, I think I have to input my own V-CV matrix into the model to account for the different spatial autocorrelations each year. This is not something I have done before AND seems overly complicated for the task at hand. However, if it is necessary, I would like to find away to do it effieciently, and more importantly help other ecologist to do so as well. This grant project has a substantial outreach component. Any good guidance on how to do this would be appreciated. The goal of the project is to detect trends over time in these metrics. The spatial aspects of the metrics are examined within each year, but they are not of interest from a trend point of view. Another goal of this project is develop models and guidance that can be used by NPS and other users for their similar data, so I am hesitant to develop models that are overly complicated and can't be reproduced. My other thought was to randomly sample from the data until the spatial autocorrelation was negligible, but the sample size was selected to maximize the power to detect changes over time. I appreciate any opinions, guidance, or references people can provide. I've been looking through the liturature, but haven't found anything that directly addreses this issue yet. Thanks, Penelope =========================================== Penelope S. Pooler, Ph.D. Quantitative Ecologist, National Park Service I&M Northeast Coastal and Barrier Network NPS email: penelope_poo...@nps.gov Adjunct Professor, Dept. of Natural Resources Science URI Coastal Institute in Kingston URI email: ppoo...@mail.uri.edu 1 Greenhouse Rd., Rm 105 Kingston, RI 02881 Ph.: (401) 874-7060 Cell: (540) 250-1096 _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology