I have a 20-yr dataset of an annual count of species abundance for a set of polygons (~200 irregularly shaped, continuous polygons). I have been using regression analysis to infer trends (change in count per year) for each polygon, as well as aggregations of polygon data based on management boundaries.
I am sure that there is spatial autocorrelation in the data, which is sure to impact the regression analysis for the aggregated data. My question is - how do I run a SAC test for time series data? Do I need to look at the SAC of residuals from my regression for each year (global Moran's I)? Or can I run one test with all years?
Once I've tested that yes there is SAC, is there an easy was to address this? My stats background is minimal and everything I've read on spatio-temporal modeling sounds very complex. I know that R has a distance-weighted autocovariate function - is this at all simple to use?
I'm really quite confused on how to assess/addess SAC for this problem and would very much appreciate any suggestions, links, or references. Thanks in advance!