I have 218 benthic sediment grabs from the continental shelf ranging from 20 to 130 meters deep. These samples were taken from eight sites spread from Northern California to Southern Washington. Within each site, samples were randomized along depth gradients.<\/p>\n
Each sample consists of species counts plus depth, latitude, and sediment characteristics such as grain size (i.e., sand versus silt), organic carbon, and nitrogen concentrations. Using Bayesian Belief Networks, species\u2013 habitat associations were calculated and established relationships were used to make regional predictive maps. Final map products depict the spatial distribution of suitable habitat where a high probability indicates a high likelihood of finding a species given a location and its combination of environmental factors. While sampling points were not taken across a consistent gridded scale, suitability maps were scaled to 250 meter resolution.<\/p>\n
As in any habitat modeling process, the \u201cbest\u201d model was chosen by looking at model performance and the amount of error, or misclassification between what was observed and what was predicted. Error of commission occurred when probability scores were high for a location where the species was actually observed to be absent. Errors of omission occurred when probability scores were low for a location where the species was actually observed to be present.<\/p>\n
I am interested in two questions. The first is whether there is a spatial pattern to the error observed and the second question is at what scale is this error significant? Error may be caused by variation in the environment that occurs at a finer scale than what my modeling structure captures.<\/p>\n
To explore these two questions, I intend to conduct a spatial autocorrelation analysis on the error for each local site to determine if there is any potential spatial pattern, and if so, if there is an associated environmental pattern to the error (i.e., does most of the errors occur in shallower or deeper water?). I am also interested in creating high resolution local maps of sediment characteristics (grain size, organic carbon, and nitrogen) through spatial interpolation technique using sediment grab data. For these local sites, I will then recreate predictive maps and compare to the 250 meter predictive maps.<\/p>\n
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