BITC / NBD protocol - 7

Okay, here is our map of inventory thoroughness. I changed the color ramp to range from green to purple. Green indicates low inventory thoroughness, and purple means high. It's a little deceptive because of this one. Notice that QGIS inserts that. Now it looks a little more realistic. You can see that these unsampled squares around the edges are no longer showing up as well-sampled. The real question is: <i>what criteria do we use to decide what is well-sampled?</i> I will note that some of the projects in this course, what we've done is to use density of sampling —the number of samples per unit area. Those are usually projects where we're unable to calculate completeness values. There are graphics that we can use to explore the relationship between completeness on the y-axis and samples per grid cell on the x-axis. Notice that sample size is on a logarithmic scale. I'm not going to go into all of the logic behind determining what is or is not well-known. Suffice it to say that you need to use multiple criteria. You can't use 'C' only. For example, look at this grid cell. It had a sample size of 7 yet managed a relatively high C-value. So, it's complicated. You can use C. You can use sample size. You can use areal density. Etc. For this particular application, maybe we would call a cell well-sampled if it has >650 samples and a C-value of 0.5 or greater. Again, a lot of thinking needs to go into that. And, it needs to be well-documented in your methods section. But, all we're going to do for the moment is select the cells with a C-value of 0.5 or better. You can see that there are 5 cells, right here. They're the red cells on the map. Because those are selected, we can go to 'Save As' and export them as a unique shapefile. Be sure to check 'Save only selected features' and 'Add saved file to map'. Now, we have a shapefile that is just those well-sampled cells. And, that's where we're going to stop this film. This will give you a moment to reflect on our identified well-known cells.