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BITC / NBD protocol - 10a

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We're down to the last major step in this process. What we're trying to do here is calculate distances in environmental space to any well-known site across our study region. This is our ½ degree aggregation grid. Pixels with a bold outline are well-known in terms of our inventory. For every site in the study region, we want to know how different or similar it is to every well-known site. So, how do we do this? We'll plot a bunch of random points. There are 5000 random points scattered across our country. To do this, go to: Vector --> Research Tools --> Random Points. I did this in advance because it takes 5-10 minutes to produce that many points. Take the country outline. Specify 5000 points. Assign a name and path to the new shapefile. And, select "add result to canvas." It's very easy. I ended up with this set of points. Now, I want you to notice something. Let's zoom in just a little bit. Here's our aggregation grid. Here's a well-known pixel. Notice that there many points that fall within that pixel. In our gdist coverage, gdist=0 for all of those points because they are inside well-known pixels. So, we'll use that to our advantage. Then, we'll characterize the environments in two dimensions: annual mean temperature and annual mean precipitation. Finally, in order for the point-sampling tool to function correctly, all of data layers that you are interested in must be turned on. Go to point-sampling tool. There is our sampling point coverage. The shapefile with the random points. Now, we can select the fields that we want to sample data from. We definitely want the identifier for the random point coverage. Then, down here at the end, we want temperature, precipitation, and gdist. Why do we want gdist? That's so that we know which points and which environments are well-known in our inventory. Then, name the output file and hit 'OK'. I'm not going to do it because I pre-created this file as well. This is another process that can take 5-10 minutes. So, run that routine. Then, look at the output shapefile. It's exactly the same random points, but if you look at the attributes table, it has: point ID, temperature, precipitation, and gdist. Once again, the points where gdist=0 are inside well-known pixels. We just got out. It's about 660 points.

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Duration: 5 minutes and 18 seconds
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Language: English
License: Dotsub - Standard License
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Posted by: townpeterson on Jul 26, 2016

This video gives a step-by-step through the protocol being used in the course on National Biodiversity Diagnoses, an advanced course focused on developing summaries of state of knowledge of particular taxa for countries and regions. The workshop was held in Entebbe, Uganda, during 12-17 January 2015. Workshop organized by the Biodiversity Informatics Training Curriculum, with funding from the JRS Biodiversity Foundation.

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