Species Richness 1 – observed species richness

 

  1. Collect data for a higher-level taxon from GBIF, for a region of interest.  There are many ways to do this from GBIF. One is to search on a country, and then choose a secondary data filter like “Class = mammalia” (or whatever you want).  Also make sure to only download those records with geospatial coordinates – there is a filter in GBIF to only get those with coordinates (Coordinate Status is “includes coordinates”).  I will email you all such a dataset for mammals of Australia from GBIF.   You should try collecting your own records for a region of interest.   If you want a smaller region of interest than a country, you could collect records for the country, import the records into DIVA-GIS and cut the layer to the desired extent. 
    1. Some important points:  you want to only have valid binomial names in your dataset.  So you need to remove all records that lack a binomial or are of the form “genusname sp.” or “genusname indet.” These are records that are likely “incomplete” but will still show up in your spreadsheet.  Check the dataset you accumulate to make sure these are weeded out.  You should also validate the records to make sure there aren’t other obvious mistakes (you can do this in DIVA).
  2. Load the spreadsheet into DIVA-GIS using Data -> Import Points to Shapefile
  3. Go to Analysis -> Point to Grid and chose Richness.   Click on the parameters tab and select “Scientific Name” or whatever you have called your binomial column.  Now go back to the Main tab and make sure that “Define Grid” says “Create a New Grid”.  Click on options, and keep cell size set to 1 for this first try.  Keep everything else as defaults, but click “output” to set an output grid file name.  Hit apply.  You should see an observed species richness grid for the input data.  Try this again with a smaller cell size under the options tab for the “define grid” text box.
  4. Now select “Analysis” -> Summarize Points.  Under the “select field” tab,  again select “Scientific Name” or whatever you have called your binomial column as the field.  Then hit apply.  You should get a summary output for Observations, richess, and some commonly used species richness estimators (Chao1 and Chao 2, Jacknife 1 and 2, ACE (abundance coverage estimator)). 
  5. Go to data -> Climate -> Map and proceed to extract a map of mean annual temperature using the richness grid layer’s extent.  You can do this by selecting “read from layer” and making sure the active layer in the side window is the richness layer.  Now you have a richness layer and annual mean temperature layer at the same extent.  You can regress richness on annual mean temperature using Analysis -> Regression, but before you do, you need to get the grid sizes to match.  The grid size for the climate data I am using was .083, while the richness layer is 1.  In order to figure out how I need to aggregate my climate grids, I divide 1 by .083, and get 12.  So I need to go to Grid->Aggregate, using kind of mean and factor of 12 (from above).  The new climate grid should now have the same grid size and extent as the richness layer and I can run a regression.  Note, this regression is “for fun”, just to see if there is a relationship between climate and richness.  We need better tools to run such analyses, and we will use one such tool – Spatial Analysis in Macroecology -  in the following weeks.