Peer Review
Article #2
Python based
GIS tools for landscape genetics: visualising genetic relatedness and measuring
landscape connectivity
Author: Thomas
R. Etherington
URL Link: http://onlinelibrary.wiley.com.ezproxy.lib.uwf.edu/doi/10.1111/j.2041-210X.2010.00048.x/full
This article discusses using GIS and
Python for landscape genetics research which will help researchers gain a
better understanding of spatial ecological processes. Using GIS is vital to
this particular area of research; however, there is a degree of customization
that is needed to processes the data which often beyond the non-specialist (Etherington, 2011). To remedy this
issue, Python was used to create a series of script based GIS tools that were
specifically designed for landscape genetic studies uses. These scripts allow
the user to convert the files, visualize genetic relatedness, and quantify the
landscape connectivity using the least-cost path analysis (Etherington, 2011). The scripts are stored in the
ArcToolbox that allows free accessibility to them along with the Python code
that created the tools. By creating these Python scripts, researchers are able
to fully utilize the current software; the user community can also provide
farther enhancements to the scripts, and this will cut down on the time spent
on developing common solutions (Etherington, 2011).
Etherington was able to elaborate
how he implemented GIS and Python to his own research very well. Even though
the article was quite short, it was packed with useful information on how
Python can customize tools for GIS. I found this article to be very interesting
because the GIS tools that Etherington created for landscape genetics can
allows be applied to the movement of past and current infectious and contagious
diseases in different parts of the world.
Etherington, T. R. (2011). Python based GIS tools for
landscape genetics: visualising genetic relatedness and measuring landscape
connectivity. Methods in Ecology and Evolution, 52-55.
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