This blog post summarizes a scientific article
entitled, “Python based GIS tools for landscape genetics: visualizing genetic
relatedness and measuring landscape connectivity” by Thomas R. Etherington
(click here to read it online). The purpose of
this assignment is to engage and expose the graduate student to peer-reviewed
publications that demonstrate the growing integration of Python programming in
a GIS environment in various interdisciplinary studies.
This article, in particular, covers an up-and-coming discipline
that is gaining more popularity in research analysis: landscape genetics. So
what is “landscape genetics”? Let’s break down these words:
- genetics – “the study of heredity and
the variation of inherited characteristics” (Google).
- landscape – “all of the visible
features of an area of countryside or land” (Google).
If we join these definitions, we can determine that landscape
genetics deals with how and why features of land are inherited the way they are.
Etherington explains that landscape genetics helps to better understand
ecological processes, such as movement of species in relation to landscape
connectivity. By conducting such research, “effects of landscape composition,
configuration, and matrix quality on gene flow and spatial genetic variation”
can be quantified based on the genetics of a population to answer issues of “habitat
fragmentation, invasive species, or wildlife diseases” (Etherington 2010). Very
interesting stuff.
There are multiple disciplines that collaborate in research
of landscape genetics. A geographic information system (GIS) alleviates a lot of
complex analysis that would otherwise be done manually, still, it requires a “degree
of customisation that is often beyond the non-specialist” (Etherington 2010).
For this reason, the author of this article created 13 Python scripts used for
landscape genetics analysis. These scripts automate large workflows by
converting files, visualizing genetics relatedness, and measuring landscape
connectivity using least-cost path (LCP) analysis (Etherington 2010).
Additionally, these scripts are housed as tools in ArcToolbox, but can also be
modified to be used in a non-ArcGIS environment.
The integration of Python programming in landscape genetics,
and the free availability of using the tools/scripts created by the author “allow
researchers to use more current software, provide the option of further
development by the user community, and reduce the amount of time that would be
spent developing common solutions” (Etherington 2010).
Article
Citation:
Etherington, T. R. (2010).
Python based GIS tools for landscape genetics: visualising genetic relatedness
and measuring landscape connectivity. Methods in Ecology and Evolution,
British Ecological Society, 2(1), 52-55.
doi:10.1111/j.2041-210x.2010.00048.x.
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