Neutral Landscape model generation with QGIS

There are many interesting things to calculate in relation to landscape ecology and its statistical metrics. However many (if not the majority) of the published toolsets are not reproducible, their algorithm code not published or open-source. Obviously this makes the easy implementation of underlying algorithms even harder for independent developers (scientists) if you don’t have the time to reproduce their work (not to mention the danger of making stupid mistakes, we are all human).

I recently found this new article in Methods in Ecology and Evolution by Etherington et al., who didn’t really present any novel techniques or methods, but instead provided a new python library that is capable of calculating Neutral Landscape Models (NLMs). NLMs are often used as nullmodel counterpart to real remote-sensing derived maps (land-cover or altitude) to test the effect of landscape structure or heterogeneity on a species (-community). Many NLM algorithms are based on cluster techniques, cellular automata or calculating randomly distributed numbers in a given 2d space. There have been critical and considerate voices stating that existing NLMS are often misused and better null-models are needed for specific hypothesis, such as a species perception of landscape structures. Nevertheless NLMs are still actively used and new papers published with it.

Etherington

Figure by Etherington et al. showing a number of different NLMs

 

The new library, called NLMpy, is open source and published under the MIT licence. Thus I can easily use and integrate into QGIS and its processing framework. Their NLMpy library only depends on numpy and scipy and thus doesn’t add any other dependency to your python setup, if you already are able to run LecoS in your QGIS installation. The NLM functions are visible in the new LecoS 1.9.6 version, but only if you have NLMpy installed and it is available in your python path. Otherwise they won’t show up! Please don’t ask me here how to install additional python libraries on your machine, but rather consult google or some of the Q&A sites. I installed it following the instructions on this page.

Midpoint displacement algorithm in QGIS

Midpoint displacement algorithm in QGIS

NLM with randomly clustered landcover patches

NLM with randomly clustered landcover patches

After you have installed it and upgraded your LecoS version within QGIS, you should be able to spot a new processing group and a number of new algorithms. Here are some screenshots that show the new algorithms and two NLMs that I calculated. The first one is based on a Midpoint displacement algorithm and could be for instance used to test against an altitude raster layer (need to reclassify to real altitude values first). The second one is aimed at emulating a random classified land-cover map. Here I first calculated a proportional NLM using a random cluster nearest-neighbour algorithm. Second I used the libraries reclassify function (“Classify proportional Raster”) to convert the proportional values (range 0-1) into a relative number of landcover classes with exactly 6 different land-cover classes. Both null model look rather realistic, don’t they 😉

This is a quick and dirty implementation, so there could occur some errors. You should use a meter-based projection as extent (such as UTM) as negative values (common in degree-based projections like WGS84 latitude-longitude) sometimes result in strange error messages. You also have to change the CRS of the generated result to the one of your project manually, otherwise you likely won’t see the result. Instead of the number of rows and columns as in the original implementation, the functions in LecoS are based on a selected extent and the desired output cellsize.

For more complex modelling tasks I would suggest that you use the library directly. To give you a good start Etherington et al. also appended some example code and data in their article´s supporting information. Furthermore a few days ago they even had a spatial MEE blog post with some youtube video demonstrations how to use their library. So it shouldn’t be that hard even for python beginners. Or you could just use the processing interface within LecoS.

In any way, if you use the library in your research, I guess the authors would really appreciate it if you could cite them 🙂

PS:

In addition I also temporarily removed LecoS ability to calculate the mean patch distance metric due to some unknown errors in the calculation. I’m kinda stuck here and anyone who can spot the (maybe obvious) bug gets a virtual hug from me!

PPS:

Happy new year!

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About Martin Jung

PhD researcher at the University of Sussex. Interested in nature conservation, ecology and biodiversity as well as statistics, GIS and 'big data'

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