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.
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.
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 🙂
- Etherington, T. R., Holland, E. P., O’Sullivan, D. (2014), NLMpy: a python software package for the creation of neutral landscape models within a general numerical framework. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12308
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!
Happy new year!
The use of null models in ecology has a long history (Connor & Simberloff,1979) and was in the epicenter of many scientific disputes. Some of them are even continuing until today (or here). I will spare the readers of this blog any further discussions or arguments as i haven’t entirely made up my own mind yet. Statistically speaking many null models make perfect sense for me if ecological data is just seen as “data”. The biological perspective of many null models however can be discussed as many of them make assumptions (random distribution of species in spatial community ecology for instance), which seem to be hardly true in natura. I agree that ecologists have to make careful considerations while designing their statistical analysis. I am going to follow the debate about null models more in the future, but for now let me introduce you to a simple null model in macroecology.
One of the most used null models in Macroecology is the so called Mid domain effect (MDE) null model. Given that the effect of all possible environmental predictors on a species distribution decreases, we would expect that the species richness peaks shift toward the center of their geometric constraints (Colwell & Lees, 2000; Colwell et al., 2004). This so called mid domain peak is build on the stochastic phenomena that if you shuffle species ranges inside a geometric constraint, you will always find that the greatest overlaps occur in the very center.
For an easy visualization: Just imagine an aluminum box full of different sized pencils. One of those you had back in primary school. The pencils inside are of varying size, some might be nearly as long as the whole box, others are nearly depleted. Close the box and shuffle it. If you now open the box again, you will find the most pencils (or parts of a pencil) in the middle of the box.
One way to generate a MDE null model from given species ranges is to use a so called spreading dye algorithm, which emulates grow of cells inside the given geometric constraints from a random starting point (emulating multiple drops of dye inside a water pont). Click the GIF image below to watch a growing MDE (CAREFUL – BIG GIF PICTURE > 4mb). As input the number of occupied grid cells per bird species in south America was used. The range was kept constant, but the starting point varies.
As you can observe the relative bird species richness peaks in the middle of the continent after some time. This patterns becomes more prominent if the algorithm runs for all 2869 bird species occurring in south America. The final image and their range quartiles look like this :
Here you can observe that the overall mid domain peak can only be observed for the fourth quartile. For the other three the relative distribution is quite random, which might explain why the MDE null model often explains quite a lot of the variance for widespread species (Dunn et al., 2007). The MDE null model has been criticized and defended again multiple times, but is still widely used in macroecology. Critics usually bring up possible influences of phylogeny (Davies et al, 2005) or geometric constrains (Connolly, 2005; McClain et al., 2007). Issues particularly with the spreading dye algorithm are, that the simulated species ranges are like spreading ink drops which are very similar in shape. In reality species ranges often have quite complex and different configurations/shapes. Furthermore the models stops at the borders of the geometric contrains (the coastline of south America). Any random drop of ink near the coast line will therefore always grow into the heart of the country, which therefore makes the shape of the used geometric constrain the most important predictor of a possible range peak. If for instance the model would be repeated for a more irregular shape (like middle America) the peaks will develop where the greatest land mass is (so around texas and bolivia). The sheer probability of an ink dye developing in panama or Ecuador is too low due to the chance of hitting this small shape. This is a property of the algorithm and might result in non-significant null models for the middle American regions.
- Colwell RK, Lees DC (2000) The middomain effect: Geometric constraints on the
geography of species richness. Trends Ecol Evol 15:70 –76.
- Colwell, R. K., Rahbek, C., & Gotelli, N. J. (2004). The Mid‐Domain Effect and Species Richness Patterns: What Have We Learned So Far?. The American Naturalist, 163(3), E1-E23.
- Connor, E. F., & Simberloff, D. (1979). The assembly of species communities: chance or competition?. Ecology, 1132-1140.
- Connolly, S. R. (2005). Process‐Based Models of Species Distributions and the Mid‐Domain Effect. The American Naturalist, 166(1), 1-11.
Davies, T. J., Grenyer, R., & Gittleman, J. L. (2005). Phylogeny can make the mid-domain effect an inappropriate null model. Biology letters, 1(2), 143-146.
- Dunn, R. R., McCain, C. M., & Sanders, N. J. (2007). When does diversity fit null model predictions? Scale and range size mediate the mid‐domain effect. Global Ecology and Biogeography, 16(3), 305-312
- McClain, C. R., White, E. P., & Hurlbert, A. H. (2007). Challenges in the application of geometric constraint models. Global Ecology and Biogeography, 16(3), 257-264.