Tools & information
Most of the tools we use are open source. In part because we believe that the use of open source software and open data contributes towards a more transparent, rapid and inclusive science, especially in ecology, biogeography and conservation sciences. But also because open source tools such as R, GRASS GIS and QGIS are amongst the best tools available today. And thanks to the extensive documentation and the incredible support offered by the developer and user communities, it is possible to relatively quickly build simple tools yourself.
For some examples of simple tools and scripts we have developed, click on one of the tabs above. They were mostly developed for own use, but perhaps they turn out to be useful for others too.
For most of our GIS work, we use GRASS GIS and QGIS. To facilitate our work, we have created a number of GRASS add-ons which are available on the GRASS add-on repository. See below a list of the add-ons I have written or contributed too. All GRASS add-ons below run in GRASS 7 and above. They can be installed using the g.extension function. If that doesn't work, see here. See also the manual pages, which include a link to the source code.
|r.meb||Compution of the multivariate environmental bias, which compares the medium environmental conditions in an area (e.g., a country) to those in a subset of that area N (e.g., the protected areas in that country). It thus provides a measure to estimate how well conditions in the subset of an area represents conditions in the whole area,or the other way around, how biased the distribution of the subset is in environmental space.|
|r.mess||To compute the multivariate environmental similarity surface (MES) and some additional supporting layers.|
|r.exdet||Detection and quantification of novel uni- and multi-variate environments following methods developed by Mesgaran et al. (2014).|
|r.niche.similarity||To compute two metrics to quantify niche similarity or overlap between all pairs of input raster layers: (D) the niche equivalency or similarity for two species following Warren et al. (2009) based on Schoeners D (Schoener, 1968).|
|r.series.diversity||Compute diversity indices over series of input layers.|
|r.forestfrag||Create a forest fragmentation index from a GRASS raster map based on a method developed by Riitters et. al (2000). The index is computed using an moving window of user-defined size.|
|r.vif||Function to compute the variance inflation factor (VIF) and the square root of the VIF. The variable with the highest VIF will be dropped and the VIF will be recomputed. This will be repeated till an user-defined VIF threshold value is reached.|
|r.out.legend||Create and export image file with legend of a raster map.|
|r.random.weight||Generates a raster layer with a weighted random selection of the raster cells (selected cells are assigned a value 1, other a value 0) based on a user defined weight raster layer.|
|r.recode.attr||Reclass/recode a raster layer based on values in a csv table.|
|v.maxent.swd||Export raster values at given point locations as text file in SWD format for input in Maxent.|
|v.what.rastlabel||GRASS GIS addon (available on github) to uploads raster values and labels at positions of vector points to the vector attribute table.|
|r.category.trim||Export categories and corresponding colors as QGIS colour file or csv file. Non-existing categories and their colour definitions will be removed.|
|r.maxent.lambdas||Compute raw and/or logistic prediction maps from a lambdas file produced with MaxEnt 3.3.3e.|
|r.edm.eval||Evaluate how well a modeled probability distribution predicts an observed distribution using all or restricted set of background / absence points.|
Combining the power of GRASS GIS, which excels in handling and analysis of spatial data, and the vast number of functions for (spatial) data visualization and analysis in R (see R Spatial View) and you have a winner at hand. Below an example of a R script that garners the strength of both tools. It uses GRASS GIS in the background for the data crunching and R for the graphical output. We use the GRASS / R combination a lot, and plan to publish more of our scripts in the future.
|r.eval.strip||R function to create a response plot to evaluate the relationship between environmental variables and the fitted probability of occurrence of individual or ensemble suitability modelling algorithms.|
Spatial data layers often come with standard legend / colour keys, which provides the visualization and even meaning to the data. As a side note, try to avoid the latter! Unfortunately, they are often shared in proprietary format which cannot be used in other programs and are not human readable. To avoid vendor lock in and to enable everybody to use it is therefore preferable to share color and legend keys in an open format that is also human readable (i.e., text based files). We maintain a collection of color, style and legend files in open formats on our github page. Please check it out here.
Zim-wiki is a graphical text editor to notes in the form of wiki pages. A great feature of Zim-wiki is that you can easily export the notebook (or part of it) as a website. You can thereby use export templates to determine how the webpages will look like. The current website is an example of an website written in Zim (with some scripted post-processing). Below you can find export templates that you can use for your own websites.
|ecodiv-responsive||Template to export your Zim notebook as a responsive/adaptive website that can be viewed in desktop and mobile devices alike. We use this template ourself for the Vegetationmap4africa website and for our tutorials.|
|Ecodiv-mobile||Template to export your Zim notebook as a website with mobile theme. Build with jquerymobile to create a website with a focus on mobile devices, but which will also look good on the desktop. The template offers some pointers to customize the design to fit the users need.|
Libreoffice / Openoffice Calc spreadsheets tools
|FeedWat||A excel tool to quickly examine the effect of changes in feed quality, diet, and level of activities on the feed and water requirement of ruminants||Information|
|Coordinates to polygon||Spreadsheet template that will take the coordinates of your boundary points and generate the GRASS GIS code to create a polygon vector layer and export this to a kml file. See also this blogpost||Download|
What we use
Customers needs, quality and openess - We use a wide variety of open source and proprietary tools for our spatial and statistical analysis and GIS work. For data analysis we rely mostly on R and its extensive ecosystems of packages, while for our data management and storage we mostly use SQLite / Spatialite and PostgreSQL / PostGIS. For our spatial analysis and GIS work, we heavily rely on GRASS GIS, QGIS and SAGA GIS, often in conjunction with R, Python and GDAL, while our graphical work is mostly done with GIMP and Inkscape. We furthermore use software tools and libraries such as Leaflet and Tilemill to create our online and mobile maps. We obviously work in any major office suite to ensure a smooth exchange of information with our partners and clients, but for our own use Libreoffice is our office suite of choice. In essence, we use the best tool at hand that also support data standards and document formats required by our clients.