What environmental conditions relate to my biodiversity patterns?
Often one wants to understand which environmental conditions are associated with the taxonomic, phylogenetic and/or trait data. Examples include edaphic and climatic variables, and publications doing so include Bickford and Laffan (2006), Gonzales-Orozco et al. (2013), González-Orozco et al. (2014a), González-Orozco et al. (2014a), Nagalingum et al. (2015) and Bein et al. (2020).
Such data are typically obtained as rasters, with spatial resolutions often of the order of hundreds of metres. This is in contrast to the resolution typically used for Biodiverse analyses (tens to hundreds of kilometres).
Up until now this has been something of a complex process. The raster data need to be aggregated to the same resolution as the Biodiverse data, and aligned as part of that process. Some sort of summary statistic needs to be calculated for each cell, usually the mean. Then the data need to be converted to a CSV format with coordinates that exactly match the Basedata group labels so they can be attached as group properties using the import process. The latter can be done by importing the rasters as their own basedatas, running numeric label statistics, exporting the results to CSV format and then attaching from there. Still not simple, and not easy when there are tens of rasters to process.
Now it is much easier
This process is greatly simplified in Biodiverse version 4, with early access via the 3.99_003 development release. (Access to releases is via the downloads page).
A set of rasters can be selected, imported and attached. Biodiverse takes care of all the spatial matching and runs the summary statistics. As a bonus, the imported data can also be attached to the project in the event the user wants to run other analyses on them.
Currently there is support for the mean, standard deviation, min, max etc. If there is demand for other statistics like the median or inter-quartile range then these can be added.
Any raster data supported by GDAL can be imported. Development has used geotiffs as they are the most common. The process could probably also be generalised to support other file formats like CSV and shapefile. It depends on demand and developer time.
The key criteria for the raster data are that they must be in the same coordinate system as your basedata and they must represent continuous data (i.e. not be numerical categories). The latter point is important because the group property analyses do not work with nominal/categorical values. If you need to summarise categorical data then use an indicator approach where each class is represented by its own raster, and that raster has values of 1 for where that class occurs, and zero elsewhere.
How it works
Some screenshots are probably the best means of showing the process.
In these examples I import two data sets from WorldClim at a 5 arc minute resolution, the Annual Mean Temperature and Mean Diurnal Range. These are just the first two of the Bioclim layers provided by WorldClim. The data have been projected into a Lambert Conic Conformal coordinate system to match the basedata being used (the example data that come with Biodiverse) and have been cropped to the Australian extent.
|Annual rainfall from WorldClim2 for Australia, using a Lambert Conic Conformal projection. Brown is low, blue is high.|
|The data are going to be attached to the example data that come with Biodiverse.|
|The process is accessed via the Basedata menu.|
|The process provides some general feedback when it completes (successfully or otherwise).|
|The outputs tab shows the intermediate basedatas have been added. Each contains a spatial analysis that was used to calculate the statistics.|
|The property data cannot be visualised directly (yet). To explore them without using an analysis you need to open the View Labels window for the basedata they were attached to and control click on a cell using your mouse. |
|The popup window shows the properties for the cell that was clicked on (you will need to change the list being shown to be Properties).|
|And here are the same clusters but this time coloured by the mean stat across all groups in the sample. (The naming scheme results in lots of "means").|
|And here is an example of the imported raster data (diurnal range) that were used to generate the group properties.|
For more details about Biodiverse, see http://shawnlaffan.github.io/biodiverse/
To see what else Biodiverse has been used for, see https://github.com/shawnlaffan/biodiverse/wiki/PublicationsList
You can also join the Biodiverse-users mailing list at https://groups.google.com/group/Biodiverse-users