The environmental variables
In this module we will see where and how find environmental layers to build our SDM. Then we will refine again our dataset taking into account the spatial resolution of the environmental layer. Finally, we will see how to deal with the problem of multi-collinearity in environmental layers. At the end on this module we will have a dataset with occurrences and a number of layer that we will use to train our SDM in module 3.
Where to find environmental variables?
Together with presence data, that will represent the dependant variable in our SDM, we need an independent variable: the predictors.
In SDM we need to add information from spatially referenced data layers to predicting the probability of occurrence of a species throughout the area of interest.
The quality and relevance of the environmental data is crucial.
It is one of the main factor (but not the only) that determines the quality of SDM output maps.
We need environmental layer with a resolution that can provide a good extrapolation of habitat suitability according to the scale of interest
(regional, continental, global), but that not compromise the analysis in term of
computational time and that take into account the uncertainty of the
occurrences.
The right chose is thus a compromise between statistical robustness, user experience and practical aspects.
The environmental layer used must represent the habitat requirements. Many sources on the web provide free environmental raster layers
such as temperature, precipitation, elevation, soil type, land use, etc.
We can use remote sensed data from MODIS (http://modis.gsfc.nasa.gov) or COPERNICUS (http://www.copernicus.eu), or interpolated climatic raster (i.e. the Worldclima
database, www.worldclim.org) or soil database (i.e.the European Soil Portal,
http://eusoils.jrc.ec.europa.eu/).
The choice of the layer to use is driven by the variables the best describe habitat requirements of the species of interest.
However, if you want project your models in the past and in the future, usually interpolated climatic data are often the only source environmental available.
However, also if in some cases climatic data only cannot be consider as the best choice, they usually represent a good because plant and animal species respond very well to climatic changes over hundred or thousand of years.
Climatic data can give good results also for species that are apparently not directly influenced by climate, as subterranean mammals (see Feuda et al., in press).