Biodiversity Data Use

Thresholding is the process by which we convert the continuous (raw) output, or continuous suitability surface, from a statistical model to a binary output. The binary output is generally interpreted as areas that are suitable/not suitable for the species. Models are rarely perfect and it is likely that they will predict species as being present where they are not actually present (commission errors) and, conversely, absent where they actually occur (omission errors). When we threshold out model we want to decide on a threshold at which we are minimising both commission and omission errors. If we have threshold value of 100 then all areas are suitable for the species and we will have a high number of commission errors and the number of omission errors will approach 0.

We choose the “threshold” value that determines a presence versus an absence of the species using the: - Minimum Training Presence (MTP) - this threshold assumes that the least suitable habitat at which the species is known to occur is the minimum suitability value for the species - MTP + user-selected error rate (e.g., E=5%, E=10%) - a user-selected threshold that omits all regions with habitat suitability lower than the suitability values for the lowest 5% or 10% of occurrence records. It assumes that the percentage of occurrence records in the least suitable habitat do not occurr in regions that are representative of the species overall habitat, and thus should be omitted. This threshold omits a greater region than the MTP.

Precise method by which you do this depends on the quality of the data that you used to build the model.