Convex Hull is a geographical model that uses the location of known occurrences and predicts that a species can be present within a spatial convex hull around these occurrence points. A convex hull is the smallest polygon that you can draw around the occurrence points enclosing all occurrence points. For any two points, the line between these two points fall completely within the convex.
This model does not use the input of environmental variables to predict the distribution of a species.
The International Union for Conservation of Nature (IUCN) uses the convex hull method to estimate the extent of occurrence for species, with the adaptation that large areas with obvious unsuitable habitat (such as ocean for terrestrial species) are excluded. Species critically endangered if the extent of occurrence is
< 100 km², endangered when the extent of occurrence is <5,000 km², and vulnerable when the extent of occurrence is < 20,000 km².
- Simple and easy to interpret
- Presence only model, no absence data needed
- Does not use environmental variables to predict species occurrence
- Likely to over/underestimate species range; For example:
A convex hull acts under the assumption that a species is distributed through the entirety of the convex hull. Major over predictions could arise if there are biomes in the output distribution which the species is not found. In the case of a coastal species, for example, a convex hull would include the whole of Australia rather than just the coastal areas where this species is found.
The second limitation is that convex hulls are susceptible to if the data is not representative of all the places a species . In the image below the northern part of the species distribution is not captured because there are no available data points from this area. This problem is common for species that occur in remote parts of Australia where there is little sampling.
The third limitation is that convex hulls are vulnerable to errors in occurrence data. If attempting to capture the distribution of wild species, an occurrence record from a zoo or would result in an overestimation of the species range. Errors in the recorded latitude and longitude are also not uncommon, but these kinds of errors can be reduced by filtering your data.
That a species distribution in the wild is well captured by the available outermost points of occurrence data.
Requires absence data
allows the user to set model arguments as specified below.
A random seed will not impact this model.
The "tails” argument can be used to ignore the left or right tail of the percentile distribution for a variable. supplied, tails should be a character vector with a length equal to the number of variables used in the model. Valid values are "both", "low" and "high". (default = NULL)
Burgman, M. A., & Fox, J. C. (2003). Bias in species range estimates from minimum convex polygons: Implications for conservation and options for improved planning. Animal Conservation Forum, 6(1), 19–28.
, R. J., Phillips, S., & , J. (2015). Elith J. : Species distribution modeling. 2014. R package version, 1-1.
IUCN, I. (2001). Red list categories and criteria: version 3.1. IUCN, Gland, Switzerland and Cambridge, UK.