Ecological Niche Factor Analysis (ENFA)

The Ecological Niche Factor Analysis (ENFA) is the central part of Biomapper. Its modules were conceived either to prepare ENFA input data or to evaluate and transform its output data.
Habitat Suitability (HS) maps are commonly built on presence/absence data, but the latter are most generally unavailable or unreliable.
HS maps are basically computed by fitting some statistical or numerical model on environmental data and species distribution data.

Classical methods (e.g. logistic regression, discriminant analysis, GLM, etc.) need both species presence and absence data; presences attest a good habitat and absences attest a bad habitat.

An “absence” (=lack of observation) may have three causes:
1° The species is present but  was not detected FALSE ABSENCE
2° The habitat is suitable, but the species is not yet/no more present FALSE ABSENCE
3° The habitat is actually not suitable TRUE ABSENCE

Input data:
Ecogeographical variables (EGV) and
Presence data
The ENFA needs to types of input data:
  1. The EGV describe environmental, topographical and anthropic parameters of the study area.
Rock frequency Elevation Distance to towns
  1. Only presence data are needed. This make the ENFA an analysis particularly robust to the quality of data.

Presence data


Ecological Niche Factor Analysis (ENFA): Marginality and Specialisation
 The ENFA’s principle is to compare the distributions of the EGV between the presence data set (species distribution) and the whole area (global distribution).

  Like the Principal Component Analysis, the ENFA summarises many EGV into a few uncorrelated factors retaining most of the information. Bur here, the factors have an ecological meaning.

Marginality Factor

  It is the direction on which the species niche differs at most from the available conditions in the global area.

  It is computed by drawing a straight line between the centroids of the global- (yellow) and the species (blue) distribution.

Specialisation Factors

 Once the marginality has bee removed, a specialisation factor can be extracted by computing the direction that maximises the ratio of the variance of the global distribution (yellow) to that of the species distribution (blue).

  It is then removed and this procedure is repeated until all the information has been explained. At the end, most of it is explained by a few of the first factors and only those will then be used.

HS map computing
The species distribution on the factors allows to compute a HS index for any set of EGV values and thus to draw the HS map.
Global distribution along the marginality (horizontal axis) and specialisation factor (vertical axis) Species distribution along the marginality (horizontal axis) and specialisation factor (vertical axis)

Habitat-Suitability map

Contact the webmaster
[Top] [Home] [LBC] [UNIL]
Updated 10.10.2000