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EGVs must be:
  • Relevant to the species ecological niche
  • Quantitative
This page explains how to achieve both.

Deriving relevant EGVs

Available data are often collected for other purposes than to model your favourite species. Most often, you need to derive more relevant variables from them. This critical to obtain a good model. By deriving variables more thightly related to the species requirements, you can often improve the model much more than by collecting more observations. So, do not hesitate to devote enough energy to this step.
This is typically performed in a GIS application (Idrisi, ArcGIS), but Biomapper provides several useful modules as well, some of which offering analyses available nowhere else.

Using Biomapper modules

Performs neighbourhood analyses
Computes various distance-related analyses
Combines several maps and perform mathematical operations on them
Transforms a continuous map into several binary maps
Converts between data types, etc.
Groups identical, adjacent cells together
Performs group analyses

Using Idrisi

Using ArcGIS

Using ArcView

Making EGVs quantitative

The ENFA requires quantitative EGVs. However, data may belong to three types, each needing a different preparation method:

Quantitative data

  • Examples: Altitude, slope, mean July temperature, amount of yearly rain, etc..
  • These data are ready to be used by the ENFA. You may want to transform them in order to make them more relevant to the species (smoothing, buffering, averaging, etc.) but they are technically acceptable.

Qualitative data

  • Examples: Soil type, land cover, vegetation type, etc..
  • These maps cannot be used in this format. There are two methods to extract quantitative data out of them. Let's imagine we are dealing with a map of vegetation types to illustrate them:
  • The first one is to determine which is the important feature of the represented categories, to reorder them using a semi-quantitative scale determined by this feature and to code it with a integer numerical value. For instance, vegetation could be ordered by height of the canopy as follows: 1.Bare ground, 2.Grassland 3.Bushes 4.Forest
  • The second method implies first to transform the map into several Boolean maps, each describing a relevant category. All available categories can be used, or only a few of them, or it is also possible to pool several categories into one Boolean map. These operations can be easily done by using Idrisi's Image calculator. These maps will then be used as described in the next paragraph. For instance, we could consider that only forests and bushes are relevant for our species and compute two Boolean maps representing presence/absence of these two entities.

Boolean(binary) data

  • Examples: Presence/absence of a species, cultivated areas, towns, roads, lakes, forests, etc..
  • These maps cannot be used in this format. There are four main methods to extract quantitative data out of them:
  • The first method is preferentially used when the map represents a resource or a shelter for the focal species, when you guess that the species needs a minimal amount of it or cannot live when it is too important in the landscape. The method implies to choose a radius of influence and to compute the frequency of occurrences into a circular area around the focal cell. Generally, the radius is chosen in order to produce a circle area equal to the home range of the species. This analysis can be performed by the BioMapper's module Circan, option Frequency (Maps/Contextual/Circular analysis). For instance, the quantitative map could represent the frequency of forests into a 1km radius buffer around each cell.
  • The second method is typically biological. Many species live at the interface between two habitats (forest edges, lake shores, coasts, etc.). The principle is then to compute the length of the boundary parting them in a circular area defined as in the first method. This can be performed by the BioMapper's module Circan, option Edge length (Maps/Contextual/Circular analysis). A few other landscape-ecology indices are available in Circan.
  • The third method is ideal when the map represent the locations of disturbance sources (towns, roads, etc.). It consists to compute a distance map, attributing to every cell the distance to the nearest occupied cell. This can be done with BioMapper's Distan module (Maps/Contextual/Distance) or Idrisi's Distance module. The real distance can be used, or a cost distance or even a function of the real distance (to lower the influence of the longer distances).
  • The fourth method is more difficult to justify biologically. It consists in smoothing the Boolean map to get values between 0 and 1. This can be done with Idrisi's Filter module or BioMapper's module Circan, option Gaussian or Mean (Maps/Contextual/Circular analysis).


  • Links to relevant URL.


  • List of bibliographical references.