Using only a few ENFA factors, you will now map the habitat suitability of your study area. The chosen algorithm will compute, for each cell, a value between 0 and 100, from unsuitable to optimal habitat. This page describes the available algorithms.

Median algorithm

Before Biomapper 3.0, it was the only algorithm available. It gives good results in most situations and is the quickest of all.
To compute the median algorithm, we divide the species range on each factor in 25 classes, in such a way that the median would exactly separate two classes. For every point in the environmental space, we count the number of observations that are either in the same class or in any class farther apart from the median. To achieve normalisation, twice this number divides the total number of observations. Thus, a point belonging to one of the two classes directly adjacent to the median will get a value of one, and a point lying outside the observation distribution will get a value of zero. Lastly, the overall suitability index for this point is computed by the weighted average of its scores on each dimension, the weights being given by the amount of information explained by each dimension.
This algorithm makes the assumption that the best habitat is at the median of the species distribution on each factor, and that these distributions are symmetric. Although this is often true, in some case itâ€™s wrong: when the distribution is bimodal, it is even completely wrong. You can also get sub-optimal results when the study area is at the border of the species distribution.

Distance geometric-mean algorithm

The principle of this method is to draw in the factor space the influence field, or suitability field, of each species observation point in such a way that, when they are close together, their attraction powers reinforce each other. For any point P in the factor space, one computes the geometric mean HG of the distances to all observations Oi. Thus, the denser the species points in the environmental space, the higher the habitat suitability. This actually comes down to calibrating a model in the environmental space to apply it to the geographic space.
This algorithm makes no assumption about the shape of the species distribution, but the density of observations must be representative of the species niche. The geometric mean produces a smooth set of envelopes around the observations points and provides a good generalisation of the niche.

Distance harmonic-mean algorithm

This algorithm is similar to the geometric mean one but uses the harmonic mean of the distances instead:
The effect of this mean is to give a (too) high weight to all observations while keeping the information of observation density in the factor space. Therefore, it has a tendency to overfit the data, which might be good when you have a small sample size.

Minimum distance algorithm

Again this algorithm is similar to the geometric mean one but uses the minimum of the distances instead:
That means that the density of observations is not taken into account anymore. Each observation has the same weight, and the closest your are from one of them, in the factor space, the more suitable your habitat. There is no generalisation at all. Actually, on all examples I have tested this algorithm never gave good results. However, as it is described in the above paper, I decided to include it in this version of BioMapper. And perhaps someone will find a case where it proved useful.

Links

References

Hirzel A.H., Hausser J., Chessel D. & Perrin N. (2002) Ecological-niche factor analysis: How to compute habitat- suitability maps without absence data? Ecology, 83, 2027-2036.

Hirzel, A.H. & Arlettaz, R. (2003) Modelling habitat suitability for complex species distributions by the environmental-distance geometric mean. Environmental Management, 32, 614-623.

Hirzel, A.H. & Arlettaz, R. (2003) Environmental-envelope based habitat-suitability models. In 1st Conference on Resource Selection by Animals (ed B.F.J. Manly). Omnipress, Laramie, USA.

This page is incomplete. You can help Biomapperians by contributing with your knowledge and experience## Table of Contents

## HABITAT SUITABILITY ALGORITHMS

Using only a few ENFA factors, you will now map the habitat suitability of your study area. The chosen algorithm will compute, for each cell, a value between 0 and 100, from unsuitable to optimal habitat. This page describes the available algorithms.## Median algorithm

Before Biomapper 3.0, it was the only algorithm available. It gives good results in most situations and is the quickest of all.To compute the median algorithm, we divide the species range on each factor in 25 classes, in such a way that the median would exactly separate two classes. For every point in the environmental space, we count the number of observations that are either in the same class or in any class farther apart from the median. To achieve normalisation, twice this number divides the total number of observations. Thus, a point belonging to one of the two classes directly adjacent to the median will get a value of one, and a point lying outside the observation distribution will get a value of zero. Lastly, the overall suitability index for this point is computed by the weighted average of its scores on each dimension, the weights being given by the amount of information explained by each dimension.

This algorithm makes the assumption that the best habitat is at the median of the species distribution on each factor, and that these distributions are symmetric. Although this is often true, in some case itâ€™s wrong: when the distribution is bimodal, it is even completely wrong. You can also get sub-optimal results when the study area is at the border of the species distribution.

## Distance geometric-mean algorithm

The principle of this method is to draw in the factor space the influence field, or suitability field, of each species observation point in such a way that, when they are close together, their attraction powers reinforce each other. For any point P in the factor space, one computes the geometric mean HG of the distances to all observations Oi. Thus, the denser the species points in the environmental space, the higher the habitat suitability. This actually comes down to calibrating a model in the environmental space to apply it to the geographic space.This algorithm makes no assumption about the shape of the species distribution, but the density of observations must be representative of the species niche. The geometric mean produces a smooth set of envelopes around the observations points and provides a good generalisation of the niche.

## Distance harmonic-mean algorithm

This algorithm is similar to the geometric mean one but uses the harmonic mean of the distances instead:The effect of this mean is to give a (too) high weight to all observations while keeping the information of observation density in the factor space. Therefore, it has a tendency to overfit the data, which might be good when you have a small sample size.

## Minimum distance algorithm

Again this algorithm is similar to the geometric mean one but uses the minimum of the distances instead:That means that the density of observations is not taken into account anymore. Each observation has the same weight, and the closest your are from one of them, in the factor space, the more suitable your habitat. There is no generalisation at all. Actually, on all examples I have tested this algorithm never gave good results. However, as it is described in the above paper, I decided to include it in this version of BioMapper. And perhaps someone will find a case where it proved useful.

## Links

## References