Contents

Practical questions
How much costs Biomapper?
Does Biomapper work with Idrisi32?
I'm working with Arcview. How can I use Biomapper?
On which platform does Biomapper run?
How to convert an ESRI grid into an Idrisi/Biomapper
raster map?
In which programming language is Biomapper developped?
Could you kindly send me the user manual.
How to quote Biomapper / ENFA?
Why is it not possible to use most of the modules of
biomapper with Idrisi16 (.img) files?
Statistics
I don't understand what mean all the validation statistics
What is the "modified Kappa coefficient"?
Do you have an example data set to test Biomapper?
I have very good absence data. Can I use them with
Biomapper?
How to use abundance data with Biomapper?
What is a BoxCox transformation and why is it needed?
What is the "brokenstick advice"?
The number of categories per factor for the
making of the HS map changes by steps of two. Is this normal?
I have computed an ENFA model and I would like
to extrapolate it on a wider / other area. Is there some equation I can
use to do it?
How is computed the score matrix?
Whate are global marginality, specialisation
and tolerance?
How do you compute the HS value for each cell from the
scores matrix?
How to interpret factor biological meaning?
BoxCox normalisation fails with some EGV maps.
Should I discard them?
How are the ROC curves and kappa calculated if Biomapper
does not use absence data?
How can I compare results from GLM and ENFA?
What is the difference between unidimensional and
multidimensional histogram algorithms for the computation of HS maps and
how strong is the impact on the resulting HS maps?
Can I compare the global marginality and specialisation
coefficients of different species if I use the same area but different
set of ecogeographical maps, in particular if have had to discard different
correlated maps in the process?
You say that sometimes the marginality
factor takes also a part of the specialisation into account. Where can
I find how much?
What's the difference between "explained
variance" and "explained information"?
I know that the habitat suitability of my
species is linearly related to some variable. Nevertheless, in the HS map,
the optimum for this variable seems not to be at an extremum. Why?
Procedures
Importing files
Problems
Miscellanies

Your Question

If you don't find here an answer to your question, please
feel free to ask
me. 
Practical questions

How much costs Biomapper?
Biomapper is free. You just have to download
it.
Does Biomapper work with Idrisi32?
Yes, Biomapper can read both Idrisi16 and Idrisi32 file format.
I'm working with Arcview. How can I use Biomapper?
Several of Biomapper's users are using Arcview. You will have
to prepare your maps in Arcview before converting
them to Idrisi/Biomapper format. Once the whole process has been completed,
you can reimport the resulting maps into Arcview for further analysis or
display.
If you are using Arcview 3.0, you can use the Biomapper module
Grid
Convertor.
How to convert an ESRI grid into an Idrisi/Biomapper
raster map and conversely?
There are several possibilities:
1° ArcView 3.x extension
There is an extension doing the job on the ESRI site, made by
Holger Schäuble. Look for Grid
Converter (av2idrisi.zip) on http://gis.esri.com/arcscripts/index.cfm.
It works for ArcView 3.x.
2° Biomapper's GridConvertor
If you have Arcview 3.0 or 3.1 and Spatial Analyst, you can
use the Biomapper module Grid Convertor. It allows to convert several
files in only one operation.
3° Manual conversion
If you have another version of ArcView, GridConvertor may not
work (always problems due to ESRI proprietary policy).You will therefore
have to convert your grids manually. In the Biomapper help file you will
find a description of the Idrisi/Biomapper file format that should help
you to transfer your data.You can try this: in Arcview, go into the file
menu and select Export Data Source and export your image as a binary raster
(has an flt extension). Exit Arcview and then change the file extension
from .flt to .rst. You should then be able to create a document file (*.rdc)
for this image with the information supplied in the help file. The flt
and rst format are identical. You will probably have to choose "real" data
type.
4° Otherwise...
You can also find useful programs at http://www.pierssen.com/idrisi/grid.htm
In which programming language is Biomapper developped?
I'm using Borland
Delphi for all my programming work. It allow me to quickly conceive
fast running procedures cloaked in a userfriendly interface. The source
code is not open but you will find the crucial procedures in the annexes
of my PhD thesis.
Could you kindly send me the user manual.
There is no user manual for Biomapper. There is the help file
though, and on the web, you will find the FAQ ( www.unil.ch/biomapper/faq.html
) and more general information.
How can I use satellite imagery pictures (or
other file format) with Biomapper?
Biomapper has no importing capabilities. It can build
a map from raw data but cannot convert alien file formats. It works
only with the Idrisi (16 and 32) file format but Idrisi itself has an extensive
set of conversion tools.
How to quote Biomapper / ENFA?
You can quote the main paper:
Hirzel, A.H., Hausser, J., Chessel, D., Perrin, N., 2002. Ecologicalniche
factor analysis: How to compute habitat suitability maps without absence
data? Ecology 83, 20272036.
and the Biomapper software itself:
Hirzel, A., Hausser, J., Perrin, N., 2002. Biomapper 2.0. Lausanne,
Lab. for Conservation Biology. URL: http://www.unil.ch/biomapper.
Why is it not possible to use most of the modules
of Biomapper with Idrisi16 (.img) files?
Only the main Biomapper program has a button to switch
from one format to the other. The other module use one of them according
to the folder in which they are placed (it is not very elegant, I agree,
and I will have to modify that some time in the future).
When the module is launched, it checks if there is an "idrisi.env"
file in the same folder as the module executable. If there is one, it switches
to the Idrisi16 mode, else it switch to Idrisi32 mode.
You can therefore enforce one mode or the other by placing a
fake "idrisi.env" in your Biomapper folder.
On which platform does Biomapper run?
Biomapper is developped for Windows32 platforms, i.e.
on all Windows since Win95. I have personally tested it on Win95, Win98,
Win NT4 and Win2000. I have been told that it worked well on a Mac with
a PCcard, but there is no version specially developed for Mac, Linux,
Unix or any other platforms.

Statistics

I don't understand what mean
all the validation statistics
I agree that this validation part is not yet very well
documented... It is the fruit of hard work to find a way to evaluate a
model without absence data. I tried thus many methods that are still present
in the output, without any further explanation... I shall try here to unveil
a few details on this subject. This will finally be incorporated into the
help file.
You must have parted your sample into two sets. For validation
purpose, you must use as "validation map" the set you did NOT used for
model calibration. This observed map is therefore a boolean map indicating
species presence. You can then evaluate the habitat suitability map produced
with the ENFA model.
When no reliable absence data are available, evaluation consist
to compare various statistics computed on the "predicted map" (the habitat
suitability map): 1° On the whole study area 2° Only on the validation
points.
The best way to understand this is to look at the boxplot displayed
after the validation process. A good model should produce high species
HSvalue (80100). The global boxplot gives you insight on how marginal
the species is in the studied area and thus how much these results could
have been obtained by chance only.
The results window gives a few statistics to resume this boxplot.
It is composed of three parts: 1° species, 2° global statistics
and 3° Comparisons.
The two first part give identical information: first, common
distribution statistics are computed (mean, median, quartiles, etc.). Then
a few more specifics statistics: the one I find the most useful is the
"proportion of presence cells >50": this is the proportion of validation
points that have a predicted HSvalue over 50. The higher this value, the
better your model.The "Prob. to be above this value by chance" statistic
use a bootstrap procedure to assess how much this value could have been
obtained by chance. Practically, it is not very interesting as I have allways
got here a 0.000 probability (good news)... Then you can see the 90th percentile
and 95th percentile. You can compare all the previous values between global
and validation sets, but only the validation set gives a really objective
idea of the quality of your model.
The last part give three comparison values between the two sets:
The "Kappa" coefficient is a modified kappa statistics that integrate both
how good is the model and how far from random it is. "Prop.of pix being
significantly above 50" is the difference between the two "Prop.>50" values.
And the "Probability to be over 50%" is the probability to be over 50%
by chance, computed on the global set distribution histogram.
These three last values are interesting to assess how the model
is different from what could be achieved by a random model but it says
nothing about the absolute quality of your model. They are highly related
to the global HS of the study area and thus, if your species is not very
marginal nor very specialised, the model could be very good but get a very
bad "far from random" score.
What is the "modified Kappa coefficient"?
I gave this name to a homemade statistic whose behaviour was
to be similar to the Cohen's Kappa coefficient. It is computed as follows:
K_{mod} = ( M_{S}M_{G })
/ (1 M_{G })
Do you have an example data set to test Biomapper?
Alas, none of our data are in public domain and we cannot give
them away.
I have very good absence data. Can I use them
with Biomapper?
If you are really sure that your absence data are good and
that no historical or spatial factors could have biased them, you will
probably get a better model by using a presence/absencebased method, like
Generalized Linear Model (GLM) or Generalized Additive Model (GAM). But
you can ever put your absence data aside and apply ENFA on presence data
only.
How to use abundance data with Biomapper?
You can use it to weight the presence data (weighted boolean
map). Just use a map with integere weights in place of 0 and 1 as species
map.
What is a BoxCox transformation and why is it
needed?
Box and Cox (1964) developed a procedure for estimating the
best transformation to normality within the family of power transformations:
Y' = (Y^{L} 1)/L (for L<>0)
Y' = ln Y
(for L = 0)
See "Biometry", Sokal & Rohlf, 1995, pp.417419 for further explanations.
Biomapper uses the BoxCox algorithm to normalise as well as possible
the ecogeographic variables. Empirically, we have found that normality
was not a crucial factor and this step could as well be ignored.
What is the "brokenstick advice"?
The distribution of the eigenvalue is compared to the
distribution of Mac Arthur's brokenstick. It is the expected distribution
when breaking a stick randomly. Therefore, the eigenvalues that are larger
than what would have been obtained randomly may be considered "significant".
You can also keep only the factors with an eigenvalue larger than 1. These
are objective means to choose how many factors to keep for HS map computing.
These are just indications designed as a support when selecting the factors.
The number of categories per factor for
the making of the HS map changes by steps of two. I mean, you can only
chose 2, 4, 6 and so on classes per factor. Is this normal?
Yes. It is because the HS computation is based on the
median of the factor distributions and the median must fall between two
classes and so the number of classes must be even.
I have computed an ENFA model and I would
like to extrapolate it on a wider / other area. Is there some equation
I can use to do it?
Presenceonly models are difficult to extrapolate to
other areas. Indeed they are based on the comparison between the locations
where the species has been observed settling down and the available habitat.
Although it can accurately make prediction on the study area, exporting
the model to another place can be very tricky. In particular if you are
comparing areas very far from each other.
Even extrapolation in a closer area can be tricky if the environmental
layers have not been collected in the same way. An environmental variable
as simple as mean water temperature can be done in very different ways
(time of the day, depth, season, etc.) which could prevent the model to
make good predictions. Or it is sufficient to move the study window a few
kilometers to make it cover a different habitat distribution, which will
bring unpredictable disturbances.
Finally, Biomapper has not been done for that purpose and there
is presently no easy way to conduct such an extrapolation.
How is computed the scores matrix?
The full mathematical details of this operation are
described in the main paper in Ecology.
You can get an intuitive understanding by looking at the help file or on
this site at http://www.unil.ch/biomapper/enfa.html
. Here is a short view of this process:
The eigenvalues and eigenvectors are extracted as follows: Compute
the matrix W=Rs^{1} * Rg where Rg
is the global correlation matrix and Rs the species covariance matrix.
From W, extract the marginality factor (I don't give here the mathematical
procedure), which gives us the matrix W^{*}. The specialisation
factors are computed by extracting the eigensystem from W^{*}.
What are global marginality, specialisation
and tolerance?
Global Marginality = M = Sqrt(S_{i=1,v}[M_{i}^{2}]
)/1.96
Global Specialisation = S = Sqrt(S_{i=1,v}(l_{i})/V)
Global tolerance = T = 1/S
where M_{i} are the coefficients of the marginality factor,
Sqrt() is the square root function, V is the number of variables
and l_{i } are the eigenvalues.
How do you compute the HS value for each cell
from the scores matrix?
This is a rather complex procedure. The full mathematical
details of this operation are described in the main paper in Ecology.
Shortly said, for each retained factor, a frequency histogram
is computed over all the cells of the map. The median of this distribution
is computed. The further a cell is from this median, the lower is its suitability
for this factor. The global suitability is then obtained by computing a
weighted mean on these "partial suitabilities". Marginality has a weight
of 1, the sum of specialisation factors has a weight of 1, proportionally
to their eigenvalue.
How to interpret factor biological meaning?
Look at the scores matrix. The first column of this
matrix is the marginality factor. The other columns are the V1
specialisation factors. (V is the number of variables). The rows
are the EGV contributions to each factor.
BoxCox normalisation fails with some EGV
maps. Should I discard them?
For myself, when the BoxCox fails, I keep the original
map. A "BoxCoxised" map gives better results than a "brute" map, but a
"brute" map is still better than no map at all.
Anyway, you may include it at the beginning (to compute the big
timeconsuming covariance map. Once it is computed, you can remove easily
variables from it, but if you add new variables, the whole matrix will
have to be recomputed) and then try to remove it to see how it affects
the result.
How are the ROC curves and kappa calculated
if Biomapper does not use absence data?
The kappa and the ROC curves that you find in the validation
dialog box assume that blank values are true absences. They are therefore
not suitable for most data sets where absences are unreliable. I put them
here for the cases where you can rely upon absences.
How can I compare results from GLM and ENFA?
Comparing ENFA and GLM is a tricky stuff. In my recent
paper (Hirzel et al, 2001, Ecological Modelling 145), I was able to compare
them because I was using virtual data and so I had access to the "reality",
the "truth". But in the general case, we do not know it and so we are constrained
to use the standard statistics (Kappa, ROC, etc.). There are three main
problems when comparing presence/absence to presenceonly methods:
1° If absences are thought to be unreliable to build a model, there
is no sense in using them to validate it afterwards. So, the standard statistics
are not useful. I tried to develop a few statistics to replace them (see
the FAQ on the Biomapper site) but the perfect statistic has still to be
invented.
2° As it is based on presence data only, the ENFA is more efficient
to model the areas with average to high suitability; its predictions for
lowsuitabitility regions should be taken with prudence. By contrast, presence/absence
methods and in particular GLMs, will tend to model good versus bad areas
causing such a kind of "stepped" response which is different from the linear
one of the ENFA.
3° Without absences  i.e. bad habitat points  to "fix the floor",
ENFA must scale its suitability index to the ceiling. That means that,
on an ENFA HS map, you will always, by construction, have at least one
cell with a HS of 1 (or 100) ; with GLM, it is generally not the case as
it is computing "probability of presence" and so the maximum values are
generally quite lower than 1.
Thus, when comparing visually GLM and ENFA maps (computed on
wellknown species at equilibrium, i.e., when absence data are largely
reliable), the results are obviously quite similar. But if you try to compare
them statistically, you get strange results biased either for one or for
the other depending on which base hypothesis you use. To compare them you
must correct both results to make them comparable:
1° Remove the "ceiling effect" by stretching the GLM results between
0 and 1.
2° Synchronise the "step effects" by transforming both GLM and ENFA
results into boolean maps (by choosing a threshold).
3° Then you can compare these results with standard presence/absence
statistics.
What is the difference between unidimensional
multidimensional histogram algorithm for the computation of HS maps and
how strong is the impact on the resulting HS maps?
The ful detail of the unidimensional algorithm are in
the main paper in Ecology. The multidimensional
is by now obsolete but I let here the explanation for history's sake. I
give here a "feeling" of what they do and how they differ:
Unidimensional algorithm:
Once the ENFA factors have been computed, it is possible
to compute for every cell of the map a value along each of them (in fact,
usually, one computes it only for a few of the first factors). The distribution
histogram of each factor is then computed taking into account only the
presence points. Each histogram will be used to attribute a "partial suitability"
value to every cell (the more the cell factor value depart from the median
of the histogram, the lower its "partial suitability". Then, a weighted
sum of these partial suitability values is made for each map cell, and
finally these sum are stretched in order to have their maximum at 100.
Multidimensional algorithm:
Here we do not address the selected factors independently.
By crossing all factors together, the factor space is divided in small
units (hypercubes). Then we count how many cells belong to each hypercube
: this is the multidimensional histogram. This multidimensional distribution
is computed both for all cells (global distribution) and the presence cells
(species distribution). Finally, the HS value is computed for each hypercube
by dividing the species hypercube by the global hypercube (and multiplied
by 100).
The problem with the multi algorithm is that it need very huge amount
of presence data to be accurate, in particular if you want to include more
than two factors, which is generally the case. It is also very memoryconsuming.
So far, I never got good results with this algorithm and it is why it will
not be described in your paper. We strongly advise Biomapper users to use
only the unidimensional algorithm. In fact, I could well have removed it
from the interface...
I am currently working on new kinds of algorithms, but it is
another story... Stay tuned! Biomapperians
will be informed of all the new developments.
Can I compare the global marginality
and specialisation coefficients of different species if I use the same
area but different set of ecogeographical maps, in particular if have had
to discard different correlated maps in the process?
Strictly speaking, you cannot. Practically, the main
biasing effect is the study area. When you have to remove a variable, it
is because it does not contain more information than is already included
into the model, so removing it should not alter significantly the global
marginality and specialisation coefficients. Thus, provided the map sets
do not differ drastically, you can still comparer them by these statistics.
Anyway, do not assume too much significance to small differences in marginality
or specialisation between species
To be true, I never tested this. You could test it by building
a common minimal EGV set and apply it to all your species. You will then
see how the marginality et specialisation differ between the common dataset
and the speciesoptimal one. Tell what you get, should you decide to try
this.
You say that sometimes the marginality
factor takes also a part of the specialisation into account. Where can
I find how much?
The amount of variance explained by the first factor
is in fact the amount of specialisation. It generally ranges from 10% to
70%. This value is given in the eigenvalue table.
To summarise, the marginality factor explains always 100% of
the marginality and some part of the specialisation. It is why it has always
a great weight (minimum 0.5) for HS computation.
What's the difference between "explained
variance" and "explained information"?
The Ecological Niche factors are conveying two kinds
of information: marginality and specialisation. In fact, the first factor
explains always 100% of the marginality and some varying part of specialisation;
the subsequent factors explain no marginality and the rest of the specialisation.
Historically, I was using the concept of "explained variance",
which in fact was related only to "explained specialisation". Marginality
was not used in this value. Therefore, when using this index for deciding
how many factors had to be kept for the HS analysis, the user could be
misled by this value. Accordingly, I introduced the concept of "explained
information" to cope with this by giving marginality and specialisation
the same "information power".
Mathematically, if L1,L2,...,Lf are the
eigenvalues of the f retained factors, and SL is the sum
of all n eigenvalues (= L1+L2+...+Lf+...+Ln),
we have :
Explained variance = Explained specialisation = Se = (L1+L2+...+Lf)/SL
In the explained information index, the marginality gets the
same weight as the specialisation and we have therefore:
Explained information = Ie = (Se+1)/2
Ie is therefor always greater than Se and can never
be smaller than 0.5. If the user chooses to keep only the first factor,
he will explain at least half of the information, the one included into
marginality. This Ie value is to be seen as a decision support value
to choose how many factors are to be included into the HS analysis.
I know that the habitat suitability
of my species is linearly related to some variable. Nevertheless, in the
HS map, the optimum for this variable seems not to be at an extremum. Why?
The HS computing algorithm
is not linear but bases itself on the observed distribution. Your problem
arises generally on the marginality factor. Let's imagine a species linearly
related to the frequency of forest and that this variable is strongly correlated
to the marginality factor (but the reasoning hold also for specialisation
factors) ; it means that, the more forest there is around a given cell,
the more suitable it is for the species, the maximum being a frequency
of 100%. This optimum is what we know from our knowledge of the species,
field studies, etc. Now, let's see the Biomapper's point of view: To it,
the optimal frequency is the one where the species is the most frequent
(More precisely, the median of the species distribution along the marginality
factor. As the distribution of this factor is generally unimodal and more
or less symmetrical, the median corresponds also to the most frequent.)
Therefore, if large forests are rare in the study area, points with 100%
forest freq. will be rare too, and the optimum for the species will not
be 100% but lower (say 80%). Then, when computing the HS index, 80% freq.
will provide the highest partial suitability value and this will decrease
when freq. is either increasing or decreasing. The rarer the large forests,
the steeper the rate of decrease.
Sometimes this effect is welcome (when dealing with median optima)
and sometimes counterproductive (with extreme optima). I am currently working
at new algorithms which will hopefully address this problem.

Procedures

Do you have a stepbystep explanation
of how doing a whole habitat suitability analysis?
Yes. You will find it in the menu Help/Modus operandi.
The whole process is outlined there and by clicking on the main titles
you access a more detailed stepbystep description of the process.
How can I create the speciespresence
map?
There are several possibilities depending on what kind of data you
have at hand:

List of observations coordinates: Put them in an ASCII file, using a structure
xcoordinate tabulation ycoordinate (You can do this with Microsoft Excel)
and the use the "Convertor" module to create a boolean presence map from
this file.

Observation map in a pointvectorfile: Simply rasterise it (with Idrisi
function "PointRas"), using the same resolution and window as your ecogeographical
maps.

Population map in a polygonvectorfile: First, rasterise it (with Idrisi
function "PolyRas"), using the same resolution and window as your ecogeographical
maps. Then make this map boolean (1=inside populations). Finally, use the
module "Sampler" to divide this map into a calibration and a validation
data sets.
How to convert a point vector map
into a species map?
In Idrisi32:

Menu Reformat/Rastervector conversion/POINTRAS

Select your vector map, e.g. "Species.vct"

Choose a name for the "image file to be updated". It must be a new name
(not an existing raster) (e.g. "Species_bl.rst")

Choose "Change cells to record the presence of 1 or more points"
Idrisi asks you if you want to bring up INITIAL: Answer YES.

In the INITIAL dialog box:

Select "Copy spatial parmeters..."

Select one of your EGV maps in "Image to copy parameters from"

In "Output data type", select "Byte"

Click OK
And this should do the job. Species_bl can now be used as species
map. You may want to partition it into calibration and validation data
sets. You can do this with the Biomapper module Sampler.
Rememember that all the EGV maps and the species map must be
in the same directory.
How to convert a polygon vector
map into a species map?
In Idrisi32:
Menu Reformat/Rastervector conversion/POLYRAS
Select your vector map, e.g. "Species.vct"
Choose a name for the "image file to be updated". It must be a new name
(not an existing raster) (e.g. "Species.rst")
Idrisi asks you if you want to bring up INITIAL: Answer YES.

In the INITIAL dialog box:

Select "Copy spatial parmeters..."

Select one of your EGV maps in "Image to copy parameters from"

In "Output data type", select "Byte"

Click OK
Then, you must booleanise the rasterised polygons:

Menu Analysis/Database query/image calculator

Select "Logical expression"

Type Species_BL = [Species]>0

Click OK
Species_BL can now be used as species map. You may want to partition
it into calibration and validation data sets. You can do this with the
Biomapper module Sampler.
Rememember that all the EGV maps and the species map must be
in the same directory.
How to insert the speciespresence
map?
Once the speciespresence map has
been created, you must insert it in Biomapper in order to use it in
the analyses. The speciesmap must be inserted in the Work
maps list (NOT the EGV maps). This can be done in the Files/Work
maps/Add maps menu. Once inserted in this list, you must declare it
as the current species map by selecting it, rightclicking on it and selecting
"Mark as species map". The current species map is then marked by a red
circle in front of its name.
Why have the EGV maps to be quantitative?
The ENFA is based on quantitative computations. For
example, the marginality factor is based on means. Therefore, if you use
a purely categorical map these computations will be misleading. Example:
say you have a vegetation map with 1.grassland, 2.forest, 3.agriculture
field, 4.bushes, but which is mainly constitute of types 1 and 4. The ENFA
will computes that the global mean for this map is about 2.5 ( 0.5*(4+1)
), which at least doesn't mean anything and at worst is strongly misleading
(could be taken as forest or agriculture land).
I have a categorical map (say a vegetation
type map). How would you handle preparing this map?
I would use BOOLEANISATOR to get a boolean map of each
relevant category and then feed these boolean maps into DISTAN and CIRCAN
to get distance and frequency maps of them. You could even choose several
radius for the moving window in order to take into account several influence
distances.CIRCAN is useful for all resources (food, shelter, etc.) variables,
which the species could need in its home range. DISTAN is useful to makes
disturbance (mainly human impact like tourism, noise, pollution,etc.) variables.
Generally, I compute both distance and frequency maps for all my boolean
variables, compute the ENFA and look at the score to see if there is any
difference between how the species is sensible to both aspects.
You could also consider to use some fragmentation index according
to the species you are mapping, as "Border length" in CIRCAN (It works
well for animals/plants living or feeding near forest boundaries, like
Ungulates)
When I verify the EGV maps, I get a warning
message telling "Map "xxxxx" is not continuous enough. What does it mean
and how to fix this problem?
Is it possible to obtain a better model
by reducing the amount of explained variance (i.e. the number of factors
used) and, consquently, increasing the number of classes per factor?
Yes. You must find the best tradeoff between explained variance
and smoothness of the HS model. Note that it is generally not useful to
select more than 10 classes.
How to compare models obtained by various
factor number/class number tradeoff ?
You can use the validation module of Biomapper. Look at the
boxplot it generates. Focus on the species box; it must be as high as
possible. The higher and the narrower the better. (The globalbox is not
useful to assess model quality; it is here to see how different from randomness
is your model. If you built your model on an area globally good for your
species, you will get a good model simply because the species can live
everywhere.)
How can I print a map from biomapper? I guess
I can do it from Idrisi but I´d like to use the rainbow palette.
Display the map and save it as a BMP file. This file you can
insert in a word document or print with any picture software. Alternatively,
you will find the rainbow palette in the Biomapper package: Biomapper.smp.
What are these "Biomapper extensions" used for?
Can I ignore them?
These extensions are made to simplify the browsing and
help the user to select among all the maps, those having the right data
type. But these extensions are not used by Biomapper to verify the maps;
it uses the raster documentation file (*.rdc). Thus you can as well ignore
the biomapper extensions.
In the Options dialog box, it is possible
to switch between correlation and covariance matrix, and to change the
norming of the eigenvectors. However, this doesn't seem to affecte ENFA
outputs.
These options are intended for the Principal Components Analysis.
They do not affect ENFA indeed.

Problems

I get an error message "... is not
a valid floating point value"
It happens because your computer is not set to use "."
as decimal separator. It is mandatory if you want to use Biomapper and
greatly advised if you want to use Idrisi. You can change this setting
in the Start Menu/parameters/Control panel/Regional settings/Numbers.
When I try to open a project, I get a message
"Documentation file not found".
This message means that Biomapper could not find a map. This
happen generally because the map is not in the same directory as the project
file (*.bio). All EGV maps must be in the same directory. This constraint
was introduced in autumn 2000 in order to make the transfer of project
between different computers transparent. By now, when you create a new
project, Biomapper will verify that all your files are in the same directory.
However, you could have problem with project created with previous versions.
I get error messages when I add EGV maps, or try
to normalise them, or mask them, etc.
This kind of problems generally happen when the metadata
file is incorrect.

If you are working with ArcView grids that you have imported into Idrisi
format, your maps probably suffer from the infamous 9999 background bug.
Check in the metadata file (*.rdc) what's the flag value: if it is 9999,
this bug is probably at work. There is a tool in the MapManager
module that will help you to fix this problem.

If this does not solve your problmen, check the metadata file (*.rdc. You
can open it in the NotePad, for example). Check for inconsistancies between
your EGV maps and species maps, in particular in the following fields:
columns, rows, Max X, Min X, Max Y, Min Y and ref. system.

If this doesn't help, please contact me.

Miscellanies

Do you have a published paper
about ENFA?
There are already several papers. More are coming. You
can get a list of related publications on the Bibliography
page. There, you can also download a PDF version of my PhD
thesis. Registered users will
be informed when new papers are coming out.
Is Biomapper better suitable for plants
or animals modelling?
So far, Biomapper has been applied on a wide range of
the life tree: Mammals, Birds, Insects,
tropical Trees and Ferns. Most of them are animals but it's mainly because
I use to meet more zoologists than botanists...
In fact, absence data tend to be poorer for animals and so Biomapper
could be particularly adapted to this case.
I would like to look for any word in the Biomapper
help file. How can I do it?
The Windows helpfile system allows you to generate
a word search from any help file. Open the help file and click on the "Find"
tab (rightmost tab). The first time you do this, you can choose the size
of the database you want to use; generally I use the smallest size, but
if you want more words to be included in the database, you can select a
larger size. Then, the "Find" tab will provide you with the kind of tool
you are looking for. Just type a word and the you will see a list of the
"chapters" containing this word.
Some checkboxes, buttons and options are
not documented in the help file.
The help file is actually written along the thread of
the modus operandi (or stepbystep guide) to guide the user through Biomapper
to allow her/him to compute and validate a HS map easily and understand
the main outputs. I agree there are a lot of undocumented small controls
(checkboxes, buttons, etc.) I added for the comfort of the user. Whilst
only a few are described in the help file or in this FAQ
on the website, all of them have a builtin help message that appears when
you hover the mouse cursor on the control; the help message  or "mouse
hint"  appears both in the bottom bar of Biomapper main window and in
a floating temporary panel; they are present for most controls in all modules.
These hints try to explain what's the use of the control. When a more complete
explanation was needed, I generally put it either in the help file or in
the FAQ. If I did not, please send me an email and I shall clarify the
situation.
I found inconsistencies and undocumented
options between the software and the help file.
There are some minor inconsistencies between the help
file and the program as Biomapper is evolving continuously and I
don't modify the help file each time. I hope you understand that I am a
scientific researcher and not a professional programmer. My main interest
is in solving ecological questions by developing more efficient algorithms
and applying them to real cases. Biomapper is free and I'm alone to program
it, therefore, I cannot devote  and am not interested in devoting  too
much time and energy in helpfile writing and updating work. So far, Biomapper
development is a pleasure; I try to keep it so. Should it become a pain,
I would probably have to find a way to get some moneycompensation.
However, I am always happy to answer the questions
of the Biomapperians and do my best to fulfill their needs. All their questions
and my answers are filed in this FAQ,
which is therefore a good knowledgebase. This FAQ is updated regularly,
each time I answer a new question, in fact. The helpfile is more painful
and timeconsuming to update, and so I update it only about once a year,
if there is enough matter to add and modify.
Why is it not possible to use EGV maps having
different scales, or not overlayable?
The ENFA requires to have for every cell of the map
a value for each EGV. If the maps are not overlayable, they would have
to be made such internally. That would mean to include into Biomapper a
complete interpolation algorithm. Although it would be possible, interpolation
is a whole world in itself and I could not implement it exhaustively into
Biomapper nor keep uptodate with all the new development in the domain.
My strategy is therefore to leave this aspect to the softwares dedicated
to this problem. Idrisi for example has a whole geostatistic module allowing
to do all kind of interpolations, from the simpler to the most complex
ones.
The function of Biomapper is to go where other GIS softwares
do not go, not to repeat what they are already doing. There is of course
some overlap but I keep it minimal.
