Error in Maxent maps

Sat, Jan 16, 2021 12:33 AM

Dear Irene,
Yes, you're right, the values should be between 0 and 1. And all the 
values are between 0 and 1 (even if they do not span through the whole 
interval), so the prediction is OK.
I guess you used complementary log-log link function (this is the 
default) to get the predicted values. This results in correct 
probability values if we assume that typical presences have a 1/point 
(1/cell) expected abundance (Phillips et al. 2017), but you have no data 
to reliably accept or reject this assumption. So you can state only 
that, for a certain species, predicted value 0.05 is larger than 0.02, 
which means that the environment is more suitable for the species in the 
first location than in the second one.
I have no publication about this theoretical issue, sorry.
Have a nice weekend,
?kos


2021.01.15. 10:46 keltez?ssel, Irene Rojo ?rta:

Dear ?kos,

Thank you very much for your reply. I have been thinking about it but
I still don't understand it very well. As I am having the logistic
output from Maxent, as far as I know, the values should be between 0
and 1 because Maxent does a transformation in order to get a
probability of finding the species referred?to my study area (as you
say it is not a probability of occurrence in absolute terms). I don't
want to compare among species, but for the rest of the species the
predicted values obtained fall within that range of 0-1, while for a
couple of them the values are between 0-0.05. That is why I am
surprised. Maybe you have any publication which could help me to
understand it better?

Thank you so much,

Irene

El mar, 12 ene 2021 a las 15:47, Bede-Fazekas ?kos
(<bfalevlist at gmail.com <mailto:bfalevlist at gmail.com>>) escribi?:

Dear Irene,

Since your background points are not real occurrences (so you do not
know the prevalence of the species), and MaxEnt is a presence-only
method, the predicted value can not directly treated as
probability of
occurrence. Also you should never compare the raw predicted values
between species (or between different models), even if you use
presence-absence data/method. 0.005 for Species1 is not better/worse
than 0.95 for Species2. So I would say that the predicted values
are OK.
If you really want to compare predictions between species, I
recommend
you to rescale the raw values to a 5-level ordinal scale using
specific
thresholds that account for observed presences. Please refer to
Somodi
et al. (2017):
https://www.researchgate.net/publication/318561746_Implementation_and_application_of_multiple_potential_natural_vegetation_models_-_a_case_study_of_Hungary
<https://www.researchgate.net/publication/318561746_Implementation_and_application_of_multiple_potential_natural_vegetation_models_-_a_case_study_of_Hungary>

Have a nice week,
?kos Bede-Fazekas
Hungarian Academy of Sciences

2021.01.12. 13:47 keltez?ssel, Irene Rojo ?rta:

    > Dear all,
    >
    > My name is Irene and I am performing Maxent SDM in R with the dismo
    > package.
    >
    > I am working with several species, performing one analysis for each
    > species, and all are working fine, i.e. the models have sense,

    except two

    > of them. For those species, I can run Maxent models and I get a

    very high

    > value of AUC (0.99) but the resulting maps show values between 0

    and 0.005,

    > when for the rest of the species they are between 0 and (almost)

    1. So maps

    > have no sense. The predictor variables are the same, and those

    two species

    > are not the ones with the lowest number of occurrences (this is

    the first

    > reason I thought that could be affecting the results).
    >
    > I know it is not easy to know what is going on, but I am doing

    exactly the

    > same with all species. If anyone can guess the error or know

    which part of

    > the analysis is more sensitive so I can check it again I would

    be very

    > grateful.
    >
    > Thanks a lot in advance,
    >
    > Irene
    >
    >? ? ? ?[[alternative HTML version deleted]]
    >
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