[Biomod-commits] Biomod-commits Digest, Vol 34, Issue 15

[Andreas Soteriades]

Hi all,

Thank you so much for your willingness to help! Here are some additional questions, as derived by your useful comments:

>This is not necessarily the case, and (in my opinion) is one of the main arguments for using an ensemble approach to >forecasting species distributions onto new time period or locations.

>However, as recommended by Brenna, this is not something really recommended anymore as ensemble forecasts are >much more robust than the best model.  

>We let the function BestModel for people who wants to quickly extract which is the best model for a given data but >this is something which should be applied when extrapolating the models into new areas or time. 

> Ensemble.Forcasting try to give you a consensus modeling projection, that means that every selected model will be >contribute (more or less depending on arguments given) to a consensus and supposed better projection. 


From the comments above I can see that what you suggest is to avoid using the model that predicted best my already known localities for projections onto other areas or into the future; I should use ensemble forecasting instead, right?

Can you please confirm that I have understood all steps of the methodology:

To initialise:
1) I load the file with the coordinates, variables and presence/absence table of my species;
2) I use Initial.State() to initialise my dataset;
3) I run the Models() function by choosing the models I want and by setting different parameters;

To predict:
1) For future predictions: I load a file with the same coordinates as the current file and with the future values of the variables/ For projections in other areas: I load a file with different coordinates and with the values of the variables observed on the new coordinates;
2) I use Ensemble.Forecasting() setting as Proj.name my new dataset;
3) I analyse the outputs;

Is the above procedure correct?

Cheers,

Andreas
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