Hi all!

I have got a question on habitat-suitability modelling and I was hopeful you could maybe help me with it.

I read this article: Astete et al. (2017), Living in extreme environments Modelling habitat suitability for jaguars, pumas, and their prey in a semiarid habitat I have emailed the author but he hasn't come back to me yet. In the paper he wrote the following: "To evaluate the resulting HS models, we used a 10-fold jack-knife cross-validation".

So, I am aware that I need to split my dataset into k parts for cross-validation. Then, k iteractions will be conducted between the validation and training datasets through bootstrapping methods. The outcome of such iteractions will produce prediction errors and estimate their mean and standard deviation (and thus, the variance) and from that point onwards, one can infer whether, or not, the model is robust. Is my interpretation correct? Is there any step I am missing here? What would be the best bootstraping methods?

Thank you so much in advance