Bootstrapping is a resampling analysis that involves taking columns of characters out of your analysis, rebuilding the tree, and testing if the same nodes are recovered. This is done through many (100 or 1000, quite often) iterations. If, for example, you recover the same node through 95 of 100 iterations of taking out one character and resampling your tree, then you have a good idea that the node is well supported (your bootstrap value in that case would be 0.95 or 95%). If you get low support, that suggests that only a few characters support that node, as removing characters at random from your matrix leads to a different reconstruction of that node.
Nicolas' suggestion regarding the Hillis and Bull paper is good, though I would suggest that a maximum likelihood tree with bootstrap values of 70% throughout would probably not go over well with reviewers. I can't comment on the analysis of Hillis and Bull (they obviously know what they are doing) but I frequently see BS values of ~70% referred to as moderate support.
As for comparison of maximum likelihood programs, I would recommend either Garli or RAxML. MEGA seems to get a lot of support on this site, but I do not use it because there are limited options for tweaking parameters.
From：
Weston Testo ：https://www.researchgate.net/post/How_can_I_interpret_bootstrap_values_on_phylogenetic_trees_built_with_Maximum_Likelihood_method
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