mean and weighted_mean average the probabilities, the second in
proportion to how well each member scored. median averages them robustly,
which is the one to reach for when a single member is capable of going badly
wrong somewhere on the grid — a boosted tree extrapolating, usually — since
a mean lets that member drag a cell and a median does not.
Details
committee is different in kind, and is biomod2's committee averaging: each
member binarises its own prediction at its own TSS-optimal cutoff, and the
cell gets the fraction of members that called it a patch. So it is already on
a 0-to-1 scale and reads directly as agreement — 0.75 means three of four
algorithms say patch — but it throws away how confident each member was.
Every rule is computed and written on every run. model.ensemble.rule picks
which one is the suitability layer, and the others go beside it, because
the disagreement between rules is itself worth looking at and recomputing
them means refitting.
References
Araújo MB, New M (2007). Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22(1), 42-47. doi:10.1016/j.tree.2006.09.010 — why an ensemble of algorithms rather than a chosen best one
Marmion M, Parviainen M, Luoto M, Heikkinen RK, Thuiller W (2009). Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions 15(1), 59-69. doi:10.1111/j.1472-4642.2008.00491.x — the rules compared against each other