In this study, we used the Multivariate Environmental Similarity Surface (MESS) index to quantify model uncertainty associated to extrapolation. Considering the reference dataset of environmental conditions for which species presence-only records are modelled, extrapolation corresponds to the part of the projection area for which one environmental value at least falls outside of the reference dataset.

Six abundant and common sea star species of marine benthic communities of the Southern Ocean were used as case studies. Results show that up to 78% of the projection area is extrapolation, i.e. beyond conditions used for model calibration. Restricting the projection space by the known species ecological requirements (e.g. maximal depth, upper temperature tolerance) and increasing the size of presence datasets were proved efficient to reduce the proportion of extrapolation areas. We estimate that multiplying sampling effort by 2 or 3-fold should help reduce the proportion of extrapolation areas down to 10% in the six studied species.