Factors influencing transferability in species distribution models

Species distribution models (SDMs) provide insights into species’ ecology
and distributions and are frequently used to guide conservation
priorities. However, many uses of SDMs require model transferability,
which refers to the degree to which a model built in one place or time can
successfully predict distributions in a different place or time. If a
species’ model has high spatial transferability, the relationship between
abundance and predictor variables should be consistent across a
geographical distribution. We used Breeding Bird Surveys, climate and
remote sensing data, and a novel method for quantifying model
transferability to test whether SDMs can be transferred across the
geographic ranges of 129 species of North American birds. We also assessed
whether species’ traits are correlated with model transferability. We
expected that prediction accuracy between modeled regions should decrease
with 1) geographical distance, 2) degree of extrapolation, and 3) were
affected by a ‘core-boundary’ effect, which assesses distances to the
boundary of a distribution. Our results suggest that very few species have
a high model transferability index (MTI). Species with large
distributions, with distributions located in areas with low topographic
relief, and with short lifespans are more likely to exhibit low
transferability. Transferability between modeled regions also decreased
with geographical distance and degree of extrapolation. We expect that low
transferability in SDMs potentially resulted from both ecological
non-stationarity (i.e., biological differences within a species across its
range) and over-extrapolation. Accounting for non-stationarity and
extrapolation should substantially increase prediction success of species
distribution models, therefore enhancing the success of conservation
efforts.