Choosing predictors and complexity for ecosystem distribution models: effects on performance and transferability

There is an increasing need for ecosystem-level distribution
models (EDMs) and a better understanding of which factors affect
their quality. We investigated how the performance and transferability of
EDMs are influenced by (1) the choice of predictors, and (2)
model complexity. We modelled the distribution of 15 pre-classified
ecosystem types in Norway using 252 predictors gridded to 100 m ×
100 m resolution. The ecosystem types are major types in the "Nature
in Norway" system mainly defined by rule-based criteria such
as whether soil or specific functional groups (e.g., trees) are present.
The predictors were categorised into four groups, of which three
represented proxies for natural, anthropogenic, or terrain processes
(‘ecological predictors’) and one represented spectral and
structural characteristics of the surface observable from above (’surface
predictors’). Models were generated for five levels of model
complexity. Model performance and transferability were evaluated with data
collected independently of the training data. We found that (1)
models trained with surface predictors only, performed considerably better
and were more transferable than models trained with ecological
predictors, and (2) model performance increased with model complexity,
levelling off from around 10 parameters and reaching a peak
around 20 parameters, while model transferability decreased with model
complexity. Our findings support that surface predictors enhance
EDM performance and transferability, most likely because they represent
discernible surface characteristics of the ecosystem types. A
poor match between the rule-based criteria that define the ecosystem types
and the ecological predictors, which represent ecological
processes, is a plausible explanation for why surface predictors better
predict the distribution of ecosystem types. Our results indicate
that, in most cases, the same models are not well suited focontrasting
purposes, such as predicting where ecosystems are and explaining
why they are there.