Datensatz

Zusammenfassung

A fundamental challenge in experimental ecology is to capture nonlinearities of ecological
responses to interacting environmental drivers. Here, we demonstrate that gradient designs outperform
replicated designs for detecting and quantifying nonlinear responses. We report the results
of (1) multiple computer simulations and (2) two purpose-designed empirical experiments. The
findings consistently revealed that unreplicated sampling at a maximum number of sampling locations
maximised prediction success (i.e. the R² to the known truth) irrespective of the amount of
stochasticity and the underlying response surfaces, including combinations of two linear, unimodal
or saturating drivers. For the two empirical experiments, the same pattern was found, with gradient
designs outperforming replicated designs in revealing the response surfaces of underlying
drivers. Our findings suggest that a move to gradient designs in ecological experiments could be a
major step towards unravelling underlying response patterns to continuous and interacting environmental
drivers in a feasible and statistically powerful way.