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Abstract

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.