To replicate, or not to replicate - that is the question: how to tackle nonlinear responses in ecological experiments
To replicate, or not to replicate - that is the question: how to tackle nonlinear responses in ecological experiments
复制标题
DOI：
10.1111/ele.13134
复制
发表时间：
2018-11-01
期刊：
影响因子：
8.8
通讯作者：
Larsen, Klaus Steenberg
中科院分区：
文献类型：
--
作者：
Kreyling, Juergen;Schweiger, Andreas H.;Larsen, Klaus Steenberg
在实验生态学的一个基本挑战是捕捉生态响应相互作用的环境驱动程序的非线性。在这里，我们证明了梯度设计优于复制设计检测和量化非线性响应。我们报告的结果（1）多个计算机模拟和（2）两个目的设计的实证实验。研究结果一致表明，在最大数量的采样位置的无重复采样最大限度地提高了预测成功率（即R-2已知的真相），而不管随机性和潜在的响应面，包括两个线性，单峰或饱和驱动程序的组合。对于这两个实证实验，发现了相同的模式，梯度设计在揭示潜在驱动因素的响应面方面优于重复设计。我们的研究结果表明，在生态实验中转向梯度设计可能是以可行和统计上强大的方式揭示连续和相互作用的环境驱动因素的潜在反应模式的重要一步。
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 R-2 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.