Ecological forecasting is critical in understanding of ecological responses to climate change and is increasingly used in climate mitigation plans. The forecasts from correlative models can be challenged by model complexity, training collinearity, collinearity shift and novel conditions of predictors that are common during model extrapolation. The individual effect of these four factors has been investigated, but it is still unclear how these four factors interactively affect forecasting. To fill this gap, we conducted a comprehensive simulation experiment to quantify how the four factors interactively influence model forecasting.

中文翻译：

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模型复杂性、训练共线性、共线性偏移、预测变量新颖性及其相互作用对生态预测的影响

生态预测对于理解气候变化的生态反应至关重要，并且越来越多地用于气候缓解计划。相关模型的预测可能会受到模型外推过程中常见的模型复杂性、训练共线性、共线性偏移和预测变量的新颖条件的挑战。已经研究了这四个因素的个体效应，但仍不清楚这四个因素如何交互影响预测。为了填补这一空白，我们进行了全面的模拟实验，以量化这四个因素如何交互影响模型预测。