Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8‐million‐km² area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non‐random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

中文翻译：

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使用大数据进行宏观尺度的生态预测：抽样设计重要吗？

尽管生态系统在区域到大陆范围（即宏观尺度）上对全球变化做出响应，但是对生态系统响应的模型预测通常依赖于来自特定地理区域内一小部分采样生态系统的目标监测数据。在这项研究中，我们研究了用于收集此类模型数据的抽样策略如何影响预测性能。我们对空间上广泛的大型数据集进行了二次采样，以研究宏观采样策略如何影响180万平方公里^2的6,784个湖泊中生态系统特征的预测区。我们估计了数据集不同子集的模型预测性能，以模仿三种常见的采样策略来收集生态系统特征的观测值：随机采样设计，分层随机采样设计和目标采样。我们发现抽样策略会影响模型的预测性能，因此（1）与简单随机抽样设计相比，分层随机抽样设计不会提高预测性能；（2）尽管模仿目标抽样（非随机抽样）的情况之一最差在执行预测模型时，其他目标抽样方案会产生与随机抽样方案具有相似预测性能的模型。