Ecological prediction at macroscales using big data: Does sampling design matter?
Ecological prediction at macroscales using big data: Does sampling design matter?
复制标题
使用大数据进行宏观生态预测：抽样设计重要吗？
DOI：
10.1002/eap.2123
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发表时间：
2020
影响因子：
5
通讯作者：
Bartley, Meridith
中科院分区：
文献类型：
--
作者：
Soranno, Patricia A.;Cheruvelil, Kendra Spence;Liu, Boyang;Wang, Qi;Tan, Pang‐Ning;Zhou, Jiayu;King, Katelyn B. S.;McCullough, Ian M.;Stachelek, Jemma;Bartley, Meridith
虽然生态系统在区域到大陆尺度(即宏观尺度)对全球变化作出反应，但对生态系统反应的模型预测往往依赖于对特定地理区域内的一小部分抽样生态系统进行有针对性的监测的数据。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。我们对一个大的、空间上广泛的数据集进行了二次采样，以调查宏观采样策略如何影响对180万平方公里面积的6784个湖泊的生态系统特征的预测。我们估计了数据集不同子集的模型预测性能，以模拟收集生态系统特征观测的三种常见抽样策略：随机抽样设计、分层随机抽样设计和定向抽样。我们发现抽样策略对模型预测性能的影响如下：(1)与简单随机抽样设计相比，分层随机抽样设计并没有提高预测性能；(2)尽管模拟目标(非随机)抽样的情景之一的预测模型的性能最差，但其他目标抽样情景的模型的预测性能与随机抽样情景的预测性能相似。我们的结果表明，尽管来自某些形式的定向抽样的数据集的潜在偏差可能会限制预测性能，但汇编现有的空间广泛的数据集可以产生具有良好预测性能的模型，这些模型可能会为与全球变化相关的广泛的科学问题和政策目标提供信息。
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‐km2area. 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.
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影响因子：
10.1
作者：
Peters, Debra P. C.;Burruss, N. Dylan;Vivoni, Enrique R.
通讯作者：
Vivoni, Enrique R.
影响因子：
5
作者：
Read, Emily K.;Patil, Vijay P.;Weathers, Kathleen C.
通讯作者：
Weathers, Kathleen C.
影响因子：
10.3
作者：
Poisson, Autumn C.;McCullough, Ian M.;Soranno, Patricia A.
通讯作者：
Soranno, Patricia A.
影响因子：
9.2
作者：
Soranno PA;Bissell EG;Cheruvelil KS;Christel ST;Collins SM;Fergus CE;Filstrup CT;Lapierre JF;Lottig NR;Oliver SK;Scott CE;Smith NJ;Stopyak S;Yuan S;Bremigan MT;Downing JA;Gries C;Henry EN;Skaff NK;Stanley EH;Stow CA;Tan PN;Wagner T;Webster KE
通讯作者：
Webster KE
空间结构的相似性限制了生态系统关系：建立对湖泊的宏观理解
影响因子：
6.4
作者：
Lapierre, Jean‐Francois;Collins, Sarah M.;Seekell, David A.;Spence Cheruvelil, Kendra;Tan, Pang‐Ning;Skaff, Nicholas K.;Taranu, Zofia E.;Fergus, C. Emi;Soranno, Patricia A.
通讯作者：
Soranno, Patricia A.