Ecological prediction at macroscales using big data: Does sampling design matter?. Issue 6 (27th April 2020)

• Record Type: Journal Article
• Authors: 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; Filstrup, Christopher T.; Hanks, Ephraim M.; Lapierre, Jean‐François; Lottig, Noah R.; Schliep, Erin M.; Wagner, Tyler; Webster, Katherine E.
• Is Part Of: Ecological applications. Volume 30:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-27
• Subjects: data‐intensive ecology -- ecological context -- extrapolation -- interpolation -- lakes -- macroscale -- monitoring -- prediction -- sampling -- sampling design; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2123
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: 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‐km 2 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.