Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon–taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.
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AC, CD, and SMG were supported by a Washington Research Foundation Distinguished Investigator Award and by startup funds from the Institute for Systems Biology. AC and NSB were supported by Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research under Contract number DE-AC02-05CH11231. NSB was supported by the National Science Foundation under Grant Nos. OCE—1558924, CBET—1606206, MCB—1518261, DBI—1565166, and MCB—1616955 to NSB. The authors thank the editor and two anonymous peer reviewers for helping us to improve the quality of this Perspective.
Conflict of interest
The authors declare that they have no conflict of interest.
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