EDUCATION Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and qual-ity. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typ-ical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottle-necks in R code and determine whether optimization can be effective. Our review is comple-mented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research. This is part of the PLOS Computational Biology Education collection