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The public perception of viruses has historically been negative. We are now at a stage where the development of tools to study viruses is at an all-time high, but society’s perception of viruses is at an all-time low. The literature regarding viral interactions has been skewed towards negative (i.e., pathogenic) symbioses, whereas viral mutualisms remain relatively underexplored. Viral interactions with their hosts are complex and some non-pathogenic viruses could have potential benefits to society. However, viral research is seldom designed to identify viral mutualists, a gap that merits considering new experimental designs. Determining whether antagonisms, mutualisms, and commensalisms are equally common ecological strategies requires more balanced research efforts that characterize the full spectrum of viral interactions. 

Abstract The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples ( ɑ -diversity) and to assess how these profiles change in response to experimental treatments or across gradients ( β -diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ - and β -diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data.