The community ecology perspective of omics data

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.

Seasonal Metabolic Dynamics of Microeukaryotic Plankton: A Year-long Metatranscriptomic Study in a Temperate Sea

“…Likewise, there is a weak negative correlation between functional richness and log transformed FlowCam cell abundances (r = -0.28, df = 55, p = 0.036). These negative correlations likely arise from non-homogeneous distribution of species in high biomass situations, which impacts the detection of rare species with the given sequencing depth 30 (Fig. S6).…”

Salinity and resource availability as drivers of Baltic benthic fungal diversity

“…The DNA metabarcoding technique is associated with some limitations (Jurburg et al., 2022; Ruppert et al., 2019) that were carefully considered and attempted to be minimized during the development of this study. One of them is related to the use of eDNA that is heterogeneously distributed in the environment and is susceptible to degradation (Mauvisseau et al., 2022).…”

Rapid taxonomic categorization of short, abundant virus sequences for ecological analyses

“…Although this methodology, and thus this work, does not identify novel viruses, we show progress in integrating viral ecology with community ecology, as both fields involve focusing on emergent properties of a profile of taxonomic units, instead of concerning single entities, the identity and function of which may be unknown (Jurburg et al., 2022; McGill et al., 2007). Microbial ecologists facing a similar lack of specific bacterial identities have advanced their field by emphasizing compositional data, relative analyses such as ratios, and global associations between community structure and phenotypes of interest (Gloor et al., 2016; Jurburg et al., 2022; Pawlowsky‐Glahn et al., 2015; Rivera‐Pinto et al., 2018). Therefore, we are confident that descriptions of herpesvirus communities that lack properly identified species can still provide foundational data to inform the nascent field of viral community ecology.…”