1 Département Des Sciences Biologiques, Université du Québec à Montréal, Montréal, Québec, Canada.
2 Groupe de Recherche Interuniversitaire en Limnologie (GRIL), Montréal, Québec, Canada.
3 Department of Biology, McGill University, Montréal, Québec, Canada.
4 Department of Biology, Concordia University, Montreal, Québec, Canada.

Biomass and abundance data are critical for ecosystem monitoring and management of fish populations, yet their acquisition represents a growing challenge as anthropogenic pressures intensify the need for monitoring. Environmental DNA (eDNA) has the potential to provide cost-effective biomass and abundance estimations, but its high molecular stability makes it prone to false positives. By contrast, environmental RNA (eRNA) has a faster turnover rate, potentially increasing spatio-temporal resolution and reducing false positives. Yet no study to date has explored the potential for eRNA to predict organismal biomass and abundance in nature. We compared eDNA and eRNA markers with different turnover rates to: (1) test if eRNA could be used to predict fish biomass from mark-recapture in lakes, (2) determine the relative capacity of different markers to predict fish biomass (and abundance when considering fish mass distribution) and (3) evaluate if integrating allometrically scaled mass strengthens fish biomass predictions using eRNA. We tested mitochondrial cyt b and nuclear ribosomal 18S markers on eRNA and eDNA samples. Concentrations of all markers were positively correlated with fish biomass. However, the eRNA markers (mRNA cyt b and rRNA 18S) generally had stronger relationships with fish biomass than eDNA (mtDNA cyt b and nuDNA 18S). This was especially the case for the potentially faster degrading mRNA cyt b marker that resolved differences amongst lakes in biomass and abundance that the other markers did not. Unlike for eDNA, relationships between eRNA concentration and fish biomass were not strengthened by allometric scaling. Our results show the strong potential of eRNA as a molecular tool for population biomass and abundance estimation in aquatic ecosystems.