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Innovative mark–recapture experiment shows patterns of selection on transcript abundance in the wild
Author(s) -
Josephson Matthew P.,
Bull James K.
Publication year - 2021
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.15946
Subject(s) - biology , selection (genetic algorithm) , evolutionary biology , trait , abundance (ecology) , natural selection , salmo , population , phenotypic trait , genetics , gene , computational biology , ecology , phenotype , fish <actinopterygii> , fishery , demography , sociology , computer science , programming language , artificial intelligence
A fundamental aspect of evolutionary biology is natural selection on trait variation. Classically, selection has been estimated primarily on external morphological traits such as beak size and coloration, or on easily assayable physiological traits such as heat‐tolerance. As technologies and methods improved, evolutionary biologists began examining selection on molecular traits such as protein sequences and cellular processes. In a From the Cover paper in this issue of Molecular Ecology , Ahmad et al . continue this trend by estimating parasite‐driven selection on the molecular trait of transcript abundance in a wild population of brown trout ( Salmo trutta ) by uniquely combining a mark–recapture experimental design with noninvasive RNA sampling. Using transcript abundance to estimate selection allows for many different traits (each unique gene's transcript counts) to be tested in a single experiment, providing the opportunity to examine trends in selection. Ahmad et al . find directional selection strength on transcript counts is generally low and normally distributed. Surprisingly, transcripts under nonlinear selection showed a disruptive selection bias, contradicting previous comparative studies and theoretical work. This highlights the importance of within‐generation selection studies, where mechanisms may differ from longer time frames. Their paper also highlights the benefits of a cost‐effective 3′ RNA sequencing technique to measure gene expression.