Search for: All records

ABSTRACT Maladapted immigrants may reduce wild population productivity and resilience, depending on the degree of fitness mismatch between dispersers and locals. Thus, domesticated individuals escaping into wild populations is a key conservation concern. In Prince William Sound, Alaska, over 700 million pink salmon (Oncorhynchus gorbuscha) are released annually from hatcheries, providing a natural experiment to characterize the mechanisms underlying impacts to wild populations. Using a dataset of > 200,000 pink salmon sampled from 30 populations over 8 years, we detected significant body size and phenological differences between hatchery‐ and wild‐origin spawners, likely driven by competitive differences during maturation and broodstock selection practices. Variation in traits was reduced in hatchery fish, raising biodiversity concerns. However, phenotypic traits of immigrants and locals were positively correlated. We discuss possible mechanisms that may explain this pattern and how it may reduce adverse impacts associated with reduced trait variation. This study suggests that domestication impacts are likely widespread, but local adaptation may be maintained by phenotypic sorting. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.