Patterns of environmental variance across environments and traits in domestic cattle
(2020) In Evolutionary Applications 13(5). p.1090-1102- Abstract
The variance in phenotypic trait values is a product of environmental and genetic variation. The sensitivity of traits to environmental variation has a genetic component and is likely to be under selection. However, there are few studies investigating the evolution of this sensitivity, in part due to the challenges of estimating the environmental variance. The livestock literature provides a wealth of studies that accurately partition components of phenotypic variance, including the environmental variance, in well-defined environments. These studies involve breeds that have been under strong selection on mean phenotype in optimal environments for many generations, and therefore represent an opportunity to study the potential evolution... (More)
The variance in phenotypic trait values is a product of environmental and genetic variation. The sensitivity of traits to environmental variation has a genetic component and is likely to be under selection. However, there are few studies investigating the evolution of this sensitivity, in part due to the challenges of estimating the environmental variance. The livestock literature provides a wealth of studies that accurately partition components of phenotypic variance, including the environmental variance, in well-defined environments. These studies involve breeds that have been under strong selection on mean phenotype in optimal environments for many generations, and therefore represent an opportunity to study the potential evolution of trait sensitivity to environmental conditions. Here, we use literature on domestic cattle to examine the evolution of micro-environmental variance (CVR—the coefficient of residual variance) by testing for differences in expression of CVR in animals from the same breed reared in different environments. Traits that have been under strong selection did not follow a null expectation of an increase in CVR in heterogenous environments (e.g., grazing), a pattern that may reflect evolution of increased uniformity in heterogeneous environments. When comparing CVR across environments of different levels of optimality, here measured by trait mean, we found a reduction in CVR in the more optimal environments for both life history and growth traits. Selection aimed at increasing trait means in livestock breeds typically occurs in the more optimal environments, and we therefore suspect that the decreased CVR is a consequence of evolution of the expression of micro-environmental variance in this environment. Our results highlight the heterogeneity in micro-environmental variance across environments and point to possible connections to the intensity of selection on trait means.
(Less)
- author
- Schou, Mads F. LU ; Kristensen, Torsten N. and Hoffmann, Ary A.
- organization
- publishing date
- 2020-05
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- cattle, directional selection, environmental variance, evolution, selection intensity, stabilizing selection
- in
- Evolutionary Applications
- volume
- 13
- issue
- 5
- pages
- 13 pages
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85081320870
- pmid:32431754
- ISSN
- 1752-4571
- DOI
- 10.1111/eva.12924
- language
- English
- LU publication?
- yes
- id
- cf3e18e2-0c60-4edb-9e13-747eee855f82
- date added to LUP
- 2020-04-10 16:46:46
- date last changed
- 2024-05-29 11:45:20
@article{cf3e18e2-0c60-4edb-9e13-747eee855f82,
abstract = {{<p>The variance in phenotypic trait values is a product of environmental and genetic variation. The sensitivity of traits to environmental variation has a genetic component and is likely to be under selection. However, there are few studies investigating the evolution of this sensitivity, in part due to the challenges of estimating the environmental variance. The livestock literature provides a wealth of studies that accurately partition components of phenotypic variance, including the environmental variance, in well-defined environments. These studies involve breeds that have been under strong selection on mean phenotype in optimal environments for many generations, and therefore represent an opportunity to study the potential evolution of trait sensitivity to environmental conditions. Here, we use literature on domestic cattle to examine the evolution of micro-environmental variance (CV<sub>R</sub>—the coefficient of residual variance) by testing for differences in expression of CV<sub>R</sub> in animals from the same breed reared in different environments. Traits that have been under strong selection did not follow a null expectation of an increase in CV<sub>R</sub> in heterogenous environments (e.g., grazing), a pattern that may reflect evolution of increased uniformity in heterogeneous environments. When comparing CV<sub>R</sub> across environments of different levels of optimality, here measured by trait mean, we found a reduction in CV<sub>R</sub> in the more optimal environments for both life history and growth traits. Selection aimed at increasing trait means in livestock breeds typically occurs in the more optimal environments, and we therefore suspect that the decreased CV<sub>R</sub> is a consequence of evolution of the expression of micro-environmental variance in this environment. Our results highlight the heterogeneity in micro-environmental variance across environments and point to possible connections to the intensity of selection on trait means.</p>}},
author = {{Schou, Mads F. and Kristensen, Torsten N. and Hoffmann, Ary A.}},
issn = {{1752-4571}},
keywords = {{cattle; directional selection; environmental variance; evolution; selection intensity; stabilizing selection}},
language = {{eng}},
number = {{5}},
pages = {{1090--1102}},
publisher = {{Wiley-Blackwell}},
series = {{Evolutionary Applications}},
title = {{Patterns of environmental variance across environments and traits in domestic cattle}},
url = {{http://dx.doi.org/10.1111/eva.12924}},
doi = {{10.1111/eva.12924}},
volume = {{13}},
year = {{2020}},
}