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Title: Selection, genetics and evolution of growth and size
Author: Janeiro Silva, Maria João
ISNI:       0000 0004 8502 3032
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2019
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A considerable body of work in recent decades in the field of evolutionary quantitative genetics has been motivated by the paradox of stasis. Mismatches between observed dynamics of size in wild populations and evolutionary predictions must arise from deficient understanding of the theoretical grounds underlying the evolution in a particular system, and/or the adoption of methodological tools making assumptions that are unrealistic. Although different in their nature, these classes of explanation are difficult to tear apart, as very often quantitative genetics (statistical) tools make either implicit or explicit assumptions about biology and ecology. In this thesis, I investigate inheritance and/or selection mechanisms when conventional applications of theory are expected to lead to biased or erroneous predictions of evolutionary change in size. Specifically, I adopt a methodology to handle genetic constraints in a fairly phenotypic perspective, which facilitates quantification of bias that would exist if such constraint was not accounted for (Chapter 3). I use this methodology to tear apart the selection in Soay sheep body mass that occurs directly through its effect on fitness and indirectly through its effect on pregnancy during the first year of life. Next, I provide analytical proofs of several issues with applications of integral projection models (IPMs) that incorporate inheritance and development, concluding that these will predict no evolutionary change regardless of whether it should, will, or has occurred (Chapter 4). Another main topic of this thesis is the development of a two-sex individual-based model (IBM) of horn length (Chapter 6), equivalent to an IPM, that uses quantitative genetics theory to model trait transmission (with development functions estimated in Chapter 5). This IBM, parameterised using data from the bighorn sheep (Ovis canadensis) of Ram Mountain, is used to quantify the evolutionary response to trophy hunting, while accounting for a large number of ecological complexities.
Supervisor: Morrissey, Michael Blair Sponsor: Fundação para a Ciência e Tecnologia (Portugal)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Bias ; Evolutionary prediction ; Evolutionary quantitative genetics ; Genetic constraint ; Growth ; Individual-based models ; Integral projection models ; Natural selection ; Ontogenetic trajectories ; Ovies aries ; Ovies canadensis ; Paradox of stasis ; Size ; Trophy hunting ; QH390.J2 ; Evolutionary genetics ; Sheep--Growth ; Sheep--Evolution ; Mountain sheep hunting