On the effect of phenotypic dimensionality on adaptation and optimality

Area of Science:

• Evolutionary biology
• Quantitative genetics
• Mathematical modeling

Background:

• Understanding the efficiency of natural selection in shaping complex organisms is a fundamental question in evolutionary biology.
• Previous models often simplify the genotype-phenotype map or trait variation.

Purpose of the Study:

• To estimate the maximum proportion of traits optimally shaped by natural selection in multitrait organisms.
• To investigate the influence of fitness functions and the number of traits on selection efficiency.
• To explore the evolutionary dynamics of trait number (n).

Main Methods:

• Development of a mathematical model for natural selection under ideal conditions (simple genotype-phenotype map, independent trait variation).
• Simulations incorporating various fitness functions (additive linear, additive nonlinear, Gaussian, multiplicative).
• Analysis of trait age and its correlation with proximity to optimum and contribution to absolute fitness.

Main Results:

• Optimal phenotypes are rarely achieved, primarily only for a single trait (n=1).
• A large proportion of traits remain far from their optimum, particularly as the number of traits (n) increases.
• The degree of deviation from optimum varies with different fitness functions.
• Earlier evolving traits contribute more to absolute fitness and are closer to their optimum.

Conclusions:

• Natural selection is less efficient at optimizing multiple traits simultaneously, even under favorable conditions.
• Phenotypic complexity leads to a higher proportion of non-optimal traits.
• Trait age is a significant factor influencing adaptation levels, with older traits being more refined.