scholarly journals Indirect genetic effects clarify how traits can evolve even when fitness does not

2018 ◽  
Author(s):  
David N. Fisher ◽  
Andrew G. McAdam
Keyword(s):  
Natural Selection ◽  
Fundamental Theorem ◽  
Genetic Effects ◽  
Social Effects ◽  
Indirect Genetic Effects ◽  
The Social ◽  
Mean Fitness ◽  
Related Individuals ◽  
Fundamental Theorems ◽  
Fisher’S Fundamental Theorem

AbstractThere are many situations in nature where we expect traits to evolve but not necessarily for mean fitness to increase. However, these scenarios are hard to reconcile simultaneously with Fisher’s Fundamental Theorem of Natural Selection and the Price identity. The consideration of indirect genetic effects on fitness reconciles these fundamental theorems with the observation that traits sometimes evolve without any adaptation, by explicitly considering the correlated evolution of the social environment, which is a form of transmission bias. While transmission bias in the Price identity is often assumed to be absent, here we show that explicitly considering indirect genetic effects as a form of transmission bias for fitness has several benefits: 1) it makes clear how traits can evolve while mean fitness remains stationary, 2) it reconciles the fundamental theorem of natural selection with the evolution of maladaptation, 3) it explicitly includes density-dependent fitness through negative social effects that depend on the number of interacting conspecifics, and 4) its allows mean fitness to evolve even when direct genetic variance in fitness is zero, if related individuals interact and/or if there is multilevel selection. In summary, considering fitness in the context of indirect genetic effects aligns important theorems of natural selection with many situations observed in nature and provides a useful lens through which we might better understand evolution and adaptation.

scholarly journals Joint phenotypes, evolutionary conflict and the fundamental theorem of natural selection

2014 ◽  
Vol 369 (1642) ◽  
pp. 20130423 ◽  
Author(s):  
David C. Queller
Keyword(s):  
Natural Selection ◽  
Species Interactions ◽  
Fundamental Theorem ◽  
Genetic Variance ◽  
Inclusive Fitness ◽  
Evolutionary Conflict ◽  
Mean Fitness ◽  
The Mean ◽  
Multiple Species ◽  
Fisher’S Fundamental Theorem

Multiple organisms can sometimes affect a common phenotype. For example, the portion of a leaf eaten by an insect is a joint phenotype of the plant and insect and the amount of food obtained by an offspring can be a joint trait with its mother. Here, I describe the evolution of joint phenotypes in quantitative genetic terms. A joint phenotype for multiple species evolves as the sum of additive genetic variances in each species, weighted by the selection on each species. Selective conflict between the interactants occurs when selection takes opposite signs on the joint phenotype. The mean fitness of a population changes not just through its own genetic variance but also through the genetic variance for its fitness that resides in other species, an update of Fisher's fundamental theorem of natural selection. Some similar results, using inclusive fitness, apply to within-species interactions. The models provide a framework for understanding evolutionary conflicts at all levels.

Adding Dynamical Sufficiency to Fisher’s Fundamental Theorem of Natural Selection

2011 ◽  
Author(s):  
Philip J. Gerrish ◽  
Paul D. Sniegowski ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras ◽  
...  
Keyword(s):  
Natural Selection ◽  
Fundamental Theorem ◽  
Fisher’S Fundamental Theorem

scholarly journals The Price equation and reproductive value

2020 ◽  
Vol 375 (1797) ◽  
pp. 20190356 ◽  
Author(s):  
Alan Grafen
Keyword(s):  
Natural Selection ◽  
Fundamental Theorem ◽  
Structured Populations ◽  
Reproductive Value ◽  
Price Equation ◽  
Class Structure ◽  
Theme Issue ◽  
Starting Point ◽  
Important Idea ◽  
Fisher’S Fundamental Theorem

The Price equation is widely recognized as capturing conceptually important properties of natural selection, and is often used to derive versions of Fisher’s fundamental theorem of natural selection, the secondary theorems of natural selection and other significant results. However, class structure is not usually incorporated into these arguments. From the starting point of Fisher’s original connection between fitness and reproductive value, a principled way of incorporating reproductive value and structured populations into the Price equation is explained, with its implications for precise meanings of (two distinct kinds of) reproductive value and of fitness. Once the Price equation applies to structured populations, then the other equations follow. The fundamental theorem itself has a special place among these equations, not only because it always incorporated class structure (and its method is followed for general class structures), but also because that is the result that justifies the important idea that these equations identify the effect of natural selection. The precise definitions of reproductive value and fitness have striking and unexpected features. However, a theoretical challenge emerges from the articulation of Fisher’s structure: is it possible to retain the ecological properties of fitness as well as its evolutionary out-of-equilibrium properties? This article is part of the theme issue ‘Fifty years of the Price equation’.

scholarly journals Adaptive fitness landscape for replicator systems: to maximize or not to maximize

2018 ◽  
Vol 13 (3) ◽  
pp. 25 ◽  
Author(s):  
Alexander S. Bratus ◽  
Yuri S. Semenov ◽  
Artem S. Novozhilov
Keyword(s):  
Natural Selection ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Hill Climbing ◽  
Adaptive Landscape ◽  
Basic Model ◽  
Mean Fitness ◽  
The Mean ◽  
The Hill ◽  
Fisher’S Fundamental Theorem

Sewall Wright’s adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher’s fundamental theorem of natural selection and Kimura’s maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a severe misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system’s dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system’s dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.

scholarly journals The Fundamental Theorem of Natural Selection

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1436
Author(s):  
John C. Baez
Keyword(s):  
Natural Selection ◽  
Continuous Function ◽  
Fundamental Theorem ◽  
Time Dependent ◽  
Original Result ◽  
Fisher Information Metric ◽  
Different Types ◽  
Information Metric ◽  
Fisher’S Fundamental Theorem ◽  
Dependent Probability

Suppose we have n different types of self-replicating entity, with the population Pi of the ith type changing at a rate equal to Pi times the fitness fi of that type. Suppose the fitness fi is any continuous function of all the populations P1,⋯,Pn. Let pi be the fraction of replicators that are of the ith type. Then p=(p1,⋯,pn) is a time-dependent probability distribution, and we prove that its speed as measured by the Fisher information metric equals the variance in fitness. In rough terms, this says that the speed at which information is updated through natural selection equals the variance in fitness. This result can be seen as a modified version of Fisher’s fundamental theorem of natural selection. We compare it to Fisher’s original result as interpreted by Price, Ewens and Edwards.

scholarly journals Selection for feed efficiency using the social effects animal model in growing Duroc pigs: evaluation by simulation

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
William Herrera-Cáceres ◽  
Juan Pablo Sánchez
Keyword(s):  
Animal Model ◽  
Direct Reduction ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Social Effects ◽  
High Rate ◽  
Phenotypic Variance ◽  
Feed Conversion ◽  
The Social

Abstract Background Traits recorded on animals that are raised in groups can be analysed with the social effects animal model (SAM). For multiple traits, this model specifies the genetic correlation structure more completely than the animal model (AM). Our hypothesis was that by using the SAM for genetic evaluation of average daily gain (ADG) and backfat thickness (BF), a high rate of improvement in feed conversion ratio (FCR) might be achieved, since unfavourable genetic correlations between ADG and BF reported in a Duroc pig line could be partially avoided. We estimated genetic and non-genetic correlations between BF, ADG and FCR on 1144 pigs using Bayesian methods considering the SAM; and responses to selection indexes that combine estimates of indirect (IGE) and direct (DGE) genetic effects for ADG and BF by stochastic simulation. Results Estimates of the ratio of the variance of DGE to the phenotypic variance were 0.31, 0.39 and 0.25 and those of the total genetic variance to the phenotypic variance were 0.63, 0.74 and 0.93 for ADG, BF and FCR, respectively. In spite of this, when the SAM was used to generate data and for the genetic evaluations, the average economic response was worse than that obtained when BV predictions from the AM were considered. The achieved economic response was due to a direct reduction in BF and not to an improvement in FCR. Conclusions Our results show that although social genetic effects play an important role in the traits studied, their proper consideration in pig breeding programs to improve FCR indirectly is still difficult. The correlations between IGE and DGE that could help to overcome the unfavourable genetic correlations between DGE did not reach sufficiently high magnitudes; also, the genetic parameters estimates from the SAM have large errors. These two factors penalize the average response under the SAM compared to the AM.

scholarly journals The purpose of adaptation

2017 ◽  
Vol 7 (5) ◽  
pp. 20170005 ◽  
Author(s):  
Andy Gardner
Keyword(s):  
Natural Selection ◽  
Fundamental Theorem ◽  
Social Evolution ◽  
Inclusive Fitness ◽  
Central Feature ◽  
Biological Adaptation ◽  
Fisher’S Fundamental Theorem

A central feature of Darwin's theory of natural selection is that it explains the purpose of biological adaptation. Here, I: emphasize the scientific importance of understanding what adaptations are for, in terms of facilitating the derivation of empirically testable predictions; discuss the population genetical basis for Darwin's theory of the purpose of adaptation, with reference to Fisher's ‘fundamental theorem of natural selection'; and show that a deeper understanding of the purpose of adaptation is achieved in the context of social evolution, with reference to inclusive fitness and superorganisms.

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