A generalization of the informational view of non-random mating: Models with variable population frequencies

Carvajal-Rodríguez, Antonio

doi:10.1016/j.tpb.2018.12.004

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…However, the above assumption is likely to be violated in the case of monogamous species with low population size, or even in large population sizes with local competition for mates (if the number of individuals in the patches is low) and/or space-temporal constraints. In such cases, the mating process resembles a sampling without replacement and the population phenotype frequencies may be altered during the reproductive season so that the sexual selection and assortative mating patterns would be more difficult to detect (Carvajal-Rodríguez 2019). In fact, the simulations (see Appendix C) showed that the performance of the multi-model inference is affected by the sampling and the mating system (polygamous or monogamous) but it is still quite robust for detecting non-random mating in the parameter values except in the worst scenario of monogamous species with small population sizes.…”

Section: Simulationsmentioning

confidence: 99%

Multi-model inference of non-random mating from an information theoretic approach

Carvajal-Rodríguez

2020

Theoretical Population Biology

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Non-random mating has a significant impact on the evolution of organisms. Here, I developed a modelling framework for discrete traits (with any number of phenotypes) to explore different models connecting the non-random mating causes (mate competition and/or mate choice) and their consequences (sexual selection and/or assortative mating). I derived the formulas for the maximum likelihood estimates of each model and used information criteria to perform multi-model inference. Simulation results showed a good performance of both model selection and parameter estimation. The methodology was applied to ecotypes data of the marine gastropod Littorina saxatilis from Galicia (Spain), to show that the mating pattern is better described by models with two parameters that involve both mate choice and competition, generating positive assortative mating plus female sexual selection. As far as I know, this is the first standardized methodology for model selection and multi-model inference of mating parameters for discrete traits. The advantages of this framework include the ability of setting up models from which the parameters connect causes, as mate competition and mate choice, with their outcome in the form of data patterns of sexual selection and assortative mating. For some models, the parameters may have a double effect i.e. they produce sexual selection and assortative mating, while for others there are separated parameters for one kind of pattern or another. From an empirical point of view, it is much easier to study patterns than processes and, for this reason, the causal mechanisms of sexual selection are not so well known as the patterns they produce. The goal of the present work is to propose a new tool that helps to distinguish among different alternative processes behind the observed mating pattern. The full methodology was implemented in a software called InfoMating (available at http://acraaj.webs6.uvigo.es/InfoMating/Infomating.htm).

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Section: Simulationsmentioning

confidence: 99%

Multi-model inference of non-random mating from an information theoretic approach

Carvajal-Rodríguez

2020

Theoretical Population Biology

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“…See Refs. [2,3] for extensive explanation about individual or mass-encounters.Out_MateSim_massEM_8_ac2-3_driftnonU_sample50_N10000_MPF0.1:Mass-encounter model 8 with female competition ( a = 2) plus choice compound-parameter model ( c = 3). Sample size is 50 matings from a population of size N = 10 000.…”

Section: Data Descriptionmentioning

confidence: 99%

Data and models from multi-model inference of non-random mating from an information theoretic approach

Carvajal-Rodríguez

2020

Data in Brief

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This is a co-submission with Multi-model inference of non-random mating from an information theoretic approach [1]. These data corresponds to the complete simulated data set jointly with the set of models defined for analysing the data. The simulated data set was obtained using the program MateSim [2].The simulated cases correspond to one-sex competition and mate choice models. For each simulation run, the population frequencies (premating individuals) and the sample of 500 mating pairs were generated randomly for a hypothetical trait with two classes at each sex.Some datasets represent larger population size species (n = 10 000) and the mating process was represented as a sampling with replacement, and the population frequencies were constant over the mating season. The minimum phenotype frequency (MPF) allowed was 0.1.Five different model cases were simulated, namely random mating, female competition with mate choice (with independent or compound parameters) and male competition with mate choice (with independent or compound parameters). Each case was simulated 1000 times.Other datasets represent monogamous species (with large or small population size) and the mating process was without replacement (from the point of view of the available phenotypes).These data sets were used to test the performance of the multi-model inference methodology proposed in [1]. The data may be useful for testing any new/old statistics for measuring sexual selection or assortative mating patterns.

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“…To compare different types’ fitness fairly, we compute the relative fitness, w i , as the ratio of their absolute fitness to the mean population fitness. Thus, for a given type i with frequency p i , the new frequency p ’ i after a selection episode will be given by (Bürger 2000; Hansen 2018) Recently, a simple theoretical framework, similar to the model presented in (1), has been developed to describe the influence of mating competition and mate choice on the emergence of sexual selection and assortative mating patterns (Carvajal-Rodríguez 2018b, 2019, 2020). This theoretical framework has enabled the development of new statistical tests for detecting sexual selection and assortative mating.…”

Section: Introductionmentioning

confidence: 99%

“…In a previous studies (Carvajal-Rodríguez, 2019, 2018a), it was demonstrated that non-random mating can be effectively quantified using the Jeffreys (Kullback-Leibler symmetrized) divergence. This measure captures the information acquired when mating is influenced by mutual mating propensities, rather than occurring randomly.…”

Section: Introductionmentioning

confidence: 99%

“…The use of information theory to describe evolutionary processes is not new as it has a long tradition in evolutionary ecology, (see for example Sherwin, 2018; Sherwin et al, 2017; Sherwin and Prat i Fornells, 2019) and population genetics (Frank, 2012; Hledík et al, 2022; Kimura, 1961). Previous work on information theory and non-random mating was done on discrete traits (Carvajal-Rodríguez, 2018a, 2019, 2020). Here we extend that work to quantitative traits to describe patterns of sexual selection and assortative mating in terms of information.…”

Section: Introductionmentioning

confidence: 99%

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The information theory formalism unifies the detection of the patterns of sexual selection and assortative mating for both discrete and quantitative traits

Carvajal-Rodríguez

2023

Preprint

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Sexual selection plays a crucial role in modern evolutionary theory, offering valuable insight into evolutionary patterns and species diversity. Recently, a comprehensive definition of sexual selection has been proposed, defining a trait as subject to sexual selection when variations in that trait are nonrandomly associated with variations in access to gametes, a limiting resource. Previous research on discrete traits demonstrated that non-random mating can be effectively quantified using the Kullback-Leibler symmetrized divergence, capturing information acquired through mating influenced by mutual mating propensities instead of random occurrences. This novel theoretical framework allows for detecting and assessing the strength of sexual selection and assortative mating. In this study, we aim to achieve two primary objectives. Firstly, we demonstrate the seamless alignment of the previous theoretical development, rooted in information theory and mutual mating propensity, with the new sexual selection definition. Secondly, we extend the theory to encompass quantitative traits. Our findings reveal that sexual selection and assortative mating can be quantified effectively for quantitative traits by measuring the information gain concerning the random mating pattern. Specifically, the gain in information within the sexual selection pattern can be expressed as a function of the sexual selection intensity index and the ratio of variances. Additionally, the measure capturing the underlying information of assortative mating is a function of the square of the correlation coefficient, taking values within the non-negative real number set [0, +∞).

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A generalization of the informational view of non-random mating: Models with variable population frequencies

Cited by 8 publications

References 15 publications

Multi-model inference of non-random mating from an information theoretic approach

Multi-model inference of non-random mating from an information theoretic approach

Data and models from multi-model inference of non-random mating from an information theoretic approach

The information theory formalism unifies the detection of the patterns of sexual selection and assortative mating for both discrete and quantitative traits

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