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

Carvajal-Rodríguez, Antonio

doi:10.1016/j.tpb.2019.11.002

Cited by 12 publications

(24 citation statements)

References 53 publications

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“…The model sets were automatically provided by the program InfoMating [1]. For example, to generate all the models available in the program for the first data file in the Out_MateSim_6_ac2-3_driftnonU_sample500_N10000_MPF0.1 folder, the command line arguments were:…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“…The second kind of data correspond to the set of models assayed with the program InfoMating [1]. There are two different sets of models in these data:Simulation model setThe file Simulations_Model_Set corresponds to the set of models assayed with the simulated data set.Empirical model setThe file Empirical_Model_Set corresponds to the set of models assayed with the empirical data from Ref.…”

Section: Data Descriptionmentioning

confidence: 99%

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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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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

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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“…Usually, the model with the lowest AIC and BIC is selected as the best model. The AIC coe cient is ordinarily positive, but what is important is the difference between the two AIC values (or better AICc), which indicates the suitability of the two models [40]. According to Goodnessof-t statistics and Kolmogorov-Smirnov and Cramer-von Mises statistical tests, the best distribution for the data were the Weibull distribution, but the AIC and BIC coe cients prefer the normal distribution.…”

Section: Fluoride Concentrationmentioning

confidence: 99%

Concentration, distribution and probabilistic health risk assessment of exposure to fluoride in drinking water of Hormozgan province, Iran

Mohammadpour

Tabatabaee

Dehbandi

et al. 2021

Stoch Environ Res Risk Assess

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In the present study, the health risk assessment of exposure to uoride in drinking water of southern Iran province was performed with a de nite and probabilistic approach. Monte Carlo simulation and sensitivity analysis were used for uncertainty in risk estimation. The results showed that uoride concentration ranged between 0.1-0.9 mg/l with an average of 0.454 ± 0.205 mg/l. The distribution function followed the normal distribution. The highest uoride concentration was observed in the central and northern regions of the province. Also, the lowest concentration was obtained 0.01 mg/l. In the de nitive method, the non-carcinogenic risks associated with uoride in drinking water were in acceptable range (HQ < 1). Conducting Monte Carlo simulation indicated that the potential non-carcinogenic risk for children in the 95th percentile exceeded the safe limit of 1, which showed that there were a potential noncarcinogenic in this group. Sensitivity analysis showed that uoride concentration and water consumption had the most considerable effect in the model. Therefore, consumption of water and foods containing uoride along with excessive consumption of tea may increase human's health risks. Therefore, continuous monitoring of water sources in terms of uoride concentration along with proper diet training for residents of this province should be done.

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“…This measure captures the information acquired when mating is influenced by mutual mating propensities, rather than occurring randomly. By employing this novel theoretical framework, it becomes possible to detect and assess the strength of sexual selection and assortative mating (Carvajal-Rodríguez 2020; Estévez et al 2020; Lau et al 2021). However, it should be noted that the original framework was designed for discrete classes, and in this current work, we aim to extend its applicability to continuous cases.…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

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

Cited by 12 publications

References 53 publications

Data and models from 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

Concentration, distribution and probabilistic health risk assessment of exposure to fluoride in drinking water of Hormozgan province, Iran

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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