Simulations of life history variation for demographic inference from population genomic data

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Abstract

Ecological differences among species influence population response to historical environmental changes, and genetic simulations now allow us to directly incorporate this variation into inferential models. However, the impact of life history strategies in demographic inference has been far less explored relative to the impact of other ecological differences, such as dispersal capacity and habitat preference. Here, we utilize individual-based simulations of a non-Wright-Fisher population to ask whether differences in the average age of first reproduction of individuals, the average adult mortality and the average number of mates per reproductive season lead to consistent and predictable differences in summary statistics of genetic diversity commonly used for simulation-based parameter estimation and demographic inference. Using a Random Forest model, we estimate three population parameters (variance in reproductive success, generation time, and effective population size) from genome-wide SNP variation for two bird species with distinct life history strategies that are directly built into the simulation machinery. Our results show that life history variation leads to predictable differences in patterns of genetic diversity. While prediction accuracy is low, parameter estimates from empirical datasets agree with the expectation that species with extreme polygamy, long adult longevity and later onset of reproduction will exhibit higher variance in reproductive success, longer generation time and smaller effective population sizes. Since the signal of life history differences is observed in the genetic summary statistics, we suggest that simulation- and model-based multi-species demographic inference should incorporate life history parameters.
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Simulations of life history variation for demographic inference from population genomic data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Molecular Ecology This is a preprint and has not been peer reviewed. Data may be preliminary. 29 January 2025 V1 Latest version Share on Simulations of life history variation for demographic inference from population genomic data Authors : Rilquer Mascarenhas 0000-0002-9905-4281 [email protected] , Michael Hickerson , and Ana Carolina Carnaval Authors Info & Affiliations https://doi.org/10.22541/au.173814305.53825759/v1 Published Molecular Ecology Version of record Peer review timeline 418 views 209 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Ecological differences among species influence population response to historical environmental changes, and genetic simulations now allow us to directly incorporate this variation into inferential models. However, the impact of life history strategies in demographic inference has been far less explored relative to the impact of other ecological differences, such as dispersal capacity and habitat preference. Here, we utilize individual-based simulations of a non-Wright-Fisher population to ask whether differences in the average age of first reproduction of individuals, the average adult mortality and the average number of mates per reproductive season lead to consistent and predictable differences in summary statistics of genetic diversity commonly used for simulation-based parameter estimation and demographic inference. Using a Random Forest model, we estimate three population parameters (variance in reproductive success, generation time, and effective population size) from genome-wide SNP variation for two bird species with distinct life history strategies that are directly built into the simulation machinery. Our results show that life history variation leads to predictable differences in patterns of genetic diversity. While prediction accuracy is low, parameter estimates from empirical datasets agree with the expectation that species with extreme polygamy, long adult longevity and later onset of reproduction will exhibit higher variance in reproductive success, longer generation time and smaller effective population sizes. Since the signal of life history differences is observed in the genetic summary statistics, we suggest that simulation- and model-based multi-species demographic inference should incorporate life history parameters. Supplementary Material File (mascarenhas_et_al_2025-main-text.doc) Download 284.00 KB Information & Authors Information Version history V1 Version 1 29 January 2025 Peer review timeline Published Molecular Ecology Version of Record 5 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Molecular Ecology Keywords age-structured populations amazon forest comparative phylogeography nucleotide diversity polygamy Authors Affiliations Rilquer Mascarenhas 0000-0002-9905-4281 [email protected] The City College of New York View all articles by this author Michael Hickerson The City College of New York View all articles by this author Ana Carolina Carnaval The City College of New York View all articles by this author Metrics & Citations Metrics Article Usage 418 views 209 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rilquer Mascarenhas, Michael Hickerson, Ana Carolina Carnaval. Simulations of life history variation for demographic inference from population genomic data. Authorea . 29 January 2025. DOI: https://doi.org/10.22541/au.173814305.53825759/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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