Why there are so many definitions of fitness in models

This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Evolutionary “fitness” is operationalized in many different ways. Its role is to quantify that which is favored by natural selection. Generally, short-term ability to survive and reproduce (e.g., expected number of surviving offspring) is assigned to genotypes or phenotypes, and used to non-trivially derive longer-term quantities (e.g. invasion rate or fixation probability) that provide insight as to which organismal strategies tend to evolve due to natural selection. Assigned fitness operationalizations either explicitly or implicitly specify organismal vital rates (i.e. births, deaths, organismal growth). Derived operationalizations also depend on assumptions regarding demographic stochasticity, environmental stochasticity, feedbacks whereby births, deaths, and organismal growth cause environmental change, and the impact of migration and niche construction on which environment is experienced. The choice of derived operationalization can impact conclusions, as we illustrate for the evolution of bet-hedging, when treated by invasion probability versus expected Malthusian parameter within an adaptive dynamics approach. After reviewing existing derived fitness operationalizations, we propose a new one that meets the particular challenges posed by balancing selection. Population genetic models generally sidestep ultra-high-dimensional phenotype and genotype spaces by instead deriving the long-term evolutionary fate/fitness of a lower-dimensional set of genetically encoded “strategies”. Strategies (e.g. costly developmental commitment to producing armaments) are causally upstream from realized phenotypes (e.g. armament size), but downstream from how an organism’s early environment (e.g. maternal effects) might inform developmental commitments. While selection is best understood in terms of differences in organismal vital rates, its derived outcomes are most easily understood as properties of genetic lineages. https://doi.org/10.32942/X2V61T Biology, Computational Biology, Ecology and Evolutionary Biology, Evolution, Genetics, Genetics and Genomics, Life Sciences, Population Biology Invasion fitness, Malthusian parameter, individuality, theoretical population genetics, bet-hedging, life history strategy, density-dependent selection, Malthusian parameter, individuality, theoretical population genetics, Bet-hedging, life history strategy, density-dependent selection Published: 2024-04-11 23:10 Last Updated: 2025-11-10 12:33 CC-BY Attribution-NonCommercial-ShareAlike 4.0 International Conflict of interest statement: None Data and Code Availability Statement: Code is publically available at: https://github.com/DanielSmithEcology/Fitness_Definitions_Code Language: English