Complex life cycles drive community assembly through immigration and adaptive diversification

Abstract

Most animals undergo ontogentic niche shifts during their life. Yet, standard ecological theory builds on models that ignore this complexity. Here, we study how complex life cycles, where juvenile and adult individuals each feed on different sets of resources, affect community richness. Two different modes of community assembly are considered: gradual adaptive evolution and immigration of new species with randomly selected phenotypes. We find that under gradual evolution complex life cycles can lead to both higher and lower species richness when compared to a model of species with simple life cycles that lack an ontogenetic niche shift. Thus, complex life cycles do not per se increase the scope for gradual adaptive diversification. However, complex life cycles can lead to significantly higher species richness when communities are assembled trough immigration, as immigrants can occupy isolated peaks of the dynamic fitness landscape that are not accessible via gradual evolution.

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“…is the establishment probability of the mutant phenotype with trait y i , i.e., the probability that the mutant survives demographic stochasticity and successfully invades the population (Allen, 2010; Saltini et al, 2023), and where µ is the per-capita mortality rate defined in Equation ( 9). From Equation (S2) it follows that the rate at which new mutations establish depends on the phenotypic traits in the populations which are variable quantities subject to evolutionary dynamics.…”

“…The simulation algorithm, developed by Saltini et al (2023) is programmed in C++ and works as follows:…”

“…is the establishment probability of the mutant phenotype with trait y i , i.e., the probability that the mutant survives demographic stochasticity and successfully invades the population (Allen, 2010; Saltini et al, 2023), and where µ is the per-capita mortality rate defined in Equation ( 10).…”

“…More specifically, in our simulations we included demographic stochasticity of emerging mutants, stochasticity in the selection and mutation process, and we relaxed the assumption of clear separation between population dynamic and evolutionary time scales. In order to do so, we used a computational algorithm developed by Saltini et al (2023) and an adapted version of the code, see Supplementary Information S2 for details.…”

“…Then, the rate Ω at which y i invades the resident population is (Dieckmann & Law, 1996; Champagnat et al, 2006). In the last equation, ω is the per-capita probability to produce a mutation, is the equilibrium density of the phenotype from which the mutant has occurred, β is the per-capita reproductive rate of the phenotype i and is defined in Equation (9), and is the establishment probability of the mutant phenotype with trait y i , i.e., the probability that the mutant survives demographic stochasticity and successfully invades the population (Allen, 2010; Saltini et al, 2023), and where µ is the per-capita mortality rate defined in Equation (10).…”