Evolution of specialized microbial cooperation in dynamic fluids

Uppal, Gurdip; Vural, Dervis Can

doi:10.1111/jeb.13593

Cited by 7 publications

(4 citation statements)

References 93 publications

(137 reference statements)

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“…In contrast, if we destroy the self-organised pattern by mixing the population ( i.e ., randomly assigning positions to individuals every time step), altruism does not evolve at all (Fig 2c). The emergent spatial patterns are hence crucial for the evolution of altruism, consistent with previous modelling work [15–17, 30, 31].…”

Section: Resultssupporting

confidence: 88%

Multiscale selection in spatially structured populations

Doekes

Hermsen

2021

Preprint

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The spatial structure of natural populations is key to many of their evolutionary processes. Formal theories analysing the interplay between natural selection and spatial structure have mostly focused on populations divided into distinct, non-overlapping groups. Most populations, however, are not structured in this way, but rather (self-)organise into dynamic patterns unfolding at various spatial scales. Here, we present a mathematical framework that quantifies how patterns and processes at different spatial scales contribute to natural selection in such populations. To that end, we define the Local Selection Differential (LSD): a measure of the selection acting on a trait within a given local environment. Based on the LSD, natural selection in a population can be decomposed into two parts: the contribution of local selection, acting within local environments, and the contribution of interlocal selection, acting among them. Varying the size of the local environments subsequently allows one to measure the contribution of each length scale. To illustrate the use of this new multiscale selection framework, we apply it to two simulation models of the evolution of traits known to be affected by spatial population structure: altruism and pathogen transmissibility. In both models, the spatial decomposition of selection reveals that local and interlocal selection can have opposite signs, thus providing a mathematically rigorous underpinning to intuitive explanations of how processes at different spatial scales may compete. It furthermore identifies which length scales - and hence which patterns - are relevant for natural selection. The multiscale selection framework can thus be used to address complex questions on evolution in spatially structured populations.

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Section: Resultssupporting

confidence: 88%

Multiscale selection in spatially structured populations

Doekes

Hermsen

2021

Preprint

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“…randomly assigning positions to individuals) at every time step, altruism does not evolve at all (figure 2c). The spatial patterns that emerge from local reproduction and ecological interactions are hence crucial for the evolution of altruism [49], consistent with previous modelling work [15][16][17]51,52].…”

Section: (I) Example I: Evolution Of Altruism Aided By Self-organizin...supporting

confidence: 88%

Multiscale selection in spatially structured populations

Doekes,

Hermsen

2024

Proc. R. Soc. B.

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The spatial structure of populations is key to many (eco-)evolutionary processes. In such cases, the strength and sign of selection on a trait may depend on the spatial scale considered. An example is the evolution of altruism: selection in local environments often favours cheaters over altruists, but this can be outweighed by selection at larger scales, favouring clusters of altruists over clusters of cheaters. For populations subdivided into distinct groups, this effect is described formally by multilevel selection theory. However, many populations do not consist of non-overlapping groups but rather (self-)organize into other ecological patterns. We therefore present a mathematical framework for multi scale selection. This framework decomposes natural selection into two parts: local selection , acting within environments of a certain size, and interlocal selection , acting among them. Varying the size of the local environments subsequently allows one to measure the contribution to selection of each spatial scale. To illustrate the use of this framework, we apply it to models of the evolution of altruism and pathogen transmissibility. The analysis identifies how and to what extent ecological processes at different spatial scales contribute to selection and compete, thus providing a rigorous underpinning to eco-evolutionary intuitions.

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“…If we perturb the selforganised pattern by mixing the population (i.e., randomly assigning positions to individuals every time step), altruism does not evolve at all (Figure 5.2c). The emergent spatial patterns are hence crucial for the evolution of altruism, a result consistent with previous modelling work (Nowak and May, 1992;Killingback et al, 1999;Le Galliard et al, 2003;Wakano et al, 2009;Uppal and Vural, 2020).…”

Section: Example I: Evolution Of Altruism In Self-organising Coloniessupporting

confidence: 88%

Microbial Evolution at Multiple Scales

Doekes¹

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Consider a population of parent entities, each of which is characterised by some characteristic ( = 1, 2, … , ). This characteristic might be a continuous phenotypic value of the entities, such as a bacterium's investment in public good production or a pathogen's virulence, an indicator variable that is 1 if the entity displays a certain behaviour (e.g., altruism) and 0 if it does not, or a measure of allelic dosage. Let = 1 ∑ =1 denote the mean value of in the population.2 If public-good producers and cheaters are initially mixed at a 1:1 ratio and the inoculum size is large, the initial frequency of public-good producing cells is very similar for all cultures, i.e., Φ ≈ 0.5 for all cultures . The between-culture variation is then small, and the within-culture variation is large (within-culture variation is maximal if Φ = 0.5). If the inoculum size is small, by chance populations are more often initialised with either a large or small frequency of public-good producers. This increases the between-culture variation and decreases within-culture variation.12 Chapter 1. IntroductionSo far, we have focused on the effects of spatial structure on microbial evolution. Next, we will turn our attention to another form of multilevel structure.3 0 is defined as the number of new infections caused by an infected individual in an otherwise susceptible host population. Multilevel pathogen evolution1 13 the set-point viral load: the relatively stable concentration of viral particles in the blood during chronic infection (Fraser et al., 2007). 14Chapter 1. Introduction a A model of within-host dynamics is nested through functional relationships into a between-host epidemiological model Time Strain frequency Replication rate Replication rate Transmission rate Time to deathtransmission death b c Figure 1.3. Nested dynamical model of pathogen evolution. (a) A mechanistic or population genetic model is used to describe within-host dynamics of different pathogen strains. (b)The within-host model is nested in a between-host model by defining functional relationships between the within-host pathogen population and epidemiological characteristics. In the example shown here, the replication rate of the dominant pathogen strain affects the transmission rate and life expectancy of the host. These curves are based on observations of the relationship between the set-point viral load of an HIV-infection and infectiousness and time until the onset of AIDS (Fraser et al., 2007(Fraser et al., , 2014. (c) The epidemiological model describes the population dynamics of hosts and between-host transmission.

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Evolution of specialized microbial cooperation in dynamic fluids

Cited by 7 publications

References 93 publications

Multiscale selection in spatially structured populations

Multiscale selection in spatially structured populations

Multiscale selection in spatially structured populations

Microbial Evolution at Multiple Scales

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