Abstract
Microbial populations in nature generally inhabit extended environments with substantial spatial variation in ecological factors: light intensity in the ocean, temperature in geothermal hot springs, or a variety of chemical concentrations including salt and pH. In such continuously varying environments, it remains unclear why a finite number of subpopulations form and how this number is set. Here we show that a model of asexual evolution in a gradient maps onto a no-gradient neutral model, and by mapping this model to a gas of kinks and antikinks, we derive the full distribution of the number of coexisting lineages, and their correlation functions. Testing these predictions in controlled laboratory experiments would provide valuable insights into many real-world microbial communities.Received 28 March 2021Revised 13 September 2021Accepted 12 October 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L042026Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBiological complexityEcological population dynamicsEvolutionary dynamicsBiological Physics
Highlights
We show that a model of asexual evolution in a gradient maps onto a no-gradient neutral model, and by mapping this model to a gas of kinks and antikinks, we derive the full distribution of the number of coexisting lineages, and their correlation functions
Even a single bacterial species may evolve into different ecologically distinct lineages [1,2,3,4] depending on environmental conditions
—though in a continuous environment— Synechococcus subclades in hot spring cyanobacterial mats [Fig. 1(a)] were found to be uniquely adapted to specific temperatures [3,7]
Summary
By mapping the neutral model onto a gas of kinks and antikinks, we calculate exactly the distribution of lineage sizes and their pair correlation function, which agree with numerical results from the full model. As a strong test of this correspondence, we have simulated our model and compared, in Fig. 2, the average time tmax at which a lineage reaches its maximum size Nlin,max, as a function of that maximum size, for the full model with a temperature gradient (red diamonds), and the rescaled no-gradient model with uniform environment and effective spatial diffusion D = Dφ/α2 (green triangles).
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