Fig 1.
(A) Schematic of artificial community selection. (B) Costly community function. Dark cells contribute more to community function per cell and thus divide more slowly than light cells. In other words, high contributors are disfavored by intracommunity selection during community maturation. However, communities dominated by high contributors are favored by intercommunity selection and have a higher chance to reproduce. (C) A Helper-Manufacturer community that converts substrates into a product. Helper H consumes agricultural waste and Resource R to grow biomass, and concomitantly releases Byproduct B at no fitness cost to itself. H’s Byproduct B is required by Manufacturer M. M consumes Resource and H’s Byproduct, and invests a fraction fP of its potential growth gM to make Product P while channeling the remaining to biomass growth. When biomass growth ceases, Byproduct and Product are no longer made. The 5 state variables (italicized) H, M, R, B, and P correspond to the amount of H biomass, M biomass, Resource, Byproduct, and Product in a community, respectively. Agricultural waste is present in excess, and thus does not enter equations. (D) Simulating artificial selection of H-M communities. (i) In our simulations, cycles of selection were performed on a total of ntot = 100 communities with the indicated initial conditions. At the beginning of the first cycle, each Newborn had a total biomass of the target value (BMtarget = 100; 60 M and 40 H each of biomass 1). In subsequent cycles, as dictated by experiments that we simulate, each Newborn’s total biomass would fluctuate around the target total biomass BMtarget, and each Newborn’s species ratio would fluctuate around its parent Adult’s species ratio. The amount of Resource in each Newborn was fixed at a value that could support a total biomass of 104 (unless otherwise stated). (ii) The maturation time T was chosen so that, for an average community, Resource was not depleted by time T (in experimental terms, this would avoid complications of the stationary phase). During maturation, Resource R, Byproduct B, Product P, and each cell’s biomass were calculated from differential equations (Methods, Section 6). Once a cell’s biomass had grown from 1 to 2, it divided into 2 identical daughter cells. Death occurred stochastically to individual cells (not depicted). After division, mutations (different shades of oval) occurred stochastically to change a cell’s phenotypes (e.g., M’s fP). (iii) At the end of a cycle, community functions (total Product P(T)) were ranked. (iv) During community reproduction, high-functioning Adults were chosen and diluted into Newborns so that, on average, each Newborn had a total biomass of approximately the target biomass BMtarget. A total of ntot = 100 Newborns were generated for the next selection cycle. In this study, communities never mixed with each other. Adult, Adult community; B, Byproduct; H, Helper; M, Manufacturer; Newborn, Newborn community; P, Product; R, Resource.
Fig 2.
Species coexistence and optimal community function at an intermediate cost.
Calculations were based on Eqs 6–10 with H and M’s growth parameters fixed at their respective evolutionary upper bounds (Table 1, “Preadapted”). (A) Stable species coexistence at moderate to low cost. Bottom: When fP, the fraction of potential growth Manufacturer diverts for making Product, is high (e.g., fP = 0.7), M will eventually go extinct (i.e., fraction of M < 1 ÷ BMtarget). Top: At moderate and low cost fP (e.g., fP = 0.4 and fP = 0.1), H and M can stably coexist. That is, different initial species ratios will converge to a steady-state value. At the end of the first cycle (time T = 17), Byproduct and Resource were reset to the initial conditions at time zero (0 and 104, respectively), and total biomass was reduced to the target value BMtarget = 100, while the fraction of M biomass ϕM remained the same as that of the parent community. See main text for how values of maturation time and Resource were chosen. (B) Optimal community function occurs at an intermediate cost fP. Community functions at various combinations of fP and fraction of M biomass (out of BMtarget = 100 total biomass) were computed by integrating Eqs 6–10. Maximal community function P(T) is achieved at an intermediate cost (magenta dashed line) when Newborn species composition is also optimal (46 H and 54 M cells). Note that, at zero fP, no Product would be made; at fP = 1, M would go extinct. The maximal P*(T) could not be further improved even if we allowed all growth parameters and fP to mutate (S10 Fig). Thus, P*(T) is locally maximal in the sense that small deviation will always reduce P(T). Ancestral fP (gray) is lower than
. The central question is this: can intercommunity selection improve ancestral fP to
despite intracommunity selection favoring lower fP? The Matlab code can be found in S1 Code. H, Helper; M, Manufacturer.
Fig 3.
Community selection can be stalled by routine experimental procedures, and can succeed when community function correlates with its heritable determinant or when using the top-tier strategy.
(A–I) Evolution dynamics when the maturation time T was sufficiently short to avoid Resource depletion and stationary phase (T = 17). (A–C) Adults were chosen using the top-dog strategy and diluted into progeny Newborns as if via pipetting (i.e., H and M biomass fluctuated around their expected values). Community selection was not effective: Average fp and community function failed to improve to their theoretical optima. Community function poorly correlated with its heritable determinant (the average cost paid by M in Newborn). Black and magenta dots: unchosen and chosen communities from 1 selection cycle, respectively. (D–F) Adults were chosen using the top-dog strategy. A fixed H biomass and a fixed M biomass from a chosen Adult were allocated into each progeny Newborn as if using a cell sorter. Community selection was successful. Community function also correlated with its heritable determinant
. Here, Newborn total biomass BM(0) and fraction of M biomass ϕM(0) were, respectively, fixed to BMtarget = 100 and ϕM(T) of the parent Adult of the previous cycle. (G–I) When we chose the top 10% Adults and let each reproduce 10 Newborns as if via pipetting, community function improved somewhat despite poor correlation between community function and its heritable determinant
. For selection dynamics over many cycles, see S14 Fig. (J–L) Evolution dynamics when maturation time was long (T = 20) such that, by the end of T, most Resource was consumed (stationary phase). Adults were chosen using the top-dog strategy and reproduced as if via pipetting. Community selection was successful due to high correlation between community function and its heritable determinant
, assuming that variable time in stationary phase would not introduce nonheritable variations in community function. Black, cyan, and gray curves represent independent simulation trials.
was the average of P(T) across all chosen Adults.
was obtained by first averaging among M within each chosen Adult and then averaging across all chosen Adults. The simulation codes can be found in S2 Code, and the data can be found in S1 Data. Adult, Adult community; H, Helper; M, Manufacturer; Newborn, Newborn community.
Fig 4.
During ineffective community selection, community function correlates weakly with its heritable determinant and strongly with nonheritable determinants.
(A) Schematic of community lineages across “previous” and “current” selection cycles. (B-G) Data of Newborns and corresponding Adults (previous cycle) were taken from the 180th cycle of the simulation displayed in black in Fig 3A and 3B. We then allowed each Adult to reproduce Newborns (current cycle), forming 100 lineages (tubes with the same color outline belong to the same lineage). (B–D) Among the 3 determinants of community function, (fP averaged among M cells in Newborn) is heritable, but BM(0) (total biomass of Newborn) and ϕM(0) (fraction of M biomass in Newborn) are not. For each lineage, the community function determinant at the previous cycle was scatter plotted against the average value at the current cycle. (E–G) During ineffective community selection (Fig 3B), community function P(T) correlates weakly with heritable determinant but strongly with nonheritable determinants. Each dot represents one community. Magenta dots: "successful" Newborns that achieved the highest community function at adulthood and therefore were chosen to reproduce in the top-dog strategy. The Matlab code for B–D can be found in S2 Code, and the data for E–G can be found in S1 Data. Adult, Adult community; M, Manufacturer; Newborn, Newborn community.
Fig 5.
Ineffective selection due to community function measurement noise can be rescued by the top-tier strategy acting in synergy with cell sorting.
Adult communities were chosen to reproduce based on "measured community function P(T)"—the sum of actual P(T) and a "noise term" randomly drawn from a normal distribution with zero mean and standard deviations of 5% or 10% of the ancestral P(T). Dynamics of average fP and average community function of the chosen Adult communities ( and
) are plotted. When community function measurement noise is low (5%), cell sorting largely rescues ineffective community selection (A–D, left panels). When community function measurement noise is high (10%), both cell sorting and top-tier strategy are required (A–D, right panels). Black, cyan, and gray curves represent independent simulation trials.
was averaged across the chosen Adults.
was obtained by first averaging among M within each chosen Adult and then averaging across the chosen Adults. The simulation codes can be found in S3 Code, and the data can be found in S2 Data. Adult, Adult community; M, Manufacturer.
Fig 6.
Effective community selection can encourage species coexistence.
Here, the evolutionary upper bound for gHmax () was larger than that for gMmax (
), opposite to that in Figs 2–5. (A) When the top-dog strategy and pipetting were used to choose and reproduce Adult communities, M was almost outcompeted by H as H evolved to grow faster than M (rows 3 and 4). Although M would ordinarily go extinct, community selection managed to maintain M at a very low level (bottom). This imbalanced species ratio resulted in very low community function (top). (B-D) When community selection was effective, using top-dog with cell sorting (panel B), top-tier with pipetting (panel C), or top-tier with cell sorting (panel D), community selection successfully improved community function and
. In these cases, H’s growth parameter did not increase to its evolutionary upper bound (panel B-D row 3, also see S17B–S17D Fig), allowing a balanced species ratio (panel B-D bottom) and high community function (panel B-D top). Resource supplied to Newborn communities here supports 105 total biomass to accommodate faster growth rates (and hence community function is larger than in other figures). Black, cyan, and gray curves represent independent simulation trials.
(average community function) and
(average fraction of M biomass in Adult communities) were averaged across the chosen Adults.
was obtained by first averaging among M within each chosen Adult and then averaging across all chosen Adults. The simulation codes can be found in S4 Code, and the data can be found in S3 Data. Adult, Adult community; H, Helper; M, Manufacturer.
Table 1.
Parameters for ancestral and preadapted H and M.
Table 2.
Additional symbols used in the simulation.