Population size shapes trade-off dilution and adaptation to a marginal niche unconstrained by sympatric habitual conditions
Data files
Dec 06, 2023 version files 21.32 KB
-
Dryad_data_Evo_2023.xlsx
-
README.md
Abstract
How does niche expansion occur when the habitual (high-productivity) and marginal (low-productivity) niches are simultaneously available? Without spatial structuring, such conditions should impose fitness maintenance in the former while adapting to the latter. Hence, adaptation to a given marginal niche should be influenced by the identity of the simultaneously available habitual niche. This hypothesis remains untested. Similarly, it is unknown if larger populations, which can access greater variation and undergo more efficient selection, are generally better at niche expansion. We tested these hypotheses using a large-scale evolution experiment with Escherichia coli. While we observed widespread niche expansion, larger populations consistently adapted to a greater extent to both marginal and habitual niches. Owing to diverse selection pressures in different habitual niches (constant versus fluctuating environments; environmental fluctuations varying in both predictability and speed), fitness in habitual niches was significantly shaped by their identities. Surprisingly, despite this diversity in habitual selection pressures, adaptation to the marginal niche was unconstrained by the habitual niche’s identity. We show that in terms of fitness, two negatively correlated habitual niches can still have positive correlations with the marginal niche. This allows the marginal niche to dilute fitness trade-offs across habitual niches, thereby allowing costless niche expansion. Our results provide fundamental insights into sympatric niche expansion.
README: Population size shapes trade-off dilution and adaptation to a marginal niche unconstrained by sympatric habitual conditions
https://doi.org/10.5061/dryad.k0p2ngff8
The dataset consists of an .xlsx file with six tabs, as described below.
Description of the data and file structure
We founded E. coli MG1655 populations from a single common ancestral clone and cultured them in eight different environmental conditions at two distinct population sizes, large (L) and small (S). All eight environments offered Acetate as the low productivity carbon source that constituted the marginal niche. Out of the eight environments, four offered a single distinct high productivity carbon source (constant habitual niche) for bacterial growth (one of thymidine (Thy), galactose (Gal), sorbitol (Sor) or arabinose (Ara)). In the other four environments, the habitual niche (high productivity carbon source) fluctuated over time in all combinations of predictable versus unpredictable and fast (switching every ~13.3 generations) versus slow (switching every ~40 generations) fluctuations. We designated these fluctuations in the habitual niche as PF (predictable and fast), PS (predictable and slow), UF (unpredictable and fast) and US (unpredictable and slow). Taken together, a combination of two different population sizes and eight different environments gave rise to 16 separate evolutionary regimens (Thy-L, Thy-S, Gal-L, Gal-S, Sor-L, Sor-S, Ara-L, Ara-S, PFL, PFS, PSL, PSS, UFL, UFS, USL and USS; here L and S refer to large and small population sizes respectively). We propagated six independently evolving biological replicates of each regimen, making a total of 96 independently evolving experimental populations.
Each of these 96 populations were assayed in five different environments: thymidine (Thy), galactose (Gal), sorbitol (Sor) or arabinose (Ara), acetate (Ace). We used the maximum slope of the growth curves, calculated over a moving window of ten readings, as the measure of fitness. These values were divided by the correspnding mean fitnesses of the ancestors in these five environments (data presented in the tab "Absolute ancestral growth rate") to obtain the relative fitness values. These relative fitness values are presented in the five tabs:
- Relative fitness in Ara
- Relative fitness in Gal
- Relative fitness in Sor
- Relative fitness in Thy
- Relative fitness in Acetate
Methods
This data was collected on laboratory populations of E. coli subjected to experimental evolution.