Post-association barrier to host switching maintained despite strong selection in a novel mutualism
Dinges, Zoe; Phillips, Raelyn; Lively, Curtis; Bashey, Farrah (2022), Post-association barrier to host switching maintained despite strong selection in a novel mutualism, Dryad, Dataset, https://doi.org/10.5061/dryad.xd2547dj3
Following a host shift, repeated co-passaging of a mutualistic pair is expected to increase fitness over time in one or both species. Without this adaptation, a novel association may be short-lived evolutionarily as it is likely to be outcompeted by native pairings. Here we test whether experimental evolution can rescue a low-fitness novel pairing between two sympatric species of Steinernema nematodes and their symbiotic Xenorhabdus bacteria. Despite low mean fitness in the novel association, considerable variation was observed across replicate infections of this entomopathogen in the number of nematodes that emerged from infected insects. We selected the most productive infections, co-passaging this novel mutualism nine times to determine whether selection could improve fitness of either or both partners. We found that neither partner showed increased fitness over time. Our results suggest that the variation in association success was not heritable and that mutational input was insufficient to allow evolution to facilitate this host shift. Thus, post-association costs of host switching may represent a formidable barrier to novel partnerships among sympatric mutualists.
We infected each Galleria mellonella caterpillar by pipetting 100 nematodes (carrying bacteria) in 500uL of ddH2O on to the dorsum of the caterpillar. Infection success (Infection_Success) was measured as the proportion of infected caterpillars with any nematode emergence. Mean nematode emergence (Estimated_IJ_Count) was estimated by volumetric subsampling. We estimated bacterial carriage (CFUs) by crushing a sample of 1000 IJs for at least 5 collections per treatment group per generation. If no colonies grew from the crushing, we used the detection limit of 0.003 CFU/IJ as the estimated carriage the in statistical analyses.
Data Description and file format
The data file “Dinges et al. Biology Letters.csv” contains 7 columns:
- The data file “Dinges et al. Biology Letters.csv” contains 7 columns:
A. Passage: numeric description (1-9) of the co-passage infection.
B. Treatment: factor (Experimental, Bacteria, Nematode) describing the nematode-bacteria pairing used for each infection. Experimental is made up of S. kraussei nematodes paired with bacteria from S. affine. Bacteria is the native bacteria pairing – S. affine nematodes carrying their native bacteria. Nematode is the native nematode pairing – S. kraussei nematodes carrying their native bacteria.
C. Host_Mass: numeric (0.11-0.313) mass of infected caterpillar in grams. Host Mass was not recorded for Passages 5-9, instead "NA" is entered.
D. Estimated_IJ_Count: numeric number of juvenile nematodes which emerged from successful infections. Where these estimates were not calculated, NA is used.
E. Infection_Success: binary – 1 indicates an infection had nematode emergence, 0 indicates no nematodes emerged from the infection
F. CFUs: numeric (0.003 – 16.32) bacterial carriage calculated as the number of colony forming units (CFUs) divided by the number of nematodes crushed in the sample, adjusted for dilution. If no colonies grew, the detection limit, defined as the fewest cells detectable in our study was used instead. If no sample was crushed, NA is recorded.
G. LogCFUs: numeric, calculated as log(CFUs). If no sample was crushed, NA is recorded.
Software and data processing description
All statistical analyses were performed in R version 3.6.3. Differences in infection success (i.e. the proportion of caterpillars with nematode emergence) between the experimental and native-control pairings were analyzed as a binomial response variable using a generalized linear model including the interaction between pairing treatment and passage number. Differences in nematode emergence, defined as the mean number of nematodes that emerged per caterpillar, were analyzed using a generalized linear model assuming normal distribution with pairing treatment, passage number, and their interaction as main effects, with caterpillar mass as a covariate. Differences in the average number of bacterial cells carried per IJ were analyzed using a linear mixed model with the pairing treatment, passage number, and their interaction as main effects. For each model, we computed the estimated marginal means (EMMs) and 95% confidence intervals for each pairing treatment using the emmeans package.
License and Restriction statement
There are no licenses or restrictions placed on access to the dataset or any associated files.
The statistical analyses were performed in R using the file “Dinges et al. Biology Letters.Rmd” in R Studio.
The first chunk of code loads libraries and set up options for the run. This file uses libraries ggplot2 (to generate plots), dplyr (to filter and process data), emmeans (to calculate estimated marginal means), and rstatix (to calculate type 3 fixed effects). To simplify the output, I set scipen = 999, to prevent scientific notation of p-values. I also set contrasts to “contr.sum” or “contr.poly” to accurately calculate the type 3 fixed effects.
The second chunk reads in the .csv data file, sets “Passage” as a factor (instead of a numeric), replaces missing data in “Estimated_IJ_Count” with 0, creates a data frame, “IJ” excluding unsuccessful infections, and creates a data frame, “X” excluding samples with no bacterial carriage data.
The third chunk creates objects for plot aesthetics: PlotTheme, fill and color for points.
The fourth chunk analyses infection success for the experimental and nematode treatments (excluding bacteria treatment because it is introduced in passage 7). It also analyzes all treatments for Passages 7-9. Finally, it creates Figure 2A.
The fifth chunk analyzes nematode emergence with the data subset “IJ”, and creates Figure 2B.
The sixth chunk analyzes bacterial carriage, with the data subset “X” and creates Figure 2C.
National Science Foundation, Award: DEB-1906465
National Science Foundation, Award: DEB-0919015