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Adaptive phenotypic plasticity is under stabilizing selection in Daphnia

Nature Ecology & Evolution volume 6, pages 1449–1457 (2022)Cite this article

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Abstract

The adaptive nature of phenotypic plasticity is widely documented. However, little is known about the evolutionary forces that shape genetic variation of plasticity within populations. Whether genetic variation in plasticity is driven by stabilizing or diversifying selection and whether the strength of such forces remains constant through time, remain open questions. Here, we address this issue by assessing the evolutionary forces that shape genetic variation in antipredator developmental plasticity of Daphnia pulex. Antipredator plasticity in D. pulex is characterized by the growth of a pedestal and spikes in the dorsal head region upon exposure to predator cue. We characterized genetic variation in plasticity using a method that describes the entire dorsal shape amongst >100 D. pulex strains recently derived from the wild. We observed the strongest reduction in genetic variation in dorsal areas where plastic responses were greatest, consistent with stabilizing selection. We compared mutational variation (Vm) to standing variation (Vg) and found that Vg/Vm is lowest in areas of greatest plasticity, again consistent with stabilizing selection. Our results suggest that stabilizing selection operates directly on phenotypic plasticity in Daphnia and provide a rare glimpse into the evolution of fitness-related traits in natural populations.

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Fig. 1: High-throughput phenotypic assessment via automated image analysis tool DAPCHA.
Fig. 2: Effects of predation risk on morphological changes in genetically unique strains.
Fig. 3: Effects of natural selection on predator-induced plastic responses in second instar Daphnia.

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Data availability

All raw images and processed data used to generate figures are deposited in Zenodo (https://doi.org/10.5281/zenodo.4738526). All sequencing reads are available from the Sequence Read Archive (PRJNA725506).

Code availability

All scripts and code used for data analysis and plotting are available at https://github.com/beckerdoerthe/SelectionPlasticity. DAPCHA is available at https://github.com/beckerdoerthe/Dapcha_v.1.

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Acknowledgements

A.O.B. was supported by the National Institutes of Health (R35 GM119686) and by start-up funds provided by the University of Virginia. D.B. and A.P.B. were supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 841419. The authors acknowledge Research Computing at The University of Virginia for providing computational resources and technical support that have contributed to the results reported within this publication (https://rc.virginia.edu). We would also like to thank the Dorset Wildlife Trust for granting access to the field site.

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Authors and Affiliations

  1. Department of Biology, University of Virginia, Charlottesville, VA, USA

    Dörthe Becker, Karen Barnard-Kubow, Robert Porter, Austin Edwards, Erin Voss & Alan O. Bergland

  2. School of Biosciences, Ecology and Evolutionary Biology, University of Sheffield, Sheffield, UK

    Dörthe Becker & Andrew P. Beckerman

  3. Department of Biology, University of Marburg, Marburg, Germany

    Dörthe Becker

  4. Department of Biology, James Madison University, Harrisonburg, VA, USA

    Karen Barnard-Kubow

  5. Biological Imaging Development CoLab, University of California San Francisco, San Francisco, CA, USA

    Austin Edwards

  6. Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA

    Erin Voss

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Contributions

D.B., A.O.B. and A.P.B. were responsible for conceptualization. D.B. and A.O.B. undertook data curation, formal analysis, methodology, resources, software and visualization and wrote the original draft of the manuscript. A.O.B. obtained funding and was responsible for project administration and supervision. D.B., K.B.-K., R.P., A.E., E.V. and A.O.B. undertook the investigations. All authors were involved in reviewing and editing the manuscript.

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Extended data

Extended Data Fig. 1 Inconsistency in manual assessments of defence morphologies.

Jitter plot contrasting manual estimates of pedestal scores in first (A-C) and second (D-F) instar animals across three independent observers indicates inconsistent manual assessment: while the majority of estimates overlap between the three observers, manual assessments of the pedestal scores differ between observers, particularly under predation conditions.

Extended Data Fig. 2 Genetic diversity among genetically similar and genetically unique strains.

(A) Distribution of pairwise IBS values between all genetically similar (left) and genetically unique (right) strains. (B) Relationship between IBS0 and kinship as calculated in the program King for pairwise combinations of individuals genotyped from the sampled population. Red and blue circles depict genetically similar and genetically unique strains, respectively. Note that in (B) all comparisons between clonally related strains (red points) are stacked on top of each other.

Extended Data Fig. 3 Experimental design.

(A) Full genome sequence analyses from 105 isofemale lines identified 49 genetically unique strains and one cluster of 56 genetically similar strains. (B) Phenotypic data were collected for these 105 isofemale lines: For experimental exposures, two mature Daphnia pulex carrying embryos in E3 stage (~18 hours before parturition; sensu72) were placed in individual jars containing medium with (bottom panel) and without (top panel) predator cue. After parturition, two neonates were randomly selected from each of the two mothers and placed in individual vials containing the same medium as their maternal environment. Subsequently, animals were monitored for 3-4 consecutive days, with daily photographs taken. Using an automated image analysis pipeline (DAPCHA, see Materials and methods and Suppl. Methods), phenotypic responses to control and predation conditions were assessed (see section ‘Robust and accurate phenotyping’, Fig. 1, Fig. 2a–d, Extended Data Fig. 6a–d). Next, heritability estimates for the observed within generation phenotypic response at each dorsal position were investigated for both genetically similar and genetically unique strains in the absence and presence of predator cue. These data allowed to contrast levels of standing genetic variation (Vg) with mutational variation (Vm) across the dorsal region. Comparing the patterns of the ‘dorsal region - Vg/Vm relationship’ within and between treatments ultimately provided evidence of differential selection across the phenotypic trait (see sections ‘Evidence for stabilizing selection in an outbred sample’ and ‘Mutational variation and further evidence of stabilizing selection’; Fig. 2e-h, Fig. 3, Extended Data Fig. 6e, f).

Extended Data Fig. 4 Chaoborus induced shape variation in D. pulex.

Visualization of the first three main axes of dorsal shape variation in first (A) and second instar (B) Daphnia using principal component (PC) analysis of procrustes data. Colours indicate treatment conditions (control: black points, predation: red points). Warp-shape diagrams highlight distinctive patterns of shape variations along the principal components: PC1 represents shape differences in dorsal height, while PC2 and PC3 characterize the development of predator-induced defence morphologies and shifts in their dorsal position, respectively.

Extended Data Fig. 5 Modularity of predator-induced defences along the dorsal axis in genetically unique strains.

A formal modularity analysis, testing for the presence of distinct phenotypic modules along the dorsal axis, indicates that plastic responses in the nuchal area of second instar animals are independent of changes in other body parts: there is strong statistical evidence for three independent dorsal modules (see model H) separating dorsal regions where plastic defence morphologies are expressed (that is, head region) and other body parts along the dorsal edge. The extent of modularity is described by a covariation ratio (CR) coefficient and respective effect sizes (Z scores) in proposed modules (for details see Materials and Methods). Note, while model L indicates the strongest modular signal (that is, most negative effect size Z), there is no statistical difference to models H, M and N (Supplementary Table 1, section V). Due to its parsimonious nature (that is, fewest model parameters), model H was used for all subsequent analyses.

Extended Data Fig. 6 Effects of predation risk on morphological changes in genetically similar strains.

(A) Risk of predation induces plastic responses, with strongest phenotypic changes observed in the head region. (B,C) In response to predation, maximum dorsal height increases and shifts towards anterior head regions. (D) In addition, the number of neckteeth increases under predation risk. Notably, variation in morphological changes within genetically similar clones is as pronounced as that observed among genetically unique clones (see Fig. 2). (E) Effect sizes from analyses of variance along the dorsal shape reveal distinctive patterns of treatment (that is, predation risk, red line), genotype (blue line), and GxE (grey line) effects on morphological changes in second instar animals. (F) Broad-sense heritability estimates of dorsal height in second instar Daphnia vary along the dorsal axis in response to control conditions (black line) and predation risk (red line) in genetically similar strains. Data in panels E and F are presented as mean values, with shaded areas indicating upper (0.95) and lower (0.05) confidence intervals. Vertical lines highlight morphological independent shape modules, separating head and posterior body areas (see Extended Data Fig. 5).

Extended Data Fig. 7 Split-block experimental design.

Clonally related and genetically unique strains were phenotype concurrently across 20 experimental batches (A), with treatment conditions (control vs predation) relatively evenly split across batches (B). Note, due to technical failures, batches 2 and 3 were excluded from the experiment.

Extended Data Fig. 8 Genetic differences drive phenotypic variation in antipredator defences.

Phenotypic variation in genetically similar strains arises due to genetic effects: phenotypic responses (here: animal length and maximal dorsal height) of offspring released from the same mother (‘within clutch’) and same strain (‘within clone’) are more similar to each other than to offspring released from a randomly drawn member of the clonal assemblage (‘among clones’). Correlation coefficients broadly exceed coefficients calculated for permuted data. Moreover, phenotypic correlations among randomly paired individuals from the same experimental batches are low, with actual data not exceeding permuted data ranges. Black and red points indicate control and predation risk conditions with darker and lighter colours depicting actual and permuted data, respectively. Data are presented as mean values, with error bars indicating upper (0.95) and lower (0.05) confidence intervals. Asterisks indicate actual data exceeding permuted data ranges (see Supplementary Table 1).

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Becker, D., Barnard-Kubow, K., Porter, R. et al. Adaptive phenotypic plasticity is under stabilizing selection in Daphnia. Nat Ecol Evol 6, 1449–1457 (2022). https://doi.org/10.1038/s41559-022-01837-5

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