Over the course of evolution, organism size has diversified markedly. Changes in size are thought to have occurred because of developmental, morphological and/or ecological pressures. To perform phylogenetic tests of the potential effects of these pressures, here we generated a dataset of more than ten thousand descriptions of insect eggs, and combined these with genetic and life-history datasets. We show that, across eight orders of magnitude of variation in egg volume, the relationship between size and shape itself evolves, such that previously predicted global patterns of scaling do not adequately explain the diversity in egg shapes. We show that egg size is not correlated with developmental rate and that, for many insects, egg size is not correlated with adult body size. Instead, we find that the evolution of parasitoidism and aquatic oviposition help to explain the diversification in the size and shape of insect eggs. Our study suggests that where eggs are laid, rather than universal allometric constants, underlies the evolution of insect egg size and shape.
Size is a fundamental factor in many biological processes. The size of an organism may affect interactions both with other organisms and with the environment1,2, it scales with features of morphology and physiology3, and larger animals often have higher fitness4. Previous studies have aimed to identify the macroevolutionary forces that explain the observed distributions in animal size1,5,6. However, the limited availability of data on the phylogenetic distribution of size has precluded robust tests of the predicted forces4,7. Here we address this problem by assembling a dataset of insect egg phenotypes with sufficient taxon sampling to rigorously test hypotheses about the causes and consequences of size evolution in a phylogenetic framework.
Insect eggs are a compelling system with which to test macroevolutionary hypotheses. Egg morphologies are extraordinarily diverse8, yet they can be readily compared across distant lineages using quantitative traits. Changes in egg size have been studied in relation to changes in other aspects of organismal biology9, including adult body size10,11,12, features of adult anatomy13 and offspring fitness through maternal investment14. Eggs must also withstand the physiological challenges of being laid in diverse microenvironments, including in water, air, or inside plants or animals15. Furthermore, because the fertilized egg is the homologous, single-cell stage in the lifecycle of multicellular organisms, egg size diversity is relevant to the evolution of both cell size and organism size8,14.
Three classes of hypotheses have been proposed to explain the evolution of egg size and shape. The first suggests that geometric constraints due to the physical scaling of size and shape explain the diversity of egg morphology13,16,17,18,19. The second suggests that there is an interaction between egg size and the rate of development20,21,22. Finally, the third suggests that the diversification of size and shape is a response to ecological or life-history changes10,13,15,23. We use a phylogenetic approach to test all three of these hypotheses, and show that many presumed universal patterns in the size, shape and embryonic development of eggs are not supported across insects. Instead, we find that models that account for ecological changes best explain the morphological diversity in eggs of extant insects..
Using custom bioinformatics tools, we assembled a dataset of 10,449 published descriptions of eggs, comprising 6,706 species, 526 families and every currently described extant hexapod order24 (Fig. 1a and Supplementary Fig. 1). We combined this dataset with backbone hexapod phylogenies25,26 that we enriched to include taxa within the egg morphology dataset (Supplementary Fig. 2) and used it to describe the distribution of egg shape and size (Fig. 1b). Our results showed that insect eggs span more than eight orders of magnitude in volume (Fig. 1a, c and Supplementary Fig. 3) and revealed new candidates for the smallest and largest described insect eggs: respectively, these are the parasitoid wasp Platygaster vernalis27 (volume = 7 × 10−7 mm3; Fig. 1c) and the earth-boring beetle Bolboleaus hiaticollis28 (volume = 5 × 102 mm3; Fig. 1c).
Plotting eggs by morphology revealed that some shapes evolved only in certain clades (Fig. 1a and Supplementary Figs. 4–7). For example, oblate ellipsoid eggs (aspect ratio < 1) are found only in stoneflies, moths and butterflies (Plecoptera and Lepidoptera; Fig. 1c, Supplementary Figs. 4, 5). Egg cases (oothecae) have evolved in multiple insect lineages29. To test whether oothecae constrain shape or size, we measured individual eggs within cases, and found that these eggs are morphologically similar to those of freely laid relatives (Supplementary Fig. 8). The most prominent pattern was that distantly related insects have converged on similar morphologies many times independently (Fig. 1a and Supplementary Fig. 7). This high degree of morphological convergence allowed us to robustly test trait associations across independent evolutionary events.
Evolutionary allometry of insect eggs
Two opposing hypotheses based on predicted geometric constraints have been proposed to explain the evolutionary relationship between egg shape and size. One hypothesis posits that when eggs evolve to be larger, they become wider (increases in egg size are associated with decreases in aspect ratio)17,18. This hypothesis predicts a reduction in relative surface area as size increases, which has been proposed as a solution to the presumed cost of making eggshell material18. The alternative hypothesis proposes that when eggs evolve to be larger, they become longer (increases in egg size are associated with increases in aspect ratio)13,18,19. This hypothesis predicts a reduction in relative cross-sectional area as eggs become larger, which has been proposed as a solution to the need for eggs to pass through a narrow opening during oviposition13,19.
To test these hypotheses about the physical scaling of size and shape, we began by modelling the evolutionary history of each morphological trait. This allowed us to determine whether distributions of extant shape and size have been shaped by phylogenetic relationships. For egg volume, aspect ratio, asymmetry and angle of curvature (Fig. 1d), we compared four models of evolution: Brownian motion, Brownian motion with evolutionary friction (Ornstein–Uhlenbeck), Brownian motion with a decreasing rate of evolution (early burst) and a non-phylogenetic model of stochastic motion (white noise). We found that models that accounted for phylogenetic covariance fit our data better than a non-phylogenetic model (white noise); in other words, the morphology of insect eggs tends to be similar in closely related insects (Supplementary Table 5). For egg size and aspect ratio, an early burst model in which evolutionary rate decreases over time, best describes the data (Supplementary Figs. 9–11). In previous studies, early burst models were rarely detected30. However, our findings are consistent with recent studies evaluating datasets that—similar to our data—comprise many taxa and orders of magnitude in morphological variation31,32. Having established appropriate phylogenetic models, we used these results to test hypotheses about the relationship between egg shape and size.
To test which aforementioned scaling relationship best describes insect egg evolution, we compared support for each of the two opposing hypotheses described above using a phylogenetic generalized least-squares approach to determine the scaling exponent of length and width (the slope of the regression of log-transformed length and log-transformed width). A slope less than one would support the first hypothesis (Fig. 2a), whereas a slope greater than one would support the second hypothesis33 (Fig. 2b). An alternative third hypothesis is that egg shape remains the same as size changes; this would result in a slope near one (an isometric relationship; Fig. 2c). The relationships describing these hypotheses are shown in Fig. 2a–d. We found that across all insects, the second hypothesis is best supported: larger eggs have higher aspect ratios than smaller eggs (0 < P < 0.005, slope = 0.78; Fig. 2e and Supplementary Table 6), even when controlling for adult body size (Supplementary Fig. 14 and Supplementary Table 8). We found no support for the first hypothesis, which suggests that future hypotheses of egg shell evolution may need to account for additional factors such as chorion composition and thickness when considering potential fitness cost. However, the allometric relationship between size and shape evolves dynamically across the phylogeny, which has also been shown for metabolic scaling in mammals34. The third hypothesis, isometry, could not be rejected for beetles and their relatives, nor for butterflies, moths and caddisflies (respectively, Neuropteroidea P = 0.04 and Amphiesmenoptera P = 0.01; Fig. 2f, Supplementary Fig. 12 and Supplementary Table 7). Calculating the scaling relationship on lineage subgroups revealed that additional clades, including mayflies, crickets and shield bugs, also show an isometric relationship (Supplementary Fig. 13). The marked differences in scaling exponents are evidence that egg evolution was not governed by a universal allometric constant. Instead, evolutionary forces beyond the constraints of physical scaling (for example, development or ecology) are required to explain the morphological diversification of insect eggs.
Developmental traits and egg evolution
The egg is the starting material for embryogenesis, and the size of the hatchling is directly related to the size of the egg at fertilization35. It has been reported that embryogenesis takes longer in species with larger eggs22 and that this relationship could influence size evolution20,21. This would be consistent with the observation that larger adult species have lower metabolic rates than smaller species36. To test this prediction across our egg dataset, we assembled published embryological records, and found that simply comparing egg volume and duration of embryogenesis yields the previously reported positive relationship22 (Supplementary Fig. 17). However, a linear regression that does not account for phylogenetic relationships is inappropriate for this analysis owing to the covariance of traits on an evolutionary tree37. When we accounted for phylogenetic covariance, we found that there was no significant relationship between egg size and duration of embryogenesis across insects, such that eggs of very different sizes develop at a similar rate and vice versa (0.02 < P < 0.10; Fig. 3b and Supplementary Table 11). These results suggest that the often-invoked trade-off between size and development20,21,22 does not hold across insects.
We also tested the hypothesis that the size of the egg has a positive relationship with adult body size. Previous studies have reported this relationship in subsets of insects and have suggested that smaller insects lay proportionally larger eggs for their bodies11,35,38. Such a relationship between egg size and body size would result in an allometric scaling exponent that is less than one. We combined our dataset of egg size with published adult body length data for insect families39, and found that this relationship was not generalizable across all insect lineages. For example, in flies and their relatives (Antliophora), as well as in mayflies and odonates (Palaeoptera), egg size is not predicted by body size, meaning that insects of similar body size lay eggs of different sizes (Antliophora P = 0.02, Palaeoptera P = 0.19; Fig. 3c, d and Supplementary Table 13). In Polyneoptera, thrips and true bugs (Condylognatha), and bees, ants and wasps (Hymenoptera), an isometric relationship between egg size and body size cannot be rejected (Polyneoptera P = 0.02, Hymenoptera P = 0.01, Condylognatha P = 0.01; Supplementary Fig. 18 and Supplementary Table 13). In general, the predictive power of the relationship between body size and egg size is low: average egg volume can vary by up to four orders of magnitude among species with a similar body size (Fig. 3c).
At the time of fertilization an egg is a single cell. We therefore tested whether the size of this cell evolved with the size of the genome, as has been observed for other cell types40, using a database of genome size for hexapods41. Although the data appeared to show a positive relationship between egg size and genome size (Supplementary Table 14), we found that this relationship was driven entirely by the lineage Polyneoptera (specifically grasshoppers, Acrididae). This lineage has evolved genome sizes that are an order of magnitude larger than other insects and has relatively large eggs (Supplementary Fig. 19). Across other insect lineages, egg volume and genome size are not significantly related (0 < P < 0.08; Supplementary Table 14), and egg volume can range over six orders of magnitude for species with a similar genome size (Supplementary Fig. 19c). This indicates that genome size is not a general driver of egg size. The decoupling of genome size, body size and developmental rate from the evolution of egg sizes suggests that the diversification of insect eggs has not been universally constrained by development.
Oviposition ecology explains egg morphology
Egg size and shape have been predicted to evolve in response to changes in life history and ecology. Recent studies in birds have highlighted one such relationship, suggesting that birds with increased flight capability have more elliptical and asymmetrical eggs13. We investigated whether an analogous relationship exists between insect flight capability and egg shape. Unlike birds, insects have undergone hundreds of evolutionary shifts to flightless and even wingless forms42. We focused on two clades in which flight evolution has been extensively studied. Stick insects (Phasmatodea) have flightless and wingless species43,44 (Supplementary Fig. 22), and many butterflies (Lepidoptera) show migratory behaviour45, which we used as a proxy for increased flight capability relative to non-migratory taxa (Supplementary Fig. 22). We found that, in contrast to birds, evolutionary changes in flight ability in these two insect clades were not associated with changes in egg shape (Ornstein–Uhlenbeck model with multiple optima per regime; ∆AICc (Akaike information criterion) < 2, exact values are included in Supplementary Tables 18, 19).
Similar to flight capacity, the microenvironment that insect eggs experience varies widely, including being exposed to air, submerged or floating in water, or contained within a host animal8. Each microenvironment places different demands on the egg, such as access to oxygen and water during development15. Preliminary studies in small groups of insects have suggested that evolutionary changes in oviposition ecology and life history may drive the evolution of egg size and shape10,23. To test this prediction across all insects, we compiled records on two modes of oviposition ecology that have been extensively studied: oviposition within an animal host (internal parasitic oviposition) and oviposition in or on water. For each mode, we reconstructed ancestral changes along the insect phylogeny, and found that both aquatic and internal parasitic oviposition modes have been gained and lost multiple times independently (Fig. 4a, b and Supplementary Figs. 20, 21). This extensive convergent evolution allowed us to perform a strong test of whether egg size and shape evolution are explained by the evolution of oviposition ecology.
We found that the evolution of new oviposition environments is linked to changes in egg size and shape. Models that accounted for shifts to either aquatic or internal parasitic oviposition better explained size and shape distributions than models that did not (Ornstein–Uhlenbeck model, ∆AICc > 2, exact values are shown in Supplementary Tables 15–17). In this analysis, we compared model fit for each ecology–trait pair separately, and found that these two ecological states were correlated with different egg morphologies. Specifically, shifts to aquatic oviposition were significantly associated with the evolution of smaller eggs with a lower aspect ratio (Fig. 4c, d and Supplementary Table 17), whereas shifts to internal parasitic oviposition were significantly associated with smaller, more asymmetric eggs (Fig. 4c, e and Supplementary Table 15). Moreover, we note that the smallest eggs are from parasitoid wasps that develop polyembryonically (that is, multiple embryos form from a single egg46; Supplementary Fig. 23). Neither oviposition mode is associated with consistent changes in the allometric relationship between size and shape (Supplementary Fig. 24).
Given that Ornstein–Uhlenbeck models can be favoured when dataset size and measurement error are large47, we repeated these analyses 100 times using simulated ecological states independent of egg morphological traits. The results of this bootstrap analysis showed that our observed result, which favoured ecological models of morphological evolution, is unlikely to be caused by dataset size alone (P = 0.01; Supplementary Table 20). Moreover, these results were robust to uncertainty in phylogenetic relationships, and to uncertainty in how taxa were classified for oviposition ecology (Supplementary Table 16). These findings provide evidence that the microenvironment that is experienced by the egg has had an important role in morphological evolution.
Insect eggs present an ideal case for testing the predictability of macroevolutionary patterns in size and shape. By comparing insect egg size and shape, we found that previous hypotheses about evolutionary trade-offs with developmental time, body size or the presumed cost of egg shells do not hold. Although we showed that developmental time is not linked to egg size, we suggest that other features of development (for example, cell number and distribution) may scale in predictable ways across eight orders of magnitude in egg size. Finally, we provide evidence that the ecology of oviposition drives the evolution of egg size and shape.
Creating the insect egg dataset
A list of the 1,756 literature sources used to generate the egg dataset is provided in the Supplementary Information. A full description of the methods used to assemble the insect egg dataset has been published elsewhere24. Egg descriptions were collected from published accounts of insect eggs using custom software to parse text from PDFs and measure published images (Fig. 1d), followed by manual verification. Each entry in the egg dataset includes a reference to an insect genus and, when reported, species name. Scientific names were validated using TaxReformer24, which relies on online taxonomic databases52,53,54,55,56. The final sample size of the dataset (over 10,000 egg descriptions) was determined to be sufficient because it included thousands of instances of repeated evolution of similar egg size and shape.
Measuring egg features
Full trait definitions are described in the Supplementary Information and summarized in brief below. To resolve ambiguous cases and to measure published images, we used the definitions below.
We defined egg length as the distance in millimetres (mm) from one end to the other of the axis of rotational symmetry.
We defined egg width as the widest diameter (mm), measured perpendicular to the axis of rotational symmetry of the egg. For eggs described in published records as having both a width and breadth or depth (that is, the egg is a flattened ellipsoid57), we defined width as the wider of the two diameters, and breadth as the diameter perpendicular to both the width and length.
Egg aspect ratio
Aspect ratio was calculated as the ratio of length to width.
Asymmetry was calculated as the ratio between the two egg diameters at the first and third quartile of the length axis, minus one. The first quartile was always defined as the larger of the two diameters.
Angle of egg curvature
The angle of curvature was measured as the angle (degrees) of the arc created by the end points and mid-point of the length axis.
A genus-level phylogeny was built by combining mitochondrial 18S and 28S sequencing data from the SILVA database59,60,61,62 with phylogenetic constraints from published higher-level insect phylogenies25,26. To account for phylogenetic uncertainty in comparative analyses, trees were estimated using a hierarchical approach63,64. Separate phylogenies for each insect order were inferred in a Bayesian framework using MrBayes v.3.2.665 and 100 post-burn-in trees were randomly chosen for each order using the order-level backbone trees of two previous studies25,26. See Supplementary Information for further details.
Annotating the egg dataset with developmental trait data
For developmental traits, a set of references was assembled from the embryological and ecological literature, and then used to compile data on interval between syncytial mitoses, time to cellularization and duration of embryogenesis. Developmental rate observations were rescaled to approximate rates at a standardized temperature of 20 °C following previous studies66. For a full list of sources, methods used in this calculation, and further discussion of developmental trait definitions, see Supplementary Information.
Annotating the egg dataset with life-history trait data
For each of the ecological features of interest (internal parasitic oviposition, aquatic oviposition, flightlessness and migratory behaviour), taxonomic descriptions from the literature were matched to taxa in the insect egg dataset. For some taxonomic groups, it was not possible to classify all members unambiguously. In these cases, the ecological state was coded ‘uncertain’ and the potential effect of this uncertainty on results was tested. For each trait the ancestral state reconstruction was estimated using an equal-rates model (R package corHMM67, function rayDISC, node.states = marginal). For a full list of sources and methods used see Supplementary Information.
Data analysis and evolutionary model comparison
Egg length, width, volume and aspect ratio were log10-transformed. Angle of curvature and asymmetry were square-root-transformed.
Models of evolution were compared using the R package geiger68. For each trait (egg length, width, volume, aspect ratio, asymmetry and angle of curvature), the model fits of Brownian motion, Ornstein–Uhlenbeck and early-burst models were compared against a null hypothesis of a white noise model that assumes no evolutionary correlation (see Supplementary Information for details). The performance of the best-fitting model was further analysed by comparing expected values of parameters from simulations under the model to observed parameters using the R package arbutus69.
The ancestral state of volume, aspect ratio and angle of curvature were mapped on the summary phylogeny using the R package phytools70 (v.0.6-44, function contMap). Evolutionary rate regimes of volume, aspect ratio and the angle of curvature were fitted on the summary phylogeny using the program BAMM71,72 (v.2.5.0, R package BAMMtools v.2.1.6, setBAMMpriors, prior for expected number of shifts set to 10, for 10,000,000 generations).
All evolutionary regression analyses were performed using a phylogenetic generalized least-squares approach in the R packages ape73 (v.5.0, correlation structure = corBrownian) and nlme74 (v.3.1-131.1). Given that the early-burst models best fit the data, we also tested a corBlomberg correlation structure, which invokes an accelerating–decelerating model of evolution, with the decelerating rate of trait change fixed at 1.3.
For comparisons performed at the genus level, each regression was repeated over 100 trees randomly drawn from the posterior distribution randomly selecting a representative entry per genus from the egg dataset. For comparisons performed at the family level, each regression was repeated 100 times calculating the family level average egg data from 50% of entries per family.
For phylogenetic regressions controlling for a third variable, we calculated the phylogenetic residuals of each variable against the dependent variable, and then calculated the phylogenetic regression of the residuals75. To test alternative hypotheses, new data were simulated using a fixed scaling exponent and the parameters of the best-fitting model with the R package phylolm76 (v.2.5, function ‘rTrait’).
Allometric regressions were performed over all insect taxa as well as for seven monophyletic groups of insects individually (Palaeoptera, Polyneoptera, Condylognatha, Hymenoptera, Neuropteroidea, Amphiesmenoptera and Antliophora). In addition, the scaling exponent between egg length and width was calculated for each monophyletic group of taxa that had more than 20 tips but fewer than 50 tips.
Following ancestral state reconstruction of ecological regimes, for each ecology–trait pair (internal parasitic or aquatic oviposition combined with volume, aspect ratio, asymmetry or curvature) the fit of a Brownian motion model, an Ornstein–Uhlenbeck model with a single optimum and an Ornstein–Uhlenbeck model with an independent optimum for each ecological state were compared using the R package OUwie77 (version 1.50). These analyses were repeated over 100 trees randomly drawn from the posterior distribution, and randomly selecting a representative egg for each genus.
Plots were generated in R. Figures were assembled with Adobe Illustrator. Egg images that were reproduced from other publications were converted to greyscale, contrast adjusted, rotated, and then masked from their backgrounds using Adobe Photoshop.
For evolutionary regressions and parametric bootstraps, a significance threshold of 0.01 was used. All P values were rounded to the nearest hundredth. Exact values for all statistical comparisons are available in the figure legends and Supplementary Information. For evolutionary model comparisons, weighted AICc values were compared at a significance threshold of 2. Evolutionary regressions were performed 100 times each, taking into account phylogenetic and phenotypic uncertainty. For more details see Supplementary Information.
Further information on research design is available in the Nature Research Reporting Summary linked to this paper.
The dataset of insect eggs is publicly available at Dryad (https://datadryad.org) with doi:10.5061/dryad.pv40d2r and has been described elsewhere24. The phylogenetic posterior distributions are provided as Supplementary Information (phylogeny_posterior distribution_misof_backbone.nxs and phylogeny_posterior_distribution_rainford_backbone.nxs).
All code required to reproduce the analyses and figures shown here is available at https://github.com/shchurch/Insect_Egg_Evolution.
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This work was supported by the National Science Foundation (NSF) under grant no. IOS-1257217 to C.G.E., NSF GRFP DGE1745303 to S.H.C. and by a Jorge Paulo Lemann Fellowship to B.A.S.d.M. from Harvard University. We thank members of the Extavour laboratory and B. Farrell, C. Dunn, D. McCoy, D. Rice, A. Kao, E. Kramer, J. Boyle, L. Bittleston, M. Srivastava, M. Johnson, P. Wilton, R. Childers and S. Prado-Irwin for discussion, and the Ernst Mayr Library at the Museum of Comparative Zoology at Harvard, and specifically M. Sears, for assistance in gathering references.
Nature thanks Clay Cressler and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
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This document contains Supplementary Methods, Supplementary Figures S1-S24, and Supplementary Tables S1-S22. These provide additional methodological details, a more complete description of the diversity of insect eggs, ancestral state reconstructions, and evolutionary model fitting results.
This file contains the Egg Dataset Bibliography. This document is a list of the 1,756 published sources that were used to generate the assembled dataset of insect egg traits.
This nexus file contains 100 phylogenetic trees randomly sampled from the posterior distribution, assembled using the Rainford et al. 2014 (ref. 26 in the main text) phylogeny as a backbone.
This nexus file contains 100 phylogenetic trees randomly sampled from the posterior distribution, assembled using the Misof et al. 2014 (ref. 25 in the main text) phylogeny as a backbone.
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Church, S.H., Donoughe, S., de Medeiros, B.A.S. et al. Insect egg size and shape evolve with ecology but not developmental rate. Nature 571, 58–62 (2019). https://doi.org/10.1038/s41586-019-1302-4
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