DOI: 10.5061/dryad.08kprr599
title: Data from: Macroevolution along developmental lines of least resistance

description: A reigning paradigm in biology is that short-term evolution can be
 predicted from measures of genetic variation within populations, but that
 the accuracy of such predictions should decay with time. Here, we show
 that intrinsic developmental variability and standing genetic variation in
 wing shape of the two flies, Drosophila melanogaster and Sepsis punctum,
 are tightly aligned and predict deep divergence in the dipteran phylogeny,
 spanning &gt;900 taxa and 185 My of evolution. This finding is hard to
 reconcile with constraint hypotheses invoking a lack of genetic variation
 as the reason for slow-evolving wing traits unless most of the observed
 variability is associated with deleterious side effects and effectively
 unusable for evolution. However, phenotyping of 71 genetic lines of S.
 punctum revealed no association between variation in wing shape and
 fitness correlates unrelated to flight, lending no credence to this
 hypothesis. We also find no evidence for genetic constraints on the pace
 of wing shape evolution along individual branches of the phylogeny.
 Instead, correlational selection related to allometric scaling,
 simultaneously shaping both developmental bias and deep divergence in fly
 wings, emerges as the most plausible explanation for the observed
 patterns. This suggests that past forces of selection have shaped the
 developmental architecture of the dipteran wing such that its long-term
 evolution can be predicted from its intrinsic variability. These findings
 challenge our understanding of the fundamental processes governing the
 emergence of phenotypic variation and its evolution.
description: <em>Quantifying wing shape and divergence across fly
 taxa </em> We focus on the evolution of wing
 shape within the Eremoneura, a clade within the Brachycera characterized
 by the presence of three larval instars. This clade is about 185 million
 years old and includes the dance and long-legged flies (Empidoidea) as
 well as the Cyclorrhapha (flies that pupate within the cuticle of the last
 larval instar (i.e., the puparium)). To quantify the
 morphological variation within and between families, we took advantage of
 illustrations and pictures of fly wings from the taxonomic and systematic
 literature. An initial data set was sourced from the <em>Manuals of
 Nearctic Diptera</em> and the <em>Manuals of Afrotropical
 Diptera</em> . We focused on those families that are represented in
 the phylogenetic hypothesis generated by Bayless et al.. Additional
 pictures and illustrations were collected from a wide range of
 publications. We only included observations where the
 location of all 11 two-dimensional landmarks used in Rohner &
 Berger could be assigned. In total, we collected wing shape data from 827
 individuals belonging to 53 families. Using tpsDig2, we manually
 quantified wing morphology as depicted on the illustrations and images.
 Additional morphometric data originally collected from images was added
 for 119 species of drosophilids from Houle et al. 2017 and 36 species for
 sepsids from Rohner & Berger 2023. The final data set contained
 993 observations of 933 species in 530 genera and 68 families. Family
 affiliation of individual genera was checked using <em>Systema
 Dipterorum</em>. The number of observations
 varied strongly across families (mean = 17.40, median = 12, minimum = 1,
 maximum = 130). This uneven sampling was caused by i) a varying number of
 species per family (e.g., Australimyzidae is a monogeneric family
 containing just 9 described species compared to Tachinidae with 9,626
 species), the loss of landmarks in several species (e.g., Sphaeroceridae),
 and often incomplete illustrations or pictures showing only part of the
 wing (e.g., Muscidae and Calliphoridae). The landmark coordinates were
 aligned to the mean configuration of Houle et al. 2017 using Procrustes
 analysis in MorphoJ. <em>Quantifying variation in
 genetic quality to test for deleterious pleiotropy associated with wing
 shape</em> To quantify fitness variance across
 the same isofemale lines of <em>S. punctum</em> as measured
 for wing shape, we reared all 71 lines (originating from the 7 European
 populations) in a common garden experiment including 9 temperature
 treatments ranging from 15-31°C. Five fitness-correlates were measured. F0
 containers with fly cultures were seeded with vials of cow dung to attain
 freshly laid eggs. Each line was seeded with four vials per temperature
 treatment. For each vial, juvenile development rate was estimated as the
 inverse of the time (in days) between the date of a laid clutch and the
 subsequent emergence of F1 adults. Juvenile survival was calculated at the
 fraction of laid eggs that emerged as adults. Emerged F1 females were
 measured for their tibia length as an estimate of body size and then
 paired with a male from the same line and placed in a 50ml vial with
 access to sugar, water, and cow dung as egg laying substrate. Sugar,
 water, and dung was replaced every 5 days for the first 15 days. Early
 reproductive success was estimated as the total number of offspring
 produced within the first 15 days of adult female life, excluding females
 that died during this timeframe (likely due to accidental deaths). Females
 that did not lay any eggs during this period were not included in
 estimates of early reproductive success. Lifespan was estimated as the
 time from the start of the experiment until the focal female died. 245
 females (17%) did not die during the period of observation and were
 recorded as censused data. In total, 1,445 females were measured across
 all lines and temperature treatments. In total, these females produced
 173,556 offspring. To test for heritable variation in
 early reproductive success, juvenile survival, developmental rate, and
 body size, we used mixed effects models using restricted maximum
 likelihood as implemented in<em> ASReml-R </em>. Temperature
 treatment, population, as well as their interaction were fitted as fixed
 effect. Line was added as random effect. Note that we did not estimate
 line by treatment interactions (i.e. G-by-E) as our aim was to capture
 overall differences in genetic quality among lines. Repeating the analysis
 excluding the highest (31℃) and lowest (15℃) (i.e. most stressful)
 temperature treatments led to similar results. Residual variances were
 allowed to vary across treatments. The significance of the random effect
 of line was tested using LRTs. Best linear unbiased predictors (BLUPs)
 were extracted and used for further analysis. For the analysis of adult
 lifespan, we fitted a censored mixed effects cox model using the
 <em>coxme</em> package [90] using treatment and population as
 fixed effects and line as random effect. Likelihood Ratio Tests (LRTs)
 were used to test whether line effects were significant. We extracted
 hazard ratios for each line and used its inverse as our estimates for
 adult longevity.
description: # **Data from: Macroevolution along developmental lines of least
 resistance**
 [https://doi.org/10.5061/dryad.08kprr599](https://doi.org/10.5061/dryad.08kprr599) ## Description of the data and file structure This dataset contains three files. One file contains all wing shape coordinates that were sourced from the literature. The second file contains wing shape information used to compare shape data sourced from the literature to those generated from pictures. The third file contains average wing shape data and best linear unbiased predictors (BLUPs) for five different fitness components. Missing data is indicated with "NA". Each file contains a separate sheet with a more detailed variable description. #### Description of variables in DRYAD\_all\_shape\_data.xlsx | variable | description | | :------- | :------------------------------------------------------------------------ | | ID | unique numerical identifier | | filename | name of the image file analyzed | | family | family-ranked taxon | | genus | genus name | | species | species name  | | source | reference to the original source | | type | indicates whether landmarks were collected from illustrations or pictures | | x1-x11 | x component of Procrustes coordinates | | y1-y11 | y component of Procrustes coordinates | #### Description of variables in DRYAD\_compare\_pictures\_and\_illustrations.xlsx | variable | description | | :------- | :------------------------------------------------------------------------ | | genus | genus name | | species | species name  | | source | reference to the original source | | type | indicates whether landmarks were collected from illustrations or pictures | | x1-x11 | x component of Procrustes coordinates | | y1-y11 | y component of Procrustes coordinates | #### Description of variables in DRYAD\_line\_means\_wing\_shape\_and\_fitness.xlsx | variable | description | | :------------------ | :------------------------------------------------------------------------------------ | | population | population of origin | | pop\_line | iso-female line identifier | | x1-x11 | average x component of Procrustes coordinates | | y1-y11 | average y component of Procrustes coordinates | | CS | average centroid size in millimeter | | fecundity | z-scored best linear unbiased predictors (BLUPs) for fecundity | | survival | z-scored best linear unbiased predictors (BLUPs) for adult lifespan | | offspring dev. rate | z-scored best linear unbiased predictors (BLUPs) for offspring developmental duration | | offspring survival | z-scored best linear unbiased predictors (BLUPs) for offspring survival | | offspring size | z-scored best linear unbiased predictors (BLUPs) for offspring body size | | PC1 | first principal component of all five fitness estimates |