Indirect parental effects on offspring fitness by egg-derived fluids in an external fertiliser
Cite this dataset
Lymbery, Rowan; Berson, Jacob; Evans, Jonathan (2020). Indirect parental effects on offspring fitness by egg-derived fluids in an external fertiliser [Dataset]. Dryad. https://doi.org/10.5061/dryad.fttdz08r3
The capacity for parents to influence offspring phenotypes via nongenetic inheritance is currently a major area of focus in evolutionary biology. Intriguing recent evidence suggests that sexual interactions among males and females, both before and during mating, are important mediators of such effects. Sexual interactions typically extend beyond gamete release, involving both sperm and eggs, and their associated fluids. However, the potential for gamete-level interactions to induce transgenerational parental effects remains under-investigated. Here, we test for such effects using an emerging model system for studying gamete interactions, the external fertiliser Mytilus galloprovincialis. We employed a split-ejaculate design to test whether exposing sperm to egg-derived chemicals (ECs) from one female would affect fertilisation rate and offspring survival when those sperm were used to fertilise a different female’s eggs. We found significant and separate effects of ECs from non-fertilising females on both fertilisation rate and offspring survival. The offspring survival effect indicates that EC-driven interactions can have transgenerational implications for offspring fitness independent of the genotypes inherited by those offspring. These findings provide a rare test of indirect parental effects driven exclusively by gamete-level interactions, and to our knowledge the first evidence that such effects occur via the gametic fluids of females.
We conducted our experiment in a series of ‘blocks’ (see Fig. 1 for schematic overview of our experimental design). The experiment was designed to estimate whether chemicals derived from the eggs of non-fertilising females can contribute to variance in fertilisation rate and the viability of offspring from a different female. Each block was comprised of sperm from a single male, the ‘egg water’ (seawater in which eggs had previously been suspended and releasing chemicals; see below) from four non-fertilising females, and eggs from a separate ‘standard’ female. Within a block, the male’s ejaculate was split into eight aliquots, and two aliquots were mixed with egg water from each non-fertilising female (i.e. separate aliquots were exposed to each egg water in replicate). Following exposure to egg water, the aliquots were then mixed with eggs from the standard female for the fertilisation trials. After allowing fertilised eggs to develop to the multi-cell stage (see below), we split the fertilisation pool into subsamples for (a) measuring fertilisation rates, and (b) development over an additional 48 h in order to derive an estimate of offspring viability. We collected data from a total of ten experimental blocks (i.e. egg water from 40 focal females, sperm from ten males, eggs from ten standard females, n = 80 fertilisations total).
Data were processed using generalised linear mixed-effects models in R version 3.6.0. We modelled fertilisation rate as a binomial response variable (number of fertilised and unfertilised eggs out of 100 in each sample) using a logit link function, with a fixed intercept term and random effects for ‘block’ ID and ‘egg water (non-fertilising) female’ ID within block, along with an observation-level random effect to account for overdispersion. We next offspring survival (count of surviving offspring) as a Poisson response variable using a log link function, again with random effects for ‘block’ and ‘non-fertilising female’, aling with an observation-level random effect to account for overdispersion, and in this case with a fixed covariate of fertilisation rate (proportion). This fixed covariate was necessary to control for variation in offspring numbers among samples that was due to variation in fertilisation rate.
The dataset contains variables in columns and samples in rows. The columns included are 'Block', 'Female' (non-fertilising females within blocks from which egg water was collected), 'Repeated_measure' (denoting the two repeated measures for each block-female combination), 'ID' (a unique ID for every sample), 'N_Fertilised_Eggs' (number of fertilised eggs out of haphazard sample of 100), 'N_Unfertilised_Eggs' (number of unfertilised eggs out of haphazard sample of 100), and 'N_Offspring_Surviving' (count of surviving offspring after 48 hours).
Note that the female-level outlier for offspring survival (after natural-log transforming and correcting by fertilisation rate) we identify in our manuscript is B3_EW1. The five outliers at the individual data point level that we identify in our manuscript are B3_EW1_2, B7_EW1_1, B7_EW3_1 and B10_EW2_1.
Australian Research Council, Award: DP150103266