Fitness consequences of early-life environmental conditions are often sex-specific, but corresponding evidence for invertebrates remains inconclusive. Here we use meta-analysis to evaluate sex-specific sensitivity to early-life nutritional conditions in insects. Using literature-derived data for 85 species with broad phylogenetic and ecological coverage, we show that females are generally more sensitive to food stress than males. Stressful nutritional conditions during development typically lead to female-biased mortality and thus increasingly male-biased sex ratios of emerging adults. We further demonstrate that the general trend of higher sensitivity to food stress in females can primarily be attributed to their typically larger body size in insects and hence higher energy needs during development. By contrast, there is no consistent evidence of sex-biased sensitivity in sexually size-monomorphic species. Drawing conclusions regarding sex-biased sensitivity in species with male-biased size dimorphism remains to wait for the accumulation of relevant data. Our results suggest that environmental conditions leading to elevated juvenile mortality may potentially affect the performance of insect populations further by reducing the proportion of females among individuals reaching reproductive age. Accounting for sex-biased mortality is therefore essential to understanding the dynamics and demography of insect populations, not least importantly in the context of ongoing insect declines.

Data collection

These data were collected for a meta-analysis to assess sex-specific sensitivity to early-life nutritional conditions in insects. We made use of experimental case studies reporting sex ratios at adult emergence in conspecifics reared under two or more diet treatments (food quality or availability). We collated primary studies in two complementary ways. The majority of primary data sets for this synthesis were collected systematically by the lead author (T. Teder) from an extensive list of journals in the field of entomology, ecology and evolutionary biology, partly as a result of one-time retrospective screening (articles published before 2003) and partly as a result of continuous screening (articles published between 2004 and 2021) of journals' tables of contents. Our systematic screening meant that the journals' tables of contents were routinely examined, and all papers identified as potentially containing relevant data on the basis of article titles were subjected to full-text review. As data of this type are typically reported in tables and figures, their identification within articles was straightforward.

To increase the amount of primary data, additional studies were identified by a thorough search in major literature databases (Google Scholar, Web of Science, Scopus, published until 2021). These complementary searches in the literature databases were undertaken to find relevant data in journals that remained uncovered by our main data collection method. Accordingly, while exploring the search results, we primarily focused on studies published in journals that were not subjected to systematic screening. The basic procedure for identifying relevant primary papers among search results was basically identical to that used when screening journals' contents: papers identified as potentially containing relevant data based on article titles were retrieved for full-text review. To minimize any search-related biases, we used only search queries that were strictly neutral concerning the focal questions of our study (i.e. sex-specific sensitivity to nutritional stress). Accordingly, our search queries included only combinations of very generic search terms: one of several synonyms of sex ratio ('sex ratio', 'proportion/percentage/fraction of males/females'), 'mortality' and one of particular insect order names ('Diptera', 'Hemiptera', 'Lepidoptera', 'Coleoptera', 'Orthoptera', etc., or 'insect*'). No restriction was set on the language or publication year of primary studies.

As a major exception, we systematically ignored studies focusing on Hymenoptera and Thysanoptera during the process of data collection. These groups of insects have haplodiploid sex determination (males develop from unfertilized and females from fertilized eggs) which provides mothers with an efficient mechanism for manipulating offspring sex ratio. We also did not consider taxa regularly exhibiting asexual reproduction, such as aphids.

Data extraction and criteria for eligibility

For a study to be considered, it had to provide two types of information: i) sex ratios at adult emergence for multiple (two or more) diet treatments together with sample sizes, and ii) corresponding juvenile mortality rates. Typically, sex ratios in primary studies were reported as the proportion/percentage of males/females or the ratio of the two sexes at adult emergence (or, in a few cases, at the pupal stage). As sample sizes for sex ratio estimates were not always explicitly indicated, we applied various indirect approaches to derive them, most often combining information on sample sizes at the start of the experiment with data on mortality throughout juvenile stages.

The combined juvenile mortality rate of the two sexes was used as a proxy for nutritional stress. Accordingly, our research relies on the premise that, within each primary study, food stress was most severe in treatments with the highest mortality rates and least pronounced in treatments with the lowest mortality rates. Both egg-to-adult and larval mortality rates (often reported as survival rates) were considered equally acceptable measures of juvenile mortality. In a few cases, we also accepted mortality rates estimated over a particular fixed part of the larval stage (two primary studies) or the pupal stage (three studies).

We limited our inclusion criteria to studies where major external mortality agents – predators and parasitoids – were explicitly excluded. In all studies included, experimental treatments were applied to the F1 generation only, whereas their parents were maintained under identical conditions, excluding in this way any parental effect on sex ratios. Among-treatment differences in nutritional stress were solely due to variations in food quality (e.g., different host plants, different prey species, also different artificial diets) or food availability. Otherwise, the conditions were uniform within the experiments. Data from multifactorial experiments (e.g. those manipulating both diet and temperature) were divided into different data sets so that the environmental factor of our interest was allowed to vary while other factors were held constant. In some primary studies, food quality and amount were manipulated indistinguishably within the same experimental setup. Data extracted from different studies were always treated as different data sets. However, data from a single study could also be split into multiple primary data sets if obtained from different experiments or using different species/populations/genotypes. We deliberately did not consider studies in which diet treatments applied contained pesticides or their residues. WebPlotDigitizer 4.3 (A. Rohatgi; https://automeris.io/WebPlotDigitizer) was used to extract graphically presented data.

One should note that the overwhelming majority of primary studies were conducted in contexts other than the focus of our synthesis: sex differences in stress responses per se were rarely addressed in these papers. Therefore, a considerable share of primary studies found, between-treatment differences in juvenile mortality were relatively small, indicating low variation in environmental stress levels. Naturally, in order to meaningfully evaluate sex-specific responses to food stress, there must be some variation in food stress across treatments. We therefore arbitrarily limited our main database to a subset of primary studies in which mortality rates across treatments had at least a 10 % difference (calculated as the difference between the maximum and minimum mortality rates across treatments). This way we ensured that growth conditions within studies were not "too similar" across treatments. Applying this threshold retained us altogether 125 primary data sets which formed the backbone of our analyses.

Data are provided as .xlsx files, and R scripts are provided for analyses. We encourage researchers interested in the re-use of this data set and code to contact the authors of the data set.