Sex allocation theory has long generated insights into the nature of natural selection. Classical models have elucidated causal phenomena such as local mate competition and inbreeding on the degree of female bias exhibited by various invertebrates. Typically, these models assume mothers facultatively adjust sex allocation using predictive cues of future offspring mating conditions. Here we relax this assumption by developing a sex allocation model for haplodiploid mothers experiencing local mate competition that lay a fixed number of male eggs first. Female egg number is determined by remaining oviposition sites or remaining eggs of the mother, depending on which is exhausted first. Our model includes parameters for variation in foundress number, patch size, fecundity and offspring mortality that allow us to generate secondary sex ratio predictions based on specific parameterizations for natural populations. Simulations show that: 1) in line with classical models, factors that increase sib‐mating result in mothers laying relatively more female eggs; 2) high offspring mortality leads to relatively more males as fertilization insurance; 3) unlike classical model predictions, sub‐optimal predictions, such as more males than females are possible. In addition, our model provides the first quantitative predictions for the expected number of males and females in a patch where typically only one mother utilizes a given patch. We parameterized the model with data obtained from seven species of southern African fig wasps to predict expected means and variances for numbers of male and female offspring for typical numbers of mothers utilizing a patch. These predictions were compared to secondary sex ratio data from single foundress patches, the most commonly encountered situation for these species. Our predictions matched both the observed number and variance of male and female offspring with a high degree of accuracy suggesting that facultative adjustment is not required to produce evolutionary stable sex ratios.
The microbiome plays a central role in biochemical cycling and nutrient turnover of most ecosystems. Because it can comprise myriad microbial prokaryotes, eukaryotes and viruses, microbiome characterization requires high-throughput sequencing to attain an accurate identification and quantification of such co-existing microbial populations. Short-read next-generation-sequencing (srNGS) revolutionized the study of microbiomes and remains the most widely used approach, yet read lengths spanning only a few of the nine hypervariable regions of the 16S rRNA gene limit phylogenetic resolution leading to misclassification or failure to classify in a high percentage of cases. Here we evaluate a synthetic long-read (SLR) NGS approach for full-length 16S rRNA gene sequencing that is high-throughput, highly accurate and low-cost. The sequencing approach is amenable to highly multiplexed sequencing and provides microbiome sequence data that surpasses existing short and long-read modalities in terms of accuracy and phylogenetic resolution. We validated this commercially-available technology, termed LoopSeq, by characterizing the microbial composition of well-established mock microbiome communities and diverse real-world samples. SLR sequencing revealed differences in aquatic community complexity associated with environmental gradients, resolved species-level community composition of uterine lavage from subjects with histories of misconception and accurately detected strain differences, multiple copies of the 16S rRNA in a single strain’s genome, as well as low-level contamination in soil cyanobacterial cultures. This approach has implications for widespread adoption of high-resolution, accurate long-read microbiome sequencing as it is generated on popular short read sequencing platforms without the need for additional infrastructure.
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