Extreme natural size variation in both sexes of a sexually cannibalistic mantidfly

In sexually cannibalistic animals, the relative sizes of potential mates often predict the outcome of aggressive encounters. Mantidflies are spider egg predators as larvae and generalist predators as adults. Unlike most cannibalistic species, there is considerable individual variation in body size in both sexes. Using preserved collections of Dicromantispa sayi, we focused on three body size metrics that we found to be positively correlated and accurately measured across researchers. We found extreme size variation in both sexes: the largest 10% of females were 1.72× larger than the smallest 10%, and the largest 10% of males were 1.65× larger than the smallest 10%. On average, females were 7.94% larger than males. In exploring possible causes of this variation, we uncovered differences among populations. To explore the effect of spider egg sac size on adult mantidfly size, we reared mantidfly larvae on egg sacs from two jumping spider species with small or large egg sacs. Mantidfly larvae reared on small egg sacs were smaller than those reared on large egg sacs. This study provides the groundwork to design ecologically relevant experiments exploring the causes and consequences of extreme size variation in an understudied system with intriguing natural history

The use of synthetic microbial communities to improve plant health

Despite the numerous benefits plants receive from probiotics, maintaining consistent results across applications is still a challenge. Cultivation-independent methods associated with reduced sequencing costs have considerably improved the overall understanding of microbial ecology in the plant environment. As a result, now, it is possible to engineer a consortium of microbes aiming for improved plant health. Such synthetic microbial communities (SynComs) contain carefully chosen microbial species to produce the desired microbiome function. Microbial biofilm formation, production of secondary metabolites, and ability to induce plant resistance are some of the microbial traits to consider when designing SynComs. Plant-associated microbial communities are not assembled randomly. Ecological theories suggest that these communities have a defined phylogenetic organization structured by general community assembly rules. Using machine learning, we can study these rules and target microbial functions that generate desired plant phenotypes. Well-structured assemblages are more likely to lead to a stable SynCom that thrives under environmental stressors as compared with the classical selection of single microbial activities or taxonomy. However, ensuring microbial colonization and long-term plant phenotype stability is still one of the challenges to overcome with SynComs, as the synthetic community may change over time with microbial horizontal gene transfer and retained mutations. Here, we explored the advances made in SynCom research regarding plant health, focusing on bacteria, as they are the most dominant microbial form compared with other members of the microbiome and the most commonly found in SynCom studies

A greenhouse‐based high‐throughput phenotyping platform for identification and genetic dissection of resistance to Aphanomyces root rot in field pea

Abstract Aphanomyces root rot (ARR) is a devastating disease in field pea (Pisum sativum L.) that can cause up to 100% crop failure. Assessment of ARR resistance can be a rigorous, costly, time‐demanding activity that is relatively low‐throughput and prone to human errors. These limits the ability to effectively and efficiently phenotype the disease symptoms arising from ARR infection which remains a perennial bottleneck to the successful evaluation and incorporation of disease resistance into new cultivars. In this study, we developed a greenhouse‐based high‐throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea. We pilot tested this platform alongside with conventional visual scoring in five experimental trials under greenhouse conditions, assaying over 12,600 single plants of advanced breeding lines developed by the North Dakota State University Pulse Breeding Program. Precision estimated through broad‐sense heritability (H2) was consistently higher for RGB‐derived indices (H2, Exg = 0.86) than the conventional visual scores (H2, disease severity index = 0.59). Prediction of disease severity using a random forest modeling of RGB‐derived indices achieved 0.69 accuracy on the test sets, with inaccurate classification partly attributed to the presence of tolerant lines (displaying root rot but no foliar symptoms) and within‐line genetic heterogeneity. We genetically dissected variation for ARR resistance from the population using RGB‐derived indices and visual scores through genome‐wide association mapping and identified a total of 260 associated single nucleotide polymorphism (SNP). The number of associated SNP for RGB‐derived indices was consistently higher than the number of associated SNP identified using visual scores, with the most significant SNP explaining about 5%–9% of variance per index. We identified previously mapped genes known to be involved in the biological pathways that trigger immunity against ARR and a few novel QTLs with small‐effect sizes that may be worthy of validation in the future. The newly identified QTLs and underlying genes, along with genotypes with promising resistance identified in this study, can be useful for improving a long‐term and durable resistance to ARR