The influence of humidity and temperature on the vertical richness and abundance of blood-sucking flies (Culicidae and Ceratopogonidae) in a montane cloud forest in Mexico.

Because the vertical distribution and diversity of blood-sucking flies are poorly known, we determined the diversity, structure, and composition of culicids between vertical vegetation strata. We evaluated the influence of microclimatic variables during different times of the day over a year. We used eight CDC traps baited with CO2 at a height of 1.5 m and 12-15 m. We conducted rank-abundance curves, similarity analysis (ANOSIM and SIMPER), and multivariate clustering with incidence and abundance data. We used GAM models to analyze the influence of strata (understory vs canopy), humidity, and temperature on insect richness and abundance. During the day, the difference between strata was mainly due to higher abundance of Wyeomyia arthrostigma and Wyeomyia ca. adelpha in the understory. During the night, the differences were mainly due to higher abundance of Culex stigmatosoma, Culex salinarius, and Aedes allotecnon in the canopy, and Wyeomyia arthrostigma in the understory. Seasonality played a role in the similarity between the strata. Diversity during the day was positively related to humidity and temperature, and nocturnal diversity increased with temperature but decreased with higher humidity. The effects of environmental factors on the spatiotemporal distribution of fly species are essential for epidemiological surveillance.

Antifungal Effect of Copper Nanoparticles against Fusarium kuroshium, an Obligate Symbiont of Euwallacea kuroshio Ambrosia Beetle.

Copper nanoparticles (Cu-NPs) have shown great antifungal activity against phytopathogenic fungi, making them a promising and affordable alternative to conventional fungicides. In this study, we evaluated the antifungal activity of Cu-NPs against Fusarium kuroshium, the causal agent of Fusarium dieback, and this might be the first study to do so. The Cu-NPs (at different concentrations) inhibited more than 80% of F. kuroshium growth and were even more efficient than a commercial fungicide used as a positive control (cupric hydroxide). Electron microscopy studies revealed dramatic damage caused by Cu-NPs, mainly in the hyphae surface and in the characteristic form of macroconidia. This damage was visible only 3 days post inoculation with used treatments. At a molecular level, the RNA-seq study suggested that this growth inhibition and colony morphology changes are a result of a reduced ergosterol biosynthesis caused by free cytosolic copper ions. Furthermore, transcriptional responses also revealed that the low- and high-affinity copper transporter modulation and the endosomal sorting complex required for transport (ESCRT) are only a few of the distinct detoxification mechanisms that, in its conjunction, F. kuroshium uses to counteract the toxicity caused by the reduced copper ion.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species.

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

Genomic Signals of Adaptation towards Mutualism and Sociality in Two Ambrosia Beetle Complexes.

Mutualistic symbiosis and eusociality have developed through gradual evolutionary processes at different times in specific lineages. Like some species of termites and ants, ambrosia beetles have independently evolved a mutualistic nutritional symbiosis with fungi, which has been associated with the evolution of complex social behaviors in some members of this group. We sequenced the transcriptomes of two ambrosia complexes (Euwallacea sp. near fornicatus⁻Fusarium euwallaceae and Xyleborus glabratus⁻Raffaelea lauricola) to find evolutionary signatures associated with mutualism and behavior evolution. We identified signatures of positive selection in genes related to nutrient homeostasis; regulation of gene expression; development and function of the nervous system, which may be involved in diet specialization; behavioral changes; and social evolution in this lineage. Finally, we found convergent changes in evolutionary rates of proteins across lineages with phylogenetically independent origins of sociality and mutualism, suggesting a constrained evolution of conserved genes in social species, and an evolutionary rate acceleration related to changes in selective pressures in mutualistic lineages.

Impact of Rearing Conditions on the Ambrosia Beetle's Microbiome.

Ambrosia beetles, along with termites and leafcutter ants, are the only fungus-farming lineages within the tree of life. Bacteria harbored by ambrosia beetles may play an essential role in the nutritional symbiotic interactions with their associated fungi; however, little is known about the impact of rearing conditions on the microbiota of ambrosia beetles. We have used culture-independent methods to explore the effect of rearing conditions on the microbiome associated with Xyleborus affinis, Xyleborus bispinatus, and Xyleborus volvulus, evaluating different media in laboratory-controlled conditions and comparing wild and laboratory conditions. Our results revealed that rearing conditions affected the fungal and bacterial microbiome structure and had a strong influence on bacterial metabolic capacities. We propose that the rearing conditions influence the ambrosia-associated fungal and bacterial communities. Furthermore, bacterial microbiome flexibility may help beetles adapt to different substrates.