An inordinate fondness for Fusarium: Phylogenetic diversity of fusaria cultivated by ambrosia beetles in the genus Euwallacea on avocado and other plant hosts

Ambrosia beetle fungiculture represents one of the most ecologically and evolutionarily successful symbioses, as evidenced by the 11 independent origins and 3500 species of ambrosia beetles. Here we document the evolution of a clade within Fusarium associated with ambrosia beetles in the genus Euwallacea (Coleoptera: Scolytinae). Ambrosia Fusarium Clade (AFC) symbionts are unusual in that some are plant pathogens that cause significant damage in naive natural and cultivated ecosystems, and currently threaten avocado production in the United States, Israel and Australia. Most AFC fusaria produce unusual clavate macroconidia that serve as a putative food source for their insect mutualists. AFC symbionts were abundant in the heads of four Euwallacea spp., which suggests that they are transported within and from the natal gallery in mandibular mycangia. In a four-locus phylogenetic analysis, the AFC was resolved in a strongly supported monophyletic group within the previously described Cade 3 of the Fusarium solani species complex (FSSC). Divergence-time estimates place the origin of the AFC in the early Miocene similar to 21.2 Mya, which coincides with the hypothesized adaptive radiation of the Xyleborini. Two strongly supported clades within the AFC (Clades A and B) were identified that include nine species lineages associated with ambrosia beetles, eight with Euwallacea spp. and one reportedly with Xyleborus ferrugineus, and two lineages with no known beetle association. More derived lineages within the AFC showed fixation of the clavate (club-shaped) macroconidial trait, while basal lineages showed a mix of clavate and more typical fusiform macroconidia. AFC lineages consisted mostly of genetically identical individuals associated with specific insect hosts in defined geographic locations, with at least three interspecific hybridization events inferred based on discordant placement in individual gene genealogies and detection of recombinant loci. Overall, these data are consistent with a strong evolutionary trend toward obligate symbiosis coupled with secondary contact and interspecific hybridization. (C) 2013 Elsevier Inc. All rights reserved.

Induced carotenoid accumulation in Dunaliella salina and Tetraselmis suecica by plant hormones and UV-C radiation

Carotenoids prevent different degenerative diseases and improve human health. Microalgae are commercially exploited for carotenoids, including astaxanthin and β-carotene. Two commercially important microalgae, Dunaliella salina and Tetraselmis suecica, were treated with plant hormones salicylic acid (SA) and methyl jasmonate (MJ), or by UV-C radiation (T. suecica only) and a combination thereof. Significant increases in total carotenoids were found for D. salina and T. suecica after treatment with MJ (10 μmol/L) and SA (70–250 μmol/L), respectively. T. suecica also had significant increases in total carotenoids following UV-C radiation compared to control cultures. Among the carotenoids, lutein was the highest induced carotenoid. A combination of these two treatments also showed a significant increase in total carotenoids and lutein for T. suecica, when compared to controls. Plant hormones and UV-C radiation may be useful tools for increasing carotenoid accumulation in green microalgae although the responses are species- and dose-specific and should be trialed in medium to large scale to explore commercial production.

Post-head-emergence frost in wheat and barley: defining the problem, assessing the damage, and identifying resistance

Radiant frost is a significant production constraint to wheat (Triticum aestivum) and barley (Hordeum vulgare), particularly in regions where spring-habit cereals are grown through winter, maturing in spring. However, damage to winter-habit cereals in reproductive stages is also reported. Crops are particularly susceptible to frost once awns or spikes emerge from the protection of the flag leaf sheath. Post-head-emergence frost (PHEF) is a problem distinct from other cold-mediated production constraints. To date, useful increased PHEF resistance in cereals has not been identified. Given the renewed interest in reproductive frost damage in cereals, it is timely to review the problem. Here we update the extent and impacts of PHEF and document current management options to combat this challenge. We clarify terminology useful for discussing PHEF in relation to chilling and other freezing stresses. We discuss problems characterizing radiant frost, the environmental conditions leading to PHEF damage, and the effects of frost at different growth stages. PHEF resistant cultivars would be highly desirable, to both reduce the incidence of direct frost damage and to allow the timing of crop maturity to be managed to maximize yield potential. A framework of potential adaptation mechanisms is outlined. Clarification of these critical issues will sharpen research focus, improving opportunities to identify genetic sources for improved PHEF resistance.

Post-head-emergence frost in wheat and barley: defining the problem, assessing the damage, and identifying resistance

Radiant frost is a significant production constraint to wheat (Triticum aestivum) and barley (Hordeum vulgare), particularly in regions where spring-habit cereals are grown through winter, maturing in spring. However, damage to winter-habit cereals in reproductive stages is also reported. Crops are particularly susceptible to frost once awns or spikes emerge from the protection of the flag leaf sheath. Post-head-emergence frost (PHEF) is a problem distinct from other cold-mediated production constraints. To date, useful increased PHEF resistance in cereals has not been identified. Given the renewed interest in reproductive frost damage in cereals, it is timely to review the problem. Here we update the extent and impacts of PHEF and document current management options to combat this challenge. We clarify terminology useful for discussing PHEF in relation to chilling and other freezing stresses. We discuss problems characterizing radiant frost, the environmental conditions leading to PHEF damage, and the effects of frost at different growth stages. PHEF resistant cultivars would be highly desirable, to both reduce the incidence of direct frost damage and to allow the timing of crop maturity to be managed to maximize yield potential. A framework of potential adaptation mechanisms is outlined. Clarification of these critical issues will sharpen research focus, improving opportunities to identify genetic sources for improved PHEF resistance.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.