Search Results (11)

Rhodomyrtus psidioides (G.Don) Benth. (Myrtaceae) is a critically endangered rainforest species from the east coast of Australia, where populations have severely and rapidly declined due to the effects of repeated myrtle rust infection. With very limited material available in the wild and freezing-sensitive seeds that have prevented storage in a seed bank, ex situ conservation of this exceptional species has proven difficult. Material from a seed orchard grown at the Australian Botanic Garden Mount Annan was successfully used to initiate three new accessions into tissue culture from cuttings, and to undertake cryopreservation experiments using a droplet-vitrification (DV) protocol for both seeds and cultured shoot tips. Use of seedling material for tissue culture initiation was very effective, with a 94–100% success rate for semi-hardwood explants and a 50–62% success rate for softwood explants. Although no survival of seeds after cryopreservation was observed, seeds of R. psidioides showed some tolerance of desiccation and exposure to cryoprotective agents. Regeneration after cryopreservation using a DV protocol was demonstrated in only one shoot tip precultured on basal medium containing 0.4 M sucrose and incubated in PVS2 for 20 min prior to immersion in liquid nitrogen. These results demonstrate the value of living collections in botanic gardens for conservation research, highlight the importance of germplasm choice for tissue culture initiation, and demonstrate the potential of cryobiotechnologies for the ex situ conservation of exceptional plant species. Full article

Myrtle rust is a very damaging disease, caused by the fungus Austropuccinia psidii, which has recently arrived in New Zealand and threatens the iconic tree species pōhutukawa (Metrosideros excelsa). Canopy-level hyperspectral and thermal images were taken repeatedly within a controlled environment, from 49 inoculated (MR treatment) and 26 uninoculated (control treatment) pōhutukawa plants. Measurements were taken prior to inoculation and six times post-inoculation over a 14-day period. Using indices extracted from these data, the objectives were to (i) identify the key thermal and narrow-band hyperspectral indices (NBHIs) associated with the pre-visual and early expression of myrtle rust and (ii) develop a classification model to detect the disease. The number of symptomatic plants increased rapidly from three plants at 3 days after inoculation (DAI) to all 49 MR plants at 8 DAI. NBHIs were most effective for pre-visual and early disease detection from 3 to 6 DAI, while thermal indices were more effective for detection of disease following symptom expression from 7 to 14 DAI. Using results compiled from an independent test dataset, model performance using the best thermal indices and NBHIs was excellent from 3 DAI to 6 DAI (F1 score 0.81–0.85; accuracy 73–80%) and outstanding from 7 to 14 DAI (F1 score 0.92–0.93; accuracy 89–91%). Full article

Austropuccinia psidii is a biotrophic fungus that causes myrtle rust. First described in Brazil, it has since spread to become a globally important pathogen that infects more than 480 myrtaceous species. One of the most important commercial crops affected by A. psidii is eucalypt, a widely grown forestry tree. The A. psidii–Eucalyptus spp. interaction is poorly understood, but pathogenesis is likely driven by pathogen-secreted effector molecules. Here, we identified and characterized a total of 255 virulence effector candidates using a genome assembly of A. psidii strain MF-1, which was recovered from Eucalyptus grandis in Brazil. We show that the expression of seven effector candidate genes is modulated by cell wax from leaves sourced from resistant and susceptible hosts. Two effector candidates with different subcellular localization predictions, and with specific gene expression profiles, were transiently expressed with GFP-fusions in Nicotiana benthamiana leaves. Interestingly, we observed the accumulation of an effector candidate, Ap28303, which was upregulated under cell wax from rust susceptible E. grandis and described as a peptidase inhibitor I9 domain-containing protein in the nucleus. This was in accordance with in silico analyses. Few studies have characterized nuclear effectors. Our findings open new perspectives on the study of A. psidii–Eucalyptus interactions by providing a potential entry point to understand how the pathogen manipulates its hosts in modulating physiology, structure, or function with effector proteins. Full article

Austropuccinia psidii is an introduced plant pathogen known to have caused significant declines in populations of several Australian native Myrtaceae species. However, limited research has focused on the impacts of the pathogen on plant communities in the aftermath of its invasion. This study investigated the relationship between disease impact level, plant species diversity, and functional richness in seedling communities in a wet sclerophyll forest in southeast Queensland. A clear shift was found from early colonizer Myrtaceae species in the mid- and understory to a more diverse non-Myrtaceae seedling community indicative of secondary succession. Comparisons of key Myrtaceae species and the seedling community suggest that there may also be a shift towards species that produce drupes and larger seeds, and overall, a current reduction in fruit availability due to the dramatic loss of previously dominant species. Seedling diversity showed no significant correlation with tree mortality, possibly due to favorable rainfall conditions during the study period. The more subtle changes in forest composition, such as changes in fruit type and availability due to myrtle rust, however, could affect the visitation of local bird species in the short term and certainly reduce the store of early colonizing native shrub and tree species. Full article

There is no published information on the seed germination or seed storage physiology of Lophomyrtus bullata, Lophomyrtus obcordata, and Neomyrtus pedunculata. This lack of information is hampering conservation efforts of these critically endangered species. This study investigated the seed morphology, seed germination requirements, and long-term seed storage methods for all three species. The impact of desiccation, desiccation and freezing, as well as desiccation plus storage at 5 °C, −18 °C, and −196 °C on seed viability (germination) and seedling vigour was assessed. Fatty acid profiles were compared between L. obcordata and L. bullata. Variability in storage behaviour between the three species was investigated through differential scanning calorimetry (DSC) by comparing thermal properties of lipids. L. obcordata seed were desiccation-tolerant and viability was retained when desiccated seed was stored for 24 months at 5 °C. L. bullata seed was both desiccation- and freezing-sensitive, while N. pedunculata was desiccation-sensitive. DSC analysis revealed that lipid crystallisation in L. bullata occurred between −18 °C and −49 °C and between −23 °C and −52 °C in L. obcordata and N. pedunculata. It is postulated that the metastable lipid phase, which coincides with the conventional seed banking temperature (i.e., storing seeds at −20 ± 4 °C and 15 ± 3% RH), could cause the seeds to age more rapidly through lipid peroxidation. Seeds of L. bullata, L. obcordata and N. pedunculata are best stored outside of their lipid metastable temperature ranges. Full article

The Myrtaceae is a very large and diverse family containing a number of economically and ecologically valuable species. In Australia, the family contains approximately 1700 species from 70 genera and is structurally and floristically dominant in many diverse ecosystems. In addition to threats from habitat fragmentation and increasing rates of natural disasters, infection by myrtle rust caused by Austropuccinia psidii is of significant concern to Australian Myrtaceae species. Repeated infections of new growth have caused host death and suppressed host populations by preventing seed set. Although most Myrtaceae species demonstrate orthodox seed storage behavior, exceptional species such as those with desiccation sensitive seed or from myrtle rust-suppressed populations require alternate conservation strategies such as those offered by cryobiotechnology. Targeting seven key Australian genera, we reviewed the available literature for examples of cryobiotechnology utilized for conservation of Myrtaceae. While there were only limited examples of successful cryopreservation for a few genera in this family, successful cryopreservation of both shoot tips and embryonic axes suggest that cryobiotechnology provides a viable alternative for the conservation of exceptional species and a potential safe storage method for the many Myrtaceae species under threat from A. psidii. Full article

Austropuccinia psidii is a fungal plant pathogen that infects species within the Myrtaceae, causing the disease myrtle rust. Myrtle rust is causing declines in populations within natural and managed ecosystems and is expected to result in species extinctions. Despite this, variation in response to A. psidii exist within some species, from complete susceptibility to resistance that prevents or limits infection by the pathogen. Untargeted metabolomics using Ultra Performance Liquid Chromatography with Ion Mobility followed by analysis using MetaboAnalyst 3.0, was used to explore the chemical defence profiles of resistant, hypersensitive and susceptible phenotypes within Melaleuca quinquenervia during the early stages of A. psidii infection. We were able to identify three separate pools of secondary metabolites: (i) metabolites classified structurally as flavonoids that were naturally higher in the leaves of resistant individuals prior to infection, (ii) organoheterocyclic and carbohydrate-related metabolites that varied with the level of host resistance post-infection, and (iii) metabolites from the terpenoid pathways that were responsive to disease progression regardless of resistance phenotype suggesting that these play a minimal role in disease resistance during the early stages of colonization of this species. Based on the classes of these secondary metabolites, our results provide an improved understanding of key pathways that could be linked more generally to rust resistance with particular application within Melaleuca. Full article

The ability of deep convolutional neural networks (deep learning) to learn complex visual characteristics offers a new method to classify tree species using lower-cost data such as regional aerial RGB imagery. In this study, we use 10 cm resolution imagery and 4600 trees to develop a deep learning model to identify Metrosideros excelsa (pōhutukawa)—a culturally important New Zealand tree that displays distinctive red flowers during summer and is under threat from the invasive pathogen Austropuccinia psidii (myrtle rust). Our objectives were to compare the accuracy of deep learning models that could learn the distinctive visual characteristics of the canopies with tree-based models (XGBoost) that used spectral and textural metrics. We tested whether the phenology of pōhutukawa could be used to enhance classification by using multitemporal aerial imagery that showed the same trees with and without widespread flowering. The XGBoost model achieved an accuracy of 86.7% on the dataset with strong phenology (flowering). Without phenology, the accuracy fell to 79.4% and the model relied on the blueish hue and texture of the canopies. The deep learning model achieved 97.4% accuracy with 96.5% sensitivity and 98.3% specificity when leveraging phenology—even though the intensity of flowering varied substantially. Without strong phenology, the accuracy of the deep learning model remained high at 92.7% with sensitivity of 91.2% and specificity of 94.3% despite significant variation in the appearance of non-flowering pōhutukawa. Pooling time-series imagery did not enhance either approach. The accuracy of XGBoost and deep learning models were, respectively, 83.2% and 95.2%, which were of intermediate precision between the separate models. Full article

Background and objectives: The global forest economy is threatened by eucalypt pathogens which are often latent or cryptic species that escape common quarantine and detection methods. Plantation forestry using eucalypts is of considerable importance to Paraguay, but knowledge regarding the pests and diseases affecting these plantations is limited. This study identified fungal diseases present in these plantations. Materials and Methods: We surveyed eucalypt plantations in four provinces in Paraguay and collected material from diseased trees for identification of the causal agents. The samples were analyzed using a combination of morphological and molecular methods. Results: Diseases encountered included Botryosphaeria stem canker, Calonectria leaf blight, Chrysoporthe stem canker, myrtle/eucalypt rust, Coniella leaf spot, heartwood rot and Teratosphaeria stem canker. Contrary to expectations, the causal agent of Teratosphaeria stem canker was identified as Teratosphaeria zuluensis (M.J. Wingf., Crous & T.A. Cout.) M.J. Wingf. & Crous and not Teratosphaeria gauchensis (M.-N. Cortinas, Crous & M.J. Wingf.) M.J. Wingf. & Crous, that is commonly documented for the South American region. Conclusions: This study updates the knowledge on forest fungal pathogens in Paraguayan eucalypt plantations and is the first report of T. zuluensis in Paraguay and in South America. Full article

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models. Full article

The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ${}^{\circledR }$ camera, orthorectified in Headwall SpectralView ${}^{\circledR }$ , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools. Full article