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Questions:Fire is a crucial component of many ecosystems. Plants whose seeds germinate in response to smoke may benefit from resource availability in the post-fire environment. Smoke can influence germination timing and success, as well as seedling vigour, resulting in burgeoning research interest in smoke-responsive germination. Research in this field has largely focused on four key 'Mediterranean-type' fire-prone ecosystems: the Mediterranean Basin, South African fynbos, Californian chaparral and Western Australia. There are far fewer studies from south-eastern Australia, a fire-prone but not "Mediterranean-type" region. How does smokeresponsive germination in this region vary according to ecological, phylogenetic, and methodological variables?Location: South-eastern Australia. Methods:We investigated patterns of smoke-promoted germination in southeastern Australian plants across habitat types, growth forms, fire response strategies, phylogeny, taxonomic levels and smoke application methods. We compiled and interrogated data comprising 303 entries on germination responses to smoke in 233 south-eastern Australian plant species, from 33 different sources. Results:Smoke-responsive germination occurs at a lower rate (~41% of tested species) in south-eastern Australian flora than it does in fynbos and Western Australian floras, and there is clear patterning within these data. Obligate-seeding species were more likely to respond, Leguminosae and Rubiaceae were less likely to respond (although we question the generality of these results), while Poaceae were more likely to respond to smoke. Finally, studies using aerosol smoke and studies conducted in situ were most likely to find smoke-promoted germination. Conclusions:Obligate seeders and Poaceae may be selected for in habitats with higher fire frequencies, consistent with literature suggesting that short inter-fire intervals favour grasslands over forests. These findings may be particular to southeastern Australia, or more widely applicable; more broad-scale comparative research will reveal the answer. By synthesizing the south-eastern Australian smoke germination literature we broaden our understanding beyond the better-studied Mediterranean-type floras.

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Since 2010 Australian ecosystems and managed landscapes have been severely threatened by the invasive fungal pathogen Austropuccinia psidii. Detecting and monitoring disease outbreaks is currently only possible by human assessors, which is slow and labour intensive. Over the last 25 years, spectral vegetation indices (SVIs) have been designed to assess variation in biochemical or biophysical traits of vegetation. However, diagnosis of individual diseases based on classical SVIs is currently not possible because they lack disease specificity. Here, a novel spectral disease index (SDI), the lemon myrtle–myrtle rust index (LMMR), has been developed. The index was designed from hyperspectral leaf‐clip data collected at a lemon myrtle plantation in New South Wales, Australia. A total of 236 fungicide‐treated (disease free) and 228 untreated (diseased) lemon myrtle leaves were sampled and a random forest classifier was used to show that the LMMR discriminates those classes with an overall accuracy of 90%. Compared to three classical SVIs (PRI, MCARI, NBNDVI), commonly applied for stress detection, the LMMR clearly improved classification accuracies (58%, 67%, 60%, respectively). If the LMMR can be validated on independent datasets from similar and different host species, it could enable land managers to reduce disease impact by earlier control. There might also be potential to collect useful data for epidemiology models. Calculating the LMMR based on hyperspectral data collected from aerial platforms (e.g. drones) would allow for rapid and high‐capacity screening for disease outbreaks.

Species richness is a widespread measure to evaluate the effect of different management histories on plant communities and their biodiversity. However, analysing the phylogenetic structure of plant communities could provide new insights into the effects of different management methods on community assemblages and provide further guidance for conservation decisions. Heathlands require permanent management to ensure the existence of such a cultural landscape. While traditional management with grazing is time consuming, mechanical methods are often applied but their consequences on the phylogenetic community assemblages are still unclear. We sampled 60 vegetation plots in dry sandy heathlands (EU habitat type 2310) in northern Germany stratified by five different heathland management histories: fire, plaggen (turf cutting), mowing, deforestation and intensive grazing. Due to the distant relationship of vascular plants and lichens, we assembled two phylogenetic trees, one for vascular plants and one for lichens. We then calculated phylogenetic diversity (PD) and measures of phylogenetic community structure for vascular plant and lichen communities. Deforested areas supported significantly higher PD values for vascular plant communities. We found that PD was strongly correlated with species richness (SR) but the calculation of rarefied PD was uncorrelated to SR leading to a different ranking of management histories. We observed phylogenetic clustering in the lichen communities but not for vascular plants. Thus, management by mowing and intensive grazing promotes habitat filtering of lichens, while management histories that cause greater disturbance such as fire and plaggen do not seem to affect phylogenetic community structure. The set of management strategies fulfilled the goals of the managers in maintaining a healthy heathland community structure. However, management strategies that cause less disturbance can offer an additional range of habitat for other taxonomic groups such as lichen communities.

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host–pathogen–environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

While saltmarsh communities are endangered in many parts of the world due to anthropogenic impact, the risk of invasion by exotic plants is considered to be low because of their saline conditions. However, in urban areas, saltmarshes receive high nutrient freshwater input through stormwater discharge. We tested if invasion of saltmarsh by exotic plant species was facilitated by increased nutrients and reduced salinity associated with urban stormwater input. In a manipulative glasshouse experiment, we grew saltmarsh communities under four treatments: high salinity-low nutrients (control), high salinity-high nutrients, low salinity-low nutrients and low salinity-high nutrients. We then invaded the saltmarsh communities with four common invasive exotic plants. Their survival rates were monitored weekly for seven weeks before final harvesting. All exotic species showed significantly higher survival in the 'low salinity' treatment compared to the 'high salinity' treatment. There was variability among species, with three of four having low survival rates (0-3%) under 'high salinity' conditions, while survival of Protasparagus aethiopicus was reduced to only 53-59%. Our findings suggest that under natural conditions of saltmarshes, the establishment of exotic plant seedlings is restricted. Additional freshwater increased the survival of invading exotic species significantly, whereas adding nutrients increased biomass production but not necessarily survival of exotics. However, the results can be highly species dependant as shown by the unexpected salinity tolerance of P. aethiopicus. Reduction in salinity of saltmarsh due to stormwater input facilitates invasion by exotic plant species that would otherwise be unable to tolerate the highly saline environment.