Epidemiology and control strategies applied to ash dieback and chestnut ink disease

Main goal of forest diseases’ management is to reduce economic, biological and aesthetic damages and biodiversity loss caused by plant parasites. The many strategies used can be grouped under two main actions, prevention (prophylaxis in some early writings) and therapy (treatment or cure). Prevention is limited primarily by the lack of knowledge of the organisms involved, including host plants. Mathematical models have been used to extend the understanding of plant disease epidemiology on a number of fronts, providing an opportunity for a more rational use of resources on expensive field trials and representing a step towards more sustainable control measures. From a curative point of view, current efforts by scientists have focused on developing diseases management (Pest Management = PM) concepts in order to balance the benefits of pesticides with the ecological concerns of their residues contaminating the environment. In this thesis, the two PM principles were applied from an innovative point of view on two case studies: ash dieback caused by Hymenoscyphus fraxineus, which can be considered the most serious disease for Fraxinus genus in Europe, and chestnut ink disease, caused by Phytophthora cambivora and P. cinnamomi. In the first part of the thesis, the two diseases are introduced, in order to permit the evaluation of similarities and differences (chapter I). Subsequently, from chapter II to chapter V, the experimental trials performed are described. In particular, in chapter II a study of the ecological niche of H. fraxineus, with the characterization of the environmental variables associated with naturally infected zones, is reported. This procedure was realized with Species Distribution Models (SDM), widely utilized in the ecological field and only recently applied to plant pathology. The presence of the pathogen was highly correlated to three summer predictors: abundant precipitation, high soil moisture and low air temperature, in comparison with the averages of the study area. The ensemble forecasting technique was then applied to obtain a prediction of the potential distribution of the pathogen at European scale, considering the distribution maps of Fraxinus excelsior and Fraxinus angustifolia, susceptible to the parasite. At last, an innovative method of network analysis permitted to identify the suitable areas that are not reachable by the pathogen with a natural spread. Chapter III reports a study conducted to evaluate six fungicides for their potential to control ash dieback. Initially, in vitro tests of the active ingredients against five different strains of the pathogen indicated thiabendazole, propiconazole and allicin as the most effective fungicides, with lower median lethal doses than procloraz. In contrast, copper sulphate and potassium phosphite were totally ineffective. Subsequently, the antifungal activities of the best three compounds were investigated in planta against H. fraxineus by trunk injection on European ashes inoculated with an indigenous strain. The test was preceded by preliminary trials to maximize the efficacy of injections; in the experimental conditions highest speed was reached with the addition of 1.2 % acetic acid to the aqueous solution and making treatments in early morning or late afternoon. Considering the results of in planta trial, thiabendazole and allicin significantly slowed down the growth of the necroses in the growing season, in contrast propiconazole injections were impracticable. The studies in chapters IV and V recall the methodologies applied to ash dieback, with application to chestnut ink disease complex. In particular, in chapter IV fuzzy logic theory was applied considering the environmental variables, such as minimum winter temperature, summer drought, slope's aspect, streams' distance and soil's permeability, that mainly can influence the development of the disease. The model was validated with a broad field survey conducted in a chestnut area in Treviso province. Moreover, uncertainty maps (regarding model structure, inputs and parameters) were produced for the correct interpretation of the prediction. Great part of the chestnut area in the study zone resulted as suitable for the development of ink disease, whereas only the 18.8 %, corresponding to higher elevation zones, presented inferior risks. In a second study (chapter V), a comparative efficacy trial on four potassium phosphite formulations by means of endotherapy against chestnut ink disease is performed. P. cinnamomi was isolated with baiting technique from symptomatic chestnuts and was inoculated on 50 asymptomatic trees. As a result of endotherapic treatments, the unique solution that significantly slowed down necroses' growth was potassium phosphite (35 %) with an addition of 0.1 % micronutrient solution. An additional endotherapic trial was conducted in a preliminary way in the chestnut where P. cinnamomi was isolated, with the main aim to evaluate growth stimulation of active growing callus next to the shape flame necroses by the injected solution of potassium phosphite 70 %. In this case, results did not highlight a significant difference between treated trees and water control ones, probably for the need of longer times for older trees. On the base of the achieved results, epidemiological modelling and endotherapic treatments, applied both to ash dieback and chestnut ink disease, can represent fundamental tools in the management of these important diseases and should be applied in an Integrated Pest Management (IPM) approach, together with appropriate cultural techniques to maximize benefits

Forest Fire Risk Prediction

Globally, fire regimes are being altered by changing climatic conditions and land use changes. This has the potential to drive species extinctions and cause ecosystem state changes, with a range of consequences for ecosystem services. Accurate prediction of the risk of forest fires over short timescales (weeks or months) is required for land managers to target suppression resources in order to protect people, property, and infrastructure, as well as fire-sensitive ecosystems. Over longer timescales, prediction of changes in forest fire regimes is required to model the effect of wildfires on the terrestrial carbon cycle and subsequent feedbacks into the climate system.This was the motivation to publish this book, which is focused on quantifying and modelling the risk factors of forest fires. More specifically, the chapters in this book address four topics: (i) the use of fire danger metrics and other approaches to understand variation in wildfire activity; (ii) understanding changes in the flammability of live fuel; (iii) modeling dead fuel moisture content; and (iv) estimations of emission factors.The book will be of broad relevance to scientists and managers working with fire in different forest ecosystems globally

Ensemble models to assess the risk of exotic plant pathogens in a changing climate

In recent decades, species distribution models (SDMs) have been widely used in many ecological, environmental and climate-change research studies to model invasive species establishment. These models associate recorded locations of species with environmental variables. Nevertheless, the few studies that attempt to model the climate suitability of plant pathogens before their arrival into a new area mainly rely on a single model projection. In this research, eleven species distribution models (in the form of three modelling approaches which include correlative and mechanistic models) were used to project the climate suitability of three target species; kiwifruit bacterial canker (Pseudomonas syringae pv. actinidia) (Psa), dwarf bunt of wheat (Tellitia controversa) and guava rust (Puccinia psidii) for New Zealand and over a global scale. The climate suitability of target species was modelled using CLIMEX as a semi-mechanistic model, MaxEnt as a presence-only correlative model and Multi-Model Framework (which includes nine correlative models). While there were similarities with regard to climate suitability for target species projected by the models over both local and global scales, there were differences in their projection with respect to the degree and extent of suitability, making it hard to select one “best” model. All models were found to have their differences and weaknesses that are largely the result of difference in the theoretical basis and structure of each model. For example, compared with CLIMEX and the Multi-Model Framework, MaxEnt showed lower transferability of projection into new areas. Additionally, as a semi -mechanistic model, the uncertainty of CLIMEX projections was found to be increased by subjectivity in the parameter setting process. To illustrate the impact of parameter variability on the uncertainty of CLIMEX projections, a sensitivity analysis was performed on one of the target species (dwarf bunt) to measure the effect of error in important parameters on model output. The sensitivity analysis showed that for dwarf bunt, CLIMEX outputs were very sensitive to upper temperature threshold and soil moisture parameters, which highlight that sensitivity analysis, should be an integral part of any CLIMEX modelling. For Multi-Model, despite the advantages such as calculating different performance criteria, the importance and contribution of selected variables and their influence on model output is not given. Because of differences in model projection, a method was developed to benefit from the information provided by all the types of models, by combining the results of different model output into an ensemble, or more specifically, a consensus model. A variant of committee averaging was used where model outputs are converted to binary maps (presence- absence) which allow any kind of algorithm and output to be included. The resulting consensus model highlighted the areas where more than half of the models agreed on the climate suitability for target species establishment. Such a model that relies on agreement of model projections indicates with a level of certainty or uncertainty what is likely to happen and consequently can highlight areas, both locally and globally, that have a higher risk of target species establishment. Finally, the effect of climate change on climate suitability of target species was investigated using two scenarios (A1B and A2) for 2030 and 2090. The results showed that, the suitable areas decreased for Psa and dwarf bunt at different levels while guava rust suitability increased. The results of this thesis confirm that models with different theoretical foundation will give dissimilar predictions, and it is difficult to determine conclusively whether one model is superior to others. Among other recommendations, I strongly advise that researchers and risk assessors should not rely on a single-model projection. If time and resources are available, an appropriate ensemble of models should be used to investigate the climate suitability of plant pathogens. Keywords: Plant pathogens, kiwifruit bacterial canker (Psa), dwarf bunt, guava rust, climate suitability, Species distribution models (SDMs), CLIMEX, MaxEnt, Multi-Model Framework, correlative models, semi-mechanistic models, sensitivity analysis, consensus model, ensemble models, climate change, range expansion

Remote Sensing of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Cybersecurity for Nuclear Power Plants Working with Simulator's Data and Machine Learning Algorithms to Find Abnormalities at Nuclear Power Plants

Cybersecurity has the utmost importance for nuclear power plants (NPPs). Demand for clean and constant energy has increased the need and use of NPPs. Countries want to have and maintain secure NPPs both physically (well-studied area) and digitally. We live in a digital world, and cyber-attacks have skyrocketed in recent years. This study explores the cyber risk for NPPs, digital attacks, potential future attacks, international aspects, and law and policy requirements of cyber protection for nuclear power plants. With the help of data analysis and machine learning algorithms, extra monitoring can be conducted on plants' data. Data monitoring applications require comprehensive data to build models and develop solutions. However, nuclear facilities do not share their data because of security concerns. Plant simulators are heavily used for training people and conducting experiments. In this thesis, we inspect plant simulators to assess their usability by people with a technical background such as cyber experts, information technology technicians, and software developers. People responsible for protecting digital systems can benefit from the help of data analytic tools and machine learning models to detect abnormalities. We study machine learning models on simulator data to examine their potential in identifying anomalies

Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

Advances in Remote Sensing and GIS applications in Forest Fire Management: from local to global assessments

This report contains the proceedings of the 8th International Workshop of the European Association of Remote Sensing Laboratories (EARSeL) Special Interest Group on Forest Fires, that took place in Stresa, (Italy) on 20-21 October 2011. The main subject of the workshop was the operational use of remote sensing in forest fire management and different spatial scales were addressed, from local to regional and from national to global. Topics of the workshops were also grouped according to the fire management stage considered for the application of remote sensing techniques, addressing pre fire, during fire or post fire conditions.JRC.H.7-Land management and natural hazard

Modelling the impact of future climate and land use change on vegetation patterns, plant diversity and provisioning ecosystem services in West Africa

Global climate change and land use change will not only alter entire ecosystems and biodiversity patterns, but also the supply of ecosystem services. A better understanding of the consequences is particularly needed in under-investigated regions, such as West Africa. The projected environmental changes suggest negative impacts on nature, thus representing a threat to the human well-being. However, many effects caused by climate and land use change are poorly understood so far. Thus, the main objective of this thesis was to investigate the impact of climate and land use change on vegetation patterns, plant diversity and important provisioning ecosystem services in West Africa. The three different aspects are separately explored and build the chapters of this thesis. The findings help to improve our understanding of the effects of environmental change on ecosystems and human well-being. In the first study, the main objectives were to model trends and the extent of future biome shifts in West Africa that may occur by 2050. Also, I modelled a trend in West African tree cover change, while accounting for human impact. Additionally, uncertainty in future climate projections was evaluated to identify regions with reliable trends and regions where the impacts remain uncertain. The potential future spatial distributions of desert, grassland, savanna, deciduous and evergreen forest were modelled in West Africa, using six bioclimatic models. Future tree cover change was analysed with generalized additive models (GAMs). I used climate data from 17 general circulation models (GCMs) and included human population density and fire intensity to model tree cover. Consensus projections were derived via weighted averages to: 1) reduce inter-model variability, and 2) describe trends extracted from different GCM projections. The strongest predicted effect of climate change was on desert and grasslands, where the bioclimatic envelope of grassland is projected to expand into the Sahara desert by an area of 2 million km2. While savannas are predicted to contract in the south (by 54 ± 22 × 104 km2), deciduous and evergreen forest biomes are expected to expand (64 ± 13 × 104 km2 and 77 ± 26 × 104 km2). However, uncertainty due to different GCMs was particularly high for the grassland and the evergreen forest biome shift. Increasing tree cover (1–10%) was projected for large parts of Benin, Burkina Faso, Côte d’Ivoire, Ghana and Togo, but a decrease was projected for coastal areas (1–20%). Furthermore, human impact negatively affected tree cover and partly changed the direction of the projected climate-driven tendency from increase to decrease. Considering climate change alone, the model results of potential vegetation (biomes) showed a ‘greening’ trend by 2050. However, the modelled effects of human impact suggest future forest degradation. Thus, it is essential to consider both climate change and human impact in order to generate realistic future projections on woody cover. The second study focused on the impact and the interplay of future (2050) climate and land use change on the plant diversity of the West African country Burkina Faso. Synergistic forecasts for this country are lacking to date. Burkina Faso covers a broad bioclimatic gradient which causes a similar gradient in plant diversity. Thus, the impact of climate and land use change can be investigated in regions with different levels of species richness. The LandSHIFT model from the Centre of Environmental System research CESR (Kassel, Germany) was adapted for this study to derive novel regional, spatially explicit future (2050) land use simulations for Burkina Faso. Additionally, the simulations include different assumptions on the technological developments in the agricultural sector. Oneclass support vector machines (SVMs), a machine learning method, were performed with these land use simulations together with current and future (2050) climate projections at a 0.1° resolution (cell: ~ 10 × 10 km). The modelling results showed that the flora of Burkina Faso will be primarily negatively impacted by future climate and land use changes. The species richness will be significantly reduced by 2050 (P < 0.001, paired Wilcoxon signed-rank test). However, contrasting latitudinal patterns were found. Although climate change is predicted to cause species loss in the more humid regions in Southern Burkina Faso (~ 200 species per cell), the model projects an increase of species richness in the Sahel. However, land use change is expected to suppress this increase to the current species diversity level, depending on the technological developments. Climate change is a more important threat to the plant diversity than land use change under the assumption of technological stagnation in the agricultural sector. Overall, the study highlights the impact and interplay of future climate and land use change on plant diversity along a broad bioclimatic gradient in West Africa.Furthermore, the results suggest that plant diversity in dry and humid regions of the tropics might generally respond differently to climate and land use change. This pattern has not been detected by global studies so far. Several of the plant species in West Africa significantly contribute to the livelihoods of the population. The plants provide so-called non-timber forest products (NTFPs), which are important provisioning ecosystem services. However, these services are also threatened by environmental change. Thus, the third study aimed at developing a novel approach to assess the impacts of climate and land use change on the economic benefits derived from NTFPs. This project was carried out in cooperation with Katja Heubach (BiK-F) who provided data on household economics. These data include 60 interviews that were conducted in Northern Benin on annual quantities and revenues of collected NTFPs from the three most important savanna tree species: Adansonia digitata, Parkia biglobosa and Vitellaria paradoxa. The current market prices of the NTFPs were derived from respective local markets. To assess current and future (2050) occurrence probabilities of the three species, I calibrated niche-based models with climate data (from Miroc3.2medres) and land use data (LandSHIFT) at a 0.1° resolution (cell: ~ 10 × 10 km). Land use simulations were taken from the previous study on plant diversity. Three different niche-based models were used: 1) generalized additive models (regression method), 2) generalized boosting models (machine learning method), and 3) flexible discriminant analysis (classification method). The three model simulations were averaged (ensemble forecasting) to increase the robustness of the predictions. To assess future economic gains and losses, respectively, the modelled species’ occurrence probabilities were linked with the spatially assigned monetary values. Highest current annual benefits are obtained from V. paradoxa (54,111 ± 28,126 US$/cell), followed by P. biglobosa (32,246 ± 16,526 US$/cell) and A. digitata (9,514 ± 6,243 US$/cell). However, in the prediction large areas will lose up to 50$/Gridzelle), gefolgt von Parkia biglobosa (32.246 ± 16.526 US$/Gridzelle) und Adansonia digitata (9.514 ± 6.243 US$/Gridzelle). Allerdings zeigen die Zukunftsprojektionen für das Jahr 2050, dass große Flächen in Nordbenin bis zu 50% dieses ökonomischen Wertes verlieren könnten. Am stärksten betroffen sind dabei die Arten Vitellaria paradoxa und Parkia biglobosa, die derzeit den höchsten Nutzwert haben. Adansonia digitata wird weniger stark aber ebenfalls negativ beeinflusst werden in weiten Teilen von Nordbenin. Hier zeigen sich jedoch regional ausgeprägte Unterschiede. Vor allem im Westen und im Osten des Untersuchungsgebietes können die Umweltveränderungen zu erhöhten Auftrittswahrscheinlichkeiten und damit höheren Erlösen im Jahr 2050 führen. Insgesamt zeigen die Ergebnisse, dass Adaptationsmaßnahmen erforderlich sind um alternative Einkommensquellen zu erschließen. Dies gilt insbesondere für Frauen, die für das Sammeln der NTFPs verantwortlich sind. Die Ergebnisse liefern Politikern eine Orientierungsmöglichkeit um verschiedene Landnutzungsoptionen ökonomisch zu vergleichen und etwaige Anpassungen von rezenten