Survival between cropping seasons is a challenge for crop pathogens and can dramatically impact epidemic recurrence and adaptation to management strategies and environmental changes. However, the survival phase is often treated as a ‘black box’. In this paper, we focus on the case of wheat rusts, which are among the most damaging diseases attacking wheat, a major crop worldwide. Though expert-based statements on rust survival are frequent, experimental evidence and quantitative observations that support these statements sometimes need clarification. The objective of this paper was to review available knowledge on the different processes involved in the survival of wheat rusts (considering stripe, leaf and stem rusts) and to assess their possibility of occurrence and probability. We focused on four main survival strategies reported in the literature: (i) sexual reproduction on alternate hosts, asexual reproduction on (ii) wild grasses related to wheat or (iii) volunteers and (iv) migration to more favourable areas during critical periods. Available reports suggest that all four strategies can occur considering all three wheat rusts. However, these processes can be complicated to detect due to the low probability of occurrence and significant variability in space and time. After a detailed description of available knowledge considering each process, we compare and contrast different survival strategies and discuss complementary methods available to tackle these questions. We suggest that using complementary methods would make it possible to better understand wheat rusts' survival and anticipate their epidemic potential in the context of global change.

Thermal ecology studies on the ecophysiological responses of organisms to temperature involve two paradigms: physiological rates are driven by body temperature and not directly by the environmental temperature, and they are largely influenced not only by its mean but also its variance. These paradigms together have been largely applied to macro invertebrates and vertebrates but rarely to microorganisms. According to these paradigms, foliar fungal pathogens are expected to respond directly to the fluctuations in leaf temperature, rather than in air temperature. We determined experimentally the impact of two patterns of leaf temperature variation of equal mean temperature, but differing in their daily amplitude, on the development of Zymoseptoria tritici, a fungus infecting wheat leaves. The highest daily thermal amplitude resulted in two detrimental effects for the pathogen fitness: an increase in the length of the latent period, i.e. the 'generation time' of the fungus when infecting its host plant, and a decrease in the density of fruiting bodies on the leaves. We discussed these empirical results, mainly the impact of both the daily thermal amplitude and the fluctuation frequency on the pathogen development in planta, in the light of the mathematical effect of the integration of non-linear functions. We concluded that it is necessary to take into account daily leaf temperature amplitudes to improve our understanding and prediction of the development of foliar fungal pathogens and other micro-organisms living in the phyllosphere in the climate change context.

Spread of airborne plant diseases from a propagule source is classically assessed by fitting a gradient curve to aggregated data coming from field experiments. But, aggregating data decreases information about processes involved in disease spread. To overcome this problem, individual count data can be collected; it was done in the case of short-distance spread of wheat brown rust. However, for such data, the gradient curve is a limited model since heterogeneity of hosts is ignored and, consequently, overdispersion occurs. So, we propose a parametric frailty model in which the frailties represent propensities of hosts to be infected. The model is used to assess dispersal of propagules and heterogeneity of hosts.

The structure of pathogen populations is an important driver of epidemics affecting crops and natural plant communities. Comparing the composition of two pathogen populations consisting of assemblages of genotypes or phenotypes is a crucial, recurrent question encountered in many studies in plant disease epidemiology. Determining whether there is a significant difference between two sets of proportions is also a generic question for numerous biological fields. When samples are small and data are sparse, it is not straightforward to provide an accurate answer to this simple question because routine statistical tests may not be exactly calibrated. To tackle this issue, we built a computationally intensive testing procedure, the generalized Monte Carlo plug-in test with calibration test, which is implemented in an R package available at https://doi.org/10.5281/zenodo.635791 . A simulation study was carried out to assess the performance of the proposed methodology and to make a comparison with standard statistical tests. This study allows us to give advice on how to apply the proposed method, depending on the sample sizes. The proposed methodology was then applied to real datasets and the results of the analyses were discussed from an epidemiological perspective. The applications to real data sets deal with three topics in plant pathology: the reproduction of Magnaporthe oryzae, the spatial structure of Pseudomonas syringae, and the temporal recurrence of Puccinia triticina.

The efficiency of plant resistance to fungal pathogen populations is expected to decrease over time, due to their evolution with an increase in the frequency of virulent or highly aggressive strains. This dynamics may differ depending on the scale investigated (annual or pluriannual), particularly for annual crop pathogens with both sexual and asexual reproduction cycles. We assessed this time-scale effect, by comparing aggressiveness changes in a local Zymoseptoria tritici population over an 8-month cropping season and a 6-year period of wheat monoculture. We collected two pairs of subpopulations to represent the annual and pluriannual scales: from leaf lesions at the beginning and end of a single annual epidemic and from crop debris at the beginning and end of a 6-year period. We assessed two aggressiveness traitslatent period and lesion sizeon sympatric and allopatric host varieties. A trend toward decreased latent period concomitant with a significant loss of variability was established during the course of the annual epidemic, but not over the 6-year period. Furthermore, a significant cultivar effect (sympatric vs. allopatric) on the average aggressiveness of the isolates revealed host adaptation, arguing that the observed patterns could result from selection. We thus provide an experimental body of evidence of an epidemiological trade-off between the intra- and interannual scales in the evolution of aggressiveness in a local plant pathogen population. More aggressive isolates were collected from upper leaves, on which disease severity is usually lower than on the lower part of the plants left in the field as crop debris after harvest. We suggest that these isolates play little role in sexual reproduction, due to an Allee effect (difficulty finding mates at low pathogen densities), particularly as the upper parts of the plant are removed from the field, explaining the lack of transmission of increases in aggressiveness between epidemics.

A qualitative pest modeling platform, named Injury Profile Simulator (IPSIM), provides a tool to design aggregative hierarchical network models to predict the risk of pest injuries, including diseases, on a given crop based on variables related to cropping practices as well as soil and weather environment at the field level. The IPSIM platform enables modelers to combine data from various sources (literature, survey, experiments, and so on), expert knowledge, and simulation to build a network-based model. The overall structure of the platform is fully described at the IPSIM-Web website ( www6.inra.fr/ipsim ). A new module called IPSIM-Wheat-brown rust is reported in this article as an example of how to use the system to build and test the predictive quality of a prediction model. Model performance was evaluated for a dataset comprising 1,788 disease observations at 13 French cereal-growing regions over 15 years. Accuracy of the predictions was 85% and the agreement with actual values was 0.66 based on Cohen's κ. The new model provides risk information for farmers and agronomists to make scientifically sound tactical (within-season) decisions. In addition, the model may be of use for ex post diagnoses of diseases in commercial fields. The limitations of the model such as low precision and threshold effects as well as the benefits, including the integration of different sources of information, transparency, flexibility, and a user-friendly interface, are discussed.

Black pod disease is caused by several species of Phytophthora. In Cameroon, the disease is mainly due to Phytophtora megakarya. The pathogen attacks cocoa pods and can lead to almost total production losses in a plot if no control measures are applied. To control the disease, several research programmes are being conducted: breeding for increased resistance, development of several biocontrol or agronomic methods. However, for better use of a specific method it is useful to have a good understanding of several epidemiological processes and more effectively know how the disease is distributed in the field. The purpose of this study was to describe the spatial development of the disease in several cocoa fields in Cameroon. In particular, we determined the spatial relation of the disease using several tools, including geostatistics models and Moran indices. The results indicated that the disease was not randomly distributed, while correlations between neighbouring cocoa trees existed. The relationships were detected up to a distance of between 7 and 9 m, revealing the wide dispersal pattern of the pathogen over short distances. No spatial structure was found in the spread of the disease in the oldest cocoa plantations and the inoculum was dispersed throughout the plot. Disease dispersal over short distances should make it possible to adapt control methods by attempting to confine the first disease foci in young plots. Research should also be undertaken to limit inoculum dispersal.

The worldwide trade of agricultural products and high levels of disturbance and fertilisation make arable lands particularly vulnerable to biological invasions. Clearing for the development of arable land has been an unprecedented event that created a new and more homogeneous habitat which allowed many species to spread to become (sub) cosmopolitan weeds, pests, and pathogens. Through competition for light, water, and nutrients (weeds), or destruction of plant tissue (pests and pathogens), harmful organisms can potentially reduce crop yield by 10-40 % on average. Historically, some non-native species produced spectacular invasions and caused incalculable damage by annihilating crop production at large scales: for example, potato late blight, Phytophthora infestans, which was one of the factors causing the Irish Potato Famine, and the American vine phylloxera, Daktulosphaira vitifoliae, which devastated vineyards across the whole of Europe. Nowadays, it is estimated that non-native weeds, pests, and pathogens cause as much as US$248 billion in annual losses to world agriculture, making this the sector most affected by the introduction of non-native species. The use of pesticides has long protected crop yield satisfactorily. However, because of the undesirable side effects that may be associated with pesticide use (e.g., development of resistant biotypes and water pollution), more integrated approaches to combat invasive species are needed, - including prevention (phytosanitary control) and cropping systems with higher potential for ecological regulation.

Risks from intentional releases of organisms to agriculture, the food chain or the environment must be assessed to ensure proportionate planning, just as accidental releases from trade or natural spread must be predicted so that management can be organised. Pest risk assessment methods are well established for trade related introductions and it is efficient to build on these and adapt available risk assessment components from agricultural and environmental assessment tools. Some additional risk considerations, particularly related to the motivation, capacity and intended impact of a perpetrator should be included, and some key elements of trade related assessments, such as the volume of trade, may be irrelevant for intentional targeted releases. Risk levels from the various causes and impacts should be comparable to allow authorities to direct responses appropriately. Preventative actions, for both intentional and unintentional introductions, are particularly important. For intentional release this puts emphasis on motivation, capacity and sources. A scenario based approach to assessing intentional release risks is taken to develop a pest risk assessment tool that can cover the range of levels of potential activity. A risk assessment framework is illustrated and a range of example scenarios is described.

We investigated whether the origin of primary inoculum of the fungal wheat pathogen Zymoseptoria tritici can be inferred from evidence of adaptation to host cultivars. We compared the aggressiveness of two pathogen populations collected locally, the first considered as resident (collected from debris in a wheat cv. Soissons monoculture plot) and the second considered as immigrant (collected from leaf lesions in a 300 m apart wheat cv. Soissons plot containing no debris, exposed to an inoculum pool of distant origin), with the aggressiveness of a third population (collected from early leaf lesions in the same monoculture plot) the origin of which we wanted to determine (local vs. distant). The three populations were sampled twice, in 2009 and 2012, from a 6-year field trial. Latent period and sporulating area of 6 × 12 isolates were assessed in greenhouse on adult plants of cv. Soissons and of Apache, another cultivar commonly grown around the field for several years. Firstly, we detected a differential host adaptation: after several years of monoculture, the resident pathogen populations became less adapted to the other cultivar. Secondly, we showed that when the inoculum pressure was high (2009), the aggressiveness profile of the third pathogen population was more similar to that of the resident populations than of the immigrant. This indicates that early lesions in the monoculture plot were mostly caused by within-field (local) primary inoculum. The comparison of aggressiveness profiles when the inoculum pressure was lower (2012) was less conclusive, suggesting that the tested population could have a mixed origin.

In a cross-infection experiment, we investigated how seasonal changes can affect adaptation patterns in a Zymoseptoria tritici population. The fitness of isolates sampled on wheat leaves at the beginning and at the end of a field epidemic was assessed under environmental conditions (temperature and host stage) to which the local pathogen population was successively exposed. Isolates of the final population were more aggressive, and showed greater sporulation intensity under winter conditions and a shorter latency period (earlier sporulation) under spring conditions, than isolates of the initial population. These differences, complemented by lower between-genotype variability in the final population, exhibited an adaptation pattern with three striking features: (i) the pathogen responded synchronously to temperature and host stage conditions; (ii) the adaptation concerned two key fitness traits; (iii) adaptation to one trait (greater sporulation intensity) was expressed under winter conditions while, subsequently, adaptation to the other trait (shorter latency period) was expressed under spring conditions. This can be interpreted as the result of short-term selection, driven by abiotic and biotic factors. This case study cannot yet be generalized but suggests that seasonality may play an important role in shaping the variability of fitness traits. These results further raise the question of possible counterselection during the interepidemic period. While we did not find any trade-off between clonal multiplication on leaves during the epidemic period and clonal spore production on debris, we suggest that final populations could be counterselected by an Allee effect, mitigating the potential impact of seasonal selection on long-term dynamics.

The question Why to disperse? has been extensively investigated from an evolutionary perspective, and the strategy to disperse can be explained by several proximate and ultimate factors. The amazing diversity of dispersal mechanisms that animals, plants, fungi, peat mosses and other organisms have developed leads to the following question: How to disperse? In this article, we introduce an original modeling framework to study the evolution of dispersal in asexual populations where reproducing individuals release propagules and can adopt (by mutation) three strategies: independent movements of all propagules, clump dispersal (i.e. clumps of propagules attached together and settling at the same location), or group dispersal (i.e. groups of propagules simultaneously released and settling at different but positively correlated locations). We show how the spatial limits and fragmentation of the species' habitat shape the frequencies of the three strategies in the population and the sizes of groups and clumps. The co-existence of the independent, clump and group dispersal strategies at the stationary state of the population dynamics is of particular note. However, group dispersal never appeared as a dominant strategy, whereas independent and clump dispersal were both dominant for different parameter ranges (essentially because dispersal is either adaptive or maladaptive) .

The thermal performance curve is an ecological concept relating the phenotype of organisms and temperature. It requires characterization of the leaf temperature for foliar fungal pathogens. Epidemiologists, however, use air temperature to assess the impacts of temperature on such pathogens. Leaf temperature can differ greatly from air temperature, either in controlled or field conditions. This leads to a misunderstanding of such impacts. Experiments were carried out in controlled conditions on adult wheat plants to characterize the response of Mycosphaerella graminicola to a wide range of leaf temperatures. Three fungal isolates were used. Lesion development was assessed twice a week, whereas the temperature of each leaf was monitored continuously. Leaf temperature had an impact on disease dynamics. The latent period of M.graminicola was related to leaf temperature by a quadratic relationship. The establishment of thermal performance curves demonstrated differences among isolates as well as among leaf layers. For the first time, the thermal performance curve of a foliar fungal pathogen has been established using leaf temperature. The experimental setup we propose is applicable, and efficient, for other foliar fungal pathogens. Results have shown the necessity of such an approach, when studying the acclimatization of foliar fungal pathogens.

BACKGROUND AND AIMS:Experiments have shown that biotrophic fungi divert assimilates for their growth. However, no attempt has been made either to account for this additional sink or to predict to what extent it competes with both grain filling and plant reserve metabolism for carbon. Fungal sink competitiveness with grains was quantified by a mixed experimental-modelling approach based on winter wheat infected by Puccinia triticina.METHODS:One week after anthesis, plants grown under controlled conditions were inoculated with varying loads. Sporulation was recorded while plants underwent varying degrees of shading, ensuring a range of both fungal sink and host source levels. Inoculation load significantly increased both sporulating area and rate. Shading significantly affected net assimilation, reserve mobilization and sporulating area, but not grain filling or sporulation rates. An existing carbon partitioning (source-sink) model for wheat during the grain filling period was then enhanced, in which two parameters characterize every sink: carriage capacity and substrate affinity. Fungal sink competitiveness with host sources and sinks was modelled by representing spore production as another sink in diseased wheat during grain filling.KEY RESULTS:Data from the experiment were fitted to the model to provide the fungal sink parameters. Fungal carriage capacity was 0·56 ± 0·01 µg dry matter °Cd(-1) per lesion, much less than grain filling capacity, even in highly infected plants; however, fungal sporulation had a competitive priority for assimilates over grain filling. Simulation with virtual crops accounted for the importance of the relative contribution of photosynthesis loss, anticipated reserve depletion and spore production when light level and disease severity vary. The grain filling rate was less reduced than photosynthesis; however, over the long term, yield loss could double because the earlier reserve depletion observed here would shorten the duration of grain filling.CONCLUSIONS:Source-sink modelling holds the promise of accounting for plant-pathogen interactions over time under fluctuating climatic/lighting conditions in a robust way.

Air temperature measured by weather stations is commonly used in epidemiological models to forecast the effect of temperature on the development of foliar fungal pathogens. However, leaf temperature is the temperature actually perceived by such pathogens. The leaf temperature depends on the leaf energy budget (e.g. air temperature, radiation, wind, transpiration, etc.), which itself strongly depends on the crop architecture (e.g. leaf position, leaf angle, leaf area density). Consequently, differences between air and leaf temperatures vary spatially between canopies with contrasted architectures and between leaves within a given architecture, especially between leaf layers, as well as temporally throughout the course of the epidemic (seasonal variations). We already characterized the effect of leaf temperature on the latent period of the fungus Mycosphaerella graminicola infecting wheat leaves. In this simulation study, we aimed at estimating whether the use of either air or leaf temperature as input data influences the development of the infectious cycle of M. graminicola within contrasted wheat canopy architectures. Various weather conditions were generated using actual weather data. For each leaf layer, leaf temperature was calculated using the one-dimensional Soil-Vegetation-Atmosphere Transfer model CUPID for different canopy architectures. From the thermal performance curves of the latent period established in the aforementioned study, the pathochron, defined as the number of leaves emerging per latent period, was calculated, using either air temperature or leaf temperature as input data. At the leaf scale, the type of temperature used as input data modified the pathochron, which could generate various disease dynamics into the canopy. Our results highlighted the weather conditions for which it is necessary to take into account leaf temperature rather than air temperature to estimate accurately the development of M. graminicola. Our simulation method could be applied to other foliar fungal pathogens. In a further step, we will use future climatic scenarios to explore the impact of climate change on disease dynamics. In a longer term, the integration of these findings to more elaborated epidemiological models is expected to improve their forecasting accuracy.