Climatic Variables And Plant Disease

Understanding the factors that trigger the development of plant disease epidemics is essential if we are to create and implement effective strategies for disease management [11]. This has motivated a large body of research addressing the effects of climate on plant disease [11,12]. Plant disease occurrence is generally driven by three factors: a susceptible host, the presence of a competent pathogen (and vector if needed) and conducive environment [9,10]. All three of these factors must be in place, at least to some degree, for disease to occur (Fig. 1). A host resistant to local pathogen genotypes or unfavourable weather for pathogen infection will lessen disease intensity. The synchronous interaction between host, pathogen and environment governs disease development. These interactions can be conceptualised as a continuous sequence of cycles of biological events including dormancy, reproduction, dispersal and pathogenesis [1]. In plant pathology this sequence of events is commonly referred to as a disease cycle. Although plant pathologists have long realised the importance of the disease cycle and its component events and the apparent relationships with environment, the quantification of these interactions did not begin in earnest until the 1950s [11]. The past five decades of research have

Disease Conducive Environment

FIGURE 1 Plant disease results from the interaction of host, pathogen and environment. Climatic features such as temperature, humidity and leaf surface wetness are important drivers of disease, and inappropriate levels of these features for a particular disease may be the limiting factor in disease risk.

Disease Conducive Environment

FIGURE 1 Plant disease results from the interaction of host, pathogen and environment. Climatic features such as temperature, humidity and leaf surface wetness are important drivers of disease, and inappropriate levels of these features for a particular disease may be the limiting factor in disease risk.

established a vast body of literature documenting the impact of temperature, rainfall amounts and frequency and humidity, on the various components of the disease cycle [11].

The quantification of the relationship between the disease cycle of a given plant disease and weather is also the foundation of many prediction models that can be used to advise growers days or weeks before the onset of an increase in disease incidence or severity [1]. Such prediction tools can allow a grower to respond in a timely and efficient manner by adjusting crop management practices. Given enough time to respond, a disease prediction might allow a grower to alter the cultivar they select for planting, the date on which the crop is sown, or the scheduling of cultural practices such as fertilisation or irrigation. A prediction of a low disease risk may also result in reduced pesticide use with positive economic and environmental outcomes. Larger scale predictions of disease risk, such as the typical risk for regions or countries based on climatic conditions, can be used to form policy and priorities for research (e.g. [13]).

Interestingly, the quantification of these relationships and application of this information as part of disease prediction models has also facilitated the simulation of potential impacts of climate change. For example, Bergot et al. [14] have used models of the impact of weather variables on the risk of infection by Phytophthora cinnamomi to predict the future distribution of disease caused by this pathogen in Europe under climate change scenarios. As more detailed climate change predictions are more readily available, many plant disease forecasting systems may be applied in this context.

Some relationships between climate and disease risk are obvious, such as some pathogens' inability to infect without sufficient surface moisture (i.e. dew or rain droplets) [7] or other pathogens' or vectors' inability to overwinter when temperatures go below a critical level. Other effects of climate may be more subtle. For example, a given pathogen may only be able to infect its host(s) when the plants are in certain developmental stages. This also means that in order to maximise their chance of infection, the life cycle of pathogen populations must be in sync with host development. Since climate change can influence the rate of both host and pathogen development, it could affect the development and impact of plant diseases. Here, we discuss a few examples where host phenology is the key to disease development.

Some pathogens depend on flower tissues as a point of entry to the host. For example, Botrytis cinerea, which causes gray mold of strawberry and other fruits (producing a gray fuzz-balled strawberry, which you may have seen at a grocery store or in your refrigerator), infects strawberry at the time of flowering [15]. It stays in flower parts until the sugar level of the berry increases, and then causes gray mold disease. Another example is Fusarium head blight of wheat and barley, which causes large yield losses, reductions in grain quality and contamination with mycotoxins (toxic substances created by the fungi) [16,17]. Several fungal species including Fusarium graminearum (teleomorph: Gibberella zeae) cause this disease, and anthesis (flowering) period seems to be the critical time for infection [17,18]. An important bacterial disease of apple and pears, called fire blight, also utilises flowers as a major point of entry [19]. The causal agent (Erwinia amylovora) can be disseminated by pollinating insects such as bees and moves into flowers to cause rapid wilting of branch tips.

Certain hosts become more resistant after a particular developmental stage, some exhibiting a trait referred to as adult plant resistance. There are many examples of genes that follow this pattern in wheat, including leaf rust (caused by the fungus Puccinia triticina) resistance genes Lr13 and Lr34 [20] and stripe rust (caused by Puccinia striiformis f. sp. tritici) resistance gene Yr39 [21]. These genes are activated by a combination of wheat developmental stage and temperature changes. In grape, there are many cases of ontogenic (or age-related) resistance against pathogens. Once grape fruit tissue matures, certain fungal pathogens such as Erysiphe necator (formerly Uncinula necator, causing powdery mildew) [22], or Guignardia bidwellii (causing black rot) [23], or the oomycete pathogen Plasmopara viticola (causing downy mildew) [2] are less successful at infecting plants.

With changes in climate, host development patterns may be altered. For the examples above, the timing and duration of flowering in wheat are a function of the average daily temperature. Heavy rain and/or strong wind events can shorten flowering duration in strawberry and apple through flower damage. Some pathogen species may be able to maintain their synchrony with target host tissue, and others may become out of sync. Thus, there are some efforts to modify disease prediction systems to accommodate potential impacts from climate change. For example, in efforts to predict the risk of apple scab (caused by the fungus Venturia inaequalis), the concept of onto-genic resistance was utilised along with inoculum production [24] because tissues become less susceptible as the rate of tissue expansion decreases.

There is no doubt that weather influences plant disease; that relationship is fundamental to the modelling of plant disease epidemiology. Thus, it is fairly straightforward to predict that where climate change leads to weather events that are more favourable for disease, there will be increased disease pressure. But the relationship between climate change and associated weather events, and resulting changes in disease development will generally not be a simple one-to-one relationship (Fig. 2). The impacts will tend to be most dramatic when climatic conditions shift above a threshold for pathogen reproduction, are amplified through interactions, or result in positive feedback loops that decrease the utility of disease management strategies [25]. For example, the Karnal bunt pathogen, Tilletia indica, which reduces wheat quality, will tend to have lower reproductive rates per capita when populations are low because individuals of different mating types must encounter each other for reproductive success [26]. If climatic conditions change to favour pathogen reproduction, the pathogen will be released from this constraint and show a larger response to the change than would otherwise have been anticipated. The trend toward greater global movement of humans and materials also produces new types of interactions as pathogens are introduced to new areas and may hybridise to produce new pathogens [27,28].

Nhd Exhibit Examples

FIGURE 2 Interactions among components of the disease triangle and potential outcomes. Amount of disease [quantity (incidence, severity, etc.) or quality (risk)] is indicated by the area of the triangle. Changes in host, pathogen and climate can increase or decrease the amount of disease as a result of their interactions.

FIGURE 2 Interactions among components of the disease triangle and potential outcomes. Amount of disease [quantity (incidence, severity, etc.) or quality (risk)] is indicated by the area of the triangle. Changes in host, pathogen and climate can increase or decrease the amount of disease as a result of their interactions.

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