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Since human pathogens sometimes live on the surface of plant leaves(read here and and here), and there are plant-pathogen microbes also living on leaf surfaces, is it possible for plant virulence genes to transfer to the human pathogens (perhaps via horizontal gene transfer) and make the human pathogen also pathogenic to plants? Has there ever been evidence of this happening?
This review paper addresses why plant pathogens haven't "crossed the Kingdom border" to being human pathogens, but what about the other way around (human pathogens affecting plants). Since there is such a wide array of plants out there with a wide range of pathogen interactions, it seems to me that it would be statistically more likely that a human pathogen could mutate to be pathogenic to some plant, than some plant pathogen could mutate to be pathogenic to specifically humans. Is that assumption valid? Also, I am talking about human-animals specifically, since it looks like there is a known virus infects both animal and plant cells in the lab (Lepidoptera/ cow pea)
As a currently-relevant example, could some plant virus, say Southern Bean Mosaic Virus, somehow share genes with a coronavirus strain and become a morph plant-human pathogen? (I know this last question is a little "out-there"). I chose that example plant virus since it is a positive-strand RNA virus like SARS-CoV-2. Thank you!
Can human pathogens present on plants become plant pathogens? - Biology
Plants defend against herbivores with mechanical wounding, barriers, secondary metabolites, and attraction of parasitoids.
Identify plant defense responses to herbivores
- Many plants have impenetrable barriers, such as bark and waxy cuticles, or adaptations, such as thorns and spines, to protect them from herbivores.
- If herbivores breach a plant’s barriers, the plant can respond with secondary metabolites, which are often toxic compounds, such as glycol cyanide, that may harm the herbivore.
- When attacked by a predator, damaged plant tissue releases jasmonate hormones that promote the release of volatile compounds, attracting parasitoids, which use, and eventually kill, the predators as host insects.
Defense Responses Against Herbivores
Herbivores, both large and small, use plants as food and actively chew them. Plants have developed a variety of strategies to discourage or kill attackers.
The first line of defense in plants is an intact and impenetrable barrier composed of bark and a waxy cuticle. Both protect plants against herbivores. Other adaptations against herbivores include hard shells, thorns (modified branches), and spines (modified leaves). They discourage animals by causing physical damage or by inducing rashes and allergic reactions. Some Acacia tree species have developed mutualistic relationships with ant colonies: they offer the ants shelter in their hollow thorns in exchange for the ants’ defense of the tree’s leaves.
Acacia collinsii: The large thorn-like stipules of Acacia collinsii are hollow and offer shelter for ants, which in return protect the plant against herbivores.
Modified leaves on a cactus: The spines on cactus plants are modified leaves that act as a mechanical defense against predators.
A plant’s exterior protection can be compromised by mechanical damage, which may provide an entry point for pathogens. If the first line of defense is breached, the plant must resort to a different set of defense mechanisms, such as toxins and enzymes. Secondary metabolites are compounds that are not directly derived from photosynthesis and are not necessary for respiration or plant growth and development.
Many metabolites are toxic and can even be lethal to animals that ingest them. Some metabolites are alkaloids, which discourage predators with noxious odors (such as the volatile oils of mint and sage) or repellent tastes (like the bitterness of quinine). Other alkaloids affect herbivores by causing either excessive stimulation (caffeine is one example) or the lethargy associated with opioids. Some compounds become toxic after ingestion for instance, glycol cyanide in the cassava root releases cyanide only upon ingestion by the herbivore. Foxgloves produce several deadly chemicals, namely cardiac and steroidal glycosides. Ingestion can cause nausea, vomiting, hallucinations, convulsions, or death.
Foxgloves: Foxgloves produce several deadly chemicals, namely cardiac and steroidal glycosides. Ingestion can cause nausea, vomiting, hallucinations, convulsions, or death.
Mechanical wounding and predator attacks activate defense and protective mechanisms in the damaged tissue and elicit long-distancing signaling or activation of defense and protective mechanisms at sites farther from the injury location. Some defense reactions occur within minutes, while others may take several hours. In addition, long-distance signaling elicits a systemic response aimed at deterring predators. As tissue is damaged, jasmonates may promote the synthesis of compounds that are toxic to predators. Jasmonates also elicit the synthesis of volatile compounds that attract parasitoids: insects that spend their developing stages in or on another insect, eventually killing their host. The plant may activate abscission of injured tissue if it is damaged beyond repair.
Plants as alternative hosts for human and animal pathogens
Many of the most prevalent and devastating human and animal pathogens have part of their life-cycle outwith the animal host. These pathogens have a remarkably wide capacity to adapt to a range of quite different environments: physical, chemical and biological, which is part of the key to their success. Many .
Many of the most prevalent and devastating human and animal pathogens have part of their life-cycle outwith the animal host. These pathogens have a remarkably wide capacity to adapt to a range of quite different environments: physical, chemical and biological, which is part of the key to their success. Many of the well-known pathogens that are able to jump between hosts in different biological kingdoms are transmitted through the fecal-oral and direct transmission pathways, and as such have become important food-borne pathogens. Some high-profile examples include fresh produce-associated outbreaks of Escherichia coli O157:H7 and Salmonella enterica. Other pathogens may be transmitted via direct contact or aerosols are include important zoonotic pathogens. It is possible to make a broad division between those pathogens that are passively transmitted via vectors and need the animal host for replication (e.g. virus and parasites), and those that are able to actively interact with alternative hosts, where they can proliferate (e.g. the enteric bacteria). This research topic will focus on plants as alternative hosts for human pathogens, and the role of plants in their transmission back to humans. The area is particularly exciting because it opens up new aspects to the biology of some microbes already considered to be very well characterized. One aspect of cross-kingdom host colonization is in the comparison between the hosts and how the microbes are able to use both common and specific adaptations for each situation.
The area is still in relative infancy and there are far more questions than answers at present. We aim to address questions underlying the interactions for both the microbe and plant host in the research topic by including the following areas:
• the ecology underlying persistence and growth of the pathogens in association with plants
• the epidemiology of the pathogens in plant hosts and associated risk analyses
• classical evolutionary aspects and population genetics, including emerging pathogens
• the molecular basis to pathogen adaptation to plant hosts
• the plant defense response, for which we know far more for the phytopathogens than animal pathogens
• differences between pre- and post-harvest colonization
• cycling between animal and plant hosts, such as transmission dynamics in the farm setting
While the majority of the research already published has focused on the enteric bacterial pathogens, there is no doubt that other human pathogens can also interact with plants as part of the life-cycle. Therefore, we would also welcome articles examining the interactions for some of the less well reported pathogens. We are interested in hearing from potential authors who can provide original research articles, reviews and opinion pieces.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
7 Major Factors Affecting Disease Development in Plants
The following points highlight the seven major factors affecting disease development. The factors are: 1. Inoculum Level 2. Cultivars 3. Cultural Practices 4. Crop Rotation 5. Environmental Conditions 6. Fruit Ripening Stage 7. Harvesting.
Disease Development: Factor # 1. Inoculum Level:
An assembly of vital pathogen and its suitable host in favourable environment should take place for a successful infection to occur. However, successful infection may be dependent on the level of the inoculum (generally comprising fungal spores or bacterial cells) available.
Gaumann (1946) have claimed that a minimum number of pathogen spores are necessary to establish certain diseases even under favourable conditions.
Such a theory denies, for many fungi, the possibility of a single spore infection and has not been accepted by other investigators. For Post-harvest pathogens, which depend on a wound to enable them to penetrate into the host, it has generally been accepted that disease development is related to both the pathogen spore load on the fruit or vegetable surface and the availability of wounds for penetration.
Regular sanitation in the field, packinghouse and in storerooms will all contribute to the reduction of the spore load on the harvested produce, while careful handling and prevention of mechanical damage helps to reduce the number of entry points for the pathogen.
However, the amount of inoculum present is closely related to weather conditions during the growing season, particularly when the spores are dispersed by rain as in the cases of Gloeosporium and Phytophthora species. An interaction between wounding and inoculum level has been described for the brown rot fungus Monilinia fructicola on stone fruits.
The presence of a high level of fungal inoculum on the fruit surface and the penetration of the unwounded fruit can take place through the stomata or directly through the peel.
The inoculum level of the pathogen may determine the success of biological control of Post-harvest diseases with antagonistic microorganisms. The efficacy of the antagonistic microorganism in reducing decay has frequently been affected by the inoculum levels of both the pathogen and the antagonistic microorganism.
Disease Development: Factor # 2. Cultivars:
The initial preharvest factor which may affect disease development is cultivars which are vary greatly in their susceptibility to diseases. Differences in cultivar characteristics can markedly affect the keeping quality of fresh produce. Melons with a thick skin and firm texture are better than others to withstand in rigors of harvesting and handling.
Variability in Post-harvest decay among apple cultivars has been related to differences in wounding resistance of their skins, a feature which may be of major importance for decaying pathogens that depend on a wound to initiate infection.
Disease Development: Factor # 3. Cultural Practices:
They can reduce the inoculum level through sanitation or to produce conditions less favourable for disease development by modifying the canopy microclimate. Practices such as pruning of fruit trees and destruction of crop debris markedly affect the survival of pathogenic microorganisms. Application of preharvest fungicides can directly reduce the level of infestations.
However, preharvest chemical sprays with the same chemical that is designated for Post-harvest application, can enhance the production of new fungal strains resistant to that fungicide. Plant spacing within the row may also affect the incidence of rots.
Legard (2000) found that wider spacing reduced Botrytis rot in strawberries compared with narrower spacing. This may be due to the increased number of target hosts available in closer spacing intercept more inoculum or many fruit may escape timely harvesting and thus contribute to increased levels of inoculum.
Disease Development: Factor # 4. Crop Rotation:
Rotations can reduce the source of infection and thus influence the quality of the harvested commodity by affecting the health of the subsequent crop. Field nutrition can have an impact on the development of storage decay.
Thus, the rapid development of bacterial soft rot in tomato fruits depends on the application of nitric fertilizer in the field to a great extent and the resistance of pears to Post-harvest decay increases after the nitrogen and calcium nutrition.
Disease Development: Factor # 5. Environmental Conditions:
These may affect the pathogens directly. Many pathogens persist in soil or survive on plant debris in the field, from which winds and rain may be directly responsible for their dispersal to potential hosts.
Pathogens, such as Phytophthora spp. which infect potato tubers or citrus fruits are actually dependent on rainwater for spores germination and initiation of infections. In fact, the percentage of brown rot caused by Phytophthora parasitica in orange orchards was directly related to the amount of rainfall during infection period.
Disease Development: Factor # 6. Fruit Ripening Stage:
The susceptibility of harvested produce to pathogens primarily depends on ripening stage at the time of picking. Several types of fruits are more susceptible to injuries and as they ripen, become more susceptible to pathogen attack.
Various tissue characteristics such as the acidity level, turgidity of tissues, nutrient availability and change in senescence at ripening stages separately or in combination enhance the susceptibility to diseases.
Other factors affecting the impact of the ripening stage on disease susceptibility involve the enhanced virulence of the pathogen, weakened host resistance and protection. One of the primary factors enhancing the susceptibility of the fruits to infection is the enhanced susceptibility of the plant cell walls to the activity of enzymes produced and secreted by the pathogens.
A strong link between the ripening stage of a fruit and its sensitivity to decay may explain as chemical substances stimulate ripening and generally enhances the decay. A classic example of conditions that stimulate ripening is the exposure of various citrus fruit cultivars to low concentrations (50 ppm) of ethylene for degreening.
Ethylene treatments are applied commercially at the beginning of citrus fruit picking season to degreen the fruits that have reached maturity but have not yet developed the desired colour.
However, this economically important procedure which enhances chlorophyll decomposition and exposes the yellow, orange or red colour in the fruit peel is accompanied by enhanced sensitivity of the fruits to decay.
It was found that along with the enhancement of ripening, ethylene stimulates senescence and disruption of the stem end button, consequently activating the quiescent infection of Diplodia natalensis at this location and leading to increased incidence of stem-end rot.
Disease Development: Factor # 7. Harvesting:
Harvesting by hand is the predominant method for fruits and vegetables intended for fresh produce market. Proper training of pickers for selecting optimal maturity stage of commodity can keep damage to a minimum extent. Mechanized harvesting, even used correctly can cause substantial damage to the commodity, which may serve as suitable areas of penetration for wound pathogens.
It is, therefore, confined mainly to less vulnerable commodities such as carrots, potato or to crops which are proposed for immediate processing. The time of harvesting during the day may also affect the keeping quality of the produce. For most crops the cool hours of the night or the early morning can be advantageous.
The harvesting date may be of great significance for fruits proposed for prolonged storage. However, despite the use of various criteria determining the appropriate stage of maturity such as colour, size, shape, flesh firmness, content of starch, sugar and juice for the prediction of optimal harvest date is often imprecise.
LOOK BACK TO MOVE FORWARD
New knowledge and methods create opportunities for progress on old questions, and indeed there are few truly new questions. It is humbling to discover that our scientific predecessors thought deeply and usefully about our subject. Perceptive articles and book chapters that were written long before the advent of PubMed can be overlooked in an online search. Readers curious about plant pathogenic bacteria are encouraged to explore the following and other older sources, which describe key research questions that remain unsolved ( Smith, 1920 Walker, 1963 Schuster and Coyne, 1974 Vidaver, 1981 Mount and Lacy, 1982 Starr, 1984 Billing, 1987 Nester et al., 2004). In the same spirit, readers are encouraged to remain open to the curiosity about the natural world that drew us to science. Fancy tools are one route to novel findings, but paradigm-shifting discoveries often come from simple observation. Charles Darwin had travel funds, notebooks, pencils, and a few dead birds.
- Small (3mL) plastic pipette or eyedropper
- Rubber band
- 3 medium binder clips
- 1 box (a printer paper box works well)
- 25 mL water, tinted with food coloring
- Small potted plant
The plant should be upright, but does not have to be one of the crops we’ve discussed. We recommend a common ivy houseplant, such as Hedera helix , or a philodendron.
- Polar graph paper
Create your graph paper by drawing concentric circles with diameters at 5 cm, 10 cm, 15 cm, 20 cm, and 25 cmusing this technique . Draw an X in the center and label “Leaf tip.”
- 2 colored pencils (different colors)
- Optional: Yard stick to help center your leaf under the pipette or eyedropper
- Optional: Drop cloth to protect floor
- Optional: Camera or phone with video recording set to burst or slow motion
Plants Get Sick Too!
This is the first fact sheet in a series of ten designed to provide an overview of key concepts in plant pathology. Plant pathology is the study of plant disease including the reasons why plants get sick and how to control or manage healthy plants.
Why Worry About Plant Diseases?
|Figure 1. Injury: Ice damage. The weight of the ice causes the tree branches to break. Photo by Keith Kresina, Course Superintendent, The Golf Club New Albany.|
Since the beginning of agriculture, farmers have had to develop means for managing weeds, insect pests, and diseases. Even today, with all the scientific research on crop protection, it is estimated that insects, diseases, weeds and animal pests eliminate half the food produced in the world during the growing, transporting and storing of crops. In the tropics, where the environment favors the development of diseases, two-thirds of some crops are lost, thereby adding to the world hunger problem. Although not directly related to the world's supply of safe food, similar losses occur in timber, ornamental, floricultural and turfgrass production systems. In economic terms, annual losses in food, fiber and ornamental production systems caused by plant pests and diseases are estimated in the hundreds of billions of dollars. Because of the significant impact plant diseases have on human and animal health, as well as on the economy, it is important for those interested in growing plants to develop a firm understanding of weeds science, entomology (study of insects) and plant pathology (study of plant disease) and how to eradicate, manage or otherwise minimize losses caused by these important plant pests. The goal of this fact sheet is to provide a general introduction to how plants get sick and some basic principles of plant pathology.
Sick Plants vs. Injured Plants
All plants can get sick and are prone to injury. Disease is defined as “suboptimal plant growth brought about by a continuous irritant, such as a pathogen (an organism capable of causing disease) or by chronic exposure to less than ideal growing conditions.” In contrast, injury is suboptimal plant growth resulting from an instantaneous event, such as a lightning strike, ice damage, a bad trim job, hail damage, road salt damage, chemical burn or mechanical damage. For example, someone gets too close to a tree while mowing their grass and ends up cutting the bark. This cut is an injury. Because of the instantaneous and “cause-and-effect” nature of injuries, they are often easy to diagnose. Injured plants are sometimes predisposed to disease.
|Figure 2. Injury: Bad trim job. This can be seen in most city parking areas. The branches have been trimmed down so much that it often leads to death of the tree. Bad trimming also opens up the tree to disease by causing wounds that pathogens can enter through. Photo by Jim Chatfield, Ohio State University Extension.||Figure 3. Injury: Road salt damage. This is frequently seen online roadsides where the salt has caused a toxicity and nothing can grow in that area. Photo by Joe Rimelspach, The Ohio State University, Turfgrass Pathology.|
Two Types of Plant Disease
|Figure 4. Biotic Disease: Corn Smut. The black color seen is the actual teliospores that infect neighboring plants. Photo by Michael J. Boehm, The Ohio State University, Department of Plant Pathology.|
In the case of disease, the source of continual irritation may be abiotic (non-living) or biotic (caused by a pathogen). Abiotic diseases are also referred to as noninfectious diseases as they do not spread from plant to plant. In lay terms, they are “not contagious.” Abiotic diseases are very common and should be considered the likely suspect when attempting to diagnose the cause of decreased plant vigor or death. This is very important when working with intensively managed cropping systems in which a high degree of manipulation or handling takes place. Examples of abiotic plant diseases include damage caused by chronic exposure to air pollutants such as nitrogen dioxide from automobile exhaust, sulfur dioxide from factories, and ground level ozone, a byproduct of photochemical reactions in the atmosphere, nutritional deficiencies and toxicities, and growth under less than ideal light, moisture or temperature conditions.
Biotic diseases are caused by pathogens and are often referred to as infectious diseases because they can move within and spread between plants. Plant pathogens are very similar to those that cause disease in humans and animals. Pathogens may infect all types of plant tissues to include leaves, shoots, stems, crowns, roots, tubers, fruit, seeds and vascular tissue and can cause a wide variety of disease types ranging from root rots and rusts to cankers, blights and wilts. Most plants are immune (resistant) to most pathogens however, all are susceptible to attack by at least one pathogen—some plants are susceptible to many. Some pathogens like Rhizoctonia, Pythium, Fusarium and Sclerotinia have a broad host range while others only infect a given species.
|Figure 5. Abiotic Disease. This may look like a pathogen or insect has attacked the plant, but in fact hail caused the damage to the apple tree. Photo by American Phytopathological Society, 2008.||Figure 6. Disease: Dollar spot. Note the white, mycelia threads and hour-glass lesions that are characteristic of the dollar spot pathogen. Photo by Michael J. Boehm, The Ohio State University, Department of Plant Pathology.|
Organisms That Make Plants Sick Are Called Pathogens
Plant pathogens are very similar to those that cause disease in humans and animals. The pathogens responsible for causing most biotic plant diseases include viruses, bacteria and phytoplasmas, fungi and fungal-like organisms, nematodes and parasitic higher plants. A description of each of these groups is listed below.
- Viruses: Viruses are intracellular (inside the cells) pathogenic particles that infect other living organisms. They live off the host's nutrients.
- Bacteria: Bacteria are microscopic, single-celled prokaryotic organisms that reproduce asexually by binary fission (one cell splitting into two). Phytoplasmas are a certain type of bacteria that lack a cell wall.
- Fungi and Fungal-Like Organisms: Collectively, fungi and fungal-like organisms (FLOs) cause more plant diseases than any other group of plant pathogens. Fungi and FLOs are heterotrophic (cannot make their own food), eukaryotic organisms that have a filamentous growth habit and which may or may not produce spores.
- Nematodes: Nematodes are simple, microscopic, multi-cellular animals—typically containing 1,000 cells or less. They are worm-like in appearance but are taxonomically distinct from earthworms, wireworms or flatworms.
- Parasitic Higher Plants: Some plants cannot make their own food and parasitize other plants to obtain nutrients and water. Examples include mistletoe, dwarf mistletoe and dodder.
Three Factors Influencing the Development of Disease: The Disease Triangle
For an infectious disease to develop there must be a susceptible host, a pathogen capable of causing disease, and a favorable environment for pathogen development. Collectively, these three aspects are known as the “Disease Triangle.” If any one of these factors is not present, disease will not develop (See Figure 7). In the case of infectious plant diseases, any practice that favors plant growth and reduces either the amount of pathogen present or its development or activity will result in significantly less disease. Visit this module on the Disease Triangle for a detailed explanation: go.osu.edu/Disease Triangle.
|Figure 7. The Disease Triangle. A susceptible host, a virulent pathogen and environmental conditions that favor the pathogen must be present in the right mix to yield disease. If any one of these three components is missing or minimized, disease will not occur. Source: Michael J. Boehm, The Ohio State University, Department of Plant Pathology.|
How a Pathogen Infects a Plant: The Disease Cycle
A disease cycle takes into account the disease triangle: host, pathogen and environment. The development of visual disease symptoms on a plant requires that the pathogen must (a) come into contact with a susceptible host (referred to as inoculation) (b) gain entrance or penetrate the host through either a wound, a natural opening (stomates, lenticels, hydathodes) or via direct penetration of the host (c) establish itself within the host (d) grow and reproduce within or on the host and ultimately, (e) be able to spread to other susceptible plants (referred to as dissemination). Successful pathogens must also be able to survive prolonged periods of unfavorable environmental conditions in the absence of a susceptible plant host. Collectively, these steps are referred to as the disease cycle (see Figure 8). If this cycle is disrupted, either naturally or via the concerted efforts of a grower, the disease will be less intense or fail to develop. In general, there are five methods used to manage plant diseases. These include use of genetically resistant plants, cultural practices, chemical application, beneficial microorganisms to suppress or counter the activity of the pathogen (known as biological control), and the use of quarantines and other regulatory practices. The collective use of all of these strategies is referred to as Integrated Pest Management. For additional information, refer to the fourth fact sheet in this series, Keeping Plants Healthy: An Overview of Integrated Plant Health Management.
|Figure 8. The Disease Cycle: A disease with only one cycle (outbreak) per season is called a monocyclic or “one-cycled” disease. A disease with multiple cycles (outbreaks) per season is called a polycyclic or “many-cycled” disease. Source: Michael J. Boehm, The Ohio State University, Department of Plant Pathology.|
What Do Sick Plants Look Like?
Proper diagnosis is a critical step in the management of plant diseases. Before you can establish what control strategies should be taken, you must first determine the exact culprit and rule out all other possibilities. To understand the steps in properly diagnosing a sick plant, please view the following fact sheets: Diagnosing Sick Plants and 20 Questions on Plant Diagnosis. Two terms often used when discussing plant disease are sign and symptom. Knowing how to properly diagnose and distinguish a sign versus a symptom is the first step in determining why your plant is sick. The term sign is used when the pathogen or part of the pathogen is observed. Examples include fungal hyphae, mycelium, spores, fruiting bodies, bacterial ooze and nematodes. Although many plant diseases can be diagnosed in the field based on the observation of diagnostic signs, many require observation by trained specialists in the laboratory or clinic. In contrast to signs, symptoms are visual or otherwise detectable alterations in a plant that result from the plant being sick or injured. Symptoms of disease often change over time as the disease progresses. Initial symptoms are often invisible or very small and nondescript. Symptoms can generally be placed in the following categories:
Insect visitation to flowers and leaves infected with Mvc
Video data indicated that bees, including honey bees, bumble bees and native solitary bees, most frequently contacted flowers, making up 75–80% of total flower visits each year (Fig 2). Flies, including syrphids, muscoids, mosquitos and gnats were the next most common floral visitors, making up 14–23% of total flower visits. Flies more commonly contacted Mvc-infected leaves compared to bees (Fig 2), with the remainder of contacts made by ants, beetles, moths, true bugs and other insects. The difference between flower vs. Mvc-infected leaf contacts for bees vs. flies was significant in both years (2008: Pearson χ 2 = 60.5, P < 0.001 2009: Pearson χ 2 = 392.7, P < 0.001).
Number of contacts (approaches that led to contacting each respective tissue) and approaches by Diptera (flies), Hymenoptera (bees) and other insects to blueberry flowers, healthy leaves, and leaves infected with Monilinia vaccinii-corymbosi (Mvc) in three Michigan blueberry plantings in 2008 (a) and 2009 (b) as observed in video-recordings. The difference between flower vs. Mvc-infected leaf contacts for Hymenoptera (Bees) vs. Diptera (Flies) was significant in both years (2008: Pearson χ 2 = 60.5, P < 0.001 2009: Pearson χ 2 = 392.7, P < 0.001).
Molecular detection of Mvc DNA on insect visitors
In 2009, 159 live insects from 28 families were captured, identified and assayed using nested PCR analysis to confirm the presence or absence of Mvc DNA on heads and forelegs. PCR analysis found Mvc DNA on five of the six insect orders and eighteen of the twenty-eight insect families assayed (Fig 3A, S5 Table). DNA was found most frequently on Hymenopterans and Dipterans, with a higher percentage of bees and wasps testing positive for Mvc compared to flies (56% vs. 31%, respectively Pearson χ 2 = 5.7, P = 0.017, Fig 3B). Overall, 33% of insects captured tested positive for Mvc DNA (S5 Table). BLAST analysis of PCR amplification products showed that all 53 Mvc-positive insect samples shared a 99% maximum identity with ITS regions of Monilinia spp.
Data are summarized by insect orders sampled (a) and family for Hymenoptera and Diptera (b). The number of insects sampled for PCR detection totaled 159. A higher percentage of Hymenoptera tested positive for Mvc compared to Diptera (56% vs. 31%, respectively Pearson χ 2 = 5.7, P = 0.017).
Quantification of volatiles from flowers, healthy leaves, and leaves infected with Mvc
VOC profiles were significantly different among the three tissues sampled (pMANOVA F2,28 = 8.1, P = 0.009, Fig 4). Pair-wise comparisons among each tissue revealed that VOC profiles of healthy leaves were different from those of flowers (F1,19 = 9.8, P = 0.009) and infected leaves (F1,18 = 9.3, P = 0.010), while the profiles of flowers vs. infected leaves were marginally, but not significantly different (F1,18 = 3.0, P = 0.083). Concentrations of 17 of the 28 volatile compounds quantified differed significantly among tissues (Table 1), which was unlikely due to chance (binomial expansion test: P < 0.001). Overall, compound concentrations were highest in infected leaves, moderate in flowers and lowest in healthy leaves (Table 1 F2,25 = 6.7, P = 0.005).
Non-metric multidimensional scaling (NMDS) plot of volatile profiles from Vaccinium corymbosum flowers (red points), uninfected leaves (black), and leaves infected with Mvc (green). Total area occupied by each sample type as represented in 2-dimensional space is shaded in gray. Stress = 0.134. VOC profiles differed significantly among the three tissues sampled (pMANOVA F2,28 = 8.1, P = 0.009), with healthy leaves differing from flowers (F1,19 = 9.8, P = 0.009) and infected leaves (F1,18 = 9.3, P = 0.010), while flowers vs. infected leaves were marginally, but not significantly different (F1,18 = 3.0, P = 0.083).
Attraction of insects to floral and Mvc volatiles
In total, we found 6,524 arthropods in the volatile lure Delta traps. Diptera were the most abundant insect order, comprising 64% of individuals, followed by Lepidoptera (12%), Hymenoptera (9%), Coleoptera (7%), Hemiptera (4%) and arachnids (2%). While there was substantial variation in the total abundance of arthropods among blueberry plantings (F14,105 = 10.6, P < 0.001), total abundance did not differ among Massachusetts, Michigan and New Jersey (F2,117 = 0.2, P = 0.8). We found no overall difference in total arthropod abundance among treatments (F7,112 = 0.6, P = 0.7), largely because Diptera did not respond to the treatments (F7,112 = 0.4, P = 0.9, Fig 5A). However, the abundance of Hymenoptera was affected by treatments (F7,112 = 4.2, P < 0.001, Fig 5B) and there were trends for differences in both Lepidoptera (F7,112 = 1.9, P = 0.075, S4A Fig) and Coleoptera (F7,112 = 1.9, P = 0.073, S4B Fig). Furthermore, the patterns among treatments were similar among Hymenoptera, Lepidoptera and Coleoptera. The combination of cinnamic aldehyde and cinnamyl alcohol was more attractive to Hymenoptera (bees and wasps) than the control blank (P < 0.05: Tukey’s post-hoc contrast, Fig 5B), and this general pattern was similar for both Lepidoptera and Coleoptera (S4 Fig). Cinnamic aldehyde and cinnamyl alcohol had the lowest diffusion rates of all treatments (S4 Table), indicating that increased attraction to these lures was not simply due to a greater quantity of volatile emission. All lures had greater than half of their content remaining at weekly swaps.
Attraction of Diptera (a) and Hymenoptera (b) to individual volatiles and synthetic blends of compounds from blueberry flowers and Monilinia vaccinii-corymbosi (Mvc) shoot strikes. Data are from five blueberry plantings in each of three states (Massachusetts, Michigan and New Jersey n = 15 plantings total). Diptera did not respond to the volatile treatments (F7,112 = 0.4, P = 0.9), however, the abundance of Hymenoptera was affected by the treatments (F7,112 = 4.2, P < 0.001). Different letters correspond to treatments that are significantly different via Tukey’s post-hoc contrasts (α = 0.05). Means ± SE shown.
I. A primer on machine learning: what is it and what are the common pitfalls?
Machine learning (ML) is the application of statistical methods to identify patterns in data and is commonly divided into unsupervised and supervised approaches (Witten et al., 2016 ). Unsupervised ML utilizes unlabelled training data and includes exploratory analysis (e.g. k-means clustering) or dimensionality reduction (e.g. principal component analysis). Supervised learning typically occurs in classification problems and utilizes labelled training data, or partially labelled data (semi-supervised learning). Supervised ML aims to learn a function from the training data that has the ability to classify unseen data. The major ingredients in classification are: training data consisting of positive and negative examples feature selection for training the model selection of the appropriate model and an independent test set for validation. Deep learning is part of a family of models that do not need explicit feature selection however, they require vast amounts of data that are rarely available in biological domains. Combining multiple ML models that have learned from the same data and are diverse in their predictions is called ensemble learning and can drastically improve prediction accuracy (Dietterich, 2000 ). Whereas training a ML model is a relatively simple process, careful feature engineering and evaluation are time-consuming and there are common pitfalls to avoid. When first embarking on ML, researchers are often confronted with the following common misnomers and pitfalls.
Pitfall 1: ‘What data sets do I need?’
The goal in ML is to train a model to accurately classify unseen data. Consider a simple classification task of images, say if a plant leaf is diseased through photos. Your classifier will use positive examples (images of diseased leaves), negative examples (images of healthy leaves), specific features (e.g. presence of chlorosis through yellow-to-green pixel ratio) and a validation set that should be a representation of the population. You would then train a ML model on diseased leaf images as the positive set and healthy leaf images as the negative set, and evaluate its performance on the validation set. In biology, positive instances can be sparse and are often needed for training in their entirety. Without a separate validation set, a common technique of assessing performance is to use k-fold cross-validation (Wong, 2015 ). In k-fold cross-validation, the training data are partitioned into k sets of equal size. The classifier is trained on k – 1 datasets and tested on the one holdout set. This procedure is repeated k times and average performance is reported. Even with a validation set, one can tune the performance to fit that particular validation set best by taking sneak peeks, and performance falls apart when a new validation set is introduced.
Pitfall 2: ‘Getting the fit right’
The most common pitfall is training a model to simply memorize the training examples this is called ‘overfitting’ (Domingos, 2012 ). An overfitted model will have outstanding accuracy on the training data, but is essentially randomly guessing predictions for unseen data. It fits the data too well, learning all the detail and noise of the training data. Overfitting can occur for various reasons, for example, by training a model for too long or by fitting a function that is too complex. For example, a linear trend in a set of data points is best fitted with a simple line, whereas a 100 th -order polynomial function will merely fit the noise of the data, and will not be able to predict the general trend (Fig. 1). Overfitting is a key issue if you have no independent validation set to easily spot it. One should always be cautious if prediction accuracy seems too good to be true.
Pitfall 3: ‘I have no in-depth knowledge of the application domain, for example, the biology’
Careful feature selection and engineering are the key to success in traditional ML. One is likely to spend only 5% of the time training a model and 95% of the time finding appropriate features and engineering representation of them. For example, a protein sequence must be encoded in a vector representation that a ML model can understand. Although sometimes the overall amino acid composition is the most relevant information, for other applications one has to preserve the position of certain amino acids in a sequence. It is not a simple task to encode proteins that are of varying lengths in feature vectors of equal length (Kuo-Chen, 2009 ). Irrelevant feature selection or training set selection often occurs when there is insufficient understanding of the problem domain. For example, consider the problem of classifying whether a pathogen protein is a secreted effector that modulates the host cell to facilitate infection. One might use secreted effectors as positive training data and nonsecreted proteins as negative training data, and focus on the amino acid composition of the N-terminal sequence, as it will give the strongest signal for discrimination. However, a classifier will then pick up merely the signal peptide composition, and signal peptides also occur in secreted noneffectors. Without background knowledge of the biology, this will only become evident with a solid validation set.
Pitfall 4: ‘I have no in-depth knowledge of machine learning’
Insufficient background in ML can lead to major glitches when training models. For example, proper scaling of features is crucial for particular classifiers, such as support vector machines, whereas classifiers such as naïve Bayes are invariant to it (Hsu et al., 2003 ). Imbalanced training sets are another issue. Often one might have only 100 positive examples but 100 000 negative examples for training. A classifier can achieve 99.9% accuracy by always predicting the predominant negative class on this set. Performance evaluation on imbalanced datasets must not use accuracy, but rather metrics such as precision, recall, F-score, kappa or Receiver Operating Characteristic (ROC) curves (Chawla, 2010 ). The curse of dimensionality refers to a scenario where the number of features is significantly higher than the number of samples (Domingos, 2012 ). It seems counterintuitive that adding more features for classification will decrease the model performance. However, consider classifying images of leaves into diseased and healthy, using 100 RGB images of size 100 × 100 pixels. We might assume that using all the pixel information would be beneficial for accurate classification and therefore we use the feature space of 100 × 100 × 3 = 30 000 dimensions. However, our training set does not cover this high-dimensional feature space and will overfit, for example, to the presence of yellow pixels at certain positions. On unseen images this classifier will have poor accuracy. Instead, a simple classifier based on a reduced number of features (e.g. the ratio of yellow to green pixels, indicating the presence of chlorosis) would have captured the trend in the data and avoided the curse of dimensionality.
Infectious diseases of animals and plants: an interdisciplinary approach
Animal and plant diseases pose a serious and continuing threat to food security, food safety, national economies, biodiversity and the rural environment. New challenges, including climate change, regulatory developments, changes in the geographical concentration and size of livestock holdings, and increasing trade make this an appropriate time to assess the state of knowledge about the impact that diseases have and the ways in which they are managed and controlled. In this paper, the case is explored for an interdisciplinary approach to studying the management of infectious animal and plant diseases. Reframing the key issues through incorporating both social and natural science research can provide a holistic understanding of disease and increase the policy relevance and impact of research. Finally, in setting out the papers in this Theme Issue, a picture of current and future animal and plant disease threats is presented.
Incidents of animal or plant disease are not solely natural occurrences. Human actions are extensively implicated in the spread and outbreak of disease. In turn, disease affects human interests widely, and much effort is spent in the control of disease. Consequently, it is difficult to prise apart the natural phenomena of disease and the social phenomena of the drivers, impacts and regulation of disease. Yet, our understanding of animal and plant diseases is riven by a great divide between the natural and social sciences—a divide that is entrenched in differences of research methods, approaches and languages. The resulting fragmentation of knowledge hinders progress in understanding and dealing with disease.
The aim of this Theme Issue is to bring together different academic disciplines to offer fresh insights into contemporary animal and plant disease threats. In this introductory paper, we outline the complex interactions between the natural and the social in animal and plant diseases, and present the case for an interdisciplinary approach, combining natural and social sciences, to disease management. Firstly, we address the two most pressing drivers of disease spread—climate change and globalization—to illustrate the interplay of human and natural factors. Secondly, we explore the inter-relationship between disease and the political, social and economic context in which it occurs, demonstrating the significance of that context by comparing and contrasting the different regimes surrounding plant and animal health. The paper then introduces the concept of interdisciplinarity and the ways in which it can prompt new insights into the transmission, effects and management of disease. Finally, we set out the papers in this Theme Issue and the prospect they provide on the present and future disease threats.
2. Drivers of future disease threats
Two contemporary processes stand out in their transformative and far-reaching impact on the spread of infectious animal and plant diseases. The first is climate change, which is profoundly altering the distribution of disease organisms, at the same time as it is increasing the vulnerability of agriculture in certain regions owing to drought, salinity, flooding or extreme weather events. The second is globalization, the increasing movement of people, goods and information, that poses challenges for border controls, food supply chains and trade patterns, but is also a force behind the development of national and international systems of regulation.
Plant and animal disease experts in the UK were surveyed in 2006 regarding the most important drivers of future disease threats . For plant diseases, the major drivers identified were pesticide-resistant disease strains and a lack of new pesticides, an increase in trade and transport of crops and plants, and an increase in ambient temperatures. For animal diseases, the major drivers were inadequate systems for disease control and weaknesses in their international implementation, the threat of bioterrorism, emergence of drug resistance and a lack of new drugs, increased trade in animals, the spread of illicit trading and other risky practices, and increased temperatures. Interestingly, lack of understanding of the biology of the pathogens did not figure, but aspects of climate change and globalization appeared under both headings.
(a) Climate change
Climate change in its contemporary form is not simply a ‘natural’ process, but is increasingly caused by human behaviour. In turn, climate change affects disease transmission at three levels: firstly, it acts directly on the biology and reproduction of pathogens, hosts or vectors secondly, it affects the habitats present in a region, the community of hosts that can live in them and the lifecycles, or lifestyles, of those hosts and thirdly, climate change induces social and economic responses, including adaptive and mitigating measures, which alter land use, transport patterns, human population movements, and the use and availability of natural resources . While the first is a matter of biology, the second and third levels include increasing social components.
The effects of climate change on disease will differ between pathogens. A Foresight analysis identified increasing disease risks as a result of warmer temperatures in Europe, including from powdery mildew and barley yellow dwarf virus, and from vector-borne diseases such as Bluetongue, Lyme disease and West Nile virus . Plant diseases may increase or decrease depending on their biology, temperature and water requirements. However, there is evidence that certain pathogens such as wheat rust that currently flourish in cool climates could adapt to warmer temperatures and cause severe disease in previously unfavourable environments . For animal diseases, increases are likely for vector-borne diseases, because insect and tick reproduction and activity are particularly sensitive to increases in temperature. As well as affecting the incidence and severity of disease, climate change will also influence the spread and establishment of non-native plants and animals. If they prove invasive, they too may impact on crop management, livestock husbandry, silviculture and infrastructure maintenance, as well as the native fauna and flora. Such changes to host ecology and environment are additionally important as even relatively small changes in the basic reproduction rate can have large impacts on the incidence of infection in a population, as pathogens more successfully jump species .
While we can thus identify some likely trends in the status of particular diseases, a second and equally important feature of climate change is the increased uncertainty it ushers in. As the Foresight report notes, there is ‘considerable uncertainty arising from the many, often conflicting, forces that climate imposes on infectious diseases, the complex interaction between climate and other drivers of change, and uncertainty in climate change itself’ . Effects of climate change that act indirectly on infectious diseases, via effects on other drivers, are particularly hard to predict. These include the social and economic responses to climate change such as shifts in land use and transport and trade patterns.
Agricultural processes, for example, have an active interplay with climate change, altering the conditions for disease. While agriculture is affected by rising temperatures and changing precipitation patterns, and must adapt, the production of food is a significant generator of greenhouse gases and is under pressure to mitigate them. Agriculture contributes about 7% of the UK's greenhouse gas emissions . Changes in agricultural systems are therefore likely to have complex consequences for disease threats. For example, agricultural adaptation will necessitate geographical shifts in cropping zones, potentially introducing disease into new areas and prompting novel disease challenges. Even agricultural mitigation measures may have unintended consequences. For example, one technology recently promoted to combat greenhouse gas emission is on-farm anaerobic digestion as a means of processing farm waste and generating green energy simultaneously . However, pathogens can enter digestors in slurry and other feedstock and be re-introduced to the field when the digestate residue, if not properly treated, is applied to a crop .
Globalization is the other major process exacerbating disease spread, through rising volumes of trade in plants and animals within and between countries, growing numbers of tourists and other travellers potentially transporting disease organisms, and an increasingly international food supply chain that extensively moves around plant and animal products for processing and sale. The effects are more strongly seen in the less regulated world of plants. In the UK, a rapid growth in horticultural trade has led to many new disease introductions including the fungus Phytophthora ramorum [8,9], which poses a serious threat to a range of indigenous trees and shrubs. Forestry in general has seen a dramatic pattern of new disease and pest introductions, particularly through the recent opening up of trade between East Asia and other regions . Over the twentieth century, the number of new plant fungal, bacterial and viral diseases appearing in Europe has risen from less than five to over 20 per decade . Much of this is attributable to increased trade, transport and travel, and there is no indication that the trend is abating.
Again, the agricultural sector is implicated in increasing disease threats, in this instance through changes to the scale of production and trade in response to globalizing markets. For example, structural change in the international horticultural industry has been towards fewer and larger producers and an increasing involvement of multiple retailers, leading to a concentration in the number and size of companies together with a major expansion of trade pathways [12–14]. The geographical concentration and intensification of production that globalization has fostered also favours certain diseases. For example, extremely high densities of European wheat crops have been linked to the increasing transmission potential of diseases such as yellow rust . Similar restructuring processes are heightening disease vulnerability in livestock. The reduction in income per animal, coupled with mechanization, has led to fewer farmers managing more animals per farm, and more animal movements between farms. For example, pig farms purchase breeding stock to maximize uptake of new genetics, and young pigs from many farms are moved and reared together in their thousands. These behaviours, and similar developments in other livestock sectors, help pathogens survive in metapopulations .
The threat posed by increasing trade and tourist movements is largely a threat to the biosecurity systems of individual farms and those put in place to prevent disease entering particular countries. These systems are increasingly sophisticated, underpinned by advances in rapid diagnostic technologies and, particularly in the horticultural sector, new approaches to risk assessment and management of emerging pathogens. However, the volume and diversity of threats is challenging these systems. Some pathways of disease introduction are difficult to measure and regulate efficiently, for instance, illegal trafficking of bushmeat or booming horticultural imports. Globalization also circumscribes the autonomy of traditional, nation-state-based systems of authority, emphasizing additionally: individual and collective arrangements and responsibilities among farms and businesses in sectors and supply chains as well as transnational systems of regulation.
The open internal borders within the European Union and the variable exercise of external border controls reduce the capacity of any European nation to keep out diseases of animals and plants on its own . European regulatory frameworks on animal and plant diseases are nested within international frameworks that determine what organisms and products can be denied trade access and under what circumstances, without contravening the rules of the World Trade Organization. International plant health protocols, for example, compile lists of harmful organisms, principally pathogens that have spread beyond their centres of origin causing disease elsewhere. However, many of these ‘newly escaped’ organisms were previously unknown to science and were not therefore on any international list before they escaped and began to wreak havoc, including Dutch elm disease, sudden oak death, phytophthora and box blight in the UK .
As this brief overview has illustrated, the spread of animal and plant diseases is heavily influenced by human behaviour in direct and indirect ways. Human-induced globalization and climate change are increasing the spread of disease, both separately and in conjunction. Disease organisms may be transported more easily as a result of extended trading systems, but they may also find more favourable conditions for reproduction and transmission as a consequence of global warming. Not just in relation to disease incidence, though, but in disease management also, one can see parallel inter-relationships between the natural and social aspects. The regulation of animal and plant diseases is a fluid and multifaceted collection of impacts and management responses. We now review some of these impacts and responses, demonstrating how scientific understanding of disease spread must be understood in the context of human responses to disease threats.
3. Regulatory relations of infectious diseases
The management of disease takes place within regulatory frameworks set out by national governments and intergovernmental organizations. In the UK, there are different regulatory frameworks for animal and plant diseases, partly reflecting biological differences between the two. For example, there are many more species of plant farmed than livestock. Key crop species and threats vary depending upon geography and climate, making a global shortlist of crop threats less relevant, and favouring local risk analysis as a means of identifying national priorities .
However, there are also historical political factors affecting the ways that plant and animal diseases are dealt with. Animals are high-value investments relative to crops, which may account for the greater protection afforded against animal disease historically . Over the past 150 years, diseases have been controlled for a whole variety of different reasons, including protecting the nation's reputation abroad, lobbying by livestock breeders, safeguarding public health and avoiding disruption of trade . The political imperatives to control disease have important consequences for the governance structures that are put in place to regulate trade and monitor and combat diseases . The ways in which different attitudes towards animal and plant diseases are manifested in different political and policy regimes are summarized in table 1.
Table 1. The different regimes for plant and animal disease in the UK.
The regulation of animal and plant diseases should be informed by scientific evidence about the likely spread of diseases and the severity of the animal and plant health problems they pose. Government policy for regulating disease is also determined, however, by the wider impacts that disease outbreaks have upon society and the economy. The differences between the two regimes outlined in table 1 stem largely from the fact that certain animal diseases are considered to have more detrimental social and economic effects than plant diseases. The following two sections examine more specifically how the social and economic relations of infectious diseases shape the way diseases are managed.
4. The social relations of infectious diseases
A range of social factors, including consumer concerns, human health risks, concerns for wildlife and risks to countryside users influence the political and regulatory context for the management of infectious disease. Consumers expect wholesome and healthy food, and food-borne illnesses place vulnerable groups at risk of infection. Certain infectious diseases of animals are controlled because the human health impacts of animal diseases can be severe: approximately 75 per cent of all recent emerging human diseases seem to originate from an animal source . The Foresight report argues that this trend is ‘likely to continue and to be exacerbated by increasing human–animal contact and a growing demand for foods of animal origin’ . There are few direct risks to human health from plant diseases, notable exceptions being mycotoxins produced by some strains/species of Fusarium, which also cause head blight in cereal crops.
Consumers are also concerned with the provenance of food and in particular with animal welfare. Indeed, welfare standards in food production and the safety of meat produced by intensive farming methods are among the concerns most frequently expressed by consumers about food . Likewise, with regard to crop production, many consumers express preferences for organically produced food or food grown with minimal chemical pesticides . The use of chemical pesticides continues to rise, however, with Defra estimating that over 30 million ha of crops were treated in 2004, compared with 13.9 million ha in 1984. The rising incidence of plant diseases makes it a matter of urgency therefore that research and development work be done to improve the utility and take-up of biopesticides , although there are limits to the protection they can provide . Alternative strategies such as the use of transgenic, disease-resistant crops appear to be a distant possibility owing to public concern over genetically modified organisms (GMOs) .
An emerging concern, that is beginning to influence government policy-making, is the potential for disease outbreaks to interfere with public use or appreciation of the countryside. There are emerging human health risks, such as the threat of Lyme disease to countryside users, which has reached almost 2000 cases per year in the UK. Such risks pose dilemmas particularly regarding sensitive risk communication to inform people of sensible precautions to take without unduly alarming them . On the other hand, risk management responses such as the blanket closure of rural footpaths in a foot and mouth disease (FMD) outbreak (as happened in the UK in 2001) are now regarded as draconian, in preventing public use of the countryside: FMD poses no significant human health risk, and the rationale for the ban was to prevent the theoretical risk of recreational users spreading the virus . This issue and others, such as serious incidences of Escherichia coli 0157 at farm visitor attractions, highlight tensions between the recreational and productive use of the countryside which considerably complicate the objectives and tactics of disease management. The effects of plant diseases may be less immediate, but, in certain cases, they may have more profound impacts on the enjoyment of the landscape. The outbreak of Dutch elm disease in the 1970s, for example, brought about the destruction of the majority of mature elms in the Northern Hemisphere, thus eliminating a prominent and ubiquitous feature of much of the open countryside . The lessons to be learned from the Dutch elm disease outbreak in the UK relate not only to the original scientific assessments made but the ways in which these were turned into official policies that downplayed the potential seriousness of the outbreak and failed to comprehend the cultural loss it would entail .
The final significant societal influence on government policy for disease control concerns the interplay between wildlife, livestock and society. There is substantial conflict surrounding wild mammals in agricultural ecosystems particularly in relation to the perceived impact of predation and disease on domestic stock. Wild mammals can infect livestock with a variety of diseases, including bovine tuberculosis , which has provoked significant conflict between badger conservation and farming groups [31,32]. Likewise, the increase in deer populations in the countryside is causing discord with agriculture, in part because of the potential of the deer to act as sources of infectious disease for livestock . There is a tension between the management and regulation of wildlife for food chain security and that for biodiversity conservation. The former implies the need for a rigid protective boundary around any animal system connected with the human food chain. However, that could militate against the conservation of more ‘natural’ ecosystems, ‘co-produced’ with farming and landscape-level approaches to biodiversity conservation . An analogous situation arises with the interplay between crop or trade plants and natural plant communities, where there is a shared pathogen, as seen for P. ramorum and Phytophthora kernoviae affecting a wide range of host plants in both the ornamental nursery trade and woodland and heathland habitats.
The regulatory context and the social impacts of diseases are inextricably linked. Understanding the importance of societal attitudes and preferences is essential to understanding why attempts to control disease succeed or fail, because seemingly ‘irrational’ behaviour may undermine the premises or application of policy. This is particularly apparent in the case of public judgements of risk where there is much evidence to suggest that risk assessment in practice draws upon a wide variety of knowledge and experience, of which scientific information may be only a small part . Mills et al.  demonstrate through their comparison of the ornamental and mushroom sectors (for diseases such as P. ramorum or Mushroom Virus X) and also the cereal and potato sectors that growers and their consultants make complex assessments of the risk of diseases. These risk assessments are based not only on technical analysis but on intuitive reactions and political judgements also .
The consequences of public concerns can be far-reaching in the changing political and regulatory frameworks. An example is the recent decision to move from a risk-based to a hazard-based assessment system for chemical pesticides in the EU (the amendment of 91/414/EEC). Risk assessment is based on a combination of the intrinsic properties of a chemical and likely exposure hazard assessment takes account of only the intrinsic properties. This will have a significant impact on the range of pesticides that can be used. The next section examines shifts occurring in the onus of responsibilities for disease management between the public and private sectors in response to the changing public and political perceptions of the scale and fairness of the distribution of costs involved.
5. The economic relations of infectious diseases
The second dimension that must be considered is the economic costs of managing disease and how these are distributed. Again, this is linked to, and has an influence on, the regulatory context. The economic impacts of disease are felt in terms of culled animals, damaged crops, lost productivity, loss of international trade, control and compensation costs, and rising food prices. As explained above, animal and plant diseases are treated differently by government and consequently their economic impacts are determined and distributed differently between state and industry.
For plant diseases, the costs of outbreaks are borne almost entirely by producers who receive no compensation from the government. Historically, given that many plant pests and pathogens require expert (often laboratory-based) identification, plant health controls have primarily relied on government plant health inspectors (supported by an extensive government-funded diagnostic testing programme) intercepting regulated pest and pathogens in order to reduce the likelihood of serious outbreaks. As a consequence, although legislation allows Ministers to pay for the destruction of plants in certain circumstances, government has not normally relied on compensation to incentivize notification of regulated pests by producers. Should it become necessary to destroy plants in large private gardens, however, plant disease control would become a much more contentious and politicized issue. Such a situation has already arisen in the USA where attempts to control citrus canker in Florida have involved the destruction of trees in residential areas .
The costs that growers have to bear from plant diseases are considerable. For example, the Mushroom Virus X disease complex has undermined the viability of the UK mushroom industry, causing losses of over £50 million per annum in recent years . Economic losses to crops from invasive pests are estimated at £4 billion per annum in the UK alone . Sectoral losses of up to £80 million per annum have been estimated if statutory controls were to fail and an exotic plant disease such as ring rot of potato was to become established . Plant pests are a significant constraint on agricultural production, responsible for around 40 per cent loss of potential global crop yields, caused roughly equally by arthropods, plant pathogens and weeds. A further 20 per cent loss is estimated to occur after harvest .
Endemic diseases of livestock that do not affect humans, like plant diseases, are left largely to farmers to manage as they choose, within legal limitations focused on public health and animal welfare. There may be a wider industry interest in the epidemiology of these diseases expressed in technical norms for example, management of mastitis in dairy cows focuses on minimizing the levels of immune cells in milk while maximizing milk yield. One consequence of the absence of external social and political interest in these endemic diseases, though, is a lack of funding for research. A major exception that reinforces government's reluctance to intervene in others is bovine tuberculosis, which government has been seeking to control and eradicate in the UK for more than a century. In 2007–2008, Defra spent £77 million—one-fifth of its animal health and welfare budget—in dealing with this disease alone . With bovine tuberculosis, payment of compensation appears to have fostered a self-perpetuating reliance on government to manage the disease, with farmers not incentivized to take sufficient biosecurity and precautionary measures .
For exotic livestock diseases (FMD, avian influenza, Newcastle disease, etc.), the government conventionally pays for the eradication of the disease and compensation to affected producers. In the case of large outbreaks, this can be a significant expense, as with the 2001 FMD outbreak, where costs of the epidemic were estimated at £3 billion to the public sector and £5 billion to the private sector . A 2008 National Audit Office report cited animal disease outbreaks as one of the reasons why the responsible government department—the Department of Environment, Food and Rural Affairs (Defra)—repeatedly overspends on its budget, while a more recent report highlighted the fact that this leads to shortfalls in other important areas such as animal welfare [39,42]. The costs involved run on between outbreaks, in the maintenance of surveillance and disease-control systems and the capacity to fight future large-scale outbreaks, including vaccine banks and levels of mobilizable veterinary staff. These public costs are generally justified in terms of the production, trade and welfare benefits of the disease-free status of UK livestock.
There are wider costs of disease beyond the impact on government and the agricultural sector. This is particularly true for livestock diseases. In the 2001 FMD outbreak, the economic impact on tourism and rural businesses—caused by footpath closures, disturbing images of ‘funeral pyres’ and appeals from the government and farming groups for people to ‘stay away’ from the countryside—was more severe than the losses to farming [43,44]. For example in Cumbria, one of the worst affected counties, losses to the tourism sector were £260 million, compared with £136 million losses to agriculture . Moreover, culled-out farmers received compensation for their losses from the government, whereas the mainly small rural businesses that suffered losses received no compensation.
The economic impact of plant and animal diseases is inextricably linked to the regulatory context. As the cost to the government of controlling animal diseases continues to rise to publicly unacceptable levels, the regulatory framework is beginning to change in order to curb and reallocate these costs. New developments such as the government's responsibility and cost-sharing agenda could potentially transform the nature of disease control [46–48]. Through the sharing of responsibilities, government wants to achieve better management of animal disease risks so that the overall risks and costs are reduced and rebalanced between government and industry. Industry will assume a greater responsibility in developing policy and deciding what forms of intervention might be needed. Producers will have greater ownership of the risks, but will face less of a regulatory burden. This will entail greater attention to farm-level biosecurity, private measures such as insurance to compensate for disease losses, collective preventative schemes within farming sectors and government–industry partnerships to tackle disease. Overall, there will be greater emphasis on farmer and industry responsibilities. This may be problematic because farmers' ability to control animal disease is subject to a range of influences and constraints [49,50]. Even so, the pace of change is likely to be forced by wider pressures on public expenditure which demand that government prioritize its commitments ever more ruthlessly.
Plant disease management with its history of private sector responsibility offers examples that the livestock sector might follow. Indeed, growers have devised imaginative programmes for biosecurity and crop insurance for major crops such as potatoes. However, the threats posed by horticultural imports to growers in general and to the wider environment may elicit a more demonstrative response from the government. Recently, some horticultural growers have experienced severe financial difficulties, particularly as a result of the ongoing P. ramorum outbreak, persuading the government to explore the possibility of contributing to an industry-financed hardship fund for seriously affected producers. This may or may not set a precedent. The wider application of responsibility and cost-sharing to plant disease, though, would face a number of technical obstacles, quite apart from the reluctance of the government to enter into open-ended financial commitments . There are a number of different sectors with different characteristics and disease vulnerabilities. It is also difficult if not impossible to assess the scale of the threat from as yet unrecognized pests and pathogens that could be introduced by unscrupulous or ill-informed traders. This leads to intractable issues about identifying who the risk takers and risk acceptors actually are in different situations and how the responsibilities and costs of risk assessment and management could be shared rationally and equitably between the taxpayer and different trade sectors.
6. An interdisciplinary approach
All of the emerging threats and challenges described above invite new framings of disease management as the relationship between agricultural production, the rural environment and society changes. It is imperative that debates around disease control take into account their intrinsic biological and physical factors. It is taken as given that we need to have a thorough understanding of the epidemiology of the diseases, the diagnostics available to recognize their presence and the available means of treating them. However, our understanding of the biology of animal and plant diseases must also inform and be informed through social science research. As this review illustrates, animal and plant diseases impact upon society in many ways, including through changing landscapes and land use, issues of food security and safety, concerns over animal welfare and ethical food production, and the use of pesticides and GMOs. Societal drivers, in turn, impact upon the conditions for and transmission of disease, ranging from influencing the changing governance and nature of agriculture, food production and trade, to efforts to prevent or control disease outbreaks. The ability to predict future disease risks, taking into account drivers such as climate change, is a fundamental research priority .
The management of animal and plant diseases involves important political and economic choices that are more contestable the more the science is uncertain. For example, early in the BSE crisis, there was considerable scientific uncertainty about whether the prion could transmit to humans, what were the routes and probability of transmission and the likely extent of mortality. Many persistent, food-borne, public health diseases such as E. coli 0157 are a function of complex, multi-causal relationships operating across food chains . Such uncertainty and indeterminacy demand both interdisciplinary framings in research and holistic governance approaches that can incorporate a broader range of evidence . In the past, policy-makers attempting to deal with disease and the contention it causes have taken a narrow scientific approach, sometimes with disastrous consequences. These experiences have led the government to signal its desire to take a more holistic approach. In the 2004 Animal Health and Welfare Strategy, Defra stated its aim to ‘make a lasting and continuous improvement in the health and welfare of kept animals while protecting the society, the economy and the environment from the effect of animal diseases’. Likewise, Defra's Plant Health Strategy (2005) broadened the objectives of plant health to include preserving the natural environment for recreation and protecting the country's natural heritage and ecosystems.
At the same time, policy-makers are beginning to recognize the benefits of a broader range of expertise in decision-making . There has been a drive to incorporate social science into policy to complement the more established sources of natural science advice. Defra has always been a heavy user of science, but the role for social science has been almost non-existent beyond the narrowly defined economic and legal advice. Traditions of social science research in this field are much weaker than natural science traditions. With the exception of economic analyses of disease control and political science accounts of policy-making, social scientific research into the management and impact of infectious plant and animal diseases has been marginal [54,55]. The lack of conceptual frameworks for analysing disease as an economic or politico-social phenomenon has been blamed on the tendency for veterinarians to claim animal health as their field of expertise . There is also an increasing demand for stakeholder engagement with the policy process. For the international regulation of plant health, arguments have been made that the full knowledge base should be called on, involving a broader stakeholder community than regulatory scientists and policy-makers . A role here for social scientists may be to provide robust tools for stakeholder identification and analysis to enable effective participation in disease management.
A 2006 report by Defra's Science Advisory Council identified the various potential contributions of social science evidence including: setting strategic direction identifying policy need (i.e. key needs and drivers) providing evidence on the likely impact of policy changes policy implementation (assessing how to engage people) and policy evaluation (evaluating the impacts of policies once implemented) . Moreover, the Science Advisory Council identified examples of ‘big social science challenges’ central to Defra's main policy objectives, including: combating and adapting to climate change promoting customer-focused sustainable farming managing food/farming/environmental risk events while avoiding panic and changing stakeholder behaviour in relation to biosecurity . While recognizing that social issues are integral to current policy objectives and that social scientists can provide important evidence for policy formulation, the Science Advisory Council also acknowledged that a rigid separation of natural and social science was not conducive to effective policy-making. The report argued against an ‘end of pipe’ role for social science, whereby it exists solely to make natural scientific developments more publicly acceptable. Instead, the Science Advisory Council suggested that ‘Social science can be relevant and useful to Defra in clarifying and refining the processes through which natural scientific evidence is itself generated and interpreted. In particular, it can assist in making more robust the shaping, framing and prioritizing of scientific research, as well as the analysis and policy interpretation of uncertainties, divergent views and gaps in knowledge’ . Defra's own 10 year Forward Look recognized the inter-relationship between scientific developments and societal reactions, and the role of interdisciplinarity in managing this inter-relationship, stating that ‘Mixed and variable public attitudes to the roles and applications of science and technology will remain a major driver for our science policy for the foreseeable future. This will be shaped by broader social trends (e.g. in attitudes to risk, ethical and privacy issues) coupled with increasing aspirations towards public accountability and democratic control of the direction of development of science and technology’ [59,60].
True interdisciplinarity means not only that scientists and social scientists work together but that both parties have a role to play in problem formulation, strategy formation and problem-solving. This requires a willingness on the part of each to familiarize themselves with the others' scientific literature and vocabulary so that a meaningful exchange can occur. Collaboration with the social sciences can bring different perspectives and methodologies to help reframe problems, or indeed reveal multiple or disputed understandings and thus expose diverse possibilities and alternative meanings . In the context of infectious disease, this means challenging the artificial barriers that are created by governmental institutions and research cultures, including the divisions between plant and animal diseases, between diseases that affect agricultural production and those that do not, and between endemic and exotic diseases. Transcending the social/natural science divide thus throws open the field of inquiry and the range of possible solutions. Inevitably, therefore, there are diverse approaches to interdisciplinary collaboration . The papers in this Theme Issue illustrate the range of possible ways for natural and social scientists to work together.
7. Contents of this issue
This Theme Issue sees the pairing of many different disciplines in a set of papers that address many of the most pressing issues in animal and plant disease management. The papers by Woods , Enticott et al.  and Potter et al.  demonstrate the value of introducing historical perspectives to contemporary problems. In Woods' paper, the history of animal disease management is traced in order to improve our understanding of contemporary disease control policy, its determinants and its deficiencies. Importantly, it demonstrates the limitations of the sciences to provide solutions to problems that have an inherently political and economic character. Enticott et al.  make a complementary argument about the changing use of disease expertise as the privatization of the veterinary profession leads to a weakened capacity for state intervention in disease control. Potter et al.  adopt a rather different approach to historical data, by using models of the Dutch elm disease epidemic of the 1970s to understand the current P. ramorum outbreak both in terms of its likely epidemiology and the social and economic effects that a large-scale tree disease outbreak will have. The paper highlights the relationship between scientific information and government's capacity to respond, a theme which also occurs in the analysis of endemic livestock diseases by Carslake et al. . The latter paper brings together a scientific analysis of the differing threats posed by a range of endemic cattle diseases with a political model of governance options, to show that policy responses are not always appropriate or proportional to disease risk. Together, these papers offer a critique of prevailing approaches to disease control that fail to take adequate account of the full range of scientific knowledge available.
The inter-relationships between government regulation, industry and trade, and their effects on disease, are further developed by Chandler et al.  who explore the role of biopesticides within an Integrated Pest Management approach, and consider the opportunities and limitations caused by public demand for alternative, non-chemical pest control and burdensome regulations developed primarily to deal with chemical pesticides.
The communication of risk to the public is a crucial element of any disease control strategy and the effective communication of complex information is explored in three papers in this issue. Strachan et al.  marry an epidemiological assessment of E. coli 0157 risk with a sociological approach that uncovers public perceptions of risk. By combining the two, the paper increases our understanding of the correspondence between disease risk and disease incidence. Quine et al.  study the epidemiology of Lyme disease in order to integrate scientific knowledge of the disease with models of risk communication. Their paper looks for ways to prevent disease spread without disproportionate adverse effects on the use of the countryside for work and leisure. Fish et al.  take the issue of risk assessment for a range of diseases and pathogens (FMD, avian influenza and cryptosporidiosis) and develop a unifying framework to explain how scientific uncertainty across the sciences about disease spread can be incorporated into decisions about control measures.
The last two papers of the issue consider the future of disease, using predictive models to extrapolate future trends. Mills et al.  integrate natural and social science perspectives on risk to compare control strategies for P. ramorum and Mushroom Virus X, two plant diseases with the potential to impact seriously on the horticultural sector. Woolhouse  reviews methods of predicting the future of animal diseases such as BSE and avian influenza as well as the emergence of novel pathogens. The paper discusses the tendency for modellers to focus on particular drivers of change (such as global warming) to the detriment of other potentially important social factors such as civil disruption. Ultimately, then, each paper in this issue illuminates a part of the complex context in which disease outbreaks occur and are managed, and demonstrates the value of bringing multiple perspectives to bear on this inherently interdisciplinary problem.