A proposal to use plant demographic data to assess potential weed biological control agents impacts on non-target plant populations

Weed biocontrol programs aim to reduce the spread and population growth rate of the target plant while stabilizing or increasing populations of those native species considered under threat by invasive plants. This goal is not unique to weed biocontrol but applies to all other invasive plant management techniques, though such information is rarely collected. Without this information, success of management interventions can be ambiguous, and regulatory agencies, the public, policy makers, funders and land managers cannot be held accountable for chosen treatments. A fundamental reform, including use of demographic studies and long-term assessments, are essential to guide weed biocontrol programs. We propose to add use of plant demography (an assessment of how environmental factors and ecological interactions, for example competition, disease or herbivory, may affect plant populations by altering survival, growth, development and reproductive rates of plant individuals) during host specificity risk assessments of potential biological control agents. Demographic models can refine assessments of potential impacts for those plant species that experience some feeding or larval development during host specificity testing. Our proposed approach to focus on impact on plant demography instead of attack on plant individuals is useful in appropriately gauging threats potential weed biocontrol agents may pose to non-target species after field release.     

The distribution of eggs per host in a herbivorous insect--intersection of oviposition, dispersal and population dynamics

1. The dynamics of parasitic organisms depend critically upon the frequency distribution of parasite individuals per host. However, the processes giving rise to this frequency distribution have rarely been modelled and tested for organisms with complex host selection behaviour. 2. In this study Microrhopala vittata, a chrysomelid beetle, was used to investigate how oviposition behaviour, movement and density of host plants interact in shaping the frequency distribution of egg clusters per host in the field. 3. Enclosures were stocked with two different host species and different beetle densities and various stochastic process models were fitted to egg cluster count data obtained from these enclosures. The different models were derived considering different scenarios, in particular whether or not plant density limits oviposition rate, whether or not ovipositing females actively seek out the most attractive plant within their perception radius and whether a female's oviposition rate is determined by plant intrinsic factors, the plant's egg cluster load or the surrounding beetle density. 4. The model parameters fitted to cage data were used to describe the frequency distribution of egg cluster counts obtained in a release experiment in the field. A total of 220 beetle pairs were released at five locations in a field where this beetle was not observed previously. Each release point was at a border between the two host species. 5. One model predicted for the preferred host species the egg cluster count frequencies in the field from parameters estimated in the cages. This model assumed that egg clusters present on a plant increased subsequent oviposition on this plant. All other models could not describe the distribution of egg cluster counts for either of the two host species. 6. The results suggest that females seek out attractive hosts actively and the attractiveness of a plant increases with its egg cluster load. This behaviour creates a frequency distribution of egg clusters per host that depends only on beetle density but not on plant density. This conclusion has important implications for modelling insect-plant interactions.     

Management of corn leafhopper (Homoptera: Cicadellidae) and corn stunt disease in sweet corn using reflective mulch

Plastic reflective mulches significantly reduced populations of corn leafhopper, Dalbulus maidis (DeLong & Wolcott), adults and the incidence of corn stunt disease caused by Spiroplasma kunkelii (CSS) in late planted sweet corn (Zea mays L.). The reflective mulches were more effective than were either foliar or soil applied insecticides in managing both the leafhopper and the pathogen it transmits. Yields of marketable ears were 1.5 to 2 times greater in reflective mulch plots than from fallow plots. This was due to larger ears (individual ear weight and length) rather than an increase in the number of ears. The use of reflective mulches provides an alternative strategy to insecticides in the management of both D. maidis and corn stunt disease. Such a strategy may prove useful to growers in Latin America and to limited resource growers and organic growers in the United States who wish to grow corn without the use of insecticides.     

Using Peer Discussion Facilitated by Clicker Questions in an Informal Education Setting: Enhancing Farmer Learning of Science

Blueberry growers in Maine attend annual Cooperative Extension presentations given by university faculty members. These presentations cover topics, such as, how to prevent plant disease and monitor for insect pests. In 2012, in order to make the sessions more interactive and promote learning, clicker questions and peer discussion were incorporated into the presentations. Similar to what has been shown at the undergraduate level, after peer discussion, more blueberry growers gave correct answers to multiple-choice questions than when answering independently. Furthermore, because blueberry growers are characterized by diverse levels of education, experience in the field etc., we were able to determine whether demographic factors were associated with changes in performance after peer discussion. Taken together, our results suggest that clicker questions and peer discussion work equally well with adults from a variety of demographic backgrounds without disadvantaging a subset of the population and provide an important learning opportunity to the least formally educated members. Our results also indicate that clicker questions with peer discussion were viewed as a positive addition to university-related informal science education sessions.     

How host plant variability influences the advantages to learning: a theoretical model for oviposition behaviour in Lepidoptera

Learning can allow individuals to increase their fitness in particular environments. The advantage to learning depends on the predictability of the environment and the extent to which animals can adjust their behaviour. Earlier general models have investigated when environmental predictability might favour the evolution of learning in foraging animals. Here, we construct a theoretical model that predicts the advantages to learning using a specific biological example: oviposition in the Lepidoptera. Our model includes environmental and behavioural complexities relevant to host selection in these insects and tests whether the predictions of the general models still hold. Our results demonstrate how the advantage of learning is maximised when within-generation variability is minimised (the local environment consists mainly of a single host plant species) and between-generation variability is maximised (different host plant species are the most common in different generations). We discuss how our results: (a) can be applied to recent empirical work in different lepidopteran species and (b) predict an important role of learning in lepidopteran agricultural pests.     

Imitation dynamics predict vaccinating behaviour

There exists an interplay between vaccine coverage, disease prevalence and the vaccinating behaviour of individuals. Moreover, because of herd immunity, there is also a strategic interaction between individuals when they are deciding whether or not to vaccinate, because the probability that an individual becomes infected depends upon how many other individuals are vaccinated. To understand this potentially complex interplay, a game dynamic model is developed in which individuals adopt strategies according to an imitation dynamic (a learning process), and base vaccination decisions on disease prevalence and perceived risks of vaccines and disease. The model predicts that oscillations in vaccine uptake are more likely in populations where individuals imitate others more readily or where vaccinating behaviour is more sensitive to changes in disease prevalence. Oscillations are also more likely when the perceived risk of vaccines is high. The model reproduces salient features of the time evolution of vaccine uptake and disease prevalence during the whole-cell pertussis vaccine scare in England and Wales during the 1970s. This suggests that using game theoretical models to predict, and even manage, the population dynamics of vaccinating behaviour may be feasible.     

Integrating natural and social science perspectives on plant disease risk, management and policy formulation

Plant diseases threaten both food security and the botanical diversity of natural ecosystems. Substantial research effort is focused on pathogen detection and control, with detailed risk management available for many plant diseases. Risk can be assessed using analytical techniques that account for disease pressure both spatially and temporally. We suggest that such technical assessments of disease risk may not provide an adequate guide to the strategies undertaken by growers and government to manage plant disease. Instead, risk-management strategies need to account more fully for intuitive and normative responses that act to balance conflicting interests between stakeholder organizations concerned with plant diseases within the managed and natural environments. Modes of effective engagement between policy makers and stakeholders are explored in the paper, together with an assessment of such engagement in two case studies of contemporary non-indigenous diseases in one food and in one non-food sector. Finally, a model is proposed for greater integration of stakeholders in policy decisions.     

Fees or refuges: which is better for the sustainable management of insect resistance to transgenic Bt corn?

The evolution of resistance in insect pests will imperil the efficiency of transgenic insect-resistant crops. The currently advised strategy to delay resistance evolution is to plant non-toxic crops (refuges) in close proximity to plants engineered to express the toxic protein of the bacterium Bacillus thuringiensis (Bt). We seek answers to the question of how to induce growers to plant non-toxic crops. A first strategy, applied in the United States, is to require Bt growers to plant non-Bt refuges and control their compliance with requirements. We suggest that an alternative strategy is to make Bt seed more expensive by instituting a user fee, and we compare both strategies by integrating economic processes into a spatially explicit, population genetics model. Our results indicate that although both strategies may allow the sustainable management of the common pool of Bt-susceptibility alleles in pest populations, for the European corn borer (Ostrinia nubilalis) one of the most serious pests in the US corn belt, the fee strategy is less efficient than refuge requirements.     

The dynamical implications of human behaviour on a social-ecological harvesting model

The dynamic aspects of human harvesting behaviour are often overlooked in resource management, such that models often neglect the complexities of dynamic human effort. Some researchers have recognized this, and a recent push has been made to understand how human behaviour and ecological systems interact through dynamic social-ecological systems. Here, we use a recent example of a social-ecological dynamical systems model to investigate the relationship between harvesting behaviour and the dynamics and stability of a harvested resource, and search for general rules in how relatively simple human behaviours can either stabilize or destabilize resource dynamics and yield. Our results suggest that weak to moderate behavioural and effort responses tend to stabilize dynamics by decreasing return times to equilibria or reducing the magnitude of cycles; however, relatively strong human impacts can readily lead to human-driven cycles, chaos, long transients and alternate states. Importantly, we further show that human-driven cycles are characteristically different from typical resource-driven cycles and, therefore, may be differentiated in real ecosystems. Given the potentially dramatic implications of harvesting on resource dynamics, it becomes critical to better understand how human behaviour determines harvesting effort through dynamic social-ecological systems.     

The evolutionary basis of human social learning

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.     

Erratic flu vaccination emerges from short-sighted behavior in contact networks

The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models. When individuals make decisions based on past epidemics, we find that individuals with many contacts vaccinate, whereas individuals with few contacts do not. However, the threshold number of contacts above which to vaccinate is highly dependent on the overall network structure of the population and has the potential to oscillate more wildly than has been observed empirically. When we increase the number of prior seasons that individuals recall when making vaccination decisions, behavior and thus disease dynamics become less variable. For some networks, we also find that higher flu transmission rates may, counterintuitively, lead to lower (vaccine-mediated) disease prevalence. Our work demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact patterns, and behavior.     

Large-scale management of insect resistance to transgenic cotton in Arizona: can transgenic insecticidal crops be sustained?

A major challenge for agriculture is management of insect resistance to toxins from Bacillus thuringiensis (Bt) produced by transgenic crops. Here we describe how a large-scale program is being developed in Arizona for management of resistance to Bt cotton in the pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae), and other insect pests of cotton. Financial support from growers makes this program possible. Collaboration between the Arizona Cotton Research and Protection Council, the University of Arizona, and government agencies has led to development of resistance management guidelines, a remedial action plan, and tools for monitoring compliance with the proposed guidelines. Direct participation in development of resistance management policies is a strong incentive for growers to invest in resistance management research. However, more research, regularly updated regulations, and increased collaboration between stakeholders are urgently needed to maintain efficacy of Bt toxins in transgenic crops.     

Early detection of white mold caused by <Emphasis Type="Italic">Sclerotinia sclerotiorum</Emphasis> in potato fields using real-time PCR

Potato white mold caused by Sclerotinia sclerotiorum is an important plant disease occurring in many potato-producing areas worldwide. This disease causes considerable yield losses in a large number of potato fields of Hamedan province, Iran. Although several non-chemical methods have been suggested against the pathogen, application of chemicals is considered a major method employed by the growers in Hamedan fields leading to considerable pollution of underground water. Therefore, it is necessary to find techniques to help growers make a more timely decision for chemical application. In this research, a pair of PCR primers was designed according to the nucleotide sequence of a region of DNA amplified by the primer M13. This primer set amplified a 352-bp fragment from all isolates of S. sclerotiorum collected from Hamedan, Iran, and Hangzhou, China. However, this situation was not observed in other tested fungi. A real-time PCR assay was conducted to detect early infection of the pathogen on potato petals. This real-time PCR assay is suggested for growers to make timely decisions on chemical application.     

Should we expect population thresholds for wildlife disease?

Host population thresholds for the invasion or persistence of infectious disease are core concepts of disease ecology and underlie disease control policies based on culling and vaccination. However, empirical evidence for these thresholds in wildlife populations has been sparse, although recent studies have begun to address this gap. Here, we review the theoretical bases and empirical evidence for disease thresholds in wildlife. We see that, by their nature, these thresholds are rarely abrupt and always difficult to measure, and important facets of wildlife ecology are neglected by current theories. Empirical studies seeking to identify disease thresholds in wildlife encounter recurring obstacles of small sample sizes and confounding factors. Disease control policies based solely on threshold targets are rarely warranted, but management to reduce abundance of susceptible hosts can be effective.     

Individual‐based ecology of coastal birds

Conservation objectives for non‐breeding coastal birds (shorebirds and wildfowl) are determined from their population size at coastal sites. To advise coastal managers, models must predict quantitatively the effects of environmental change on population size or the demographic rates (mortality and reproduction) that determine it. As habitat association models and depletion models are not able to do this, we developed an approach that has produced such predictions thereby enabling policy makers to make evidence‐based decisions. Our conceptual framework is individual‐based ecology, in which populations are viewed as having properties (e.g. size) that arise from the traits (e.g. behaviour, physiology) and interactions of their constituent individuals. The link between individuals and populations is made through individual‐based models (IBMs) that follow the fitness‐maximising decisions of individuals and predict population‐level consequences (e.g. mortality rate) from the fates of these individuals. Our first IBM was for oystercatchers Haematopus ostralegus and accurately predicted their density‐dependent mortality. Subsequently, IBMs were developed for several shorebird and wildfowl species at several European sites, and were shown to predict accurately overwinter mortality, and the foraging behaviour from which predictions are derived. They have been used to predict the effect on survival in coastal birds of sea level rise, habitat loss, wind farm development, shellfishing and human disturbance. This review emphasises the wider applicability of the approach, and identifies other systems to which it could be applied. We view the IBM approach as a very useful contribution to the general problem of how to advance ecology to the point where we can routinely make meaningful predictions of how populations respond to environmental change.     

Sham surgery trial controls: perspectives of patients and their relatives

this study reports on qualitative research conducted in the UK with people with Parkinson's Disease and their relatives on the subject of "sham surgery." It explores attitudes toward sham surgery and reasoning about hypothetical participation in a sham-controlled trial. Results showed that attitudes toward sham surgery may not necessarily predict trial participation behavior. A small majority of interviewees deemed sham surgery ethically acceptable with certain provisos, but hypothetical participation was driven primarily by disease severity and a lack of standard treatment options, with a preference for receiving the real surgery over sham. Ethical implications for patient equipoise and the autonomy of patients' research participation decisions are discussed.     

How Microbes Shape Their Communities? A Microbial Community Model Based on Functional Genes

Exploring the mechanisms of maintaining microbial community structure is important to understand biofilm development or microbiota dysbiosis. In this paper, we propose a functional gene-based composition prediction(FCP) model to predict the population structure composition within a microbial community. The model predicts the community composition well in both a low-complexity community as acid mine drainage(AMD) microbiota, and a complex community as human gut microbiota. Furthermore, we define community structure shaping(CSS) genes as functional genes crucial for shaping the microbial community. We have identified CSS genes in AMD and human gut microbiota samples with FCP model and find that CSS genes change with the conditions. Compared to essential genes for microbes, CSS genes are significantly enriched in the genes involved in mobile genetic elements, cell motility, and defense mechanisms, indicating that the functions of CSS genes are focused on communication and strategies in response to the environment factors. We further find that it is the minority, rather than the majority, which contributes to maintaining community structure. Compared to health control samples, we find that some functional genes associated with metabolism of amino acids, nucleotides, and lipopolysaccharide are more likely to be CSS genes in the disease group. CSS genes may help us to understand critical cellular processes and be useful in seeking addable gene circuitries to maintain artificial self-sustainable communities. Our study suggests that functional genes are important to the assembly of microbial communities.     

Seed fate and decision‐making processes in scatter‐hoarding rodents

A mechanistic understanding of seed movement and survival is important both for the development of theoretical models of plant population dynamics, spatial spread, and community assembly, and for the conservation and management of plant communities under global change. While models of wind‐borne seed dispersal have advanced rapidly over the past two decades, models for animal‐mediated dispersal have failed to make similar progress due to their dependence on interspecific interactions and complex, context‐dependent behaviours. In this review, we synthesize the literature on seed dispersal and consumption by scatter‐hoarding, granivorous rodents and outline a strategy for development of a general mechanistic seed‐fate model in these systems. Our review decomposes seed dispersal and survival into six distinct sub‐processes (exposure, harvest, allocation, preparation, placement, and recovery), and identifies nine intermediate (latent) variables that link physical state variables (e.g. seed and animal traits, habitat structure) to decisions regarding seed allocation to hoarding or consumption, cache placement and management, and deployment of radicle‐pruning or embryo excision behaviours. We also highlight specific areas where research on these intermediate relationships is needed to improve our mechanistic understanding of scatter‐hoarder behaviour. Finally, we outline a strategy to combine detailed studies on individual functional relationships with seed‐tracking experiments in an iterative, hierarchical Bayesian framework to construct, refine, and test mechanistic models for context‐dependent, scatter‐hoarder‐mediated seed fate.