An integrated model for pre- and post-harvest aflatoxin contamination in maize.

Aflatoxin contamination caused by colonization of maize by Aspergillus flavus continues to pose a major human and livestock health hazard in the food chain. Increasing attention has been focused on the development of models to predict risk and to identify effective intervention strategies. Most risk prediction models have focused on elucidating weather and site variables on the pre-harvest dynamics of A. flavus growth and aflatoxin production. However fungal growth and toxin accumulation continue to occur after harvest, especially in countries where storage conditions are limited by logistical and cost constraints. In this paper, building on previous work, we introduce and test an integrated meteorology-driven epidemiological model that covers the entire supply chain from planting to delivery. We parameterise the model using approximate Bayesian computation with monthly time-series data over six years for contamination levels of aflatoxin in daily shipments received from up to three sourcing regions at a high-volume maize processing plant in South Central India. The time series for aflatoxin levels from the parameterised model successfully replicated the overall profile, scale and variance of the historical aflatoxin datasets used for fitting and validation. We use the model to illustrate the dynamics of A. flavus growth and aflatoxin production during the pre- and post-harvest phases in different sourcing regions, in short-term predictions to inform decision making about sourcing supplies and to compare intervention strategies to reduce the risks of aflatoxin contamination.

An epidemiological framework for modelling fungicide dynamics and control.

Defining appropriate policies for controlling the spread of fungal disease in agricultural landscapes requires appropriate theoretical models. Most existing models for the fungicidal control of plant diseases do not explicitly include the dynamics of the fungicide itself, nor do they consider the impact of infection occurring during the host growth phase. We introduce a modelling framework for fungicide application that allows us to consider how "explicit" modelling of fungicide dynamics affects the invasion and persistence of plant pathogens. Specifically, we show that "explicit" models exhibit bistability zones for values of the basic reproductive number (R0) less than one within which the invasion and persistence threshold depends on the initial infection levels. This is in contrast to classical models where invasion and persistence thresholds are solely dependent on R0. In addition if initial infection occurs during the growth phase then an additional "invasion zone" can exist for even smaller values of R0. Within this region the system will experience an epidemic that is not able to persist. We further show that ideal fungicides with high levels of effectiveness, low rates of application and low rates of decay lead to the existence of these bistability zones. The results are robust to the inclusion of demographic stochasticity.

How does abundance scale with body size in coupled size-structured food webs?

1. Widely observed macro-ecological patterns in log abundance vs. log body mass of organisms can be explained by simple scaling theory based on food (energy) availability across a spectrum of body sizes. The theory predicts that when food availability falls with body size (as in most aquatic food webs where larger predators eat smaller prey), the scaling between log N vs. log m is steeper than when organisms of different sizes compete for a shared unstructured resource (e.g. autotrophs, herbivores and detritivores; hereafter dubbed 'detritivores'). 2. In real communities, the mix of feeding characteristics gives rise to complex food webs. Such complexities make empirical tests of scaling predictions prone to error if: (i) the data are not disaggregated in accordance with the assumptions of the theory being tested, or (ii) the theory does not account for all of the trophic interactions within and across the communities sampled. 3. We disaggregated whole community data collected in the North Sea into predator and detritivore components and report slopes of log abundance vs. log body mass relationships. Observed slopes for fish and epifaunal predator communities (-1.2 to -2.25) were significantly steeper than those for infaunal detritivore communities (-0.56 to -0.87). 4. We present a model describing the dynamics of coupled size spectra, to explain how coupling of predator and detritivore communities affects the scaling of log N vs. log m. The model captures the trophic interactions and recycling of material that occur in many aquatic ecosystems. 5. Our simulations demonstrate that the biological processes underlying growth and mortality in the two distinct size spectra lead to patterns consistent with data. Slopes of log N vs. log m were steeper and growth rates faster for predators compared to detritivores. Size spectra were truncated when primary production was too low for predators and when detritivores experienced predation pressure. 6. The approach also allows us to assess the effects of external sources of mortality (e.g. harvesting). Removal of large predators resulted in steeper predator spectra and increases in their prey (small fish and detritivores). The model predictions are remarkably consistent with observed patterns of exploited ecosystems.