Food Security: The Role of Biological Modelling

Nicole Augustin (University of Bath)

Fish accounts for a substantial portion of the global population's intake of animal protein. Estimating fish abundance is required for stock management but survey data on fish abundance is not always available.  Fishery catch data offer a rich potential source of information for management, if modelling can separate out the effects of fishing effort, species behaviour and population abundance. Here we model catch data from the blue ling fishering industry off the northwest coast of Scotland, using Generalized Additive Mixed Models with a space time interaction represented via a novel tensor product of a soap film smooth of space with a penalized regression spline of time. The use of soap film smoothers avoids imposing correspondences between spatially adjacent areas that are in fact separated by the stock boundary.  The blue ling data poses several challenges for model checking and validation due to its unbalanced, spatially clustered and preferential nature. After model selection, checking and validation there is evidence for increasing relative blue ling abundance from 2000-2010 in some spatial locations.

Nik Cunniffe (University of Cambridge)

Plant pathogens reduce yield, often greatly, and are significant threats to food security.  Effective control is therefore very important. I describe recent work using stochastic spatial models to optimize control strategies for plant diseases, initially focusing on citrus pathogens.  The citrus industries in Florida and Brazil - two of the largest production regions in the world - have had to deal with citrus canker for a number of years. However, more recently, citrus greening (huanglongbing) has invaded these areas, leading to significant losses. I show how spatial individual-based models can be fitted to the spread of both pathogens, and how fitted models can be used to optimize cultural disease controls. I also show how the same framework can be used to scale-up from experimental data on Bahia bark scaling, a citrus disease for which the etiology - and even the causal agent - is in fact unknown. I will then change tack, and present work concentrating on role of arthropods in plant-pathogen interactions. In particular, I will describe initial modelling work showing how the preference of individual insect vectors for infested vs. non-infected plants can affect disease spread, and how this has different consequences for persistently and non-persistently transmitted diseases. I will also present results of a population genetics model that tracks how similar decisions made by plant pollinators can interact with plant disease resistance. As time permits, I will conclude by introducing a large-scale spatial stochastic meta-population model for landscape-scale spread, as has recently been used for a number of pests and pathogens. The application I will present will be modelling the spread and control of the tree disease sudden oak death in Californian forests, but the models can also be applied to crop plants, as will be shown via an application to sampling presented as joint work with Dr Stephen Parnell).