Inference of ecological and environmental models

Ecological and environmental models are continually evolving. Within fisheries research , individual movement, environmental drivers, and interspecific interactions are key areas of interest stimulating the development of new and complex modelling efforts. Individual-based models (IBMs) are one example of such model in which individual animals interact with one another and the landscape in which they live, with population metrics emerging from the actions of collective individuals. When used in spatially-explicit landscapes IBMs can show how populations are expected to change over time in response to management actions, and have therefore been shown to be effective management tools in many systems. For instance, IBMs are being used to design strategies for the conservation and exploitation of fisheries, and for assessing the effects on populations of major construction projects and novel agricultural chemicals. However, good understanding of fits to data and associated uncertainty are needed before such models can be used to support decision making.

Hence, there is urgent need to improve methods of calibrating complex multiparameter models: existing methods are too slow, and not always accurate. This project aims to improve the best existing method: Approximate Bayesian Computation, ABC. ABC is currently being used for statistical inference in a diverse range of applications in ecology, evolution and more widely, including for example: models of elephants in Amboseli; mackerel in the North East Atlantic; local butterfly populations; but also evolution of pathogens; social network analysis; and statistical physics (see Didelot et al. 2011; Prangle et al. 2016; van der Vaart et al. 2016). In most of these cases the challenges of parameter estimation and model comparison are both of importance, but implementation can prove computationally expensive. This project aims to improve ABC methods and apply them in collaboration with environmental researchers, to help them in fitting models to data. Initial focus will be on IBMs developed for fisheries management by Cefas, the UK governments marine and freshwater science experts, https://www.cefas.co.uk/.

ABC compares model outputs with data and is particularly useful for statistical inference where the model is only available as a computer simulator such as an IBM. ABC is a relatively new field of research, and is a hot topic in statistics and several applied fields (Beaumont 2010). There are many open problems in this area, some of which lie at the heart of this project, including:

ABC for high-dimensional parameter spaces. IBMs often have more than 10 parameters that have to be estimated by fitting the model to data: more than in many current applications of ABC.

ABC for computationally expensive simulators. Some IBMs take several minutes to complete a run. This is a problem because existing ABC methods require thousands of runs to obtain reliable results.

This project will develop new methods to address these issues, driven by the need for accurate fisheries ecological models to guide fisheries management.