Modelling techniques are needed that allow prediction of how many animals occur, and where, in mapped environments at specified times in the future. Models of this type are constructed in the Individual Based Modellers’ group at the University of Reading (https://ibmreading.wordpress.com/about/
). Each individual in these models starts life as a newborn, and then grows and when big enough reproduces, depending on the availability of food and environmental temperature in the mapped environment. Allocation of energy to maintenance, growth and/or breeding is guided by the individual’s energy budget. The way individuals feed, interact, mate and care for offspring is modelled, and the population is simulated in the mapped environment. Interest then centres on what happens to the population: where are the animals, and how many are there. Existing models are available for animals from earthworms to elephants, but the models are adaptable for use with other species. Existing applications include management of nature reserves, assessment of the environmental impacts of wind farms and highways, and assessment of the effects on non-target organisms of new chemicals for the control of agricultural pests. In your PhD you will adapt an existing model and obtain data to parameterise and validate the model for a species and environment of your choice. Then you will investigate the predictions of the model for what happens to the population. Some existing models are available on request from [email protected]
. To run them you need to install free NetLogo software.
To complete a PhD it is necessary (i) to research the behavioural and physiological ecology of the chosen species so that you can make needed changes to an existing model; ii) to obtain data sets with which to validate the model. These data sets should include population size over time, together with maps showing over time where the animals were found, and where the sources of food and water were. We can help obtain satellite images if required to map the vegetation in the study area and so find the quality and availability of plant food using the Modis satellite series, which has been used for vegetation mapping since 2000 and provides a spatial resolution of 250m with a temporal resolution of 16 days; iii) to digitise these maps and data sets into formats suitable for input to the model; iv) to construct, parameterise and run the model for the study species in the environment in which it was studied; and v) to assess the trustworthiness of the model by comparing its outputs to the data obtained for validation.
To link the population outputs to the principal drivers, i.e., food supply and environmental factors such as temperature, each individual in the model has to have its own energy budget. This is accomplished using a method of modelling individual energy budgets developed at Reading. Modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is a shortfall, the priorities are maintenance and then growth/reproduction until reserves fall to a critical threshold below which all is allocated to maintenance. Thus individuals fail to reproduce or die of starvation when their energy reserves are exhausted.
Successful Individual Based Models tread a delicate line between simplicity and complexity, and their calibration and evaluation are challenging. However, much progress is being made using a new technique, Approximate Bayesian Computation (ABC), which calibrates models and compares how well different possible models fit the available data. By careful use of ABC model structure can be optimised. ABC is a technique partly developed at Reading http://www.sciencedirect.com/science/article/pii/S0304380015003750
You will be based in Biological Sciences, where you can work alongside others making similar models. The satellite imaging will be supervised by Tristan Quaife http://www.met.reading.ac.uk/userpages/db903833.php
Student profile: We encourage applications from all biological disciplines, particularly if you have numerical or computational interests. We will provide training in ecology, use of satellite data and computer programming as needed. Please provide an indication of the data sets available for validation of the model of your chosen species (see (ii), above) with your application.
Cartwright, S. J., Bowgen, K. M., Collop, C., Hyder, K., Nabe-Nielsen, J., Stafford, R., Stillman, R. A., Thorpe, R. B. and Sibly, R. M. (2016) Communicating complex ecological models to non-scientist end users. Ecological Modelling, 338. pp. 51-59.
van der Vaart, E., Johnston, A.S.A., Sibly, R.M. 2016. Predicting how many animals will be where: How to build, calibrate and evaluate individual-based models. Ecological Modelling, 326, 113-123.
Johnston, A. S. .A., Sibly, R., Hodson, M. E., Alvarez, T. and Thorbek, P. (2015) Effects of agricultural management practices on earthworm populations and crop yield: validation and application of a mechanistic modelling approach. Journal of Applied Ecology, 52, 1334-1342.
van der Vaart, E., Beaumont, M.A., Johnston, A.S.A., Sibly, R.M. 2015. Calibration and evaluation of individual-based models using Approximate Bayesian Computation. Ecological Modelling, 312, 182-190.
Johnston, A.S.A., Holmstrup, M., Hodson, M.E., Thorbek, P., Alvarez, T., Sibly, R.M. (2014). Earthworm distribution and abundance predicted by a process-based model. Applied Soil Ecology, 84, 112-123.
Nabe-Nielsen, J., Sibly, R.M., Tougaard, J., Teilmann, J. & Sveegaard, S. (2014). Effects of noise and by-catch on a Danish harbour porpoise population. Ecological Modelling, 272, 242-251.
Liu, C., Bednarska, A., Sibly, R.M., Murfitt, R.C., Edwards, P., Thorbek, P. (2014).Incorporating toxicokinetics into an individual-based model for more realistic pesticide exposure estimates: A case study of the wood mouse. Ecological Modelling, 280, 30-39.
Johnston, A.S.A., Hodson, M.E., Thorbek, P., Alvarez, T., Sibly, R.M. (2014). An energy-budget agent based model of earthworm populations and its application to study the effects of pesticides. Ecological Modelling, 280: 5-17.
Kulakowska, K.A., Kulakowski, T.M. , Inglis , I.R., Smith, G.C., Haynes, P.J., Prosser, P., Thorbek, P., Sibly, R.M. (2014). Using an individual-based model to select among alternative foraging strategies of woodpigeons: data support a memory-based model with a flocking mechanism. Ecological Modelling, 280: 89-101.
Dalkvist, T., Sibly, R.M., Topping, C.J. (2013). Landscape structure mediates the effects of a stressor on field vole populations. Landscap Ecology, 28:1961-1974. DOI 10.1007/s10980-013-9932-7