Modelling techniques are needed that allow prediction of how many animals occur, and where, in mapped environments at specified times in the future. Potential applications include management of nature reserves, assessment of the environmental impacts of building proposals including wind farms and highways, and assessment of the effects on non-target organisms of new chemicals for the control of agricultural pests. Models of this type have been constructed by Richard Sibly’s group at the University of Reading. 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: how are the animals distributed in their environment, and how many are there. The model is currently set up for earthworms, but is adaptable for use with mammals and other species. In your PhD you will adapt the 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. The existing model is available on request from [email protected]
. To run it you need to install free NetLogo software.
To make a PhD you will need (i) to research the behavioural ecology of your chosen species so that you can make needed changes to the existing model; ii) to obtain data sets with which to validate the model. These data sets should include population size and structure over time, together with maps showing over time where the animals were found, and where the sources of food and water were. We will help you obtain satellite images to map the vegetation in your study area and so find the quality and availability of plant food using the Landsat satellite series, which has been used for vegetation mapping since 1972 and provides a spatial resolution of 30m 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 your 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.
Models of the type you will use are called Agent Based Models (ABMs) and are the only way to investigate the population consequences of different management scenarios in mapped environments. ABMs are dynamical systems containing many autonomous interacting individuals, and are used where, broadly, the factors influencing the behaviour of individuals are known, but interest centres on what happens at the population level. Will the population increase or decrease? How fast will be the response? What are the effects of time-varying food supplies? 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. Here we will use a method of modelling individual energy budgets developed at Reading by Richard Sibly’s group http://www.reading.ac.uk/biologicalsciences/about/staff/r-m-sibly.aspx
. 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.
You will start off based in Biological Sciences, where you will produce an initial simple model working alongside others making similar models. The satellite imaging will be supervised by Tristan Quaife http://www.met.reading.ac.uk/users/users/1769
. 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 a description of the data sets available for validation of the model of your chosen species (see (ii), above) with your application.
Nabe-Nielsen, J., Sibly, R.M., Tougaard, J., Teilmann, J. & Sveegaard, S. (in press). Effects of noise and by-catch on a Danish harbour porpoise population. Ecological Modelling, in press.
Johnston, A.S.A., Hodson, M.E., Thorbek, P., Alvarez, T., Sibly, R.M. (in press). An energy-budget agent based model of earthworm populations and its application to study the effects of pesticides. Ecological Modelling, in press.
Kulakowska, K.A., Kulakowski, T.M. , Inglis , I.R., Smith, G.C., Haynes, P.J., Prosser, P., Thorbek, P., Sibly, R.M. (in press). 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, in press.
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
Okie, J.G. et al. (2013). Effects of allometry, production, and lifestyle on rates and limits of body size evolution. Proceedings of the Royal Society B-Biological Sciences. http://dx.doi.org/10.1098/rspb.2013.1007.
Bednarska, A., Edwards, P., Sibly, R.M., Thorbek, P. (2013). A toxicokinetic model for Thiamethoxam in rats: implications for higher-tier risk assessment. Ecotoxicology (in press) DOI 10.1007/s10646-013-1047-z
Liu, C., Sibly, R.M., Grimm, V., Thorbek, P. (2013). Linking pesticide exposure and spatial dynamics: an individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes. Ecological Modelling, 248, 92-102. Available at http://ac.els-cdn.com/S0304380012004826/1-s2.0-S0304380012004826-main.pdf?_tid=f1ff474e-2e3e-11e2-90e4-00000aacb35d&acdnat=1352886046_2b508d5445bcd4e1a69d0f5e66507253
Dalkvist, T., Sibly, R.M., and Topping, C.J., 2011. How predation and landscape fragmentation affect vole population dynamics. PLoS One 6, e22834. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0022834
Nabe-Nielsen. J., Sibly, R.M., Forchhammer, M.C., Forbes, V.E., Topping, C.J. (2010).
The effects of landscape modifications on the long-term persistence of animal populations. PloS ONE, 5, e8932. http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0008932
Quaife, T. and Lewis, P. (2010). Temporal Constraints on Linear BRDF Model Parameters. Ieee Transactions on Geoscience and Remote Sensing. 48: 2445-2450.
Quaife, T., et al. (2008) Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sensing of Environment.112: 1347–1364.
Sibly, R.M., Nabe-Nielsen. J., Forchhammer, M.C., Forbes, V.E., Topping, C.J. (2009). The effects of spatial and temporal heterogeneity on the population dynamics of four animal species in a Danish landscape. BMC Ecology, 9:18 doi:10.1186/1472-6785-9-18. http://www.biomedcentral.com/1472-6785/9/18
Sibly, R.M., Barker, D., Denham, M.C., Hone, J. Pagel, M. (2005) On the regulation of populations of Mammals, Birds, Fish and Insects. Science, 309, 607-610.