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University of Reading Featured PhD Programmes

Predicting the numbers and location of seabass for sustainable management

  • Full or part time
  • Application Deadline
    Monday, January 30, 2017
  • Competition Funded PhD Project (UK Students Only)
    Competition Funded PhD Project (UK Students Only)

Project Description

The European seabass (Dicentrarchus labrax) is a large slow growing and high value fish, that is an important target for both commercial and recreational fishers. There is large interannual variation in recruitment that is largely driven by environmental factors with seabass doing well in warmer years. In the North Sea, Irish Sea and English Channel, the spawning stock biomass (SSB) of bass is declining rapidly and zero catch has been proposed for 2017 by the International Council for the Exploration of the Sea (ICES - http://www.ices.dk), a global organization that develops science and advice to support the sustainable use of the oceans. To conserve the stock, various management measures have been imposed including seasonal closures, monthly limits for commercial fishers, and bag limits for anglers. Spatial individual based models (IBMs) have been developed for seabass that include growth, mortality, spawning, migration, settlement and exploitation (recreational & commercial), and these are being used to assess the impact of different management strategies.

Further work is needed to optimise the structure and calibration of the IBMs and to add in a new submodel representing the behaviour of both recreational and commercial fishers. Both of these represent significant challenges. IBMs are computer-based models that represent the location and state of all the individuals in the population in a spatially-explicit map of their environment. They synthesise all available information and are evaluated on their ability to match population data; in our case the data are in the form of fish surveys and records of the catches of fishers collected by ICES and Cefas (part of the UK government - https://www.cefas.co.uk/). Successful IBMs 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.

You will:

• Obtain relevant satellite data to construct maps of sea surface temperature in the sea areas for the years for which seabass survey and landing data are available

• Construct a new Fisher behaviour IBM for both commercial and recreational fishermen based on examples from other fisheries and link it to the biological model to investigate how the fishing decisions of fishers respond to management measures and to local availability of seabass

• Use long-term seabass survey and landings data and records of the fishing schedules of fishers to optimise the structure of the models using Approximate Bayesian Computation

• Use the models to explore the impact of different management strategies and provide outputs that can be used to support the management of seabass at a European level.

The impact of management measures on both biological sustainability and social dimension will be investigated and the potential tradeoffs assessed. Finally, results from the models will be used to inform decision-making through contributions to the relevant ICES working groups and UK advisory process. Development of these models represent significant scientific challenges, but have the potential to provide insight into the impact of management and improve the conservation of seabass.

Working environment and training opportunities: You will be cosupervised by Kieran Hyder and Robert Thorpe of Cefas, and Shovonlal Roy of University of Reading, and will work in a group of IBM modellers building and evaluating ecological Individual Based Models of species from earthworms to elephants, see https://ibmreading.wordpress.com/. One of the models is of mackerel, and this is also with Cefas. Your project offers the opportunity to learn modern techniques of remote sensing and practical ecological modelling and to deploy them to help the management of fish stocks. Model development and evaluation will use ABC, a technique partly developed at Reading http://www.sciencedirect.com/science/article/pii/S0304380015003750. You may attend in-house training courses on Python and ABC. There will be opportunities to travel and spend time at Cefas. Whilst at Cefas, you can develop a broad knowledge of marine policy and fisheries assessment, see at first-hand how science is used to underpin decision-making, and gain experience of the fisheries assessment process at ICES.

Student profile: We encourage applications from all relevant disciplines. We will provide training in ecology and computer programming as needed.

Further details can be found at http://www.met.reading.ac.uk/nercdtp/home/available/

Funding Notes

This project includes CASE sponsorship from Cefas. Further details can be found at View Website


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

How good is research at University of Reading in Biological Sciences?

FTE Category A staff submitted: 20.60

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