Doctoral Programme on Marine Ecosystem Health and Conservation
 PhD Subject Catalogue Fifth Edition - 2014
Informing improved management of mixed fisheries through comparative modelling of fleet dynamics
PhD Code: MARES_14_15:
Mobility
  • Host institute 1: P4 - Galway Mayo Institute of Technology (GMIT)
  • Host institute 2: P18 - AZTI-Tecnalia
  • Host institute 3: Université Pierre et Marie Curie (UPMC)
Research fields:
  • T4 - Natural Resources: overexploitation, fisheries and aquaculture
Promotor(s):
  • Cóilín Minto
  • Jean-Marc Guarini
  • Dorleta Garcia (AZTI)
Contact Person and email: Cóilín Minto - coilin.minto@gmit.ie

Subject description
Managing fisheries is managing people –Hilborn (2007).
 
Recent policy imperatives have attracted more attention to resolving the tension between biological, economic and conservation objectives in fisheries; particularly, the ecosystem considerations of the Marine Strategy Framework Directive and the discard ban imposed in the reformed European Common Fisheries Policy. The European Marine Board’s strategy for sustainable harvest of seafood demands the investigation of policies, regulations and incentives in fisheries that will improve application of evidence-based and adaptive policy making (European Marine Board, 2013). This proposal aims to inform these policy imperatives through the provision of solid applied scientific advice.
 
Single species quota management applies to situations where a single fleet targets a single stock (Hilborn and Walters, 1992). Where multiple species are caught by multiple fleets, single species quotas often fail to achieve targeted levels of fishing mortality (Vinther et al., 2004; Ulrich et al., 2011). Multiple species caught in the same fishery along with fleet diversity serve to invalidate the assumption that once a single species quota is exhausted, fishing will cease on that species. As a result, catches often exceed quotas in mixed fisheries with resultant biological impacts, illegal landings and discarding at sea (Hentrich and Salomon, 2006; Hamon et al., 2007; Kraak et al., 2013). Mixed fisheries management is increasingly focussed on enabling the simultaneous achievement of conservation targets for multiple species caught in multiple fleets (Gerritsen et al., 2012). 
 
It is now recognized that understanding the dynamics and drivers of individual fishers and fleets, particularly the economic influences, is crucial to successful fisheries management (Tidd et al., 2012). This, coupled with the fact that most fisheries, particularly demersal, are mixed (Iriondo et al., 2012) requires that greater attention is paid to fleet dynamics, i.e., determining how and where a group of fishers will fish in response to biological and economic drivers.
 
The pressure exerted on different stocks is a product of the dynamics of effort allocation of different fleet segments responding to many biological, economic and regulatory drivers. As such, effort allocation is the central link between the commercial activity and the biological resources of mixed fisheries. Understanding how and where a fleet will fish is therefore central to developing effective long-term management plans.
 
At least three fleet dynamic effort allocation models are currently in use:
  • 1. Dynamic state variable models, which incorporate spatial information into effort allocation (Poos et al., 2010; Batsleer et al., 2013);
  • 2. Random utility models that model discrete choice based on a set of drivers (Hutton et al., 2004), and;
  • 3. Markov models, which model the transition probability between discrete states; Venables et al. (2009) successfully applied Markov models to the spatial dynamics of the Australian northern prawn fishery.
 
Comparing the predictive performance of at least these three methods is the first major component of the proposed PhD. Such an in-depth applied comparison has not been attempted previously. Additional deliverables are envisaged from application of these models to particular fleets operating in the transboundary mixed fisheries of the Celtic Sea; an area fished by vessels from all three countries partnering in this proposal. This component of the proposal extends successful PhD work at GMIT that modelled Markov fleet dynamics (Davie, 2013).
 
The applied utility of understanding fleet dynamics lies in the development of long-term management plans. These plans are based on large scale management strategy evaluations broadened to incorporate multiple species and fleets. The FLBEIA (Fisheries Library Bio-Economic Impact Assessment) package (Garcia et al., 2013; Jardim et al., 2013) is an exciting development toward these goals and greatly extends the capabilities to include multistock, multifleet and seasonal elements in a dynamic stochastic environment. The significant challenge of incorporating realistic fleet dynamics in mixed fisheries bioeconomic models is the second substantial component of the proposal. This component will focus on the comparative implementation of fast and reliable effort dynamics models in FLBEIA.
 
The student will divide their time between GMIT (Ireland), AZTI-Tecnalia (Spain) and UPMC (France) under the joint supervision of Dr Cóilín Minto (GMIT), Prof. Jean-Marc Guarini and Ms Dorleta Garcia (AZTI). During years 1 and 2, the student will spend most of their time in Ireland working on candidate fleet dynamic models with intermittent visits to AZTI and UPMC. Year 3 will focus on the implementation of selected models in FLBEIA when time will be divided between GMIT and AZTI.
 
References
  • Batsleer, J., Poos, J. J., Marchal, P., Vermard, Y., and Rijnsdorp, A. D. (2013). Mixed fisheries management: protecting the weakest link. Marine Ecology Progress Series, 479:177-190.
  • Davie, S. 2013. The drivers and dynamics of fisher behaviour in Irish fisheries. PhD thesis. Galway-Mayo Institute of Technology, 213 p.
  • European Marine Board.(2013). Navigating the Future IV. Position Paper 20. Ostend: European Marine Board.
  • Garcia, D., Urtizberea, A., Diez, G., Gil, J., and Marchal, P. (2013). Bio-economic management strategy evaluation of deepwater stocks using the flbeia model. Aquatic Living Resources, 26(04):365-379.
  • Gerritsen, H. D., Lordan, C., Minto, C., and Kraak, S. B. M. (2012). Spatial patterns in the retained catch composition of Irish demersal otter trawlers: High-resolution fisheries data as a management tool. Fisheries Research, 129:127-136.
  • Hamon, K., Ulrich, C., Hoff, A., and Kell, L. T. (2007). Evaluation of Management Strategies for the Mixed North Sea Roundfish Fisheries with the FLR Framework. In Oxley, L and Kulasiri, D, editor, Modsim 2007: International Congress on Modelling and Simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability, pages 2813-2819. Lincoln Univ; HEMA Consulting Pty Ltd; Modelling and Simulat Soc Australia & New Zealand (MSSANZ); Univ Canterbury; SGI; Lincoln Ventures Ltd; Hoare Res Software Ltd; IMACS; IEMSS; Environm Modelling & Software; IBM. ISBN 978-0-9758400-4-7.
  • Hentrich, S. and Salomon, M. (2006). Flexible management of fishing rights and a sustainable fisheries industry in europe. Marine Policy, 30(6):712-720.
  • Hilborn, R. (2007). Managing fisheries is managing people: what has been learned? Fish and Fisheries, 8(4):285-296.
  • Hilborn, R. and Walters, C. J. (1992). Quantitative Fisheries Stock Assessment: Choice, Dynamics, and Uncertainty. Chapman and Hall.
  • Hutton, T., Mardle, S., Pascoe, S., and Clark, R. (2004). Modelling fishing location choice within mixed fisheries: English North Sea beam trawlers in 2000 and 2001. ICES Journal of Marine Science, 61(8):1443-1452.
  • Iriondo, A., Garcia, D., Santurtun, M., Castro, J., Quincoces, I., Lehuta, S., Mahevas, S., Marchal, P., Tidd, A., and Ulrich, C. (2012). Managing mixed fisheries in the European Western Waters: Application of Fcube methodology. Fisheries Research, 134:6-16.
  • Jardim, E., Urtizberea, A., Motova, A., Osio, C., Ulrich, C., Millar, C., Mosqueira, I., Poos, J. J., Virtanen, J., Hamon, K., et al. (2013). Bioeconomic modelling applied to fisheries with r/flr/flbeia. STECF ISBN, pages 978-92.
  • Kraak, S. B. M., Bailey, N., Cardinale, M., Darby, C., De Oliveira, J. A. A., Eero, M., Graham, N., Holmes, S., Jakobsen, T., Kempf, A., Kirkegaard, E., Powell, J., Scott, R. D., Simmonds, E. J., Ulrich, C., Vanhee, W., and Vinther, M. (2013). Lessons for fisheries management from the EU cod recovery plan. Marine Policy, 37:200-213.
  • Poos, J. J., Bogaards, J. A., Quirijns, F. J., Gillis, D. M., and Rijnsdorp, A. D. (2010). Individual quotas, fishing effort allocation, and over-quota discarding in mixed fisheries. ICES Journal of Marine Science, 67(2):323-333.
  • Tidd, A. N., Hutton, T., Kell, L. T., and Blanchard, J. L. (2012). Dynamic prediction of effort reallocation in mixed fisheries. Fisheries Research, 125:243-253.
  • Ulrich, C., Reeves, S. A., Vermard, Y., Holmes, S. J., and Vanhee, W. (2011). Reconciling single-species TACs in the North Sea demersal fisheries using the Fcube mixed-fisheries advice framework. ICES Journal of Marine Science, 68(7):1535-1547.
  • Venables, W. N., Ellis, N., Punt, A. E., Dichmont, C. M., and Deng, R. A. (2009). A simulation strategy for fleet dynamics in australia's northern prawn fishery: effort allocation at two scales. ICES Journal of Marine Science: Journal du Conseil, 66(4):631-645.
  • Vinther, M., Reeves, S., and Patterson, K. (2004). From single-species advice to mixed-species management: taking the next step. ICES Journal of Marine Science, 61(8):1398-1409.
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Expected outcomes
The student will gain experience working with international institutes dedicated to the provision of management advice for fish stocks. Collaboration and engagement will occur with government institutes, fisheries Regional Advisory Councils to the European Commission, universities and experts at the cutting edge of fisheries science. The work will contribute directly to the development of mixed fisheries management plans and inform European fisheries policy implementation. The likelihood of adoption of research outcomes is enhanced by engaging directly with industry and regulatory stakeholders. The student will gain challenging and highly sought-after skills in statistical and econometric modelling and implementation in an applied setting directly linked to the objectives of MARES. 
In addition to other discoveries along the way, expected publications include: 
  • A manuscript on the application of the dynamic state variable model to selected fisheries;  
  • A high-impact comparative paper on the predictive performance of multiple models;  
  • A technical paper on implementation in FLBEIA, and; 
  • A comparative application to two or more mixed fisheries.


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