Predicting fisher response to competition for space and resources in a mixed demersal fishery

Understanding and modelling fleet dynamics and their response to spatial constraints is a prerequisite to anticipating the performance of marine ecosystem management plans. A major challenge for fisheries managers is to be able to anticipate how fishing effort is re-allocated following any permanent or seasonal closure of fishing grounds, given the competition for space with other active maritime sectors. In this study, a Random Utility Model (RUM) was applied to determine how fishing effort is allocated spatially and temporally by the French demersal mixed fleet fishing in the Eastern English Channel. The explanatory variables chosen were past effort i.e. experience or habit, previous catch to represent previous success, % of area occupied by spatial regulation, and by other competing maritime sectors. Results showed that fishers tended to adhere to past annual fishing practices, except the fleet targeting molluscs which exhibited within year behaviour influenced by seasonality. Furthermore, results indicated French and English scallop fishers share the same fishing grounds, and maritime traffic may impact on fishing decision. Finally, the model was validated by comparing predicted re-allocation of effort against observed effort, for which there was a close correlation.

[1]  Olivier Thébaud,et al.  Modeling fleet response in regulated fisheries: An agent-based approach , 2006, Math. Comput. Model..

[2]  E. Fulton,et al.  The role of behavioural flexibility in a whole of ecosystem model , 2013 .

[3]  Ray Hilborn,et al.  Fleet Dynamics and Individual Variation: Why Some People Catch More Fish than Others , 1985 .

[4]  D. McFadden,et al.  Specification tests for the multinomial logit model , 1984 .

[5]  Kenneth E. Train Discrete Choice Methods with Simulation: Logit , 2003 .

[6]  Darren M Gillis,et al.  Ideal free distributions in fleet dynamics: a behavioral perspective on vessel movement in fisheries analysis , 2003 .

[7]  T. Pitcher,et al.  Towards sustainability in world fisheries , 2002, Nature.

[8]  P. Leung,et al.  Modeling trip choice behavior of the longline fishers in Hawaii , 2004 .

[9]  David C. Smith,et al.  Human behaviour: the key source of uncertainty in fisheries management , 2011 .

[10]  Etienne Rivot,et al.  Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models , 2010 .

[11]  K. Train Discrete Choice Methods with Simulation , 2003 .

[12]  K. McLeod,et al.  Confronting the challenges of implementing marine ecosystem‐based management , 2007 .

[13]  M. Dorn Fishing behavior of factory trawlers: a hierarchical model of information processing and decision-making , 2001 .

[14]  SeaWeb Turning the tide : saving fish and fishers - building sustainable and equitable fisheries and governance , 2005 .

[15]  M. Nourry,et al.  The influence of fiscal regulations on investment in marine fisheries: A French case study , 2011 .

[16]  Laurence T. Kell,et al.  Exit and entry of fishing vessels: an evaluation of factors affecting investment decisions in the North Sea English beam trawl fleet , 2011 .

[17]  Villy Christensen,et al.  ECOPATH II − a software for balancing steady-state ecosystem models and calculating network characteristics , 1992 .

[18]  Laurent Millischer,et al.  Information transfer, behavior of vessels and fishing efficiency: an individual-based simulation approach , 2006 .

[19]  Martin D. Smith,et al.  Avoiding surprises: Incorporating fisherman behavior into management models , 2002 .

[20]  Ray Hilborn,et al.  Ecosystem-based fisheries management: the carrot or the stick? : Perspectives on eco-system-based approaches to the management of marine resources , 2004 .

[21]  Sophie Bertrand,et al.  Lévy trajectories of Peruvian purse-seiners as an indicator of the spatial distribution of anchovy ( Engraulis ringens ) , 2005 .

[22]  Price Uncertainty, Expectations Formation and Fishers' Location Choices , 1993, Marine Resource Economics.

[23]  Daniel S. Holland,et al.  An empirical model of fleet dynamics in New England trawl fisheries , 1999 .

[24]  Carl J. Walters,et al.  Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries , 2000 .

[25]  Olivier Thébaud,et al.  A dynamic model of the Bay of Biscay pelagic fleet simulating fishing trip choice: the response to the closure of the European anchovy (Engraulis encrasicolus) fishery in 2005 , 2008 .

[26]  H. Browman,et al.  Perspectives on ecosystem-based approaches to the management of marine resources , 2004 .

[27]  S. Pascoe,et al.  Theories and behavioural drivers underlying fleet dynamics models , 2012 .

[28]  Yves Croissant,et al.  multinomial logit model , 2013 .

[29]  PingSun Leung,et al.  Modeling entry, stay, and exit decisions of the longline fishers in Hawaii , 2004 .

[30]  Lizzie Buchen Battling scientists reach consensus on health of global fish stocks , 2009 .

[31]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[32]  O. R. Eigaard,et al.  Short-term choice behaviour in a mixed fishery: investigating métier selection in the Danish gillnet fishery , 2012 .

[33]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[34]  J. Sutinen,et al.  Location choice in New England trawl fisheries: old habits die hard. , 2000 .

[35]  Paul Marchal,et al.  A comparative analysis of métiers and catch profiles for some French demersal and pelagic fleets , 2008 .

[36]  Tim Futing Liao,et al.  Multinomial Logit Models , 1994 .

[37]  A. D. Rijnsdorp,et al.  Competitive interactions among beam trawlers exploiting local patches of flatfish in the North Sea , 2000 .

[38]  K. Stokes,et al.  The relative weight of traditions, economics, and catch plans in New Zealand fleet dynamics , 2009 .

[39]  D. McFadden MEASUREMENT OF URBAN TRAVEL DEMAND , 1974 .

[40]  S. Pascoe,et al.  Fisher behaviour: exploring the validity of the profit maximising assumption , 1997 .

[41]  Sean Pascoe,et al.  Modelling fishing location choice within mixed fisheries: English North Sea beam trawlers in 2000 and 2001 , 2004 .

[42]  O. Thébaud,et al.  Investment Behaviour and Capacity Adjustment in Fisheries: A Survey of the Literature , 2011, Marine Resource Economics.

[43]  Dietrich Earnhart,et al.  Combining Revealed and Stated Data to Examine Housing Decisions Using Discrete Choice Analysis , 2002 .

[44]  Sean Pascoe,et al.  Input Controls, Input Substitution and Profit Maximisation in the English Channel Beam Trawl Fishery , 1998 .

[45]  Rolf Wüstenhagen,et al.  The influence of eco‐labelling on consumer behaviour – results of a discrete choice analysis for washing machines , 2006 .

[46]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[47]  Stéphanie Mahévas,et al.  Spatially explicit fisheries simulation models for policy evaluation , 2005 .

[48]  Pier Paolo Gatta,et al.  state of the world fisheries and aquaculture 2010 , 2011 .