Preference for Landings’ Smoothing and Risk of Collapse in Optimal Fishery Policies: The Ibero-Atlantic Sardine Fishery


Several world fish stocks are being explored at unsustainable levels and require management plans to rebuild stock abundance. Defining a management plan is, however, a complex task that entails multidisciplinary work. In fact, while it requires solid scientific knowledge of fish stocks, the inclusion of economic and social objectives is crucial to a successful management implementation. In this paper we develop an age-structured bioeconomic model where the objective function is modified to accommodate preferences from different stakeholders. In particular, we consider important characteristics that a management plan should take into account: profit maximization, fishermen’s preference for reducing landings’ fluctuations and risk of fishery collapse. Modeling preferences for reducing landings’ fluctuations is accomplished by defining a utility function with aversion to intertemporal income fluctuations. Building upon biology literature, we model precautionary concerns by incorporating a probability of collapse that depends on current spawning biomass. We illustrate how this framework is able to assist in the analysis and design of harvest control rules applying it to the Ibero-Atlantic sardine stock.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    Based on recruitment series for the sardine stock, ICES (2013b) proposes a separation of the stock in two productivity regimes, before and after 1993. It is argued that the mean productivity (recruits per spawner) of the period after 1993 is a good indicator of future stock productivity.

  2. 2.

    The Scientific, Technical and Economic Committee for Fisheries (STECF) is the entity responsible for publishing information on the structure and economic performance of EU Member States fishing fleets. There was only one data point available for Spanish purse-seiners costs. Thus we assumed Portuguese purse-seiners to be representative of the entire fleet.

  3. 3.

    For small pelagic species like sardines the component where this issue is more relevant is in recruitment and, in particular, in the existence of spawning stock biomass levels impairing it, thus causing low species abundance and risk of stock collapse (Katara 2014).

  4. 4.

    Another distribution could be used. We assume the normal distribution for ease of exposition.

  5. 5.

    Chen et al. 2002 define an extinction probability function of the stock for a single brood line in one generation. We introduce here a stronger assumption by defining an extinction probability for the entire stock. While it would be possible to model a collapse in recruitment, our simplified approach is able to offer an analysis of the main implications resulting from the introduction of an expected profit that explicitly takes into account the current status of the species’ stock.


  1. Beddington JR, Agnew DJ, Clark CW (2007) Current problems in the management of marine fisheries. Science 316(5832):1713–1716

    Article  Google Scholar 

  2. Bjorndal T, Brasão A (2006) The East Atlantic bluefin tuna fisheries: stock collapse or recovery? Mar Resour Econ 21:193–210

    Article  Google Scholar 

  3. Byrd RH, Nocedal J, Waltz RA (2006) KNITRO: an integrated package for nonlinear optimization

    Google Scholar 

  4. Chen DG, Irvine JR, Cass AJ (2002) Incorporating Allee effects in fish stock recruitment models and applications for determining reference points. Can J Fish Aquat Sci 59(2):242–249

    Article  Google Scholar 

  5. Clark C (2010) Mathematical bioeconomics, 3rd edn. Wiley, New York

    Google Scholar 

  6. Dichmont CM, Pascoe S, Kompas T, Punt AE, Deng R (2010) On implementing maximum economic yield in commercial fisheries. Proc Nat Acad Sci 107(1):16–21

    Article  Google Scholar 

  7. FAO (2014) The state of world fisheries and aquaculture 2014. Food and Agriculture Organization, Rome

    Google Scholar 

  8. Froese R, Branch TA, Proelß A, Quaas M, Sainsbury K, Zimmermann C (2011) Generic harvest control rules for European fisheries. Fish Fish 12(3):340–351

    Article  Google Scholar 

  9. Garrido S, Silva A, Pastor J, Dominguez R, Silva AV, Santos AM (2015) Trophic ecology of pelagic fish species off the Iberia: diet overlap, cannibalism and intraguild predation. Mar Ecol Prog Ser 539:271–286

    Article  Google Scholar 

  10. Gulland JA (1983) Fish stock assessment: a manual of basic methods. FAO/Wiley Inter-Science, New York

    Google Scholar 

  11. ICES (2013a) Report of the workshop to evaluate the management plan for Iberian sardine (WKSardineMP). ICES CM 2013/ACOM 62

  12. ICES (2013b) Reference points for the Iberian sardine stock (ICES areas VIIIc and IXa). Working document to ICES-ACOM

  13. ICES (2015a) Advice basis. In: Report of the ICES advisory committee, 2015. ICES Advice 2015, Book 1. Section 1:2

  14. ICES (2015b) Report of the working group on southern horse mackerel, anchovy and sardine (WGHANSA). ICES CM 2015/ACOM:16

  15. ICES (2015c) ICES advice basis. ICES advice 2015, Book 1, p 13

  16. ICES (2016) Advice basis. In: Report of the ICES advisory committee 2016. ICES advice 20165. Book 1

  17. INE (2008–2012) Estatísticas da Pesca, annual. Available from:

  18. Kapaun U, Quaas MF (2013) Does the optimal size of a fish stock increase with environmental uncertainties? Environ Resour Econ 54(2):293–310

    Article  Google Scholar 

  19. Katara I (2014) Recruitment variability. In: Ganias K (ed) Biology and ecology of sardines and anchovies. CRC Press, Boca Raton, pp 242–282

    Google Scholar 

  20. Lassen H, Medley P (2001) Virtual population analysis: a practical manual for stock assessment (no. 400). Food and Agriculture Organization

  21. Mardle S, Pascoe S (1999) A review of applications of multiple-criteria decision-making techniques to fisheries. Mar Resour Econ 14:41–63

    Article  Google Scholar 

  22. Maroto JM, Moran M (2014) Detecting the presence of depensation in collapsed fisheries: the case of the Northern cod stock. Ecol Econ 97:101–109

    Article  Google Scholar 

  23. Martins MM, Skagen D, Marques V, Zwolinski J, Silva A (2013) Changes in the abundance and spatial distribution of the Atlantic chub mackerel (Scomber colias) in the pelagic ecosystem and fisheries off Portugal. Sci Mar 77(4):551–563

    Article  Google Scholar 

  24. Pascoe S, Bustamante R, Wilcox C, Gibbs M (2009) Spatial fisheries management: a framework for multi-objective qualitative assessment. Ocean Coast Manag 52:130–138

    Article  Google Scholar 

  25. Pascoe SD, Plagányi EE, Dichmont CM (2017) Modelling multiple management objectives in fisheries: Australian experiences. ICES J Mar Sci 74:464–474

    Google Scholar 

  26. Ricker WE (1954) Stock and recruitment. J Fish Board Can 11(5):559–623

    Article  Google Scholar 

  27. Schaefer M (1954) Some aspects of the dynamics of the populations important to the management of the commercial marine fisheries. Bull Inter Am Trop Tuna Comm 1(2):27–56

    Google Scholar 

  28. Schaefer M (1957) Some considerations of population dynamics and economics in relation to the management of marine fisheries. J Fish Res Board Can 14:669–681

    Article  Google Scholar 

  29. Santos AM, Borges MF, Groom S (2001) Sardine and horse mackerel recruitment and upwelling off Portugal. ICES J Marit Sci 58:589–596

    Article  Google Scholar 

  30. Smith V (1969) On models of commercial fishing. J Polit Econ 77(2):181–198

    Article  Google Scholar 

  31. Silva A, Moreno A, Riveiro I, Santos B, Pita C, Garcia Rodrigues J, Villasante S, Pawlowski L, Duhamel E (2015) Research for pech committee-sardine fisheries: resource assessment and social and economic situation

  32. Stage J (2006) Optimal harvesting in an age-class model with age-specific mortalities: an example from Namibian line fishing. Nat Resour Model 19(4):609–631

    Article  Google Scholar 

  33. STECF (2014) The 2014 annual economic report on the European fishing fleet, STECF-14-16. In: Guillen J, Virtanen J (eds) Joint Research Centre, Ispra

  34. Steinshamn SI (2011) A conceptual analysis of dynamics and production in bioeconomic models. Am J Agric Econ 93(3):799–808

    Article  Google Scholar 

  35. Tahvonen O (2008) Harvesting an age-structured population as biomass: does it work? Nat Resour Model 21(4):525–550

    Article  Google Scholar 

  36. Tahvonen O (2009a) Optimal harvesting of age-structured fish populations. Mar Resour Econ 24(2):147–169

    Article  Google Scholar 

  37. Tahvonen O (2009b) Economics of harvesting age-structured fish populations. J Environ Econ Manag 58:281–299

    Article  Google Scholar 

  38. Tahvonen O, Quaas M, Schmidt J, Voss R (2013) Optimal harvesting of an age-structured schooling fishery. Environ Resour Econ 54:21–39

    Article  Google Scholar 

  39. Trenkel VM, Hintzen NT, Farnsworth KD, Olesen C, Reid D, Rindorf A, Shephard S, Dickey-Collas M (2015) Identifying marine pelagic ecosystem management objectives and indicators. Mar Policy 55:23–32

    Article  Google Scholar 

  40. Ward JM, Kelly M (2009) Measuring management success: experience with United States fisheries. Mar Policy 33(1):164–171

    Article  Google Scholar 

  41. Worm B, Hilborn R, Baum JK, Branch TA, Collie JS, Costello C, Jensen OP (2009) Rebuilding global fisheries. Science 325(5940):578–585

    Article  Google Scholar 

Download references


The authors gratefully acknowledge financial support from Fundação Calouste Gulbenkian. This research work was developed in the context of the project entitled “The Economic Valuation and Governance of Marine and Coastal Ecosystem Services (MCES)”. CENSE is financed through Strategic Project Pest-OE/AMB/UI4085/2013 from the Fundação para a Ciência e Tecnologia, I.P., Portugal. This work was funded by National Funds funds through FCT—Fundação para a Ciência e Tecnologia under the project Ref. UID/ECO/00124/2013 and by POR Lisboa under the project LISBOA-01-0145-FEDER-007722. We acknowledge the support of FCT via scholarship SFRH/BPD/81880/2011 (Rui Mota). Finally, we acknowledge also EU/DGMARE Fisheries Data Collection Framework (DCF) for funding A. Silva.

Author information



Corresponding author

Correspondence to Renato Rosa.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rosa, R., Vaz, J., Mota, R. et al. Preference for Landings’ Smoothing and Risk of Collapse in Optimal Fishery Policies: The Ibero-Atlantic Sardine Fishery. Environ Resource Econ 71, 875–895 (2018).

Download citation


  • Fishery management
  • Optimal harvesting
  • Age-structured model
  • Stock collapse
  • Harvest control rules
  • Bioeconomic model
  • Multiple objectives