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Seasonal Harvest Patterns in Multispecies Fisheries

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Abstract

Fishers face multidimensional decisions: when to fish, what species to target, and how much gear to deploy. Most bioeconomic models assume single-species fisheries with perfectly elastic demand and focus on inter-seasonal dynamics. In real-world fisheries, vessels hold quotas for multiple species with heterogeneous biological and/or market conditions that vary intra-seasonally. We analyze within-season behavior in multispecies fisheries with individual fishing quotas, accounting for stock aggregations, capacity constraints, and downward-sloping demand. Numerical results demonstrate variation in harvest patterns. We specifically find: (1) harvests for species with downward-sloping demand tend to spread out; (2) spreading harvest of a high-value species can cause lower-value species to be harvested earlier in the season; and (3) harvest can be unresponsive or even respond negatively to biological aggregation when fishers balance incentives in multispecies settings. We test these using panel data from the Norwegian multispecies groundfish fishery and find evidence for all three. We extend the numerical model to account for transitions to management with individual fishing quotas in multispecies fisheries. We show that, under some circumstances, fishing seasons could contract or spread out.

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Notes

  1. The multispecies context refers to a set of participants who target multiple fish species within a year, either sequentially or simultaneously, and typically under the same management plan and using the same gear. For example, cod, haddock, and saithe are often caught together in similar ocean environments and, in the Norwegian context, they are jointly managed under the same license defined by vessel and gear type even though the quotas are set on an individual species basis.

  2. Although harvest can be fairly selective in multispecies fisheries—e.g., pelagics in the northeast Atlantic (Asche et al. 2007)—duality models are largely unable to distinguish between a fleet that targets a sequence of species in completely selective fisheries and a fleet in a non-selective fishery in which the share of each species is relatively constant within the year.

  3. Smith et al. (2008) find evidence of effort substitution in response to spawning aggregations of gag (a species of grouper), but forward-looking behavior is not modeled explicitly.

  4. In a model of species choice (Zhang and Smith 2011), the structure of the decision assumes one of three possible targets is chosen in each period and thus rules out the possibility of multispecies targeting. This feature largely reflects the general approach of discrete choice modeling. Some of the fine-scale empirical literature analyzes behavioral responses to changing stock abundance (Smith et al. 2008; Zhang 2011; Huang and Smith 2014).

  5. Cod (Gadus morhua), haddock (Melanogrammus aeglefinus), and saithe (Pollachius virens) are also important species in the New England groundfish complex (saithe is commonly referred to as pollock but is a different species from the Alaskan walleye pollock, Gadus chalcogrammus).

  6. We note three issues that our model does not address and that suggest future research directions: 1) strategic interactions and coordination failures among multiple IFQ holders; 2) within-season leasing and trading of IFQs; and 3) the complexities of an IFQ management regime co-existing with open access and regulated open access regimes that apply to other target fisheries.

  7. The model described here produces the same results as a symmetric Nash equilibrium with Cournot competition as long as the aggregate industry-wide quota is set at a level that eliminates incentives of the fleet to withhold production from the market. This situation is highly relevant for our case study (Norwegian cod, haddock, and saithe), in which industry-wide quotas bind. Arguably, this situation also describes most fisheries; other explanations such as ecological co-occurrence and bycatch are typically offered when non-binding quotas occur at the industry level.

  8. Equation (3) could be written as a more general demand model to allow market interactions between species, but we assume that species are neither substitutes nor complements. Since the species we model empirically are considered substitutes, incentives to concentrate harvest of one species with a relatively elastic demand would be moderated by positive cross-price elasticities with species having relatively less elastic demand.

  9. The number of vessels, n, is fixed in the short term as we only analyze intra-seasonal behavior. Within the time period considered—a single fishing year—the fleet size is not expected to change. Over the longer term, profitability can motivate new participation, for example, when \(\alpha_{i} < 1\), if this is possible. However, in the Norwegian fleet, as in most managed fisheries, entry is limited.

  10. This implicitly assumes that there is not a liquid rental market for fishing capacity, which is reasonable in most real-world settings. Such a market does not exist at all in many fisheries, and, as vessels need a license, it is a complicated and time-consuming process to be allowed to use a new vessel. Another setting where such a constraint has an impact is “high-grading,” which refers to throwing lower-value fish overboard because the hold capacity on each trip is limited (Vestergaard 1996).

  11. When stock effects and discounting are removed and the production technology is otherwise constant returns (\(\alpha_{i} = 1\)), the harvest and effort paths are completely flat (Supplemental Figure 1).

  12. Removing either discounting or the stock effect leads to the same result as long as the production technology is otherwise constant returns (\(\alpha_{i} = 1\)) (Supplemental Figure 2). With decreasing returns (\(\alpha_{i} < 1)\), the effort path reflects tradeoffs across concavity of the harvest function, which smooths effort, and discounting and the stock effect, which concentrate effort (Supplemental Figure 3).

  13. This result is corroborated by Wakamatsu and Anderson (2018) in a single-species experimental game setting.

  14. When stock effects and discounting are removed but the production technology is otherwise constant returns (\(\alpha_{i} = 1)\), the harvest path is perfectly flat and the effort path still has a dip during biological aggregation (Supplemental Figure 4).

  15. For these parameters, this biological effect on catchability outweighs within-season discounting. Again, this is conditional on having no constraint on per-period effort capacity combined with constant returns to scale technology. Relaxing either of these assumptions induces some smoothing in catch and effort (Supplemental Figure 5).

  16. We choose values for the constraints such that the tightest is an amount of capacity that does not allow the entire quota of the higher-value species to be caught, whereas the loosest is one that allows total quotas for both species to be caught flexibly. Effort can, for example, be interpreted as the number of weeks in a month that the fleet is out fishing.

  17. To illustrate this, we consider a moderate capacity constraint (Emax= 3) and both 80-20 gear selectivity and 60-40 gear selectivity (Supplemental Figures 8 and 9).

  18. The results are qualitatively similar when the two species’ biological aggregations are offset; the peaks and troughs in harvest patterns follow the biological patterns predictably (Supplemental Figure 6).

  19. Note that with an even tighter effort constraint (Emax = 1), none of species 2 is taken, not all of species 1 quota is taken, and effort is allocated uniformly to species 1 (Supplemental Figure 7).

  20. The three-species case explored in Fig. 4 is discussed in Appendix B. This provides an extension of the intuition for the two-species scenario.

  21. The “trawl ladder” is a quota allocation instrument used in Norwegian fisheries that is based on historical rights. In an effort to keep the coastal fleet’s yearly catches stable, they are granted a larger part of the fishing quota in years with relatively moderate biomass. By comparison, the larger vessels such as trawlers have more fluctuating quota quantities.

  22. Norwegian groundfish are targeted by a heterogeneous fleet broadly divided into coastal vessels, longliners, and trawlers.

  23. Larsen and Dreyer (2012) indicate that under 20% of the total cod catch from trawlers is landed fresh. Almost all Norwegian-caught cod, regardless of product form, is exported.

  24. The aggregate landings for the trawler fleet and the computed within-season variation are presented in Supplemental Table 1.

  25. Supplemental Figure 13 shows average landings per month for the three sample years, focusing on the three IFQ species and normalizing each year to the average monthly landings in the year.

  26. Although there is not a comparable analysis for Norway, Lee (2014) demonstrates that U.S. cod prices are responsive to quantity landed at a daily time step, and Gordon and Hannesson (1996) establish links between the U.S. and European cod markets. As such, it is reasonable to assume that the Norwegian cod prices are responsive to quantity landed at the monthly scale.

  27. To illustrate the differences across species, we also plot the hazard rates (Supplemental Figure 14).

  28. The topic of how incentives to target and associated behaviors change under institutional change in fisheries is of growing interest and has many complications (Abbott et al. 2015; Reimer et al. 2017). Our intention here is to illustrate how simple mechanisms in our model offer some possibilities for what to expect in multispecies fisheries.

  29. Alternatively, one can think of this conceptual model as one in which quota is tradable but already efficiently allocated—that is, one in which a market equilibrium has been achieved following a period of trading.

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Acknowledgements

The authors thank the Research Council of Norway and the Knobloch Family Foundation for financial support of this research.

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Authors and Affiliations

  1. School of Marine Science and Policy, College of Earth, Ocean, and Environment, University of Delaware, 111 Robinson Hall, Newark, DE, 19716, USA

    Anna M. Birkenbach

  2. Department of Economics, University of Delaware, 111 Robinson Hall, Newark, DE, 19716, USA

    Anna M. Birkenbach

  3. Department of Innovation, Management and Marketing, University of Stavanger Business School, Ullandhaug, 4036, Stavanger, Norway

    Andreea L. Cojocaru

  4. Institute for Sustainable Food Systems and School of Forest Resources and Conservation, University of Florida, P.O. Box 110180, Gainesville, FL, 32611-0180, USA

    Frank Asche

  5. Department of Safety, Economics and Planning, University of Stavanger, Ullandhaug, 4036, Stavanger, Norway

    Frank Asche

  6. School of Economics and Business, Norwegian University of Life Sciences, NMBU, Box 5003, 1432, Ås, Norway

    Atle G. Guttormsen

  7. Nicholas School of the Environment, Duke University, Box 90328, Durham, NC, 27708, USA

    Martin D. Smith

  8. Department of Economics, Duke University, Box 90328, Durham, NC, 27708, USA

    Martin D. Smith

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  1. Anna M. Birkenbach
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Correspondence to Anna M. Birkenbach.

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Birkenbach, A.M., Cojocaru, A.L., Asche, F. et al. Seasonal Harvest Patterns in Multispecies Fisheries. Environ Resource Econ 75, 631–655 (2020). https://doi.org/10.1007/s10640-020-00402-7

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