Evolutionarily sustainable consumption

The Evolution and Ecology Program (EEP) assembles evidence and insights on how human exploitation alters the heritable traits of targeted populations and explores options for reducing unwanted alterations.

© Kittichart Potithat | Dreamstime

© Kittichart Potithat | Dreamstime

While aquatic food resources and fisheries-induced evolution are at the focus of this research, analogous challenges and solutions apply to any food-production system relying on self-renewing animal or plant populations:

  • EEP continued to develop tools for tackling both fundamental and applied challenges related to exploitation-induced evolution. The resultant toolbox is reviewed in the forthcoming book edited by EEP scientists, Fisheries-Induced Evolution [1], and includes modeling tools for strategic and tactical evaluations (eco-genetic models and adaptive dynamics models [2][3], as well as statistical tools for analyzing trends in exploited fish stocks [4]).
  • Progress in the field of fisheries-induced evolution was reviewed in an invited article published in the prestigious journal Annual Review of Ecology, Evolution, and Systematics [5] (Figure 1). The review showcased the widespread use of tools and approaches developed by EEP.
  • The Evolutionary Impact Assessment (EvoIA) is a new framework developed under EEP’s leadership by an international consortium of fishery experts. The first comprehensive EvoIA has been conducted on North Sea plaice, a major fishery target [6].
  • A standard protocol for estimating fisheries-induced selection pressures is being developed in the international Working Group on Fisheries-Induced Evolution, co-chaired by EEP scientists. The working group operates under the auspices of the International Council for the Exploration of the Sea and collaborates with a large number of international experts to obtain the first global-scale estimates of fisheries-induced selection pressures.
  • Eco-genetic models have been developed and calibrated for plaice in the North Sea [6][7][8], for Atlantic salmon in Norway [9], and for Atlantic cod in the Barents Sea [10].
  • Such eco-genetic models can be used to help interpret phenotypic trends documented in time series of field observations [7][10]. This helps to assess the extent to which evolutionary dynamics are responsible for the observed patterns.
  • Another use of eco-genetic models is their use to further understanding of the interactions between fisheries-induced evolution and some fundamental aspects of evolutionary biology, such as the evolutionary drivers of sexual size dimorphism [8] and the maintenance of long-term adaptive potential [11].
  • Bifurcation analysis was used to understand when fisheries may lead to disruptive selection [12], promoting diversification in exploited stocks, and how fleet dynamics can sometimes accelerate fisheries-induced adaptations [13].
  • Experimental approaches have demonstrated how behavioral traits can undergo fisheries-induced evolution [14].
  • Multiple lines of inquiry have led to a better understanding of management options for mitigating unwanted fisheries-induced evolution [5][6][15].
  • The aquaculture industry can cause evolutionary threats to wild fish stocks. In salmon, escaped farmed fish can undermine local adaptations in wild fish. EEP developed an eco-genetic model to assess the threat escaped salmon pose to the viability of wild salmon populations [9], and experimental releases of farmed salmon have been used to provide better estimates of the numbers of escaped salmon [16].

Figure 1. Trends in maturation have been studied in many marine and freshwater fish stocks based on extensive data sets that typically extend over many decades. Most stocks show trends indicative of fisheries-induced evolution (FIE) (blue), while others are ambiguous (red) or do not show such trends (green) [5].

References

[1] Dieckmann U, Godø OR & Heino M eds. Fisheries-induced Evolution. Cambridge University Press, UK. In preparation.

[2] Dunlop ES, Heino M & Dieckmann U. Eco-genetic models of fisheries-induced adaptive change. In Dieckmann U, Godø OR & Heino M eds. Fisheries-induced Evolution, Cambridge University Press, UK, in press.

[3] Ernande B, Dieckmann U & Heino M. The adaptive dynamics of reaction norms. In Dieckmann U, Godø OR & Heino M eds. Fisheries-induced Evolution, Cambridge University Press, UK, in press.

[4] Heino M, Ernande B & Dieckmann U. Reaction-norm analysis of fisheries-induced adaptive change. In Dieckmann U, Godø OR & Heino M eds. Fisheries-induced Evolution, Cambridge University Press, UK, in revision.

[5] Heino M, Díaz Pauli B & Dieckmann U (2015). Fisheries-induced evolution. Annual Review of Ecology, Evolution, and Systematics 46: 461–480.

[6] Mollet FM, Poos JJ, Dieckmann U & Rijnsdorp AD (2015). Evolutionary impact assessment of the North Sea plaice fishery. Canadian Journal of Fisheries and Aquatic Sciences 11/2015: doi 10.1139/cjfas-2014-0568.

[7] Mollet FM, Dieckmann U & Rijnsdorp AD (2016). Reconstructing the effects of fishing on life history evolution in North Sea plaice (Pleuronectes platessa). Marine Ecology Progress Series 542: 195–208.

[8] Mollet FM, Enberg K, Boukal DS, Rijnsdorp AD & Dieckmann U. An evolutionary explanation of female-biased sexual size dimorphism in North Sea plaice (Pleuronectes platessa). In revision.

[9] Castellani M, Heino M, Gilbey J, Araki H, Svåsand T & Glover KA (2015). IBSEM: An individual-based Atlantic salmon population model. PLoS ONE 10: e0138444.

[10] Eikeset AM, Dunlop ES, Heino M, Dieckmann U & Stenseth NC The role of density-dependent growth and life-history evolution in accounting for fisheries-induced trait changes. In preparation.

[11] Marty L, Dieckmann U & Ernande B (2015). Fisheries-induced neutral and adaptive evolution in exploited fish populations and consequences for their adaptive potential. Evolutionary Applications 8: 47–63.

[12] Landi P, Hui C & Dieckmann U (2015). Fisheries-induced disruptive selection. Journal of Theoretical Biology 365: 204–216.

[13] Landi P, Hui C & Dieckmann U. Fleet dynamics can accelerate fisheries-induced evolution. In preparation.

[14] Díaz Pauli B, Wiech M, Heino M & Utne-Palm AC (2015). Opposite selection on behavioural types by active and passive fishing gears in a simulated guppy Poecilia reticulata fishery. Journal of Fish Biology 86: 1030–1045.

[15] Heino M, Dunlop ES, Godø OR & Dieckmann U. Management implications of fisheries-induced evolution. In Dieckmann U, Godø OR & Heino M eds. Fisheries-induced Evolution, Cambridge University Press, UK, in press.

[16] Skilbrei OT, Heino M & Svåsand T (2015). Using simulated escape events to assess the annual numbers and destinies of escaped farmed Atlantic salmon of different life stages from farm sites in Norway. ICES Journal of Marine Science 72: 670–685.


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Last edited: 06 April 2016

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