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The Cost of Co-viability in the Australian Northern Prawn Fishery

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Abstract

Fisheries management must address multiple, often conflicting objectives in a highly uncertain context. In particular, while the bio-economic performance of trawl fisheries is subject to high levels of biological and economic uncertainty, the impact of trawling on broader biodiversity is also a major concern for their management. The purpose of this study is to propose an analytical framework to formally assess the trade-offs associated with balancing biological, economic and non-target species conservation objectives. We use the Australian Northern Prawn Fishery (NPF), which is one of the most valuable federally managed commercial fisheries in Australia, as a case study. We develop a stochastic co-viability assessment of the fishery under multiple management objectives. Results show that, due to the variability in the interactions between the fishery and the ecosystem, current management strategies are characterized by biological and economic risks. Results highlight the trade-offs between respecting biological, economic and non-target species conservation constraints at each point in time with a high probability and maximizing the net present value of the fishery.

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Notes

  1. The MSC is an international non-profit organisation established to promote solutions to the problem of overfishing.

  2. Year y(t) is a function of week t, where weeks are numbered 1,…, 52, 53,…, 102, 103, …

  3. A number of intermediate combinations were also analysed; however, for the sake of simplicity, only three are displayed in this paper. The selected strategies presented here were of particular interest in the analysis carried out by [32].

  4. scilab is a freeware http://www.scilab.org/ dedicated to engineering and scientific calculus. It is especially well-suited to deal with dynamic systems and control theory.

  5. As the fleet size of the NPF has historically been reduced, this study assesses the marginal cost of removing vessels instead of the marginal effects of increasing the fleet.

  6. The NPF is a limited entry fishery and changes in the maximum fleet size are not allowed. The work presented here is thus an artificial case study to assess effects of potential changes in fleet size.

  7. Comparison of results with and without discounting enabled us to dismiss the hypothesis that discounting is driving the trade-off between CVA and mean NPV.

  8. Year y(t) is a function of week t, where weeks are numbered 1,…, 52, 53,…, 102, 103, …

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Acknowledgements

This work was supported by the French Research Agency ANR through the project Adhoc, the Australian Fisheries Research and Development Corporation (FRDC), the University of Tasmania (UTAS)/ Commonwealth Scientific and Industrial Research Organisation (CSIRO) PhD Program in Quantitative Marine Science and the French Research Institute for Exploitation of the Sea (Ifremer). We are grateful to the CSIRO researchers providing us with access to data and knowledge of the fishery, and especially to Rodrigo Bustamante and Rik Buckworth for helpful discussions on the model and the case study. We also thank two anonymous reviewers and the associate editor for their helpful comments on an earlier version of the manuscript.

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Authors and Affiliations

  1. IFREMER, UMR M101, AMURE, Unité d’Economie Maritime, BP 70, F-29280, Plouzané Cedex, France

    Sophie Gourguet & Olivier Thébaud

  2. School of Economics and Finance, Queensland University of Technology, Brisbane, Australia

    Olivier Thébaud

  3. Tasmanian School of Business and Economics, University of Tasmania, Private Bag 84, Hobart, 7000, Australia

    Sarah Jennings

  4. CSIRO Oceans and Atmosphere Flagship, PO Box 1538, Hobart, TAS, 7001, Australia

    L. Richard Little

  5. Centre for Marine Socioecology, University of Tasmania, Private Bag 129, Hobart, 7000, Australia

    Sarah Jennings, L. Richard Little, Sean Pascoe & Roy A. Deng

  6. CSIRO Oceans and Atmosphere, PO Box 2583, Brisbane, QLD, 4001, Australia

    Catherine M. Dichmont & Sean Pascoe

  7. CNRS, GREThA, University of Bordeaux, Avenue Léon Duguit, 33608, Pessac Cedex, France

    Luc Doyen

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  1. Sophie Gourguet
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Correspondence to Sophie Gourguet.

Appendices

Appendix A: Bio-economic Model

1.1 A.1 Annual Spawning Stock Size Indices

The annual spawning stock size indices S s (y(t)) of the grooved and brown tiger and blue endeavour prawns (s = 1,2 and 3, respectively) for the year y(t) are calculated as in [49] and are described in Eq. 20.

(20)

where N s,♀, l (t) is the abundance of prawns of species s of sex x = ♀ (for female) in size-class l alive at the start of time t which corresponds to one time step (i.e. 1 week). Grooved and brown tiger prawns are represented by 1-mm size-classes between lengths of 15 to 55 mm, while blue endeavour prawns are modeled as a single aggregated length class. y(t) is the year Footnote 8 corresponding to the time t, β s (t) measures the relative amount of spawning of species s during the time t, and γ s, l corresponds to the proportion of females of species s in size-class l that are mature. Z s, l (t) is the total mortality on animals of species s in size-class l during time t and is defined by:

$$ Z_{s,l}(t)=M_{s}+F_{s,l}(t). $$
(21)

with M s the natural mortality of animals of species s and F s, l (t) the fishing mortality of animals of species s and size-class l at time t. Details on fishing mortality are given in Appendix A.3.

1.2 A.2 White Banana Prawn: an Uncertain Resource

Abundance of white banana prawns (species s = 4) appears to be more heavily influenced by the environment than by fishing pressure [21, 40] and its year to year availability is highly variable. More specifically, stocks are strongly influenced by weather patterns, generally peaking in years in which there has been high rainfall. It is assumed that spawning stock biomasses of white banana prawns do not influence significantly the stock abundances the following years and that annual environmental influences are independent. Therefore, in the present study, white banana prawn annual biomass is modeled as a uniform i.i.d. random variable:

$$ B_{s}(y(t)) \leadsto \mathcal{U}(B^{-}_{s}, B^{+}_{s}), \qquad s=4. $$
(22)

with B s = 4(y(t)) the stochastic biomass of white banana prawn for the year y(t), and \(B^{-}_{s=4}\) and \(B^{+}_{s=4}\) the uniform law bounds (values are given in [32]).

1.3 A.3 Fishing Mortality and Catch

Fishing mortality F s, l, f (t) due to fishing effort of fishing strategy f (with f = 1 and 2 for the grooved and brown tiger prawn fishing strategies, respectively) on animals of species s in size-class l during time t is given by:

$$ F_{s,l,f}(t) = u_{s}(t)\mathrm{E}_{f}(t), \qquad s=1,2,3 \text{ and } f=1,2. $$
(23)

where E f (t) corresponds to the effort of fishing strategy f during time t. Fishing mortality functions u s are detailed in [32].

Weekly catches C s, l, f (t) of species s = 1,2 and 3 in length-class l by tiger prawn fishing strategy f(f = 1,2); and annual catches C s = 4, f = 3(y(t)) of white banana prawns (s = 4) by banana prawn sub-fishery (f = 3) for the year y(t) are defined by the system of Eq. 24:

$$ \left\{\begin{array}{ll} C_{s,l,f}(t) = \displaystyle{{\sum}_{x}\upsilon_{s,x,l} \mathrm{N}_{s,x,l}(t) F_{s,l,f}(t) \frac{1-\exp\left( -M_{s} -{\sum}_{f=1,2} F_{s,l,f}(t)\right)}{ M_{s} + {\sum}_{f=1,2}F_{s,l,f}(t)}} & s=1,2,3 \text{ and } f=1,2\\ C_{s,f}(y(t)) = q_{s,f}B_{s}(y(t))\mathrm{E}^{\mathrm{y}}_{f}(y(t)) & s=4 \text{ and } f=3. \end{array} \right. $$
(24)

with υ s, x, l the mass of an animal of species s = 1,2 and 3 and sex x (x = ♀ for female, and x = ♂ for male) in size-class l, F s, l (t) the fishing mortality of animals of species s and size-class l at time t, and \(\mathrm {E}^{\mathrm {y}}_{f}(y(t))\) the annual effort of fishing strategy or sub-fishery f during year y(t).

1.4 A.4 Annual Profit

Gross incomes Inc f (y(t)) for grooved (f = 1) and brown (f = 2) tiger prawn fishing strategies are calculated from catches C s, l, f (t) of tiger and blue endeavour prawns (s = 1,2 and 3), and gross income Inc f = 3(y(t)) for banana prawn sub-fishery (f = 3) is calculated from catches C s = 4, f = 3(y(t)) of white banana prawns (s = 4), as described by Eq. 25.

$$ \left\{\begin{array}{ll} \text{Inc}_{f}(y(t)) =\kern-.8pc \displaystyle{\sum\limits_{t= 52(y(t)-1)+1}^{52y(t)}} \left( \sum\limits_{s=1}^{3}{\sum}_{l}p_{s,l} C_{s,l,f}(t) \right), & s=1,2,3 \text{ and } f=1,2.\\ \text{Inc}_{f}(y(t))=p_{s} C_{s,f}(y(t)) , & s=4 \text{ and } f=3. \end{array} \right. $$
(25)

where p s, l is the average market price per kilogram for animals of species s = 1,2 and 3 in size-class l (related to five market categories for the tiger prawns and corresponding to an average price for the blue endeavour prawns, as they are represented through an aggregated length-class). Grooved and brown tiger prawns are marketed together as ‘tiger prawns’ under a common size-dependent price, therefore p s, l are identical for s = 1 and s = 2. The average price per kilogram of white banana prawns is denoted p s = 4.

Total annual profit of the whole fishery π(y(t)) for year y(t) is then expressed by:

$$\begin{array}{@{}rcl@{}} \pi(y(t))\! =\! \text{Inc}_{3}(y(t))\! -\! c^{var}_{3}{\mathrm{E}^{y}_{3}}(y(t)) + \sum\limits_{f=1}^{2} \sum\limits_{t=52(y(t)-1)+1}^{52y(t)} \\ \left( \text{Inc}_{f}\left( t,\mathrm{E}_{f}(t)\right) - c^{var}_{f}\mathrm{E}_{f}(t) \right)-c^{fix}_{v}\mathrm{K}(y(t)).\\ \end{array} $$
(26)

where Inc f (t,E f (t)) is the annual gross income of fishing strategy f for the time t and related to E f (t) the fishing effort (expressed in days at sea) of the fishing strategy f during time t. \(c^{var}_{f}\) corresponds to the variable cost for one unit of fishing effort of fishing strategy or sub-fishery f, and \(c^{fix}_{v}\) is the annual fixed cost by vessel. Details on costs are given in [49] and [32]. K(y(t)) is the number of vessels involved in the NPF during the year y(t).

Appendix B: Co-Viability Approach Thresholds

This appendix displays the values of the biological, economic and sea snake conservation viability thresholds used in Sections 4.1, 4.3 and 4.4. More specifically, Table 5 displays the threshold values tested in the sensitivity analyses for the spawning stock size indices (Section 4.1) and Table 6 summarizes the threshold values used in the analyses Sections 4.2 to 4.4.

Table 5 Values of the biological thresholds tested in sensitivity analyses
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Table 6 Values of the thresholds used in co-viability analyses
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Appendix C: Statistics

This appendix displays in Fig. 5 the linear regressions between historical annual sea snake catch C snake,f(y(t)) by sub-fishery f (with f = 1+2 for tiger prawn sub-fishery and f = 3 for banana prawn sub-fishery) and associated annual fishing effort E f (y(t)). Table 7 displays the statistics of these regressions.

Fig. 5
figure 5

Linear regression between historical annual sea snake catch by sub-fishery and associated annual effort. Regression for the tiger prawn sub-fishery is represented in (a) and banana prawn sub-fishery in (b)

Full size image
Table 7 Statistics of the linear regression between annual sea snake catches by tiger and banana prawn sub-fisheries and associated annual efforts (intercept at 0)
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Gourguet, S., Thébaud, O., Jennings, S. et al. The Cost of Co-viability in the Australian Northern Prawn Fishery. Environ Model Assess 21, 371–389 (2016). https://doi.org/10.1007/s10666-015-9486-y

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  • DOI: https://doi.org/10.1007/s10666-015-9486-y

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