Using simulation to determine standard requirements

Marine Policy 73 (2016) 146–153

Contents lists available at ScienceDirect

Marine Policy journal homepage: www.elsevier.com/locate/marpol

Using simulation to determine standard requirements for recovery rates of fish stocks Thomas R. Carruthers a,n, David J. Agnew b a b

Fisheries Centre, AERL, University of British Columbia, 2202 Main Mall, Vancouver, B.C., Canada V6T1Z4 Marine Stewardship Council, 1 Snow Hill, London EC1A 2DH, United Kingdom

art ic l e i nf o

a b s t r a c t

Article history: Received 2 February 2016 Received in revised form 28 July 2016 Accepted 28 July 2016

In many fisheries settings, managers must reconcile reference points relating to fishing mortality rate with recovery objectives relating to stock biomass. Simulation was used to evaluate the relationship between fishing mortality rate and stock recovery. Simulation was also used to determine feasible recovery times and the circumstances under which fishing mortality rates can be used as a proxy for biomass targets for a range of life history traits. Stock type was as important in determining stock recovery as the level of stock depletion. For most life-history types there was a relatively low likelihood of achieving rebuilding targets unless fishing rates were at least 40% lower than those associated with maximum sustainable yield. The relatively slow response to these levels of fishing mortality needs to be taken into account in recovery plans that aim to achieve a target stock status, and in outcome-based certification standards. & 2016 Published by Elsevier Ltd.

Keywords: Simulation evaluation Management strategy evaluation Exploitation rate Management reference points Marine certification Certification standards

1. Introduction Currently the Marine Stewardship Council (MSC) fishery standard evaluates fisheries according to biological reference points related to biomass at Maximum Sustainable Yield (BMSY) [1] following the targets set out in UNCLOS and the UN Straddling Stocks Agreement [2]. The MSC default standard has adopted 40% of biomass prior to fishing as a proxy for BMSY based on the modelling conducted in the US and elsewhere in the early 1990 s [1]. However, increasingly management systems are moving away from biomass-based reference points and instead looking towards those associated with exploitation rate (F) [3–5]. For example, the European Commission's Common Fisheries Policy is now based on achieving fishing mortality levels consistent with MSY (FMSY) [4]. The International Council for the Exploration of the Seas (ICES) provides advice in respect to FMSY and Btrigger which is considered to be a lower bound of spawning stock biomass fluctuation around BMSY, but since most ICES stocks have not been fished at FMSY for long, BMSY has generally not yet been reached and BMSY variability has not been characterized [5]. The central difference is that FMSY reference points focus on stock trajectory rather than current stock status, and offer management a greater number of available and measureable proxies than biomass based reference points [6]. However, in taking n

Corresponding author. E-mail address: t.carruthers@fisheries.ubc.ca (T.R. Carruthers).

http://dx.doi.org/10.1016/j.marpol.2016.07.026 0308-597X/& 2016 Published by Elsevier Ltd.

account of the move to fishing mortality reference points, MSC (like ICES [5]) has recognised that where stock levels are lower than BMSY, several years of rebuilding are required before the stock can be expected to be fluctuating around BMSY[7]. MSC requires certified fisheries to have biomass fluctuating around BMSY or above, but allows for limited time periods when biomass has dropped below BMSY so long as it is still higher than the point at which recruitment is impaired (often defined as a biomass based limit reference point Blim), and the stock is projected to recover within a specified timeframe [7]. Several reviews of the factors that influence stock recovery have identified the existence of specific recovery plan, with well-defined objectives, pre-defined time scales, political leadership and an open and transparent process involving all stakeholders as being important, but the most important element is usually a significant and immediate reduction in fishing mortality [8,9]. Experience with the implementation of required rebuilding timeframes under the Magnuson Stevens Act in the USA has shown that while many stocks can recover within 10 years, a large number of those under formal rebuilding plans show little sign of improvement even after 10 years, and the principal cause of this is a failure of many plans to reduce fishing mortality and particularly to avoid situations where fishing mortality is greater than FMSY [10,11]. Clearly minimum recovery timeframes will be dependent upon the reduction in fishing mortality delivered, intrinsic growth rate of the stock and its generation time, and the depletion of the stock in relation to BMSY. Recognising the existing experience and literature cited above, MSC sets its maximum recovery times, in

T.R. Carruthers, D.J. Agnew / Marine Policy 73 (2016) 146–153

which BMSY must be achieved from a status close to the point at which recruitment is likely to be impaired, to the lesser of 20 years or 2 generation times. However, with the increasing move for fisheries management targets to only be defined in terms of fishing mortality, and to use this as a proxy for biomass based reference points, there is a need to understand the relationship between delivered fishing mortality, life history characteristics, the duration of a recovery plan and the likely resultant biomass relative to BMSY. This paper sets out to answer two questions. Firstly, is it feasible to recover from biomass levels at which recruitment starts to be impaired (a common Biomass Limit Reference Point) to BMSY within 2 generation times or 20 years, whichever is the lower? Secondly, if exploitation rates are used as reference points are there precautionary rules that can be applied that provide confidence that a stock is fluctuating around its biomass reference point even when starting as low as Blim? For example is it reasonable, that in most circumstances, fishing mortality held at 80% of FMSY for 10 years or one generation time can be assumed to have resulted in a current population status consistent with BMSY? To answer these questions a peer-reviewed Management Strategy Evaluation (MSE) framework [12] was used to simulation test the effects of varying exploitation rate over a range of fish life-history types. MSE is a simulation approach for identifying management procedures (e.g. a stock assessment and harvest control rule) that are robust to uncertainty in stock and fishery dynamics and also revealing trade-offs among various management performance metrics [13,14] (Fig. 1).

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taken exactly. The outcomes of the projections were recorded in terms of stock biomass relative to BMSY and the biological limit reference point Blim (biomass at which recruitment is impaired). In this analysis Blim is defined as the biomass where recruitment is 50% of unfished levels. Blim was calculated using the simulated deterministic stock-recruitment curves. Since Beverton-Holt recruitment dynamics were assumed for each stock type, there is only one value of Blim which was lower than BMSY in all simulations. In order to control for differences in longevity among stock types, in some instances projected durations were evaluated in terms of Mean Generation Time (MGT). MGT was calculated as the average age of a mature fish in the simulated population. A rebuilding analysis was carried out to quantify the required duration for a stock to recover to above BMSY levels from a starting biomass level Blow. Blow was defined as a biomass greater than Blim and less than the midpoint between Blim and BMSY. Projected biomass was calculated for discrete years. It follows that during rebuilding in one projection year t, biomass B, is less than BMSY and the next year tþ1, it is higher than BMSY. In these cases, the continuous rebuilding time tr, was calculated by linear interpolation where tr ¼tþ (BMSY-Bt)/(Bt þ 1-Bt). In some instances where a stock did not reach BMSY during the 100 year projection, a cubic polynomial (e.g. Bt ¼at3 þbt2 þct þ d) was fitted to the biomass trajectory and used to predict the year at BMSY by extrapolation. Calculations were carried out in the statistical environment R (v3.03, 64 bit) [21] using the parallel processing package ‘snowfall’.

3. Results 2. Materials and methods The simulation framework of this paper was identical to that of a previous management strategy evaluation [12] and readers are referred to that paper for details regarding methods. Six stock types were simulated that span a range of scenarios for longevity and resilience to overfishing (Table 1). To ensure that the population and fishery dynamics (the known simulated ‘truth’ or ‘operating model’) are credible these were informed by data-rich stock assessments. For each of the six stock types 3000 historical simulations were carried out. Over a 50 year period a range of fishery scenarios were simulated leading to varying levels of current stock depletion and exploitation rate. For each simulation the appropriate quantities relating to maximum sustainable yield were calculated, such as BMSY and FMSY (these are known perfectly without error). Every simulation (3000 per stock type) was then projected forwards for 100 years for each of six FMSY strategies (FMSY, 80% FMSY, 60% FMSY, 40% FMSY, 20% FMSY, no fishing). It was assumed that there was no implementation error and that catches were

Life-history type was as important as exploitation level in determining rebuilding time to above BMSY levels from Blow. For example, fishing at 80% FMSY led to mean recovery times of 55.1 years and 4.7 years for the rockfish and butterfish stock types, respectively (Table 2). With zero exploitation, recovery times for these stocks shortened to 11.4 and 3.7 years, respectively. In general, recovery times were markedly shorter for the exploitation levels at 60% FMSY or lower (as reflected by the spacing of the median recovery time estimates plotted as vertical lines in Fig. 2). The very short-lived butterfish stock that has high inter-annual variability in recruitment was unlike the other simulated stocks and was unresponsive to varying exploitation levels. When duration was standardized according to MGT, the stocks showed more comparable expectation of stock status in response to varying exploitation levels (i.e. a more consistent set of curves among rows of Figs. 3 and 4). As might be expected, recovery to BMSY levels was strongly determined by initial stock level. With the exception of the snapper and butterfish stocks there was a relatively low probability of exceeding BMSY in one MGT even with very low exploitation levels. For example given exploitation at 40%

Fig. 1. A flow diagram of the components of the MSE. The dashed box represents the projection of the model and update according to a particular FMSY rule (e.g. 40% FMSY).


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Table 1 The six simulated stocks. Where two values are included these specify the upper and lower bounds from which a uniform random variable was sampled. Inter-annual variability refers to the standard deviation among the log-normal recruitment deviations. Stocks are generalized to a family level (e.g. snapper, sole) and a primary source of information to support the specification of the operating model is included for each stock. Stock types Stock Assessment reference Longevity Resilence Number of simulated ages Generation time Natural mortality rate Steepness Age at maturity Inter-annual variability in recruitment (log SD)

Rockfish [15] High Moderate 64 24 0.04 0.08 0.35 0.72 6.50 8.50 0.30 0.70

Snapper [16] High High 45 14 0.05 0.50 1.50 0.40

0.12 0.80 2.50 0.80

Porgy [17] Moderate Low 16 6 0.20 0.25 0.31 0.51 1.00 3.00 0.40 0.70

Mackerel [18] Moderate Moderate 18 8 0.10 0.30 0.25 0.70 2.20 2.80 0.30 0.70

Sole [19] Moderate High 21 11 0.13 0.22 0.60 0.90 4.00 6.00 0.30 0.50

Butterfish [20] Short High 6 2 0.70 0.90 0.30 0.80 0.60 1.40 0.70 1.10

Table 2 The number of years taken for a stock to reach biomass at maximum sustainable yield (BMSY) when biomass started higher than the biological limit (Blim) but lower than half way between the biological limit and biomass at maximum sustainable yield ((Blim þ BMSY)/2). The fifth, median and ninety-fifth percentiles are represented by the columns labelled 5%, Med. and 95%, respectively. Cells that are shaded darker represent longer recovery times. Stock

FMSY

80% FMSY

60% FMSY

Name

Longevity

Resilience

Mean

5%

Med.

95%

Mean

5%

Med.

95%

Mean

Rockfish Snapper Porgy Mackerel Sole Butterfish Stock Name Rockfish Snapper Porgy Mackerel Sole Butterfish

High High Moderate Moderate Moderate Short

Moderate High Low Moderate High High

17.3 5.2 1.0 6.0 6.2 1.5

80.8 42.0 45.5 37.1 27.8 5.0

150.0 134.6 150.0 111.7 94.4 25.7

55.1 17.6 40.5 19.8 12.3 4.7

101.2 70.3 145.8 71.5 34.3 19.5

Resilience Moderate High Low Moderate High High

5% 8.1 3.9 4.3 3.9 3.9 1.5

Med. 17.5 7.4 12.6 10.3 6.5 4.2

95% 44.1 16.9 44.6 36.7 11.1 16.1

56.4 23.7 54.6 26.7 15.3 7.2 20% FMSY Mean 15.8 6.7 10.4 10.9 5.7 5.2

14.4 5.1 3.6 4.5 4.8 1.5

Longevity High High Moderate Moderate Moderate Short

84.1 49.5 61.4 46.6 36.9 8.5 40% FMSY Mean 21.1 8.5 16.5 13.8 6.8 5.7

5% 7.0 3.5 3.7 3.6 3.6 1.5

Med. 13.8 6.2 8.3 8.6 5.5 4.0

95% 31.1 11.2 22.6 28.3 8.2 12.3

32.6 10.4 12.1 4.3 30.6 4.8 18.7 4.3 8.8 4.3 6.3 1.5 No exploitation Mean 5% 13.0 6.1 5.7 3.3 7.8 3.4 9.3 3.5 5.0 3.3 4.8 1.5

FMSY levels less than 1 in 10 simulations starting at half BMSY recovered to above BMSY. Over 1.5 MGT, recovery was much more pronounced particularly for stocks with greater recruitment compensation (higher steepness e.g. sole and snapper). For example given the same exploitation rate of 40% FMSY, 9 in 10 sole simulations recovered to above BMSY when starting below half BMSY. Stocks starting below 30% of BMSY were unlikely to recover to above BMSY over the longer rebuilding duration 1.5 MGT. Similarly, half an MGT is unlikely to be a meaningful recovery time for stocks starting below 70% of BMSY levels. The critical difference between rebuilding relative to Blim versus BMSY was recovery given high exploitation levels. While recovery to above Blim is achievable from even FMSY fishing levels for most stocks (Fig. 2), recovery to above BMSY levels is much less certain (Fig. 3). For example very few simulations recover to BMSY levels after 1.5 MGT for rock fish when exploited at FMSY levels. However half of the same simulations recovered to above Blim. For some stocks rebuilding times could be very slow. For example even with exploitation rate at 40% of FMSY, it could take more than 20 years for a rockfish population to reach BMSY levels from Blow (Fig. 4, panel a). It should be noted that for the higher exploitation rate scenarios (above 60% FMSY) and longer lived stock types, biomass often did not recover to BMSY levels within the projected time of 100 years, and a larger number of calculations were dependent on extrapolation (e.g. 80.2% in the case of the FMSY strategy and the rockfish stock type, Table 3). While the pattern of these results seems consistent with calculations that were not based on extrapolation, we suggest that these results should be treated with

5%

Med.

95%

29.7 10.0 21.7 14.6 8.0 4.5

70.1 25.6 88.0 49.5 15.4 17.4

Med. 11.4 5.4 6.5 7.2 4.9 3.7

95% 24.3 9.5 16.7 24.1 7.1 11.3

caution (particularly where the fraction of calculations by extrapolation is above 10% that are shaded grey in Table 3).

4. Discussion The central question of this research was whether exploitation rate relative to FMSY can be used as a proxy of stock status when biomass reference points are undefined, and the original state of the stock is unknown but assumed at least to be above the point at which recruitment is impaired, their Biomass limit point. The reason for choosing this as a lower limit is that fisheries with biomass at or below their Biomass limit point cannot meet MSC certification requirements. The results indicate that for most of the species here fishing mortality needs to be significantly lower than FMSY for at least two generation times before a stock can be generally assumed to be fluctuating around BMSY given these starting conditions. This answers the second question posed in the introduction: yes, for many species with reasonably productive life histories fishing mortality is likely to be an appropriate proxy. Regarding the first question, our results show that in the absence of any exploitation the soonest most stocks can recover is one generation time, but that achieving recovery within two generation times is achievable with sufficiently low, but non-zero, fishing mortality. These findings are consistent with established fisheries theory: stock status is determined by a complex interaction of previous depletion, life-history type and exploitation level. Our analysis does however highlight life-histories for which reference points


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Fig. 2. The probability of exceeding the biological limit reference point Blim for runs starting below Blim given the four FMSY related harvest strategies. The three columns refer to three time periods at which the stock status was evaluated, half of Mean Generation Time (0.5 MGT), the MGT and one and a half MGT (1.5 MGT). Two horizontal lines represent 50% and 90% probability of exceeding the Blim reference level.

are unlikely to be meaningful, in particular the long-lived species such as snapper, and also informs expectations for stock recovery over a range of scenarios for stock depletion and exploitation rate. A further important result is that simply maintaining fishing

mortality at FMSY is unlikely to be a successful recovery strategy. The value of various sources of information varies across simulations; in some instances it is possible to have confidence in stock rebuilding without precise information regarding one or

150


Fig. 3. The probability of exceeding BMSY for runs starting below BMSY given the four FMSY related harvest strategies. The three columns refer to three time periods at which the stock status was evaluated, half of Mean Generation Time (0.5 MGT), the MGT and one and a half MGT (1.5 MGT). Two horizontal lines represent 50% and 90% probability of exceeding the BMSY reference level.

more of the four key determinants: historic stock depletion, duration of exploitation, life history or exploitation rate. For example, it may not be necessary to understand historic stock depletion and have precise knowledge of recent exploitation rates (providing these have been relatively low) to have confidence in

the recovery of a short-lived species. Similarly over short durations, substantial stock rebuilding is unlikely for long-lived species such as rockfish regardless of exploitation rate. Considering a typical assessment cycle is 3–6 years, recovery times could be relatively large for some stocks even at


151

Fig. 4. Distributions for the number of years taken for a stock to reach biomass at maximum sustainable yield (BMSY) when biomass started higher than the biological limit (Blim) and half way between the biological limit and biomass at maximum sustainable yield ((Blim þBMSY)/2). Table 3 The percentage of rebuilding calculations (i.e. Table 2) for which biomass did not reach BMSY in 100 years of projections and instead this was calculated by extrapolation of a third order polynomial function. Numbers shaded in red represent instances in which over 10% of calculations were by extrapolation. Stock Name

Longevity

Resilience

Rockfish Snapper Porgy Mackerel Sole Butterfish

High High Moderate Moderate Moderate Short

Moderate High Low Moderate High High

FMSY

80% FMSY

60% FMSY

40% FMSY

20% FMSY

No exploitation

80.2 29.9 71.1 17.4 10.0 0.2

17.5 1.6 32.4 1.4 0.0 0.2

0.0 0.0 4.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

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Fig. 5. Variability in simulated unfished biomass (standardized to a mean of 1).

intermediate exploitation rates. For example in the case of the snapper simulation the median estimate of recovery of from Blow to BMSY was 20 years given an exploitation rate of 60% FMSY. It should be noted that these recovery times are based on population assumptions that in many instances have overstated the speed of stock rebuilding [22,23]. Very short-lived species that exhibit high recruitment variation can be considered separate from the other stocks. Natural changes in productivity for butterfish comprehensively outweighed the effect of exploitation rate on stock status. In the remaining stock types much of the variability in recovery time was driven by differences in longevity, a problem addressed by phrasing rebuilding in terms of MGT. The remaining variability was mainly due to recruitment compensation (the steepness of the stock-recruitment curve) and historical stock depletion. Figs. 2 and 3 suggest that it may be possible to develop simple rules to provide approximate recovery times (in units of MGT) based on estimates of stock depletion, F/FMSY and steepness. MSC certification standards operate within the relevant management frameworks that may use either FMSY or BMSY reference points. As noted in the introduction, MSC originally defined its requirements in the context of biomass reference points in line with the understanding of international best practice applicable in the mid-2000s, principally the FAO Code of Conduct and the FAO Ecolabelling Guidelines [24], but in its latest standard recognises the need to acknowledge and allow for the use of non-biomass reference points, particularly fishing mortality based references. In most applications the use of BMSY reference points relies on comparison of current stock size relative to unfished levels. There is however considerable debate not only about how well unfished biomass may be quantified [25] but also whether the concept is appropriate for all stocks. Empirical analysis has revealed that for many marine populations there may not be strong relationship between stock size and productivity [26] which strongly undermines the theoretical basis for biomass reference points. Other research has found fixed exploitation rate scenarios to offer benefits for the management of stock that exhibit non-stationary productivity that may support evaluation of fisheries according to F-based reference points [27]. Our simulations included only temporal variability in recruitment but even so unfished biomass could easily vary by 7 25% in our simulations (coefficient of variation of 25%, Fig. 5). Temporal

auto-correlation in recruitment or ecological processes that could lead to different productivity regimes (varying natural mortality rates) were not simulated. It follows that unfished biomass is likely to be much more variable for a real population. When coupled to estimates of current stock size that are estimated with a comparable level of precision [25] biomass reference points can be expected to be highly uncertain and potentially seriously biased. While FMSY based reference points are still dependent on a number of variables that are generally poorly characterized in stock assessments (natural mortality rate, recruitment compensation and fishery vulnerability) they are more strongly determined by current information and are not reliant on estimation of unfished biomass. There are circumstances in which there may be greater confidence in estimates of F-based reference points due to better information regarding current exploitation rate. For example stocks for which ‘fishery independent’ surveys are available over a long period of time and reliable catch-at-age data are also available. Tagging programs may also provide relatively reliable estimates of current fishing mortality rate [28]. This research reinforces the view that evaluation of fisheries according to exploitation rate requires careful consideration of population life-history, further highlighting the fundamental difference in fishery evaluation according to F- and biomass-based reference points. F-based reference points focus on sustainability of current exploitation rates, are more dependent on current information and do not rely on estimates of historical biomass that is often difficult to quantify reliably, and may be less useful for management of stocks subject to environmental regime change [27]. Standards and recovery programs that require determination of or a target stock status as well as exploitation rate (for instance in consistency with the GSSI benchmarking [29]) need to build in an understanding of this relationship to make accurate certification determinations.

Acknowledgements We are grateful for the support of the Gordon and Betty Moore Foundation and the Natural Resources Defense Council.

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