General Exam

Response to Prof. Gordon (Sandy) McFarlane’s questions.

Aneesh Hariharan, QERM Question 1: Given what is known about sardine population dynamics, what are the trade offs associated with implementing a variable exploitation rate fishing strategy both for the total stock or for the Pacific Northwest component?

Abstract: I approach Question 1 by first describing key features of the Pacific sardine (Northern subpopulation) population dynamics. A large portion of what is known is derived from Hill et.al. (2012). The significance of incorporating climate induced regime shifts into Pacific sardine management is highlighted while presenting the background material, The trade­offs associated with implementing a variable exploitation rate fishing strategy is presented along the lines of King, J. R., and G. A. McFarlane (2006). Since the question addresses 2 separate sub­questions, one for the total stock and one for the Pacific Northwest component, I attempt to build upon the results presented in King, J. R., and G. A. McFarlane (2006) by explicitly taking into account the sardine age structure affecting different regions of the sardine fishery, in the presence of environmental stochasticity (Gaussian white noise for the purposes of this question) and regime shifts. Following guidelines from King et.al. (2015), the simulation study illustrating the trade­offs of implementing a climate induced variable exploitation rate is based on the regime’s productivity (high, low and medium).

Pacific Sardine population dynamics: Stock structure In this question, I focus only on the Pacific sardine northern subpopulation ( from northern Baja California, México to British Columbia, Canada and extends up to 300 nm offshore). Typically, the default approach was to assume that all catches landed in ports from Ensenada (ENS) to British Columbia (BC) were from the northern subpopulation. However, there have been very recent developments and there is now general consensus that catches landed in ENS likely represent a mixture of southern subpopulation (warm months) and northern subpopulation (cold months) (Felix­Uraga et al. 2004; Zwolinski et al. 2011). That being said, the adult spawning stocks likely move north and south in synchrony and do not occupy the same space simultaneously. Thus, for the purposes of this question, we focus on a single stock of the northern subpopulation from ENS to BC.

Biological parameters: Age, Length, Mortality (Region specific) The largest recorded Pacific sardine length is 41.0 cm (Hill et.al. 2012). However the largest Pacific sardine commercially captured fish since 1981 is only 29.7 cm. The heaviest sardine weighed 0.323 kg. The oldest recorded Pacific sardine was 15 years old, but commercially­caught Pacific sardine are typically less than seven years old. Sardines are typically larger and two to three years older in regions off the Pacific Northwest.(Hill et.al. 2012) In this work, both the above facts are useful for developing age classes and existence of an age dependant availability to fishery. Recruitment Until 1953, sardines fully recruited to the fishery when they were ages three and older (MacCall 1979). Recent fishery data indicate that sardines begin to recruit at age zero and are fully recruited to the southern California fishery (SCA) by age two. Age­dependent availability to the fishery likely depends upon the location of the fishery, with young fish unlikely to be fully available to fisheries located in the north and older fish less likely to be fully available to fisheries south of Point Conception. Size and Age at Maturity Size­ and age­at­maturity may decline with a decrease in biomass, latitude, and temperature and at relatively low biomass levels, sardines appear to be fully mature at age one, whereas at very high biomass levels, only some of the two­year­olds are mature (MacCall 1979). In this report, it is assumed that the age at 50% maturity is 1 year. Regime shifts, CCE, SST and distribution of Pacific sardine The abundance and distribution of the Pacific sardine within the California Current Ecosystem (CCE) is greatly influenced by climate variability. Throughout the last century, the northern stock of Pacific sardine exhibited extreme fluctuations in its abundance and distribution, which has largely been attributed to climate variability inherent in the CCE (Norton et al., 2005; Herrick et al., 2006). The CCE extends up to southern Vancouver Island from Baja California and exhibits high biological productivity. Through the last century, the CCE has experienced shifts between warm and cold climate regimes reflected in changes in sea surface temperature (SST). Four regime shifts in the California Current are currently proposed and under discussion; 1925, 1947, 1977 and 1988/89. The years of 1925, 1947 and 1977 have been confirmed as major climate regime shifts in several papers ( Hare and Mantua 2000), but climate changes in 1988/89 were relatively small and considered a minor regime shift (McFarlane et al., 2000). These years characterized a warm regime from 1925 to the 1947, a cold regime between the 1940s and late 1970s, and a warm regime from 1977 to the present (McFarlane et al., 2000). The abundance and distribution of Pacific sardine is extremely sensitive to SST(currently a big debate if the correlation to SST is true. Most recent assessment use the CalCOFI index. That will not be the major focus in the answer to this question) changes caused by the above ocean climate variability in the CCE (Hill et al.,

2012). Between 1934 and 1944, the estimated biomass of Pacific sardine varied between 1.2 million and 2.8 million tonnes and it was the most abundant fish in the CCE. Overfishing was blamed for the collapse of Pacific sardine stock. Now, it is believed that the beginning of the cold regime shift in the CCE during the 1940s decreased the biological productivity of Pacific sardine and accelerated the collapse of the stock, along with intensive fishing pressure. The collapse of Pacific sardine has therefore been attributed to a combination of overfishing and lowered biological productivity reflected to the cold regime (Herrick et al., 2006). The abundance of Pacific sardine remained below 5,000 tonnes during the 1950s and 1960s. As the CCE shifted to a warm regime in the late 1970s, the Pacific sardine stock began to rebuild. It is estimated that the biomass peaked at 1.71 million tonnes in 2000. The estimated biomass in 2006 was 1.31 million tonnes. The total catch by Mexico, the U.S. and Canada has remained greater than 120,000 tonnes since 2000 (Hill et al., 2012). With all the above background about Pacific sardine population dynamics in place, we are able to look at the trade­offs associated with a variable exploitation rate.

A variable exploitation rate fishing strategy and associated trade­offs A regime is a period of a decade or more in which the state, or characteristic behaviour, of the climate or ocean system is steady . Year­to­year differences may exist, but overall the state of the system varies around a persistent baseline. Marine resource management should be able to deal with the decadal­scale variability of climate–ocean regimes. In this section, we look at the trade­offs that arise between yield and conservation of the pacific sardine fish stock and fishery in the presence of ocean regime shifts. MacCall (2002) suggested that for short­lived species such as the Pacific sardine, a regime­specific harvest rate was optimum for maintaining high yield and low variation in spawning stock biomass. Polovina (2005) suggested that for fisheries with regime impacts on productivity, if a constant harvest rate strategy is used, the harvest rate must be well below traditional benchmarks (i.e. approximately 10% of the exploitable biomass), to avoid overfishing during low productivity regimes. An immediate trade­off that one can observe is that a low harvest rate results in low overall yield from the stock, particularly during high productivity regimes. These two scenarios formed the basis for setting up climate induced constant and variable harvests inKing, J. R., and G. A. McFarlane (2006) and similar scenarios will be used here. King et.al. (2015) outline the most common obstacles to implementation of a management philosophy that is amenable to including regime shifts into fisheries management. One of the obstacles that is particularly relevant in the current state of the Pacific sardine fishery is that the environmental and recruitment series have a high within regime variability, especially if biomass estimates are based off a single measure of

environmental variability (SST) to reflect overall changes in an ecosystem state. (Fig. 1).

Fig 1: Regime shifts and Average temperature over time. (Adapted from Ishimura. 2010) Note: Within regime SST variability is very high.

In addition, it is unclear if there is a reliable way to anticipate a regime shift and thus use it for predicting the sardine biomass. Thus, it was proposed in King et.al. (2015) that current attempts should not focus on directly integrating regime shifts and states in stock assessments or in estimating biological reference points, rather it should be used as supporting information to stock assessment advice. Given all the above pieces of expert information, it seems fitting to incorporate low frequency environmental forcing to the Pacific sardine abundance and distribution with regime shifts and determine if its introduction improves the balance between yield and conservation under various harvest scenarios and the consequences of ignoring regime shifts while providing tactical advice. The following section is based off of King, J. R., and G. A. McFarlane (2006) to evaluate the trade­offs associated with implementing a variable exploitation rate fishing strategy both for the total stock and for the Pacific Northwest component based on a simulated fish population in the presence of regime shifts.

Trade­offs associated with implementing a variable exploitation rate fishing strategy: Modeling details Simulating a fish population A sardine population is simulatedwith a Beverton­Holt­based age­structured model, with regime impacts added as a multiplicative effect on the slope at the origin:

where R is the number of young in year t, P are the number of spawners at the end of the previous year after any harvest (expressed as number fish), ɛ is the regime effect assigned to year t and α and β are parameters. Parameters of the model (from King(2006), short lived species) The following parameters are mostly derived from King, J. R., and G. A. McFarlane (2006). The age at 50% maturity is assumed to be 1, to reflect the relatively complex relationship between maturity and abundance of the Pacific sardine i.e. At relatively low biomass levels, sardines appear to be fully mature at age one, whereas at very high biomass levels, only some of the two­year­olds are mature (MacCall 1979).

Age at 50% maturity 1

Annual mortality 0.4

Maximum size (kg) 0.3

0.00000017

0.02

Regime 1 effect, ɛ

(Good productivity)

1

Regime 2 effect, ɛ

(Low productivity)

2

Regime 3 effect, ɛ

(Moderate productivity)

1.3

Modeling Regime shifts Three, 20­year regime periods were modelled, with the first 20 years as a period of good productivity (regime 1), the second 20 years as a period of poor productivity (regime 2) and the final 20 years as a period of moderate productivity (regime 3). Values of ɛwere selected such that the resultant unfished biomass approximated 50% in regime 2 and 75% in regime 3 of the regime 1 biomass. Age classes, assumptions on spawners and recruits For the purposes of this simulation, I consider 7 age classes to reflect the fact that age­dependent availability to the fishery likely depends upon the location of the fishery, with young fish unlikely to be fully available to fisheries located in the north and older fish less likely to be fully available to fisheries south of Point Conception. In addition, California commercial catches are usually younger than five years. Sardine are typically larger and two to three years older in regions off the Pacific Northwest. The age classes are: 0, 1, 2, 3, 4, 5, 6+ Age 0 are considered as recruits and ages 1+ are fully mature and constitute the spawning stock. Additionally, it is assumed that ages classes 1 and 2 appear in Californian waters and ages 3+ are in the Pacific Northwest regions.

The reason separate age classes are explicitly modeled is to evaluate variable harvest strategy and its associated trade­offs for the total spawning stock vs. effects on specific age classes. This modeling approach also seems to adequately reflects the fact that age­dependent availability to the fishery likely depends upon the location of the fisher, with young fish unlikely to be fully available to fisheries located in the north and older fish less likely to be fully available to fisheries south. One aim of evaluating variable harvest strategies over separate age classes in this question is an attempt to build on the work presented in King (2006) and discuss it during the committee meeting. Environmental Stochasticity A small amount of stochastic white noise is added at every time step to take into account random environmental variability surrounding the Beverton­Holt stock recruit model. The aim of adding white noise is to study if scenarios are drastically altered in the presence of environmental stochasticity (naively modeled as white noise here!). Harvest scenarios Constant harvest rate (Scenario 1 to 4) Harvest rate=F over the 60 years (3 regimes), irrespective of changes in productivity, is set to contant: Scenario 1: No harvest (Used to develop reference points) Scenario 2: F = annual mortality (M); Scenario 3: F = 0.5M Scenario 4: F = 0.25M. (No scenarios for F>M were considered, since F=M led to fishery closure and conservation concern in all scenarios 1 and even the variable harvesting scenario 5): Scenario 5: 1st Regime­specific harvest rates (Reg1): Harvest rate is switched coincidental with the regime shift year. F = M, F = 0.5M and F = M variable harvesting across regime 1, regime 2 and regime 3 respectively. (Common management practice) Scenario 6:: 2nd Regime­specific harvest rates (Reg2): Harvest rate is switched coincidental with the regime shift year.. F = M, F = 0.25M and F = 0.5M for variable harvesting across regime 1, regime 2 and regime 3 respectively. (King and McFarlane (2006) present a couple of other scenarios, where the harvest switch is not implemented coincidentally with the regime shifts but implemented with a lag of 50% mortality (assumed to be 1 year in this model) after the regime switch. Due to time constraints for answering this question, I am not presenting those results but can be easily added on to the study. Instead, an attempt is made to study the effect of environmental stochasticity,regime shifts and variable harvesting on age specific distributions and the total stock) For each of the above scenarios, the following simulation results, which builds upon King and McFarlane (2006) are also presented.

1. Age specific SS harvest trade­off scenarios Assumption: “South” refers to the Southern Pacific sardine stock (south of Pt. Conception) and are assumed to be smaller fish (ages 1 and 2). As pointed in the introduction, this may not be a truly valid

assumption, since there are older fish in the South and occasional younger fish in the North. However, this assumption presumably gets as close to the age­based differentials in the fishery. Only results for Reg 2(F=M, F=0.25M, F=0.5M, Scenario 6) is presented in this report. 2. Effect of varying the environmental stochasticity (white noise). In all the simulations, a small white noise (sigma=0.01, low stochasticity) is added to indicate recruitment variability. Results for the total spawning stock harvest trade­offs are also presented for sigma=0.1 (high stochasticity), to study the effect of environmental stochasticity and how it affects the regime component. Evaluating management trade­offs: Fishery closures, conservation concerns and specifying a reference point The maximum number of spawners observed over the 60 years without any harvest was used to set the criteria for fishery closures and conservation concerns. The 25% and 35% levels of this maximum were used as representatives of critical spawning biomass (fishery closure) and a level for which there would be conservation concerns respectively. For age­specific harvest trade­off scenarios, it was assumed that the 25% and 35% levels of max with no harvesting for the “South” (recruits and age 1) and the PacNW (age 2+) stock corresponded to reference values for that particular age class. (E.g. If 25% max of recruits with no harvesting was 17, then this would be the criterion to identify fishery closure for recruits in all other harvest scenarios). It isn’t clear if this a valid assumption for a reference point and might need further review at the committee meeting.

Trade­offs associated with implementing a variable exploitation rate fishing strategy: Results and discussion I wrote all code for this question in R. Since the code is too long to paste in this document, Appendix A contains the main function module (“AgeStructMatRegime”) that is repeatedly called to evaluate different scenarios. The environmental stochasticity parameter is set at a constant 0.01 and is the same for all simulations with low stochasticity and at 0.1 for high stochasticity. The following section (Fig 2 to 10) presents the results from a simulated fish population with different harvest scenarios, with and without environmental stochasticity in the presence of regime shifts. Discussion of results immediately follow the relevant figures.

Results:

Fig 2: Spawning stock (millions of fish) from a Beverton Holt age structured model for 6 harvest scenarios. In all the scenarios, the first 20 years is “high” productive regime, the second 20 years is “low” productive regime and final 20 years is “medium” productive regime.

Fig 3: Number of years of each model scenario that lead to conservation concern or fishery closure.

Fig 4: Yield, expressed as number of fish caught (IDEALLY IT SHOULD BE WEIGHT) for Scenarios 2 to 6. Discussion of results from Fig 2, 3 and 4 Fig 4. follows the intuition that the total yield decreased with decreasing constant harvest rate, such that the yield at F=0.25M was only approximately 30% of the yield at more aggressive constant fishing, F=M. This result is very similar to King et.al (2006) and thus, encouraging:) From Fig 3., it is clear that with constant F=0.25M, there is no fishery closure and only 17 years of conservation concern. Aggressive fishing with constant harvesting isn’t even an option in ANY of the regimes, since it leads to 100% fishery closure and conservation concerns. This result vastly differes from King et.al (2006), since, contrary to their assumtion, here it was assumed that age of 50% maturity is 1+ and recruits that enter the fishery are 0+. Overall, lower constant harvest rates, F=0.5M and F=0.25M resulted in lower years of fishery closure and lower years of conservation concern, but this came with a tradeoff of much lower yield. Reg1 (Scenario 5) (where Harvest rate is switched coincidental with the regime shift year. F = M, F = 0.5M and F = M harvests for regime 1, regime 2 and regime 3 respectively) isn’t an option either, since it led to 57/60 days of fishery closure and 100% days of conservation concern for only a 19% increase in yield compared to Reg 2(Scenario 6). Since Reg1 led close to 100% fishery closure and conservation concerns, it may not provide a viable trade­off for the increased yield realized. The trade­offs are minimized with the selection of regime specific harvest rates, Reg2 (scenario 6), that reflects relative productivity of different regimes (F=M, F=0.25M and F=0.5M). The total yield obtained under

this harvest rate scenario was approx 15% higher than that obtained with constant F=0.5M. The number of years of fishery closure in Reg. 2 was in fact slightly lesser than constant harvest F=0.5M but the number of years of conservation concern is much higher (15 years) than constant F=0.5M. Given the increase in yield and the lower number of fishery closure years, further research is needed to study the impact of particular regimes that leads to a greater number of years of conservation concern in Reg2. One possible attempt is to look at these effects under environmental stochasticity. Fig.5 and Fig 6 do not address comparison of yields but are meant to present a visual of the effect of environmental stochasticity under under different harvesting scenarios.

Fig 5: Different harvest scenarios under the presence of large environmental stochasticity

Fig 6: Number of years of each model scenario that lead to conservation concern or fishery closure with large environmental stochasticity

Discussion of results from Fig 5 and 6 Clearly from Fig.5, environmental stochasticity added to the Beverton Holt age structured model produces a lot of noise in all different scenarios. Without looking at yield, from Fig.6, it can be seen that Reg2 and constant harvest F=0.5M are only 2 years apart for the number of years of fishery closure and conservation concern. However, the yield in constant harvest, F=0.5M across all regimes is 5% higher than Reg2 ! This finding could be explained purely by the noise and additional work needs to be done along the lines of King and McFarlane (2006) to look at a scenario that delays harvest by 1 year after a regime shift with environmental stochasticity to study if Reg2 is indeed the most optimal tradeoff between yield and conservation with added environmental stochasticity. Finally we look at age specific harvests with a very conservative constraint to see if older fish are subject to more fishery closures compared to age 0 and age 1 fish. The aim of this section is to study if smaller fish (age 0 and 1) that reside in the “South” (MX, CA) inherently are subject to lower fishery closures and conservation constraints than bigger fish in the PacNW (Age 2 and higher). Though there is no existing literature that explicitly supports this exact size differential assumption, it is presumably a valid assumption based on characteristics of existing fishery. The reference criterion, that I use in Fig 7, 8, 9 and 10 (with/without environmental stochasticity) is to compare each age class under different scenarios to the 25% and 35%max of the corresponding age class under the no harvest scenario. This isn’t practical for implementation in management but gives a reasonable outlook of the scale of difference in management outcomes.

Fig 7: Reg2 variable harvesting and number of fish of each age class.

Fig 8: Reg2 variable harvesting, fishery closure and conservation concern. Extremely conservative reference

of 25% max no harvest for each age class is used correspondingly in the Reg2 scenario.

Fig 9: Similar to Fig 7 with environmental stochasticity.

Fig 10: Similar to Fig 8 with environmental stochasticity.

Discussion of results from Fig 7,8, 9 and 10 It can be seen that fishery closures and conservation concerns (Fig 8 and Fig 10) are much lesser for smaller aged fish (age0 and age 1) compared to age 2+ under Reg2 (the optimal harvest that minimized tradeoff between yield and conservation). Though a comparison of yield and the trade­offs associated with conservation has not been accomplished yet, it might be prudent to incorporate separate reference points age2+ stock than age 0 and age 1 stock under a variable harvest plan in the presence of regime shifts? From figures 8 and 10, the implication is that PacNW will always remain closed, which is far from reality! However, the point of this section was to introduce that under regime shifts with scenarios 1­6 as defined in the previous section, the South has much lesser fishery closures. Environmental stochasticity seems to have little effect on fishery closures for the PacNW and South stock (Compare Fig 8 and 10). Conclusion: Reg2 (F=M, F=0.25M, F=0.5M) minimized the trade­off between yield and conservation in the absence of environmental stochasticity and hence should be implemented under the six scenarios considered for this question. In the presence of environmental stochasticity, constant harvest F=0.5M produced similar results as Reg2 for the conservation piece (fishery closure and conservation concern) but at a higher yield. Thus, more research is needed to assert the effect of environmental stochasticity in the different regime specific harvest scenarios. The variable harvest strategy might not be as effective in the presence of environmental stochasticity. Further option, such as lagging harvest a year (similar to King et.al. (2006)) after the regime switch appears could lead to interesting comparisons. Finally, age specific targeting with an extremely conservative reference point, showed that the South fishery was subject to lesser closure and was less of a conservation concern than older fish. The yield trade­off has not been studied yet but would be great avenue

to further continue this research. Future work would also include incorporating the age differentials to illustrate characteristics of the fishery (South and PacNW) to study the possibility of different reference points (e.g. not one single 0.25* max (no harvest) of the total spawning stock)) under variable harvest scenarios. ­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­­ Question 2: There is an increasing focus on determining appropriate harvest strategies for forage fish such as sardine. Discuss the current thinking on management appropriate approaches for those types of species, especially approaches incorporating decadal scale climate change. A large aspect of this question was addressed in Question 1 without specifically alluding to decadal scale climate change. This question is answered with the help of 3 main papers and other cross­references derived from these papers. The papers are: King and McFarlane 2003; King and McFarlane 2006; King, McFarlane and Punt 2014 In the North Pacific, climate and environmental conditions have been observed to be relatively stable on decadal scales, during periods called regimes, but they can abruptly shift from one state to another during regime shifts . Abundance and distribution of fish such as the Pacific sardine are known to fluctuate concurrently with climate–ocean regimes. For example, the years of 1925, 1947 and 1977 have been confirmed as major climate regime shifts in literature ( Hare and Mantua 2000), but climate changes in 1988/89 were relatively small and considered a minor regime shift (McFarlane et al., 2000). These years characterized a warm regime from 1925 to the 1947, a cold regime between the 1940s and late 1970s, and a warm regime from 1977 to the present (McFarlane et al., 2000). Within a regime period, the abundance of Pacific sardine increases or decreases over time, and across regime periods the populations experience high amplitude of variability. Extreme fishing pressure (as shown in Question 1) combined with the the magnitude of variability in survival makes fish species such as the sardine susceptible to rapid depletion.

Marine resource management for small pelagics typically incorporate decadal­scale variability of climate–ocean regimes. As already pointed out, a regime is a period of a decade or more in which the state, or characteristic behaviour, of the climate or ocean system is steady . However, a regime shift is rapid, usually occurring within a year, and is a substantial change from one regime period to another.

It has also been found that differing levels of productivity appear to be decadal in nature and correspond to regimes; further changes in productivity coincide with regime shifts. Ecosystem based assessments should provide an indication to the stock assessment scientists, and to the managers, which productivity scenario is most appropriate.

The current approach in fisheries science and management couples risk management of exploitation with conservation goals. Science advice provides the possible consequences and the associated risk of various harvest strategies given the best estimates of current and projected stock status and regime shifts. The drivers of those ecosystem impacts include climate–ocean variability and ecosystem reorganization, both of which are contained in low­frequency (decadal­scale) ecosystem regime shifts and states. King et.al (2006) provides the follwing flowchart to serve as a framework for determing appropriate harvest strategies in the presence of regime shifts.

Flowchart 1: (From King et.al. 2006)

For sardines, there have been repeated attempts to link biomass fluctuations to indices of climatic variability, like the Pacific Decadal Oscillation (PDO)as a proxy for regime shifts. Management advice, until 2012, implicitly used the relationship between recruits­per­spawner and the environment, as quantified by sea surface temperature (SST) at the pier of the Scripps Institute of Oceanography in La Jolla, CA, USA to establish the fishing mortality corresponding to maximum sustainable yield.

However, this use of SST was removed from the harvest control rule in 2012 after analysis of updated data. It was found that the relationship wasn’t significant. Since then, the updated data have been re­assessed using the same methodology originally employed. It was found very recently that significant part of the fish recruitment and recruits per spawner variability is indeed explained by the relationship with temperature. The new finding averaged SST over the main spawning area and not the pier temperature. The CalCOFI index, instead of the PDO SST was the basis for the result of re­introduction of SST into the Harvest Control Rule.

The above debate highlights the limitations of depending on a single measure of environmental variability to reflect overall changes in ecosystem state, and the need to update and revise relationships when necessary.

Simulations might be of great use in such situations. Simulation testing of the performance of management strategies given uncertainty in stock assessment, model estimates, data collection, reference points and management actions to achieve specified management objectives falls compose a toolset in fisheries management called Management Strategy Evaluation (MSE). MSE (Management Strategy Evaluation) is the most relevant field of investigation for incorporating regime shifts into fisheries management since the

strategic models can ask:

(i) Does ignoring environmental impacts lead to poor performance?

(ii) Can we do better using an environmental control?

The answers might be contradictory.

Some examples of previous attempts to include regime shifts were:

Dynamic B0: (MacCall 1985) This method takes the parameters that are estimated by the stock assessment model and then projects the population forward from the first year when there are catches, but with no fishing to estimate B0. This projection of B0 has time­varying recruitment, growth and natural mortality which can be simulated with regime­like characteristics.

The Moving window approach: (Punt et.al. 2013): This method estimates B0 and BMSY using stock assessment estimates of recruitment for a specified number of years. It may not a viable management appropriate approach for sardines, since the regime shifts should be expected to be consistently less than 20 years.

Given the above approaches, it remains that biological reference points should not be regime specific, but should be based on the fit of the stock–recruitment relationship, if catch and survey data do not span multiple regime states. Thus, survey data cannot be relied upon to capture a full suite of environmental signals for managing and setting harvest guidelines for fish such as the sardine.

Question 1 was approached with the above goal in mind, where specific regimes were not modeled.

Though MSE provides an avenue for testing tactical models given decisions made based on knowledge of regime shifts and states, is was recently proposed by scientists (King et.al. 2015) that current attempts should not focus on directly integrating regime shifts and states in the stock assessments or in estimating biological reference points, but rather are used as supporting information to stock assessment advice. In this regard, the nature of the regime’s productivity could be useful when future projections or assumptions about recruitment are needed for management decisions (Punt, et.al. 2013).

An algorithm that summarizes thinking of current management appropriate approaches for determining harvest strategies (extracted from King et. al . 2015):

1. Historical data on environmental factors and fish productivity could be synthesized and classified into regimes to give an indication of previous regime­state attributes and corresponding relative fish productivity.

2. Biomass estimates from stock assessments (without inclusion of environmental forcing) would be produced for recruitment assumptions for below average, average and above average recruitment­state scenarios

3. Tactical advice based on these scenarios would be summarized in decision tables with associated risk.

4. Current information on ecosystem attributes to compare with synthesized historical regime­state

characteristics could be used to assign plausibility weights to each fish productivity scenario presented in the decision table, and the corresponding tactical advice.

5. Knowing the consequences of each management action given each alternative state of nature, along with their relative plausibility, will provide decision­makers with the information to allow them to select management actions given the available uncertainties.

The above points are highlighted in Flowchart 1.

Regime shifts can thus slowly be incorporated into a tactical MSE. Incorporating regime shifts impacts on fish productivity requires converting fish productivity into regime­specific states to inform the projection of recruitment and the ‘health’ of fish stocks.

This needs to be done carefully in a decision analysis framework, rather than as ‘best case’ forecasts. Purely relying on ‘best case’ forecasts is not reliable and may in the longer term lead to a lack in trust on ecosystem factors.

References: Felix­Uraga, Roberto., et al. "On the existence of Pacific sardine groups off the west coast of Baja California and Southern California." California Cooperative Oceanic Fisheries Investigations Report 45 (2004): 146. Hare, Steven R., and Nathan J. Mantua. "Empirical evidence for North Pacific regime shifts in 1977 and 1989." Progress in oceanography 47.2 (2000): 103­145. Herrick, S. F., Kevin Hill, and Christian Reiss. "An optimal harvest policy for the recently renewed United States Pacific sardine fishery." Climate change and the economics of the world’s fisheries: examples of small pelagic stocks. Edward Elgar, Glasgow (2006): 126­150. Hill, Kevin T., et al. Assessment of the Pacific sardine resource in 2012 for US management in 2013. Technical report, Pacific Fishery Management Council, 7700 NE Ambassador Place, Portland, OR 97220, USA, 2012. Ishimura, Gakushi. "Transboundary management of a fish stock under climate variability: the case of Pacific sardine in the California Current Ecosystem." (2010). King, J. R., and G. A. McFarlane. "Marine fish life history strategies: applications to fishery management." Fisheries Management and Ecology 10.4 (2003): 249­264. King, J. R., and G. A. McFarlane. "A framework for incorporating climate regime shifts into the management of marine resources." Fisheries Management and Ecology 13.2 (2006) King, Jacquelynne R., Gordon A. McFarlane, and André E. Punt. "Shifts in fisheries management: adapting to regime shifts." Philosophical Transactions of the Royal Society of London B: Biological Sciences 370.1659 (2015): 20130277.: 93­102. Jacobson, Larry D., and Alec D. MacCall. "Stock­recruitment models for Pacific sardine (Sardinops sagax)."

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Appendix A #R code for main function module: “AgeStructMatRegime” #Only 1 sub function, all other sub­functions look similar #Author: Aneesh Hariharan; aneesh@uw.edu # This is written for answering a general exam question. The new ideas presented here need further # exploration and thus will undergo major revisions. AgeStructMatRegime_F = function(sx, sy, sz, a, b, tf, N0, sig_r) #sig_r = 0.01 #Parameter to describe environmental stochasticity ncls = length(N0) #Number of age classes

Nt_F = matrix(0, tf, ncls) #Initialize output matrix with time steps as rows, age classes as columns Nt_F[1, ] = N0 #initial values into first row of output matrix for (t in 1:19) Pt = Nt_F[t, 2]+Nt_F[t, 3] + Nt_F[t, 4] + Nt_F[t, 5] + Nt_F[t, 6]+ Nt_F[t, 7] Nt_F[t + 1, 1] = (1/(eps_1*a + (b/Pt))) * (exp(sig_r * rnorm(1, mean = 0, sd = 1))) #number of recruits with environmental stochasticity Nt_F[t + 1, 2:ncls] = sx * Nt_F[t, 1:(ncls ­ 1)] #number of age classes 2­7: 1st regime for (t in 20:39) Pt = Nt_F[t, 2]+Nt_F[t, 3] + Nt_F[t, 4] + Nt_F[t, 5] + Nt_F[t, 6]+ Nt_F[t, 7] Nt_F[t + 1, 1] = (1/(eps_2*a + (b/Pt)))* (exp(sig_r * rnorm(1, mean = 0, sd = 1))) #number of recruits with environmental stochasticity Nt_F[t + 1, 2:ncls] = sy * Nt_F[t, 1:(ncls ­ 1)] #number of age classes 2­7: 2nd regime for (t in 40:64) Pt = Nt_F[t, 2]+Nt_F[t, 3] + Nt_F[t, 4] + Nt_F[t, 5] + Nt_F[t, 6]+ Nt_F[t, 7] Nt_F[t + 1, 1] = (1/(eps_3*a + (b/Pt)))*(exp(sig_r * rnorm(1, mean = 0, sd = 1))) #number of recruits with environmental stochasticity Nt_F[t + 1, 2:ncls] = sz * Nt_F[t, 1:(ncls ­ 1)] #number of age classes 2­7: 3rd regime return(Nt_F)