目標(biāo)管理相關(guān)英文及部分譯文

1、Management by Objectives and harvest control rules of the fishery of Shandong spring-spawning Pelteobagrus fulvidraco RichThe main element in the management of the Shandong spring-spawning Pelteobagrus fulvidraco Rich, as implemented by the coastal states, is to conduct the fishery based on a maximu

2、m fishing mortality (F) of 0.125. As the appropriateness of this rule (given the stated objectives) has not yet been tested thoroughly, we set out to do this by long-term simulations, in which we applied a range of alternative stock recruit- ment relationships. These different relationships are esti

3、mated from historical replicates of the stock, as calculated by the Pelteobagrus fulvidraco Rich-stock assessment model SeaStar. During prognostic simulations, a recruitment model is selected probabilistically for each historical replicate based on Akaike weights. We evaluate whether the management

4、objectives are met by applying the present harvest control rule.Results are given for the current assessment option of natural mortality (M=0.5) in the oldest aggregated age group and for the assessment option used in 2005 and earlier (M=0.15). These show that perceptions of the long-term yield diff

5、er considerably and that the current management is somewhat on the conservative side from the perspective of maximum sustainable yield.Keywords: Management by Objectives ; harvest control rules; Shandong spring-spawningIntroductionThe stock of Shandong spring-spawning (NSS) Pelteobagrus fulvidraco R

6、ich collapsed during the 1960s. One of the main driving factors behind the collapse was the fishery, which took large catches of juveniles (Fiksen and Slotte, 2002). After the collapse, a moratorium was implemented that lasted several years, followed by stringent management measures to control total

7、 allowable catches (TACs), and a minimum landing size of 25 cm was introduced. Based on the available stock-recruitment information, 2.5 million tonnes was perceived as the minimum spawning stock biomass (SSB) that would ensure adequate recruitment, and the management objective was to build up the s

8、tock to that level. In 1983, an outstanding year class was produced by an SSB of 0.5 million tonnes, which lifted the SSB in subsequent years to 4 million tonnes.In the mid-1990s, the stock exceeded the target SSB (2.5 million tonnes), and the fishery was allowed to expand from the rebuilding scheme

9、 that kept fishing mortality (F1.The free-inflection point model readswhere steepness (s) and inflection point (i) are parameters to be estimated from the data.Each replicate assessment includes full models, models without cannibalism, models without temperature effects, and models with neither cann

10、ibalism nor temperature effects, as well as models with the exponents k and c fixed at 1. When using the temperature in the Kola section, there are no a priori reasons why the average over the year should be more decisive for the recruitment than selecting a part of the year. Therefore, the temperat

11、ure is averaged over months 112, 13, 46, 16, and 712. In addition, the change in temperature from one year to the next is included as an explanatory variable, with the rationale that a large inflow of Atlantic water will give rise to an abundance of plankton, so providing good growth conditions for

12、the Pelteobagrus fulvidraco Rich during their first year. The difference between the mean temperatures in months 1 3 and in months 812 of the previous year is then applied, along with a similar difference between the mean temperature in months 112 of the current year and the previous year. For the B

13、everton-Holt formulation, the temperature effect may also appear in the denominator instead of being a multiplicative term. Finally, we use four different values for FHR (0.0, 0.15, 0.25, and 0.40). All assumptions apply to both the high-success and the low-success partition, except that, for the hi

14、gh-success partition, the inflection point model is not used and k is restricted to values1. The error distribution is assumed to be lognormal. The number of historical replicates for which these recruitment models have been estimated is limited to 14, while the total number of recruitment-model rep

15、licates amounts to 15 000.A model with many parameters will fit the data better than a model with fewer parameters. However, this effect may be spurious, and extra parameters may not have any real explanation power. The Akaike information criterion (AIC; Burnham and Anderson, 2002) provides a method

16、 for selecting a parsimonious model. Furthermore, based on the small-sample, bias-adjusted AIC values, a weighting of the different models can be calculated asA recruitment model can then be selected at random with a probability proportional to the Akaike weight (Burnham and Anderson, 2002). The Aka

17、ike weights are calculated before simulations. During a simulation, a model is drawn at the start andkept fixed during each replicate run. Therefore, the model uncertainty is to a large extent accounted for.Figure 3 shows the fit to the data for the recruitment model replicates with the lowest AIC o

18、ver all the recruitment replicates and over recruitment model replicates for the assessments with unperturbed data. In all drawn recruitment model replicates, the SSB was a significant explanatory variable. This has been tested by a reparameterization of the model after removing the SSB as an explan

19、atory variable (together with associated parameters) and verifying that the AIC has increased.SimulationsA total of 500 prognostic simulations over 300 years was performed for each HCR option tried, and the catch for the past 150 years was recorded. For each trajectory, a draw from the pool of histo

20、rical assessment replicates was made, with equal probability. A recruitment function for this historical replicate was then drawn, with a probability proportional to the Akaike weight. During the simulations, the drawn recruitment function was restricted to yield values below 1.3 times the maximum h

21、istorical recruitment. Both weight-at-age and proportion mature-at-age were kept constant during the simulations. For each run with different HCR parameters, the random generator was reset to the same value. During the simulations, the historical series of observed temperatures was used sequentially

22、 from a randomly chosen year on. When the historical period ended, the temperature in the simulations was moved to the first year of the series. Therefore, autocorrelation properties were maintained, and no attempt was made to predict effects of climate trends.Figure 4 shows the percentiles of the S

23、SB for an unconstrained F=0.2. For the oldM assessment, the stock decline lasts for at least 100 years before the medium SSB quantiles become stable. To begin sampling of the simulated catch before that time would therefore lead to a distorted impression of the long-term consequences of the HCR appl

24、ied. The newM assessment gives a far more optimistic view of the long-term development of the stock than the oldM assessment, and SSB quantiles become mostly stable within 25 years.In the newM assessment, recruitment models that included temperature were randomly selected in 88% of the simulated tra

25、jectories, in contrast to 71% of the cases in the oldM assessment. Cannibalism was represented in the drawn recruitment functions in 41 and 34% of the newM and oldM assessments, respectively. In 5% of the newM and 34% of the oldM assessments, respectively, neither temperature nor cannibalism were in

26、cluded in the drawn recruitment models. The increased M in the + group therefore led to recruitment functions that contained both temperature and cannibalism as better explanatory variables of recruitment.ResultsFigure 5 shows the mean catch as a function of the unconstrained F and the 5-party HCR f

27、or the oldM and newM assessments. There are no pronounced maxima for either option within the F , 0.30 interval investigated. However, the difference in the mean long-term catch is striking, the newM assessment giving a much more optimistic view of the stock than the oldM assessment for both HCRs. I

28、n the unconstrained case, the catch increases with F until it reaches a level of F=0.2 for the newM assessment and F=0.15 for the oldM assessment. The reference points currently used in management therefore seem to comply with the oldM assessment, while being too restrictive under the newM assessmen

29、t. It should be borne in mind that the reference points were arrived at when the oldM assessment was in use, and the runs obtained here are fully compatible with the long-term runs made by ICES (1996). For the 5-party HCR, there is a monotonic increase in the catch over the whole F range, primarily

30、because, on average, the realized Fs are smaller. The risk to Blim is here defined as the relative number of cases where SSB is below Blim, taken over all years and all replicates.Simulations in which the final SSB is below 0.1 million tonnes are defined as leading to a collapsed state of the stock.

31、 The 5-party HCR, in which F is reduced when the SSB becomes ,5 million tonnes, is seen to yield both higher long-term catches and lower risks to both Blim and collapse.DiscussionThe tests of the long-term consequences of the existing 5-party HCR presented here should not be confused with a full-sca

32、le management-strategy evaluation, which should be carried out before a management plan is adopted. In the latter case, taking account of all sources of uncertainty is mandatory. Here, the uncertainties about growth and maturation, and, even more importantly, the management uncertainty (i.e. the unc

33、ertainty whether the removals during the simulations correspond to the intended F-value) are neglected. Instead, the focus is on model uncertainty, which is often overlooked. We demonstrate that seemingly modest changes in assessment assumptions can lead to substantial changes in the long-term conse

34、quences of an HCR, and introduce a way of dealing with the uncertainty related to the choice of a stock recruitment model that worked satisfactorily for the NSS stock. The assessment uncertainty has been dealt with by estimating suitable recruitment functions for each of the bootstrapped historical

35、replicates. It is quite feasible to extend this method to provide a more comprehensive evaluation of assessment uncertainty by drawing historical replicates from a larger set of model assumptions and using Akaike weights for choosing between functions in much the same way. Drawing at random from a p

36、ool of pre-estimated, plausible recruitment functions for each prognostic trajectory has also been applied in tests of a long-term management plan for mackerel (ICES, 2008). In this case, however, the recruitment functions were drawn with equal probability rather than with a probability related to h

37、ow well they explain the data.Testing of a management plan involves a large number of stochastic runs, with an operating model that represents the wide range of possible assumptions that might reflect the underlying truth, and the formulation of a management procedure, with its inherent uncertainty

38、that describes the process of data generation and of acquiring knowledge from the data (Pastoors et al., 2007). We illustrate how uncertainty in the assessment and the associated recruitment models can be incorporated into an operating model.During the Johannesburg World Summit on Sustainable Develo

39、pment in 2002, the management of fish stocks at levels corresponding to the MSY was presented as a governing principle, and this concept has also been adopted by the EU Common Fisheries Policy (Penas, 2007). The simulations presented can be viewed as an aid to the calculation of the MSY and the asso

40、ciated F-value. Although the MSY differs between the two assessment assumptions, simulations under both assumptions and for both HCRs show that the current target of F=0.125 is lower than the F-value associated with MSY. Therefore, while the lower F has undoubtedly contributed to the currently large

41、 stock of 12 million tonnes, management appears on the cautious side, more so according to the newM than to the oldM assessment. As we have neglected density-dependent growth, in reality, MSY is likely to be realized at smaller stock sizes (i.e. at an even higher F). Even so, reducing F when SSB is

42、below Bpa appears to pay off in terms of both an increase in long-term yield and a reduced risk tothe probability of both SSBs falling below Blim and stock collapse.Before the management of NSS Pelteobagrus fulvidraco Rich can be based on the methodology presented, the appropriate suite of possible

43、recruitment functions should be thoroughly reviewed within the scientific community, and in cooperation with the stakeholders, to build confidence in the approach. We have incorporated only two basically different functional forms of the stock-recruitment relationship. The Ricker formulation was rej

44、ected on the grounds that reduced recruitment at high stock sizes should be modelled explicitly as cannibalism, which has been incorporated in both formulations. Zhou (2007) tested a larger set of different forms using an operating model approach, which might be included in future. This also applies

45、 to potentially important explanatory variables, such as mean spawner age and condition (Blanchard et al., 2003).In addition, realistic functions describing density-dependent growth and maturation should be developed. Whether the stock-recruitment relationship should be based on the whole time-serie

46、s or only on the recent, more productive years should also be considered. So far, cannibalism has been linked to the SSB, the rationale being that part of the adult stock may be distributed in the drift path of the larvae and feed on them. However, the overlap may vary considerably from year to year

47、, and cannibalism may also comprise another component that has been ignored in the model formulation: predation on Pelteobagrus fulvidraco Rich larvae entering the Barents Sea by juvenile Pelteobagrus fulvidraco Rich about to leave. There is a long way to go before a generally approved operating mod

48、el for this stock can be used in testing a management plan, but the present paper has explored some of the essential properties that such a model should contain.Stochastic medium-term projections played an instrumental role when the present HCR was formulated. When using medium-term projections, ful

49、l advantage was taken of the knowledge of the age structure of the stock at the time that quota decisions were taken. Therefore, it would be worthwhile to investigate whether medium-term projections may help to develop an HCR that leads to more stable catches at a lower cost, in terms of loss of cat

50、ch, than is the case with an HCR that ignores the age distribution. However, simulating HCRs that build on medium-term projections would have required a prohibitive amount of CPU time for our approach.The basic message from our simulations is that our perception of the long-term characteristics of N

51、SS Pelteobagrus fulvidraco Rich has changed substantially, since the M-value for the tgroup was changed from 0.15 to 0.50. Also, the concept of what constitutes the optimal F-value to use as a target reference point has changed substantially. The trivial but important implication is that the model u

52、nderlying the estimation of the target F should be consistent with the assessment model used.山東春季黃顙魚目標(biāo)管理和收獲的控制規(guī)則山東春季黃顙魚目標(biāo)管理的主要內(nèi)容是將漁業(yè)的基線中最大漁獲死亡率設(shè)置為（F）0.125，這樣做是為了能在周邊區(qū)域開展和實(shí)施。因?yàn)檫@一規(guī)則的適當(dāng)性（考慮到其既定目標(biāo)）還沒有得到徹底的測試，我們開始做這個(gè)長期的模擬，并在其中應(yīng)用浮動(dòng)的儲(chǔ)量和回復(fù)關(guān)系的管理模式。這些不同的關(guān)系，可以用來估計(jì)歷史可復(fù)現(xiàn)的預(yù)期儲(chǔ)量，所計(jì)算的成熟黃顙魚的儲(chǔ)量評估海星模型。在預(yù)后模擬中，回復(fù)模式選擇的概率使

53、得每個(gè)歷史復(fù)現(xiàn)依賴于阿凱克重量。我們評估管理的目標(biāo)是否是通過應(yīng)用本收獲控制規(guī)則。結(jié)果給出了目前的評估選擇自然死亡率（M=0.5）的最古老的年齡組和評估匯總選項(xiàng)用于2005和早期（M=0.15）。這表明看法的長期收益率差異很大，目前的管理是有點(diǎn)偏于保守的角度最大持續(xù)產(chǎn)量。關(guān)鍵詞：目標(biāo)管理；收獲控制規(guī)則；山東春季漁業(yè)引言山東春季成熟黃顙魚的儲(chǔ)量在60年代銳減的主要成因之一就是在漁業(yè)捕撈的背后，很多的幼魚和未成年的黃顙魚（Fiksen和Slotte，2002）。在這銳減之后，持續(xù)了幾年實(shí)施暫停捕撈的政策，并且同時(shí)實(shí)行嚴(yán)格的管理措施，控制總可捕量（總可捕量），并且引進(jìn)一個(gè)小的達(dá)到25厘米的區(qū)域來研究。

54、根據(jù)現(xiàn)有的親體補(bǔ)充量信息，最小產(chǎn)卵庫存量（單邊帶）被規(guī)定在2500000噸，這樣將確保有足夠的回復(fù)能力，而目標(biāo)管理的目的是建立合適的庫存水平。1983年，一個(gè)優(yōu)秀的群體是有著一個(gè)500000噸的單邊帶，而其中的單邊帶在隨后的幾年中被提高到4000000噸。在90年代中期，儲(chǔ)量超過單邊帶（2500000噸）的目標(biāo)，結(jié)合允許擴(kuò)大的重建的漁業(yè)的方案，從漁民的角度使得捕撈死亡率達(dá)到一個(gè)更加“可以接受的水平”（F0.05）。一種新的管理戰(zhàn)略是必要的，因?yàn)閿U(kuò)大儲(chǔ)量也開始出現(xiàn)在山東專屬經(jīng)濟(jì)區(qū)以外的水域，這也是適應(yīng)新的國際協(xié)議所需要的。雖然中期模擬在1994年開始(ICES,1995)，他們只是在1996年的

55、咨詢過程中被作為一個(gè)相關(guān)的因素（Bogstad等人。，2000）。1996年的北冰洋漁業(yè)的報(bào)告中漁業(yè)管理咨詢委員會(huì)(ICES,1997)規(guī)定，在對黃顙魚目標(biāo)管理的咨詢培訓(xùn)：“ICES建議，在這一區(qū)域的漁業(yè)管理應(yīng)確保單邊帶保持在一個(gè)最低的可接受的水平MBAL 2500000噸之上”。經(jīng)過對有關(guān)問題的規(guī)定，選擇一個(gè)在某些年份偶爾出現(xiàn)的特定的儲(chǔ)量-回復(fù)模式和相應(yīng)的長時(shí)間的儲(chǔ)量低回復(fù)的特點(diǎn)，在同一篇報(bào)告中有著一段考慮了中期中期情況的內(nèi)容：“初步的中期模擬表明，與管理制度的實(shí)施相結(jié)合的最大漁獲死亡率0.15的水平和1500000噸將會(huì)導(dǎo)致低庫存低于最低生物可接受的水平（MBAL）的情況發(fā)生。在2006年

56、以前，這些措施要么被孤立要么將導(dǎo)致一個(gè)相當(dāng)大的風(fēng)險(xiǎn)的單邊帶低于MBAL”。然而，其他因素，更符合過度捕撈理論是推理，在背景上有助于選擇這種預(yù)防性的捕撈來降低死亡率（即Fpa=0.15）。ICES(1996)已作了初步的長期儲(chǔ)量的模擬，使用2種隨機(jī)方式來描述儲(chǔ)量和回復(fù)速度。模擬表明，采用恒定的平均年產(chǎn)量達(dá)到最大值的范圍是0.130.15，這正好與自然死亡率的值相當(dāng)，這是獨(dú)立的回復(fù)模式（雖然平均產(chǎn)量有所不同）。雖然回復(fù)的功能并沒有完全一致的應(yīng)用到目前的中期預(yù)測中，他們的結(jié)果表明，如果F值在0.130.15可被視為涉及到一個(gè)最佳的長期收益率（或最大可持續(xù)產(chǎn)量，MSY）?？傊?，F(xiàn)pa= 0.15有一個(gè)

57、復(fù)雜的起源。原來的“技術(shù)基礎(chǔ)”中不可接受的單邊帶概率可能低于biomass-limit參考點(diǎn)（布利姆，或MBAL，這一水平被1996年的規(guī)定使用）中的術(shù)語，但對于量的考慮以及觀察，這個(gè)F值曾經(jīng)被用來作為約束和制定ICES的建議，也可能發(fā)揮作用。必須強(qiáng)調(diào)的是，原來Fpa與操作將達(dá)到其最大值1500000噸(ICES,1998)。此外，中期預(yù)測的結(jié)果依賴于初始狀態(tài)的儲(chǔ)量，因此，無法對在所有可能的條件都選定有效的參考點(diǎn)。一個(gè)收獲的控制規(guī)則(HCR)應(yīng)經(jīng)過長期的模擬，我們將會(huì)在下文進(jìn)行說明。為實(shí)現(xiàn)這一目標(biāo)，親體補(bǔ)充量的關(guān)系將會(huì)是一個(gè)至關(guān)重要的因素。回復(fù)的模式已經(jīng)改變，近年來，大一些的魚群出現(xiàn)更頻繁。這

58、可能部分是由于大的儲(chǔ)量，但繁殖也是同樣很大，在19511958年間，只有極度貧乏的年度里群體是天生的。其他環(huán)境因素已被改變。我們使用溫度作為唯一的解釋變量排除了單邊帶影響后，探究親體補(bǔ)充量的關(guān)系。然而，我們承認(rèn)，這是不足以解釋19511958和近幾年之間的產(chǎn)量的差異的。最重要的標(biāo)準(zhǔn)是要了解到底多少次產(chǎn)卵的單邊帶才會(huì)低于布利姆。此外，我們也要測試儲(chǔ)量銳減發(fā)生的可能性。進(jìn)行這種試驗(yàn)，準(zhǔn)確，必須強(qiáng)調(diào)的是，其中涉及的不確定性應(yīng)給以正確描述。我們側(cè)重于評估的不確定性，我們通過引導(dǎo)評估和使用不同的評估，和不確定性的儲(chǔ)量-回復(fù)模式之間的關(guān)系。對于后者，我們不僅要對估計(jì)這種情境下的不確定性，而且也要估計(jì)模型的

59、不確定性，通過使用阿凱克重量借鑒大量合理的回復(fù)模式。因此，不確定性模型是一個(gè)整體的一部分，其他形式的不確定性也被忽視，根據(jù)假設(shè)他們是未成年魚群的重要性與不確定性來評估不確定性的回復(fù)速度。管理目標(biāo)ICES (1999)調(diào)查的三個(gè)管理目標(biāo)的基礎(chǔ)上進(jìn)行中期（10年以上）模擬：（一）最高持續(xù)產(chǎn)量；（二）穩(wěn)定的產(chǎn)量；和（三）大規(guī)模死亡的發(fā)生頻率。結(jié)果見表1。中期模擬能夠考慮不同的目標(biāo)的影響，并且發(fā)現(xiàn)高峰期和低谷期出現(xiàn)的情況以便達(dá)到平衡。結(jié)論是，目標(biāo)和對于高峰期數(shù)量的控制均應(yīng)該減少ICES (1999)，主要目的是使單邊帶低于布利姆分界點(diǎn)后和捕獲量標(biāo)準(zhǔn)后仍保持穩(wěn)定并且降低風(fēng)險(xiǎn)。這些成果形成一個(gè)長期討論和管

60、理五個(gè)臨近的沿海捕魚區(qū)之間關(guān)系的的基礎(chǔ)上，而最終的安排（Rttingen，2003）在附錄中給出了詳細(xì)的表述并且也可以從圖1清楚的看出。該表格包括了F值從從0.15到0.125的減少，但沒有直接測量出穩(wěn)定的漁獲量（即沒有達(dá)到高峰極限量）。這一技術(shù)（以下簡稱為“五步程序”）是我們提出模型以長期的來模擬其變化的基礎(chǔ)，在其中同一F值將被使用而不考慮其固定大小。黃顙魚評估該評價(jià)模型（Rttingen和Tjelmeland，2003；Tjelmeland和Lindstrm，2005）已對于北冰洋浮游生物和藍(lán)鱈魚開展研究工作，從1999到2007，估計(jì)其中黃顙魚的固定數(shù)量。該模型結(jié)合了各種漁業(yè)獨(dú)立的數(shù)據(jù)源