基于CMSY和BSM的东海区重要渔业种类资源评估

有限数据方法(data-limited method)可结合少量易获得数据和相关生物学信息对渔业资源状况、生物学参考点以及生物量等进行评估,已经成为全球区域性渔业管理组织和资源评估学者的关注热点。本研究采用基于渔获量的最大可持续渔获量(catch-based maximum sustainable yield,CMSY)和基于贝叶斯状态空间的Schaefer产量模型(Bayesian Schaefer production model,BSM)评估了东海区19个重要经济种类的资源状况,并提出了基于最大可持续渔获量(maximum sustainable yield,MSY)的渔业管理建议。结果显示,19个种类中有1个种类衰竭,3个种类严重衰退,5个种类过度捕捞,5个种类轻度过度捕捞,5个种类健康。种群状态长期评估结果表明,处于生物可持续水平的鱼类种群占比已由1980年的95%下降至2019年的26%。同时对CMSY和BSM方法的结果进行了比较,整合单位捕捞努力量渔获量(catch per unit effort,CPUE)数据的BSM方法导致了置信区间较宽,并调节了生物量轨迹的变化形态...     

Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database

Meta‐analyses of stock assessments can provide novel insight into marine population dynamics and the status of fished species, but the world’s main stock assessment database (the Myers Stock‐Recruitment Database) is now outdated. To facilitate new analyses, we developed a new database, the RAM Legacy Stock Assessment Database, for commercially exploited marine fishes and invertebrates. Time series of total biomass, spawner biomass, recruits, fishing mortality and catch/landings form the core of the database. Assessments were assembled from 21 national and international management agencies for a total of 331 stocks (295 fish stocks representing 46 families and 36 invertebrate stocks representing 12 families), including nine of the world’s 10 largest fisheries. Stock assessments were available from 27 large marine ecosystems, the Caspian Sea and four High Seas regions, and include the Atlantic, Pacific, Indian, Arctic and Antarctic Oceans. Most assessments came from the USA, Europe, Canada, New Zealand and Australia. Assessed marine stocks represent a small proportion of harvested fish taxa (16%), and an even smaller proportion of marine fish biodiversity (1%), but provide high‐quality data for intensively studied stocks. The database provides new insight into the status of exploited populations: 58% of stocks with reference points (n = 214) were estimated to be below the biomass resulting in maximum sustainable yield (B_MSY) and 30% had exploitation levels above the exploitation rate resulting in maximum sustainable yield (U_MSY). We anticipate that the database will facilitate new research in population dynamics and fishery management, and we encourage further data contributions from stock assessment scientists.     

Improving estimates of population status and trend with superensemble models

Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional “superensemble” model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve estimates of population status and trend for fisheries. We fit four widely applicable data‐limited models that estimate stock biomass relative to equilibrium biomass at maximum sustainable yield (B/B_MSY). We combined these estimates of recent fishery status and trends in B/B_MSY with four ensemble methods: an ensemble average and three superensembles (a linear model, a random forest and a boosted regression tree). We trained our superensembles on 5,760 simulated stocks and tested them with cross‐validation and against a global database of 249 stock assessments. Ensemble methods substantially improved estimates of population status and trend. Random forest and boosted regression trees performed the best at estimating population status: inaccuracy (median absolute proportional error) decreased from 0.42 – 0.56 to 0.32 – 0.33, rank‐order correlation between predicted and true status improved from 0.02 – 0.32 to 0.44 – 0.48 and bias (median proportional error) declined from ?0.22 – 0.31 to ?0.12 – 0.03. We found similar improvements when predicting trend and when applying the simulation‐trained superensembles to catch data for global fish stocks. Superensembles can optimally leverage multiple model predictions; however, they must be tested, formed from a diverse set of accurate models and built on a data set representative of the populations to which they are applied.     

A tale of two seas: a meta‐analysis of crustacean stocks in the NE Atlantic and the Mediterranean Sea

Meta‐analysis of marine biological resources can elucidate general trends and patterns to inform scientists and improve management. Crustacean stocks are indispensable for European and global fisheries; however, studies of their aggregate development have been rare and confined to smaller spatial and temporal scales compared to fish stocks. Here, we study the aggregate development of 63 NE Atlantic and Mediterranean crustacean stocks of six species (Nephrops norvegicus, Pandalus borealis, Parapenaeus longirostris, Aristeus antennatus, Aristaeomorpha foliacea and Squilla mantis) in 1990–2013 using biomass index data from official stock assessments. We implemented a dynamic factor analysis (DFA) to identify common underlying trends in biomass indices and investigate the correlation with the North Atlantic Oscillation (NAO) index. The analysis revealed increasing and decreasing trends in the northern and southern NE Atlantic, respectively, and stable or slowly increasing trends in the Mediterranean, which were not related to NAO. A separate meta‐analysis of the fishing mortality (F) and biomass (B) of 39 analytically assessed crustacean stocks was also carried out to explore their development relative to MSY. NE Atlantic crustacean stocks have been exploited on average close to F_MSY and remained well above B_MSY in 1995–2013, while Mediterranean stocks have been exploited 2–4 times above F_MSY in 2002–2012. Aggregate trends of European crustacean stocks are somewhat opposite to trends of fish stocks, suggesting possible cascading effects. This study highlights the two‐speed fisheries management performance in the northern and southern European seas, despite most stocks being managed in the context of the European Union's Common Fisheries Policy.     

Comparing three indices of catch per unit effort using Bayesian geostatistics

In assessing a fish stock, indices based on catch per unit effort (CPUE) are frequently used. Estimates of three indices of catch per unit effort were compared here (CPUE₁, CPUE₂ and CPUE₃), considering the fitting of two models: (i) a bivariate geostatistical model for catch and effort; (ii) a bivariate model where catch and effort were considered spatially independent. For comparing the estimates of the three indices after the fitting of the two models, catch and effort data were simulated in different scenarios. The simulation study showed that, in general, the estimates of CPUE₁ expressed by the ratio of the means of catch and effort, present better results for different scenarios and that the estimates from (i) are better than (ii), mainly when there is a correlation between catch and effort and an additional spatial correlation.     

Generic harvest control rules for European fisheries

In European fisheries, most stocks are overfished and many are below safe biological limits, resulting in a call from the European Commission for new long‐term fisheries management plans. Here, we propose a set of intuitive harvest control rules that are economically sound, compliant with international fishery agreements, based on relevant international experiences, supportive of ecosystem‐based fisheries management and compatible with the biology of the fish stocks. The rules are based on the concept of maximum sustainable yield (MSY), with a precautionary target biomass that is 30% larger than that which produces MSY and with annual catches of 91%MSY. Allowable catches decline steeply when stocks fall below MSY levels and are set to zero when stocks fall below half of MSY levels. We show that the proposed rules could have prevented the collapse of the North Sea herring in the 1970s and that they can deal with strong cyclic variations in recruitment such as known for blue whiting. Compared to the current system, these rules would lead to higher long‐term catches from larger stocks at lower cost and with less adverse environmental impact.     

Coping with information gaps in stock productivity for rebuilding and achieving maximum sustainable yield for grouper–snapper fisheries

Maintaining fish stocks at optimal levels is a goal of fisheries management worldwide; yet, this goal remains somewhat elusive, even in countries with well‐established fishery data collection, assessment and management systems. Achieving this goal often requires knowledge of stock productivity, which can be challenging to obtain due to both data limitations and the complexities of marine populations. Thus, scientific information can lag behind fishery policy expectations in this regard. Steepness of the stock–recruitment relationship affects delineation of target biomass level reference points, a problem which is often circumvented by using a proxy fishing mortality rate (F) in place of the rate associated with maximum sustainable yield (F_MSY). Because MSY is achieved in the long term only if an F proxy is happenstance with F_MSY, characterizing productivity information probabilistically can support reference point delineation. For demersal stocks of equatorial and tropical regions, we demonstrate how the use of a prior probability distribution for steepness can help identify suitable F proxies. F proxies that reduce spawning biomass per recruit to a target percentage of the unfished quantity (i.e., SPR) of 40% to 50% SPR had the highest probabilities of achieving long‐term MSY. Rebuilding was addressed through closed‐loop simulation of broken‐stick harvest control rules. Similar biomass recovery times were demonstrated for these rules in comparison with more information‐intensive rebuilding plans. Our approach stresses science‐led advancement of policy through a lens of information limitations, which can make the assumptions behind rebuilding plans more transparent and align management expectations with biological outcomes.     

Revisiting safe biological limits in fisheries

The appropriateness of three official fisheries management reference points used in the north‐east Atlantic was investigated: (i) the smallest stock size that is still within safe biological limits (SSB_pa), (ii) the maximum sustainable rate of exploitation (F_msy) and (iii) the age at first capture. As for (i), in 45% of the examined stocks, the official value for SSB_pa was below the consensus estimates determined from three different methods. With respect to (ii), the official estimates of F_msy exceeded natural mortality M in 76% of the stocks, although M is widely regarded as natural upper limit for F_msy. And regarding (iii), the age at first capture was below the age at maturity in 74% of the stocks. No official estimates of the stock size (SSB_msy) that can produce the maximum sustainable yield (MSY) are available for the north‐east Atlantic. An analysis of stocks from other areas confirmed that twice SSB_pa provides a reasonable preliminary estimate. Comparing stock sizes in 2013 against this proxy showed that 88% were below the level that can produce MSY. Also, 52% of the stocks were outside of safe biological limits, and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 73% of the stocks, including those that were severely depleted. These results point to the urgent need to re‐assess fisheries reference points in the north‐east Atlantic and to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture.     

Identifying spawner biomass per‐recruit reference points from life‐history parameters

Analysis of spawning biomass per‐recruit has been widely adopted in fisheries management. Fishing mortality expressed as spawning potential ratio (SPR) often requires a reference point as an appropriate proxy for the fishing mortality that supports a maximum sustainable yield—F_MSY. To date, a single generic level between F_30% and F_40% is routinely used. Using records from stock assessments in the RAM Legacy Database (RAMLD), we confirm that SPR at MSY (SPR_MSY) is a declining function of stock productivity quantified by F_MSY. We then use general linear models (GLM) and Bayesian errors‐in‐variables models (BEIVM) to show that SPR_MSY can be predicted from life‐history parameters (LHPs, including maximum lifespan, age‐ and length‐at‐maturation, growth parameters, natural mortality, and taxonomic Class) as well as gear selectivity. The calculated SPR_MSY ranges from about 13% to 95% with a mean of 47%. About 64% of the stocks in the RAMLD require SPR_MSY > 40%. Modelling SPR_MSY reveals that LHPs plus Class explain 61% of the deviance in SPR_MSY. Faster‐growing, low‐survival, and short‐lived species generally require a high SPR. With equal LHPs, elasmobranchs require about 20% higher SPR_MSY than teleosts. When F_MSY is estimated from fisheries that harvest older fish, increasing the vulnerable age by one year leads to about an 8% increase in SPR_MSY. The BEIVM yields smaller variance and bias than the GLM. The models developed in this study could be used to predict SPR_MSY reference points for new stocks using the same LHPs for calculating F_x%, but without knowledge of the stock‐recruitment parameters.     

A simple method for estimating MSY from catch and resilience

The Law of the Sea requires that fish stocks are maintained at levels that can produce the maximum sustainable yield (MSY). However, for most fish stocks, no estimates of MSY are currently available. Here, we present a new method for estimating MSY from catch data, resilience of the respective species, and simple assumptions about relative stock sizes at the first and final year of the catch data time series. We compare our results with 146 MSY estimates derived from full stock assessments and find excellent agreement. We present principles for fisheries management of data‐poor stocks, based only on information about catches and MSY.     

Verifier of the maximum sustainable yield estimates of the southern Atlantic albacore,Thunnus alalunga,stock

Maximum sustainable yield (MSY) has generally been accepted as one of the target biological reference points. Albacore, Thunnus alalunga Bonnaterre, is a temperate tuna species widely distributed in marine waters. The International Commission for the Conservation of Atlantic Tunas (ICCAT) and the International Seafood Sustainability Foundation (ISSF) had reported the southern Atlantic albacore stock status with different MSY reference points. In addition, the European Commission's Advisory Committee on Fisheries and Aquaculture (ACFA), on 15 September 2006, proposed to amend the Common Fisheries Policy according to the MSY principle, but there is little information on the verifier of the MSY estimates of this albacore stock. This study verifies the MSY estimates of this albacore (T. alalunga) stock to support the management (i.e. setting of MSY) for the southern Atlantic albacore (T. alalunga) stock. The MSY estimates of the albacore stock were evaluated and verified by different models (i.e. Bayesian surplus production model [BSPM], continuous time delay‐difference model [CD‐DM] and Fox surplus production model [SPM]). The MSY estimates from BSPM and CD‐DM were lower than those from conventional estimates; the relative biomass ratio (B₂₀₁₁/B_MSY) and relative fishing mortality ratio (F₂₀₁₁/F_MSY) from BSPM and CD‐DM were higher than those from ICCAT, which showed that measures should be taken for the sustainable utilisation of this fish stock.     

Rebuilding Mediterranean fisheries: a new paradigm for ecological sustainability

In Mediterranean European countries, 85% of the assessed stocks are currently overfished compared to a maximum sustainable yield reference value (MSY) while populations of many commercial species are characterized by truncated size‐ and age‐structures. Rebuilding the size‐ and age‐structure of exploited populations is a management objective that combines single species targets such as MSY with specific goals of the ecosystem approach to fisheries management (EAF), preserving community size‐structure and the ecological role of different species. Here, we show that under the current fishing regime, stock productivity and fleet profitability are generally impaired by a combination of high fishing mortality and inadequate selectivity patterns. For most of the stocks analysed, a simple reduction in the current fishing mortality (F_cur) towards an MSY reference value (F_MSY), without any change in the fishing selectivity, will allow neither stock biomass nor fisheries yield and revenue to be maximized. On the contrary, management targets can be achieved only through a radical change in fisheries selectivity. Shifting the size of first capture towards the size at which fish cohorts achieve their maximum biomass, the so‐called optimal length, would produce on average between two and three times higher economic yields and much higher biomass at sea for the exploited stocks. Moreover, it would contribute to restore marine ecosystem structure and resilience to enhance ecosystem services such as reservoirs of biodiversity and functioning food webs.     

Improving management advice through spatially explicit models and sharing information

Recent assessments of Chilean shrimp, Heterocarpus reedi, in central Chile have been conducted separately for the northern and southern zones of the fishery and treating them as two separate stocks. However, it is not clear whether H. reedi of the two zones interact with one another or whether they share similar characteristics. Such knowledge is necessary to determine whether they should be modeled as separate “stocks” or as a single stock. This has motivated the use of the Pella–Tomlinson model to test whether there are spatial differences in the population dynamics of H. reedi in the two zones and whether sharing information between the zones improves management advice. We test if it is better, from a stock assessment point of view, to model the stock as one unit in the whole area, or as two separate stocks. In the single-stock model, we sum the catch data of both zones, but each catch-per-unit-of-effort index is fit as a separate data set, using a joint likelihood. Under the single-stock hypothesis, the best model fit was the symmetric production function (i.e. the Schaefer model for which the biomass that supports maximum sustainable yield as a proportion of carrying capacity (B_MSY/B₀) = 0.5), with different catchability coefficients for each CPUE index, but a shared standard deviation of the log-normal likelihood function. Under the two-stock hypotheses, both catch and CPUE data were separated for each zone in the model. In this case, the best model fit is also the one with symmetrical production curve, and the only parameter that differed between the zones was B₀. However, B₀ per unit of habitat was similar for the two zones. Also, the precision of estimated management quantities was improved by modeling the appropriate spatial structure and sharing information among zones. The results suggest that the demographic parameters are similar for the two zones. It appears that the main difference between the two zones is the exploitation history, with the catch in the southern zone being reduced earlier than in the northern zone and consequently the biomass in the southern zone increased earlier than in the northern zone. This implies that local depletion can occur in this stock and that differences in management among zones may require explicitly modeling sub-stocks in the assessment of this and other species.     

Selectivity metrics for fisheries management and advice

Fisheries management typically aims at controlling exploitation rate (e.g., Fbar) to ensure sustainable levels of stock size in accordance with established reference points (e.g., F_MSY, B_MSY). Population selectivity (“selectivity” hereafter), that is the distribution of fishing mortality over the different demographic components of an exploited fish stock, is also important because it affects both Maximum Sustainable Yield (MSY) and F_MSY, as well as stock resilience to overfishing. The development of an appropriate metric could make selectivity operational as an additional lever for fisheries managers to achieve desirable outcomes. Additionally, such a selectivity metric could inform managers on the uptake by fleets and effects on stocks of various technical measures. Here, we introduce three criteria for selectivity metrics: (a) sensitivity to selectivity changes, (b) robustness to recruitment variability and (c) robustness to changes in Fbar. Subsequently, we test a range of different selectivity metrics against these three criteria to identify the optimal metric. First, we simulate changes in selectivity, recruitment and Fbar on a virtual fish stock to study the metrics under controlled conditions. We then apply two shortlisted selectivity metrics to six European fish stocks with a known history of technical measures to explore the metrics’ response in real‐world situations. This process identified the ratio of F of the first recruited age–class to Fbar (Frec/Fbar) as an informative selectivity metric for fisheries management and advice.     

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/B_MSY). We also compare several other models to estimate B/B_MSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/B_MSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.     

A study on relationships between large‐scale climate indices and estimates of North Pacific albacore tuna productivity

A logistic production model was used to examine potential relationships between three climate indices, the North Pacific Gyre Oscillation (NPGO), the Pacific Decadal Oscillation (PDO), and the Multivariate El Niño‐Southern Oscillation Index (MEI), and productivity estimates of the North Pacific albacore tuna (Thunnus alalunga) population. Catch and standardized catch‐per‐unit‐effort data from three longline fisheries (Japan, US, and Taiwan) were used in the model. The climate indices were incorporated into the model by correlating time‐varying intrinsic population growth rate (r_y) of the production model with the annual mean value for each index. The estimated probability that the NPGO is positively correlated with stock productivity, as measured by r_y, was 0.99, and the calculated probability that MEI is negatively correlated with the productivity was 0.95. The time lag for these correlations is 4 yr, which is consistent with the timing of recruitment to the Japan longline fishery. The PDO did not seem to have any detectable relationship with stock productivity. However, it remains uncertain if there is a conclusive linkage between the albacore productivity and the NPGO or the MEI index, because model fit to the data is about the same as that of a base model which does not use any climate index and assumes a time‐invariant r.     

Management strategies for short lived species: The case of Australia's Northern Prawn Fishery: 2. Choosing appropriate management strategies using input controls

A Management Strategy Evaluation framework is used to evaluate management strategies based on input controls for the fishery for two tiger prawn species (Penaeus esculentus and Penaeus semisulcatus) in Australia's Northern Prawn Fishery. Three “assessment procedures” are considered and two forms of decision rule. The performance of the management strategies is evaluated in terms of whether stocks are left at (or above) the spawning stock size at which Maximum Sustainable Yield is achieved (S_MSY), the long-term discounted total catch and the extent of inter-annual variation in catches. The focus of the analysis is on management strategies based on the current method of stock assessment because an alternative method of assessment based on a biomass dynamics model is found to be highly variable. None of the management strategies tested is able to leave the spawning stock size of P. esculentus near S_MSY if the target effort level used in the management strategy is set to E_MSY. Accounting for stock structure through the application of a spatially- (stock-) structured assessment approach fails to resolve this problem. Since the assessment method is generally close to unbiased, the failure to leave the stocks close to S_MSY is because the measure of control is total effort and the two species are found (and caught) together. Reducing the target effort level to below E_MSY increases the final stock size, but the reduced risk comes at a cost of reduced catches. The best management strategy in terms of leaving both species close to S_MSY is found to be one that changes the timing of the fishing season so that effort is shifted from P. esculentus to P. semisulcatus and sets more precautionary effort targets for P. esculentus.     

Integrating recreational fisheries data into stock assessment: implications for model performance and subsequent harvest strategies

Understanding the impacts of recreational fishing on commercially fished stocks is becoming increasingly relevant for fisheries managers. However, data from recreational fisheries are not commonly included in stock assessments of commercially fished stocks. Simulation models of two assessment methods employed in Australia's Commonwealth fisheries were used to explore how recreational fishery data can be included, and the likely consequences for management. In a data‐poor management strategy for blue eye trevalla, Hyperoglyphe antarctica (Carmichael), temporal trends in recreational catch most affected management outcomes. In a data‐rich age‐structured stock assessment for striped marlin, Kajikia audax (Philippi), estimates of stock status were biased when recreational catches were large or when the recreational fishery targeted different size classes than the commercial fishery and these data were not integrated into the assessment. Including data from recreational fishing can change perceptions of stock status and impact recommendations for harvest strategies and management action. An understanding of recreational fishery dynamics should be prioritised for some species.     

有限数据渔业种群资源评估与管理——以小黄鱼为例

传统的渔业资源评估方法需以翔实的调查和渔业数据为基础,而现有的大多数种类面临着着渔获量、基础生物学、有效捕捞努力量等数据缺失问题,因此并不适合采用数据需求较高的模型进行评估和管理。面临着渔业资源衰退的严峻形势和渔获量限额管理的迫切要求,基于有限数据的评估方法和渔获量相关的管理方案正被越来越多的国家采用。本研究以东海小黄鱼(Larimichthys polyactis)种群为例,根据渔获量、自然死亡、消减率、生物学参数、开捕体长等数据,采用 54 种有限数据评估方法,模拟 3 种捕捞动态,对小黄鱼进行管理策略评价和资源评估。结果显示,以相对产量(relative yield, RY)不低于 50%、过度捕捞概率(probability of overfishing, POF)小于 50%,生物量低于最大可持续生物量的 10%(B<0.1BMSY)的概率小于 20%为风险控制水平,捕捞强度随机波动和增长情景下,分别有 6个管理方案(management procedures, MPs)满足既定管理目标;“一般型”和“增长型”捕捞强度情景下, 14个 MPs 满足管理目标。权衡分析 3 种捕捞动态下的 MPs, 50%FMSY 基准法(FMSYref50)可作为小黄鱼渔业最佳的管理方案, POF 介于 5.46%~6.70%, B<0.5BMSY概率介于 15.66%~22.73%,长期获得的相对产量介于 52%~100%;然而, FMSYref50确定的可接受生物学渔获量(acceptable biological catch, ABC)仅有 1.08×10^4 t,与当前产量相差较大。因此,考虑到降低捕捞强度为渔业管控的发展趋势,建议采用动态 F 比值法(DynF)为小黄鱼渔业管理方案,“下降型”捕捞强度情景下,POF为 37.84%, B<0.5BMSY概率为 38.63%,长期获得的相对产量为 84%, ABC为 4.03×10^4 t。根据敏感性分析,发现 DynF 评估的 ABC 对捕捞产量、资源丰度指数不敏感,而对自然死亡系数、最大可持续捕捞死亡系数与自然死亡系数比值(FMSY_M)和当前资源量均较为敏感,参数值增加会导致 ABC 增加,表明在开展渔业资源评估时需要着重提高这 3 种参数的准确性。     

东南太平洋竹?鱼资源评估与捕捞控制规则模拟研究

以东南太平洋智利竹鱼为对象、以资源量动态模型为基础,使用模拟方法构建了"真实"的智利竹鱼种群及其渔业,评估了观测误差和过程误差对智利竹鱼资源评估和管理的影响。模拟的"真实"的智利竹鱼种群及其渔业结果显示,1997—2014年太平洋智利竹鱼资源量总体上呈逐年下降趋势,且远低于B_(MSY)的50%;捕捞死亡系数波动剧烈,仅在2012—2014年低于F_(MSY)且相对稳定。渔业资源评估模拟结果显示,观测误差和过程误差使资源量和B_(MSY)被低估,捕捞死亡系数和F_(MSY)被高估,且随机误差越大,资源量、B_(MSY)被低估,而捕捞死亡系数、F_(MSY)被高估的程度越大。渔业管理模拟的结果表明,捕捞控制规则采用恒定捕捞死亡系数时,未来10年基于50%2014年捕捞死亡系数的管理措施为最佳管理措施。由于捕捞死亡系数被高估,最佳管理措施实施后使得年总可捕捞量高于预期,而年资源量低于预期,资源量增长或恢复的速度变慢,资源可能同时处于过度捕捞状态和正遭受过度捕捞。过度捕捞的风险与随机观测误差和过程误差的大小成正比。