Article

Applying a New Ensemble Approach to Estimating Stock Status of Marine Fisheries Around the World

Wiley
Conservation Letters
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Abstract

The exploitation status of marine fisheries stocks worldwide is of critical importance for food security, ecosystem conservation, and fishery sustainability. Applying a suite of data-limited methods to global catch data, combined through an ensemble modeling approach, we provide quantitative estimates of exploitation status for 785 fish stocks. Fifty six percent (439 stocks) are below BMSY and of these, 261 are estimated to be below 80% of the BMSY level. While the 178 stocks above 80% of BMSY are conventionally considered “fully exploited”, stocks staying at this level for many years, forego substantial yield. Our results enable managers to consider more detailed information than simply a categorization of stocks as “fully” or “over” exploited. Our approach is reproducible, allows consistent application to a broad range of stocks, and can be easily updated as new data become available. Applied on an ongoing basis, this approach can provide critical, more detailed information for resource management for more exploited fish stocks than currently available. This article is protected by copyright. All rights reserved

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... For scenario 2, after fishing for the first 75 years of the simulation, all COMs indicated REs dropping farther from 0, however, SSCOM was the only model with REs revolving around 0 during the last 25 years of the simulation. Other studies equally tested the performances of COMs and had similar conclusions indicating that these methods are not only influenced by fisheries changes but also fish size selectivity, life history strategies and catch misreporting 8,10,13,18,21,[25][26][27][28][29] . Although SSCOM showed better simulation results than the other four COMs, it still has its limitations and must be applied with caution for management. ...
... Therefore, the development of COMs should not be an excuse for stakeholders and fisheries managers to set up schemes for the continuous collection of data useful to perform more solid and complete assessments. While data-limited models could provide a quick assessment for management decisions, it should be noted that our use of these methods involved simple hypotheses and frequently provided biased estimates of fishery status 8,26 . Because catch-based models are susceptible to fishing history and strategy 25 , as demonstrated in this work, we propose that catch-only approaches be used as a temporary stepping stone while data (e.g., size or age composition, and valid abundance indices data) is acquired to allow for the adoption of more reliable methods. ...
... Our findings corroborate the notion that a species' life history features not only directly impact the prediction of COMs 8,26 , but also correlate to fishing situations in terms of the effect on the performance of data-limited approaches. However, despite having access to considerable data, previous research revealed that all catch-based models already in use experienced difficulties in producing credible findings that corresponded with standard stock assessment techniques 8 . ...
Article
Full-text available
Most sophisticated stock assessment models often need a large amount of data to assess fish stocks, yet this data is often lacking for most fisheries worldwide, resulting in the increasing demand for data-limited stock assessment methods. To estimate fish stock status, one class of these data-limited methods uses simply catch time series data and, in other instances, life history information or fishery characteristics. These catch-only methods (COMs) built differently are known to make assumptions about changes in fishing effort and may perform differently under various fishing scenarios. As a case study, this paper used European anchovy (Engraulis encrasicolus) caught in the northwest African waters, though very economically and ecologically important, but still unassessed. Our study investigated the performance of five COMs under different fishing scenarios using as a reference the life-history information of the European anchovy captured in this region of the Atlantic. Hence, the present study developed a simulation approach to evaluate the performance of the five COMs in inferring the stock biomass status (B/BMSY) with consideration of different fishing scenarios under prior information true to anchovy. All five COMs mostly underestimated B/BMSY throughout the simulation period, especially under constant fishing mortality, and in the last five years of the simulation during all fishing scenarios. Overall, these COMs were generally poor classifiers of stock status, however, the state-space COM (SSCOM) generally performed better than the other COMs as it showed possibilities of recovering an overfished stock. When these methods were explored using actual anchovy catch data collected in the northwest African waters, SSCOM yielded results that were deferred from the other COMs. This study being the first to assess this species’ stock in this area using a suite of COMs, presents more insights into the species stock status, and what needs to be considered before scientifically putting in place management measures of the stock in the area.
... This is directly linked to the wellestablished maximum sustainable yield (MSY) calculations, that are, despite its limitations (Larkin, 1977;Caddy and Mahon, 1995), commonly used as sustainable benchmarks for harvested species in stock assessment reports, (see for example Winker et al., 2018;Haddon, 2001;Hilborn and Walters, 1992;Streipert et al., 2019;Government et al., 2019;PEW, 2019). Especially in the cases of limited available data, simple surplus models such as the Beverton-Holt model are critical for stock assessments (Dichmont et al., 2016;Punt et al., 2015), and metaanalysis of global fisheries (Winker et al., 2018;Froese et al., 2017;Rosenberg et al., 2018;Worm et al., 2009). In this work, we address this key question for the harvested Beverton-Holt model that is often considered as discrete logistic growth model (Bohner et al., 2010;Brauer and Castillo-Chavez, 2001, Section 2.1&2.5). ...
... The fewer data are available, the fewer model parameters can be reliably estimated. Thus, simple population models are implemented specifically for species with limited data availability (Winker et al., 2018;Dichmont et al., 2016;Punt et al., 2015;Froese et al., 2017;Rosenberg et al., 2018;Worm et al., 2009). As mentioned in the Introduction, the maximum sustainable yield quantities are still used to determine sustainable harvest levels in fisheries, (see Haddon, 2001;Hilborn and Walters, 1992). ...
... However, we also demonstrate that when the square root identity is applied to populations captured by the Pella-Tomlinson model this still results in sustainable population levels, for a wide range of parameter values. Although surplus models of this type can be criticized for their simplicity, they are critical in data-limited stock assessments, (see Dichmont et al., 2016;Punt et al., 2015), and meta-analysis of global fisheries, (see Winker et al., 2018;Froese et al., 2017;Rosenberg et al., 2018;Worm et al., 2009). The Beverton-Holt recurrence possesses many mathematically desirable properties which are preserved when a multiplicative harvest survival rate is incorporated. ...
Article
We introduce the term net-proliferation rate for a class of harvested single species models, where harvest is assumed to reduce the survival probability of individuals. Following the classical maximum sustainable yield calculations, we establish relations between the proliferation and net-proliferation that are economically and sustainably favored. The resulting square root identities are analytically derived for species following the Beverton–Holt recurrence considering three levels of complexity. To discuss the generalization of the results, we compare the square root result to the optimal survival rate of the Pella–Tomlinson model. Furthermore, to test the practical relevance of the square root identities, we fit a stochastic Pella–Tomlinson model to observed Barramundi fishery data from the Southern Gulf of Carpentaria, Australia. The results show that for the estimated model parameters, the equilibrium biomass levels resulting from the MSY harvest and the square root harvest are similar, supporting the claim that the square root harvest can serve as a rule-of-thumb. This application, with its inherited model uncertainty, sparks a risk sensitivity analysis regarding the probability of populations falling below an unsustainable threshold. Characterization of such sensitivity helps in the understanding of both dangers of overfishing and potential remedies.
... Chrysafi and Kuparinen (2016) carried out a compilation of the most common quantitative assessment methods for data-limited fisheries based on catch ratios, length proportions in the catch, life-history, abundance indices, resilience, exploitation, depletion, and catch-at-age. Other recent studies have been presenting new methods to evaluate the status of fisheries (e.g., Rosenberg et al., 2014;Rosenberg et al., 2017), including the development of ensemble and super ensemble models to improve predictive accuracy Rosenberg et al., 2017;Rudd et al., 2019;. In parallel, several frameworks were developed to facilitate the use (Dowling et al., 2016), AFAM -Adaptive Fisheries Assessment and Management toolkit (McDonald et al., 2018), etc. ...
... Chrysafi and Kuparinen (2016) carried out a compilation of the most common quantitative assessment methods for data-limited fisheries based on catch ratios, length proportions in the catch, life-history, abundance indices, resilience, exploitation, depletion, and catch-at-age. Other recent studies have been presenting new methods to evaluate the status of fisheries (e.g., Rosenberg et al., 2014;Rosenberg et al., 2017), including the development of ensemble and super ensemble models to improve predictive accuracy Rosenberg et al., 2017;Rudd et al., 2019;. In parallel, several frameworks were developed to facilitate the use (Dowling et al., 2016), AFAM -Adaptive Fisheries Assessment and Management toolkit (McDonald et al., 2018), etc. ...
... Chrysafi and Kuparinen (2015) did a compilation of the most common assessment methods for data limited fisheries. Other recent studies have been presenting new methods to evaluate the fisheries status (e.g., Anderson et al., 2017;Rosenberg et al., 2014;Rosenberg et al., 2017). Fishers' local knowledge studies also have been very useful to provide information on ecological/biological data, temporal trends, and fish abundance, and can help to fill local knowledge gaps in the short term and to guide future biological studies (e.g., Aguilar-Perera et al., 2009;Begossi et al., 2016;Damasio et al., 2015;Lavides et al., 2010). ...
... Some notable progress in rebuilding fish stocks has been realised in recent years, yet the overall positive trend has been driven largely by a few high-volume and high-value industrial fisheries (Worm et al. 2009;Rosenberg et al. 2017). Many of the now well-managed fisheries are classified as data-rich, and management decision-making is often informed by rigorous stock assessments that consider multiple data sources within integrated statistical assessment models (Rosenberg et al. 2017). ...
... Some notable progress in rebuilding fish stocks has been realised in recent years, yet the overall positive trend has been driven largely by a few high-volume and high-value industrial fisheries (Worm et al. 2009;Rosenberg et al. 2017). Many of the now well-managed fisheries are classified as data-rich, and management decision-making is often informed by rigorous stock assessments that consider multiple data sources within integrated statistical assessment models (Rosenberg et al. 2017). By contrast, the vast majority of non-industrial, small-scale fisheries are considered data-limited, and their stock status remains unknown or highly uncertain (Costello et al. 2013;Rosenberg et al. 2017); consequently, overexploitation remains a major threat (Costello et al. 2013;Froese et al. 2018a). ...
... Many of the now well-managed fisheries are classified as data-rich, and management decision-making is often informed by rigorous stock assessments that consider multiple data sources within integrated statistical assessment models (Rosenberg et al. 2017). By contrast, the vast majority of non-industrial, small-scale fisheries are considered data-limited, and their stock status remains unknown or highly uncertain (Costello et al. 2013;Rosenberg et al. 2017); consequently, overexploitation remains a major threat (Costello et al. 2013;Froese et al. 2018a). The multiuser nature of many coastal small-scale fisheries, often encompassing commercial, recreational and subsistence segments, poses challenges for obtaining representative catch statistics, while the perceived lower value of the resource typically leads to less funding for scientific surveys and observer programmes. ...
Article
Full-text available
Managing coastal fisheries is challenging as the status of many fish stocks caught in these fisheries remains unknown. In the South African linefishery, regular comprehensive assessments of the status of most linefish stocks are unattainable owing to a scarcity of reliable long-term data. Length-based analysis remains the only option to determine stock status in the form of spawning potential ratio (SPR), as life-history information and representative length samples are available for many linefish species. Although per-recruit models are susceptible to bias due to violation of the steady-state assumption, the SPR has been shown to be robust for long-lived species under reasonably consistent fishing mortality. In this study we used observer-collected length-frequency data from two time-periods 20 years apart (1988–1990 and 2008–2010), before and after management regulations were implemented, in combination with life-history information, to estimate the SPRs for 17 linefish species. We then correlated the recent stock-status estimates to species-specific life-history traits to identify length-based indicators of susceptibility to exploitation. Most species showed improvements in SPR between the periods, caused mainly by decreases in fishing mortality (F) and also increases in length-at-first-capture (Lc ). The ratio between Lc and asymptotic length (Lc /L ∞), and the ratio between Lc and optimum length (Lc /L opt), had significant relationships with SPR. We suggest that length-based indicators could be used to classify risk to overfishing in data-poor fisheries for medium- to long-lived species when time-series data are not attainable, but where representative size samples and adequate life-history information exist.
... This is directly linked to the wellestablished maximum sustainable yield (MSY) calculations, that are, despite its limitations (Larkin, 1977;Caddy and Mahon, 1995), commonly used as sustainable benchmarks for harvested species in stock assessment reports, (see for example Winker et al., 2018;Haddon, 2001;Hilborn and Walters, 1992;Streipert et al., 2019;Government et al., 2019;PEW, 2019). Especially in the cases of limited available data, simple surplus models such as the Beverton-Holt model are critical for stock assessments (Dichmont et al., 2016;Punt et al., 2015), and metaanalysis of global fisheries (Winker et al., 2018;Froese et al., 2017;Rosenberg et al., 2018;Worm et al., 2009). In this work, we address this key question for the harvested Beverton-Holt model that is often considered as discrete logistic growth model (Bohner et al., 2010;Brauer and Castillo-Chavez, 2001, Section 2.1&2.5). ...
... The fewer data are available, the fewer model parameters can be reliably estimated. Thus, simple population models are implemented specifically for species with limited data availability (Winker et al., 2018;Dichmont et al., 2016;Punt et al., 2015;Froese et al., 2017;Rosenberg et al., 2018;Worm et al., 2009). As mentioned in the Introduction, the maximum sustainable yield quantities are still used to determine sustainable harvest levels in fisheries, (see Haddon, 2001;Hilborn and Walters, 1992). ...
... However, we also demonstrate that when the square root identity is applied to populations captured by the Pella-Tomlinson model this still results in sustainable population levels, for a wide range of parameter values. Although surplus models of this type can be criticized for their simplicity, they are critical in data-limited stock assessments, (see Dichmont et al., 2016;Punt et al., 2015), and meta-analysis of global fisheries, (see Winker et al., 2018;Froese et al., 2017;Rosenberg et al., 2018;Worm et al., 2009). The Beverton-Holt recurrence possesses many mathematically desirable properties which are preserved when a multiplicative harvest survival rate is incorporated. ...
Preprint
We introduce the term net-proliferation in the context of fisheries and establish relations between the proliferation and net-proliferation that are economically and sustainably favored. The resulting square root laws are analytically derived for species following the Beverton-Holt recurrence but, we show, can also serve as reference points for other models. The practical relevance of these analytically derived square root laws is tested on the the Barramundi fishery in the Southern Gulf of Carpentaria, Australia. A Beverton-Holt model, including stochasticity to account for model uncertainty, is fitted to a time series of catch and abundance index for this fishery. Simulations show, that despite the stochasticity, the population levels remain sustainable under the square root law. The application, with its inherited model uncertainty, sparks a risk sensitivity analysis regarding the probability of populations falling below an unsustainable threshold. Characterization of such sensitivity helps in the understanding of both dangers of overfishing and potential remedies.
... Fisheries and stocks lacking comprehensive datasets are commonly known as "data-poor" or "data-limited" fisheries (Costello et al. 2012;Dowling et al. 2015). Recently, many data-limited approaches have been developed to meet an increasing demand for science-based fisheries management of unassessed fisheries where data and resources are limited (Wetzel and Punt 2011;Costello et al. 2012;Dowling et al. 2015Dowling et al. , 2016Chrysafi and Kuparinen 2016;Rosenberg et al. 2018). ...
... In addition, there are some studies comparing the performance in determining stock status of catch-based (Wetzel and Punt 2015;Rosenberg et al. 2018) and length-based assessment models (Chong et al. 2020). However, a comparison of the performance of both length-based and catch-based methods to estimate stock status is needed. ...
... Catch-MSY uses catch and productivity to estimate MSY. Here we use the modified version of Catch-MSY (Rosenberg et al. 2018) to extract biomass trends from all viable r-K pairs using the R package datalimited version 0.1.0 (Anderson et al. 2016). ...
Article
The quantity of data from many small-scale fisheries is insufficient to allow for the application of conventional assessment methods. Even though in many countries they are moving to close-loop simulations to assess the performance of different management procedures in data limited situations, managers in most developing countries are still demanding information on stock status. In this study we use the common metric of harvest rate to evaluate and compare the performance of the following catch-only and length-only assessment models: Catch-Maximum Sustainable Yield (Catch-MSY), Depletion Based Stock Reduction Analysis (DBSRA), Simple Stock Synthesis (SSS), an extension of Catch-MSY (CMSY), Length Based Spawning Potential Ratio (LBSPR), Length-Based Integrated Mixed Effects (LIME), and Length-Based Bayesian (LBB). In general, results were more biased for slightly depleted than for highly depleted stocks, and for long-lived than for short-lived species. Length-based models, such as LIME, performed as well as catch-based methods in many scenarios and, among the catch-base models the one with the best performance was SSS.
... The importance of assessing the stock status of data-limited marine resources, which do not have sufficient data available for conventional models that are applied to data-rich resources, has been emphasized worldwide since the early 2000s (e.g., Costello et al., 2012;Dowling et al., 2015;Rosenberg et al., 2018). The application of a scientifically based annual catch limit (ACL) has been stressed for nearly all marine stocks, including those with data-limited resources. ...
... Furthermore, we applied CMSY and AMSY as data-limited methods; however, other data-limited methods can also be applied. Because super-ensemble models (Anderson et al., 2017;Rosenberg et al., 2018), which combine various catch-only methods, seem to perform relatively well, the results from such data-limited methods may be useful for future applications. Nevertheless, the collection of more data on stocks worldwide may be needed to ensure a sufficient sample size for analysis. ...
Article
More scientifically based stock assessments of data-limited species are rapidly increasing worldwide. Concurrently, non-negligible biases of stock status estimated using data-limited methods are becoming an issue. It is well known that the assumed prior distribution on depletion for some data-limited methods strongly affects the estimated stock status. Priors on depletion are best set based on expert knowledge, however, expert subjectivity and experience should be considered. Moreover, for a very data-limited stock, there is no information from experts. Owing to such constraints, the blind application of “default priors” on depletion often takes place. Here, we examined fishery-related, model assumption-related, biology-related, management-related, and spatial-based characteristics of stocks in which such default priors tend to work well, and vice versa, by applying a machine learning method (XGBoost) to the RAM Legacy data. The results suggest that “false healthy” misclassification of stock status in Catch Maximum Sustainable Yield (CMSY) and Abundance Maximum Sustainable Yield (AMSY) methods occurs more for stocks that are less managed with short time series length with slow growth and less variation in recruitment. In contrast, “false overexploited” misclassification of stock status in CMSY and AMSY occurs more for stocks that are well managed, have long time series length, and high variation in recruitment. Filtering out non-suitable stocks based on such characteristics or correcting the bias using machine learning methods will prevent the blind application of default priors and may prevent misclassification of stock status for data-limited species.
... Surplus Production Models (SPMs) are among the basic and most widely used population models that require minimal data but can produce reliable estimates of fisheries reference points, i.e., maximum sustainable yield (MSY), biomass at MSY (B MSY ), etc. These models are essential, especially for the data-poor fisheries where only time series of catch and effort data are available (Dichmont et al., 2016;Froese et al., 2016;Punt et al., 2015;Rosenberg et al., 2017;Sagarese et al., 2018;Worm et al., 2009). In Bangladesh, the only available data are time series of yearly landing and efforts statistics published by the Fisheries Resources Survey System (FRSS). ...
... Surplus Production Models are the simplest tools for data-limited to-moderate fisheries stock assessments because of their capabilities to approximate the dynamics of stock biomass from an assumption of initial biomass, an index of abundance-the Catch-Per-Unit-Effort (CPUE), and the time series of landing data. Most importantly, these models do not require information on size and/or age (Dichmont et al., 2016;Froese et al., 2016;Punt et al., 2015;Rosenberg et al., 2017;Winker et al., 2018). Nevertheless, it is widely acknowledged that SPMs are often failed to adequately describe the biomass dynamics of a real-world fish stock that is subjected to variability in size-structure, recruitment, selectivity, environmental conditions, etc. (Pella & Tomlinson, 1969). ...
Article
Full-text available
Pampus argenteus and Pampus chinensis form the high-value demersal Pomfret fishery of Bangladesh. But, due to a monotonic decline in catches over the last five years, it is essential to explore the current stock status concerning the removal rate to ensure the sustainability of this fishery. Therefore, given the reliability and minimal data requirements, this study employed an extended Bayesian State-Space Surplus Production Model, JABBA (Just Another Bayesian Biomass Assessment), to assess the stock rigorously. The results revealed that the stock biomass of the Pomfret fishery in the final year of the time series is significantly lower than B MSY , the biomass required to produce MSY. Consequently, this study recommends a yearly catch limit (TAC) of 10,000 metric tons to prevent further depletion of the stock biomass. Furthermore, to avoid growth overfishing by allowing all immature fishes to reproduce at least once before being caught, this study also calculated the optimum length (L opt) for catch for both species at which biologically maximum yield and revenue can be obtained. The estimated L opt is 25 cm for P. argenteus and 30 cm for P. chinensis, and not to capture fishes with lengths lower than these limits, this study further calculated the minimum mesh size limits for gill and set bag nets is 7.5 cm. Though the mesh size regulation was estimated using length-based reference points derived from an empirical equation, this regulation can be used as an associate reference point when TAC is applied to assure the sustainability of this fishery.
... Surplus Production Models (SPMs) are among the basic and most widely used population models that require minimal data but can produce reliable estimates of fisheries reference points, i.e., maximum sustainable yield (MSY), biomass at MSY (B MSY ), etc. These models are essential, especially for the data-poor fisheries where only time series of catch and effort data are available (Dichmont et al., 2016;Froese et al., 2016;Punt et al., 2015;Rosenberg et al., 2017;Sagarese et al., 2018;Worm et al., 2009). In Bangladesh, the only available data are time series of yearly landing and efforts statistics published by the Fisheries Resources Survey System (FRSS). ...
... Surplus Production Models are the simplest tools for data-limited to-moderate fisheries stock assessments because of their capabilities to approximate the dynamics of stock biomass from an assumption of initial biomass, an index of abundance-the Catch-Per-Unit-Effort (CPUE), and the time series of landing data. Most importantly, these models do not require information on size and/or age (Dichmont et al., 2016;Froese et al., 2016;Punt et al., 2015;Rosenberg et al., 2017;Winker et al., 2018). Nevertheless, it is widely acknowledged that SPMs are often failed to adequately describe the biomass dynamics of a real-world fish stock that is subjected to variability in size-structure, recruitment, selectivity, environmental conditions, etc. (Pella & Tomlinson, 1969). ...
Article
Full-text available
Abstract Pampus argenteus and Pampus chinensis form the high-value demersal Pomfret fishery of Bangladesh. But, due to a monotonic decline in catches over the last five years, it is essential to explore the current stock status concerning the removal rate to ensure the sustainability of this fishery. Therefore, given the reliability and minimal data requirements, this study employed an extended Bayesian State-Space Surplus Production Model, JABBA (Just Another Bayesian Biomass Assessment), to assess the stock rigorously. The results revealed that the stock biomass of the Pomfret fishery in the final year of the time series is significantly lower than BMSY, the biomass required to produce MSY. Consequently, this study recommends a yearly catch limit (TAC) of 10,000 metric tons to prevent further depletion of the stock biomass. Furthermore, to avoid growth overfishing by allowing all immature fishes to reproduce at least once before being caught, this study also calculated the optimum length (Lopt) for catch for both species at which biologically maximum yield and revenue can be obtained. The estimated Lopt is 25 cm for P. argenteus and 30 cm for P. chinensis, and not to capture fishes with lengths lower than these limits, this study further calculated the minimum mesh size limits for gill and set bag nets is 7.5 cm. Though the mesh size regulation was estimated using length-based reference points derived from an empirical equation, this regulation can be used as an associate reference point when TAC is applied to assure the sustainability of this fishery.
... The database is not a complete record of world fisheries, missing the multitude of unassessed stocks, particularly from developing regions of the world. However, this ever-growing collection is one of the largest conglomerates of fisheries assessment data to exist and is often used as a proxy for global data-moderate to data-rich fisheries (Rosenberg et al., 2018). The RAMLDB contains many stocks with missing data on stock assessment model usage. ...
... Our RAMLDBindependent data collection process revealed that many of these stocks in regions such as the Northwest and Northeast Atlantic were analysed using trends-based methods. It is well known that the RAMLDB covers relatively data-moderate and data-rich stocks, but has knowledge gaps for those that are data-poor (Rosenberg et al., 2018). Many of these Northern Atlantic stocks are not data-poor but do use relatively simplistic assessment models. ...
Article
Full-text available
Despite their growing socio-economic importance globally, relatively little is understood about how crustacean stocks are assessed, which has potential to compromise fishery sustainability, especially under heavy exploitation and environmental changes. To inform stock assessment model application for emergent fisheries, we evaluated model use for crustacean stocks available in the RAM Legacy Database (RAMLDB) and the evolution of model use for four case-study fisheries, emphasizing the relationship between data availability and model complexity. Differences in model use between FAO fishing regions and crustacean species sub-groups were identified. Only 60.9% of crustacean stocks in the RAMLDB identified the model used for assessment. For the remaining stocks, we collected ancillary data to fill the information gaps, amounting to 92.5% of crustacean stocks in RAMLDB. Of these, model complexity varied from count-based to environmentally explicit statistical catch-at-length methods, but tended to be data intensive, likely due to biases towards regions with more developed fishery management programmes. Furthermore, regional comparisons indicated that crustaceans are only well-assessed in a few geographical hotspots. The progression of model use over time was inconsistent between case-study fisheries, being driven by myriad factors including data availability, confidence in biological processes and ecological considerations. Our findings can be used as a resource to help inform model choice for fisheries management. Towards the goal of seeking global best practices for crustacean stock assessments, future work should address knowledge gaps in regional stock assessment model use and conduct comparative studies to evaluate stock-specific costs and benefits relating to model complexity.
... However, time series of catches are often available for unassessed fisheries. 2 The debate in fisheries science is on whether catch data alone can infer the underlying condition of the fish stock (Froese and Kesner-Reyes 2002;Pauly, Hilborn, and Branch 2013;Branch et al. 2011;Rosenberg et al. 2018). Modeling studies consistently show that the 10% rule and related catch-based metrics are overly pessimistic and generate false positives of collapse (Wilberg and Miller 2007;Branch et al. 2011;Daan et al. 2011;Carruthers, Walters, and McAllister 2012). ...
... Using assessed fisheries has some potential for doing comparative work, but it comes with a significant caveat that assessed fisheries are a small fraction of fisheries globally and are more likely to be well managed in the first place. There are also ongoing efforts to refine catch-based and other data-limited metrics of stock status using ensemble modeling (Rosenberg et al. 2018). These emerging methods may turn out to provide useful outcome measures for comparative work. ...
... Costello et al. (26) used methods relying on reported catches as the primary indicator of stock status, which have often increased in these regions, suggesting that the stocks are reasonably healthy; for example, the average B/B MSY was reported as 1.16 in China, 1.08 in Indonesia, 0.90 in the Republic of Korea, and 1.94 in Bangladesh. Similarly, Rosenberg et al. (27) used an ensemble of 4 catch-driven methods, which also suggested that most stocks in South and Southeast Asia were close to B MSY. Local experts, in contrast, have widespread concerns about the poor status of stocks in these countries (11,28) and believe that methods that rely primarily on trends in catches fail to capture these concerns. ...
... Estimating regional trends in abundance, fishing pressure and catch 26 Overview 27 The goal of the analysis was to estimate regional trends from the set of stocks within a 28 given region, accounting for imbalance in the data (individual stocks) when estimating the state 29 (regional index). A linear mixed effects model with fixed year effects and random stock effects 30 would account for some of the imbalance in the stocks available in a given year, but the year 31 effects in the final years would be sensitive to which stocks were present. ...
Article
Full-text available
Marine fish stocks are an important part of the world food system and are particularly important for many of the poorest people of the world. Most existing analyses suggest overfishing is increasing, and there is widespread concern that fish stocks are decreasing throughout most of the world. We assembled trends in abundance and harvest rate of stocks that are scientifically assessed, constituting half of the reported global marine fish catch. For these stocks, on average, abundance is increasing and is at proposed target levels. Compared with regions that are intensively managed, regions with less-developed fisheries management have, on average, 3-fold greater harvest rates and half the abundance as assessed stocks. Available evidence suggests that the regions without assessments of abundance have little fisheries management, and stocks are in poor shape. Increased application of area-appropriate fisheries science recommendations and management tools are still needed for sustaining fisheries in places where they are lacking.
... Indeed, coastal marine ecosystems are among the most socioeconomically and ecologically important habitats globally, and fluctuations in fish species diversity, distribution, and abundance have been projected to change the economic value of commercial fisheries in these areas (Hiddink and ter Hofstede, 2008;Ojea et al., 2020). Furthermore, the majority of fish stocks are currently either fully exploited, overexploited or collapsed (FAO, 2022;Pauly et al., 2002;Rosenberg et al., 2018;Worm et al., 2009). The impacts of overfishing and habitat degradation are magnified by climate-induced environmental and ecosystem-level changes, as a result of the alterations in ocean physics and chemistry resulting from the massive burning of fossil fuels (Hoegh-Guldberg and Bruno, 2010;Pörtner et al., 2019). ...
... One of the central problems in many countries is that information is only available from fishing operations, generally in the form of historical catch series, and sometimes also in the form of measures of fishing effort as an index of fishing mortality. This condition has been widely recognized and discussed, and several approaches based on the theory of the biomass dynamic model have been suggested (Prista et al., 2011;Costello et al., 2012;Martell and Froese, 2013;Froese et al., 2017;Rosenberg et al., 2018;Winker et al., 2018;FAO, 2019). ...
Article
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Assessing the state of exploitation of a resource is key to its management. In the fishery of the blue shrimp, Penaeus stylirostris, off the central-eastern coast of the Gulf of Baja California, this assessment process is critical for two reasons: the data is limited because only catch and effort data are available, and the dynamic biomass model is not applicable to short-lived (annual) species. In this study, a procedure based on the Leslie model was used, and applied to the last 15 annual fishing seasons (2006 to 2021). Estimates of the monthly biomass per fishing season, the corresponding harvest rates ( HR y ), and indicators for the survival ratio, s y , representing the remaining stock at the end of the fishing season (essentially spawners), and the fishery’s recruitment rate, ρ y , at the beginning of the fishing season, were obtained. These last two quantities, s y and ρ y , were related to identify a limit biological reference point that reflects the replacement level for the shrimp stock, defined here as the limit for population renewal rate, PRR Lim . Initially, a Kobe diagram was constructed based on HR y and s y , which indicated a sustainable fishery status that requires management measures to limit fishing to keep it sustainable, which is currently being implemented. A Kobe diagram was also constructed based on ρ y , instead of s y , yielding the same results. Additionally, we used Kobe’s diagrams to show the contribution of the environment and an ecosystem-based reference point.
... As observed in this study, it is to be anticipated that the performance of various compared methods may be different and often result in opposing status estimations based on the tested fishing intensity trends, depletion levels, data availability and resolution, and life-histories (Rosenberg et al., 2018;Pons et al., 2020;Bouch et al., 2020). The snapper and grouper stocks that are mostly included here (as well as a croaker species), cover a broad spectrum of depletion, and generally have small differences in their life-histories. ...
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The Indonesian deep-slope demersal fisheries are economically valuable and contribute to the wellbeing of millions of people. However, the sustainability of these fisheries is uncertain because they lack data and assessment. As a precursor to management, we developed and applied a framework for using standardized catch per unit effort (CPUE) and spawning potential ratio (SPR) as indicators to assess eight primary species-fishing gear complexes: the Malabar blood snapper Lutjanus malabaricus (droplines and longlines), goldbanded jobfish Pristipomoides multidens (droplines and longlines), sharptooth jobfish P. typus (droplines and longlines), crimson snapper L. erythropterus (droplines), and rusty jobfish Aphareus rutilans (droplines). We standardized CPUE by identifying relevant fishing trips using a species-association approach and removing any changes in the index not attributable to abundance by using a delta-generalized linear model. SPR values were estimated on a per-recruit basis from life-history parameters using length data. Results indicated that in 2020, all stocks were unhealthy (SPR values < 25%) with only a few exceptions (e.g., P. multidens and L. erythropterus). Most fishing grounds with low SPR values had stable or decreasing CPUE trends, suggesting that current fishing rates are suboptimal or unsustainable. However, L. erythropterus had an increasing CPUE trend but moderate to low SPR, indicating that fishing pressure has decreased so SPR may be an underestimate, leading to an optimistic but uncertain conclusion about stock health and the sustainability of current fishing rates. Such discrepancies between CPUE and SPR may be challenging for the implementation of management measures, but we have outlined and applied a framework for interpretation. The most recent yield values set by the Indonesian Ministry of Marine Affairs and Fisheries for these stocks, however, are 1.4-2.4 times higher than our calculations. This discrepancy may be attributed to several factors, such as inclusion of species that are atypical for deep demersal fish stocks in the Ministry's estimates , differences in methods or the types of data used, or annual variability.
... Most fish stocks worldwide are data-limited and lack quantitative assessments of stock and exploitation status (Rosenberg et al., 2014(Rosenberg et al., , 2018; nevertheless, requirements for scientific advice on sustainable and precautionary management for all exploited stocks continue to grow (Berkson et al., 2011;Newman et al., 2015;Flood et al., 2016). This has led to a proliferation of work to develop and test assessment methods for datalimited stocks (e.g. ...
Article
Empirical harvest control rules set catch advice based on observed indicators and are increasingly being used worldwide to manage fish stocks that lack formal assessments of stock and exploitation status. Within the International Council for the Exploration of the Sea, trend-based rules that adjust advice according to recent survey observations have been adopted; however, there is increasing evidence that such rules do not work well for short-lived pelagic species that can exhibit large inter-annual fluctuations in stock size. Constant harvest rates, removing a fixed proportion of observed biomass index, have been proposed as a suitable strategy for managing short-lived species. Unknown survey catchability has, however, remained a barrier to reliance on their application on these stocks in the past. We apply simulation testing to define a robust, sustainable constant harvest rate for a data-limited short-lived stock, using the English Channel sprat as a case study. By conditioning a management strategy evaluation framework based on existing and borrowed life-history parameters and precautionary considerations, we test and show that a constant harvest rate outperforms trend-based catch rules, maximizing yields while reducing risks of stock overexploitation, and conclude an 8.6% constant harvest rate provides sufficiently precautionary catch advice for this stock.
... The differences in the performance of catch-based models for different types of life history traits have been identified in our results. The results of our study support that the life history traits of a species not only directly affect the predictability of catch-based models [17,33], but that the traits also correspond to fishing selectivity in terms of the effect on the performance of DLMs. Given the effects of life history traits, the choice of priors for traits relevant to population growth rate and life span (e.g., r and K) would be critical in the application of these models [10][11][12]. ...
Article
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The assessment of fish stocks is often limited by a lack of comprehensive data. Therefore, catch-based methods are increasingly being used because of the availability of more catch data. However, catch-based models may perform differently for species with different traits and fishing histories. In this study, we investigated the performance of catch-based models for species with different life history traits, fishing histories, and under different length selections. We compared simulated biomass with estimated stock status from three widely used catch-based models (Catch-MSY model [CMSY]; catch-only model-sampling importance resampling model [COM-SIR]; state-space catch-only model [SSCOM]) under three fishing history scenarios (constant, increasing then decreasing, and continuously increasing fishing mortality) and three length selectivity scenarios (no selectivity, preferring smaller individuals, preferring larger individuals). Our results showed that CMSY performed the best, particularly when fishing mortality remained constant. Catch-based models performed better for opportunistic species that had larger individuals selected for fishing and equilibrium species that had smaller individuals selected. However, the models tended to overestimate stock status when fishing mortality continued to increase. Therefore, caution should be exercised when applying catch-based methods to data-poor stocks with diverse life history traits, fishing history, and those sensitive to selective fishing.
... These methods, however, rely on assumptions that are particularly difficult to verify in data-limited contexts, with risk of biased or uninformative results (e.g. Free et al., 2020;Rosenberg et al., 2018). Even when required assumptions are verified, their use is limited to gaining a snapshot of the status of a stock prior to the implementation of a management approach. ...
Article
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Fisheries managers are in need of quantitative tools to inform decisions regarding selection of robust management practices, prioritising research gaps and stocks to focus on, particularly where there are limited resources or data. To support these decisions, the use of Management Strategy Evaluation (MSE), that is, closed loop simulation‐testing of management procedures, is widely regarded as best practice. However, applying MSE is time‐ and computationally intensive, and requires highly skilled expertise and processes for stakeholder input and peer review. For data‐ and capacity‐limited fisheries, MSE may be particularly challenging to implement. Yet, these are the contexts where it is most critical to test assumptions, evaluate the implications of all sources of uncertainty and identify the most informative data sources. To facilitate wider use of MSE, the Method Evaluation and Risk Assessment (MERA) framework was developed as an accessible online interface, with quick processing time, focused on generic data‐limited management procedures, but allowing progression to tailored and more data‐rich methods. The framework links a quantitative questionnaire and data input standard to a flexible operating model with optional customisation via command line access to the back‐end open‐source R libraries. Here, we illustrate a case study application of MERA for the bocinegro (Pagrus pagrus, Sparidae) fishery in the Gulf of Cadiz, where in conjunction with fishery stakeholders, a custom management procedure was developed and tested and key research gaps and data collection priorities were identified. We discuss implications for wider use of MSE in various contexts, including eco‐certification and fishery improvement projects.
... The appeal of simple, generic solutions is understandable: when overwhelmed with potential options or lacking the technical capacity to identify or evaluate them, any method that is standard or promoted heavily and repeatedly by consultants or experts can remain a path of least resistance even if it actually is a poor fit to a particular situation upon closer examination. No single data-limited assessment method or management approach, or limited subsets thereof, is appropriate or optimised across all data conditions, fishery operational characteristics, species life histories and socioeconomic or governance contexts (Costello et al., 2016;Dowling et al., 2019;Rosenberg et al., 2014Rosenberg et al., , 2018. The requirements, assumptions, caveats (hence, suitability) and links of each component of a potential harvest strategy need to be understood. ...
Article
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Successful fisheries management systems tend to be underpinned by harvest strategies, specifying formally agreed data collection systems, assessment approaches and management measures used to regulate fishing pressure. While harvest strategies can be effective even in data‐ and capacity‐limited (DCL) situations, their development remains challenging in such contexts. We present a process and decision‐support tool, FishPath, to guide the identification of suitable harvest strategy component options given often debilitating conditions: (i) resource limitations and lack of technical management capacity; (ii) ‘uniqueness’ of DCL fisheries; (iii) the concept of harvest strategies is unfamiliar to managers and scientists, and the universe of options is hard to navigate; and (iv) the lack of an effective participatory process to identify solutions tailored to local contexts. These conditions can lead to either management paralysis or generic solutions that may be poor fits to specific conditions. The FishPath Tool uses a diagnostic questionnaire that elicits the key characteristics and specific circumstances of a fishery. It compares these with the requirements of alternative options from an inventory of possible harvest strategy components, identifies where these requirements are met and provides customised, transparent guidance on the appropriateness of component options of a harvest strategy, specific to the fishery of interest and its governance context. The FishPath Process is a facilitated multi‐stakeholder, participatory engagement process aimed to set fisheries on the path to develop a harvest strategy. The FishPath Process and Tool combine to ensure a bottom‐up, documented, transparent, replicable and efficient process.
... Several approaches have been used to assess data-limited marine fish stocks based on length, catch and CPUE to support rapid evaluation of fish stocks through reference points (RPs) (Alam et al., 2021(Alam et al., , 2022Amorim et al., 2020;Ba, 2020;Dimarchopoulou et al., 2021;Dowling et al., 2015Dowling et al., , 2016Froese et al., 2017;Herron et al., 2018;Hordyk, Ono, Sainsbury, et al., 2015a;Hordyk, Ono, Valencia, et al., 2015b;ICES, 2015;Maria et al., 2022;Miethe et al., 2019;Prince et al., 2020;Ren & Liu, 2020;Rosenberg et al., 2018;Wang et al., 2020;Zhou et al., 2018). All data-limited approaches are subject to risk due to uncertainty of input parameters (Cope & Punt, 2009;Liao et al., 2022;Meissa et al., 2021;Pons et al., 2020). ...
Article
Inland capture fisheries in many parts of the world, especially in developing nations, receive relatively low governance priority, thereby raising concerns about their sustainability. Consequently, most inland capture fisheries are data‐limited, which renders conventional capture fisheries assessment methods inapplicable for making science‐based monitoring and management decisions. Three recent data‐limited approaches for marine fish stock assessments using length frequency and catch data (Length‐Based Indicators [LBI], Growth‐Type Groups Length‐Based Spawning Potential Ratio [GTG‐LBSPR] and Catch Maximum Sustainable Yield [CMSY]), were explored for applicability to assess stock status of three commercially exploited finfish species, Daysciaena albida, Eleutheronema tetradactylum and Mugil cephalus, in Chilika lagoon, an inland water body along the east coast of India. The LBI and GTG‐LBSPR approaches are based on catch length‐frequency data, while the CMSY is based on catch‐only data. The LBI provided insight into current exploitation status of the three species in relation to sustainable fishing, the GTG‐LBSPR provided reference points (RP) for the unfished portion of spawning biomass and relative yield in relation to selectivity, and the CMSY provided RPs for MSY, biomass and fishing pressure that yield MSY, to assess stock status and fishery management decisions. All three approaches suggested that the three species were overfished in terms of catch length and quantity, which agreed with expert knowledge of the fishery in the lagoon. All three approaches can support management and policy decisions in inland fisheries and fishery management recommendations. Future research should explore and standardise such approaches to overcome data limitations in assessing and managing inland capture fisheries.
... Therefore, many data-limited approaches have been developed where data and resources are limited (e.g. Dowling et al., 2015b;Wetzel and Punt, 2015;Rosenberg et al., 2018). ...
Article
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In data and capacity limited situations, catch-only models are increasingly being used to provide summaries of the state of regional and global fisheries. Due to the lack of information on stock trends, heuristics are required for initial and final depletion priors. The lack of data for calibration means that results are sensitive to the choice of heuristics. We, therefore, evaluate the value of obtaining additional information for classifying stock status. We found that heuristics alone performed nearly as well as the catch-only model. This highlights that catch-only models cannot be used as part of management control, where data updates are used to monitor the effectiveness of interventions. To implement management for data-poor stocks, additional data and knowledge are therefore required. The value of obtaining additional information for reducing risk due to loss of yield through adopting a risk equivalence approach should be evaluated. This will help identify the value-of-information and prioritise the development of scientific management frameworks that protect marine ecosystems and the well-being of people who have a stake in the resources at regional and local levels.
... Additionally, deep learning methods propose to denoise passive acoustic recordings in order to detect specific marine mammals (Vickers et al., 2021). Other ML methods, such as the boosted regression tree model and random forests have sought to assess stock status, such as depletion (Free et al., 2017;Rosenberg et al., 2018;Zhou et al., 2017). While these approaches seem to perform better than other methods, they could have ecological consequences if they are depended on to set catch limits, as well as diminish rational trust between fishery stakeholders as partners may perceive stock quotes or fishery rules as baseless. ...
Article
Recent literature and empirical research show that both trust and collaboration are of great importance for effective fishery management. The application of Machine Learning (ML) to fishery management offers exciting new opportunities for data synthesis and analysis and integrated insights across typically siloed domains. Yet, challenges remain as ML approaches provide new means of monitoring, enforcement and data analysis. Trust is among the underlying bases of collaboration, and control is the main means of shaping collaborative decision‐making techniques. As ML changes the dynamics of governance and enhances management control mechanisms, ML affects trust. ML methods are being introduced into a context that suffers a lack of transparency and trust between fishers and managers. As ML technologies continue to be used to inform fishery management and influence knowledge sharing and communication within the fishery network, forms of trust existing in the management network will be impacted differently. This article provides a concise review of a subset of potential ML applications to fishery management to explore how these emerging methods may impact forms of trust between fishery stakeholders.
... The management success of fish stocks depends on the availability and reliability of knowledge surrounding the biological specifics of the stock, as well as on fishery-dependent information. In a situation where most of the known commercial fish stocks in the world are fully exploited or overexploited, management improvements are urgently needed [1][2][3]. Mortality caused by fishery is a significant factor affecting fish stock abundance, playing a key role in fisheries' management. The mortality information may be biased for several reasons (discarding, gear selection, escapee mortality, misreporting, etc.) [4,5]. ...
Article
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The Gulf of Riga stock of Baltic herring (Clupea harengus membras L.) has been managed through several management tools. One of them has been the restriction of vessels´ main engine power (< 221 kW). This restriction was implemented in the early 1990s based on the vessel types available in the area and on assumption that the gear size used in trawl fishery depends on vessel size (power). In the current study, we compared vessels with different engine powers using same gear as currently allowed in the gulf to identify whether vessel power has any relation to the catch structure. The results showed that the engine power did not explain the differences in catch structure, which were more dependent on season and depth of water. Easing the power restriction of the trawl vessels in the Gulf of Riga will most likely not have a major negative impact on the sustainable management of the herring population in the gulf. However, the vessels with higher engine power should not use larger trawl gear than currently used in the gulf.
... They might also prove useful in ensemble modeling, which is now gaining traction in fisheries science as a means to combine the estimates from multiple stock assessment methods (Brodziak and Legault, 2005;Brodziak and Piner, 2010). Ensemble modeling loosens the assumptions associated with selecting a single "best" assessment model (Rosenberg et al., 2018). Stewart and Martell (2015) proved that ensemble modeling benefits from guidelines for developing sets of candidate models. ...
... As observed in this study, it is to be anticipated that the performance of various compared methods may be different and often result in opposing status estimations based on the tested fishing intensity trends, depletion levels, data availability and resolution, and life-histories (Rosenberg et al., 2018;Pons et al., 2020;Bouch et al., 2020). The snapper and grouper stocks that are mostly included here (as well as a croaker species), cover a broad spectrum of depletion, and generally have small differences in their life-histories. ...
Article
The deep demersal snapper-grouper fishery in Indonesia is a data-poor fisheries resource that provides food security and a source of income to millions globally. Owing to an ongoing crew-operated data recording system implemented in Indonesia since 2015, the stocks of this fishery can now be assessed using length-frequency data and updated life-history parameters. Here, we use two length-based methods, one that is fishery-specific and another that is more generalized, to assess the status of Indonesian stocks. Specifically, we develop a literature-based assessment method based on a patchwork of conventional approaches but tailored to the studied stocks, and compare it with a newly established and broadly applicable length-based Bayesian biomass estimation method (LBB). The methods were applied to 16 stocks from 4 Indonesian Fisheries Management Areas and were compared based on simulations, as well as the convergence of the resulting stock status classification and uncertainty of the results. Analyzing the effect of using the literature-based species/family-specific life-history parameter values for asymptotic length (L inf) and relative natural mortality (M/K) in LBB showed that different values do affect the estimated biomass indicator. Nevertheless, in more than half the cases, the stock status classification did not differ between the two methods, while LBB results became more reliable with narrower confidence limits. Simulations, as well as similar status indicators between the two models support the value of the literature-based approach as an assessment methodology for the Indonesian deep demersal fisheries. Narrower confidence ranges highlight the importance of using fishery-specific information when applying generalized stock assessment methods. While most catches had few immature fish, half of the assessed stocks were consistently shown to have low biomass, indicating that important Indonesian stocks are at high risk of overfishing.
... Numerous studies in recent years have put forward versions of "data-limited" models that have attempted to provide numerical estimates of the global status of unassessed fish stocks lacking the data or capacity needed for formal stock assessment (Costello et al., 2012(Costello et al., , 2016Pauly, 2007;Rosenberg et al., 2018;. Due to data limitations, all of these global assessment efforts have used forms of "catch-only" data-limited models and references therein). ...
Article
Implementation of the United Nations Sustainable Development Goals requires assessments of the global state of fish populations. While we have reliable estimates of stock status for fish populations accounting for approximately half of recent global catch, our knowledge of the state of the majority of the world's “unassessed” fish stocks remains highly uncertain. Numerous publications have produced estimates of the global status of these unassessed fisheries, but limited quantity and quality of data along with methodological differences have produced counterintuitive and conflicting results. Here, we show that despite numerous efforts, our understanding of the status of global fish stocks remains incomplete, even when new sources of broadly available data are added. Estimates of fish populations based primarily on catch histories on average performed 25% better than a random guess. But, on average, these methods assigned fisheries to the wrong FAO status category 57% of the time. Within these broad summaries, the performance of models trained on our tested data sources varied widely across regions. Substantial improvements to estimates of the state of the world's exploited fish populations depend more on expanded collection of new information and efficient use of existing data than development of new modelling methods.
... Several papers have explained the status of fisheries based on the catch per unit effort (e.g. [47,48,49,50]). CPUE reduction suggested overfishing levels: fully exploited or over exploited, and fisheries management attributes have been hypothesized: selective fishing on large targeted species. In recent years, there is new approach to [51,52,53]) which was applicable for data-poor application [54]. ...
Article
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Yellowfin tuna ( Thunnus albacares ) is one of large pelagic potential and economical species from the family of Scombridae. Production of yellowfin tuna from several potential fisheries areas (WPP) in Maluku was reported in the condition of overfishing. Meanwhile, tuna fisheries have been the potential resource for export demand. It is therefore, the status of yellowfin tuna fisheries should be monitored regularly following the regulation of PERMEN KP no 4 year 2017. The aims of this study were to measure the size of maturity of yellowfin tuna during October to December 2018 and utilize the empirical relationship to estimate optimum length and maximum length reached during this period. Observation on yellowfin tuna was conducted by joining local fishers from Kawa village, West Seram Regency. Handlines were occupied to catch yellowfin tuna around Banda Sea. From the catch sizes ranged from was 34cm to 168cm, the length maturity (L m ) of yellowfin tuna is 107.2cm of total length for female and 112.9cm for male. From the empirical relationship, the optimum length (L opt ) of yellowfin tuna during October-December 2018 was 120.5cm and maximum length (L max ) was 181.4cm. This study showed that 90% of yellowfin catch was immature which indicated a recruitment overfishing. Only 2.4% of mega-spawners were caught during this study and indicated growth overfishing. Indication of an overfishing of the yellowfin tuna fishery contradicted to larger size of first maturity found. This contrary is discussed and balance fishing strategy is proposed as the idea to maintain the population of yellowfin tuna.
... They suggest using a small number of models with low correlations in an ensemble. Ensemble approaches have been applied to estimate stock status of marine fisheries around the world (Anderson et al., 2017;Rosenberg et al., 2018). Ensemble approaches also provide a link to management strategy evaluation through the development of multiple operating models (Stewart and Martell, 2015). ...
Article
Two approaches to address retrospective patterns in stock assessments are compared. The Rose approach is an ensemble of models that all remove the retrospective pattern through changes in data, parameter values, or model assumptions. It is time intensive and can result in a wide range of historical abundance trends. The Rho approach modifies the terminal year estimates of a single model that exhibits a retrospective pattern. It is fast and easy to apply but results in a discontinuous time series. Neither approach identifies the source of the retrospective pattern. The pros and cons of these two approaches are compared in terms of catch advice and stock status using four examples with varying strength and direction of retrospective patterns. The choice of which approach to use could be based on time and expertise available to conduct and maintain an assessment, with Rose preferred if a lot of both are available while Rho preferred otherwise. If the Rho approach is used, managers should consider adjusting their control rule or risk buffer to account for the difference between Rose and Rho results shown here.
... A future for CMSY may lie within ensemble models (Anderson et al., 2017;Rosenberg et al., 2018;Free et al., 2020), which use several catch-only methods in combination to return a more accurate assessment by removing the biases of individual methods. ...
Article
All fish stocks should be managed sustainably, yet for the majority of stocks, data are often limited and different stock assessment methods are required. Two popular and widely used methods are Catch-MSY (CMSY) and Surplus Production Model in Continuous Time (SPiCT). We apply these methods to 17 data-rich stocks and compare the status estimates to the accepted International Council for the Exploration of the Sea (ICES) age-based assessments. Comparison statistics and receiver operator analysis showed that both methods often differed considerably from the ICES assessment, with CMSY showing a tendency to overestimate relative fishing mortality and underestimate relative stock biomass, whilst SPiCT showed the opposite. CMSY assessments were poor when the default depletion prior ranges differed from the ICES assessments, particularly towards the end of the time series, where some stocks showed signs of recovery. SPiCT assessments showed better correlation with the ICES assessment but often failed to correctly estimate the scale of either F/F MSY of B/B MSY , with the indices lacking the contrast to be informative about catchability and either the intrinsic growth rate or carrying capacity. Results highlight the importance of understanding model tendencies relative to data-rich approaches and warrant caution when adopting these models.
... Costello et al. (2016) created a data base on 70% of the exploited fish stocks in the world and that data suggest that the abundance of exploited fish is roughly 50% of what it was before industrial fishing began. Rosenberg et al. (2017) have recently provided an estimate of the status of harvested fish stocks estimating the average abundance just below Maximum Sustainable Yield targets, which would also put the abundance in the range suggested by Christensen and Costello. All three of these estimates are consistent with the Lester et al. increase in abundance inside MPAs, and quite inconsistent with those in Sala and Giakoumi. ...
... Similar quantitative level of information is unrealistic to achieve when considering complex ecosystem drivers and interactions, and higher uncertainties in knowledge are likely to remain. Here, an ensemble approach to management advice could be considered as a way forward (Rosenberg et al., 2018). The ensemble approach is particularly useful when there is evidence for several plausible levels of parameter values, while the most appropriate value is difficult to determine. ...
Article
Eastern Baltic cod is an example of a fish stock where fishing pressure has substantially declined after decades of intensive exploitation. However, in contrast to the expected improvements in stock status, stock productivity has concurrently declined to a historic low level. Targeted fisheries for the eastern Baltic cod were recently banned. However, at present low growth and high natural mortality, the stock biomass is projected to remain low, even in the absence of fishing. Thus, the future development in this stock and its potential recovery are largely dependent on ecosystem drivers likely contributing to the presently poor state of the cod stock (e.g. oxygen conditions, spatial distribution of prey species, abundance of marine mammals). Some of these ecosystem drivers and associated impacts on cod may be possible to influence by management measures, which are however not straightforward to implement. Moreover, scientific knowledge to guide management decisions in a complex ecosystem context is lagging behind. The Baltic cod case exemplifies the complexity of questions emerging for management as well as scientific advice under rapidly changing ecosystem conditions, where traditional fisheries management alone may have a limited potential to rebuild the stock.
... It is widely recognized that fisheries are putting global marine ecosystems under severe pressure [1,2]. Nearly 30% of fish stocks are overexploited and 17% have collapsed [3][4][5][6][7]. Selective exploitation of large predators has led to concerns about 'fishing down the food chain' [8], and an increasing number of marine taxa, including a quarter of all sharks, are considered to be at an elevated risk of extinction [9]. ...
Article
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Overexploitation is recognized as one of the main threats to global biodiversity. Here, we report a widespread change in the functional diversity of fisheries catches from the large marine ecosystems (LMEs) of the world over the past 65 years (1950 to 2014). The spatial and temporal trends of functional diversity exploited from the LMEs were calculated using global reconstructed marine fisheries catch data provided by the Sea Around Us initiative (including subsistence, artisanal, recreational, industrial fisheries, and discards) and functional trait data available in FishBase. Our analyses uncovered a substantial increase in the functional richness of both ray-finned fishes (80% of LMEs) and cartilaginous species (sharks and rays) (75% of LMESs), in line with an increase in the taxonomic richness, extracted from these ecosystems. The functional evenness and functional divergence of these catches have also altered substantially over the time span of this study, with considerable geographic variation in the patterns detected. These trends show that global fisheries are increasingly targeting species that play diverse roles within the marine ecosystem and underline the importance of incorporating functional diversity in ecosystem management.
... To address these challenges, the development of alternative methods to increase knowledge about data-limited fisheries has been a major topic of research in recent years. Chrysafi and Kuparinen (2016) compiled the most common assessment methods for data-limited fisheries, and other recent studies have been presenting new methods to evaluate the status of fisheries (Thorson et al., 2013;Rosenberg, 2014;Dowling et al., 2015;Geromont and Butterworth, 2015;Anderson et al., 2017;Rosenberg et al., 2017;Carruthers, 2018). However, some of these methods still require some basic knowledge of the life-history of the species. ...
Article
Indonesia is the most important producer country of snapper and grouper species worldwide, with a notable increase in landings over the past decades. The Java Sea is one of the most frequently fished areas for these species in Indonesia, but in 2016 a decrease in landings was observed. This study applied two approaches (Length-based Indicators and Length-Structured Growth-Type-Group Model) to assess the status of snapper and grouper fisheries in the Java Sea, using length-composition data from the commercial fishery and covering multiple gears with different selectivity. The work focused on the dominant species in the catches: Malabar blood snapper (Lutjanus malabaricus), Areolate grouper (Epinephelus areolatus), Crimson snapper (Lutjanus erythropterus), and Goldbanded jobfish (Pristipomoides multidens). Considerable differences were observed, related to the type of gear used. Catch data obtained from the longline fishery presented good stock status indicators for all species studied. The spawning potential ratio (SPR) estimates calculated for Malabar blood snapper and crimson snapper indicated that these species are currently fished in the Java Sea at unsustainable levels (below 30 % SPR) by the dropline and mixed-gear fleets, while areolate grouper and goldbanded jobfish are not overfished (above 30 % SPR). For both methods, bias in Linf and, secondarily, bias in M/K have a stronger influence on the indicators values, in particular for the proportion of individuals above the length of optimal yield + 10 % (Pmega) estimates and SPR, than bias in Lmat values. This study highlighted other areas where improvements are critical to ensure the sustainability of the snapper and grouper fisheries in the Java Sea.
... Given the multiple sources of potential uncertainty in climate impact assessments, multi-model ensembles within and across models of different complexity are of interest. Ensemble modeling approaches will be applied to synthesize the information to derive overall system level trends Rosenberg et al., 2018;Lotze et al., 2019). Selection of models used to derive ensemble estimates may be informed by an analysis of among model correlations (Stewart and Hicks, 2018). ...
Article
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The Alaska Climate Integrated Modeling (ACLIM) project represents a comprehensive, multi-year, interdisciplinary effort to characterize and project climate-driven changes to the eastern Bering Sea (EBS) ecosystem, from physics to fishing communities. Results from the ACLIM project are being used to understand how different regional fisheries management approaches can help promote adaptation to climate-driven changes to sustain fish and shellfish populations and to inform managers and fishery dependent communities of the risks associated with different future climate scenarios. The project relies on iterative communications and outreaches with managers and fishery-dependent communities that have informed the selection of fishing scenarios. This iterative approach ensures that the research team focuses on policy relevant scenarios that explore realistic adaptation options for managers and communities. Within each iterative cycle, the interdisciplinary research team continues to improve: methods for downscaling climate models, climate-enhanced biological models, socio-economic modeling, and management strategy evaluation (MSE) within a common analytical framework. The evolving nature of the ACLIM framework ensures improved understanding of system responses and feedbacks are considered within the projections and that the fishing scenarios continue to reflect the management objectives of the regional fisheries management bodies. The multi-model approach used for projection of biological responses, facilitates the quantification of the relative contributions of climate forcing scenario, fishing scenario, parameter, and structural uncertainty with and between models. Ensemble means and variance within and between models inform risk assessments under different future scenarios. The first phase of projections of climate conditions to the end of the 21st century is complete, including projections of catch for core species under baseline (status quo) fishing conditions and two alternative fishing scenarios are discussed. The ACLIM modeling framework serves as a guide for multidisciplinary integrated climate impact and adaptation decision making in other large marine ecosystems.
... This was true when tested on a simulated dataset and when tested on empirical data from the RAM Legacy Database. Recently, Rosenberg et al. (2017) applied the same superensemble approach to assess global fish stocks status with catch data from the FAO. Beyond these two applications, the superensemble approach could be used to combine predictions from any of the methods outlined in this paper. ...
Article
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Preprint
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Rising ocean temperatures threaten marine ecosystems and food security worldwide. Yet, our current understanding of how climate change affects global marine fisheries often overlooks critical seasonal dynamics that govern ecosystem function. To address this gap, we compiled a global dataset for 187 countries between 1950 to 2018 and employed fixed effects regression to assess the effect of intra-annual sea surface temperature (SST) variation on annual marine fisheries catches. We detect a significant non-linear response with varied effects across the globe. Increases over 1°C in intra-annual SST variation estimated a 12% increase in catches for low and high latitude aseasonal countries, and a 30% decrease in catches in highly seasonal mid-latitude countries. Our analyses underscore the need to integrate ocean seasonality in efforts to better understand, mitigate and adapt to the impacts of climate change on marine fisheries, to support sustainable fisheries management as well as maintain global ocean health.
Chapter
This chapter introduces China's rich marine fishery resources, analyses the efficiency of the use of China's marine fishery resources based on the SBM (Slack Based Measure) super-efficiency model and the Malmquist-Luenberger (ML) index, and finally examines the public management policies for marine fishery resources. Section 10.1 of this chapter describes the global emphasis on marine fishery resources, including China. Section 10.2 examines the total amount and distribution of marine fishery resources globally and in particular in China. Section 10.3 discusses the issue of utilisation efficiency of marine fishery resources in China, and measures the utilisation efficiency of marine fishery resources based on the SBM super-efficiency DEA model and the Malmquist-Luenberger index, and points out the reasons for the inefficiency. Section 10.4 introduces China's public management policies for marine fishery resources, which are divided into presentations in terms of fishing costs, subsidies and individual transferable quotas. Section 10.5 summarises the above.
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Age-based stock assessments are sometimes rejected by review panels due to large retrospective patterns. When this occurs, data-limited approaches are often used to set catch advice, under the assumption that these simpler methods will not be impacted by the problems causing retrospective patterns in the age-based assessment. This assumption has never been formally evaluated. Closed-loop simulations were conducted where a known source of error caused a retrospective pattern in an age-based assessment. Twelve data-limited methods, an ensemble of a subset of these methods, and a statistical catch-at-age model with retrospective adjustment were all evaluated to examine their ability to prevent overfishing and rebuild overfished stocks. Overall, none of the methods evaluated performed best across the scenarios. A number of methods performed consistently poorly, resulting in frequent and intense overfishing and low stock sizes. The retrospective adjusted statistical catch-at-age assessment performed better than a number of the alternatives explored. Thus, using a data-limited approach to set catch advice will not necessarily result in better performance than relying on the age-based assessment with a retrospective adjustment.
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Bottom trawling is widespread globally and impacts seabed habitats. However, risks from trawling remain unquantified at large scales in most regions. We address these issues by synthesizing evidence on the impacts of different trawl-gear types, seabed recovery rates, and spatial distributions of trawling intensity in a quantitative indicator of biotic status (relative amount of pretrawling biota) for sedimentary habitats, where most bottom-trawling occurs, in 24 regions worldwide. Regional average status relative to an untrawled state (=1) was high (>0.9) in 15 regions, but <0.7 in three (European) regions and only 0.25 in the Adriatic Sea. Across all regions, 66% of seabed area was not trawled (status = 1), 1.5% was depleted (status = 0), and 93% had status > 0.8. These assessments are first order, based on parameters estimated with uncertainty from meta-analyses; we recommend regional analyses to refine parameters for local specificity. Nevertheless, our results are sufficiently robust to highlight regions needing more effective management to reduce exploitation and improve stock sustainability and seabed environmental status—while also showing seabed status was high (>0.95) in regions where catches of trawled fish stocks meet accepted benchmarks for sustainable exploitation, demonstrating that environmental benefits accrue from effective fisheries management. Furthermore, regional seabed status was related to the proportional area swept by trawling, enabling preliminary predictions of regional status when only the total amount of trawling is known. This research advances seascape-scale understanding of trawl impacts in regions around the world, enables quantitative assessment of sustainability risks, and facilitates implementation of an ecosystem approach to trawl fisheries management globally.
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We propose an alternative delayed population growth difference equation model based on a modification of the Beverton–Holt recurrence, assuming a delay only in the growth contribution that takes into account that those individuals that die during the delay, do not contribute to growth. The model introduced differs from a delayed logistic difference equation, known as the delayed Pielou or delayed Beverton–Holt model, that was formulated as a discretization of the Hutchinson model. The analysis of our delayed difference equation model identifies a critical delay threshold. If the time delay exceeds this threshold, the model predicts that the population will go extinct for all non-negative initial conditions. If the delay is below this threshold, the population survives and its size converges to a positive globally asymptotically stable equilibrium that is decreasing in size as the delay increases. We show global asymptotic stability of the positive equilibrium using two different techniques. For one set of parameter values, a contraction mapping result is applied, while the proof for the remaining set of parameter values, relies on showing that the map is eventually componentwise monotone.
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The status of fishery resources in the Yangtze estuary and its adjacent waters is still unclear for the effective implementation of fishery management strategies. To help address this gap, a new method especially for data-limited fish stocks (LBB) was applied to assess seven commercially and ecotrophically important fish stocks. Fish specimens were collected in the estuary by bottom trawling quarterly from May 2018 to February 2019. Two historical datasets were collected with the same method in the same area for Indian perch (Jaydia lineata) and sickle pomfret (Pampus echinogaster). To explore the growth features and resilience of fish stocks, auximetric plots and growth performance indices (Φ′) were used. Results showed that common hairfin anchovy (Setipinna tenuifilis) in 2018 and Indian perch in 2018 showed a healthy stock biomass status with complete length structures under a sustainable fishing pressure. The others were outside of safe biological limits or overfished. The Lmean/Lopt < 0.9 in six (67%) of nine LBB models for seven fish stocks suggested that most of the stocks were truncated in length structures. This contribution provides the main fishery reference points regarding stock status that can inform managers and form the basis for various management strategies.
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The brushtooth lizardfish Saurida undosquamis (Richardson, 1848) is a high trophic level benthic predator and is one among the most exploited demersal finfish species from eastern Arabian Sea by Indian trawlers. However, in recent years, the landings of many top predator fishes including S. undosquamis showed a declining trend resulting in a steady decline in the mean trophic levels of the fishes caught commercially in the region. We investigated the growth, mortality and stock dynamics of S. undosquamis harvested by mechanised trawls in the southeastern Arabian Sea, using length-based methods for the data collected during 2012–2016. Besides, Bayesian state-space implementation of the Schaefer model (BSM) and catch-based MSY (CMSY) estimation were also made using the data for the period 1985–2016. Total length of the fish ranged from 5.5 to 34.5 cm with average annual mean length of 22.0 cm during 2012–2016. The growth parameters L ∞ and K were 37.3 cm and 0.41 year ⁻¹ , respectively. The natural, fishing and total mortality coefficients were 0.92, 2.58 and 3.5, respectively and exploitation ratio was 0.82. The length at first maturity was estimated at 21.4 cm for females. The mean size in the catch is lower than the optimum length for exploitation. Fisheries reference points (MSY, F msy , B msy ) as well as relative stock size (B/B msy ) and exploitation (F/F msy ) estimated from catch data and broad priors for resilience ( r ), implies an exploitation of 30% below B msy level. Results from the length-based Thompson and Bell prediction model indicates that reducing the present level of fishing effort by 40% would lead to a harvest of the species at a sustainable level. As “fishing down food web” is reported in recent years from eastern Arabian Sea, the exploitation of top predators need to be maintained at sustainable levels to prevent ecosystem changes along the region.
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Worldwide, the majorities of fish stocks are data-limited and lack fully quantitative stock assessments. Within ICES, such data-limited stocks are currently managed by setting total allowable catch without the use of target reference points. To ensure that such advice is precautionary, we used management strategy evaluation to evaluate an empirical rule that bases catch advice on recent catches, information from a biomass survey index, catch length frequencies, and MSY reference point proxies. Twenty-nine fish stocks were simulated covering a wide range of life histories. The performance of the rule varied substantially between stocks, and the risk of breaching limit reference points was inversely correlated to the von Bertalanffy growth parameter k. Stocks with k>0.32 year−1 had a high probability of stock collapse. A time series cluster analysis revealed four types of dynamics, i.e. groups with similar terminal spawning stock biomass (collapsed, BMSY, 2BMSY, 3BMSY). It was shown that a single generic catch rule cannot be applied across all life histories, and management should instead be linked to life-history traits, and in particular, the nature of the time series of stock metrics. The lessons learnt can help future work to shape scientific research into data-limited fisheries management and to ensure that fisheries are MSY compliant and precautionary.
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The Mediterranean Large Marine Ecosystem (Med-LME) is a heterogeneous system that, despite its oligotrophic nature, has high diversity of marine species and high rate of endemism, making it one of the world hotspots for marine biodiversity. The basin is also among the most impacted Large Marine Ecosystems in the world due to the combined multiple stressors, such as fishing pressure, habitat loss and degradation, climate change, pollution, eutrophication and the introduction of invasive species. The complexity of Med-LME in its structure/function and dynamics, combined with the socio-political framework of the region make management of its marine resources quite challenging. This contribution aims at highlighting the importance of the Med-LME, with an emphasis on the state of its food web and of its fish/fisheries using modelling tools and national/international reporting. The purpose is to demonstrate the importance of an holistic framework, based on stock assessments and ecosystem based modelling approaches, to be adopted in support of management and conservation measures for the preservation and sustainable use of the Med-LME resources.
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Significance What would extensive fishery reform look like? In addition, what would be the benefits and trade-offs of implementing alternative approaches to fisheries management on a worldwide scale? To find out, we assembled the largest-of-its-kind database and coupled it to state-of-the-art bioeconomic models for more than 4,500 fisheries around the world. We find that, in nearly every country of the world, fishery recovery would simultaneously drive increases in food provision, fishery profits, and fish biomass in the sea. Our results suggest that a suite of approaches providing individual or communal access rights to fishery resources can align incentives across profit, food, and conservation so that few trade-offs will have to be made across these objectives in selecting effective policy interventions.
Technical Report
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Stock status is a key parameter for evaluating the sustainability of fishery resources and developing corresponding management plans. However, the majority of stocks are not assessed, often as a result of insufficient data and a lack of resources needed to execute formal stock assessments. The working group involved in this publication focused on two approaches to estimating fisheries status: one based on single-stock status, and the other based on ecosystem production. For the single-stock status work, a fully factorial simulation testing framework was developed to assess four potential data-limited models. The results suggest that Catch-MSY, a catch-based method, was the best performer, although the different models perf ormed similarly in many cases. Catch-MSY was more effective in estimating status over short time s cales and could be particularly applicable for use in developing countries where data time series are ofte n shorter. Harvest dynamics was the most important explanatory variable in determining performance, which emphasizes the importance of having accurate information on fishing effort and total removals. For the ecosystem-level production analysis, the working group used sate llite-based estimates of primary productivity by size classes and a more complete food web, which included more complete microbial pathways than earlier approaches. The working group also assembled estimates of ecological transfer efficiencies from a large number of energy flow network models to characterize uncertainty. The first-order estimates of fishery production potential indicated a potential yield of up to 180 million tonnes of fish, which could vary depending on the ca pacity to sustainably diversify the suite of species that are currently exploited. Planktivorous specie s provide the largest scope for growth. However, consideration of factors such as the ecological impact on other food web components, profitability of harvest operations, and marketability for these species must first be resolved. The realized production potential for planktivores may be much lower than their potential levels depending on the outcome of these considerations. The working group estimated that up to 50 million tonnes of benthic production could be potentially harvested, although this estimate is subject to similar constraints as those for planktivores. The greatest scope for growth in the benthic component may be found in the mariculture sector, subject to suitable environmental safeguards. Ecosystem exploitation rates should not exceed 20–25 percent of available production, considering basic energetic constraints in marine ecosystems . Current harvest levels for benthivorous and piscivorous species (principally fish) exceeded th ese levels in higher-latitude ecosystems (subarctic- boreal and temperate) and were near or slightly below them in lower latitudes and upwelling systems. The estimates of the ratio of current catches to available production for planktivorous species are substantially lower, reflecting the production potential of currentl y underutilized species. However, targeted harvesting of selected planktivorous species does lead to relatively high exploitation rates for some species. Together, these results provide globa lly applicable methods for estimating fish stock status and fishery production potential.
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Marine fish populations have high variation in cohort strength, and the production of juveniles (recruitment) may have persistent positive or negative residuals (autocorrelation) after accounting for spawning biomass. Autocorrelated recruitment will occur whenever average recruitment levels change between oceanographic regimes or due to predator release, but may also indicate persistent environmental and biological effects on shorter time-scales. Here, we use estimates of recruitment variability and autocorrelation to simulate the stationary distribution of spawning biomass for 100 real-world stocks when unfished, fished at FMSY, or fished following a harvest control rule where fishing mortality decreases as a function of spawning biomass. Results show that unfished stocks have spawning biomass (SB) below its deterministic equilibrium value (SB0) 58% of the time, and below 0.5SB0 5% of the time on average across all stocks. Similarly, stocks fished at the level producing deterministic maximum sustainable yield (FMSY) are below its deterministic prediction of spawning biomass (SBMSY) 60% of the time and below 0.5SBMSY 8% of the time. These probabilities are greater for stocks with high recruitment variability, positive autocorrelation, and high natural mortality—traits that are particularly associated with clupeids and scombrids. An elevated probability of stochastic depletion, i.e. biomass below the deterministic equilibrium expectation, implies that management actions required when biomass drops below a threshold may be triggered more frequently than expected. Therefore, we conclude by suggesting that fisheries scientists routinely calculate these probabilities during stock assessments as a decision support tool for fisheries managers.
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Anderson, S. C., Branch, T. A., Ricard, D., and Lotze, H. K. 2012. Assessing global marine fishery status with a revised dynamic catch-based method and stock-assessment reference points. – ICES Journal of Marine Science, 69: . The assessment of fishery status is essential for management, yet fishery-independent estimates of abundance are lacking for most fisheries. Methods exist to infer fishery status from catches, but the most commonly used method is biased towards classifying fisheries as overexploited or collapsed through time and does not account for still-developing fisheries. We introduce a revised method that overcomes these deficiencies by smoothing catch series iteratively, declaring fisheries developing within three years of peak catch, and calibrating thresholds to biological reference points. Compared with status obtained from stock-assessment reference points for 210 stocks, our approach provides a more realistic assessment than the original method, but cannot be perfect because catches are influenced by factors other than biomass. Applied to FAO catches, our method suggests in 2006 32% of global fisheries were developing, 27% fully exploited, 25% overexploited, and 16% collapsed or closed. Although less dire than previous assessments, this still indicates substantial numbers of overexploited stocks. Probably because median exploitation rate decreased since 1992, our catch-based results do not reflect recent stabilization of assessed-stock biomass. Whether this outlook also applies to unassessed stocks can only be revealed with increased or more representative collection of biomass- and exploitation-rate trends.
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The argument persists that the continued overexploitation by many fisheries around the world is evidence that current approaches to fisheries management are failing, and that more precautionary management approaches are needed. We review the available estimates of the status of fish stocks from three sources: the FAO's “State of Marine Resources”, a database on scientific stock assessments, and recent estimates from statistical models designed to determine the status of unassessed fish stocks. The two key results are (i) that stocks that are scientifically assessed are in better shape and indeed are not typically declining but rebuilding, and (ii) that large stocks appear to be in better shape than small stocks. These results support the view that stocks that are managed are improving, while stocks that are not managed are not. Large stocks receive far more management attention than small stocks in jurisdictions that have active fisheries management systems, and most unassessed stocks are simply not managed. We assert that fisheries management as currently practised can (and often does) lead to sustainable fisheries, and what is needed is to actively manage the unassessed fisheries of the world. More precautionary management is not necessarily needed to ensure the sustainability of managed fisheries.
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The majority of global fish stocks lack adequate data to evaluate stock status using conventional stock assessment methods. This poses a challenge for the sustainable management of these stocks. Recent requirements to set scientifically based catch limits in several countries, and growing consumer demand for sustainably managed fish have spurred an emerging field of methods for estimating overfishing thresholds and setting catch limits for stocks with limited data. Using a management strategy evaluation framework we quantified the performance of a number of data-limited methods. For most life-histories, we found that methods that made use of only historical catches often performed worse than maintaining current fishing levels. Only those methods that dynamically accounted for changes in abundance and/or depletion performed well at low stock sizes. Stock assessments that make use of historical catch and effort data did not necessarily out-perform simpler data-limited methods that made use of fewer data. There is a high value of additional information regarding stock depletion, historical fishing effort and current abundance when only catch data are available. We discuss the implications of our results for other data-limited methods and identify future research priorities.
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Research shows that population status can be predicted using catch data, but there is little justification for why these predictions work or how they account for changes in fisheries management. We demonstrate that biomass can be reconstructed from catch data whenever fishing mortality follows predictable dynamics over time (called “effort dynamics”), and develop a state-space catch only model (SSCOM) for this purpose. We use theoretical arguments and simulation modeling to demonstrate that SSCOM can, in some cases, estimate population status from catch data. Next, we use meta-analysis to estimate effort dynamics for U.S. West Coast groundfishes before and after fisheries management changes in the mid-1990s. We apply the SSCOM using meta-analytic results to data for eight assessed species, and compare results with stock assessment and data-poor methods. Results indicate general agreement between all three methods. We conclude that effort dynamics provides a theoretical basis for using catch data to reconstruct biomass, and has potential for conducting data-poor assessments. However, we still recommend that index and compositional data be collected to allow application of data-rich methods.
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Assessing fishery collapses worldwide is hindered by the lack of biomass data for most stocks, leading to the use of landings-based proxies or the assumption that existing stock assessments are globally representative. We argue that the use of sparse assessments to evaluate fishery status requires model-based inference because assessment availability varies spatially and temporally, and we derive a model that extrapolates from assessment results to available landings, life history, and location data. This model uses logistic regression to classify stocks into different prediction bins and estimates the probability of collapse in each using cross-validation. Results show that landings, life history, and location are informative to discriminate among different probabilities of collapse. We find little evidence that regions with fewer assessments have a greater proportion of collapsed stocks, while acknowledging weak inferential support regarding regions with one or fewer assessments. Our extrapolation suggests that 4.5%–6.5% of stocks defined by landings data are collapsed, but that this proportion is increasing. Finally, we propose a research agenda that combines stock assessment and landings databases while overcoming limitations in each.
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A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.
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Recent reports suggest that many well-assessed fisheries in developed countries are moving toward sustainability. We examined whether the same conclusion holds for fisheries lacking formal assessment, which comprise >80% of global catch. We developed a method using species’ life-history, catch, and fishery development data to estimate the status of thousands of unassessed fisheries worldwide. We found that small unassessed fisheries are in substantially worse condition than assessed fisheries, but that large unassessed fisheries may be performing nearly as well as their assessed counterparts. Both small and large stocks, however, continue to decline; 64% of unassessed stocks could provide increased sustainable harvest if rebuilt. Our results suggest that global fishery recovery would simultaneously create increases in abundance (56%) and fishery yields (8 to 40%).
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Concerns over fishing impacts on marine populations and ecosystems have intensified the need to improve ocean management. One increasingly popular market-based instrument for ecological stewardship is the use of certification and eco-labeling programs to highlight sustainable fisheries with low environmental impacts. The Marine Stewardship Council (MSC) is the most prominent of these programs. Despite widespread discussions about the rigor of the MSC standards, no comprehensive analysis of the performance of MSC-certified fish stocks has yet been conducted. We compared status and abundance trends of 45 certified stocks with those of 179 uncertified stocks, finding that 74% of certified fisheries were above biomass levels that would produce maximum sustainable yield, compared with only 44% of uncertified fisheries. On average, the biomass of certified stocks increased by 46% over the past 10 years, whereas uncertified fisheries increased by just 9%. As part of the MSC process, fisheries initially go through a confidential pre-assessment process. When certified fisheries are compared with those that decline to pursue full certification after pre-assessment, certified stocks had much lower mean exploitation rates (67% of the rate producing maximum sustainable yield vs. 92% for those declining to pursue certification), allowing for more sustainable harvesting and in many cases biomass rebuilding. From a consumer's point of view this means that MSC-certified seafood is 3–5 times less likely to be subject to harmful fishing than uncertified seafood. Thus, MSC-certification accurately identifies healthy fish stocks and conveys reliable information on stock status to seafood consumers. Copyright: ß 2012 Gutiérrez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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World population is expected to grow from the present 6.8 billion people to about 9 billion by 2050. The growing need for nutritious and healthy food will increase the demand for fisheries products from marine sources, whose productivity is already highly stressed by excessive fishing pressure, growing organic pollution, toxic contamination, coastal degradation and climate change. Looking towards 2050, the question is how fisheries governance, and the national and international policy and legal frameworks within which it is nested, will ensure a sustainable harvest, maintain biodiversity and ecosystem functions, and adapt to climate change. This paper looks at global fisheries production, the state of resources, contribution to food security and governance. It describes the main changes affecting the sector, including geographical expansion, fishing capacity-building, natural variability, environmental degradation and climate change. It identifies drivers and future challenges, while suggesting how new science, policies and interventions could best address those challenges.
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After a long history of overexploitation, increasing efforts to restore marine ecosystems and rebuild fisheries are under way. Here, we analyze current trends from a fisheries and conservation perspective. In 5 of 10 well-studied ecosystems, the average exploitation rate has recently declined and is now at or below the rate predicted to achieve maximum sustainable yield for seven systems. Yet 63% of assessed fish stocks worldwide still require rebuilding, and even lower exploitation rates are needed to reverse the collapse of vulnerable species. Combined fisheries and conservation objectives can be achieved by merging diverse management actions, including catch restrictions, gear modification, and closed areas, depending on local context. Impacts of international fleets and the lack of alternatives to fishing complicate prospects for rebuilding fisheries in many poorer regions, highlighting the need for a global perspective on rebuilding marine resources.
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The mean trophic level of the species groups reported in Food and Agricultural Organization global fisheries statistics declined from 1950 to 1994. This reflects a gradual transition in landings from long-lived, high trophic level, piscivorous bottom fish toward short-lived, low trophic level invertebrates and planktivorous pelagic fish. This effect, also found to be occurring in inland fisheries, is most pronounced in the Northern Hemisphere. Fishing down food webs (that is, at lower trophic levels) leads at first to increasing catches, then to a phase transition associated with stagnating or declining catches. These results indicate that present exploitation patterns are unsustainable.
Article
This book serves as an advance This book serves as an advanced text on fisheries and fishery population dynamics and as a reference for fisheries scientists. It provides a thorough treatment of contemporary topics in quantitative fisheries science and emphasizes the link between biology and theory by explaining the assumptions inherent in the quantitative methods. The analytical methods are accessible to a wide range of biologists, and the book includes numerous examples. The book is unique in covering such advanced topics as optimal harvesting, migratory stocks, age-structured models, and size models.d text on fisheries and fishery population dynamics and as a reference for fisheries scientists. It provides a thorough treatment of contemporary topics in quantitative fisheries science and emphasizes the link between biology and theory by explaining the assumptions inherent in the quantitative methods. The analytical methods are accessible to a wide range of biologists, and the book includes numerous examples. The book is unique in covering such advanced topics as optimal harvesting, migratory stocks, age-structured models, and size models.
Article
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/BMSY). We combined these estimates of recent fishery status and trends in B/BMSY 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 to 0.56 to 0.32 to 0.33, rank-order correlation between predicted and true status improved from 0.02 to 0.32 to 0.44 to 0.48 and bias (median proportional error) declined from −0.22 to 0.31 to −0.12 to 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.
Article
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ∗∗∗, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Article
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.
Article
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Article
Analyses of global fish stocks paint a mixed picture of success, with some holding fishery management responsible for the poor status of many stocks [1-3] or predicting widespread collapse [1, 4]. Some suggest a stable [5] or improving situation [6] in certain jurisdictions. The debate is particularly polarized in the European Union, where the Common Fisheries Policy (CFP) has been criticized for failing to protect stocks [2, 7-10], while others argue that a rebuilding process is underway [11, 12]. We show that substantial change in stock trends occurred in the area around the turn of the century: since then, the fishing pressure (as measured by the exploitation rate) has reduced continuously and there have been increases in biomass, demonstrating the potential for stock recovery. In 2011, for the first time, the majority of assessed stocks, where reference points are defined, were fished sustainably. The reductions in fishing pressure were associated with declines in fishing effort. The last reform of the CFP, in 2002, introduced effort control as part of more enforceable management measures, which were also based on longer-term plans. Further reforms to the CFP are currently being developed, so it is important, when correcting its weaknesses, to also acknowledge and build on the success of a major reduction in the fishing pressure on European fish stocks.
Article
The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance matrices of the ensembles. In addition, the authors model the uncertainty in using computer model results to estimate true states of nature. Based on these models and principles of decision making in the presence of uncertainty, this paper poses the problem of superensemble experimental design in a quantitative fashion. Simple examples of the resulting optimal designs are presented. The authors also provide a Bayesian climate modeling and forecasting analysis. The climate variables of interest are Northern and Southern Hemispheric monthly averaged surface temperatures. A Bayesian hierarchical model for these quantities is constructed, including time-varying parameters that are modeled as random variables with distributions depending in part on atmospheric CO2 levels. This allows the authors to do Bayesian forecasting of temperatures under different Special Report on Emissions Scenarios (SRES). These forecasts are based on Bayesian posterior distributions of the unknowns conditional on observational data for 1882-2001 and climate system model output for 2002-97. The latter dataset is a small superensemble from the Parallel Climate Model (PCM) and the Community Climate System Model (CCSM). After summarizing the results, the paper concludes with discussion of potential generalizations of the authors' strategies.
Article
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 (BMSY) and 30% had exploitation levels above the exploitation rate resulting in maximum sustainable yield (UMSY). 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.
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