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Spatial stock assessment models are recognised as increasingly important for estimation of stock status and a sustainable exploitation rate. The inclusion of movement between spatial units within a model is difficult because the data requirements are high. However, for populations with low levels of spatial exchange it is possible to reduce the data requirements by distributing information on biological parameters between neighbouring units, or units with shared environmental conditions. This can allow spatial modelling to be applied even in data-limited situations. We develop this approach here through application to orange roughy (Hoplostethus atlanticus) subpopulations inhabiting neighbouring seamounts in the South Pacific. Despite limited data for each seamount, we were able to simultaneously fit multiple, localised, process-based models of the depletion dynamics. This was achieved by sharing information on the unexploited population size via known environmental covariates, with the relationship estimated in a hierarchical and integrated manner during the model fit. Cross-validation demonstrated that this approach can compensate for a lack of seamount-specific abundance data and improve ability of the model to estimate localised depletions.

Many management bodies require applying the precautionary approach when managing marine fisheries resources to achieve sustainability and avoid exceeding limits. For data-limited stocks, however, defining and achieving management objectives can be difficult. Management procedures can be optimized towards specific management objectives with genetic algorithms. We explored the feasibility of including an objective that limited the risk of a stock falling below various limit reference points in the optimization routine for an empirical data-limited control rule that uses a biomass index, mean catch length, and includes constraints (the “rfb-rule”). This was tested through management strategy evaluation on several fish stocks representing various life-history traits. We show that risk objectives could be met, but more restrictive risk limits can lead to a potential loss of yield. Outcomes were sensitive to simulation conditions such as observation uncertainty, which can be highly uncertain in data-limited situations. The rfb-rule outperforms the method currently applied by ICES, particularly when risk limitation objectives are considered. We conclude that the application of explicit precautionary levels is useful to avoid overfishing. However, we caution against the indiscriminate use of arbitrary risk limits without scientific evaluation to analyse their impact on stock yields and sustainability.

In data-poor regions, modeled river temperatures are essential for predicting potential stressors for species at risk. With limited data from the Grand, Thames, and Sydenham rivers in southern Ontario, Canada, we evaluated simple mixed-effect regression models to predict water temperature using air temperature from nearby weather stations. Model performance was assessed for periods relevant to the fitness of the black redhorse (Moxostoma duquesni): June to August, when heat events may be likely; and May, when spawning occurs. All of the models performed better when trained on data from these periods, compared with using data from the entire growing season. The best model was a linear regression using 5 days of lagged air temperature. This model had a root mean square error for summer means of 1.5 °C. The differences in prediction error at different times of year highlight the importance of considering species ecology in model interpretation. However, the improvement in model fit when using only data from the relevant time of year suggests that relatively simple models can be used effectively in a management arena when applied appropriately.

The phenotype of infused cells is a major determinant of Adoptive T-cell therapy (ACT) efficacy. Yet, the difficulty in deciphering multiparametric cytometry data limited the fine characterization of cellular products. To allow the analysis of dynamic and complex flow cytometry samples, we developed cytoChain, a novel dataset mining tool and a new analytical workflow. CytoChain was challenged to compare state-of-the-art and innovative culture conditions to generate stem-like memory cells (TSCM ) suitable for ACT. Noticeably, the combination of IL-7/15 and superoxides scavenging sustained the emergence of a previously unidentified nonexhausted Fit-TSCM signature, overlooked by manual gating and endowed with superior expansion potential. CytoChain proficiently traced back this population in independent datasets, and in T-cell receptor engineered lymphocytes. CytoChain flexibility and function were then further validated on a published dataset from circulating T cells in COVID-19 patients. Collectively, our results support the use of cytoChain to identify novel, functionally critical immunophenotypes for ACT and patients immunomonitoring.

A pulse fishery is one where fishing occurs during a short period each year and is zero for the rest of the year. Grüss et al. proposed a continuous per-recruit model for such fisheries. However, this analysis is based on per-recruit equations that were derived under the assumption that fishing mortality is constant in time and, thus, do not apply to fisheries where F varies, such as a pulse fishery. A correct continuous per-recruit formulation for pulse fisheries is derived. In most cases, the continuous per-recruit model can be closely approximated by a discrete model, where the integrals are replaced by sums. A modification in the Baranov catch equation for pulse fisheries is also derived that can used to compute annual exploitation rates.

We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

Stock assessment of Azov sea roach Rutilus rutilus (Linnaeus, 1758) has been performed by CMSY model in period 1999-2019 by data-limited modelling in R. The current population status – in biological safe zone for stock biomass and no overfishing signals (B2019/BMSY = 1,32, F2019/FMSY = 0,53). Perhaps, current paper results can be a slightly incomplete in background that there is no relevant data about IUU fishery ever exists, which can lead to fishing mortality underestimation. Azov sea roach population continue to be in “depleted” status after river flow regulation in 1950’s. Joined continuous biomass estimates time series over whole fishing his-tory 1932–2019 showed at least 2 population collapses: in 1940’s and 1980’s years. According to model re-sults TAC (total allowed catch) should be accepted at level 516.9 t. If the recommendation is followed stock biomass will stay at safety in level 1828.1 t. Data limited modelling shows a good performance for sea roach in background of data lucking and in this reason still the best choose against cohor or surplus production models.

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment.

Per-recruit models have been widely used since the onset of modern fisheries science, particularly in data-limited situations. When the study fishery is a pulse fishery, namely a fishery operating over a brief period followed by a long fallow period, exploitation rates rather than fishing mortality rates are employed to calculate per-recruit quantities. The literature suggests that a discrete per-recruit model is more appropriate than a continuous per-recruit model when per-recruit quantities are expressed as a function of exploitation rates. For this reason, Erisman et al. [Erisman, B. E., Grüss, A., Mascarenas-Osorio, I., Lícon-González, H., Johnson, A. F., and López-Sagástegui, C. 2020. Balancing conservation and utilization in spawning aggregation fisheries: a trade-off analysis of an overexploited marine fish. ICES Journal of Marine Science, 77: 148–161.] recently developed a discrete per-recruit model to examine the impacts of altering exploitation rates for the Gulf corvina (Cynoscion othonopterus) pulse fishery. Using Erisman et al.’s (Erisman, B. E., Grüss, A., Mascarenas-Osorio, I., Lícon-González, H., Johnson, A. F., and López-Sagástegui, C. 2020. Balancing conservation and utilization in spawning aggregation fisheries: a trade-off analysis of an overexploited marine fish. ICES Journal of Marine Science, 77: 148–161.) data, we demonstrate in detail that, under certain conditions, it is reasonable to employ a continuous per-recruit model for a pulse fishery system. We then use the designed continuous per-recruit model to demonstrate how the timing of the pulse fishery within the year relative to the timing of reproduction can be accounted for in a per-recruit model, and we explore the impacts of these model developments. This article serves as a strong basis for future studies that model pulse fishery systems in data-limited situations.

Each year, millions of marine aquarium fish and invertebrates are harvested from coral reefs and enter the complex and largely unregulated marine aquarium trade (MAT). It is challenging to identify species at risk of overexploitation in this trade due to its data-limited and poorly monitored nature. We developed a new analytical approach based on a productivity-susceptibility analysis (PSA) to assess the vulnerability of wild-captured marine aquarium fish. The PSA was originally developed to assess food fisheries; however, species and operational characteristics between food fisheries and the MAT differ. Thus, we improved a prior PSA framework to assess the data-limited MAT through customization of productivity and susceptibility factors to align with the target fishery, improved data binning, calculation of susceptibility, and characterization of the vulnerability scores. Our vulnerability results align well with the most recent IUCN assessments, showing improved accuracy using this revised PSA compared to prior adaptions of the PSA to the MAT. Further, we show that this PSA approach can be used to assess species on either a global or country-specific scale. A Gaussian mixture model clustering algorithm was applied to the PSA results to objectively classify fish along a sustainability continuum. Among 32 species, a majority of species clustered as highly sustainable or sustainable indicating little management or over-harvest concern; however, the Bangaii cardinalfish Pterapogon kauderni and blue tang Paracanthurus hepatus indexed as unsustainable. This novel PSA method, and use of a clustering algorithm to classify results, provides a predictive tool for a wide range of fisheries. In addition to informing species management plans, the compilation of sustainability status data generated by our PSA can inform a consumer guide, allowing consumers and other stakeholders to make sustainable decisions when purchasing fish.

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.

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 closed-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-based models, the one with the best performance was SSS followed by CMSY.

As most exploited fisheries lack a coherent time series of biomass index, development of data-limited stock assessment methods such as stock reduction analysis (SRA), is critical for fishery stock assessment due to their modest data requirements for estimating stock status and overfishing catch limits. In this study, we propose that sporadic time series of biomass indices, if available, may be fully utilized to inform priors of recent relative biomass (BT/B1) for data-limited stocks. We evaluated the performance of SRA incorporating this index-based prior by comparing two other common SRA priors (a deterministic prior set at 40% of the unfished biomass and a catch-based prior) with estimates from the likelihood-based assessments of 91 fish stocks from the RAM Legacy database. We extended our analysis by evaluating performance based on life history attributes and two depletion levels with BT/BMSY equaling 1 as the breakpoint. Results suggest index-based priors enhance accuracy for fish stocks at both depletion levels. We demonstrate that performance of SRA can be affected by three factors: the reliability of priors for BT/B1, recent depletion level, and life history.

BACKGROUND: Risk factors for birth defects are frequently investigated using data limited to liveborn infants. By conditioning on survival, results of such studies may be distorted by selection bias, also described as “live birth bias”. However, the implications of live birth bias on risk estimation remain poorly understood. OBJECTIVES: We sought to quantify live birth bias and to investigate the conditions under which it arose. METHODS: We used data on 3,994 birth defects cases and 11,829 controls enrolled in the National Birth Defects Prevention Study to compare odds ratio (OR) estimates of the relationship between three established risk factors (antiepileptic drug use, smoking, and multifetal pregnancy) and four birth defects (anencephaly, spina bifida, omphalocele, and cleft palate) when restricted to live births as compared to among live births, stillbirths, and elective terminations. Exposures and birth defects represented varying strengths of association with live birth; all controls were liveborn. We performed a quantitative bias analysis to evaluate the sensitivity of our results to excluding terminated and stillborn controls. RESULTS: Cases ranged from 33% live born (anencephaly) to 99% (cleft palate). Smoking and multifetal pregnancy were associated with live birth among anencephaly (crude OR [cOR] 0.61 and cOR 3.15, respectively) and omphalocele cases (cOR 2.22 and cOR 5.22, respectively). For analyses of the association between exposures and birth defects, restricting to live births produced negligible differences in estimates except for anencephaly and multifetal pregnancy, which was two-fold higher among live births (adjusted OR [aOR] 4.93) as among all pregnancy outcomes (aOR 2.44). Within tested scenarios, bias analyses suggested that results were not sensitive to the restriction to liveborn controls. CONCLUSIONS: Selection bias was generally limited except for high mortality defects in the context of exposures strongly associated with livebirth. Findings indicate that substantial live birth bias is unlikely to affect studies of risk factors for most birth defects.

Within the fields of biology and ecology, animal movement is arguably one of the most basic, and yet, often one of the most difficult areas of study. Where and why animals migrate, and what patterns can be derived from individual movements in order to make population-level inferences are key areas when attempting to define basic population dynamics. These questions are of equal interest to biologists and managers, with many species assessments identifying improvements in the understanding of population-level movement as a key research need. We aimed to improve our understanding of population level movement for common thresher sharks Alopias vulpinus by leveraging the largest satellite tagging dataset available for this species. Using a Bayesian approach specifically designed to address population-level questions with sparse telemetry data, we identified that A. vulpinus off the west coast of North America are partial migrators which conditionally migrate, based on a combination of fixed intrinsic states (size, sex) and variable extrinsic states (e.g. season, environment). Waters of the Southern California Bight were identified as an area where, seasonally, a large variety of sizes of A. vulpinus can be found. While smaller juveniles can be found throughout the year, larger sub-adults and adults often move out of the Bight during certain seasons (spring and winter). Knowledge of how A. vulpinus distribute along the coast, and that season, size, and to some extent sex, play important roles in where and what type of animals are likely to be found, are key pieces of information when attempting to accurately characterize basic biological parameters like age, growth, and reproduction, as well as understanding the effects of variable fishing pressures across the species’ range.

Metastasis of a neuroendocrine tumour (NET) to the orbit is a rare occurrence, with published data limited to a few case reports. Orbital involvement usually presents with proptosis and decreased o...

“Resiliency” for communities at risk from sea level rise and its effects means preserving as much property and associated economic activity as possible without disrupting current activity or expending funds on projects that provide limited long-term utility or social value. Of interest is how the coincidence of these events impacts the need for storm water improvements and the financial obligations they will entail. This research project focused on the impacts of a non-coastal, groundwater influenced southeast Florida community and the long-term funding they will need to reduce flooding in the community using GIS. This research involved defining surface elevations and groundwater levels, assessing the impacts of sea level rise on groundwater, assessing impacts to storm water from rainfall, identifying likely improvements, and assessing the scale for improvements. The analysis used three extreme rainstorm events under the 0-, 1-, 2-, and 3-foot sea level rise scenarios to determine the magnitude of the cost of the improvements. For a 35-square mile community, our research estimated that the cost could exceed $300 million. For a community not directly adjacent to the coast, the magnitude of these costs should be of interest to similarly placed communities.

Data-limited fisheries assessment methods have great potential to help inform small island communities on the status of their fisheries resources. In this paper, we provide a length-based assessment of an artisanal fishery that primarily targets spawning aggregations of Shortjaw Bonefish Albula glossodonta at Anaa Atoll in the Tuamotu Archipelago of French Polynesia. We assessed the length-frequency distribution of the spawning stock across a 3-year period (2016–2018). During this time, male and female Shortjaw Bonefish were fully recruited to the fishery at age 4 and age 5, respectively. Fishing mortality was over two times the range of natural mortality for this species (i.e., 0.21–0.32), and based on these estimates of natural mortality, the annual spawning potential ratio of the population was between 7% and 20% across the sampling years. The majority of the catch was sexually mature, with 78, 95, and 95% of the annual female catch in 2016, 2017, and 2018, respectively, being equal to or greater than the length of first maturity (i.e., 48 cm FL). However, every fisheries indicator and biological reference point suggested that the fishery was overexploited and in need of management intervention. To this aim, the community of Anaa (1) established an Educational Managed Marine Area, which overlaps with the Shortjaw Bonefish migratory corridor adjacent to Tukuhora village and (2) instated a temporal rahui (a traditional conservation method) inside the Educational Managed Marine Area during the peak months of the spawning season.

Data availability, and unreported and unregulated fishing are significant obstacles to evaluating stock status, especially in tropical areas. Limitations in data quantity and quality can lead to model misspecification and erroneous data treatments, potentially causing important changes in model outputs and subsequent management implications. Red snapper Lutjanus purpureus (Poey) in French Guiana provides an example of a stock with a long-time series of fishery-dependent data subject to large uncertainty. A flexible catch-at-age model (Stock Synthesis) was applied to the available data and compared to an historically applied assessment approach. Inter-model variability based on different model specifications and data treatments were compared to identify better the status of the resource. Results showed that a major source of uncertainty in the model was the inclusion of a catch-per-unit-effort abundance index with questionable ability to track abundance. The Stock Synthesis model provided a more flexible and viable method than the virtual population analysis approach. Despite large uncertainty, models depicted a similar trend with a notable stock depletion in the late 1990s but with two distinct biomass trends in more recent years depending on the treatment. To reduce uncertainty and preserve this important economic resource, new data collection programmes and management policies are needed.

The mdx mouse (C57BL/10ScSn-DMDmdx/J) is the oldest model of Duchenne muscular dystrophy (DMD). Mdx remains popular and has not been replaced by newer mouse models, despite criticisms that mdx has a nearly normal lifespan and mild pathology while DMD remains a severe, fatal disease. At some point we noticed that the absence of mdx RNA-seq data limited our ability to assess the results of physiological work on the mouse model and to compare these results to human genetic data [1]. We carried out RNA-seq analysis of wild-type and mdx mice of 2 and 5 months of age, using three hindlimb muscles per mouse: the flexor digitorum brevis (FDB), the extensor digitorum longus (EDL) and the soleus (SOL), with a total of 55 samples. We then mined the data and found that each of the three muscles is a valid experimental model for DMD-related mouse work, even the FDB, despite a delayed pathology development. We also show that the mdx mouse muscles are enriched in metabolic, developmental, regenerational and structural pathways that have been found to be the “disease signature” of DMD in young and presymptomatic subjects [38, 39]. Additionally, we show that healthy human muscle fiber microtubules present the grid-like organization found in control rodents but perturbed in the mdx mouse. We conclude that the mdx mouse appropriately mimics the early stages of DMD, with its microtubule defects signaling fiber regeneration [35]. We hope that these results may contribute to a better understanding of the failure of regeneration as DMD progresses.

ObjectiveTo characterise the environmental presence of hepatitis A virus (HAV) in low- and middle-income countries (LMICs).DesignSystematic review and meta-analysis.Data sourcesEBSCOhost, PubMed, Scopus, ScienceDirect, Clinical Key and Web of Science were searched. Grey literature was sourced by searching the following electronic databases: Open Grey, National Health Research Database and Mednar.Eligibility criteria for including studiesCross-sectional and ecological studies reporting HAV environmental presence and conducted in LMICs between January 2005 and May 2019, irrespective of language of publication.Data extraction and data synthesisRelevant data were extracted from articles meeting the inclusion criteria, and two reviewers independently assessed the studies for risk of bias. High heterogeneity of the extracted data led to the results being reported narratively.ResultsA total of 2092 records were retrieved, of which 33 met the inclusion criteria. 21 studies were conducted in Tunisia, India and South Africa, and the rest were from Philippines, Pakistan, Morocco, Chad, Mozambique, Kenya and Uganda. In Tunisian raw sewage samples, the prevalence of HAV ranged from 12% to 68%, with an estimated average detection rate of 50% (95% CI 25 to 75), whereas HAV detection in treated sewage in Tunisia ranged from 23% to 65%, with an estimated average detection rate of 38% (95% CI 20 to 57). The prevalence of HAV detection in South African treated sewage and surface water samples ranged from 4% to 37% and from 16% to 76%, with an estimated average detection rates of 15% (95% CI 1 to 29) and 51% (95% CI 21 to 80), respectively. Over the review period, the estimated average detection rate of environmental HAV presence appeared to have declined by 10%.ConclusionThe quality of included studies was fair, but sampling issues and paucity of data limited the strength of the review findings.PROSPERO registration numberCRD42019119592.

Performance evaluation of data-limited, length-based methods is instrumental in determining and quantifying their accuracy under various scenarios and in providing guidance about model applicability and limitations. We conducted a simulation–estimation analysis to compare the performance of four length-based stock assessment methods: length-based Thompson and Bell (TB), length-based spawning potential ratio (LBSPR), length-based integrated mixed effects (LIME), and length-based risk analysis (LBRA), under varying life history, exploitation status, and recruitment error scenarios. Across all scenarios, TB and LBSPR were the most consistent and accurate assessment methods. LBRA is highly biased, but precautionary, and LIME is more suitable for assessments with time-series longer than a year. All methods have difficulties when assessing short-lived species. The methods are less accurate in estimating the degree of recruitment overfishing when the stocks are severely overexploited, and inconsistent in determining growth overfishing when the stocks are underexploited. Increased recruitment error reduces precision but can decrease bias in estimations. This study highlights the importance of quantifying the accuracy of stock assessment methods and testing methods under different scenarios to determine their strengths and weaknesses and provides guidance on which methods to employ in various situations.

Harvest control rules (HCRs) for sustainable fishery management have been developed for data-limited fish species for which stock assessments cannot be conducted. However, HCRs have largely not considered mixed-species catches, as when fishing-effort data are widely pooled for numerous minor species in a multispecies fishery. Presently, a feedback HCR has been successfully applied in Japanese fisheries management. By combining management strategy evaluation with a simulation to generate mixed-species data from a multispecies fishery that assume constant catchability (q) among species, we evaluated the performance of this feedback HCR and then compared its performance using species-specific data. In most cases, the biomass was controlled over that needed for maximum sustainable yield (MSY), and the fishing effort was under the fishing mortality consistent with achieving MSY (FMSY). However, for slow-growing species, the biomass might become lower than what is required to remain capable of producing MSY, even though fishing effort was controlled under FMSY. The results show that the feedback HCR is appropriate for multispecies fisheries management where only mixed-species data are available but with special monitoring for slow-growing minor species.

Specifying annual catch limits for artisanal fisheries, low economic value stocks, or bycatch species is problematic due to data limitations. Many empirical management procedures (MPs) have been developed that provide catch advice based on achieving a stable catch or a historical target (i.e., instead of maximum sustainable yield). However, a thorough comparison of derived yield streams between empirical MPs and stock assessment models has not been explored. We first evaluate trade-offs in conservation and yield metrics for data-limited approaches through management strategy evaluation (MSE) of seven data-rich reef fish species in the Gulf of Mexico. We then apply data-limited approaches for each species and compare how catch advice differs from current age-based assessment models. MSEs identified empirical MPs (e.g., using relative abundance) as a compromise between data requirements and the ability to consistently achieve management objectives (e.g., prevent overfishing). Catch advice differed greatly among data-limited approaches and current assessments, likely due to data inputs and assumptions. Adaptive MPs become clearly viable options that can achieve management objectives while incorporating auxiliary data beyond catch-only approaches.

In fisheries, vulnerability assessments - also commonly known as ecological risk assessments (ERAs) -have been an increasingly popular alternative to stock assessments to evaluate the vulnerability of non-target species in resource- and data-limited settings. The widely-used productivity-susceptibility analysis (PSA) requires detailed species-specific biological information and fishery susceptibility for a large number of parameters to produce a relative vulnerability score. The two major disadvantages of PSA are that each species is assessed against an arbitrary reference point, and PSA cannot quantify cumulative impacts of multiple fisheries. This paper introduces an Ecological Assessment of the Sustainable Impacts of Fisheries (EASI-Fish), a flexible approach that quantifies the cumulative impacts of fisheries on data-limited bycatch species, demonstrated in eastern Pacific Ocean (EPO) tuna fisheries. The method first estimates fishing mortality (F) based on the 'volumetric overlap' of each fishery with the distribution of each species. F is then used in length-structured per-recruit models to assess population vulnerability status using conventional biological reference points. Model results were validated by comparison with stock assessments for bigeye and yellowfin tunas in the EPO for 2016. Application of the model to 24 species of epipelagic and mesopelagic teleosts, sharks, rays, sea turtles and cetaceans and identification of the most vulnerable species is demonstrated. With increasing demands on fisheries to demonstrate ecological sustainability, EASI-Fish allows fishery managers to more confidently identify vulnerable species to which resources can be directed to either implement mitigation measures or collect further data for more formal stock assessment.

In the absence of abundance indices from scientific surveys or commercial sources, reliable length frequency data from sampled commercial catches can be used to provide an indirect assessment of fishing mortality. Length-based indicators are simple metrics which describe length frequency distributions. The length-based indicator Lmax5%, the mean length of the largest 5% of individuals in the catch, combined with appropriately selected reference points, can be used to evaluate the presence of very large individuals in the catch and hence determine exploitation level. Using analytical per-recruit models, we derive reference points consistent with a spawning potential ratio of 40%. The reference points depend on the life history parameters for natural mortality, maturity, and growth (M, Lmat, L∞, k, CVL∞). Using available simulation tools, we investigate the sensitivity of the reference points to errors in these parameters and explore the usefulness of particular reference points for management purposes for stocks with different life histories. The proposed reference points are robust to uncertainty in length at first capture, Lc, and take into account the maturation schedule of a species. For those stocks with high M/k ratios (>1), Lmax5%, combined with the appropriate reference point, can be used to provide a data-limited stock assessment.