Extensions and Applications of Mean Length Mortality Estimators for Assessment of Data-Limited Fisheries

For data-limited fisheries, length-based mortality estimators are attractive as alternatives to age-structured models due to the simpler data requirements and ease of use of the former. This dissertation develops new extensions of mean length-based mortality estimators and applies them to federally-managed stocks in the southeastern U.S. and U.S. Caribbean. Chapter 1 presents a review of length-based methods from the literature. Common themes regarding the methodology, assumptions, and diagnostics in these length-based methods are discussed. In Chapter 2, a simulation study evaluates the performance of the length-converted catch curve (LCCC), Beverton-Holt equation (BHE), and Length Based-Spawner Potential Ratio (LB-SPR) over a range of scenarios. Although the LCCC and BHE are older methods than LB-SPR, the former outperformed LB-SPR in many scenarios in the simulation. Overall, it was found that the three length-based mortality estimators are less likely to perform well for low M/K stocks (M/K is the ratio of the natural mortality rate and the von Bertalanffy growth parameter; this ratio describes different life history strategies of exploited fish and invertebrate populations), while various decision rules for truncating the length data for the LCCC and BHE were less influential. In Chapter 3, a multi-stock model is developed for the non-equilibrium mean length-based mortality estimator and then applied to the deepwater snapper complex in Puerto Rico. The multispecies estimator evaluates synchrony in changes to the mean length of multiple species in a complex. Synchrony in mortality can reduce the number of estimated parameters and borrows information from more informative species to lesser sampled species in the model. In Chapter 4, a new method is developed to estimate mortality from both mean lengths and catch rates (MLCR), which is an extension of the mean length-only (ML) model. to do so, the corresponding behavior for the catch rate following step-wise changes in mortality is derived. Application of both models to Puerto Rico mutton snapper shows that the MLCR model can provide more information to support a more complex mortality history with the two data types compared to the ML model. In Chapter 5, a suite of mean length-based mortality estimators is applied to six stocks (four in the Gulf of Mexico and two in the U.S. Atlantic) recently assessed with age-structured models. There was general agreement in historical mortality trends between the age-structured models and the mean length-based methods, although there were some discrepancies which are discussed. All models also agreed on the overfishing status in the terminal year of the assessment of the six stocks considered here when the mortality rates were compared relative to reference points. This dissertation develops new length-based assessment methods which consider multiple sources of data. The review guides prospective users on potential choices for assessment with length-based methods. Issues and diagnostics associated with the methods are also discussed in the review and highlighted in the example applications

Comparisons of mean length-based mortality estimators and age-structured models for six southeastern US stocks

Length-based mortality estimators have been developed as alternative assessment methods for data-limited stocks. We compared mortality estimates from three methodologically related mean length-based methods to those from an age-structured model (ASM). We estimated fishing mortality and determined overfishing status, i.e. if F/FMSY \u3e 1, for six stocks which support important recreational and commercial fisheries in the southeastern United States. The similarities in historical fishing mortality between the length-based methods and the most recent assessments varied among the case studies, but the classification of overfishing status in the terminal year did not differ based on the choice of model for all six stocks. There was also high agreement in the number of overfishing years within different historical periods. Applications of length-based methods can be consistent with the results that might be obtained from an ASM. In one case, diagnostics were used to identify the problems with the length-based estimators. The potential for determining overfishing status from these methods can encourage data collection programmes for unassessed stocks

Multispecies Extensions to a Nonequilibrium Length-Based Mortality Estimator

Recent advances in methodology allow the history of the total mortality rate experienced by a population to be estimated from periodic (e.g., annual) observations on themean length of the population. This approach is generalized to allow data on several species that are caught together to be analyzed simultaneously based on the theory that changes in fishing effort are likely to affect several species; thus, the estimation of times when the mortality rate changes for one species borrows strength from data on other, concurrently caught species. Information theory can be used to select among models describing the degree of synchrony (if any) in mortality changes for a suite of species. This approach is illustrated using data on Puerto Rican handline fishery catches of three snapper species: Silk Snapper Lutjanus vivanus, Blackfin Snapper L. buccanella, and Vermilion Snapper Rhomboplites aurorubens. We identified the best model as the one that provided for simultaneous decreases in mortality rate around the year 1997 and for separate, species-specific magnitudes of change in total mortality. The simultaneous estimation of parameters for multiple species can provide for more credibility in the inferred mortality trends than is possible with independent estimation for each species

Comparative Performance of Three Length-Based Mortality Estimators

Length‐based methods provide alternatives for estimating the instantaneous total mortality rate (Z) in exploited marine populations when data are not available for age‐based methods. We compared the performance of three equilibrium length‐based methods: the length‐converted catch curve (LCCC), the Beverton–Holt equation (BHE), and the length‐based spawning potential ratio (LB‐SPR) method. The LCCC and BHE are two historically common procedures that use length as a proxy for age. From a truncated length‐frequency distribution of fully selected animals, the LCCC estimates Z with a regression of the logarithm of catch at length by the midpoint of the length‐bins, while the BHE estimates Z as a function of the mean length. The LB‐SPR method is a likelihood‐based population dynamics model, which—unlike the LCCC and BHE—does not require data truncation. Using Monte Carlo simulations across a range of scenarios with varying mortality and life history characteristics, our study showed that neither the LCCC nor the BHE was uniformly superior in terms of bias or root mean square error across simulations, but these estimators performed better than LB‐SPR, which had the largest bias in most cases. Generally, if the ratio of natural mortality (M) to the von Bertalanffy growth rate parameter (K) is low, then the BHE is most preferred, although there is likely to be high bias and low precision. If M/K is high, then the LCCC and BHE performed better and similarly to each other. Differences in performance among commonly used truncation methods for the LCCC and BHE were small. The LB‐SPR method did not perform as well as the classical methods but may still be of interest because it provides estimates of a logistic selectivity curve. The M/K ratio provided the most contrast in the performance of the three methods, suggesting that it should be considered for predicting the likely performance of length‐based mortality estimators

Evaluating Atlantic bluefin tuna harvest strategies that use conventional genetic tagging data

An individual tagging model was implemented within the spatial, seasonal, multi-stock, multi-fleet operating models of the peer-reviewed Management Strategy Evaluation (MSE) framework for Atlantic bluefin tuna to evaluate the benefits of a harvest strategy that utilizes conventional gene tagging. A multi-year Brownie estimator was developed to test the accuracy and precision of exploitation rate estimates arising from gene tagging programs with various scenarios for spatial release distribution, release numbers and fishery exploitation rates. Harvest strategies that used the Brownie estimator were tested to evaluate yield and resource conservation performance relative to idealized management using perfect information. For the eastern stock, releasing 1,000 fish throughout the Atlantic and genotyping 27% of all landed fish at an estimated cost of US$2M was sufficient to obtain estimates of exploitation rate with a coefficient of variation of 20$2.5M. Harvest strategies using the gene tagging data provided expected yield and resource conservation performance that was not substantially lower than a harvest strategy assuming using perfect information regarding vulnerable biomass. Reducing the number of releases most strongly affected the worst-case ‘lower-tail’ outcomes for West area yield and eastern stock biomass. Conventional gene tagging harvest strategies offer a promising basis for calculating management advice for Atlantic bluefin tuna that may be cheaper, simpler, and more robust than the current conventional stock assessment paradigm

Method evaluation and risk assessment: A framework for evaluating management strategies for data-limited fisheries

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.MERA was initially commissioned by the Marine Stewardship Council, and benefits from the ongoing support of the David & Lucile Packard Foundation, the Marine Stewardship Council, the Natural Resources Defense Council, the Walton Family Foundation and the United Nations Food and Agricultural Organization.Peer reviewe

A modified Sequential Organ Failure Assessment score for dengue: development, evaluation and proposal for use in clinical trials

Background Dengue is a neglected tropical disease, for which no therapeutic agents have shown clinical efficacy to date. Clinical trials have used strikingly variable clinical endpoints, which hampers reproducibility and comparability of findings. We investigated a delta modified Sequential Organ Failure Assessment (delta mSOFA) score as a uniform composite clinical endpoint for use in clinical trials investigating therapeutics for moderate and severe dengue. Methods We developed a modified SOFA score for dengue, measured and evaluated its performance at baseline and 48 h after enrolment in a prospective observational cohort of 124 adults admitted to a tertiary referral hospital in Vietnam with dengue shock. The modified SOFA score included pulse pressure in the cardiovascular component. Binary logistic regression, cox proportional hazard and linear regression models were used to estimate association between mSOFA, delta mSOFA and clinical outcomes. Results The analysis included 124 adults with dengue shock. 29 (23.4%) patients required ICU admission for organ support or due to persistent haemodynamic instability: 9/124 (7.3%) required mechanical ventilation, 8/124 (6.5%) required vasopressors, 6/124 (4.8%) required haemofiltration and 5/124 (4.0%) patients died. In univariate analyses, higher baseline and delta (48 h) mSOFA score for dengue were associated with admission to ICU, requirement for organ support and mortality, duration of ICU and hospital admission and IV fluid use. Conclusions The baseline and delta mSOFA scores for dengue performed well to discriminate patients with dengue shock by clinical outcomes, including duration of ICU and hospital admission, requirement for organ support and death. We plan to use delta mSOFA as the primary endpoint in an upcoming host-directed therapeutic trial and investigate the performance of this score in other phenotypes of severe dengue in adults and children

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.

The Global Burden of Diseases, Injuries and Risk Factors 2017 includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. METHODS: We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting