Fisheries Stock Assessment

Demographic analysis as an aid in shark stock assessment and management

The 21st-century skills are creativity and innovation, critical thinking and problem-solving, communication and collaboration are accessible through Modelling. This is closely related to HOTS. The problem formulation is, firstly how is HOTS on Mathematical Modelling Approach in a primary school that is valid? and secondly how is HOTS in Mathematical Modelling Approach in Primary schools that are practical? This research uses a development research method which consists of 3 stages, namely analysis, design and evaluation. In the analysis step carried out student analysis, curriculum, and HOTS in Mathematics modelling approach. The second steps is design and product. Then the final step, researchers used a formative evaluation design consisting of self-evaluation, one-to-one, expert review, small group. Subjects in this study were students of SD IT Bina Insani Kayuagung in Ogan Komering Ilir Regency. Data collection techniques are, firstly, walkthrough, this is based on expert review to get tasks valid in content, construct and language aspects of HOTS on mathematics modelling approach in Primary Schools, secondly, interview, this is from one to one, small group to find out the practicality of a Hypothetical Learning Trajectory, and lastly, questionnaire. From expert validation and student answers analysis, we obtained HOTS tasks on mathematical modelling approach in primary schools that are valid and practical using “Jumat Sejahtera” Context.

Errors in fitting models to data are usually assumed to follow a normal (or log normal) distribution in fisheries. This assumption is usually used in formulating likelihood functions often required in frequentist and Bayesian stock assessment modelling. Fisheries data are commonly subject to atypical errors, resulting in outliers in stock assessment modelling. Because most stock assessment models are nonlinear and contain multiple variables, it is difficult, if not impossible, to identify outliers by plotting fisheries data alone. Commonly used normal distribution‐based frequentist and Bayesian stock assessment methods are sensitive to outliers, resulting in biased estimates of model parameters that are vital in defining the dynamics of fish stocks and evaluating alternative strategies for fisheries management. Because of the high likelihood of having outliers in fisheries data, frequentist or Bayesian methods robust to outliers are more desirable in fisheries stock assessment. This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods. Using simulated fisheries as examples, we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in fisheries data and compare the robust approaches with the commonly used normal distribution‐based approach. The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods.

Impacts of COVID-19 on at-sea data collection and regulatory activities and fisheries catches off Namibia

Within the frame of the EU Data Collection Regulation (DCR), Germany is obliged since 2002 to collect basic fisheries data to support the Common Fisheries Policy. Various governmental institutions are involved in the collection of landings and effort data, biological and economic data of the German fisheries. About 200 trips on commercial fishery vessels were sampled from 2002 to 2006. Additional stock data are collected on research surveys. The landings of cod in the recreational fisheries in the North and Baltic Seas were recorded within a pilot study. In order to assess the economic situation of the fishing fleet and processing industry, economic data were collected. The collected data are being stored in a national database and being made available for scientific working groups. At present, the legal regulations within the DCR framework are being reviewed and adapted to the changing requirements of fisheries management.

Summary The diversity of chondrichthyans in the Mediterranean Sea is relatively high; however, available data indicate that this group is declining in abundance and several species are becoming rare. As a result, the collection of biological data is a priority for demographic models, stock assessments, and food web analysis. In the present study, we report morphological parameters and length–weight relationships of several chondrichthyan species, both abundant and threatened, from the western Mediterranean Sea. Samples were obtained with commercial and scientific bottom trawl vessels between 2001 and 2013. A total of 893 individuals belonging to 11 families and 20 species were weighed and total lengths measured. In addition, seven species of large demersal sharks were measured and length–length relationships obtained to study the relationships between different body length measurements. All species showed positive allometric or isometric growth, except for Centroscymnus coelolepis. The results of the length–weight relationships reveal differences between the western Mediterranean and nearby areas, depending on the species studied.

In recent years there has been a substantial increase in the collection of biological data on social surveys. Biological data has hitherto been primarily collected by medically trained personnel in a clinic or laboratory setting or using specialist nurse interviewers in a home-visit setting. However, improvements in technology and the development of minimally or non- invasive data collection methods have made it increasingly feasible to collect bio-measures in a home setting using non-medically trained lay interviewers. In the field of genetic research, it has become increasingly common to collect DNA from saliva samples. This paper provides an account of a pilot study investigating the feasibility of collecting saliva samples for DNA extraction from mothers, fathers and children aged around 11 years old using lay interviewers on the UK Millennium Cohort Study. The pilot study was carried out in 2011 in five areas of the UK with one interviewer in each area. 45 families took part in the pilot and saliva samples were obtained from 73 per cent of mothers, 76 per cent of fathers and 74 per cent of children. We demonstrate that it is indeed viable to collect saliva samples for DNA extraction from children and parents using lay interviewers in a home setting, and provide practical suggestions about how the data collection process could be improved in order to achieve higher response rates and improved specimen quality. Our findings are relevant to other surveys planning to incorporate saliva sample collection for DNA extraction, particularly for those involving lay interviewers in a home setting.

BackgroundImplementing rigorous epidemiologic studies in low-resource settings involves challenges in participant recruitment and follow-up (e.g., mobile populations, distrust), biological sample collection (e.g., cold-chain, laboratory equipment scarcity) and data collection (e.g., literacy, staff training, and infrastructure). This article describes the use of a monitoring and evaluation (M&E) framework to improve study efficiency and quality during participant engagement, and biological sample and data collection in a longitudinal cohort study of Bolivian infants.MethodsThe study occurred between 2013 and 2015 in El Alto, Bolivia, a high-altitude, urban, low-resource community. The study’s M&E framework included indicators for participant engagement (e.g., recruitment, retention, safety), biological sample (e.g., stool and blood), and data (e.g., anthropometry, questionnaires) collection and quality. Monitoring indicators were measured regularly throughout the study and used for course correction, communication, and staff retraining.ResultsParticipant engagement indicators suggested that enrollment objectives were met (461 infants), but 15% loss-to-follow-up resulted in only 364 infants completing the study. Over the course of the study, there were four study-related adverse events (minor swelling and bruising related to a blood draw) and five severe adverse events (infant deaths) not related to study participation. Biological sample indicators demonstrated two blood samples collected from 95% (333 of 350 required) infants and stool collected for 61% of reported infant diarrhea episodes. Anthropometry data quality indicators were extremely high (median SDs for weight-for-length, length-for-age and weight-for-age z-scores 1.01, 0.98, and 1.03, respectively), likely due to extensive training, standardization, and monitoring efforts.ConclusionsConducting human subjects research studies in low-resource settings often presents unique logistical difficulties, and collecting high-quality data is often a challenge. Investing in comprehensive M&E is important to improve participant recruitment, retention and safety, and sample and data quality. The M&E framework from this study can be applied to other longitudinal studies.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32*. Add Health combines longitudinal survey data on respondents’ social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. Wave IV Wave IV was designed to study the developmental and health trajectories across the life course of adolescence into young adulthood. Taking place in 2008, approximately 92.5% of the original Wave I respondents were located and 80.3% of eligible cases were interviewed. The Wave IV public use file contains data on 5,114 respondents, aged 24 to 32*. In Wave IV, biological data was also gathered in an attempt to acquire a greater understanding of predisease pathway s, with a specific focus on obesity, stress, and health risk behavior. The Wave IV public use dataset includes the following data files: Wave IV In-home Interview File: variables from the in-home interview, including anthropometric measures Relationship Data Pregnancy Table File Live Births File Children and Parenting File Wave IV Weights Wave IV Public Use Biomarkers, Glucose Data Wave IV Public Use Biomarkers, Measures of EBV and hsCRP Wave IV Public Use Biomarkers, Lipids Data *17 respondents in the Wave IV public use sample were 33 years old at the time of the interview.

Objective.Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, the presence of various artifacts leads to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The proposed work focuses on the electromyography (EMG) artifacts, which are among the most challenging biological artifacts. The currently reported EMG artifact cleaning performance largely depends on the data used for validation, and in the case of machine learning approaches, also on the data used for training. The data are typically gathered either by recruiting subjects to perform specific EMG artifact tasks or by integrating existing datasets. Prevailing approaches, however, tend to rely on intuitive, concept-oriented data collection with minimal justification for the selection of artifacts and their quantities. Given the substantial costs associated with biological data collection and the pressing need for effective data utilization, we propose an optimization procedure for data-oriented data collection design using deep learning-based artifact detection.Approach.We apply a binary classification differentiating between artifact epochs (time intervals containing EMG artifacts) and non-artifact epochs (time intervals containing no EMG artifact) using three different neural architectures. Our aim is to minimize data collection efforts while preserving the cleaning efficiency.Main results.We were able to reduce the number of EMG artifact tasks from twelve to three and decrease repetitions of isometric contraction tasks from ten to three or sometimes even just one.Significance.Our work addresses the need for effective data utilization in biological data collection, offering a systematic and dynamic quantitative approach. By providing clear justifications for the choices of artifacts and their quantity, we aim to guide future studies toward more effective and economical data collection in EEG and EMG research.

Accounting for cohort-specific variable growth in fisheries stock assessments: A case study from south-eastern Australia

Stock assessments, which evaluate the effects of fishing on a fishery, are one basis of fishery management decisions. Stock assessments are based on fisheries data (a description of the catch and catching process), research vessel survey data, and qualitative information drawn from the observations of the harvesters and other knowledgeable observers. The fundamental model used as a basis for stock assessments and fishery management decisions takes account of four forces affecting biomass of the exploited population. These are growth, recruitment, natural mortality, and fishing mortality. Recruitment is the major source of variability in production of a fish population. The magnitude of past recruitment is estimated by application of virtual population analysis (VPA) to fisheries data. Future recruitment is predicted from current research vessel survey data and the relationship between survey catch rates and VPA estimates of recruitment during past years.

The quality of fisheries data has great impacts on the quality of stock assessment, and thus fisheries management. In this paper, using a case study I evaluate the impacts of two types of error, biased error and atypical error, that can negatively affect the quality of fisheries data in stock assessment. These errors are commonly associated with fisheries data, and assumptions on their sources and statistical properties can have great impacts on the outcome of stock assessment. Although the sources and statistical properties of these errors differed, both of them could result in errors in stock assessment if estimation methods are not appropriate. Different statistical approaches used in fitting models differ in their robustness with respect to errors of different statistical properties in data. This study showed the importance of evaluating the quality of input data and the possibility of developing an approach that is robust to errors in data. Considering the likelihood of fisheries data being affected by errors of different statistical properties, I suggest that the robustness of a stock assessment be evaluated with respect to data quality.

Challenges in assessments of Japanese eel stock

Marine species inhabit an extensive underwater environment that is largely inaccessible to humans. Consequently, we have relied on various technologies to study and manage the commercial and recreational species we depend on. Over the past century, there have been rapid advancements in optical and acoustic technology, which have coincided with an increased need for effective fisheries management. The ability to observe fish during the capture process has shed light on the role of fish behavior and the potential bias it introduces into fisheries data. Due to insufficient knowledge of most systems, scientific surveys and stock assessment models have traditionally relied on simplified assumptions about fish behavior. While it has been understood that fish have well-developed sensory systems, mobility, and complex life histories, the lack of information has limited their use in gear catchability, survey design, and assumptions about spatial and temporal population dynamics. This dissertation examines the use of optical and acoustic technology to address these limitations, improving the interpretative power of survey data and reducing potential bias. Baited remote underwater video systems (BRUVS) were employed in Chapter 2 to examine the role of a species' life history in the performance of traditional survey gears (e.g., fish pots and demersal otter trawls). The spatial distribution of black sea bass (Centropristis striata), a structure-oriented species, in Buzzards Bay was observed to vary depending on the survey gear. Conversely, scup (Stenotomus chrysops), a habitat-agnostic species, exhibited similar patterns across survey methods. Video observations of black sea bass documented an increasing affinity for structured habitats during the summer and fall. This shift in the spatial distribution of black sea bass substantially affected the trawl survey data. Catch data from the spring trawl survey generally corresponded to the video and pot data with respect to the spatial distribution and population structure of both black sea bass and scup. Conversely, the fall trawl survey data starkly contrasted with the two other surveys, with few adult black sea bass catches. The lack of catch is presumably due to the shifting residence of black sea bass to rocky habitats, which are not sampled by the trawl and, therefore, unavailable to the survey. The shifting availability between the spring and fall trawl surveys presents an inaccurate picture of black sea bass abundance in Buzzards Bay. The disparity in catches between survey gears in Chapter 2 was mainly due to the trawl's inability to survey the entire spatial extent of the survey area due to its exclusion of rocky habitats. Similar patterns of bias were observed in Chapter 3, which used a high-resolution acoustic sonar to study the behavior of river herring (Alosa pseudoharengus and Alosa aestivalis) during their spring spawning migration. This advanced acoustic system allowed for observations of fish passage throughout the entire migration period, including nighttime observations, which were impossible with previous studies relying on optical or visual data collection. Contrary to prior research suggesting that these fish primarily move during the day, the observations showed that river herring were active day and night, with interactions between the time of day and tidal state. These data indicate that river herring movement and behavior may be driven by ecological factors such as predation and energetics rather than a physical cue, such as light. The field data was subsequently used in Chapter 4 to study the role of sampling designs on survey bias and precision. These findings revealed that traditional survey designs, assuming only daytime movement, underestimated annual run counts by 45 – 55%. The interaction between tidal and diurnal signals led to daily changes in behavioral patterns, resulting in an underestimation of daily run counts by 28 – 99%. These underestimations were not random, leading to systematic bias in the survey data. The implications of these findings suggest that data collected from visual counts or daytime-only optical technology should be approached with caution. Furthermore, behavioral observations can impact our understanding of anthropogenic structures and ecological processes. In Chapter 5, the high-resolution acoustic sonar also identified a consistent presence of striped bass under a tide gate during the latter part of the river herring's annual migration. The striped bass activity exhibited strong diurnal and tidal patterns, potentially aimed at maximizing foraging opportunities. The persistent presence of striped bass downstream of artificial structures can create ecological barriers, posing additional challenges to migrating river herring. Moreover, this may disrupt predator-prey relationships, maintaining high levels of predation despite low prey abundance. These previously unknown impacts may complicate ongoing stock recovery efforts. As demonstrated in this dissertation, using novel technologies and a comparative analysis of traditional methods may provide a deeper, more nuanced understanding of marine environments and their inhabitants, laying the groundwork for future research and paving the way for more sustainable fisheries management practices.