4 Frequently Asked Questions
This is a quick reference for common questions about the course. If you don’t find what you need here, please contact the course instructors.
Why are fisheries-independent surveys important?
Fisheries-independent surveys are a foundation of fisheries stock assessment and ecosystem research. They are crucial for tracking trends in fish stocks globally. These surveys provide consistent time series data that are used in stock and ecosystem assessments.
Here are more details on how fisheries-independent surveys are useful:
- Foundation for Research and Management: Fisheries-independent surveys are considered a cornerstone for fisheries stock assessment and ecosystem research. They are instrumental in informing resource assessments and stimulating ideas, discussion, and analysis in fisheries and ecological research fields.
- Data Collection and Products: Surveys provide data products used in ecosystem processes research, ecological studies, stock assessment, and forecasting. They offer observations of fish abundance, environmental variables, species distribution, community structure, and the discovery of new taxa.
- Unbiased Information about Change: Unlike data obtained from commercial or recreational fisheries, surveys aim to obtain samples based on statistically rigorous designs. This allows for the inference that observed changes in an abundance index, or other types of data over time, are indicative of true changes in abundance and other population characteristics such age size- or age-structure.
- Stock Assessment Input: Fish stock assessment models require abundance indices as a measure of the relative or absolute abundance of a population. Estimates of abundance index uncertainty are often incorporated into these models as model-weighting criteria.
- Monitoring and Adaptation: Surveys are conducted worldwide to determine the status of marine populations and characterize the state of marine ecosystems. As marine ecosystems change and technology advances, modernizing survey tools allows us to maintain critical survey time series and deliver the best available science to support sustainable fisheries management. This includes adapting to changes in ecosystems and technology by evolving approaches to survey design, estimation, and methods.
Why would this course prepare you well for a future career in the field of fisheries science?
This course, “Principles and applications of fisheries-independent surveys” (FISH 572), is designed to prepare you well for a future career in fisheries science due to its comprehensive curriculum and practical focus:
- Foundational Knowledge: It establishes a strong understanding of fisheries-independent surveys as a cornerstone for fisheries stock assessment and ecosystem research, which are fundamental to the field.
- Essential Skills: The course covers crucial skills such as survey design for various objectives and data types, practical data analysis using diverse methods, and understanding ever-important topics like estimating uncertainty and sampling effort optimization. These are highly valuable for any career in fisheries science.
- Real-World Application: You will gain insight into navigating the logistical challenges of implementing and managing these surveys, and critically, how the data products are directly used in fisheries research, stock assessment, and informing management decisions. This bridges theoretical knowledge of population dynamics with practical management applications.
- Hands-on Experience: The course includes student-led literature reviews, discussions, and notably, hands-on survey data analysis. A significant portion of the grade is based on a final research project using survey data, which provides invaluable experience in research planning and execution that mirrors real-world scientific work.
- Career Insights: The curriculum explicitly addresses career paths in fish research surveys, offering guidance and networking opportunities.
- Relevance to Current Trends: By exploring topics like survey modernization, flexible survey design, and the use of new statistical tools and technology, the course ensures you are updated on the latest developments and challenges in survey science, preparing you for an evolving field.
Why learning about fisheries-independent surveys is critical to prepare you well for future collaboration with artificial intelligence (AI)?
Learning about fisheries-independent surveys, as a data collection tool, can prepare you well for future collaboration with AI for several key reasons:
- Understanding Data Quality and Bias: AI models are highly dependent on the quality and objectivity of the data they are trained on. Fisheries-independent surveys are specifically designed to collect high-quality data through statistically rigorous designs. By understanding these methods, you learn to recognize and appreciate the importance of data integrity. This knowledge is crucial when working with AI, as you will be able to identify potential biases in datasets and assess the reliability of AI-driven insights, ensuring the AI is learning from sound information.
- Data Structure and Organization: Surveys generate structured time series data, including observations of abundance, environmental variables, species distribution, and community structure. Understanding how these data are collected, organized, and stored (e.g., in databases) is fundamental. This knowledge directly translates to preparing data for AI algorithms, which often require well-structured and standardized inputs. You’ll be better equipped to design data pipelines and ensure compatibility between survey outputs and AI models.
- Uncertainty Quantification: Fisheries-independent surveys inherently deal with uncertainty in their estimates (e.g., in abundance indices). The course explicitly covers topics like uncertainty and how it is incorporated into stock assessment models. This focus on quantifying and managing uncertainty is highly relevant to AI, as many AI models also produce predictions with associated uncertainties, but some may not propagate uncertainty as desired. Your understanding of survey-derived uncertainty will enable you to better interpret AI model outputs, evaluate their confidence, and even contribute to developing AI models that explicitly account for data uncertainty.
- Identifying Key Variables and Relationships: Through learning about survey design and analysis, you’ll gain an understanding of which environmental and biological variables are critical for understanding fish populations and ecosystems. This domain expertise is invaluable when collaborating with AI. You can guide AI in identifying relevant features for its analysis, suggest appropriate modeling approaches, and validate whether the AI’s findings align with ecological principles. Without this important context and framing, AI models are much less useful.
- Interpreting Model Outputs: The course emphasizes how survey data products are used in ecosystem processes research, ecological studies, stock assessment, and forecasting. This gives you experience in interpreting complex scientific data and model outputs. When AI is applied to fisheries data, it will generate its own set of outputs (e.g., predictions, classifications, anomaly detections). Your background in interpreting survey-derived insights will allow you to critically evaluate AI-generated information and discern its practical implications for fisheries management.
- Adapting to New Technologies: The course highlights the importance of modernizing survey tools and adapting to new technology. This forward-looking perspective fosters an open mind towards integrating innovative solutions, including AI, into fisheries science. Your exposure to evolving design, estimation, and technology within surveys will make you more receptive to, and capable of, leveraging AI for advanced data analysis and informing management.
What is the future of fisheries-independent surveys?
The future of fisheries-independent surveys is characterized by continuous modernization and adaptation to dynamic marine ecosystems and rapid technological advancements.
Key aspects of this future include:
- Responding to Environmental and Anthropogenic Changes: Surveys will need to continuously adapt to shifts in species distribution due to climate change, increased human activities in survey areas (e.g., offshore wind farms, marine protected areas), and disruptions from extreme weather events. This requires changing sampling extents and combining information across multiple jurisdictions.
- Technological Integration and Modernization: The future will see the incremental incorporation of advanced technologies, rather than wholesale replacement of existing methods. This includes:
- New Sampling Technologies: Increasingly sophisticated and high-resolution acoustics, eDNA (environmental DNA), and optical techniques will become more widespread. eDNA, for instance, shows promise for detecting rare species, quantifying community composition, and estimating abundance.
- Evolving Platforms: While crewed research vessels will remain key, surveys will be augmented and potentially partially replaced by uncrewed and autonomous platforms like uncrewed surface vehicles (USVs), remotely operated vehicles (ROVs), and even animal-borne sensors. Stationary platforms (piers, moorings, seabed landers) and satellites will also contribute data.
- Data Processing and Transmission: Improvements in data storage, processing, and near real-time transmission from sea to land will enable adaptive sampling decisions and remote involvement of scientists and stakeholders.
- Artificial Intelligence and Machine Learning (AI/ML): AI/ML will play an increasingly significant role in rapidly processing large, multidisciplinary observational datasets, identifying and counting organisms (e.g., scallops from camera footage), and informing adaptive sampling designs.
- Expansion of Survey Objectives: Surveys are evolving beyond single-taxon stock assessments to become comprehensive platforms for ecosystem monitoring. This means collecting a wider range of biotic and abiotic data, including oceanographic variables (salinity, dissolved oxygen, pH), physiological status, condition, food habits, genetics, and even microplastics, to support ecosystem-based fisheries management and climate change forecasting.
- Advancements in Statistical Methods: Rapid advancements in spatial statistical methods, including spatio-temporal models, will be crucial. These models will facilitate estimation from unbalanced data (i.e., data with spatial or temporal gaps), bridge time series across changes in survey effort or design, and help optimize survey designs for efficiency and precision, even under budget constraints.
- Flexible and Multi-Platform Designs: Future surveys will prioritize flexibility in design to continuously adapt to changes in ecosystem conditions along with manager and stakeholder needs. This will involve using probabilistic sampling designs, parsimonious stratification, and designing surveys to be “platform-agnostic,” allowing the integration of data from various sources (e.g., trawl, acoustics, eDNA, video) and multiple platforms.
- Addressing Resource Constraints: Static budgets necessitate innovative approaches, including increased use of commercial vessel platforms for surveys and exploring cost-recovery models, while carefully assessing tradeoffs between cost-saving measures and data quality.
- Maintaining Consistency and Continuity: A core challenge and priority for the future is to manage change while ensuring the consistency and comparability of long-term time series data. This will involve careful calibration of new and old methods, running parallel operations, and using model-based approaches to account for method differences.
- Increased Automation and Data Accessibility: There will be a drive towards greater automation in sampling and data analysis to ensure timely access to data. Furthermore, data products will need to be standardized and accessible to a broad range of stakeholders, aligning with open science and data-sharing policies.
I’m considering a career in marine ecology. How would this class benefit me in that pursuit?
This course would significantly benefit you in a career in marine ecology by providing a strong foundation in data collection, analysis, and understanding of marine ecosystems:
- Understanding Ecosystem Dynamics: The course emphasizes how survey data products are used in ecosystem processes research and ecological studies. In marine ecology, understanding how populations interact with their environment and with each other is fundamental. This class will teach you how to collect and interpret data that reveals these critical ecological relationships.
- Comprehensive Data Collection Skills: Marine ecologists often rely on robust data to study populations, habitats, and environmental changes. This course will teach you about various survey designs for different objectives, types of data collection, and the resulting data products. You’ll learn about methods for observing animal abundance, environmental variables, species distribution, and community structure, which are all central to marine ecological research.
- Data Quality and Scientific Rigor: Marine ecology thrives on reliable data. The course highlights that surveys aim to obtain samples based on statistically rigorous designs, leading to unbiased information about change. This understanding of how to collect high-quality data is crucial for any marine ecologist seeking to draw accurate ecological conclusions and inform conservation or management efforts.
- Data Analysis and Interpretation: A significant part of marine ecology involves analyzing complex datasets. You will gain practical skills in analyzing fisheries-independent survey data using both design- and model-based methods. This will equip you with the analytical tools to interpret ecological patterns, assess population health, and evaluate the impact of environmental changes.
- Real-World Application and Logistical Understanding: Marine ecologists often engage in fieldwork and collaborate with management agencies. The course will provide insight into logistical challenges in implementing and managing fisheries-independent surveys. This practical understanding is vital for planning your own research and working effectively within large-scale ecological monitoring programs.
- Adaptation to Change and New Technologies: Marine ecosystems are dynamic. The course’s focus on current topics like survey modernization, sampling effort optimization, flexible survey design, and the use of statistical tools and new technology will prepare you to adapt your ecological research methods to changing environmental conditions and integrate cutting-edge tools (like eDNA, acoustics, and AI/ML) into your work.
- Ecosystem-Based Management Context: The course highlights how survey data supports ecosystem-based fisheries management. This broader perspective is highly relevant to applied marine ecology, which increasingly aims to manage and conserve entire ecosystems, not just individual species. You’ll understand how your ecological research can directly inform real-world management and policy.
- Hands-on Research Experience: The final research project using survey data provides invaluable experience in research planning and execution, mirroring the work of a professional marine ecologist. This hands-on experience will build your confidence and capabilities for future ecological research.
I’m considering a career in fisheries stock assessment. How would this class benefit me in that pursuit?
This course would be incredibly beneficial for a career in fisheries stock assessment for the following reasons:
- Foundational Role of Surveys: You will learn how fisheries-independent surveys are the cornerstone of modern fisheries stock assessments. This includes understanding why they are often the most reliable and consistent source of information for estimating population abundance, spatial distribution, and demographic structure—all crucial for stock assessments.
- Data for Assessment Models: The course will teach you precisely how survey data are used to generate the relative indices (or absolute estimates) of abundance and biomass that are essential inputs for stock assessment models. You’ll also learn how the uncertainty in these abundance indices is incorporated into models, which is vital for robust assessments.
- Ensuring Data Quality: You’ll understand the principles behind designing statistically rigorous surveys to ensure the collection of high-quality, unbiased data. This is critical because the accuracy and reliability of stock assessments directly depend on the quality and consistency of the input data.
- Managing Data Challenges: The class will prepare you for common issues in stock assessment data, such as changes in survey methodology or environmental conditions disrupting long-term data series. You will learn approaches to combine data from different methods and use model-based techniques to account for these differences, ensuring the continuity of assessments.
- Advanced Analytical Skills: The course will introduce you to sophisticated statistical methods, including spatio-temporal models. These are key tools for stock assessors to handle complex, unbalanced datasets, bridge different data sources, and optimize survey designs for maximum efficiency and precision.
- Integrating New Technologies: You will gain insight into how advanced technologies like eDNA, optical instruments, autonomous vehicles, etc. can be integrated into surveys for more efficient data collection and processing. Understanding these advancements will allow you to leverage new data streams and integrate them along with traditional data collections into analyses of future stock assessments.
- Ecosystem-Based Assessment: The class will broaden your perspective to include the collection of a wider range of environmental and biological data during surveys to support ecosystem-based fisheries management. This prepares you for assessments that consider broader factors beyond single-species population dynamics.
- Practical Research Experience: Through a hands-on final research project using real survey data, you will gain practical experience in planning, executing, and analyzing data relevant to stock assessments. This experience directly mirrors the work performed by professionals in the field.
I’m considering a career in fisheries policy and management. How would this class benefit me in that pursuit?
This class would significantly benefit you in a career in fisheries management by equipping you with a foundational understanding of the data and science that underpins management decisions:
- Understanding the Basis of Management Advice: Fisheries management fundamentally relies on scientific data to make decisions about quotas, regulations, and conservation measures. This course will teach you that fisheries-independent surveys are a foundation of fisheries stock assessment and ecosystem research, directly informing management advice. Understanding where this critical information comes from and its strengths and limitations is very important for fishery managers.
- Evaluating Data Quality and Reliability: As a manager, you’ll constantly review scientific reports and data to inform your decisions. This course emphasizes that surveys aim to obtain samples based on statistically rigorous designs to provide information about change in ecosystems. This knowledge will enable you to critically assess the quality and reliability of the data presented to you, ensuring your management decisions are based on sound science.
- Interpreting Stock Assessments and Ecosystem Reports: The course highlights how survey data products are used directly in stock assessment and to characterize the state of marine ecosystems. You will learn how abundance indices are generated and incorporated into assessment models. This understanding will allow you to better interpret the outputs of stock assessments and ecosystem reports, enabling you to grasp their implications for management actions.
- Informing Policy and Regulation: Effective management requires setting appropriate policies and regulations. By understanding survey design, data analysis, and the uncertainties involved, you’ll be better positioned to translate scientific findings into practical management measures, weigh trade-offs and risks, and explain the scientific basis for regulatory decisions to stakeholders.
- Adapting to Ecosystem Changes: Marine ecosystems are dynamic, influenced by climate change and human activities. The course’s focus on adapting to changes in ecosystems and technology through evolving survey design, estimation, and data collection methods will prepare you to understand how scientific monitoring can adapt to these changes and provide the necessary data for adaptive management strategies.
- Communicating with Scientists and Stakeholders: A key aspect of fisheries management is effective communication between scientists, managers, and the public. This course will give you the scientific vocabulary and understanding of data collection methodologies to effectively engage with scientists who provide the data and to explain complex scientific concepts to various stakeholders in plain language.
- Appreciating Logistical Challenges: Understanding the logistical challenges in implementing and managing fisheries-independent surveys will give you a practical appreciation for the effort and resources required to collect essential data. This insight can help you allocate resources more effectively and appreciate the constraints scientists face.
- Future-Proofing Your Knowledge: The course touches on current topics such as: new technology, flexible survey design, and AI/ML in data processing. This forward-looking perspective will ensure you are prepared for future advancements in how fisheries data is collected and analyzed, allowing you to incorporate innovative tools into management practices.
I’m considering a career in academia. How would this class benefit me in that pursuit?
If you’re considering a career in academia, this class would provide significant benefits, particularly if your academic focus is in marine science, ecology, or fisheries:
- Research Foundation: Academia is built on research. This course teaches you that fisheries-independent surveys are a foundation of fisheries stock assessment and ecosystem research. You will learn the rigorous methodologies for collecting data essential for designing and executing high-quality academic research projects.
- Methodological Expertise: You’ll gain deep knowledge in various survey designs for different objectives, types of data collection, and the resulting data products, as well as practical skills in analyzing fisheries-independent survey data using both design and model-based methods. This methodological expertise is crucial for developing your own field research programs, securing grants, and publishing in peer-reviewed journals.
- Bridging Theory and Practice: Academia often involves translating theoretical concepts into practical applications. Understanding the logistical challenges in implementing and managing fisheries-independent surveys, and how survey data products are used in fisheries research, assessment, and management advice will enable you to design research that is both scientifically sound and relevant to real-world problems.
- Advanced Statistical Skills: Academic research often requires sophisticated statistical approaches. The course’s focus on advancements in statistical methods, including spatio-temporal models, and their use for estimation from unbalanced data and optimizing survey designs directly enhances your quantitative skillset, making you a more capable researcher and modeller.
- Publication and Grant Writing: The final research project using survey data provides experience in research planning and execution, including the analysis and interpretation of data. This hands-on experience is directly transferable to writing manuscripts for publication and developing compelling research proposals for funding.
- Teaching and Mentorship Preparation: In academia, you’ll likely teach and mentor students. Having a strong grasp of the fundamental data collection tools and analytical methods, as provided by this course, prepares you to effectively teach these subjects and guide future researchers.
- Interdisciplinary Collaboration: Modern academic research is highly collaborative. The course’s emphasis on “Expansion of Survey Objectives” to include a wider range of biotic and abiotic data for ecosystem monitoring prepares you for interdisciplinary projects common in academia, where you might collaborate with oceanographers, geneticists, or climate scientists.
- Staying at the Forefront of the Field: Academia demands staying current with the latest advancements. The course explores current topics such as uncertainty, survey continuity, effort optimization, flexible survey design, and the use of statistical tools and new technology, including AI/ML. This ensures you’re aware of cutting-edge methods and technologies that can shape your research direction.
- Building a Research Network: Exposure to lectures from instructors and visiting experts and student-led literature reviews and discussions will help you build valuable connections within the academic community, which are crucial for collaborations, post-doctoral positions, and faculty roles.
In essence, this course provides the comprehensive scientific and methodological toolkit necessary to initiate and sustain a successful research program in marine ecology or fisheries science within an academic setting.