Fisheries-independent surveys operate on dedicated survey vessels and are the foundation of sustainable fisheries management. They provide the consistent data on marine resources we need to inform stock assessments. These data are collected using a scientifically rigorous sampling framework and are used to set catch limits. As the ocean environment changes and survey needs evolve, our monitoring approaches must adapt while maintaining the long-term data sets we need.
A new framework outlines a strategic path forward. Traditional methods include capture-based trawl and hook-and-line together with acoustic sampling from the survey vessel. The future of fisheries-independent surveys lies in the careful, incremental integration of advanced technologies alongside traditional methods.
For example, some complementary survey data can be obtained using additional acoustic, optical, and molecular sampling tools, including from autonomous vehicles and stationary platforms. These advanced technologies generally can’t provide the complete set of data necessary for stock assessment. But they can improve the scale, breadth, and efficiency of surveys.
The new framework was developed by collaborators from:
- NOAA Fisheries
- Earth Sciences New Zealand
- Washington Department of Fish and Wildlife
- Institute for Marine Research (Norway)
- Institut Français de Recherche pour l’Exploitation de la Mer (France)
Surveys Are Facing Challenges
For decades, standard methods such as bottom trawls and acoustic-trawl surveys have provided the foundational data necessary to understand fish abundance, distribution, and biological health. As key inputs to fisheries stock assessments, these datasets are essential not only for scientists and managers, but also for fishermen and industry stakeholders. The industry relies on stable, science-based management to plan their seasons and ensure sustainable harvests.
However, surveys now face unprecedented pressures. Climate-driven changes are altering marine ecosystems. Fish populations have shifted into deeper waters or farther north where surveys have not traditionally been conducted. Portions of survey areas have become inaccessible to traditional survey methods because of energy development or conservation needs. Furthermore, aging research infrastructure—including aging vessels and specialized equipment—creates significant maintenance challenges, threatening the continuity of long-term data series.
Beyond these physical constraints, fishery managers and fishermen are demanding broader, ecosystem-level information. This includes oceanographic, biodiversity, and habitat status data. The central challenge driving survey modernization efforts is balancing the continuity of crucial legacy data with the urgent need for more expansive, efficient, and robust monitoring.
Pathways to Modernize Surveys
Stan Kotwicki, lead author with the Alaska Fisheries Science Center, embraces new technology as part of the solution. He states, “These new tools and the data they produce have the potential to alleviate some of the challenges facing our current survey enterprise. And one of the biggest challenges is maintaining data consistency and continuity. This is critical to support sustainable fisheries management.”
Our new framework proposes a roadmap centered on integration, parallel testing, and calibration of new technologies, such as autonomous vehicles equipped with eDNA samplers or optical equipment. By deploying these new technologies alongside traditional survey methods, scientists can understand how new observations relate to historical records. Researchers also use advanced statistical models to bridge these datasets to account for differences in catchability and sensor sensitivity. This ensures that the incorporation of new tools remains statistically sound, transparent, and consistent with our obligation to provide the best available science for fisheries management.
Examples of Successful Integration and Calibration from the Research
Optical Sampling
In the North Pacific, many rockfish live in rough and variable bottom habitats that can be inaccessible to standard bottom trawl surveys. Researchers have explored using underwater stereo cameras to assess the abundance and size composition of these species. These camera techniques are not intended to replace hands-on sampling because physical specimens are essential for critical data, including age, feeding habits, and body condition. But stereo camera systems can provide additional abundance data to better represent the full distribution of the stocks and improve interpretation of standard bottom trawl survey results.
Acoustic Sampling
In New Zealand, acoustic data collection was added to deepwater trawl surveys to estimate deep-sea fish abundance. This provided a new index of midwater fish, which are important food for hoki. Hoki—also known as blue grenadier or blue hake—make up New Zealand’s largest fishery. This additional information allowed a new evaluation of its prey and demonstrated a clear correlation between prey availability and hoki condition. In this case, the new technology did not replace an existing method but added new, valuable information to an ongoing survey time series at a modest additional cost.
Molecular Sampling
Since 2019, eDNA collections on the U.S. West Coast acoustic-trawl survey have been used to generate abundance and distribution estimates for Pacific hake. Scientists compared the time-series of eDNA estimates from 2019–2023 with biomass trends from the acoustic data. The eDNA data reflected similar patterns to the acoustic data; together they were given more weight than other data sources in the stock assessment model. This example shows the ability for eDNA to provide quantitative information and strengthen data inputs across the full geographic extent relevant for a stock assessment.
Uncrewed Vehicle Sampling
Uncrewed vehicles can augment or extend fisheries-independent surveys. For example, uncrewed surface vehicles cannot capture fish by trawling, but they can collect acoustic measurements for commercially important fish like Alaska pollock. For example, surveys were curtailed due to the COVID-19 pandemic. But the Alaska Fisheries Science Center was able to deploy Saildrones to conduct an acoustic-only survey of midwater pollock in the Bering Sea. This provided some of the information needed to manage that fishery in a challenging time.
Ongoing work is exploring more routine use of uncrewed vehicles in surveys. The Southwest and Northwest Fisheries Science Centers have used Saildrones to augment surveys of West Coast fish populations. The Alaska Fisheries Science Center is exploring the use of uncrewed vehicles to work in tandem with research vessels to complete surveys more efficiently. This approach may allow surveys to allocate saved ship time to cover areas that are currently not surveyed. The Northeast Fisheries Science Center is testing similar platforms in offshore wind farms that are difficult to access using traditional survey vessels.
Future of Fisheries-Independent Surveys
As we head into the future, these examples will continue to build. For instance, our colleagues are also working on modernizing survey gear, statistical designs, and sampling methods on our traditional capture-based surveys.
Reliable, fisheries-independent survey data will remain critical for the foreseeable future. They provide the baseline data for fisheries stock assessments and ecosystem status reports which support effective fisheries management. Modernizing survey methods is necessary to adapt to an evolving environment and capture operational benefits from innovation; ensuring data consistency and continuity is equally vital. New innovations should serve to complement and improve—rather than disrupt—the long-term records essential for sound decision-making.
Kotwicki concludes, “Our research recommends adopting a strategic approach. This includes prioritizing calibration and comparison when implementing new methods, and fostering international collaboration. Agencies can then build the flexibility they need to adapt to changing ecosystems and technological opportunities.”
This framework offers a path to ensure we maintain the integrity of long-term data series while expanding our capacity to understand complex and changing marine ecosystems.
