A future focus: Science, technology and innovation

Drawing on the experience of our panel, in this section we highlight select examples that could shift how we fish commercially to reduce the stress it causes within the marine environment. We recognise that research and innovation relating to fisheries and the marine environment is expensive – the feasibility and prioritisation regarding implementation falls beyond this report. Not all of these ideas are will be implemented at scale by 2040, but we take a deliberate future focus to highlight a raft of changes.

The marine environment faces significant challenges in 2020 – it is under considerable stress from fishing, sedimentation, climate change, pollution and more (as discussed in ‘Challenges for the marine environment’). These challenges cannot be solved with science alone. A systems change is required that brings together a raft of solutions, some scientific but many relating to a culture change to improve relationships within, and management of, the marine environment. Despite science not providing all of the short-term answers, the application of a raft of scientific innovations may collectively provide improvements in the health of our fisheries and the marine environment. In this part of the report we focus on the tools that may help to achieve these desired improved outcomes. Some of these tools have been available for some time, but not widely adopted. Others are new and not yet ready for implementation. All have something to offer a vision of future fisheries management if prioritised within an overarching strategy for the ocean.

Climate change, ocean acidity, marine heatwaves and other anthropogenic stressors amplify the need to be able to respond to changing fisheries. As discussed in the section ‘Climate change is a huge threat to our oceans’, climate change will drive many changes across fisheries by both disrupting ecosystems and moving species into different regions in response to changing temperatures and ocean circulation. Though a changing climate is already upon us, far more significant changes are expected to come. The climate changes to date have already highlighted the need for greater data collection and analysis to feed into more rapid and responsive decision making. The management system will need to be able to urgently respond to challenges such as shifting stocks, disrupted food webs, changes in fish productivity and recruitment patterns, and ocean acidification.

Climate changes to date have already highlighted the need for greater data collection and analysis to feed into more rapid and responsive decision making.

The marine environment and fisheries will continue to be further impacted by the other stressors outlined in ‘Challenges for the marine environment’, including sedimentation, plastic pollution and invasive species, with their cumulative effects contributing to ongoing changes in fisheries that will be felt by the commercial sector.

While data is fundamental to being able to make informed decisions, a lack of data should not be a roadblock to making decisions. There will still be times where there is a need to make decisions without a complete evidence and the system should be able to account for uncertainty while we endeavour to fill in knowledge gaps.

There will still be times where there is a need to make decisions without a complete evidence base and the system should be able to account for uncertainty while we endeavour to fill in knowledge gaps.

Taking a regional approach to management at an appropriate scale for the fishery will also enable greater flexibility in decision making and more responsive management. The issues that need to be addressed may be unique to an area and require a holistic local approach to tackle a range of stressors that are causing changes to fisheries. Regional approaches to fisheries management are more resource intensive but can provide greater input and satisfaction for local communities (within the context of broader fisheries management). These are already showing promise throughout Aotearoa New Zealand (see case study: Fiordland created a novel model for managing the marine area and case study: Te Korowai o te tai ō Marokura in Kaikōura shows how regional responsibility can streamline fisheries management), and could be built on by making sure the process is able to respond to new data and knowledge in a rapid fashion. Locally held knowledge will play a critical role in more flexible decision making, specifically for inshore fisheries. Once time has been spent establishing local involvement in marine management, combining place-based evidence at an appropriate scale with a management framework that is fast and responsive will help to provide optimum management for each unique scenario.

Combining place-based evidence at an appropriate scale with a management framework that is agile and responsive will help to provide optimum management for each unique scenario.

Existing management processes could be modified to sharpen the response capacity. The recent shift within the fisheries management system to use digital technology for EM provides a strong foundation on which to build a more responsive system. Information about harvested catch gleaned from these processes can be built into models and ongoing monitoring efforts to observe changes in near real time to support ‘moving management options’. The innovations highlighted in the sections ‘How much we fish’ and ‘Where and when we fish’ will also be critical components of a more responsive system. Faster decision making will also support consistency in the application of new software and technologies in the industry, with guidance from the regulator being received prior to purchasing decisions.

The recent shift within the fisheries management system to use digital technology for electronic monitoring provides a strong foundation on which to build a more responsive system.

Regulatory swiftness underpinned by a stronger evidence base to inform decisions will be the foundation of a responsive management system. Such a framework would enable researchers to more efficiently provide input into the decision-making process and identified risk indicators could ensure rapid action to protect ecosystem health. A far greater understanding of basic fisheries science and ecosystem dynamics is a prerequisite to enable this more responsive system, along with ecosystem indicators. This in turn will enable the ongoing and sustainable use of Aotearoa New Zealand’s fisheries for generations to come.

Systems should be in place so that data is strategically collected to fill knowledge gaps, data is collected regularly, decisions are based on the present not the past, and full datasets are available for extrapolations to predict outcomes.

There are also areas where we are data rich and information poor – that is, the data is not being used to its full potential to extract knowledge. The use of electronic reporting primarily for compliance purposes is an example of this, where we have rich data that could be used more widely (see section ‘Electronic catch and position reporting is live’). Best practice examples where we have robust long-term data that informs decision making, such as in the Chatham Rise[4] (see case study: Chatham Rise is a unique fishery with consistent, long-term data), are what we should strive for in all other priority fisheries management regions throughout the country.

Data needs to be integrated in a way that it is easily accessible for decision makers, commercial fisheries, researchers and other stakeholders. Improving how we integrate and manage data, and opening up access so data is widely utilised, will help to turn data into information. The real value of the (long-term, time-stamped) data will then come from the interpretation and analyses of it, and the risk assessments and decision support tools that are driven by the data. The suggestions below can feed into the data transformation strategy that is currently underway within Fisheries New Zealand (introduced in ‘Regulator initiatives and data transformation’).

Screengrab from the Litter Intelligence website: a citizen science effort to measure litter on Aotearoa New Zealand’s coastlines and waterways.

• Automated data collection. AI and machine learning present new ways to process larger volumes of data with less resource-intensive methods (expanded on below).
• Innovative use of observers. As aspects of the role of observers become automated, there is opportunity to employ observers in more innovative research tasks on-board the vessel or to extend the types of data they capture using new tools and technology.
• Monitoring surveys. Better coordination and structuring of routine monitoring of inshore fisheries would ensure better coverage across the inshore fisheries around the country and fill data gaps of significant benefit to fisheries management.

As technology use increases and cost decreases, and it becomes easier to automate methods or build in transparency and verification processes, it should become increasingly viable to use data collected from a range of sources in marine science and fisheries management, with confidence that it is robust and high-quality. For the data collected from a range of sources to be useful, it will need to be well organised and integrated.

A conceptual diagram from [10] (CC BY 4.0) showing one example of a status quo (top) and possible high-tech (bottom) example of a fishery-dependent collection system for data such as stock and bycatch data, whereby decision making could happen on a faster time scale. Aotearoa New Zealand is already on the path to the high-tech system, though, in practice, decisions may be limited by regulatory processes such as ministerial decision making. Note that recreational fishing is out of scope for this report but would need to form part of a comprehensive fisheries data platform.

Aotearoa New Zealand’s fisheries management system is currently on a journey towards a more high-tech system, having started to collect a lot of data electronically, but there are still obstacles (discussed in the section ‘Regulator initiatives and data transformation’). The high-tech science-driven system needs to have a bridge to the observational system embedded in mātauranga so that both types of information can be integrated into decision making. Additional challenges to moving towards a high-tech system include[10]:

• High upfront costs. Switching to a new system can be very expensive compared to continuing with the status quo. Adopting new software and hardware for data collection, processing, analysis and storage has purchase, installation and training costs. The New Zealand Government has already invested in some of the EM equipment needed to move to a more high-tech system.
• Regulatory barriers. The types of data collected in a high-tech system may not align with government requirements for reporting. For example, requirements for paper-based reporting may hinder uptake of electronic reporting. Aotearoa New Zealand is already past this particular hurdle but regulation needs to continue to keep up with new ways of capturing and reporting data so as to not hold back uptake of improved systems.
• Implementation of data collection standards. Some data collection efforts may be redundant because the data is not viewed as legitimate or it does not meet the needs or technical and performance standards required by the user. Agreeing on guidelines and standards for fisheries data would enable better utilisation of externally collected data in fisheries management.
• The need for trust and buy-in from fishers. If fishers cannot access and do not benefit directly from data, they may resist being involved in data collection efforts, especially where requirements are burdensome. Concerns around privacy and commercial sensitivities may also come into play.

Aotearoa New Zealand’s fisheries management system is currently on a journey towards a more high-tech system, having started to collect a lot of data electronically, but there are still obstacles.

Getting new data systems set up correctly from the beginning requires having the data experts and end users involved and informing what questions need to be answered by the system. For example, having end-users input early would have helped to ensure fishers are reporting how they use seabird mitigations in a way that answers vital science questions. Similarly, it will be important to ensure video data (from cameras on boats) is reviewed in ways that will meet particular statistical requirements of risk assessment modelling. In some instances, fishers need data to validate new fishing approaches and there needs to be a simple connection into the system to enable this (see case study: The importance of connecting fishers to researchers).

As we streamline, link up, and set up new data systems, it is important to effectively involve stakeholders or end-users in data system design. While establishing new data systems will always be a somewhat iterative process, where there is a lack of early stakeholder or end-user input, this can result in lost opportunities for setting up effective systems for data analysis from the start, and results in unnecessary amendments. This provides opportunities for win-wins between all participants in a data system.[10]

Efforts to improve the fisheries and marine science data systems could align with the recommendations of the Parliamentary Commissioner for the Environment relating to environmental reporting and the coordinated and long-term funding of environmental research.

Need for large and secure data storage

Aotearoa New Zealand has secure data storage for current fisheries data, but if collection and access is to expand the current system may need to change. Large amounts of data (often in terabytes) must be stored – this requires either physical hard-drives that are manually exchanged, or cloud storage/wireless transmission that may be difficult for fisheries operating in remote areas.[11] Costs of data transfer and storage are also non-trivial. Data storage must be secure and access to data must be clearly defined. The format of data will also need to be considered, for example, increases in video imagery and multibeam echosounder.

Need for improved analytical processes

A broader and more automated data collection and management approach will generate a wealth of information. Ongoing monitoring efforts will see these datasets continue to grow. Some new or complementary data collection methods may also collect more data than previous approaches. For example, acoustic data collection generates much greater volumes of data than trawl samples.

New analytical processes will be required to get through this sheer volume of data. Software that is able to automate or streamline analyses in a reproducible fashion will be an essential tool to utilise data to inform fisheries management, with a quick turnaround being critical to support fast and responsive decision making (see case study: Software to streamline acoustic data analysis). These efficiencies will potentially be further improved by the use of AI and machine learning in analytical processes (discussed below).

Providing access to datasets could allow for novel analysis and new insights. Reciprocated data sharing between different groups could provide a fuller picture of the state of the marine environment and fisheries and could prevent duplicating data collection efforts. There is also opportunity to make some data even more widely available, such as in public online databases, so that it can be accessed for different research and management needs. Requiring publicly funded research to have open access datasets may be a first step to achieving this goal. It also would allow the public access to information that provides a more complete picture on the state of our fisheries and environment, which may build trust.

Future developments for camera technology might help to reduce these concerns. The Datalink system being built into Tiaki to develop a release-at-depth strategy may provide a first step to remove some intrusion in the workplace by capturing footage underwater (see case study: Precision Seafood Harvesting – Tiaki). The specific concerns of iwi relating to data rights, data privacy and intellectual property must be factored in to any processes that make data more widely accessible. Establishing trust and appropriate conditions for sharing of data is important and it may not be appropriate for that to be wholly accessible. A data trust is a possible way to provide stewardship over data while enabling wider access.

There would be great benefits in aligning with Te Mana o Te Taiao – Aotearoa New Zealand Biodiversity Strategy 2020 goal that “national, agreed common data standards and open data agreements are ensuring that everyone has access to a federated repository of biodiversity information”.

Improving access to data could build on the existing systems. See the section ‘Who collects data?’ for a list of some existing databases.

Need for effective data visualisation tools

A significant volume of data is available from four standardised time series of research trawl surveys conducted for the regulator over the last 30 years. This data can be used to develop indicators of fisheries ecosystem health and change, but access to, and communication of, this data has mostly been via individual lengthy survey reports, making it challenging to determine trends over time. Data visualisation tools that are able to be used across common platforms will be more accessible than specialist programmes.

Access to, and communication of, this data has mostly been via individual lengthy survey reports, making it challenging to determine trends over time.

Some visualisation tools already exist, such as Sea Sketch – a publicly accessible tool for visual marine planning that was used in Sea Change Tai – Timu Tai Pari. The web-based platform, Fishecoviz, in the early stages of development[12] is another step towards making data more visually accessible. The prototype includes Chatham Rise trawl survey data but could be expanded to include other ecosystem components (e.g. oceanographic indices) and other areas. The portal currently includes several interactive pages where the user will be able to explore and compare species and ecosystem indicators, biomass trends and feeding relationships and view species distribution on a map. In order to be effective, the portal needs to be publicly accessible and user friendly.

A web-based delivery platform, DOC2.0,[13] is being developed to provide data that is currently only accessible as pdf or hard copy versions of New Zealand Fisheries Assessment Reports to Fisheries New Zealand and other users. The concept is for searchable delivery (and download) of peer-reviewed published outputs (rather than of raw data). A beta testing version has been developed and is currently being reviewed by users with further implementation planned in 2021.

Schematic of the different approaches to machine learning.

There are limitations and challenges in applying AI in the commercial fisheries sector

Successful scaling up of AI technology across the fisheries sector will require collaboration and dialogue between fishers, academic researchers, NGOs and the private sector. While AI has the potential to effect change in fisheries, in practice there are several hurdles that are currently limiting implementation to small research projects and pilots.

Aspects of fish behaviour that need to be understood to inform gear design include:

• Swimming speed and movement patterns. The speed and movements of fish need to be understood in order to design non-lethal trawl methods so that the fish harvested are top quality and the bycatch can be returned unharmed.
• School movements. Japanese researchers developed a simulation model of fish-schooling behaviour to determine the most effective set net design to be selective and reduce bycatch.[30]
• How fish respond to visual cues. Understanding more about what fish see and how they react to gear will help to attract certain species.
• How fish respond to sounds. Innovations to improve the use of sound to selectively attract fish could be beneficial, but only if developed with consideration of the need to minimise anthropogenic ocean noise as it can impact marine life.[31, 32] The use of sound to guide fish to another location could possibly be applied in a way that shifts schools of fish away from a protected habitat before fishing effort takes place. Sounds can also be used to deter non-target species (as discussed in ‘Gear add-ons will be essential to deter non-target species’).
• How fish respond to olfactory stimuli. Understanding how olfactory queues could be used to select for some species and deter others may also open avenues to use these methods in new gear. Bait analysis tests for scampi that fed into potting methods are an example of this in action.[33]
• Diet. Knowledge of fish diets can inform approaches to fishing such as identifying effective baits for trap fishing. A local study that used DNA analysis (specifically metabarcoding) identified the diet of deep sea scampi and found that scampi have a varied diet, with a high reliance on scavenging a diverse range of species from the seafloor.[34] Analysis of diet also enables a way to detect if diet is changing in response to environmental shifts.

Fish behaviour has already informed some design aspects of innovative gear in use in Aotearoa New Zealand’s commercial fisheries. The net design and trawl speed of the Precision Seafood Harvester – Tiaki, were designed such that fish swam and survived until landed on deck (see case study: Precision Seafood Harvesting – Tiaki). The potting technique profiled in the case study ‘Potting as an alternative to trawling’, also based its design on prior behaviour studies.[35] Some challenges still need to be overcome so that fish behaviour can inform gear design, with multi-species fisheries posing a particular issue.

Selectivity

Much of the focus in trawling innovation relates to selectivity by selecting for fish that are a marketable size. For nets and trawling, this is largely determined by the size and shape of mesh openings in the net, particularly at the end of the net where the fish accumulate (the ‘cod-end’), and fish behaviour during the capture process (see section above ‘Knowing how fish behave is essential for gear design of the future’). The simplest way to reduce the catch of undersized fish is to increase the size of meshes in the cod-end or use selectivity grids where appropriate and allow the fish to escape. Some fish that pass through the nets die during the process.[38] In the future, greater selectivity could also be achieved if bycatch is caught alive and healthy and released back to the ocean unharmed.

Other tools such as sorting grids and exclusion devices also provide a way to select for the target species and reduce bycatch by allowing for the escape of larger animals when they come into contact with grid bars at the top or bottom of the trawl net, but not ejecting the smaller target species. An example is the sea lion exclusion devices (SLEDs) used in the squid fishery near Maungahuka the Auckland Islands (SQU6T) and the southern blue whiting fishery near Motu Ihupuku Campbell Island. A review concluded SLEDs have contributed to reduced rates of observed sea lion mortality[40], though these findings are contested.[41] Separator panels with multiple cod-ends of different mesh sizes have achieved successes in some fisheries in Aotearoa New Zealand. However, these approaches are not applicable to smaller bycatch species. There is room for improvement in bycatch reduction innovations in Aotearoa New Zealand’s commercial fisheries industry.

Combining gear innovations with other technologies such as acoustic and video technology could in future facilitate more selective methods and reduce the trawling impact as the trawling effort needed is reduced.[42] Advances in computer vision (discussed above, in ‘AI and machine learning have the potential to increase efficiencies’) coupled with more selective design will allow for selection to take place at depth and allow for underwater release to reduce discard mortality. Video imaging and acoustic technologies could be coupled for pre-catch identification and catch monitoring to potentially increase catch rates of target species and reduce the trawling footprint.[42] Among a range of international examples is a local project that aims to develop a video-guided active sorting device (see case study: The importance of connecting fishers to researchers). On a larger scale, a collaborative, multimillion dollar project led to the development of a modular harvesting system that offers significant advantages over traditional nets but does not address seabed impacts (see case study: Precision Seafood Harvesting – Tiaki). Development of underwater computer vision to add to the harvesting system is currently underway.

Reducing bottom impact

There are also opportunities to innovate in trawl design and materials to lighten the weight of equipment so that it doesn’t drag along the seafloor and damage habitat (the impacts of this are discussed in ‘Habitat’).[36] Innovations to change the extent to which these gears contact the seabed have dual conservation and production gains in that as well as avoiding habitat and species disturbance, they reduce fuel consumption and expensive damage to trawling gear.

A number of techniques have been developed overseas to reduce the impact of trawl fishing on benthic habitats, including semi-pelagic trawl where trawl doors are lifted off the bottom rather than in contact with the seabed, and raised fishing line trawls. These are thought to be suited to application in some of Aotearoa New Zealand’s fisheries and could be tested in our setting,[36] building on early testing of semi-pelagic trawls.[43]

A precision robotic shellfish picker is under development by researchers at the University of Canterbury to address the issues related to dredging (as discussed in the case study: Scallop surveys and harvest).

The second approach is to use flume tanks to test systems. Flume tanks provide a physical environment to carry out performance evaluations of the new gear and simulate underwater and near-surface conditions. There are only a handful around the world for testing trawling gear and Aotearoa New Zealand’s researchers and industry collaborate with these institutes. Independent fishers who supply Talley’s have visited the flume tank at the Marine Institute at Memorial University in Newfoundland, which is the world’s largest flume tank, to test gear innovations. A flume tank is going to be built at Plant & Food Research which may be able to be used for testing gear locally.

There are remaining challenges in testing net and trawl innovations in the marine setting. In particular, it is challenging to capture data about survivability following escape from the net. Video footage is helpful in distinguishing between live and dead bycatch being released, but cannot capture subsequent events, such as predation. Tagging of fish may give some clues, but is extremely resource intensive. Beyond the benthic impacts, we also need to study the impacts of the gear on the ecosystem more broadly. A more permissive regulatory environment that maintains rigour is needed to enable controlled fishing trials of innovative gear with careful monitoring of the impact on stock numbers and the ecosystem over time. This could be achieved through special permits to support innovation.

A more permissive regulatory environment that maintains rigour is needed to enable controlled fishing trials of innovative gear with careful monitoring of the impact on stock numbers over time. This could be achieved through special permits to support innovation.

Looking to the future, towed capture methods will need to continually embrace technology that allows more active selection, identifies unwanted catch before or during the capture process, avoids or releases species at depth unharmed, and doesn’t come into contact with the seafloor. Alternative methods also need to be evaluated for differences in fuel use or emissions. Facilitating this gear innovation would be assisted by review and amendment of the Enabling Innovations in Trawl Technology regulations to better facilitate the development and use of innovative fishing technology. Funding and technological support for gear trials could also reduce barriers to gear innovation, as would a review of how the ‘third wire’ regulations can restrict innovative uses of technology.[48]

Not every new innovation will work first time and a permissive environment is needed to allow fishers to iterate and optimise new gear within their local context to give a fair comparison to established technology.

A Nature Conservancy project supporting fishers to fish sustainably in the Gulf of Maine has equipped nine boats with this technology at no charge so that they can test it before purchasing at a discounted rate if they want to continue to use the method. Though one fisher could manage a number of electronic jiggers at once via an on-board computer, the current cost of the technology may hinder uptake at a scale that would make it economically viable to adopt this method over alternatives such as trawling. However, it does enable the harvest of selected quality fish for appropriate small, premium markets, and could be more widely adopted with investment to help operators find a route to these markets.

Underwater hook release

Gear innovations are required to address a key issue with line fishing – bycatch (see ‘Bycatch of non-target and protected species’). Hooks floating on the ocean’s surface are commonly mistaken for food by seabirds, which can get caught by the hook. This is a particular issue in longlining where hundreds or thousands of hooks are used at once. Changes in fishing practice, such as setting longlines at night, weighting lines, and bird-scaring decides go some way to reducing these impacts, but gear innovation has the potential to further reduce risks to seabirds. Innovations to address this issue focus on releasing the hook underwater out of accessible reach of seabirds. An underwater bait setter innovation developed by local fishermen is showcased in the case study: A collaborative effort to protect vulnerable seabirds. A separate but similar innovation developed in the UK, the Hookpod, encloses baited hooks in a case and then releases hooks at depth. These have been tested by commercial operators in Aotearoa New Zealand and are approved by the Ministry for Primary Industries as a standalone seabird bycatch mitigation measure. These innovations mitigate the risks of seabird bycatch on the sea surface during setting of the gear, but underwater risks of hooks or entanglement still exist for other seabirds and other marine animals.

The cost of implementing these systems might be cost prohibitive and the types of fishing locations may limit viability in some areas. However, if further fisheries expand to using potting (such as in the case study: Potting as an alternative to trawling) there will be more ropes in the water and a higher chance of entanglement. It will therefore be even more pertinent to development ropeless gear to minimise the impacts on non-target species.

Policies exist to support innovative trawl technology, but in some instances the prescriptive requirements are actually hindering innovation rather than fuelling it. True innovation needs to be assessed within a flexible monitoring framework that is not encumbered by monitoring methods inherited from old techniques. Providing a permissive environment for innovation, coupled with a requirement for monitoring stock levels and bycatch, will encourage more industry players to adopt new techniques. Extra incentives could be provided by fast tracking the special permit process through the Enabling Innovations in Trawl Technology regulations to enable simpler trial of new gear through the special permit process, so as to reduce barriers to trialling new gear within quota. Well-designed regulation that signals the need to shift to less damaging gear could incentivise technological development in this area and expedite adoption of new technologies.

It must be emphasised that simply putting cameras on boats is not the end goal for EM efforts. The promise of EM systems will be realised when the detailed data they generate is channelled into efficient and adaptive management decisions – including in real time – that enhance fisheries for all. This will include allowing these rich datasets to be mined for both commercial and environmental benefit. Some commercial fishing companies are already using their EM data to inform their own decisions, but there is potential for much wider benefit. In this way, the most successful EM programmes will have specific objectives and standards that invite collaboration and integrate with management and information systems.

The promise of EM systems will be realised when the detailed data they generate is channelled into efficient and adaptive management decisions – including in real time – that enhance fisheries for all.

In the US, pre-existing at-sea observer coverage influences the receptiveness of a fishery to EM.[25] Fisheries with higher observer coverage see a cost benefit to switching to EM, whereas fisheries with little observer coverage may incur higher costs by implementing EM.

Across the Tasman, the AFMA runs an EM programme with coverage across four fisheries.[68] Coverage is expected to expand into other fisheries over the next 5-10 years.

Barriers and benefits

Video monitoring is the most challenging component of EM to implement – but for the most part, the challenges are not technical, but rather to do with people, relationships and communication. A collaborative, co-design approach is needed to develop EM programs that suit the needs of fishers and thus ensure buy-in by enabling commercial and environmental goals to be met as well as being simply a compliance tool.

A major contributor to the success of EM in Alaska was the switch from a top-down to an all-hands approach with all stakeholders at the table developing the details.”

– Dan Falvey, Alaska Longline Fishermen’s Association, National Electronic Monitoring Workshop 2016, NOAA Fisheries.[69]

Changes in practices

The presence of on-board cameras for EM may drive a change from the portion of the industry that is misreporting and illegal practices to more accurate reporting and changed fishing practices. EM may also require fishers to alter their workstream in ways that may add labour and costs. For example, fishers may need to present their catch to the camera view, slowing down the sorting process.

Privacy

Many fishers feel that video monitoring is intrusive and that such surveillance represents a distrust in them from government.[71, 72] Guidelines for the use and retention of EM data, especially imagery, must be clearly defined and communicated: who owns and has access to the data?

Numerical data is hard to manipulate but video images and pictures can be altered and misinterpreted… one image taken out of context could negatively impact a fisherman or the entire industry.”

– Mike Russo, New England Groundfish Fisherman, National Electronic Monitoring Workshop 2016, NOAA Fisheries.[69]

Suggested uses of drones to monitor fishing effort, including the two hypothetical case studies presented in a 2019 paper involving using drones to monitor recreational fishing effort and use of public lands,[73] also raise privacy and ethical issues that must be considered if unmanned autonomous vehicles are used for human surveillance purposes – whether of the general public or of commercial fishers.

Demonstrating business benefits beyond compliance

Without the camera, my information is viewed as anecdotal to managers but a camera makes the observations more substantive. This gives fishermen power that they did not have before.”

– Mike Russo, New England Groundfish Fisherman, National Electronic Monitoring Workshop 2016, NOAA Fisheries.[69]

Some limitations compared to what observers can achieve

The additional scientific tasks that observers undertake on board a fishing vessel (e.g. tissue sampling or otolith extraction) cannot be replaced by EM. There may also be some limits to what EM can identify compared to an observer where features are not discernible with current technology, though this may improve over time.

Benefits

Although EM is an important tool for enhancing compliance with regulations, it has other less obvious benefits. EM can also:

• Ensure all fishers are operating on a level playing field.
• Empower fishers by providing concrete evidence that can back up their assertions in conversation with regulators and the public, building trust (see case study: Livestreaming commercial fishing catch).
• Improve quality of life at sea, by removing the necessity for an on-board observer who requires a berth, food etc. or, if observers are retained, expand monitoring of ecosystem health via tracking non-commercial index species.
• Verify claims about sustainability and traceability, allowing fishers to market an eco-certified, net/hook-to-plate product that is higher-value.
• Enable more flexible management of fisheries (discussed in section above: ‘Changing fisheries demand nimble and responsive decision making’). For example, identifying individual vessels with issues, meaning fishery-wide area closures can be avoided or scaled back. Fisheries managers can switch to more targeted management measures (e.g. fleet-wide, individual trip or vessel caps).
• Allow analysis of the product handling process, with a view to enhancing quality and value of the catch.
• Enable skippers to monitor activity on the vessel from multiple camera views, which has both operational and safety benefits.
• Monitor whether fishers are using the best practice mitigations (defined in regulations and the new National Plan of Action Mitigation Standards).
• Possible future benefits could be achieved through linking EM data with other real-time data to inform fishers about the environment to mitigate risks and/or enhance fishing effort.

Examples of these benefits in action are highlighted in the section ‘Dynamic ocean management will help protect non-target species in real time’, and the case study: EcoCast – an app that can help fishers decide where to fish.

Image processing

Improved computing can also enable rapid processing and analysis of images. This has a range of potential applications in fisheries and the information gleaned from it can inform fisheries management. Awalludin et al.[75] reviewed image processing techniques for fisheries, listing the following applications: fish classification, counting and abundance, fish weight and length, fish tracking, fish disease, fish tissue properties, and fish habitat identification.

The marine environment can be very challenging for computer hardware – with issues relating to power, access to the internet, and generally rough conditions. Sea-going hardware needs to be robust and resilient. Schoening[76] describes the development of a mobile sea-going high-performance computing cluster (ShiPCC), robustly designed to operate with electrically impure ship-based power supplies and based on off-the-shelf computer hardware. The SHiPCC units are envisioned to generally improve the relevance and importance of optical imagery for marine science.

AI has potential to streamline review of EM

There have been some small pilot projects related to AI and fisheries in Aotearoa New Zealand and overseas, with more currently in development.

EM can help generate reliable and transparent fisheries data at a lower long-term cost than on-board human observers. However, this information still needs to be sorted and analysed. Manual review of video footage is time-consuming and costly.[77]

Instead, we could use AI to review video with a combination of computer vision and machine learning to count and measure fish and identify species. This tech has the potential to make EM more efficient and cost-effective[25] and is being actively pursued by officials in Aotearoa New Zealand.[78]

Using genetic technologies in Aotearoa New Zealand’s fisheries

The use of genomic technologies is not commonplace in Aotearoa New Zealand fisheries management, but several workstreams are currently underway that will pave the way for such applications (see appendix 13: Genetics in fisheries in Aotearoa New Zealand). Building on the efforts of the Fisheries New Zealand workshop,[84] researchers, industry, iwi and community should work together to address barriers, prioritise applications based on sustainability concerns and economic/cultural value. Feasibility studies which aim to determine where using genetics could be a cost-effective approach to inform sustainable management of our fisheries. We can also draw on the experiences of other countries who are beginning to integrate genetic technology in fisheries management, including Norway (see case study: Real-time genetic management of a marine fishery), Australia (see case study: Genetic tagging to understand bluefin tuna population dynamics) and the US (see case study: Managing great white shark conservation through eDNA). Because the application of genetic technologies in fisheries management is in its relative infancy, there is opportunity for local work to push the frontiers in this space, especially considering the strong international scientific footprint that Aotearoa New Zealand has in the areas of bioinformatics and phylodynamics.

To maximise on the potential of these applications, efforts need to be made to ensure that the various samples being collected now can be repurposed for genetic studies. For example, marine mammals that wash up on shore, become stranded, or are caught in trawls usually have small tissue samples taken. These are stored by various Aotearoa New Zealand institutions, often in an ad hoc way in university or museum freezers. However, some samples may be compromised due to poor handling and processing. The animals that strand may not be fully representative of the genetics of the population, which also needs to be factored in when using such samples. There is an emerging need for a more centralised repository of tissue samples and environmental samples (akin to a biobank). Such an initiative would have to play close attention to issues of data sovereignty and sample use especially in the case of taonga species.

Increasing demand for these technologies will also require more local capacity and capability to do high-throughput sequencing and store data. These efforts would need to be connected to other efforts within the fisheries sector to aggregate and use data to inform fisheries management decisions. There are also initiatives underway across Aotearoa New Zealand to familiarise communities and iwi with genetic technologies in environmental applications, these include the Wai Tūwhera o te Taiao – Open Waters Aotearoa initiative led by the EPA, and the Lakes380 and Marine Biosecurity Toolbox led by the Cawthron Institute.

Currently, microchemical analyses of fish are able to inform:

• Stock assessment and delineation. The chemical signatures of otoliths or other structures can identify and delineate fish originating from distinct geographical origins to classify fish stock units, but are currently best used as part of an integrated approach with other markers.[101]
• Migration patterns. Chemical signatures can show where the fish originated, where it moved and when, which has informed understanding about Atlantic salmon’s[102] migratory patterns.[103] These techniques have also been applied to connect larvae from aquaculture to restoration of bivalve reefs in the wild.[104]
• Age and growth rates. In addition to the traditional use of otolith structure to assess age, various chemical signatures can be used to determine the age of a fish and other details around age at maturity to inform management decisions, as demonstrated in studies on orange roughy.[105]
• Dietary patterns. Researchers can assess diets and determine trophic level based on chemical signatures by looking at amino acids to reconstruct dietary patterns and shifts over time.[106, 107]

An echogram of acoustic marks for krill swarms in the Ross Sea. Image credit: NIWA.

Acoustic technologies can be broadly divided into active and passive methods. Active methods rely on sending out sound underwater to gather information. A common approach is using a tool called an echosounder to send a pulse of sound down to the seafloor via an underwater transducer. If the pulse encounters something along the way, it is reflected back as an echo and converted to electrical energy via the transducer. The time it takes between sending out the pulse and receiving the echo tells us how far away the organism is. The echo is displayed as a 2D picture known as an echogram and the details can be used to infer the types of fish or other organisms in the area.

Originally, the technology was only able to operate at a single, discrete frequency. This limited resolution and range and therefore what could be detected. Advances led to the use of multiple, discrete frequencies at once to simultaneously measure different groups, where groups could be distinguished based on their biological or acoustic properties. A further development involved moving from a discrete burst to a continuous response around a central frequency which provides greater information and better resolution of the data, so it is easier to discriminate between species. These are known as broadband (‘chirp’) echosounders. Active devices can also send out acoustics that act as deterrents.

In contrast, passive acoustic methods are used to listen to and record sounds underwater.[110] These approaches use underwater microphones (hydrophones) to pick up sounds known to be associated with specific marine organisms, such as whales or sound-producing fish, but their application relies on prior work to identify and ‘sound-truth’ species-specific sounds through verification with other methods. Scientists can identify, record and study underwater animals using passive acoustic technologies, either in the absence of visual information or coupled with optical technologies such as underwater or surface cameras to make biomass estimates (see section ‘Underwater and surface cameras give a wider and sharper view of the ocean’). The technique provides a way to perform non-invasive, non-destructive surveys of marine life, generating information to locate fish to understand habitats, spawning times and other behaviours.

Acoustic technologies are used in a range of ways in fisheries:

• Abundance estimates. Acoustic technologies provide a non-destructive method of estimating fish densities and – with the right survey design – abundance, which can feed into stock assessments.[111. 112] Fisheries-independent acoustic surveys are currently used in Aotearoa New Zealand to estimate orange roughy, southern blue whiting, hoki, oreos, and mesopelagic fish abundance. Specialised towed acoustic systems, including deep-tow bodies and a net-mounted acoustic-optical system (AOS), have been developed by NIWA and CSIRO to improve sampling of these deepwater fish.[113]
• Species composition and distribution. Acoustic techniques can be used to locate individuals and concentrations of particular species, including during their vulnerable spawning stage.[114] This in turn allows spawning habitat to be identified, mapped and protected.[115] Researchers have applied a semi-automated process to better understand fisheries areas, such as assessing the micronekton community on the Chatham Rise,[116] using the open-source software ESP3 (see case study: Software to streamline acoustic data analysis).

In order to broaden the application of acoustics in fisheries management some key challenges need to be addressed. An initial hurdle is classifying the acoustic properties of a species before active acoustic technology can be used to monitor or study it. This relies on studies to identify the species and its target strength.[117] For some species or in particular habitats (e.g. deep water) this can be more difficult, but methods are constantly developing to improve in these two areas and validation steps are performed to ensure accurate data. Further research on the non-target effects (e.g. impacts on cetacean communication) should also be investigated before deploying the technology.

Use of cameras, both underwater and on the surface, can gather essential information about ecosystem health and dynamics. Other techniques introduced earlier, including genetic and acoustic technologies, can also be applied to fill gaps in our knowledge and inform EAFM. In addition, eDNA collected from the water can provide deep multi-trophic detail on the organisms that live in any given environment – from microbes to mammals,[118] as discussed below.

Innovative modelling approaches that draw on data from these and other techniques are changing the way that ecosystems are monitored and managed. Used alongside the catch limits, these tools offer the potential to monitor ecosystem health in a predictive and more holistic way, adding an extra dimension to fisheries management.

Timeline of development of cameras for studying marine biodiversity.[119]

Camera imagery is affected by water clarity and visibility issues, which are worsened by sedimentation. Converting imagery to data that can be used is often slow and laborious. Automation may be able to speed up this process, but the technology is, for the most part, still in the pilot stage. Current regulations that limit a third wire to protect seabirds are a barrier to using innovative underwater camera technology as an extra wire is needed to transmit imagery back to the boat, though special permits can be given to trial gear.[48]

This section discusses underwater and surface cameras. For discussion on satellite imagery, see the section ‘Satellite technology allows us to track marine species in detail’.

There are different types of cameras available for different applications.

The eDNA approach overcomes many limitations when studying more complex biological systems, including time-consuming microscopy, extractive sampling, difficulties identifying different life stages and sexes, and cryptic species identification. In a fisheries management context, a one-year New Jersey study comparing eDNA and monthly trawl estimates were well correlated across fish species richness, composition, seasonality and relative abundance.[135]

A key strength of eDNA is that it is a non-invasive sampling method and can provide a high-level overview of genetic biodiversity, including presence/absence data, and detect ecosystem changes over time. eDNA has also been shown to be effective in detecting anthropogenic disturbances[136] and detecting rare marine taxa from seahorses[137] to sharks.[138] The hope is that in the future eDNA sampling methods will enable further quantitative precision (using absolute and relative abundance), noting that ‘catch’, acoustic and video surveys all have intrinsic strengths and weaknesses when it comes to quantitative analysis.

There are several limitation, comparative and proof-of-principle studies when implementing eDNA in aquatic environments, and these have been at the core of the >1,500 papers on this topic, see [118]. In a fisheries context, there’s potential for contamination from fishing gear, lab contamination and the vectoring of DNA by animals. Moreover, eDNA data provides no direct information on age, weight, life-stage or fecundity.[139] However, the potential for eDNA to move beyond ‘just barcodes’ to measure population-level metrics is being actively explored.[140]

The hope is that in the future eDNA sampling methods will enable further quantitative precision (using absolute and relative abundance), noting that ‘catch’, acoustic and video surveys all have intrinsic strengths and weaknesses when it comes to quantitative analysis.

There are a number of whole ecosystem modelling approaches of marine ecosystems and these are being increasingly used as a tool for analysis of ecosystem structure and function.[145, 148, 149] However, while full ecosystem models are desirable they are not always practical to be implemented because of data and knowledge gaps.[147] The cost of implementing full ecosystem models can also be prohibitive due to the significant data needs. By 2040, increased data and knowledge regarding our marine ecosystems will support wider application of the more complex models in the stock assessment and broader fisheries management process. Readily available and up-to-date data will also help the development, testing and deployment of more reliable models with fewer assumptions, and reduce the cost burden of running such models.

A high level overview of the different types of modelling approaches in the toolbox that can be applied, depending on available data, and some examples of these are discussed below. Additional examples are briefly described in appendix 14: Further examples of models.

Need for indicators

Ecosystem indicators are crucial to understand ecosystem dynamics because it is impractical to measure every species to inform management decisions. Ecosystem indicators can be physical, chemical or biological. Physical and chemical indicators are measures of the physical and chemical components of the ecosystem, whereas biological indicators (or bioindicators) refer to organisms, species, or communities whose characteristics show the presence of specific environmental conditions. For example, measuring the most sensitive species can indicate whether the rest of the ecosystem is healthy (discussed in ‘Ecological monitoring’). The opportunities to collect data in new ways (outlined above) could provide valuable indicators for ocean health and ecosystem dynamics.

Use going forward

• Innovative modelling and spatial analysis tools for identifying vulnerable and sensitive habitats allow for assessment of spatial management configurations and trade-offs to maximise biodiversity protection benefits and minimise costs to bottom trawling fisheries.
• It’s essential that all parties have a clear understanding of the strengths and weaknesses of a model at all stages of development, and the level of uncertainty where the model can be used for fisheries management decisions.
• Models need to be fit-for-purpose, deployed in stages for continuous refinement, and able to be effectively communicated so that people understand how the evidence is generated and the level of uncertainty.
• Models that can be modified and applied to new questions will be most beneficial to support a responsive management system.
• Models can support the basis of multispecies harvest control rules.
• Long computer run times usually preclude more complete exploration of the parameter space but as computing capacity increases this might be possible.
• These models should be fully integrated into the decision-making process and always ground-truthed with real-life fisheries experience.

Clear species-specific spatial and seasonal variability in bycatch per unit effort has been identified for seabird bycatch in gillnet fisheries, highlighting high-risk situations occur that could be avoided, while full fishing activity is appropriate at other times.[166] Significant closures to set netting have been regulated in Aotearoa New Zealand because of this.

The timing and location of commercial fisheries operations plays a large role in dictating the impacts of fishing on marine ecosystems. Some of the issues relating to fishing sustainability – catching protected species as bycatch and damaging significant habitats – could be managed by changing where and when fishing occurs. This approach has already been applied in some fisheries where, for example, fishers set their longlines at night to avoid seabird bycatch. The FAO guidelines interpreting UNGA 61/105 on the protection of vulnerable benthic ecosystems include limited spatial extent of bottom fishing as a method to mitigate significant adverse impact.[167]

Some of the issues relating to fishing sustainability – catching protected species as bycatch and damaging significant habitats – could be managed by changing where and when fishing occurs. This approach has already been applied in some fisheries where, for example, fishers set their longlines at night to avoid seabird bycatch.

There is significant opportunity to use innovative tools and techniques to make these changes more specific, dynamic and precise.

First we need to more holistically study the oceans and biota within it. There are knowledge gaps that need to be filled about habitats, lifetime and seasonal movements of marine species, and fisheries interactions with protected species to better understand how to target some species and avoid others.[168] New tools such as drones, autonomous vehicles and satellites are making it possible to gather data from otherwise inaccessible areas. Advances in biochemical techniques can further grow the knowledge base to inform management, and EM can expand data collection efforts and validate observer data.

Next, we can use this information to predict patterns of movement and behaviours. Those predictions can feed into spatial decision support tools that model the risks of coming into contact with protected species. These are already relied on in fisheries management in Aotearoa New Zealand but improving frequency and pace of data collection and analytical processes would allow quicker decision making by fisheries managers. Advances in data science can strengthen the evidence base that feeds into decision making, such as the work by Dragonfly Data Science.[169] The predictions will need to also incorporate projections about how species might respond to changing climates and oceans, as well as evolutionary changes in response to various factors, including fishing.[170]

Ultimately, the goal is for this knowledge and new tools to come together to enable dynamic ocean management, with the goal of near-real-time decision making in the marine space. Being able to dynamically manage the ocean with more precise spatial and temporal management of commercial fishing could support fishing to continue at sustainable levels while minimising impact on non-target species or particular habitats.

In this section, we discuss how innovative tools and new scientific approaches can be used to study, predict and inform in real time where and when fishing should take place, and monitor illegal fishing.

After the 2016 Kaikōura earthquake, scientists deployed drones to survey the rocky reef and intertidal habitats affected by uplift. The drones were equipped with visible and infrared cameras to investigate the recovery of kelp and other macroalgae, and the extent of juvenile pāua loss.

UAVs can also be used to track, count and measure marine mammals (see case study: Māui Drone Project). Unmanned aerial systems have been used to survey Antarctic fur seals[174] and leopard seals[175] in Antarctica, and to count and measure gray and harbour seals[176] in the UK.[177, 178] They have also been deployed to measure and photograph southern right whales/tohorā.[179, 180]

Autonomous vehicles can also be used to track invasive species. Coral-devouring crown-of-thorns starfish[184] are a threat to the Great Barrier Reef. Researchers have designed an unmanned robotic system known as ‘RangerBot’ to automatically identify crown-of-thorns starfish, and administer a lethal injection of bile salts.[185]

• Data from satellite remote sensing technologies was integrated to understand environmental factors, habitat use and movement patterns of sharks and rays to support conservation and management.[194]
• Satellite tags were used to remotely sense and monitor cases of illegal fishing in a shark sanctuary in the Republic of the Marshall Islands.[195] Illegal fishing is discussed further below.
• Information gathered from satellite tracking has been used to identify areas suitable for a fishery closure.[196]
• A study using satellite tags on killer whales[197] in Norway found that the whales were attracted to herring[198] fishing vessels which suggests deterrent approaches may be the most effective fisheries management solution.[199]

Limitations and opportunities

• Satellite tagging is labour intensive and tags are expensive.[168]
• Currently there is limited spatial resolution of satellite remote sensors[194], but this is likely to be able to be improved over time.
• Similarly, there can be temporal limitations depending on how the study or system is designed. Short studies may miss capture events or dynamics and things like cloud cover can interrupt data collection.
• Satellite tagging requires species to be caught once to deploy the tags but doesn’t require recapture, which is an advantage over mark recapture method.
• Developments with batteries and hardware will continue to reduce the size of tags.

Electronic monitoring can be used to collect data on interactions between fishing and protected species

The oceans around Aotearoa New Zealand are biodiverse. With this rich natural heritage, EM trials in Aotearoa New Zealand have often explicitly aimed to assess interactions with threatened, endangered or protected species – a focus not as central in EM trials elsewhere.

Selected EM trials in New Zealand that detected threatened, endangered or protected species interactions.

In Australia, EM for threatened, endangered or protected species has progressed beyond the trial stage and is part of at least two full-scale, operational programmes: seabird captures are monitored in pelagic longline fisheries, and pinniped and cetacean captures are monitored in gillnets.[68]

A 2018 review of EM for monitoring interactions with threatened, endangered or protected species found that EM is effective for detecting a range of events across different fishing methods, including deployment of mitigation devices, bycatch events and unusual behaviour. It is possible to identify species from EM imagery, even with the challenging conditions that can include wet or obscured specimens.[74]

EM can change the behaviour of fishers. For example, fishers in an electronic-monitoring trial increased their reporting of seabird captures (see case study: Using cameras to protect threatened seabirds).[204] Digital monitoring is expected to greatly improve the information on seabird capture events across a broad range of fisheries.[205]

The 2018 review made recommendations for next steps to improve threatened, endangered or protected species monitoring via EM in Aotearoa New Zealand.[74] Most of these centred on the review component of EM: developing training materials and programmes, as well as developing quality assurance and data standards. Key among these was the creation of bespoke ‘fishionaries’ that catalogue and store photos taken by fisheries observers to use in EM training.

There are a range of tools that model data to estimate the probability of bycatch, to inform conservation measures and fisheries management:

• Bycatch prediction tools. A tool that combines species distribution models with oceanographic data to predict bycatch of pilot whales[206] in a longline fishery was strongly and significantly correlated with observed rates of bycatch in space and time, demonstrating that such tools could be accurately predict times and places with a high risk of bycatch.[207] Models to study bycatch of seals identified water turbidity was a major driver of seasonal trends in bycatch which could inform bycatch mitigation efforts.[208]
• Spatial decision support tools weigh up different spatial management scenarios and optimise spatial plans for maintaining ecosystem health and biodiversity. There are frameworks that can be used to assess the risk of encountering protected species during fishing to determine whether fishing can take place in a region. These tools are likely to become more refined over time as further data from the methods described above feed into them. Examples include:
  • The SEFRA is an assessment that has been developed to estimate the risk to protected species posed by fishing activities.[209] The assessment can be used when there is little data available on mortality (for instance, where the species is rare, or fisheries observer coverage is very low). The assessment uses the spatial distribution and abundance of a species and combines it with the distribution and intensity of fisheries activities (or other threats) to estimate their overlap. It has been used to inform fisheries closures to protect Māui and Hector’s dolphins.[210]
  • The Risk Atlas tool enables querying of fisheries risk to protected species and what the risk reduction benefits and costs are of different spatial management configurations.
  • Freely available decision-support tool software such as Marxan[211] and Zonation[212] are also used for conservation planning. These tools help to define a system of protected areas using ecological, social and economic criteria. Both of these tools have been used in Aotearoa New Zealand to support marine management.

Other tools can be used to understand population dynamics.

• Models of spatial population dynamics and species distribution/habitat suitability. Models have been used for spatial distribution of cetaceans in Aotearoa New Zealand waters[213] and habitat suitability for corals and vulnerable marine ecosystems.[214] Integrating tagging and fisheries data into a spatial population dynamics model can improve its predictive skills.[215] These models have the same caveats outlined in the section above on models.
  • Spatial Population Model (SPM) is a modification of the CASAL software (see section above), which models fish and fisheries distribution at a higher level of spatial resolution. SPM simplifies spatial distribution of fish over time and space using ‘preference layers’ to determine the probability that fish occur in each area at each time, and can be, for example, sea surface temperature, depth, or the distance from one cell to another. SPM is valuable because this simplified and unique way of modelling spatial distributions and movements allows it to operate as an estimation model. Therefore, rather than being just a tool for investigating the potential effect of space on fish stocks and fisheries (which most spatially resolved models are), it can estimate model parameters, including those controlling spatial movement, and be directly used in stock assessment. SPM has been used as part of the stock assessment of Antarctic toothfish,[216] and more recently for investigating potential distributional changes in tuna associated with climate change.

Ideally these tools will be adapted to inform management in real time, as discussed below in the section ‘Dynamic ocean management will help protect non-target species in real time’. Their use will grow once we have more data to refine the models.

Spatial distribution of biomass (in thousands of tonnes) for immature, mature, and spawning Antarctic toothfish as estimated by SPM. From [217].

Screengrab of the Starboard interface. Image credit: Starboard.

• Data science, machine learning and AI.
  • Aotearoa New Zealand company Xerra Earth Observation Institute (Xerra) have developed a cost-effective way to monitor fishing activities and detect potential IUU fishing. The platform, known as Starboard™ Maritime Intelligence combines datasets from multiple sources to derive insights about the vessels fishing in an area (see screengrab, above). The visual presence of a vessel (through synthetic aperture radar and optical imagery data) and emissions from a vessel’s marine navigation radar (through radio frequency data) are compared to AIS data to uncover which boats are reporting their location and those that aren’t. To help determine which species vessels are targeting, Starboard incorporates sea surface temperature data. The integrated data can also be used to detect unusual and noteworthy vessel behaviour. A proof-of-concept pilot operation to detect IUU fishing of southern bluefin tuna in the Tasman Sea was successful at detecting a dark fleet of vessels. The cost of monitoring the area using satellites over a two week period was roughly equal to a typical eight-hour, maritime surveillance flight.
  • Researchers used machine learning to verify whether FADs were used in purse seine fishing, which could have useful applications where such use is banned.[224]
  • Non-profit organisation Global Fishing Watch uses AIS data to increase the transparency and sustainability of fishing on the high seas. Algorithms can separate fishing activity from non-fishing behaviours such as transiting from AIS data. In this way, Global Fishing Watch can track fishing activity and intensity across areas of the ocean that may be otherwise inaccessible. This approach revealed fishing activity before and after the establishment of an MPA near Kiribati, where all fishing activity ceased upon closure bar one vessel, which was fined by Kiribati. In 2016, Indonesia partnered with Global Fishing Watch, making data from their vessel monitoring system available publicly. This allows Global Fishing Watch to track Indonesian vessels over a certain size that do not use AIS. Other countries such as Peru, Costa Rica and Chile have subsequently followed suit. The Global Fishing Watch algorithms have also been used to identify when fishing vessels are setting longlines, with the aim of assessing how many vessels adopt best-practice night-setting to mitigate albatross bycatch.
  • Innovative New Zealand company, X-craft have developed an alternative approach to patrolling the high seas: the Proteus, an unmanned sea vessel powered by solar panels and a mini wind turbine. The Proteus is equipped with a range of sensors, an on-board drone, and an AI system that allows the vessel to make decisions – about whether to follow or track a vessel of interest, for example.

Starboard™ Maritime Intelligence combines datasets from multiple sources to derive insights about the vessels fishing in an area. The visual presence of a vessel (through synthetic aperture radar and optical imagery data) and emissions from a vessel’s marine navigation radar (through radio frequency data) are compared to AIS data to uncover which boats are reporting their location and those that aren’t.

Technological developments have opened up new opportunities to monitor our vast oceans. We can draw on these developments to build on Aotearoa New Zealand’s existing monitoring efforts. In the boxes below, we highlight the data types of importance for monitoring the marine environment, any current initiatives that contribute to monitoring in Aotearoa New Zealand and opportunities to improve monitoring. Data collected from this range of tools could be coordinated and collated into an ocean observing system, as discussed in the following section. Monitoring of all ocean measures could be improved through a nationwide series of inshore surveys that collect a range of environmental and fisheries data and there are also opportunities to utilise existing efforts (e.g. tagged animals) to gather further information, which are not explored further here.

Fisheries management and ecosystem services are stated to have been significant drivers for the requirements of GOOS over the last decade.[237] However, commentary detailing the trend towards EBM and the need for GOOS to service this need goes back 20 years in the literature.[238, 239] The OceanObs’19 conference canvassed the aspirations of what an ocean observing system can provide – including addressing the needs of fisheries and EBM practitioners.[240] At the global level, many of the benefits of an ocean observing system cannot be directly applied to fisheries management because data isn’t localised enough, though there are wider benefits – for example, improving early warnings of severe weather events like floods, droughts and storms.[236]

There are many other forms of OOS – regional, coastal, and national systems – which collect detail at a finer scale. Some of this data feeds into GOOS while other data is collected for other purposes. Australia has the Integrated Marine Observing System and the US has the Integrated Ocean Observing System.

Aotearoa New Zealand does not currently have an OOS but would benefit from one as it would assist prediction, mitigation and management of the effects of multiple stressors, including climate change, sea-level rise, ocean acidification and impacts from changing terrestrial fluxes.[241] Current ocean monitoring efforts in Aotearoa New Zealand could be built on to establish this.[241] The local research community has work underway to plan for this. In 2018, there was a planning workshop for a New Zealand OOS involving research institutes, government, and businesses. Outcomes from the workshop are presented in a paper by O’Callaghan et al.[241] The paper describes how an integrated OOS for Aotearoa New Zealand can be developed in Aotearoa New Zealand that integrates mātauranga Māori with western science. The NZ-OOS would be “bottom-up community-driven” and success requires inclusiveness and cross-institutional engagement. The biggest hurdle would likely be sufficient and sustained prioritisation of the system, with government data streams being the backbone of the system. Details of the plan and overarching structure are included in the figure below and appendix 16.

An OOS adds a more sophisticated dimension (in space and time) to enable a 3D view which is dynamic instead of spatial tools such as MPAs.

An OOS adds a more sophisticated dimension (in space and time) to enable a 3D view which is dynamic instead of spatial tools such as MPAs.

Framework for the NZ-OOS building on existing efforts in Aotearoa New Zealand, from [241].

Potential to increase the value of jack mackerel through the refined use of by-products to be of higher value. Image credit: Plant & Food Research.

Iceland’s roughly 3000% increase in by-product use has generated around US$500 million per year and around 700 direct jobs – many of which are in rural coastal towns. For a population of around 360,000 people, there are more than sixty companies in Iceland working with by-products and processing of seafood.[242] Further growth of the industry will mean more jobs and even greater economic gains.

This success is in part due to the Iceland Ocean Cluster organisation and their 100% Fish Project, whose mission is “to inspire the seafood industry and seafood communities to utilise more of each fish, increase the value of each fish landed, support new business opportunities, increase employment and decrease waste.”

Strengths of this approach that could be drawn on in Aotearoa New Zealand’s efforts to use more of the fish include:

• Taking a bottom-up approach to accelerate innovation. The private-sector initiative operates an accelerator to support start-ups in the seafood industry. There are over 70 companies in the programme and a large group of entrepreneurs starting companies to capitalise on fish waste. The Iceland Ocean Cluster invests in these companies and also brings other investors in.
• Welcoming ideas from within and outside the fisheries industry. Though the network is mostly focused on start-ups, the cluster also supports innovation from incumbent companies in the fishing industry. This is important because although many of the innovative products have come from newcomers to the industry, it is difficult to realise some of the ideas without existing fisheries expertise and industry connections.[243]
• Incubating good ideas and offering a physical meeting space. Bringing people together to support their initiatives provides networking and learning opportunities, knowledge spill over and economies of scale to reduce the risk of failure.[243] The benefit of sharing space is shown by 70% of the companies who are part of the cluster having collaborated together. It is especially helpful to connect the entrepreneurs new to the industry with the experienced fisheries companies to share insights and overcome hurdles.
• Focusing on local value add in fishing communities. To restore opportunities to smaller fishing towns, some of the new innovations are in technology development for fish utilisation that would allow processors to operate on smaller scales, e.g. through modular systems, which could help to revive more remote fishing communities.
• Expanding networks beyond borders. The team developed an Ocean Cluster Network, which includes several clusters in the US, Canada and Norway, to strengthen innovation and utilisation of fish by-products so that products can be developed through collaboration within the network. There is plenty of opportunity to share lessons and collaborate, all while preserving the unique value propositions and commercially sensitive information inherent to each company, and several such projects are already underway.

Although some companies in Aotearoa New Zealand are taking steps in this direction, the wider seafood industry would benefit from transforming thinking around by-products, moving away from seeing by-products as waste to seeing them as a rich resource from which we can draw value. We could learn from programmes such as the Bioresource Processing Alliance. This could make the seafood industry become even more productive to the broader economy and aligns with the Government’s vision in its economic plan of moving from volume to value.[244] Using the whole fish requires product innovation in the versatile use of the fish by-products and process innovation in harvesting raw materials. Technological developments related to by-products from wild fisheries are relevant to aquaculture and vice versa.

Recognising that using more of our catch is not only positive for fisheries sustainability but also has significant economic potential, Aotearoa New Zealand should take a focused approach to accelerating opportunities in this sector. A local, context-specific approach will be necessary, but we can look to Iceland for inspiration and experience. The Iceland Ocean Cluster is part of a broader suite of related initiatives that contribute to a thriving by-products industry in the country. These include an annual international one-day conference, Fish Waste For Profit, aimed at companies involved in the commercial fishing, aquaculture and processing sector, and Ocean Excellence – a one-stop-shop consulting firm to help fishing companies develop solutions to use 100% of the fish.

Demonstration of the relationship between value add and quantity available for different categories of fish by-products. Click to enlarge.

Pharmaceutical, nutraceutical and cosmetic applications of fish by-products

Pharmaceutical uses of fish by-products include biotechnology, nutraceuticals and cosmetics. Several projects funded by Seafood Innovations Ltd relate to development of such products. There has also been considerable investment by the Ministry of Business, Innovation and Employment and its predecessors in projects through the Bioresource Processing Alliance and in projects funded by industry. These types of products require a high level of sorting and processing to pharmaceutical quality standards, but this leads to a high level of value-add.[245]

There are numerous medical and surgical applications from fish by-products in development. For example, squalene from shark liver is used as an adjuvant in vaccine delivery. One example in development by is the use of a crystallin protein found in the fish eye lens in ophthalmic surgeries (e.g. as a glue rather than sutures) or treatments (e.g. to treat corneal disease). Researchers at Otago University have used the sugar chitosan from squid pens to develop a gel to prevent surgical adhesions.

The nutraceutical market is also ripe for applications from fish by-products and Aotearoa New Zealand companies are already selling and developing nutraceutical and cosmetic products. It is important that these are scientifically validated.

Examples of applications for fish by-products

A number of companies in Aotearoa New Zealand are already manufacturing high value products from fish. Products include squalene from shark livers; fish oil for health supplements; marine collagen from fish skin for cosmetics and heath supplements; and shark cartilage as a health food supplement. It is important that such product development is validated by scientific studies on efficacy and focused on secondary product streams in existing harvests.

A significant challenge for Aotearoa New Zealand’s industry is having over 100 commercial species with different potentially valuable components that cannot be processed in the same manner as each other due to variable tissue mixtures depending on the market for other uses (e.g. skins for collagen, or heads for rock lobster bait or food; fish that may not be filleted so there is no economic means to separate tissues; potential species and quality variability in response to water temperature changes). In addition, our current marine products processing infrastructure designed for manufacture of single products has no flexibility and often destroys one component when recovering another. Making our challenges into unique opportunities requires knowing exactly what is in any raw material in real time, then using this information to direct processing, choosing from a suite of integrated technologies to maximise raw material use and product value.

A culture change is also needed to shift thinking from volume to value, which has been a long-term strategy across primary industries and a long-stated government goal. Further analysis of this is outside the scope of this report but analysis to understand why we are not moving faster up the value chain could be beneficial in the context of commercial fisheries. Policy could encourage innovation and reduce these barriers so Aotearoa New Zealand’s industry as a whole can lead in this space, while ensuring that higher value by-products do not impact food security.

One industry representative articulated the opportunities and barriers clearly when they said, “We are sitting on a goldmine, but we don’t know how to tackle it.”

Key ways to streamline commercialisation of fish by-products include:

• Improving knowledge of demand and opportunities for supply. The industry’s understanding of the consumer could be improved via better access to market data to inform investment, entrepreneurship and innovation. Connecting this market knowledge with what we know about the species, location and volume of by-products generated by our fisheries companies could help to identify opportunities to extract high-value products for which there is consumer demand. At the same time, developing species-specific understanding of potential proteins of interest via chemical compositional analysis is important to develop novel products which can also be high value.
• Addressing issues in processing systems and supply chains. Streamlining processes for higher value products could help to overcome some of the cumbersome aspects of by-product processing or remove logistical inefficiencies that make it unprofitable. This may mean improving aspects related to storage on vessels, split processing operations, or gaps in supply and processing chains, though these are generally thought to be of a high standard in our industry. Vertical integration of fishing and processing in Iceland has helped streamlined processes there and the WaSeaBi project in the EU aims to address these issues.
• Planning and support to establish infrastructure. Most commercial fisheries companies are capital constrained and the cost of establishing infrastructure and manufacturing processes can hinder product development. Many by-product project proposals have stalled due to high capital costs. Collaboration between companies or processors to establish economies of scale would be one step to help address this problem. Alternatively, different companies each specialising in key products or different parts of the supply chain and offering contract manufacturing to the others could address this challenge.
• Improving access to technical expertise and applied science. Many of the higher value products such as those with pharmaceutical applications require technical expertise. This is an area where great potential may remain untapped unless people within the industry become better connected to researchers with technical expertise to fulfil this development (see case study: High quality marine collagen from Aotearoa New Zealand). Some applications may call for the business to lead the research themselves, which requires more flexibility in contracts and research funding models as well as a research industry that is able to provide applied research as a service.
• Making it easier to do clinical trials: Clinical regulatory requirements can be a challenge to meet, both financially and logistically, but developing a clinically proven ingredient means a higher and more guaranteed income from the product. Funded or subsidised clinical research would remove the barrier that many companies face in making a high-value medical product from a fish by-product.
• Supporting networks and connection. As discussed in the section ‘How we fish’, facilitating connections between fisheries stakeholders is important in innovating for a more sustainable future. The Iceland Ocean Cluster and Canada’s Ocean Assets stakeholder database are examples of ways to do this with a specific focus on using the whole fish. The Bioresource Processing Alliance could be drawn on to support this.

Some challenges for by-product processing that are unique to wild fisheries (as opposed to aquaculture) include having to manage supply issues where there are short fishing seasons with large landings, rather than a constant supply year-round.

The Food and Beverage Manufacturing Industry Transformation Plan, currently in early development, may help to address some of these issues.

Government requirements for traceability, private sector sustainability commitments, and a greater interest in supply chain transparency because of legal and social risks are some of the factors driving seafood traceability systems to expand beyond food safety and inventory management.[248] Traceability systems used for compliance purposes could be built on for traceability that supports sustainable fisheries management.[249] These may need to be adapted when the objective of the system is to share information with the consumer in order to add a market premium, to ensure the information provided meets consumers’ needs.[250] How these systems impact the fishery depend on their legal or voluntary framework. For example, restrictions from an individual country or region around what fish can be imported only control what enters the end market, not what comes out of the fishery (i.e. the fish could be sold elsewhere). In contrast, a multilateral catch documentation scheme, backed by international law via RFMO, controls what is fished and monitors it throughout the supply chain.

In Aotearoa New Zealand, it is already a legislated requirement for seafood producers to track information[251] and import markets have their own requirements that our seafood industry needs to meet. For example, fish and fishery products caught and processed outside the EU have to comply with the EU’s eCERT framework in order for their import to be authorised.

The first global standards were established by the Global Dialogue on Seafood Traceability in 2017 and these could be implemented by the Aotearoa New Zealand seafood industry. There are also calls for a global, secure, interoperable support system for seafood traceability, but that faces many challenges such as technological and financial constraints, language barriers and regulatory differences.[252] In the meantime, voluntary approaches, country specific approaches and government-to-government agreements are likely to be where progress is made.