Via Yale News, an article on how data from space unveil a global view of animals on the move:
A global team of researchers, including Yale scientists, is using advanced tagging to track the movement of individual animals across the world, an ambitious research project that is opening a new frontier in efforts to monitor biodiversity change and pinpoint areas for conservation intervention.
For the project, individual animals from 15 species worldwide were tagged with lightweight sensors that transmit data to the International Space Station, which then transmits the information to a single system for integration and interpretation. Since late 2021, the new technology has captured the movements of hundreds of small animals, such as blackbirds, artic terns, and even bats. Eventually they hope to track many species of reptiles, mammals, and insects.
The first findings are described March 8 in the journal Trends in Ecology and Evolution.
“For the first time, we are able to have a finger on the pulse of life worldwide,” said Walter Jetz, lead author of the study, co-director of the Max Planck-Yale Center for Biodiversity Movement and Global Change (MPYC), and a professor of ecology and evolutionary biology and of the environment at Yale. “This now operational technology blazes the trail for a biological earth observation with animal sensors.”
Given the low cost and small tag size researchers hope to scale the effort to thousands of species and deliver data about animal lives globally in real time, said Jetz.
The project is called ICARUS or International Cooperation for Animal Research Using Space, a collaboration of international scientists led by the Max Planck Society.
The effort relies upon trained volunteers to place the miniature sensors, which weigh less than one-tenth of an ounce, on individual animals. The sensors not only record GPS data but can also supply other information on conditions experienced by animals, such as temperature.
Data from the sensors are collected from the International Space Station and then transmitted to computers on the ground. While most of the species tagged so far are birds, future tagging could include many species of land animals, researchers say.
“Rather than globe-orbiting sensors capturing images of the planet’s surface for subsequent interpretation, animals, through countless individual movement decisions, seek out their preferred conditions, sensing the quality and health of ecosystems in real time,” said Martin Wikelski, co-director of MPYC, research director at the Max Planck Institute for Animal Behavior in Germany, and originator of ICARUS.
For instance, scientists will not only be able to identify areas essential for survival of the animals, but identify areas where biodiversity may be threatened by human encroachment or poaching when anticipated migration routes are blocked.
With the technology now in place, the Max Planck-Yale Center is currently raising funds to purchase more sensors, which cost about $300 each, support researchers worldwide to use and scale up the system, train volunteers, and integrate information sharing platforms. The group is also negotiating with NASA and the German space agencies to place new data collection devices on satellites.
“The dream is an ongoing cohort of say 100,000 animal sentinels that help us humans measure, understand, and mitigate our changes to this planet,” Jetz said.
Movements across space and environments, home ranges and migration corridors from these new data can be explored at the website https://animallives.org, an initiative of the international Max Planck-Yale Center for Biodiversity Movement and Global Change (MPYC).
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Via Sentinel, an interesting initiative to reimagine and democratize environmental data:
The Sentinel is Conservation X Labs’ new artificial intelligence device that addresses emerging extinction threats. The tool retrofits existing devices, such as trail cameras and acoustic recorders, to enhance how efficiently conservationists can act on important events.
The device instantly runs machine learning models on data as it is captured and sends notifications to users in real-time. This allows users to know if something critical, like the presence of a poacher or endangered species, is detected so they can take immediate action.
Henrik Cox, Product Management Engineer, deploys a Sentinel to monitor wildlife in Virginia.
“The Sentinel democratizes creating, running, and deploying machine learning models by providing leverage to conservationists everywhere” said Alex Dehgan, Co-Founder and CEO of Conservation X Labs. “The Sentinel will fundamentally change how we monitor and protect the environment – from catching poachers before they can get away, monitoring endangered species in real time, and detecting new diseases before it’s too late.”
Leveraging advances in artificial intelligence through tools like the Sentinel can profoundly increase the scale and efficiency of conservation efforts to better understand and respond to environmental challenges and will allow us to better protect animals around the world.
The Sentinel has been selected for use in a variety of unique projects around the world capturing wide-ranging information, including identifying rare jaguars in Costa Rica, informing wildlife crime officers of suspicious behavior in South Africa, and monitoring gorilla behavior in the Congo. It was a grand prize winner at the 2021 ASME ISHOW, an international accelerator for hardware-led social innovations.
Via Africa News, a look at how deforestation can be mitigated using remote sensing technology:
Israeli-based Albo Climate and Mauritius-based Tembo Power are partnering on a mutually exclusive basis to produce a high-resolution carbon monitoring model of vulnerable tropical forests. The maps will help to evaluate ecosystem health carefully. Furthermore, they will monitor the areas for deforestation and generate verified high-quality carbon offsets.
The collaboration will begin by developing carbon credits from two national parks in Cameroon. Both parks are home to a diverse range of unique plants and endangered animals. This includes pangolins, hippos, leopards, black colobus, mandrills, lowland gorillas and chimpanzees. It also has numerous birds, reptiles and fish species. However, the parks face increasing threats due to logging, poaching, mining, agricultural activities and coastal infrastructure development. Thus, given current deforestation rates, the two forests may lose 6,000 hectares per year.
Jacques Amselem, CEO of Albo Climate, commented: “We are thrilled to be partnering with Tembo, a key leader in developing clean energy and conservation projects across sub-Saharan Africa. Combined with our unique deep-learning, satellite-based approach to carbon credit quantification and verification, we see the potential for true impact at scale across the continent.”
The expected income from the carbon offset projects will involve maintaining the park’s boundaries. It will also include expanded support of ranger services and surveillance systems by the forest management of Cameroon. In addition, the generated income will go to supporting local communities.
Furthermore, Albo Climate and Tembo will further collaborate on additional conservation projects across East and Southern Africa. This strategic partnership will foster a robust and widely applicable remote-sensing carbon model usable in Africa’s array of tropical forests. Tembo Power’s founder, Raphael Khalifa, explained: “Tembo’s goal is to position our subsidiary Tembo Climate in full compliance with the Taskforce on Scaling Voluntary Carbon Markets led by Mark Carney and Bill Winters, advocating for the extensive use of technology to address global warming”. He added that they were glad to bring Albo’s cutting edge approach to the African continent.
Via TechRepublic, a look at the use of technology in the battle against biodiversity loss and climate change:
The potential to install a regime of benign surveillance over the natural world is immense, ranging from earth-observation satellites to smartphones listening out for chain saws in the forest.
The interrelated issues of biodiversity loss and climate change are rising fast up the popular and political agenda. One reason is that the world increasingly appears to be — on fire.
In August 2019, wildfires — many started deliberately — consumed large areas of Amazonian rainforest, reducing the Earth’s ‘lung capacity’, rendering indigenous people and wildlife homeless, and releasing copious amounts of greenhouse gases. In September, on the other side of the world, forests in Indonesian Borneo and Sumatra burned. Again, human agency is widely suspected, as palm oil planters clear the jungle to make way for their crop. Massive bushfires are currently raging in eastern Australia, which experienced its hottest recorded summer in 2018/19.
Wildfires are also occurring in the far North with increasing frequency and intensity: in June 2019 (the hottest on record in the region), fires in the Arctic emitted 50 megatons of carbon dioxide — equivalent to the total annual CO2 output from Sweden. Evidence that Arctic permafrost is melting faster than previously expected only exacerbates the carbon release problem.
Why is the world apparently fiddling while Rome burns?
The tendency for national governments to have a short-term focus, addressing immediate problems and deferring longer-term issues for successive administrations or generations, is not helpful when confronting planet-scale problems like climate change and biodiversity loss. That’s because incremental ‘business-as-usual’ activities can run into irreversible tipping points that flip systems unexpectedly into new and undesirable states (the Amazon being an increasingly urgent example).
Although supranational bodies such as the UN and the EU try to take a wider view of such issues, recent years have seen a rise in nationalism around the world, leading to suspicion and even rejection of such bodies, often accompanied by the denigration of scientific evidence and expertise.
The Internet of Things, or IoT, is an area of science and technology that can help in the fight against biodiversity loss and climate change. In this article we’ll outline the current state of the IoT and examine some examples of its use in vulnerable ecosystems.
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Via The Wall Street Journal, an article on how conservationists hope newly launched open-source tools will lend more insight into endangered species and the effects of climate change:
Cameroon’s Dja Faunal Reserve is one of the largest undisturbed areas in Africa. Elephants trample through the trees, monkeys call to one another, and insects hum, all with barely any human interference. The lack of roads and dense forest have protected the biodiversity there, but the same remoteness has left the impact of climate change on the area little understood. Instead of trying to set up camp for a few weeks at a time, researchers are turning to tools they hope will gather data delicately and indefinitely.
“So often the science stops when you leave these places,” says Shah Selbe, a former rocket scientist and the co-founder of FieldKit, a Los Angeles nonprofit building open-source tools to gather conservation data. “If we can create low-cost tools that make monitoring easier, we can start to get more data.”
Big Data has transformed industries from finance to drug discovery. Conservationists, however, haven’t had the same access to deep data sets because of the difficulty and cost involved in gathering data in the wilderness.
To help, startups are developing open-source technology built to monitor the environment, from equipment that records the sounds of the forest to devices that collect data on weather conditions. The hope is that open-source tech will make it cheap enough to gather data. In turn, the data could lend greater insights into where to focus efforts to save endangered species and tackle the effects of climate change.
The open-source movement advocates sharing design information so that anyone can inspect and improve upon the tools that are built. Often, the groups behind the tech are nonprofits, typically resulting in hardware and software that are cheaper than commercial counterparts. Researchers can adapt the tools without worrying about breaking a user agreement or warranty. With more adaptable tools, projects could range from learning about a single species to ecosystems as large as the polar regions.
FieldKit makes a water-resistant device about the size of a coffee-table book that allows users to gather data on water temperature, weather conditions, pH levels and more. Launched this month, the $150 device includes a computer chip that can accommodate a variety of environmental sensors and add-ons, which run $50 to $205 each. FieldKit also offers two prebuilt models, one for water quality and another for weather conditions. A prebuilt model for air-quality is coming soon. Prices include a small margin to support Conservify, FieldKit’s parent nonprofit, which currently relies on grants to fund its operations.
The units were built to withstand environments from rainforests to freezing tundra, and to collect data for weeks or even months at a time, says Mr. Selbe. He expects that many users will be full-time researchers affiliated with nonprofits or universities, but he says it was important to sell prebuilt kits to expand the potential audience to students and individuals without technical backgrounds.
“You can take the FieldKit out of the box, download the app, and be monitoring in five minutes,” he says.
FieldKit’s website hosts the data collected on the devices. Mr. Selbe says that he hopes that most researchers will put their data on the platform, enabling even researchers and enthusiasts without FieldKits to use it. There is an option to disguise location information to protect endangered species or sensitive locations, Mr. Selbe says.
Much of the conservation technology builds on open-source tools made for more general uses, such as Raspberry Pi’s simple, single-board computer chips and Tensorflow, Google’s machine-learning platform. These advances have been essential for the cash-strapped environmental research community, says Alasdair Davies, co-founder of the Arribada Initiative, a nonprofit that builds open-source technology with partners who require technical expertise.
“We are riding the wave going on in the commercial space and repurposing it for conservation,” Mr. Davies says.
More devices in the field mean more data, but analyzing all of it can prove impossible for solo researchers and small teams. Citizen scientists have volunteered to label, transcribe or otherwise organize data on scores of different projects, including labeling wildlife caught on camera in a New York forest and transcribing old weather logs from the U.S. Navy. Some research even requires volunteers, such as Arribada’s Carnivore Bytes project, which aims to learn more about wild dogs in part by gathering data on people’s pets.
Carnivore Bytes in January sent recording devices that clip onto collars to hundreds of dog owners. With an app, volunteers note when their dogs are doing things like panting, playing or eating. Eventually, the data is meant to help train an algorithm to go through wild-dog noises for insights into how climate change is impacting their lives. For example, wild dogs making fewer eating noises could suggest that prey is scarce and hunger is becoming an issue.
Open Acoustic Devices, based in the U.K., sells acoustic recording devices called AudioMoths for $60, compared with hundreds or thousands of dollars for a commercial version. Since launching in 2018, AudioMoth has sold about 20,000 units, according to co-founder Andy Hill. The sales, along with occasional work building custom software to go along with AudioMoth projects, allow Mr. Hill and another founder to work on the technology full-time, he says.
AudioMoths have been used to record thousands of hours of sound. In one project, researchers are recording marine mammal vocalizations in an effort to decode the different sounds that dolphins, manatees and whales use to communicate. Mr. Hill estimates that projects have, on average, used 10 to 100 AudioMoths, which has greatly improved the quality and quantity of data.
“Without open-source technology, you are going to be limited to one-species studies with one device,” he says. “That hasn’t really gotten us anywhere when it comes to conservation.”
Open-source technology is also allowing more people to engage in environmental research, says Lydia Gibson, an ecologist and National Geographic explorer who uses it in her fieldwork.
Still, conservation isn’t just about technology, she says. It is also about the people who use the tools and the local communities that participate, actively or not. Conservation has gotten better about including more voices and respecting local expertise, and those strides can’t be lost as shiny new tools are added to research tool kits, Ms. Gibson says.
“A focus on technology needs to include a holistic view of how it’s used,” she says. “Technology on its own is not the solution.”
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Via McKinsey, a look at how advanced analytics may help struggling fisheries thrive while simultaneously protecting endangered ocean resources:
At restaurants and dinner tables around the world, seafood is often the entrée of choice. Fish, crustacean, and mollusk consumption account for about 17 percent of the world’s total animal protein intake, with much of this coming from the ocean. Fish and shellfish are especially important in low-income areas where total protein intake is low and diets are less diversified.
Fishing companies—businesses that catch fish or other seafood in the wild—will play a major role in sustaining food security and supporting fishing communities. But in their quest to capture enough fish to satisfy soaring demand, they are exerting unprecedented pressure on marine and freshwater ecosystems. It now takes five times the effort (in kilowatt-hours) to catch the same amount of fish as it did in 1950, because the targeted species are now in scarce supply.1 This shortage not only jeopardizes commercial prospects for fishing companies but also greatly threatens the ability of endangered ocean species to reproduce and maintain their numbers.
Balancing fishery interests with environmental concerns is not easy, but advanced analytics (AA)—the use of sophisticated methods to collect, process, and interpret big data—might represent an untapped solution to this problem. While fishing companies, regulators, and environmentalists now apply these tools, their use is typically limited to small-scale pilots. But we may have reached the point where advanced analytics will take off within the fishing sector. In addition to the development of new technologies that support analytics in this field, both policy makers and fishing-company leaders have an increased sense of urgency because of dwindling fish stocks. Further, people entering the fishing industry or participating in regulatory development are more tech savvy than their predecessors, giving them a greater understanding of advanced analytics and other digital tools. Even fishermen from emerging markets can access information on these technologies—and their benefits—through a simple smartphone search.
The growth of advanced analytics could promote the development of precision fishing—the use of advanced tools and technologies to optimize fishing operations and management. If large-scale fishing companies around the world move to this model, they could decrease their annual operating costs by about $11 billion, and customers would benefit from lower prices for fish and seafood. Precision-fishing techniques can also contribute to improved management of ocean resources, which could increase industry profits by as much as $53 billion by 2050 while simultaneously raising the total fish biomass to at least twice the current level.2
This article attempts to paint a picture of the current situation in the fishing industry, focusing on the challenges that are making it more urgent to adapt advanced analytics and associated tools. It also discusses several of the most popular use cases that have emerged for advanced analytics, as well as others that show great potential. Finally, the article provides a practical guide to next steps for all industry stakeholders.
The appetite for tuna, salmon, shrimp, and other ocean creatures is nothing new. Demand has increased an average of 3.2 percent annually between 1961 and 2016—more than twice the 1.6 percent rate of population growth over the same period and higher than the 2.8 percent rise in consumption of terrestrial mammals.3 Overall, the world’s fish consumption is predicted to increase by 20 percent from 2016 to 2030, driven by global population growth, the expansion of the middle class, and greater urbanization (giving more people more access to seafood, as well as the electricity and refrigeration needed to store it). Consumers also increasingly prefer healthy food choices, and many view fish as a good alternative to red meat.
As boats across the world search for a good haul, wild-fish capture has been slowly declining. Since the mid-1990s, the amount of wild fish processed has fallen by about 0.6 percent on an annual basis, while the amount coming from aquaculture rose by 5.7 percent (Exhibit 1). (Aquaculture production comprises entities that breed, rear, and harvest all types of fish as well as other organisms that live in water.) The value of fish coming from aquaculture now tops $250 billion annually, compared with about $170 billion for wild catches.
Exhibit 1
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To cope with the decreased catch in their traditional fishing grounds, commercial fishing companies have considerably expanded their footprint on the oceans. In addition to targeting new species, they have increased their fishing efforts in tropical zones and extended their operations from coastal regions to the high seas, raising the total area fished from 60 percent to 90 percent of the world’s oceans.4
Overall, the world’s fish consumption is predicted to increase by 20 percent from 2016 to 2030, driven by global population growth, the expansion of the middle class, and greater urbanization.
Thanks to technological improvements, fishing companies have also penetrated further depths to target deepwater animals such as grenadiers and blue lings. Fishing these species is rarely sustainable because many have slow reproduction rates, which limits spawning and population growth. In the past, targeting such fish has often resulted in ecological disasters. In the 1980s, for instance, the deep-sea orange roughy almost suffered extinction through overfishing until researchers discovered that it was slow growing and exceptionally late to mature.
As fishing companies expand their reach, they are putting extreme pressure on the ocean environment. About half the world’s fish stocks are now classified as collapsed, rebuilding, or overexploited, and wild-catch rates are falling in most regions (Exhibit 2). This phenomenon is particularly apparent with large fish at the top of the food chain, including sharks, tuna, and billfish.5 The loss of these apex predators has cascading effects that disrupt the equilibrium of ocean ecosystems.6 Take the decline of some shark populations, which has been known to trigger sudden and undesirable population changes in species living in the same habitat. The number of shellfish or herbivores might collapse, for instance, or a large algae bloom could develop.
Exhibit 2
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Other perils also loom. By 2025, oceans could contain 250 million metric tons of plastic—one per every three tons of fish—unless companies and other stakeholders institute some mitigation measures.7 The accumulation of plastic debris may reduce the fish-survival rate, lowering stocks. Climate change, and its accompanying acidification, warming, and deoxygenation processes, is already affecting the oceans and will have profound implications for marine ecosystems, including reduced biodiversity and shifts in habitat. According to some scenarios, these shifts could decrease fishing revenues by 35 percent by 2050.8
Recognizing the growing threat to fish stocks, some countries and regions have acted to improve resource management, with mixed results.9 For instance, the United States has increased the proportion of stocks fished at biologically sustainable levels from 53 percent to 74 percent from 2005 through 2016, an increase that may be partly attributed to the Magnuson-Stevens Fishery Conservation and Management Act.10 Similarly, around 69 percent of stocks managed by the Australian Fisheries Management Authority were sustainably fished in 2015. But these regional gains are negated by overfishing in other markets, illegal fishing, and excessive waste.
Since regulations alone cannot eliminate overfishing, fisheries need other solutions to stay on a sustainable trajectory while minimizing their environmental impact. For most issues, including catch reporting, trade-information sharing, subsidies, tariff policies, and regulation enforcement, greater national and international collaboration will help. But fisheries and the public could also benefit from the increased use of advanced analytics (Exhibit 3). These algorithms have become popular across industries over the past few years as technological improvements have increased data availability, facilitated the deployment of information, and expanded data-ingestion capabilities.
Exhibit 3
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Many industry stakeholders have already incorporated advanced analytics into all components of the value chain. Here’s a look at some of the most important recent developments relevant to fisheries.
Data acquisition through sensing platforms
Sensors for collecting data have become more common, compact, and less expensive over the past few years. At the same time, the variety of platforms on which these devices can be deployed has considerably expanded, allowing them to capture data more rapidly and over greater distances. Sensing platforms that are particularly important within the fishing industry include the following:
Satellite. Optical and radar sensors on satellites can offer a holistic view of the environment at unprecedented spatial and temporal resolution, making them particularly valuable for monitoring purposes. Optical sensors measure the light reflected by the earth’s surface across a wide range of the electromagnetic spectrum. Important oceanic parameters can be derived from such data, including sea temperature and turbidity. Radar sensors emit microwave radiation and measure the portion that is scattered back to the instrument. They can provide data about ocean topography, winds, sea ice, and the movement of vessels. Unlike optical sensors, radar systems can collect information even during poor weather and lighting conditions, including times when the sky is dark or cloudy.
Drones. Equipped with cameras or other sensing devices, drones are increasingly used to explore the ocean. Some are even capable of navigating underwater. Compared with oceanographic vessels, drones are cheaper and more flexible. When sent in groups, they can also provide a more exhaustive sampling of the environment.11 Although drones cover a smaller area than satellites, they can provide more detailed images, allowing them to detect smaller objects or phenomena.
Onboard or underwater devices. Data related to fishing operations and catch are typically recorded by fishermen or observers. Common parameters include those related to vessel location, gear types, and catch, including species, volume, biophysical characteristics, and discards. Onboard sensors can automate and facilitate this laborious process while simultaneously generating more exhaustive and reliable data. The data are then integrated into platforms known as electronic monitoring systems (EMSs). Several fishing-management authorities also require large fishing vessels to be equipped with vessel-monitoring systems (VMS), a technology that the European Union established in the early 2000s to support the monitoring, control, and surveillance of fishing vessels in its waters. VMS can collect information on a vessel’s position, speed, and heading. Vessel operators can also send valuable information to authorities through their VMS, such as estimated catch and the start and end times for their fishing operations. Another onboard utility, the automatic identification system (AIS), was designed to complement radar systems and decrease the likelihood of marine collisions. Like VMS, it can be used to track the activity of fishing vessels. Other sensors, such as cameras and fuel-monitoring systems, can also be placed on board or next to underwater nets for real-time tracking.
Public organizations such as the National Oceanic and Atmospheric Administration and the Copernicus Marine Environment Monitoring Service have increased the effective usage of data obtained from satellite sensors by freely publishing them. Many start-ups and other companies also offer various products related to sensing platforms, including output from satellite sensors and data-collection systems designed for commercial fisheries.
Improved data-transmission technologies
The growth of the Internet of Things (IoT), land- and satellite-based mobile networks, and smartphones makes it much easier for fisheries to transmit data from vessels for analysis. For instance, vessels can use IoT to monitor and transmit data on fuel consumption in real time. The resulting data are then sent ashore through wireless mobile networks, including 3G and 4G, when close to shore. At further distances, vessels rely on satellite networks for transmission.
The growth of the Internet of Things (IoT), land- and satellite-based mobile networks, and smartphones makes it much easier for fisheries to transmit data from vessels for analysis.
More insightful data analysis
Computational power has increased substantially, making it easier to process and analyze information using sophisticated algorithms. Across industries, some of the most important advances relate to the rise of artificial intelligence and machine learning, which can identify hidden relationships in large amounts of data. In particular, image-recognition and object-detection tools, powered by deep learning, have made a significant leap forward during the past decade. For instance, onboard cameras, assisted by image-recognition software, can provide fishermen with important information on the content of their catch in real time, including species, volume, and fish size.
Fishing-industry stakeholders are already transforming their operational and business processes by incorporating AA into all parts of the value chain, including fishery management, detection and capture, processing, reporting, and surveillance and control (Exhibit 4). They typically use multiple AA tools and sensors in combination, and a few even apply them from end to end within the value chain (see sidebar, “How are fisheries exploring new technology? An interview with Matts Johansen, CEO of Aker BioMarine Antarctic”). We have found that in some of the most important use cases involving AA and fishing, the following actions have been taken.
Exhibit 4
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Monitoring illegal, unreported, and unregulated fishing
Authorities leverage AA to combat illegal, unregulated, and unreported fishing using geolocation data from AIS and VMS. AA can predict whether fishing vessels are actively engaged in fishing by looking at their AIS speed and course profile. For example, a vessel that slows down to one to three knots and frequently changes direction would likely be fishing. If geolocation data are not available, AA can also determine the position of vessels through image-recognition algorithms and satellite imagery (both radar and optical) that allow authorities to monitor the fishing fleet directly from space and detect any suspicious activity under their purview, such as fishing in restricted zones or the offloading of fish cargo from one vessel to a refrigerated transport vessel—a practice that is sometimes used to conceal a catch from authorities.
Some industry organizations also use sensor data to monitor fishing activity, with the goal of increasing sustainability, such as Global Fishing Watch, a not-for-profit organization that aims to increase transparency by offering free data about the activity of the global fishing fleet based on AIS, VMS, and satellite imagery.12
Improving the detection of fish
Most fisheries have scarce data about their target catch. They might assess stock yearly, rather than making more frequent observations, and their analyses focus on information about landed catches and data recorded by observers. Tools that incorporate advanced analytics can provide a more dynamic, reliable, and nuanced view of the fluctuating ocean environment.
Consider patterns related to fish aggregation and migration, which change in response to temperature, wave height, the presence of sea ice, and other ocean conditions. Fisheries can monitor these changes through satellite imagery obtained from sensors. Complemented with information from other sources, such as the location of fishing vessels and catch data, advanced analytics can help determine the distribution and migratory patterns of a target species over time and space with greater accuracy and frequency.
Some researchers have already applied advanced analytics to get better information on the distribution of fish. One team developed high-resolution predictive models by combining various ocean data, including sea-surface temperature, wind speed, and chlorophyll levels associated with plankton, with information obtained from fisheries and tagging sensors. The models provide daily recommendations about where to fish and how to avoid bycatch, increasing efficiency.13 With a more detailed and dynamic vision of fish stocks, fishing companies can decrease the amount of time, effort, and fuel required for each catch. Likewise, authorities can use the data to improve resource management.
Reporting to authorities and central managers
As noted earlier, fishermen and independent observers typically monitor and report fishing activities themselves. The results are then sent to relevant authorities or central managers within their company. EMS can automate and facilitate this time-consuming process to generate more exhaustive and reliable data based on sensor input. These systems typically consist of cameras connected to a GPS receiver and other vessel-tracking devices, such as engine-monitoring sensors that send data on fuel consumption in near real time. As fishing companies evolve toward a more data-rich environment, advanced analytics will become more and more relevant. Eventually, fishing companies will be able to combine data in ways that deliver new insights about key operational-performance drivers, such as fuel consumption and fish-catching rates.
Traceability
The supply chain in the seafood industry is complex, opaque, and lacking in international harmonization because the stakeholders involved often closely guard their information. 14 The lack of clarity makes it easier for vessels to skirt regulations and fish illegally. It also frustrates consumers, who are increasingly asking for more information about the source and freshness of the food on their plates.
The supply chain in the seafood industry is complex, opaque, and lacking in international harmonization because the stakeholders involved often closely guard their information.
To improve transparency, some researchers are investigating distributed-ledger technologies that track and store information on transactions, including data on the movement of goods along the supply chain, in a secure, distributed database. Although distributed-ledger technologies are not classified as advanced-analytics tools, they are an important enabler. The information in a distributed-ledger technology database, including insights from advanced analytics, is available to all approved users in real time.
Researchers are also investigating other technologies for tracking seafood, such as radiofrequency-identification tags and quick response codes, both of which transmit product information when scanned. With tagging, fishing companies may find it easier to receive permission to place labels on their products certifying that they are approved by the Marine Stewardship Council and other organizations that guarantee a product has been sustainably sourced, monitored along the supply chain, and correctly labeled. Consumers may increasingly look for such labels, giving an advantage to those that fish responsibly.
Although commercial fishing companies are exploring advanced analytics through pilots and other activities, their decisions about where, when, and how to fish are still largely based on intuition and experience. Similarly, most regulators are not taking full advantage of advanced analytics. They have collected and analyzed some data, but their information is often incomplete and prone to inaccuracies, especially in emerging markets.
With all industry stakeholders concerned about fishing stocks, it is now time to take a more aggressive approach to advanced analytics. As noted earlier, recent technological advances will facilitate this push, since costs for data storage and processing are decreasing each year. Their greater affordability means that most fishing companies and other stakeholders can now afford to implement more advanced-analytics tools in the near future. Likewise, talent recruitment will become less difficult for fisheries since the supply of data scientists, engineers, and technicians is growing. Fisheries will still face more challenges in acquiring talent than well-known tech companies or other industries that have traditionally promoted advanced analytics, but the recruitment pool will be larger.
Fishing companies
To guide their advanced-analytics journey, fishing companies must create a road map focusing on challenges they hope to address, such as those related to fishing efficiency, capture volatility, and fleet monitoring. To identify quick wins, companies should first assess their data stores to see what information is readily available. Most will find that they already have much relevant information on hand, including vessel-specific data on daily catch (both volume and species), GPS position, and fuel consumption.
Although commercial fishing companies are exploring advanced analytics through pilots and other activities, their decisions about where, when, and how to fish are still largely based on intuition and experience.
Simple yet powerful use cases could be built around such data. Rather than using this information for purely descriptive purposes—for instance, noting the average catch for each vessel during past months—fishing companies could adopt a forward-looking analytical approach. One analysis might involve using geospatial modeling to map fishing activity and catch rate over the course of the season, allowing fisheries to track the fleet more closely and gain a better understanding of performance drivers. Increased fishing efficiency would also reduce fuel consumption and running costs. In addition to such simple analyses, fishing companies could use geospatial modeling to predict the location of targeted fish according to various environmental conditions. Such tools could inform not only fishing operations but also downstream commercial activities, including seafood pricing and labeling.
Fishing companies will also find many other use cases for advanced analytics. For example, they could generate even greater fuel savings by examining data from IoT sensors that provide information on vessel behavior, including fuel consumption and navigation conditions. Their analyses could help them generate real-time recommendations about the most energy-efficient routes and maneuvers. Similarly, fishing companies could examine data from onboard sensors to determine if any equipment is experiencing the sorts of problems that typically occur before a breakdown. With this information, they could detect potential failures ahead of time, thereby preventing costly repairs and long downtimes. In an analysis of large fishing companies worldwide, we estimated that using advanced analytics could produce more than $11 billion in savings by reducing running costs, as well as expenses for fuel, labor, and repair and maintenance (Exhibit 5).
Exhibit 5
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While these potential gains are impressive, fishing companies will not achieve them by simply implementing advanced-analytics initiatives. Instead, they must undertake an end-to-end digital transformation throughout all their functions.15 Such transformations require employees to have the right skill sets, as well as appropriate tools, processes, and interfaces (for instance, dashboards where they can readily access data). In addition, organizations should provide training and support to help employees see the value of advanced analytics, especially if they appear reluctant to change their ways. Without this support, employees may view advanced analytics as an imposition—a mind-set that is likely to impede progress.
Government and fishery-management agencies
Advanced analytics can increase transparency about seafood from ocean capture to the dinner table
With fish stocks dwindling and environmental challenges mounting, governments and fishery-management agencies could consider investing in data-collection technologies and research programs that can provide a comprehensive, near real-time vision of both ocean resources and fishing activities. By leveraging the data, they can adopt new measures and regulations more quickly and also rapidly respond to external pressures such as climate change. Fishing quotas could also become more dynamic. Rather than setting a quota annually, at the beginning of the fishing season, authorities could make adjustments throughout the year based on real-time information about the amount and type of catch that vessels are collecting.
The current exchange of information between fishermen and authorities is not optimal.16 A collaborative problem-solving approach—potentially happening at the global or regional level—is needed to develop a clear road map defining data standards and mutual goals, such as those for by catch reduction. These efforts would build trust among stakeholders and benefit all.
Food companies
By improving both the monitoring of fishing activities and the reporting of associated catches, advanced analytics can increase transparency about the seafood supply chain from ocean capture to the dinner table. Food companies can share this information with consumers, who have a growing interest in the quality, traceability, and sustainability of food products. In addition to their own health, they are concerned about environmental impact. If advanced analytics reveals that most of a company’s catch comes from endangered species or overfished areas, the company can shift to other options to increase sustainability (either moving its own fishing fleets or changing suppliers). Certain technologies, including distributed-ledger technologies and radio-frequency-identification tags, can help companies share their insights about catch origin more efficiently and might merit additional investment.
Modern farmers already rely on sophisticated weather forecasts, sensors, and geospatial tools to optimize their harvest and manage land more sustainably. Now it’s time for fishing companies and other stakeholders to start their own digital and analytical journey. Getting fuller nets and larger fish is one goal, but a more important objective relates to sustainability. As fish stocks drop and fishing companies expand their reach, advanced analytics may be one of the best tools for protecting endangered species and other ocean resources. While data and algorithms may seem a better fit for boardrooms than boats, some fisheries have already achieved major gains by applying them. It’s now time for more widespread adoption before the environmental consequences of overfishing accelerate.
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