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Harnessing the FourthIndustrial Revolution for Life on Land

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Harnessing the FourthIndustrial Revolution for Life on Land

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World Economic Forum

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Harnessing the Fourth Industrial Revolution for Life on Land

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How artificial intelligence is changing wildlife research

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How artificial intelligence is changing wildlife research

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by Anne Casselman

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How artificial intelligence is changing wildlife research

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Can Artificial Intelligence Help Save the Natural World?

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Can Artificial Intelligence Help Save the Natural World?

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by Jackie Snow

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Can Artificial Intelligence Help Save the Natural World?

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AI-powered tools are giving conservationists new ways to combat the daunting, ongoing, human-caused problem of mass extinction. It won't be easy.

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Wildlife Corridors Conservation Act of 2019

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Wildlife Corridors Conservation Act of 2019

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Half-Earth Project

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Hummingbirds play a critical role in the ecosystems in which they live. Ranging across the Americas from Chile to Canada, healthy hummingbird populations are key pollinators for a wide range of flowering plants. From high in the forest canopy to open grasslands, from mountainsides to valley floors, this beautiful and varied family of birds, now mapped to 1km by the Half-Earth mapping team, is revealing new conservation opportunities. This information will be useful to indigenous, local, state and national entities to effectively expand landscape protections.

View the video above for a guided tour of the hummingbirds map.
Explore the hummingbirds map.

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Hummingbirds play a critical role in the ecosystems in which they live. Ranging across the Americas from Chile to Canada, healthy hummingbird populations are key pollinators for a wide range of flowering plants.

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Harnessing Artificial Intelligence for the Earth

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Harnessing Artificial Intelligence for the Earth

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World Economic Forum
In Collaboration with PwC and Stanford Woods Institute for the Environment

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Wildlife Corridors Conservation Act of 2019 Introduced in Congress

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Wildlife Corridors Conservation Act of 2019 Introduced in Congress

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by Chris Heltne

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WILDLIFE CORRIDORS CONSERVATION ACT OF 2019 INTRODUCED IN CONGRESS WITH BI-PARTISAN SUPPORT FOLLOWING UN REPORT ON GLOBAL BIODIVERSITY CRISIS

WASHINGTON, D.C. (May 16, 2019) - Marking the most significant step toward national wildlife conservation in decades, the Wildlife Corridors Conservation Act of 2019 was introduced today in both houses of Congress. Led by Sen. Tom Udall (D-NM), the bill was co-sponsored in the Senate by Richard Blumenthal (D-CT), Cory Booker (D- NJ), Kamala Harris (D-CA), Jeff Merkley (D-OR), Bernie Sanders (D-VT), Jon Tester (D-MT), Sheldon Whitehouse (D-RI), and Ron Wyden (D-OR). The bill was introduced in the House of Representatives by Congressmen Don Beyer (D-VA) and Vern Buchanan (R-FL).

This bi-partisan introduction comes just weeks after the United Nations released a harrowing report detailing the threats facing global biodiversity, including wildlife native to America. If passed, the Act will restore habitat and protect America's native wildlife by establishing a National Wildlife Corridors Program that facilitates the designation of wildlife corridors on federal lands and provides grants to maintain wildlife corridors on non-federal lands. Fragmentation of wildlife habitat has been identified as a significant threat to wildlife across America.

"The Wildlife Corridors Conservation Act would provide the most important step of any single piece of legislation at the present time in enlarging the nations protected areas and thereby saving large swaths of America's wildlife and other fauna and flora, especially in this critical time of climate change and shifting locations of the original environments in which a large part of biodiversity has existed." - E.O. Wilson

"Wildlands Network thanks Sen. Udall and Reps. Beyer and Buchanan for their leadership to protecting America's Wildlife," said Susan Holmes, Federal Policy Director, Wildlands Network. "From elk to monarch butterflies, America's wildlife needs to move across the landscape to survive. The Wildlife Corridors Conservation Act will reconnect important landscapes and protect species in the face of climate change and development. The UN report makes it clear - we cannot wait any longer to take action. The Wildlife Corridors Conservation Act is the most significant national proposal to protect wildlife we have seen in decades and gives us hope that we are not too late to protect the wildlife we love and need."

The Act grants authority to key federal agencies to create a National Wildlife Corridor system on federal public land and creates a Wildlife Movement Grant Program to incentivize the protection of wildlife corridors by state and tribal agencies, as well as interested private landowners, on non-federal lands. It also establishes Regional Wildlife Movement Councils and a National Coordination Committee to identify, prioritize and fund on-the-ground projects supported by state agencies, tribes and local stakeholders.

"America's wilderness has sustained our treasured native fish, wildlife and plant species for hundreds of years, but this vital part of our national heritage is in jeopardy," said Sen. Udall. "The habitats and migration routes that our wildlife rely on to move and thrive are under increasing pressures, and our precious biodiversity along with it. In New Mexico, our millions of acres of public lands are home to thousands of iconic species-from the desert bighorns to whooping cranes to Gila trout-that could vanish if we fail to take bold action. These species are essential to our rich natural inheritance and agricultural and economic success, and are an important legacy to pass on to our children. By designating corridors that would connect these vital habitats to one another, we can ensure the survival of some of our most iconic species, from the monarch butterfly to the Louisiana black bear, and preserve our precious wildlife for future generations to come."

"With roughly one in five animal and plant species in the U.S. at risk of extinction due to habitat loss and fragmentation, one of the simplest yet most effective things we can do is to provide them ample opportunity to move across lands and waters," said Rep. Beyer. "The U.N. report on accelerating extinctions makes it clear that the window for action to protect the planet's biodiversity is closing. We badly need to pass the Wildlife Corridors Conservation Act."

"Protecting wildlife and promoting biodiversity are of critical importance in light of a new study warning that one million species are facing extinction," said Rep. Buchanan. It's time for Congress to help restore threatened wildlife populations and safeguard our nation's lands and waters. We don't get a second chance once a species becomes extinct."

Wildlife corridors are critically important habitat areas that allow animals to move between areas of habitat, facilitating migration, range expansion, and mating. Protecting wildlife corridors also increases potential resiliency of animal populations in the face of changing landscapes and climate. The bill is supported by nationally recognized scientists, including Harvard's  Dr. E.O. Wilson, and over 220 prominent  NGOs  nationwide.

"The Wildlife Corridors Conservation Act would provide the most important step of any single piece of legislation at the present time in enlarging the nations protected areas and thereby saving large swaths of America's wildlife and other fauna and flora, especially in this critical time of climate change and shifting locations of the original environments in which a large part of biodiversity has existed," renowned biologist E.O. Wilson said of the bill.

"Defenders of Wildlife commends Sen. Udall, Rep. Beyer and Rep. Buchanan for their leadership in protecting America's wildlife. Confronting a rapidly changing climate and pressure from human development, wildlife need the freedom to move across our nation's landscape. The legislation introduced today draws all Americans into the effort to support wildlife by mapping the corridors and connecting landscapes that they need to survive and thrive," said Jamie Rappaport Clark, president and CEO of Defenders of Wildlife.

The bill would also improve wildlife-related recreational opportunities and has received support from major outdoor brands, including Patagonia and Osprey Packs.

"We are still in business today because we fight for the protection of our public lands and waters and the biodiversity that they support," stated Alison Huyett, Patagonia's Environmental Campaigns Manager. "We are proud to stand behind this critical legislation that will help protect and restore America's native wildlife and create more resilient landscapes."

Wildlife species in need of protected corridors include the pronghorn antelope, an important game species in the Southwest, whose survival depends upon the ability to migrate seasonally. Even small insects like the monarch butterfly need protected corridors to migrate up to 3,000 miles. It can take 3-4 generations to complete a full migration, and without protected places along the flyway for them to rest and reproduce, the species could be lost entirely.

"The recent UN report makes it crystal clear that America needs more tools to protect plants and animals," stated Leda Huta, Executive Director of the Endangered Species Coalition. "We have ignored biodiversity for too long and at our own peril. If we want to recover declining plant and animal populations-and reap the benefits of biodiversity for the health and safety of humans-wildlife corridors are a no-brainer."

"The Wildlife Corridors Conservation Act will provide a crucial lifeline for many of America's native species," stated Rob Ament, Senior Conservationist at the Center for Large Landscape Conservation, "so they can safely move across America's landscapes to meet their daily, seasonal and lifetime needs."

"The National Parks Conservation Association commends the legislation for the benefits that it could provide to wildlife that travel beyond park boundaries such as Los Angeles mountain lions in Santa Monica Mountains National Recreation Area," said Theresa Pierno, President and CEO of the National Parks Conservation Association. "The legislation would build upon the success of the Path of the Pronghorn outside of Grand Teton, America's first nationally protected wildlife corridor which serves as a testimony to the success of community, state and federal collaboration."
Background:
Protection of wildlife corridors is a conservation goal with bipartisan support around the country. In the last decade, the Western Governors' Association and the  New England Governors and Canadian Premiers both adopted wildlife corridor protection measures. In 2018, Secretary of the Interior Ryan Zinke issued Secretarial Order 3362, which would improve "habitat quality in Western big-game winter range and migration corridors." In addition, both red and blue states such as New Hampshire, Wyoming, New Mexico and California have recently passed measures to protect wildlife corridors.
Additional resources:
Wildlands Network created a  fact sheet for the Wildlife Corridors Conservation Act, as well as facts sheets for potential impacts of the bill on specific species, including  grizzly bears,  monarch butterflies,  Florida panthers and  pronghorns.

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Wildlife Corridors Conservation Act of 2019 Introduced in Congress | Half-Earth

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WILDLIFE CORRIDORS CONSERVATION ACT OF 2019 INTRODUCED IN CONGRESS WITH BI-PARTISAN SUPPORT FOLLOWING UN REPORT ON GLOBAL BIODIVERSITY CRISIS WASHINGTON, D.C. (May 16, 2019) - Marking the most significant step toward national wildlife conservation in decades, the Wildlife Corridors Conservation Act of 2019 was introduced today in both houses of Congress.

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E.O. Wilson Foundation

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E.O. Wilson Biodiversity Foundation » Introduction to Biodiversity

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All it takes is a stroll through your local park to see that the world is saturated with life. At a glance, the eye takes in birds, mammals, reptiles, trees. A stretch of ground chosen at random will reveal hordes of plants, insects, tiny worms, fungus and bacteria hidden from the naked eye.

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Hummingbird robot uses AI to soon go where drones can't

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Hummingbird robot uses AI to soon go where drones can't

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Purdue University

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What can fly like a bird and hover like an insect?

Your friendly neighborhood hummingbirds. If drones had this combo, they would be able to maneuver better through collapsed buildings and other cluttered spaces to find trapped victims.

Purdue University researchers have engineered flying robots that behave like hummingbirds, trained by machine learning algorithms based on various techniques the bird uses naturally every day.

This means that after learning from a simulation, the robot "knows" how to move around on its own like a hummingbird would, such as discerning when to perform an escape maneuver.

Artificial intelligence, combined with flexible flapping wings, also allows the robot to teach itself new tricks. Even though the robot can't see yet, for example, it senses by touching surfaces. Each touch alters an electrical current, which the researchers realized they could track.

"The robot can essentially create a map without seeing its surroundings. This could be helpful in a situation when the robot might be searching for victims in a dark place -- and it means one less sensor to add when we do give the robot the ability to see," said Xinyan Deng, an associate professor of mechanical engineering at Purdue.

The researchers will present their work on May 20 at the 2019 IEEE International Conference on Robotics and Automation in Montreal. A YouTube video is available at https://youtu.be/jhl892dHqfA.

Drones can't be made infinitely smaller, due to the way conventional aerodynamics work. They wouldn't be able to generate enough lift to support their weight.

But hummingbirds don't use conventional aerodynamics -- and their wings are resilient. "The physics is simply different; the aerodynamics is inherently unsteady, with high angles of attack and high lift. This makes it possible for smaller, flying animals to exist, and also possible for us to scale down flapping wing robots," Deng said.

Researchers have been trying for years to decode hummingbird flight so that robots can fly where larger aircraft can't. In 2011, the company AeroVironment, commissioned by DARPA, an agency within the U.S. Department of Defense, built a robotic hummingbird that was heavier than a real one but not as fast, with helicopter-like flight controls and limited maneuverability. It required a human to be behind a remote control at all times.

Deng's group and her collaborators studied hummingbirds themselves for multiple summers in Montana. They documented key hummingbird maneuvers, such as making a rapid 180-degree turn, and translated them to computer algorithms that the robot could learn from when hooked up to a simulation.

Further study on the physics of insects and hummingbirds allowed Purdue researchers to build robots smaller than hummingbirds -- and even as small as insects -- without compromising the way they fly. The smaller the size, the greater the wing flapping frequency, and the more efficiently they fly, Deng says.

The robots have 3D-printed bodies, wings made of carbon fiber and laser-cut membranes. The researchers have built one hummingbird robot weighing 12 grams -- the weight of the average adult Magnificent Hummingbird -- and another insect-sized robot weighing 1 gram. The hummingbird robot can lift more than its own weight, up to 27 grams.

Designing their robots with higher lift gives the researchers more wiggle room to eventually add a battery and sensing technology, such as a camera or GPS. Currently, the robot needs to be tethered to an energy source while it flies -- but that won't be for much longer, the researchers say.

The robots could fly silently just as a real hummingbird does, making them more ideal for covert operations. And they stay steady through turbulence, which the researchers demonstrated by testing the dynamically scaled wings in an oil tank.

The robot requires only two motors and can control each wing independently of the other, which is how flying animals perform highly agile maneuvers in nature.

"An actual hummingbird has multiple groups of muscles to do power and steering strokes, but a robot should be as light as possible, so that you have maximum performance on minimal weight," Deng said.

Robotic hummingbirds wouldn't only help with search-and-rescue missions, but also allow biologists to more reliably study hummingbirds in their natural environment through the senses of a realistic robot.

"We learned from biology to build the robot, and now biological discoveries can happen with extra help from robots," Deng said.

Simulations of the technology are available open-source at https://github.com/purdue-biorobotics/flappy.

Early stages of the work, including the Montana hummingbird experiments in collaboration with Bret Tobalske's group at the University of Montana, were financially supported by the National Science Foundation.

This work aligns with Purdue's Giant Leaps celebration, acknowledging the university's global advancements made in AI, algorithms and automation as part of Purdue's 150th anniversary. This is one of the four themes of the yearlong celebration's Ideas Festival, designed to showcase Purdue as an intellectual center solving real-world issues.

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birds; insects (including butterflies); behavioral science; robotics research; engineering; vehicles; robotics; artificial intelligence; neural interfaces

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Hummingbird robot uses AI to soon go where drones can't

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What can fly like a bird and hover like an insect? Your friendly neighborhood hummingbirds. If drones had this combo, they would be able to maneuver better through collapsed buildings and other cluttered spaces to find trapped victims. Purdue University researchers have engineered flying robots that behave like hummingbirds, trained by machine learning algorithms based on various techniques the bird uses naturally every day.

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Half-Earth Project

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Marine Fishes and Marine Fishing Activities layers were added to the Half-Earth in the fall of 2018. Currently, only 3-4 percent of Earth's oceans are under some form of protection. Meanwhile, fishing pressures continue to mount in seascapes around the world. The Half-Earth Map is revealing the best locations where new protections can do the most good.

View the video above for a quick how-to on viewing the marine map layers.

Explore the marine map on your own.

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Marine Fishes and Marine Fishing Activities layers were added to the Half-Earth in the fall of 2018. Currently, only 3-4 percent of Earth's oceans are under some form of protection. Meanwhile, fishing pressures continue to mount in seascapes around the world. The Half-Earth Map is revealing the best locations where new protections can do the most good.

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Use of robots and artificial intelligence to understand the deep sea

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Use of robots and artificial intelligence to understand the deep sea

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University of Plymoth

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Artificial intelligence (AI) could help scientists shed new light on the variety of species living on the ocean floor, according to new research led by the University of Plymouth.

With increasing threats facing the marine environment, scientists desperately need more information about what inhabits the seabed in order to inform conservation and biodiversity management.

Autonomous underwater vehicles (AUV) mounted with the latest cameras are now able to collect vast amounts of data, but a bottleneck is still created by humans having to process it.

In a new study published in Marine Ecology Progress Series, marine scientists and robotics experts tested the effectiveness of a computer vision (CV) system in potentially fulfilling that role.

They showed on average it is around 80% accurate in identifying various animals in images of the seabed, but can be up to 93% accurate for specific species if enough data is used to train the algorithm.

This, scientists say, demonstrates CV could soon be routinely employed to study marine animals and plants and lead to a major increase in data availability for conservation research and biodiversity management.

PhD student Nils Piechaud, lead author on the study, said: "Autonomous vehicles are a vital tool for surveying large areas of the seabed deeper than 60m (the depth most divers can reach). But we are currently not able to manually analyse more than a fraction of that data. This research shows AI is a promising tool but our AI classifier would still be wrong one out of five times, if it was used to identify animals in our images.

"This makes it an important step forward in dealing with the huge amounts of data being generated from the ocean floor, and shows it can help speed up analysis when used for detecting some species. But we are not at the point of considering it a suitable complete replacement for humans at this stage."

The study was conducted as part of Deep Links, a research project funded by the Natural Environment Research Council, and led by the University of Plymouth, in collaboration with Oxford University, British Geological Survey and the Joint Nature Conservation Committee.

One of the UK's national AUVs -- Autosub6000, deployed in May 2016 -- collected more than 150,000 images in a single dive from around 1200m beneath the ocean surface on the north-east side of Rockall Bank, in the North East Atlantic. Around 1,200 of these images were manually analysed, containing 40,000 individuals of 110 different kinds of animals (morphospecies), most of them only seen a handful of times.

Researchers then used Google's Tensorflow, an open access library, to teach a pre-trained Convolutional Neural Network (CNN) to identify individuals of various deep-sea morphospecies found in the AUV images. They then assessed how the CNN performed when trained with different numbers of example images of animals, and different of numbers of morphospecies to choose from.

The accuracy of manual annotation by humans can range from 50 to 95%, but this method is slow and even specialists are very inconsistent across time and research teams. This automated method reached around 80% accuracy, approaching the performance of humans with a clear speed and consistency advantage.

This is particularly true for some morphospecies that the algorithms work very well with. For example, the model correctly identifies one animal (a type of xenophyophore) 93% of the time.

While the study does not advocate the replacement of manual annotation, it does demonstrate that marine biologists could be able to implement AI for specific tasks if carefully assessing the reliability of their predictions. This would greatly enhance the capacity of scientists to analyse their data.

The researchers say the combination of specialist ecological knowledge with the high-tech AUV's capacity to survey large areas of the seabed, and the fast data processing capacity of AI, could greatly speed up deep-ocean exploration, and with it our wider understanding of marine ecosystems.

Dr Kerry Howell, Associate Professor in Marine Ecology and Principal Investigator for the Deep Links project, added: "Most of our planet is deep sea, a vast area in which we have equally large knowledge gaps. With increasing pressures on the marine environment including climate change, it is imperative that we understand our oceans and the habitats and species found within them. In the age of robotic and autonomous vehicles, big data, and global open research, the development of AI tools with the potential to help speed up our acquisition of knowledge is an exciting and much needed advance."

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marine biology; fish; nature; vehicles; engineering; optics; information technology; artificial intelligence; hacking

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Use of robots and artificial intelligence to understand the deep sea

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Artificial intelligence (AI) could help scientists shed new light on the variety of species living on the ocean floor, according to new research led by the University of Plymouth. With increasing threats facing the marine environment, scientists desperately need more information about what inhabits the seabed in order to inform conservation and biodiversity management.

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Hacking conservation: how a tech start-up aims to save biodiversity

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Hacking conservation: how a tech start-up aims to save biodiversity

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by Lisa Palmer

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Beads of sweat form on Alex Dehgan's brow as he steps through the mist and heat of the tropical conservatory at the US Botanic Garden in Washington DC. The air is not as hot and humid as in the forests of Madagascar, but the warmth reminds him of the two and a half years he spent there, studying lemur populations and surviving bouts of cerebral malaria, schistosomiasis and dysentery.

Dehgan enters the medicinal plant garden and finds the Madagascar periwinkle ( Catharanthus roseus), a plant that led to key treatments for childhood leukaemia and is now endangered in its natural habitat. Famous for its biodiversity, Madagascar lost nearly 40% of its forests between the 1950s and 2000 - and probably many unknown plant and animal species, says Dehgan, who trained as an evolutionary biologist. "How many more things could we have found?" he wonders.

That's the kind of issue that has driven Dehgan over the past two decades. After an unorthodox career in science that has included setting up a national park in an active war zone in Afghanistan, Dehgan is betting his retirement on hacking the field of conservation. He wants to harness the power of technological innovation to transform the practice of preserving ecosystems and species.

Borrowing a page from Silicon Valley, he has launched a non-profit technology start-up called Conservation X Labs in Washington DC. Just four years old, the initiative is bringing new people into the field of conservation - including entrepreneurs, engineers, computer scientists and anthropologists. It aims to support research and development into technology that might aid conservation. Shaking up the field is essential, Dehgan says. "Unless we fundamentally change the model, the tools and the people working on conserving biodiversity, the prognosis is not good."

One of the group's key tactics is setting up prizes to lure in fresh talent and ideas. So far, it has launched six competitions for tools to, among other things, limit the spread of infectious diseases, the trade in products made from endangered species and the decline of coral reefs. The first commercial product to be spun out of the start-up - a portable DNA scanner - is slated for release by the end of the year.

Conservation X Labs stands out both for the funding it is bringing to the field and for building collaborations with technologists who might not otherwise have thought about conservation work, says Geoffrey Dabelko, a political scientist focused on the environment at Ohio University in Athens. As co-founder and chief executive, Dehgan has big hopes for the organization and the prospect of combatting a precipitous loss in biodiversity around the world. "Alex challenges assumptions of who is a conservationist," Dabelko says, "and his level of ambition is uncommon."

Innovation infusion

Conservation biologists tend to travel widely, but few have such a diverse background as Dehgan. Born in Iran, he grew up in the United States, and studied zoology and political science at university before earning a law degree. In the 1990s, he went to Russia to help develop the country's first environmental laws after the collapse of the Soviet Union, and he even lived for a time in the Moscow Zoo to avoid being kidnapped. He then earned a PhD in evolutionary biology and eventually went to Iraq in 2004, where he trained former weapons scientists to work in fields such as ecology and conservation biology. A few years later, he joined the Wildlife Conservation Society in Afghanistan's post-conflict war zone to complete the country's first extensive wildlife surveys in 30 years and establish its first national park, Band-e-Amir.

Dehgan caught the tech-innovation bug in his roles as science adviser and chief scientist at the US Agency for International Development (USAID), the federal agency responsible for civilian foreign aid and development help. While there in the early 2010s, he resuscitated the agency's science programme and launched its Global Development Lab, which is devoted to blue-skies research. He also saw the successes of the Saving Lives at Birth programme, an international effort co-sponsored by USAID. That initiative helped to widen adoption of the Pratt Pouch - technology designed to preserve antiretroviral medications that can be used in infants to prevent HIV infections shortly after birth. One of its challenges also spurred the development of the Odon Device - an innovation for assisting in vaginal births that was developed by a car mechanic from Argentina.

After he left USAID in 2014, Dehgan turned down executive leadership roles in academia and established organizations, and decided instead to take a leap into something out of the ordinary. He launched Conservation X Labs in 2015 in partnership with Paul Bunje, then chief scientist at the XPrize Foundation.

Since its founding, Conservation X Labs has awarded US$3.3 million through its challenges. It has, for example, partnered with the US National Park Service and the US Department of the Interior on a $70,000 prize for early detection of the fungi that are killing Hawaii's iconic 'Ōhi'a ( Metrosideros polymorpha) tree, a backbone of native forests and watersheds. Fungal pathogens, carried by an invasive ambrosia beetle, have been killing the trees at alarming rates since at least 2010, and the disease is spreading rapidly. J. B. Friday, a forester at the University of Hawai'i at Mānoa, says that a competition could help spur a rapid response to the outbreak. "If good, smart people over the world are thinking about this and can bring us ideas we haven't thought of," he says, "that would be great to know." Proposals are currently being evaluated for the competition, which closed for submissions in April.

Dehgan hopes that the organization's prizes and other initiatives will bring innovative solutions to conservation's deepest problems. Hundreds of people have already been lured in through challenges and engineering programmes such as Make for the Planet - a multi-day, in-person event - and an online tech collaboration platform called Digital Makerspace, which matches conservationists with technical talent.

One innovation that has come out of Conservation X Labs is ChimpFace, facial-recognition software designed to combat chimpanzee trafficking that happens through sales over the Internet. A conservationist came up with the idea, Dehgan explains, but she didn't have the technical expertise needed to achieve her vision. Digital Makerspace helped her to form a team to develop the technology, which uses algorithms that have been trained on thousands of photos provided by the Jane Goodall Institute. ChimpFace can determine whether a chimp for sale has been taken illegally from the wild, because those animals have been catalogued.

Conservation X Labs, which boasts a full-time staff of 15, is also developing its own technology, including a hand-held, battery-powered device known as the DNA Barcode Scanner. The device is designed to allow park rangers, customs officers, supply-chain inspectors and law-enforcement officials to find the genetic identity of items at very low cost, including endangered and protected wood and seafood species, and oft-disguised wildlife products such as rhino horn or pangolin scales.

The scanner could be available as early as the end of this year, through a newly formed for-profit company called Thylacine Biosciences in Seattle, Washington. It is expected to cost around $100, which would make it an order of magnitude less expensive than other hand-held DNA devices, says Dehgan. An added bonus is that people don't need to be able to read to use it.

There is a race to deploy such technologies, says Kim Warner, senior scientist at Oceana, an environmental non-profit organization in Washington DC. Inexpensive and easy-to-use scanners are really needed to "make decisions at the border, to know what's coming in and what is going out", she says. "The most rapid method now takes two or three days, and you need trained personnel to interpret the results."

Dehgan says that fresh approaches are needed because the field has been slow to change and is struggling to find solutions to huge issues. One problem is that the field is "filled with conservationists", he says. "The Society for Conservation Biology is a society of professional mourners who lament and describe the passing of species." Dehgan, who is a member of the society and served on its board of governors from 2012 to 2015, asserts that too much human behaviour and innovation are left out of conservation.

Michael Mascia, who became president of the society in 2017, says the group has been focused on solutions for some time, but that it has evolved to include more social science and, recently, more technology. Mascia is the first social scientist to serve as president, and leads research at the Betty and Gordon Moore Center for Science at Conservation International in Arlington, Virginia. His institution is partnering with Dehgan's group on a prize-backed challenge aimed at artisanal mining, and he says that Conservation X Labs is playing a key part in building a bridge between emerging technology and field deployment.

Others are contributing, too, Mascia says. Microsoft has launched its AI for Earth programme, a five-year, $50-million effort to apply artificial intelligence to environmental challenges. And various projects have arisen from Google X, among them one that aims to bring Internet connectivity to remote places by balloon. But, Mascia says, "tech is not going to solve all our problems. Conservation is a social process, and it requires people to make choices and potentially change their behaviour."

As it seeks to refashion the field, Conservation X Labs is facing some challenges. Foundations find it difficult to support the group's atypical mission as a non-profit conservation-tech effort, Dehgan says. The company must compete with large tech firms to hire engineers to build devices. And collaborating with conventional conservation organizations brings problems, too. Often, he says, the missions don't align: many are focused on creating preserves instead of on specific human factors that might be driving extinction, such as the economics of animal trafficking.

Still, Dehgan sees ample opportunity to make progress. "Humans have caused these problems," he says. "And we have the ability to solve them."

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Hacking conservation: how a tech start-up aims to save biodiversity

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Beads of sweat form on Alex Dehgan's brow as he steps through the mist and heat of the tropical conservatory at the US Botanic Garden in Washington DC. The air is not as hot and humid as in the forests of Madagascar, but the warmth reminds him of the two and a half years he spent there, studying lemur populations and surviving bouts of cerebral malaria, schistosomiasis and dysentery.

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by Roberta Kwok

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When researchers collect audio recordings of birds, they are usually listening for the animals' calls. But conservation biologist Marc Travers is interested in the noise produced when a bird collides with a power line. It sounds, he says, "very much like the laser sound from Star Wars ".

In 2011, Travers wanted to know how many of these collisions were occurring on the Hawaiian island of Kauai. His team at the University of Hawaii's Kauai Endangered Seabird Recovery Project in Hanapepe was concerned specifically about two species: Newell's shearwaters ( Puffinus newelli) and Hawaiian petrels ( Pterodroma sandwichensis). To investigate, the team went to the recordings.

With some 600 hours of audio collected - a full 25 days' worth - counting the laser blasts manually was impractical. So, Travers sent the audio files (as well as metadata, such as times and locations) to Conservation Metrics, a firm in Santa Cruz, California, that uses artificial intelligence (AI) to assist wildlife monitoring. The company's software was able to detect the collisions automatically and, over the next several years, Travers' team increased its data harvest to about 75,000 hours per field season.

Results suggested that bird deaths as a result of the animals striking power lines numbered in the high hundreds or low thousands, much higher than expected. "We know that immediate and large-scale action is required," Travers says. His team is working with the utility company to test whether shining lasers between power poles reduces collisions, and it seems to be effective. The researchers are also pushing the company to lower wires in high-risk locations and attach blinking LED devices to lines.

For underfunded conservation scientists, AI provides an attractive alternative to manually processing huge troves of data, such as camera-trap images or audio recordings. A PhD student could "spend months labelling it all by hand before they can get anywhere near answering their hypothesis", says Dan Stowell, a computer scientist at Queen Mary University of London. Citizen science is one option to help with the data, but it isn't always the right one: volunteers might work too slowly, and recruiting for projects that involve non-charismatic species can be difficult. AI tools don't experience fatigue-related performance deterioration, as humans do, and they might be better at detecting infrequent or complex patterns.

Scientists need answers to pressing questions, such as whether conservation actions are working. And some problems need near real-time results - for instance, law-enforcement agencies pursuing illegal wildlife traffickers need to determine quickly if an animal for sale on social media is protected. AI could fit the bill.

Despite its reputation for requiring advanced computing skills, AI is now more accessible than ever, thanks to point-and-click tools and dedicated programming libraries. The software isn't as accurate or as sensitive as humans at many conservation research tasks, and the amount of data needed to train an AI algorithm to recognize images and sounds can present hurdles. But early adopters in conservation science are enthusiastic. For Travers, AI enabled a massive boost in monitoring. "It's a huge increase over any other method available," he says.

Automated identification

Researchers interested in AI can, as Travers did, outsource the work. Conservation Metrics' pricing starts at US$1-3 per hour of audio, but depends on data volume and project complexity; image-classification pricing is variable. The company also informally takes on for free three to five projects per year that involve interesting conservation questions or technical challenges; researchers can contact the company for consideration.

Scientists can also use browser-based tools. One option is Wildbook, a software framework produced by the non-profit organization Wild Me in Portland, Oregon, and its academic partners. Wildbook uses neural networks and computer-vision algorithms to detect and count animals in images, and to identify individual animals within a species. This information enables more precise estimates of wildlife population sizes, says Tanya Berger-Wolf, Wildbook co-founder and a computer scientist at the University of Illinois at Chicago.

Wildbook works for any species with stripes, spots, wrinkles, fin or ear notches, or other unique physical features. The number of manually annotated images needed to start a project depends on the species, says Jason Holmberg, executive director of Wild Me. The team also uses AI to collect images of specific species from YouTube, and will start trawling through Twitter this year. Researchers and citizen scientists can add images to be identified automatically. So far, scientists have created projects for more than 20 types of animals, including whale sharks ( Rhincodon typus), manta rays ( Manta birostris and M. alfredi), Iberian lynxes ( Lynx pardinus) and giraffes ( Giraffa sp.). New users can join existing Wildbook projects for free; setting up a project for a new species typically costs $10,000-20,000, Holmberg says.

Other tools process acoustic data. The Automated Remote Biodiversity Monitoring Network (ARBIMON) is a browser-based tool produced by Sieve Analytics in San Juan, Puerto Rico. Researchers upload their recordings, and the company recommends that they manually identify a couple of hundred clips that contain the species call of interest, and hundreds more that do not. ARBIMON then uses machine learning to classify the remaining data. So far, about 3.4 million 1-minute recordings have been uploaded, and researchers have monitored animals such as birds, amphibians and cetaceans. The company charges $0.06 per minute of audio.

Refind Technologies in Gothenburg, Sweden, has even incorporated AI software into custom hardware. The firm created a device called the Fish Face ID Tunnel to help researchers identify fish subspecies on the basis of photographs taken inside the device. "You could say it's a photo booth for fish," says Johanna Reimers, the firm's chief executive. Refind and the environmental charity The Nature Conservancy, based in Arlington, Virginia, installed the device on a fishing boat in Indonesia last year, and the charity is analysing the data. Researchers interested in a similar device, or a customized version, should expect to pay around $50,000, Reimers says.

Training data

Tech-savvy researchers can take advantage of 'command-line' AI software to answer conservation questions. Most cutting-edge results in deep learning use the open-source machine-learning libraries TensorFlow, developed by Google, and PyTorch, led by Facebook, Stowell says. For the past few years, his team has run contests, called the Bird Audio Detection Challenge, in which participants develop and test algorithms to determine whether bird calls are present in sound clips. Prizes go to the highest-scoring open-source entries; code from some of the other entries is also posted online.

TensorFlow in particular has a large user community, and many code examples are available online, says Scott Loarie, co-director of the nature app iNaturalist, a joint initiative of the National Geographic Society in Washington DC and the California Academy of Sciences in San Francisco.

Conservation biologists can get up to speed with AI through online classes offered by DataCamp, Coursera, Udacity or the NVIDIA Deep Learning Institute. The online guide Machine Learning for Humans (see go.nature.com/2sbjasb) offers resources for beginners, and the podcast This Week in Machine Learning & AI (see go.nature.com/2ts36bv) can help biologists to stay up to date.

Collaborations with AI specialists can also help. Ruth Oliver, an ecologist at Yale University in New Haven, Connecticut, took this approach when analysing about 1,200 hours of audio from the Arctic. She worked with an electrical engineer, who had experience in machine learning, and ran AI algorithms using the software tool MATLAB to estimate when songbirds arrived in the Arctic and how environmental factors affected vocalizations ( R. Y. Oliver et al. Sci. Adv. 4, eaaq1084; 2018).

Despite its apparent ubiquity, AI isn't an easy solution. Estimates of the amount of training data required for machine learning vary widely, from hundreds to tens of thousands of manually classified examples, depending on the model, study goals and task complexity. But for endangered species, it might be "difficult or impossible" to collect a very large sample, says Mitch Aide, a tropical ecologist at the University of Puerto Rico Río Piedras Campus in San Juan and founder of Sieve Analytics. "In these cases, you do the best you can."

AI software can also be more error-prone than trained people. Stowell suggests validating results with a small sample and testing the model on data from a different research project or country to ensure that it generalizes as expected.

To assess accuracy in its work, Travers' team performed extensive tests comparing Conservation Metrics' results with on-the-ground observations. The AI software picked up about half of the strikes seen and heard by humans, and an analyst at the firm manually removed false positives. To estimate the actual collision rate, the scientists roughly doubled the number detected by the software. The AI program "doesn't have to be 100% accurate", Travers says. "We just have to know exactly how accurate it is."

And then there's the concern that users might blindly accept AI results without understanding how they were generated. To mitigate that, Peter Ersts, a software developer at the American Museum of Natural History's Center for Biodiversity and Conservation in New York City, suggests using open-source tools; biologists could ask a colleague with software-development expertise to review and explain the code.

Ersts and his colleagues hope to build a curated set of labelled wildlife images that researchers can use to test new models. Their open-source program Andenet-Desktop allows users to label species manually and export training data in formats that can be read by frameworks such as TensorFlow and PyTorch. Researchers can use those libraries to create their machine-learning model, load model parameters back into Andenet and automatically annotate remaining data.

Although early results are promising, human input is still needed. "We can't fully replace people yet, and nor should we," Ersts says.

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When researchers collect audio recordings of birds, they are usually listening for the animals' calls. But conservation biologist Marc Travers is interested in the noise produced when a bird collides with a power line. It sounds, he says, "very much like the laser sound from Star Wars ".

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Artificial Intelligence—A Game Changer for Climate Change and the Environment

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Artificial Intelligence—A Game Changer for Climate Change and the Environment

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by Renee Cho

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Artificial Intelligence-A Game Changer for Climate Change and the Environment

As the planet continues to warm, climate change impacts are worsening. In 2016, there were 772 weather and disaster events, triple the number that occurred in 1980. Twenty percent of species currently face extinction, and that number could rise to 50 percent by 2100. And even if all countries keep their Paris climate pledges, by 2100, it's likely that average global temperatures will be 3˚C higher than in pre-industrial times.

But we have a new tool to help us better manage the impacts of climate change and protect the planet: artificial intelligence (AI). AI refers to computer systems that "can sense their environment, think, learn, and act in response to what they sense and their programmed objectives," according to a World Economic Forum report,

In India, AI has helped farmers get 30 percent higher groundnut yields per hectare by providing information on preparing the land, applying fertilizer and choosing sowing dates. In Norway, AI helped create a flexible and autonomous electric grid, integrating more renewable energy.

And AI has helped researchers achieve 89 to 99 percent accuracy in identifying tropical cyclones, weather fronts and atmospheric rivers, the latter of which can cause heavy precipitation and are often hard for humans to identify on their own. By improving weather forecasts, these types of programs can help keep people safe.
What are artificial intelligence, machine learning and deep learning?
Artificial intelligence has been around since the late 1950s, but today, AI's capacities are rapidly improving thanks to several factors: the vast amounts of data being collected by sensors (in appliances, vehicles, clothing, etc.), satellites and the Internet; the development of more powerful and faster computers; the availability of open source software and data; and the increase in abundant, cheap storage. AI can now quickly discern patterns that humans cannot, make predictions more efficiently and recommend better policies.

The holy grail of artificial intelligence research is artificial general intelligence, when computers will be able to reason, abstract, understand and communicate like humans. But we are still far from that-it takes 83,000 processors 40 minutes to compute what one percent of the human brain can calculate in one second. What exists today is narrow AI, which is task-oriented and capable of doing some things, sometimes better than humans can do, such as recognizing speech or images and forecasting weather. Playing chess and classifying images, as in the tagging of people on Facebook, are examples of narrow AI.

When Netflix and Amazon recommend shows and products based on our purchasing history, they're using machine learning. Machine learning, which developed out of earlier AI, involves the use of algorithms (sets of rules to follow to solve a problem) that can learn from data. The more data the system analyzes, the more accurate it becomes as the system develops its own rules and the software evolves to achieve its goal.

Deep learning, a subset of machine learning, involves neural networks made up of multiple layers of connections or neurons, much like the human brain. Each layer has a separate task and as information passes through, the neurons give it a weight based on its accuracy vis a vis the assigned task. The final result is determined by the total of the weights.

Deep learning enabled a computer system to figure out how to identify a cat-without any human input about cat features- after "seeing" 10 million random images from YouTube. Because deep learning essentially takes place in a "black box" through self-learning and evolving algorithms, however, scientists often don't know how a system arrives at its results.
Artificial intelligence is a game changer
Microsoft believes that artificial intelligence, often encompassing machine learning and deep learning, is a "game changer" for climate change and environmental issues. The company's AI for Earth program has committed $50 million over five years to create and test new applications for AI. Eventually it will help scale up and commercialize the most promising projects.

Columbia University's Maria Uriarte, a professor of Ecology, Evolution and Environmental Biology, and Tian Zheng, a statistics professor at the Data Science Institute, received a Microsoft grant to study the effects of Hurricane Maria on the El Yunque National Forest in Puerto Rico. Uriarte and her colleagues want to know how tropical storms, which may worsen with climate change, affect the distribution of tree species in Puerto Rico.

Hurricane Maria's winds damaged thousands of acres of rainforest, however the only way to determine which tree species were destroyed and which withstood the hurricane at such a large scale is through the use of images. In 2017, a NASA flyover of Puerto Rico yielded very high-resolution photographs of the tree canopies. But how is it possible to tell one species from another by looking at a green mass from above over such a large area? The human eye could theoretically do it, but it would take forever to process the thousands of images.

The team is using artificial intelligence to analyze the high-resolution photographs and match them with Uriarte's data-she has mapped and identified every single tree in given plots. Using the ground information from these specific plots, AI can figure out what the various species of trees look like from above in the flyover images. "Then we can use that information to extrapolate to a larger area," explained Uriarte. "We use the plot data both to learn [i.e. to train the algorithm] and to validate [how well the algorithm is performing]."

Understanding how the distribution and composition of forests change in response to hurricanes is important because when forests are damaged, vegetation decomposes and emits more CO2 into the atmosphere. As trees grow back, since they are smaller, they store less carbon. If climate change results in more extreme storms, some forests will not recover, less carbon will be stored, and more carbon will remain in the atmosphere, exacerbating global warming.

Uriarte says her work could not be done without artificial intelligence. "AI is going to revolutionize this field," she said. "It's becoming more and more important for everything that we do. It allows us to ask questions at a scale that we could not ask from below. There's only so much that one can do [on the ground] ... and then there are areas that are simply not accessible. The flyovers and the AI tools are going to allow us to study hurricanes in a whole different way. It's super exciting."

Another project, named Protection Assistant for Wildlife Security (PAWS) from the University of Southern California, is using machine learning to predict where poaching may occur in the future. Currently the algorithm analyzes past ranger patrols and poachers' behavior from crime data; a Microsoft grant will help train it to incorporate real-time data to enable rangers to improve their patrols.

In Washington State, Long Live the Kings is trying to restore declining steelhead and salmon populations. With a grant from Microsoft, the organization will improve an ecosystem model that gathers data about salmon and steelhead growth, tracks fish and marine mammal movements, and monitors marine conditions. The model will help improve hatchery, harvest, and ecosystem management, and support habitat protection and restoration efforts.
How AI is used for energy
AI is increasingly used to manage the intermittency of renewable energy so that more can be incorporated into the grid; it can handle power fluctuations and improve energy storage as well.

The Department of Energy's SLAC National Accelerator Laboratory operated by Stanford University will use machine learning and artificial intelligence to identify vulnerabilities in the grid, strengthen them in advance of failures, and restore power more quickly when failures occur. The system will first study part of the grid in California, analyzing data from renewable power sources, battery storage, and satellite imagery that can show where trees growing over power lines might cause problems in a storm. The goal is to develop a grid that can automatically manage renewable energy without interruption and recover from system failures with little human involvement.

Wind companies are using AI to get each turbine's propeller to produce more electricity per rotation by incorporating real time weather and operational data. On large wind farms, the front row's propellers create a wake that decreases the efficiency of those behind them. AI will enable each individual propeller to determine the wind speed and direction coming from other propellers, and adjust accordingly.

Researchers at the Department of Energy and National Oceanic and Atmospheric Administration (NOAA) are using AI to better understand atmospheric conditions in order to more accurately project the energy output of wind farms.

Artificial intelligence can enhance energy efficiency, too. Google used machine learning to help predict when its data centers' energy was most in demand. The system analyzed and predicted when users were most likely to watch data-sucking Youtube videos, for example, and could then optimize the cooling needed. As a result, Google reduced its energy use by 40 percent.
Making cities more livable and sustainable
AI can also improve energy efficiency on the city scale by incorporating data from smart meters and the Internet of Things (the internet of computing devices that are embedded in everyday objects, enabling them to send and receive data) to forecast energy demand. In addition, artificial intelligence systems can simulate potential zoning laws, building ordinances, and flood plains to help with urban planning and disaster preparedness. One vision for a sustainable city is to create an "urban dashboard" consisting of real-time data on energy and water use and availability, traffic and weather to make cities more energy efficient and livable.

In China, IBM's Green Horizon project is using an AI system that can forecast air pollution, track pollution sources and produce potential strategies to deal with it. It can determine if, for example, it would be more effective to restrict the number of drivers or close certain power plants in order to reduce pollution in a particular area.

Another IBM system in development could help cities plan for future heat waves. AI would simulate the climate at the urban scale and explore different strategies to test how well they ease heat waves. For example, if a city wanted to plant new trees, machine learning models could determine the best places to plant them to get optimal tree cover and reduce heat from pavement.
Smart agriculture
Hotter temperatures will have significant impacts on agriculture as well.

Data from sensors in the field that monitor crop moisture, soil composition and temperature help AI improve production and know when crops need watering. Incorporating this information with that from drones, which are also used to monitor conditions, can help increasingly automatic AI systems know the best times to plant, spray and harvest crops, and when to head off diseases and other problems. This will result in increased efficiency, enhanced yields, and lower use of water, fertilizer and pesticides.
Protecting the oceans
The Ocean Data Alliance is working with machine learning to provide data from satellites and ocean exploration so that decision-makers can monitor shipping, ocean mining, fishing, coral bleaching or the outbreak of a marine disease. With almost real time data, decision-makers and authorities will be able to respond to problems more quickly. Artificial intelligence can also help predict the spread of invasive species, follow marine litter, monitor ocean currents, keep track of dead zones and measure pollution levels.

The Nature Conservancy is partnering with Microsoft on using AI to map ocean wealth. Evaluating the economic value of ocean ecosystem services-such as seafood harvesting, carbon storage, tourism and more-will make better conservation and planning decisions possible. The data will be used to build models that consider food security, job creation and fishing yields to show the value of ecosystem services under differing conditions. This can help decision-makers determine the most important areas for fish productivity and conservation efforts, as well as the tradeoffs of potential decisions. The project already has maps and models for Micronesia, the Caribbean, Florida, and is expanding to Australia, Haiti, and Jamaica.
More sustainable transport on land
As vehicles become able to communicate with each other and with the infrastructure, artificial intelligence will help drivers avoid hazards and traffic jams. In Pittsburgh, an artificial intelligence system incorporating sensors and cameras that monitors traffic flow adjusts traffic lights when needed. The systems are functioning at 50 intersections with plans for 150 more, and have already reduced travel time by 25 percent and idling by more than 40 percent. Less idling, of course, means fewer greenhouse gas emissions.

Eventually, autonomous AI-driven shared transportation systems may replace personal vehicles.
Better climate predictions
As the climate changes, accurate projections are increasingly important. However, climate models often produce very different predictions, largely because of how data is broken down into discrete parts, how processes and systems are paired, and because of the large variety of spatial and temporal scales. The Intergovernmental Panel on Climate Change (IPCC) reports are based on many climate models and show the range of predictions, which are then averaged out.

Averaging them out, however, means that each climate model is given equal weight. AI is helping to determine which models are more reliable by giving added weight to those whose predictions eventually prove to be more accurate, and less weight to those performing poorly. This will help improve the accuracy of climate change projections.

AI and deep learning are also improving weather forecasting and the prediction of extreme events. That's because they can incorporate much more of the real-world complexity of the climate system, such as atmospheric and ocean dynamics and ocean and atmospheric chemistry, into their calculations. This sharpens the precision of weather and climate modeling, making simulations more useful for decision-makers.
AI has many other uses
AI can help to monitor ecosystems and wildlife and their interactions. Its fast processing speeds can offer almost real-time satellite data to track illegal logging in forests. AI can monitor drinking water quality, manage residential water use, detect underground leaks in drinking water supply systems, and predict when water plants need maintenance. It can also simulate weather events and natural disasters to find vulnerabilities in disaster planning, determine which strategies for disaster response are most effective, and provide real-time disaster response coordination.
What are the risks of artificial intelligence?
While AI enables us to better manage the impacts of climate change and protect the environment in addition to transforming the fields of business, finance, health care, medicine, law, education and more, it is not without risks. Some prominent individuals such as the late physicist Stephen Hawking and Tesla CEO Elon Musk have warned of the existential dangers of uncontrolled artificial intelligence.

The World Economic Forum report identified six categories of AI risk:

Performance. The black box conclusions of AI may not be understandable to humans and thus it may be impossible to determine if they are accurate or desirable. Deep learning could be risky for applications such as early warning systems for natural disasters where more certainty is needed.

Security. AI could potentially be hacked, enabling bad actors to interfere with energy, transportation, early warning or other crucial systems.

Control risks. Since AI systems interact autonomously, they can produce unpredictable outcomes. For example, two systems came up with a language of their own that humans couldn't understand.

Economic risks. Companies that are slower to adopt AI may suffer economic consequences as their AI-based competition advances. We are already seeing how brick and mortar stores are closing as the economy becomes increasingly digitized.

Social risk. AI is resulting in more automation, which will eliminate jobs in almost every field. Autonomous weapon systems could also hasten and exacerbate global conflicts.

Ethical risks. Since AI uses inferred assumptions about groups and communities in making decisions, it could lead to increased bias. The collection of data also raises privacy issues.

To deal with these risks, the World Economic Forum states that government and industry "must ensure the safety, explainability, transparency and validity of AI application." More interaction among public and private entities, technologists, policy-makers and even philosophers, and more investments in research are needed to avert the potential risks of artificial intelligence-and to realize its potential benefits to the environment and humanity.

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Artificial Intelligence-A Game Changer for Climate Change and the Environment

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As the planet continues to warm, climate change impacts are worsening. In 2016, there were 772 weather and disaster events, triple the number that occurred in 1980. Twenty percent of species currently face extinction, and that number could rise to 50 percent by 2100.