This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. For the first time in four centuries, it's good to be a beaver. Long persecuted for their pelts and reviled as pests, the dam-building rodents are today hailed by scientists as ecological saviors. Their ponds and wetlands store water in the face of drought, filter out pollutants, furnish habitat for endangered species, and fight wildfires. In California, Castor canadensis is so prized that the state recently committed millions to its restoration.

The phone is not lost, quite the opposite. Incongruous but invaluable, the phone is part of a network of devices placed throughout the forest to listen for the telltale sounds of illegal logging. Amidst the rustle of leaves, the scampering of critters, and the steady drip of moisture, the sound of a lorry or chainsaw is an alarm bell that can bring forest rangers hurrying to the scene. It's just one of the ways that technology is helping conservationists in the fight to protect wildlife and the planet. The advance of technology is often seen as a risk for the environment: From the invention of the plough to carve the landscape, through the industrial revolution, to the electronic age's thirst for Earth's limited resources.

Off the coast of Southern California, amid a literal sea of troubles--warming waters, microplastic pollution, overfishing--is a 96-square-mile conservation success story. Santa Cruz Island once teemed with feral pigs and invasive Argentine ants until the Nature Conservancy unleashed a coordinated campaign of eradication. That's allowed the adorable island fox to bounce back from the brink of extinction. The battle was won, but the war wasn't over, because the Nature Conservancy now has to defend that territory from yet another invader: rats. The scourge of islands everywhere, rats get ashore and breed like crazy, devouring just about everything in their paths--native plant seeds, bird and reptile eggs, local people's crops.

For the past three decades, geologist Carlos Souza has worked at the Brazil-based nonprofit Imazon, exploring ways he and the teams he coordinates can use applied science to protect the Amazon rainforest. For much of that time, satellite imagery has been a big part of his job. In the early 2000s, Souza and colleagues came to understand that 90 percent of deforestation occurs within 5 kilometers of newly created roads. While satellites have long been able to track road expansion, the old way of doing things required people to label those findings by hand, amassing what would eventually become training data. Those years of labor paid off last fall with the release of an AI system that Imazon says reveals 13 times more roadway than the previous method, with an accuracy rate of between 70 and 90 percent.

Effective water resource management requires information on water availability, both in terms of quality and quantity, spatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in data-sparse regions.

Sean Cusack has been a backyard beekeeper for 10 years and a tinkerer for longer. That's how he and an entomologist friend got talking about building an early warning system to alert hive owners to potentially catastrophic threats. They envisioned installing a motion-sensor-activated camera at a beehive entrance and using machine learning to remotely identify when invaders like mites or wasps or potentially even the Asian giant hornet were getting in. "A threat like that could kill your hive in a couple of hours, and it'd be game over," Cusack said. "But had you known within 10 minutes of it happening and could get out there and get involved, you could potentially rescue whole colonies."

The technology coupled with hands-on work and measurements is used to create a forest management plan. The Duncannon Borough Watershed is a 1,600-acre property key to generating money in the local community. "In 300 spots, we measured every tree for a tenth of an acre," said Josh Parrish, the director of the Working Woodlands program at the Nature Conservancy. Understanding what you have is important in moving forward. So, the Nature Conservancy is doing just that by working with a company that uses artificial intelligence.

I get the feeling you don't dislike rats enough. Because your struggles with the rodents chewing through your house pale in comparison to the problems wrought by rodents chewing through entire island ecosystems. Release just one pregnant rat on an island and soon enough the invasive predators will have decimated that pristine environment like an atom bomb. Sure, rats on their own are pretty neat, but we've got a nasty habit of transporting them where they don't belong, at which point they transform into menaces. Such is the plight of the Galapagos Island of Seymour Norte, a speck of 455 acres off the coast of Ecuador. In 2007, conservationists succeeded in ridding the island of invasive rats, but a decade later, the fiends had returned, likely by swimming from the neighboring island of Baltra.

Video reviewers will convert lengths to weight to be used for science and management.

Say Machine Learning to someone, and if they recognize the term, they'll probably think, "tech company." But while the origin stories of transformative technologies like machine learning, deep learning, and artificial intelligence often seem to take root in Silicon Valley, the truth is these are industry-agnostic innovations. Their impact is being felt across countless fields you might never have thought of as being ripe for technological advancement. Think about it like this: If you were a farmer, and someone came to you and said, there's a technology out there that can accurately predict your crop yields, would you be interested? Well, this is exactly what Descartes Labs does.