The BirdNET app, a free machine-learning powered tool that can identify more than 3,000 birds by sound alone, generates reliable scientific data and makes it easier for people to contribute citizen-science data on birds by simply recording sounds, according to new Cornell research. "The most exciting part of this work is how simple it is for people to participate in bird research and conservation," said Connor Wood, research associate in the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology and lead author of The Machine Learning-Powered BirdNET App Reduces Barriers to Global Bird Research by Enabling Citizen Science Participation, which was published on 28 June in PLOS Biology. "You don't need to know anything about birds, you just need a smartphone, and the BirdNET app can then provide both you and the research team with a prediction for what bird you've heard," Wood said. "This has led to tremendous participation worldwide, which translates to an incredible wealth of data. It's really a testament to an enthusiasm for birds that unites people from all walks of life."

For the first time, researchers have shown that there is a genetic component underlying the amazing spatial memories of mountain chickadees. These energetic half-ounce birds hide thousands of food items every fall and rely on these hidden stores to get through harsh winters in the mountains of the West. To find these caches, chickadees use highly specialized spatial memory abilities. Although the genetic basis for spatial memory has been shown for humans and other mammals, direct evidence of that connection has never before been identified in birds. Their research, "The Genetic Basis of Spatial Cognitive Variation in a Food-Caching Bird," published Nov. 3 in the journal Current Biology.

We can learn a lot from nature if we listen to it more--and scientists around the world are trying to do just that. From mountain peaks to ocean depths, biologists are increasingly planting audio recorders to unobtrusively eavesdrop on the groans, shrieks, whistles and songs of whales, elephants, bats and especially birds. This summer, for example, more than 2,000 electronic ears will record the soundscape of California's Sierra Nevada mountain range, generating nearly a million hours of audio. To avoid spending multiple human lifetimes decoding it, researchers are relying on artificial intelligence. Such recordings can create valuable snapshots of animal communities and help conservationists understand, in vivid detail, how policies and management practices affect an entire population.

Do you hear the birds chirping outside your window? There are more than 10,000 bird species in the world, and they can be found in nearly every environment, from untouched rainforests to suburbs and cities. Birds play an essential role in nature. They are high up in the food chain and integrate changes occurring at low levels. As such, birds are excellent indicators of deteriorating habitat quality and environmental pollution.

Billions of birds in North America make the trek south each fall, migrating in pursuit of warmer winter temperatures. Many of these migratory birds fly under the cover of night, making it challenging for birdwatchers and ornithologists to observe them and track long-term trends. But the need to monitor avian population levels is critical. Recent research estimates that the number of birds in North America has fallen by 3 billion in the past 50 years, impacted by climate change, habitat loss, hunting and pesticides. Spring migration has declined by 14 percent in the last decade.

Dear EarthTalk: What are some ways Artificial Intelligence (AI) is being used to fight climate change and otherwise protect the environment? Artificial Intelligence (AI), defined as the capability of machines to imitate intelligent human behavior and learn from data, is considered by many to be the final frontier of computing. And environmentalists and tech companies are now harnessing the power of AI to service to the environment. To wit, Microsoft announced in December 2017 that it is expanding its "AI for Earth" program and committing $50 million over the next five years to put AI technologies in the hands of individuals and organizations working to solve global environmental challenges, including climate change as well as water, agriculture and biodiversity issues. Lucas Joppa, Microsoft's first Chief Environmental Scientist, is convinced that AI is now mature enough and the global environmental crisis acute enough to justify the creation of an AI platform for the planet.

Published Sunday, Jun. 24, 2018, 12:48 pm Dear EarthTalk: What are some ways artificial intelligence is being used to fight climate change and otherwise protect the environment? Artificial intelligence (AI), defined as the capability of machines to imitate intelligent human behavior and learn from data, is considered by many to be the final frontier of computing. And environmentalists and tech companies are now harnessing the power of AI to service to the environment. To wit, Microsoft announced in December 2017 that it is expanding its "AI for Earth" program and committing $50 million over the next five years to put AI technologies in the hands of individuals and organizations working to solve global environmental challenges, including climate change as well as water, agriculture and biodiversity issues. Lucas Joppa, Microsoft's first Chief Environmental Scientist, is convinced that AI is now mature enough and the global environmental crisis acute enough to justify the creation of an AI platform for the planet.

Ecological systems are inherently complex. The processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales, presenting a unique challenge for the development and coordination of effective conservation strategies, particularly for wide-ranging species. In order to study ecological systems across scales, data must be collected at fine resolutions across broad spatial and temporal extents. Crowdsourcing has emerged as an efficient way to gather these data by engaging large numbers of people to record observations. However, data gathered by crowdsourced projects are often biased due to the opportunistic approach of data collection. In this article, we propose a general class of models called AdaSTEM, (for adaptive spatio-temporal exploratory models), that are designed to meet these challenges by adapting to multiple scales while exploiting variation in data density common with crowdsourced data. To illustrate the use of AdaSTEM, we produce intra-seasonal distribution estimates of long-distance migrations across the Western Hemisphere using data from eBird, a citizen science project that utilizes volunteers to collect observations of birds. Subsequently, model diagnostics are used to quantify and visualize the scale and quality of distribution estimates. This analysis shows how AdaSTEM can automatically adapt to complex spatiotemporal processes across a range of scales, thus providing essential information for full-life cycle conservation planning of broadly distributed species, communities, and ecosystems.