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Colossal and the US Fish and Wildlife Service will sequence the genomes of all endangered species in the US, storing biological samples in Colossal's BioVault and making genomic data openly accessible to aid conservation efforts.
The US Fish and Wildlife Service is partnering with de-extinction company Colossal Biosciences to build a national 'bio vault' of genetic material from endangered species, with samples cryopreserved and sequenced for conservation research.
Earthwatch Expeditions partners with biologist Richard Bodmer to involve tourists in participatory science in the Peruvian Amazon, collecting data that supports conservation and government protection of the region.
NVIDIA highlights five initiatives using AI and accelerated computing to address climate change, wildlife conservation, and recycling efficiency. Key features include Earth-2 climate models, orangutan nest detection, and robotic waste sorting.
Nature article discussing how scientists are collecting and analyzing environmental DNA from the air to monitor biodiversity, detect species, and track ecosystem health, with applications for conservation efforts and biological threat detection.
A note about kākāpō parrots with a sponsorship pitch for a monthly LLM briefing.
Researchers from MIT and the Woodwell Climate Research Center published a paper on using computer vision to automate fish monitoring, improving upon traditional citizen science methods for river herring conservation.
DeepMind announces new AI research applications for conservation, including a high-resolution deforestation risk model, species distribution mapping using Graph Neural Networks, and updates to the Perch bioacoustics model.
Google DeepMind released an updated version of Perch, an AI model for bioacoustic analysis that helps conservationists monitor endangered species through audio data. The new model improves bird species prediction, adapts better to underwater environments, and expands to include mammals, amphibians, and anthropogenic noise, with over 250,000 downloads of the original version.