Ecology & Conservation

Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified f

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Science Summary: In this guest post, Prof. Matthew Schwartz returns to describe a new approach to AI-accelerated science. In Vibe Physics, Schwartz discussed similarities in capability between Claude and a physics graduate student. Here, he describes what happened when he stopped fighting Claude and allowed Claude to find “Claude-shaped” problems: ones best suited to the capabilities of the current generation of LLM

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Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet AI pioneer Ken Goldberg, use AI techniques to track animal populations, investigate how to improve recommender systems, and find out how external knowledge is used in AI systems. Combining cultures, from code to canvas: an interview with Ken Goldb

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Jennifer Raynor Dr. Jennifer Raynor discusses the rapid evolution of AI-enabled vessel tracking technologies and how this technology can drive better decisions at sea. As chair of Global Fishing Watch’s science advisory board and member of the organization’s board of directors, Dr. Jennifer Raynor has a front-row seat to the rapidly changing picture capturing human activity at sea. Advances in artificial intelligence

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Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Mon

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Image credit: Bejan Adrian / Pexels Key Takeaways BirdCODE is a supervised deep learning model that offers zero-shot sound event detection , enabling researchers to detect and classify bird vocalizations with temporal precision and without dataset-specific fine-tuning. The model was developed using a combination of training strategies using weakly labelled recordings, synthetic soundscapes, and pseudo-labelled data t

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Developer @RightSideOfAI built AUGURY, an open-source AI pipeline that uses the MiniCPM5-1B model to turn weed observations into meaningful soil-health insights, avoiding reliance on large cloud models.

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Google AI Developers demonstrated a workflow that gave Gemini 3.7 Flash hundreds of PDFs from a Victorian-era book of botanical illustrations, extracted the historical plants, classified them with the modern APG IV system, and presented the result as an interactive visualization.

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Image credit: José Neiva Mesquita-Neto Understanding Pollination Through Ecological Function Pollination is one of the most important ecological processes on Earth, but understanding how different insects contribute to it remains surprisingly difficult. While many bee species visit flowers, they do not all transfer pollen. Distinguishing between different ecological functions has traditionally required labor-intensiv

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The OlmoEarth Platform: Geospatial inference at planetary scale July 28, 2026 Ai2 The OlmoEarth models are our family of Earth observation foundation models, pretrained on roughly 10 terabytes of multimodal satellite data. Governments, NGOs, and other mission-driven organizations are already adapting OlmoEarth for applications including deforestation monitoring, food security, and wildfire risk. At Ai2, we know how t

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The air in forests is filled with animal sounds, containing rich information about biodiversity. However, analyzing the massive recordings collected by autonomous devices is a huge challenge. Dan Stowell, a researcher at Naturalis Biodiversity Centre in the Netherlands, leads BioacAI, a four-year EU-funded project with European and UK partners, to develop AI tools that automatically identify species from sound record

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Image credit: Mantis Design / Adobe Stock The Growing Challenge of Biodiversity Data Citizen science projects are generating more data than ever before. Global biodiversity databases now contain millions of species observations , creating an incredible resource for ecological research. While collecting the data is not a problem, researchers still need to analyze the sounds hidden within thousands or even millions of

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Our 2025 Annual Report The past few years have seen a steady drumbeat of discoveries that illustrate the complex communications and culture of other animals, many of them accelerated by significant advances in technology. We are proud to be part of this incredible progress in understanding our fellow species. Our 2025 Annual Report highlights the advances we’ve made with our partners over the past year in the burgeon

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Image credit: Quang / Adobe Stock Individual ID in Chatty Species In the wild, zebra finches don’t sing in isolation. They move through dynamic social groups, calling and responding to one another across space and time. For ecologists, understanding this communication requires knowing who is communicating with whom , and how information flows through a group. Tracking communication at the level of individuals, howeve

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Tanya Berger-Wolf, professor of computer science engineering; of electrical and computer engineering; and of evolution, ecology and organismal biology at Ohio State University, gave a lecture titled “AI for Nature: From Science to Action" as part of the Distinguished Lectures in Life Sciences series presented by the Huck Institutes of the Life Sciences.

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Image credit: Elianne Dipp / Pexels Over the last decade, work on animal communication has reached a convergence as the growing availability of large-scale behavioral and bioacoustic data merges with relatively recent advances in machine learning. As a result, we’re starting to see the study of animal communication shift from narrowly defined, species-specific, and hypothesis-limited analyses toward data-driven disco

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