March Mammal Madness is a science outreach project that, over the course of several weeks in March, reaches hundreds of thousands of people in the United States every year. We combine four approaches to science outreach – gamification, social media platforms, community event(s), and creative products – to run a simulated tournament in which 64 animals compete to become the tournament champion. While the encounters between the animals are hypothetical, the outcomes rely on empirical evidence from the scientific literature. Players select their favored combatants beforehand, and during the tournament scientists translate the academic literature into gripping “play-by-play” narration on social media. To date ~1100 scholarly works, covering almost 400 taxa, have been transformed into science stories. March Mammal Madness is most typically used by high-school educators teaching life sciences, and we estimate that our materials reached ~1% of high-school students in the United States in 2019. Here we document the intentional design, public engagement, and magnitude of reach of the project. We further explain how human psychological and cognitive adaptations for shared experiences, social learning, narrative, and imagery contribute to the widespread use of March Mammal Madness.

Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network’s Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.