Digital twins are transforming ecology by enabling scientists to simulate complex animal behaviors and ecosystem dynamics in real time. One notable example is the Crane Radar, a digital twin developed by ecologist Koen de Koning at Wageningen University & Research in the Netherlands. Using real-time data from birdwatchers, satellite imagery, and migration patterns from Movebank, the tool predicts where common cranes will be in the next four hours, helping both researchers and birdwatchers locate flocks more accurately. Since its launch in 2022, the platform has drawn up to 300,000 daily users during peak migration, demonstrating strong public engagement. Digital twins are virtual replicas of physical systems, originally developed for manufacturing and healthcare. NASA used an early version during the Apollo 13 mission to troubleshoot an oxygen tank explosion. Today, the technology is expanding rapidly, driven by advances in IoT, AI, and cloud computing. The global digital-twin market is expected to grow from $21 billion in 2024 to $150 billion by 2030, with healthcare leading in growth due to applications in surgery planning and diagnostics. In ecology, digital twins offer powerful tools for studying ecosystems under climate change and human pressure. The Crane Radar is part of the EU-funded Nature FIRST project, which aims to improve biodiversity monitoring through digital modeling. De Koning chose cranes because of their abundant data and predictable migration patterns, but he also saw a personal benefit—no more missing the birds due to poor timing. More complex models are being developed for ecosystems like Doñana National Park in Spain, a critical biodiversity hotspot threatened by water overuse, agriculture, tourism, and climate change. Maria Paniw’s team is building a digital twin that models interactions between vegetation, rabbits, and the Iberian lynx—a keystone species recently downgraded from endangered to vulnerable. The model uses GPS data from lynx, rabbit population records, and satellite-derived vegetation indices to predict how changes in plant health affect prey and predator dynamics. This helps guide future lynx reintroductions and conservation planning. Similar projects are underway for rivers across the globe. In England, a digital twin of the River Stiffkey is helping to protect rare chalk-stream habitats. In Kenya, Lawrence Nderu is developing a twin of the Mara River basin to forecast floods and support early warnings for nomadic herders. By integrating data from weather stations, soil sensors, and satellite imagery, the model aims to reduce loss of life and livestock during flood events. Despite their promise, digital twins face significant challenges. Reliable, real-time data is essential, but field sensors must be connected to the internet—a barrier in remote areas. Citizen science data, while valuable, can be inconsistent in time, location, or species identification. Storage costs are also high; Nderu estimates $800 annually just for cloud storage of video data. Furthermore, digital twins require long-term funding, yet most research grants are short-term, making sustained maintenance difficult. Scientists stress the need to engage users early, explaining how the models can support decision-making. As digital twins evolve, they could become vital tools for conservation, climate adaptation, and sustainable management of natural systems.