'I rarely get outside': scientists ditch fieldwork in the age of AI (www.nature.com)

Recent developments in ecology highlight a significant shift towards machine learning and data analysis, as scientists increasingly rely on digital datasets rather than traditional fieldwork. Tadeo Ramirez-Parada’s PhD research exemplifies this trend, utilizing a machine-learning algorithm to analyze flowering times from over a million herbarium specimens without ever setting foot in a field. His findings illustrate how plants are adapting to rising temperatures, shedding light on ecological responses to climate change. This transition is part of a broader movement in ecology, where advancements in AI, sensor technology, and data digitization allow for unprecedented monitoring of biodiversity, promising insights into global change. However, this shift raises concerns among ecologists about the diminishing hands-on experience in the field, which they argue is vital for nuanced understanding and robust research. Critics warn that an over-reliance on digital data gathering could lead to biases or oversimplifications in ecological studies. Furthermore, the phenomenon of 'AI colonialism' is discussed, highlighting ethical issues regarding data collection from poorer regions without local engagement. The conversation underscores a tension between leveraging technological advancements for ecological insights and maintaining essential fieldwork traditions that nurture a deeper connection with nature.