Article by Anusha Krishnan: “What does a global map of plant life look like, and what happens when the data behind it is incomplete?
A recent study published in Nature Communications in January 2026, describes such a map, built from field surveys, earth observation systems, and millions of observations recorded by citizen scientists around the world.
This map now offers one of the most in-depth views of how plants function across ecosystems. However, the map also exposes something else. These are large, persistent gaps in the data that scientists rely on to understand the Earth’s vegetation, which means that quite a bit of the world’s plant life is still poorly documented.
The study used 31 plant traits such as size, growth strategy, leaf characteristics, wood density, reproductive traits, and resource use to outline a global ‘plant economics’ spectrum. These characteristics, also known as functional traits, can help us understand how plant strategies change in response to climate and ecosystem stress.
Currently, most global biodiversity data only tell us what species are found where; they don’t tell us what roles they play in carbon storage and ecosystem dynamics. Mapping these traits on a global scale gives us a spectrum of characteristics spanning fast-growing, nutrient-hungry plants to slow-growing, stress-tolerant ones and how these traits support plant growth, survival, adaptation, and persistence in an ever-changing world. This is especially important for informing models on energy, nutrient, and water cycles which are increasingly being used to plan infrastructure, agricultural, and energy strategies in a world faced with climate change.
The researchers used a combination of data from detailed field surveys collected by scientists, millions of observations from citizen scientists, and environmental information derived from satellites and climate records to create this global plant trait map.
They then used machine-learning models to link the plant traits with environmental conditions like temperature, rainfall, and soil properties to predict plant traits in places where direct measurements were unavailable. The models were generated using three approaches, namely, scientific surveys only, citizen science only, and both combined…(More)”.
