Tropical forests are among the most biodiverse and ecologically vital ecosystems on Earth, hosting more than two-thirds of all tree species known. Their canopies are the biosphere’s most concentrated atmospheric interface for carbon, water and energy. However, the functional diversity – how different species operate, grow, and respond to their environment – is less understood. A study by Aguirre-Gutiérrez and colleagues, including Joice Klipel, research associate at the Institute of Ecology at Leuphana University, paints a varied and dynamic picture of canopy traits of tropical forests across continents, revealing insights on functional richness and divergence.
Info: Functional richness & functional divergence
Functional Richness (FRich) describes the range of different functional traits found in a community. A high FRich means many ecological strategies are present, suggesting greater ecosystem adaptability. Functional Divergence (FDiv) measures how species’ traits are distributed within that range. High FDiv indicates that species are functionally distinct, occupying different niches, which is often linked to resource specialization or strong competition. Together, these metrics help assess biodiversity beyond species counts, focusing on how species function.
What are funtional traits – and why do they matter?
Functional traits are measurable properties of plants, such as leaf thickness, nutrient content, or wood density, that influence how plants interact with their environment. These traits determine important processes like photosynthesis, water use, nutrient cycling, and carbon storage. Understanding how these traits vary helps scientists assess how forests function, how resilient they are to change, and how they may respond to climate stressors like droughts or rising temperatures.
Most Earth System Models hdo neglect the diverse and heterogeneous tropical forest biome by representing it as a largely uniform ecosystem. By this oversimplification, the accuracy of predictions about ecosystem functioning, climate feedbacks, and biodiversity resilience is limited.
Info: Earth System Models
Earth System Models (ESMs) are complex computer simulations that integrate physical, chemical, and biological processes across the atmosphere, biosphere, hydrosphere, and geosphere to understand and predict how the Earth system responds to natural and human-induced changes. They track the flow of energy, water, carbon, and nutrients to predict changes in climate, vegetation, and biogeochemical cycles.
A unique effort to map global canopy traits
In response, the researchers of the study undertook a comprehensive analysis of tropical forest canopy traits. They combined field-collected data from more than 1,800 vegetation plots and tree traits with Sentinel-2 satellite remote-sensing, terrain, climate and soil data, to predict variation across 13 morphological, chemical and structural traits of trees and to map the functional diversity of forests across the tropical Americas, Africa, and Asia. The forest sites under study span a total of almost 800 hectares, covering diverse climates and landscapes.
Infobox: Sentinel-2 satellite imagery
Sentinel-2 is a pair of Earth observation satellites from the European Space Agency (ESA). They provide high-resolution optical imagery every 5 days, capturing data in 13 spectral bands. This allows scientists to monitor vegetation, land use, water bodies, and more. In ecology, Sentinel-2 is crucial for detecting plant traits, forest structure, and environmental change at fine spatial scales.
Distinct forest identities: Americas, Africa, Asia
The analysis reveals strong biogeographical differences in canopy functional traits:
American tropical forests exhibit the highest functional richness, meaning they span a broader range of trait combinations. This reflects the high species diversity and environmental heterogeneity, found in the tropical Americas.
African forests, by contrast, show the highest functional divergence, indicating a more specialized pattern of resource use. This might be driven by long-term environmental pressures such as historical drought.
Asian tropical forests (including parts of Australia) display high average values for traits like leaf size, water content, and nutrient concentrations, likely linked to the dominance of the Dipterocarpaceae family in Southeast Asia.
These trait distributions suggest that each region has evolved distinct canopy strategies shaped by evolutionary history, soil fertility, rainfall seasonality, and past climate conditions.
Wet vs. dry
Dry forests (e.g., in Brazil’s cerrado or African savannas) have species with high SLA and nutrient-rich, fast-turnover leaves. This indicates acquisitive strategies optimized for rapid growth during short wet periods.
Wet forests (like Amazonia or Borneo) show traits associated with conservative strategies with thicker, denser leaves and higher carbon investment.
Implications for science, conservation, and climate models
This study improves the realism of Earth System Models by providing detailed trait maps. It identifies regions with high uncertainty or data gaps, guiding future field work (especially in parts of Africa and Asia). Furthermore, the study showcases the rising importance of AI and remote sensing technologies in mapping plant traits and biodiversity on a large scale. But while these tools are powerful, they are meant to complement – not replace – classic ecological methods like field sampling and species identification. To really understand how functional diversity changes over time, we still need to keep investing in good old-fashioned fieldwork, which then feeds into more advanced models and predictions. The insights of this fascinating study can serve as a foundation for forecasting changes in functional forest composition under shifting climates and contribute to building a more process-based and accurate ecological modelling framework across different spatial and temporal scales.
You want to learn more about the study and its findings? Then read the full article here: https://www.nature.com/articles/s41586-025-08663-2#citeas
