Towards global maps of vegetation trait change - the VESTA project

Exploring the intricate interplay between global biodiversity patterns and the looming impact of climate change stands as a paramount inquiry within the realm of earth system science. Furthermore, the acknowledgment of shifts in plant functional diversity emerges as a key catalyst, wielding substantial influence over pivotal ecosystem processes like the carbon cycle. Various essential plant traits, intricately tied to vegetation function—ranging from photosynthesis to carbon storage and water/nutrient uptake—underscore the significance of comprehensive global trait maps. These maps prove indispensable for unraveling environmental interactions, identifying threats to the biosphere, and fostering a profound understanding of our planet's intricacies. However, the sparse and non-representative nature of current trait observations poses a formidable challenge. Presently, global maps of vegetation traits are constructed by bridging observational gaps, primarily relying on empirical or statistical relationships between trait observations, climate and soil data, and remote sensing information. However, these approaches exhibit limited explanatory power, struggle to encompass a myriad of traits, and face constraints in ensuring ecological consistency in their extrapolations.

The VESTA (Vegetation Spatialization of Traits Algorithm) project emerges as a groundbreaking initiative aimed at refining our grasp on global above and belowground plant traits. This endeavor involves integrating a trait-based dynamic global vegetation model (DGVM) with Earth observation (EO) data. Trait-based DGVMs, rooted in a process-based foundation, forge a direct nexus between the environment, plant ecology, and emerging vegetation patterns. Leveraging insights from contemporary global trait databases, the model is initialized to mirror real-world conditions. Subsequently, EO data enters the equation to fine-tune the model through a calibration process, adjusting trait relationship curves having as reference satellite measurements of vegetation structure and productivity.

Drawing parallels to prior methods used in climate reanalysis, EO-constrained trait-based DGVMs yield a multivariate, spatially comprehensive, and coherent record of global vegetation traits. The resultant dataset encapsulates trait distributions, offering detailed insights into plant functional diversity metrics—mean, variance, skewness, and kurtosis—at specific locations. Notably, these trait maps extend beyond mere snapshots, evolving into a temporal series that affords a nuanced comprehension of the prevailing state of functional diversity and its temporal shifts. Ultimately, the fruition of this project manifests as an invaluable EO product, showcasing leaf, wood, and root traits and their change through time.