EGU23-7428, updated on 25 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Global patterns of plant functional traits and their relationships to climate

Jiaze Li and Iain Colin Prentice
Jiaze Li and Iain Colin Prentice
  • Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, SL5 7PY, UK

Plant functional traits (FTs) determine the survival strategies of plants and their adaptations to the environment, which affect the structure and productivity of vegetation. However, global patterns of many FTs remain uncertain. Currently available global maps of FTs generated by different upscaling approaches show considerable divergence. Potentially different trait responses could be induced by climate change in herbaceous, evergreen and deciduous taxa. Better understanding of these variations should improve our ability to predict global trait patterns and the consequences of global environmental change for vegetation.

We compiled a global data set for 18 FTs, including 42,676 species from 89,478 natural vegetation plots. All FTs in the data set have community-weighted mean values for each plot; seven also have species-mean values. We grouped the species into non-woody, woody deciduous and woody evergreen categories according to their life form and leaf phenology. Then we calculated community-weighted mean values of the seven FTs having species-mean records for the three plant groups. We selected three bioclimatic variables: a moisture index (MI, representing plant-available moisture), mean temperature of the coldest month (MTCO, representing winter cold), and mean growing season temperature (MGST, representing summer warmth) to create a three-dimensional global climate space and define global climate classes. Principal Component Analysis (PCA) was used to estimate the main functional continua on which FTs converge for each plant group. Redundancy Analysis (RDA) was used to describe the extent to which variation in trait combinations can be explained by bioclimatic variables. Correlations between each trait and bioclimatic variables for the three plant groups were described by Generalized Additive Models (GAMs). We used the GAMs to visualise the trait distribution in global climate space for all seven FTs, group by group. We finally fitted new comprehensive GAMs considering bioclimatic variables and remotely-sensed global cover of the three plant groups in order to predict global patterns for all 18 FTs at 0.1° spatial resolution.

Bioclimatic variables explain more trait variance for woody than non-woody plants. Two trait combinations are common to all plant groups: one is plant height (H) – diaspore mass (DM), positively associated with seasonal temperatures; the other is leaf mass per unit area (LMA) – leaf nitrogen content per unit area (Narea), decreasing with moisture availability. Stem specific density (SSD) of non-woody plants is correlated with the LMA–Narea axis, but SSD of woody evergreen plants is correlated with the H–DM axis. For woody deciduous plants, SSD is correlated with leaf nitrogen content per unit mass (Nmass). Leaf area (LA) is positively correlated with all bioclimatic variables and shows variation in all plant groups that is independent of other traits. FTs within the same trait combination tend to present similar patterns in the global climate space. GAMs based on bioclimatic variables and vegetation cover explain up to three-quarters (on average about a half) of global trait variation.

Our study reveals universal relationships among traits and between traits and climates, highlights certain key differences between within non-woody, woody deciduous and woody evergreen taxa, and produces high-resolution global maps for plant functional traits.

How to cite: Li, J. and Prentice, I. C.: Global patterns of plant functional traits and their relationships to climate, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7428,, 2023.