Mapping local and global variability in plant trait distributions

Abstract

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50 × 50-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.odeling global climate and the carbon cycle with Earth system models (ESMs) requires maps of plant traits that play key roles in leaf-and ecosystem-level metabolic processes (1-4). Multiple traits are critical to both photosynthesis and respiration, foremost leaf nitrogen concentration (Nm ) and specific leaf area (SLA) (5-7). More recently, variation in leaf phosphorus concentration (Pm ) has also been linked to variation in photosynthesis and foliar respiration (7-12). Estimating detailed global geographic patterns of these traits and corresponding trait-environment relationships has been hampered by limited measurements (13), but recent improvements in data coverage (14) allow for greater detail in spatial estimates of these key traits.Previous work has extrapolated trait measurements across continental or larger regions through three methodologies: (i) grouping measurements of individuals into larger categories that share a set of properties [a working definition of plant functional types (PFTs)] (4, 15), (ii) exploiting trait-environment relationships (e.g., leaf Nm and mean annual temperature) (1,(16)(17)(18)(19)(20), or (iii) restricting the analysis to species whose presence has been widely estimated on the ground (21-24). Each of these methods has limitations-for example, trait-environment relationships do not well explain observed trait spatial patterns (1, 25), while species-based approaches limit the scope of extrapolation to only areas with well-measured species abundance. More critically, the first two global methodologies emp...

Plant traits inform predictions of tundra responses to global change

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Evaluation and selection of functional diversity metrics with recommendations for their use in life cycle assessments

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Geographic scale and disturbance influence intraspecific trait variability in leaves and roots of North American understorey plants

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