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Activity Number: 391 - Statistical Advancements in Forestry, Ecology and Climate Modeling
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #326810
Title: Hierarchical Spatial Model for Creating Global Maps of Plant Trait Distribution
Author(s): Abhi Datta*
Companies: Johns Hopkins Bloomberg School of Public Health
Keywords: Bayesian model; Spatial statistics; Gaussian Processes; Plant traits; Earth System Models; Uncertainty quantification

In Earth Systems Models (ESMs), characterization of plant diversity is limited to grouping related species into Plant Functional Types (PFTs) and representing all trait variation within each PFT with a single mean value that is applied globally. This parsimonious representation suppresses knowledge about local plant diversity. We use the largest global plant trait database and state-of-the-art Bayesian spatial model to create fine-grained global maps of plant trait distributions will allow for a more accurate representation of the land surface in ESMs. We characterize how the three traits --- specific leaf area (SLA), dry mass-based concentrations of leaf nitrogen (Nm), and phosphorus (Pm) vary within and among over 50,000 ? 50 × 50 km cells across the entire vegetated land surface. This is the first approach that leverages both environmental and spatial information which are critical to predict trait values at sparsely sampled regions of the earth. The Bayesian approach advances prior trait mapping endeavors by generating global maps that preserve local variability. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Authors who are presenting talks have a * after their name.

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