Multiscale analysis of global variation in tree allometric relationships: parameter sets for global vegetation models - Ian Grant; Wenge Ni-Meister; Nancy Y Kiang (Environmental Research: Ecology, 2025)
Global models of vegetation dynamics and remote sensing data products require allometric relationships governing trees’ size and shape to predict such quantities as vegetation spatial structure and biomass. However, empirical variation in allometric relationships poses a challenge for global scale models that rely on limited classifications of plant functional types (PFTs), as site-derived parameters often fail to apply appropriately out of sample. To identify empirical allometric variation at multiple scales salient to these global models, we analyzed trees from the global Tallo database, fitting allometric models to biome-continent and biome-PFT aggregations as well as to individual sites. Our results provide new insight into global allometric variability compared to metabolic scaling theory (MST) for relations between height, stem diameter, crown radius (CR), and crown volume. We found that the degree of within-biome variation across continents depends on biome, with tropical rain forests exhibiting much less variation than temperate broadleaf forests. Moreover, the PFT (distinguished by angiosperms vs. gymnosperms, leaf type, phenology) can explain allometric divergence in similar climates. The scaling exponents of most biome-PFT groups fall below the MST predictions for both H – D and CR-allometry. Needleleaf trees (both evergreen and deciduous) have high H – D scaling exponents, while broadleaf trees (both evergreen and deciduous) trees have low H–D scaling exponents. Overall, leaf type played a more important role in determining height ( H )-stem diameter ( D ) allometry than phenology in our biome-PFT analyses, corresponding closely to disparate scaling exponents in very arid groups. At the site level, we found a clear positive relationship between within-site 95th percentile tree height and H – D scaling exponent. Since 95th percentile tree height can be inferred from remote sensing, we suggest that this relationship could support models of global variation in H – D allometry in the context of remote-sensing-based vegetation models. We found the geographic scale of spatial correlation among H – D and CR– H scaling exponents to be significant for sites less than 250 km apart and to decline at greater distances. Finally, in order to demonstrate our results’ relevance to biomass models, we demonstrated how these allometric relationships can be used to estimate allometric parameters for a model that predicts aboveground biomass from Global Ecosystem Dynamics Investigation (GEDI) lidar waveform measurements.