1. Studies of forest carbon stocks and fluxes rely on estimates of specific wood density to convert tree diameter and height measurements made within permanent plots into carbon stock estimates. However, measurements of wood density are often available for only a subset of species. As there is strong phylogenetic trait conservatism of wood density, missing data are usually estimated by averaging the wood densities of other species within the same genus or family, using whatever data are available locally.
2. The Global Wood Density (GWD) database (http://hdl.handle.net/10255/dryad.235) provides wood densities for 8412 species from around the world, providing an opportunity to utilize data from further afield when faced with missing values in a study area. We investigated whether the GWD provides better estimates than local data sets when conventional averaging methods (AM) are used. Secondly, we develop Hierarchical Bayesian Models (HBM) that incorporate phylogenetic covariance to estimate missing wood densities.
3. Using AM, we found that correlations between observations and estimates were higher when the GWD was used in place of local data sets, mostly because of larger sample size. Missing wood densities were more accurately estimated from the global data set than from local data sets, indicating that the GWD should be used as a common standard when calculating carbon stocks.
4. Estimates based on including phylogenetic dependency in HBMs were also closely correlated to observations, but were no better than those obtained from the simpler AM. Estimations based on HBM could become more useful when phylogenetic trees resolved to the species level are available. Until such improvements are made, we conclude that building more data into the reference data set, rather than improving the method itself, is the most productive way to refine estimates of unknown wood densities.