Résumé
Accurately monitoring tropical forest carbon stocks is an outstanding
challenge. Allometric models that consider tree diameter, height and wood
density as predictors are currently used in most tropical forest carbon
studies. In particular, a pantropical biomass model has been widely used
for approximately a decade, and its most recent version will certainly
constitute a reference in the coming years. However, this reference model
shows a systematic bias for the largest trees. Because large trees are key
drivers of forest carbon stocks and dynamics, understanding the origin and
the consequences of this bias is of utmost concern. In this study, we
compiled a unique tree mass dataset on 673 trees measured in five tropical
countries (101 trees > 100 cm in diameter) and an original dataset
of 130 forest plots (1 ha) from central Africa to quantify the error of
biomass allometric models at the individual and plot levels when
explicitly accounting or not accounting for crown mass variations. We
first showed that the proportion of crown to total tree aboveground
biomass is highly variable among trees, ranging from 3 to 88 %. This
proportion was constant on average for trees < 10 Mg (mean of 34 %)
but, above this threshold, increased sharply with tree mass and exceeded
50 % on average for trees ≥ 45 Mg. This increase coincided with a
progressive deviation between the pantropical biomass model estimations
and actual tree mass. Accounting for a crown mass proxy in a newly
developed model consistently removed the bias observed for large trees
(> 1 Mg) and reduced the range of plot-level error from −23–16 to
0–10 %. The disproportionally higher allocation of large trees to crown
mass may thus explain the bias observed recently in the reference
pantropical model. This bias leads to far-from-negligible, but often
overlooked, systematic errors at the plot level and may be easily
corrected by accounting for a crown mass proxy for the largest trees in a
stand, thus suggesting that the accuracy of forest carbon estimates can be
significantly improved at a minimal cost.