Contributions of height allometry and within-species trait variation to uncertainty in estimates of tropical forest carbon stocks

Abstract Tropical forests host globally significant carbon stocks, which are relied on to help mitigate the effects of human-induced climate change. Characterizing the uncertainty around carbon stock estimates is thus essential to inform the carbon budgets needed to safely limit global warming, with implications for policy and decision-making worldwide. We used a large forest inventory dataset from the Australian tropical rainforest (7208 stems) to evaluate height:diameter (H:DBH) allometric variation and quantified within-species variation in wood density (WD) and woody tissue carbon ([C]) content (98 trees). Together, H:DBH, wood density, and woody tissue C were used to estimate forest carbon stocks (i.e. the carbon content of woody biomass: CAGB). Using simulations, we then provide estimates and recommendations on the uncertainty around CAGB. H measurement errors contributed more random error (4.5%) to CAGB than the best performing (site-level) H:DBH allometric models (±2.4%) and led to under-estimation of CAGB by roughly 15%. Comparison of H:DBH allometric models that were developed at different biogeographic scales shows that widely used pantropical models substantially over-estimated tree H, and thus tropical forest CAGB, at some Australian sites by close to 100%. By contrast, wood trait variation generated just 3% uncertainty in CAGB, which is reassuring since global wood trait datasets often contain a single record or a few records for tropical species. Our findings reinforce the promise of laser scanning technologies to improve biomass estimation via more accurate measurements of canopy height and nondestructive development of local-to-regional allometric models, which provide clear advantage over pantropical equations.