Landscape-level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type.

Mitigation of climate change depends on accurate estimation and mapping of terrestrial carbon stocks, particularly in carbon dense tropical forests. Allometric equations can be used to robustly estimate biomass of tropical trees, but often require tree height, which is frequently unknown. Researchers and practitioners must, therefore, decide whether to directly measure a subset of tree heights to develop diameter : height (D:H) equations or rely on previously published generic equations. To date, studies comparing the two approaches have been spatially restricted and/or not randomly allocated across the landscape of interest, making the implications of deciding whether or not to measure tree heights difficult to determine. To address this issue, we use inventory data from a systematic-random forest inventory across Gabon (102 forest sites; 42,627 trees, including 7,036 height-measured trees). Using plot-specific models of D:H as a benchmark, we compare the performance of a suite of locally fitted and commonly used generic models (parameterized national, georegional, and pantropical equations) across a variety of scales, and assess which abiotic, anthropogenic, and topographical covariates contribute the most to bias in height estimation. We reveal marked spatial structure in the magnitude and direction of bias in tree height estimation using all generic models, due at least in part to soil type, which compounded to substantial error in site-level AGB estimates (of up to 38% or 150 Mg/ha). However, two generic pantropical models (Chave 2014; Feldpausch 2012) converged to within 2.5% of mean AGB at the landscape scale. Our results suggest that some (not all) pantropical equations can extrapolate AGB across large spatial scales with minimal bias in estimated mean AGB. However, extreme caution must be taken when interpreting the AGB estimates from generic models at the site-level as they fail to capture substantial spatial variation in D:H relationships, which could lead to dramatic under- or over-estimation of site-level carbon stocks. Validated allometric models derived at site- or soil-type-levels may be the best way to reduce such biases arising from landscape-level heterogeneity in D:H model fit in the Afrotropics.

Certified and uncertified logging concessions compared in Gabon: changes in stand structure, tree species, and biomass.

Forest management certification is assumed to promote sustainable forest management, but there is little field-based evidence to support this claim. To help fill this gap, we compared a Forest Stewardship Council (FSC)-certified with an adjacent uncertified, conventionally logged concession (CL) in Gabon on the basis of logging damage, above-ground biomass (AGB), and tree species diversity and composition. Before logging, we marked, mapped, and measured all trees >10 cm dbh in 20 and twelve 1-ha permanent plots in the FSC and CL areas, respectively. Soil and tree damage due to felling, skidding, and road-related activities was then assessed 2-3 months after the 508 ha FSC study area and the 200 ha CL study area were selectively logged at respective intensities of 5.7 m(3)/ha (0.39 trees/ha) and 11.4 m(3)/ha (0.76 trees/ha). For each tree felled, averages of 9.1 and 20.9 other trees were damaged in the FSC and CL plots, respectively; when expressed as the impacts per timber volume extracted, the values did not differ between the two treatments. Skid trails covered 2.9 % more of the CL surface, but skid trail length per unit timber volume extracted was not greater. Logging roads were wider in the CL than FSC site and disturbed 4.7 % more of the surface. Overall, logging caused declines in AGB of 7.1 and 13.4 % at the FSC and CL sites, respectively. Changes in tree species composition were small but greater for the CL site. Based on these findings and in light of the pseudoreplicated study design with less-than perfect counterfactual, we cautiously conclude that certification yields environmental benefits even after accounting for differences in logging intensities.