Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees. However, limited datasets, uncertainty in model form, and the implications of among-stand variation and nonlinearity warrant a re-examination of allometric relationships using modern statistical techniques. We ask the following questions: (1) Is there among-stand variation in allometric relationships? (2) Is there nonlinearity in allometric relationships? (3) Can among-stand variation or nonlinearities in allometric equations be attributed to differences in stand age? (4) What are the implications for biomass estimation? To answer these questions, we synthesize data from six different studies in the White Mountains of New Hampshire and compare the performance of generalized additive models (GAMs) -- a nonparametric statistical model -- and the linear models which are most commonly used.
Results/Conclusions
We find that GAMs consistently outperform linear models. This result, as well as the inclusion of stand age terms in the best-fitting models, indicate the presence of among-stand variation. A planned contrast analysis revealed significant differences in allometric parameters for each of the four considered species between stands of young and old ages, young and mid ages, and mid and old ages, indicating the potential importance of stand age. However, variability in these results point to additional sources of variability. We additionally found that linear models overestimate tree biomass compared to both the GAM generated estimates and the true biomass values. These overestimations are proportionally small, but nevertheless indicate the opportunity for important refinements in the accuracy of biomass estimation equations.