Validation of allometric biomass models: How to have confidence in the application of existing models

Abstract The development of biomass estimation models is highly resource intensive as it generally entails harvesting (or excavating) trees of a range of sizes to determine dry weight of above-ground (or below-ground) biomass. To maximise the cost effectiveness of such sampling, guidance is required on whether an allometric model that already exists is suitable for a new site or species, or whether further sampling and model development is necessary. With the aim to provide such guidance, we collated 12 pairs of well-sampled ( N  > 50) data sets of the same species at two sites, or two species at the same site. These provided case studies for: (i) assessing alternative statistical approaches to validate the application of a model developed using one data set to predict biomass of independent data from another site or species, and (ii) applying scenario analyses to explore the impact of sample size on uncertainty of validation, e.g. minimising type I and type II errors. Our results indicate that although an allometric model for a given species or plant functional type may be applied across multiple sites, validation will be important when an existing generic multi-site and multi-species model is applied to a new species. Results obtained demonstrated that an independent sample size of N  ≤ 15 frequently (37–46% of the time) provides insufficient power to avoid incorrectly accepting “validation” (type II errors). Hence, to ensure a useful outcome from resources spent in sampling biomass, it is recommended that at least 50 trees be sampled for each species. An equivalence test may then be applied to determine if the minimum detectable negligible difference between the existing model and the new independent data is