Developing generalized, calibratable, mixed-effects meta-models for large-scale biomass prediction

Large-scale prediction of forest biomass is of interest for forest science, ecology, and issues related to climate change. Previous research has attempted to provide allometric models suitable for large-scale biomass prediction using different methods. We present a new approach for meta-analysis of existing biomass equations using mixed-effects modelling on simulated data. The resulting generalized meta-models can be calibrated for local conditions. This meta-analytical approach allows for directly benefiting from previous research to minimize data collection and properly take into account the unknown differences between different locations within large areas. The approach is demonstrated by developing pan-Mediterranean mixed-effects meta-models for Pinus brutia Ten. The fixed part of the meta-models enables sound aboveground biomass predictions throughout practically the full native range of the species. Significant improvement in the predictive performance can be further gained by using small local datasets for model calibration. The calibration procedure for location-specific biomass prediction is based on best linear unbiased predictor of random effects. The predictive performance of the meta-models under different sampling strategies is validated in an independent dataset. The results show that mixed-effects meta-models may enable accurate and robust large-scale biomass predictions. Calibration for specific locations based on minimal data collection effort performs better than fitting location-specific equations based on much larger samples. The advantages of mixed-effects meta-models are of interest not only for further biomass-related research and applications, but also for other modelling disciplines within forest science.