Developing and Extrapolating Allometric Models for Prediction of Wyoming Big Sagebrush (Artemisia tridentata subsp. wyomingensis) Aboveground Biomass

First name: 
Jon
Last name: 
Michel
Class Year: 
2022
Advisor: 
William Lauenroth
Essay Abstract: 
Despite widespread global focus on estimating aboveground carbon storage, relatively few tools have been developed to estimate aboveground biomass of drylands in the western United States. We created allometric models that estimate leaf, small branch, large branch, and total biomass of Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis). These models outperform existing global and regional shrub biomass models. The newly developed models can be used to non-destructively estimate the aboveground biomass and carbon storage of Wyoming big sagebrush. In addition, we investigated the potential of various allometric model forms to accurately extrapolate data. Log-transformed linear models, weighted non-linear power models, and weighted logistic models performed similarly on training datasets. When these models were extrapolated to larger datasets, power and log-transformed linear models outperformed logistic models. Log-transformed linear models slightly outperformed power models in extrapolation, but exhibited a negative bias. These principles of model development should be considered when developing allometric models in the future.