Partitioning and predicting forage biomass from total aboveground biomass of regenerating tree species using dimensional analyses
- Download PDF (389.0 KB)
- This publication is available only online.
Canadian Journal of Forest Research
Foresters and wildlife biologists use biomass estimates as proxies of habitat structure, productivity, and carrying capacity. Determining biomass, however, is challenging without destructive harvests. We provide a dimensional analysis approach to partition browse biomass (BB) from total aboveground biomass (AGB) of six regenerating hardwoods in the Allegheny forests of Pennsylvania, USA. First, we determined the average diameter of browsed twigs for each species. Then, we created a subset of potential browsable twig and foliage biomass from total AGB in 439 individuals harvested within paired exclosure (fenced) and control (unfenced) plots at 15 sites. We fit species-specific allometric equations to estimate BB and AGB using basal diameter and height as predictors and tested the effects of fencing. Although overall stem height and BB were greater within exclosures, fencing did not significantly affect relationships between either predictor and BB or AGB, thereby enabling general and robust (R2 ≥ 0.80) equations for most species. Our work provides biomass equations for regionally dominant species and size classes that are underrepresented in the literature, yet critical to forest renewal and wildlife. Moreover, by sampling variable sites and levels of browse pressure, reported equations lessen site-specific biases. Finally, our methodology provides a template to generate forage biomass prediction equations for other plant and ungulate species.
KeywordsOdocoileus virginianus, herbivory, allometry, regression, Allegheny hardwoods
Morgan, Quinn; Johnstone-Yellin, Tamara L.; Pinchot, Cornelia C.; Peters, Matthew; Royo, Alejandro A. 2019. Partitioning and predicting forage biomass from total aboveground biomass of regenerating tree species using dimensional analyses. Canadian Journal of Forest Research. 49(3): 309-316. https://doi.org/10.1139/cjfr-2018-0119.