• Russell, Matthew B.
  University of Minnesota
  russellm@umn.edu
  
• Weiskittel, Aaron R.
  University of Maine
  
• Kershaw Jr., John A.
  University of New Brunswick
  

Various methods for predicting annual tree height increment (Δht) and height-to-crown base increment (Δhcb) were developed and evaluated using remeasured data from permanent sample plots compiled across the Acadian Forest of northeastern North America. Across these plots, 25 species were represented upon which total height (ht) measurements were collected from mixed-species stands displaying both single- and multi-cohort structures. For modeling Δht, development of a unified equation form was found to result in higher accuracy and less bias compared to a maximum-modifier approach. Incorporating species as a random effect resulted in predictions that were not significantly different compared to predictions from species-specific equations for nine of the ten most abundant species examined. For Δhcb, equations that modeled changes in hcb over two time periods (i.e., an incremental approach) were either not significantly different from or significantly closer to zero compared to predictions that estimated hcb at two time periods (i.e., a static approach). Results highlight the advantages of incorporating species as a random effect in individual-tree models and demonstrate the effectiveness of modeling tree crown recession directly for application in mixed-species forest growth and yield models.