Date of Award

Fall 12-16-2022

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Forest Resources

Advisor

Shawn Fraver

Second Committee Member

Robert Seymour

Third Committee Member

Laura Kenefic

Additional Committee Members

J. Pascal Berrill

Nicole Rogers

Abstract

Quantitative tools used to guide the management of important northern conifer species require updating and refinement to address changes in the contemporary resource and evolving objectives of ownership. This work builds on an extensive body of knowledge about stand density management and innovates some new approaches. In sum, the three chapters presented herein: 1) seek to strengthen and more fully articulate arguments for adopting relative density as a primary metric of stand density assessment, 2) quantify minimum stand densities to achieve full site occupancy and argue for more parity with treatment of maximum stand density, and 3) present an empirically based analysis of the performance of common density metrics in the context of irregular stand structures.

Relative density, defined as the ratio of observed stand density index to the maximum value (SDI/SDIMAX), provides a parsimonious expression of key information about stand density. Two approaches to the graphical display of size-density information, density management diagrams and stocking guides, may usefully be integrated on this basis. Doing so facilitates the unambiguous design of silvicultural prescriptions, and as importantly, communication about them. A historical review of both approaches is followed by recommendations and methods for unification. An example of use is presented using a newly developed size-density management charts for eastern spruce-fir.

Whereas the maximum size-density relationship has been a focus of considerable research efforts, culminating in the expression of an allometric scaling equation tantamount to a biological law, comparatively little attention has been paid to the quantification of minimum densities required to fully occupy that same growing space. To address this need, existing models for predicting crown dimensions of softwood and hardwood species in northern New England were used to develop robust estimates of this quantity. Mixed-effects models were used to explore the influence of selected covariates on key aspects of these relationships.

Finally, an extensive long-term dataset was assembled and used to test a series of hypotheses about stand density metrics. Performance was assessed in relation to growth prediction under structurally and compositionally complex stands. Growth-density relationships articulated under more homogeneous conditions were extended and tested in this novel context.

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