A decision-support system for forest density management within upland black spruce stand-types

The objective of this study was to develop an enhanced modular-based structural stand density management model (SSDMM) and associated algorithmic analogue for upland black spruce (Picea mariana (Mill) BSP.) stand-types situated within the central portion of the Canadian Boreal Forest Region. For a given density management regime, site quality, rotation age, stock-type, cost structure and set of merchantability standards, the hierarchical-based SSDMM enabled estimation of the following metrics: overall productivity (e.g., mean annual volume, biomass and carbon increments), volumetric yields (e.g., total and merchantable volumes per unit area), log-product distributions (e.g., number of pulp and saw logs by diameter class), biomass production and carbon sequestration outcomes (e.g., oven-dried masses of above-ground components and associated carbon equivalents by diameter class), recoverable end-products and associated monetary values (e.g., volume and economic value of recovered chip and dimensional lumber products by diameter class and sawmill-type (stud and randomized length processing protocols)), and fibre quality attributes (e.g., maximum branch diameter and wood density). The core modules which were responsible for describing stand dynamics and structural change were developed using 407 (122 from natural stands and 285 from managed stands) temporal tree-list measurements obtained from 269 (142 in natural stands and managed 127 in managed stands) sample plots (note, natural stands are those that naturally regenerated following a stand-replacing disturbance and have no history of density regulation whereas managed stands are those that naturally or artificially regenerated following a stand-replacing disturbance and have a history of density regulation). The modules responsible for predicting log product distributions, and end-product volumes and values, were developed employing relationships derived from taper and sawmill simulation studies. The modules responsible for predicting biomass and carbon outcomes, and log and fibre quality attributes, were developed using data obtained from initial espacement and thinning experiments. The resultant model introduces a number of advancements over its predecessors including those that (1) ensured mathematical compatibility among yield estimates, (2) accounted for intrinsic density-independent mortality factors, response delay following thinning, and genetic worth effects, and (3) provided increased flexibility in terms of enabling end-users to change merchantability standards, specify product degrade factors, and adjust cost profiles, according to their unique requirements. As demonstrated, the decision-support model can assist in facilitating the transformative shift towards the production of high value end-products, bio-energy feed stocks, carbon credits, and ecosystem services, currently underway within the Canadian forest sector.