When economically optimal is ecologically complicated: modeling tree-by-tree cutting decisions to maximize financial returns from northern hardwood stands

Abstract

This study challenges a long-standing and often uncontested assertion in the forestry discourse that maximizing financial returns always requires ecologically simplified stands. We developed a high-resolution simulation tool for northern hardwood stands in eastern North America and integrated advanced numerical optimization methods to model the tree-level harvest decisions that maximize financial returns. We modeled each individual tree’s growth and its probability of natural mortality, conditioned on the evolving neighborhood-scale competitive environment it resides in. We developed size-, species-, and grade-specific price functions to assign potential harvest revenue values to each discrete bole section of each standing tree, and we used an evolutionary search algorithm to specify the financially optimal timing of tree-by-tree removals. We modeled three different case studies, representing a broad range of northern hardwood stand conditions, including a hypothetical 50-year-old, even-aged stand and two inventoried stands in northern New York, USA, with contrasting management histories. We observed consistent results across all three cases: maximizing financial returns from northern hardwood forests requires silvicultural finesse and results in ecologically complicated stands.

Management recommendations for Alpine protection forests: the importance of regeneration quality and initial stand composition

“…Instead, we derived management recommenda-<ons based on the simultaneous interpreta<on of the sta<s<cal model's outcomes and selected underlying simula<on results. With a single and more reliable target variable, management regimes could be itera<vely op<mized, or the best management regime could be iden<fied automa<cally from a larger number of predefined simula<ons, as done for other ecosystem services (e.g., Altamirano-Fernández et al, 2023;Buongiorno et al, 2012;Díaz-Yáñez et al, 2021;Foppert and Maker, 2024). Such techniques would also allow for a larger set of varying management parameters to be inves<gated.…”