The idea at the heart of the research is deceptively simple. In boreal Canada, forests are not uniform carpets of trees; they are patchworks of stands of different ages, shaped by a history of crown fires, insect outbreaks, succession, and, more recently, industrial logging. Before large-scale forestry, eastern Canadian boreal ecosystems supported far more mature and old-growth forest than they do today, and those old stands provided critical ecosystem services, from wildlife habitat and carbon storage to the regulation of surface water. Industrial harvesting has generally reduced the area of these stands and scrambled their distribution, simplifying the mosaic and threatening remnant old forests in some regions. Ontario’s response has been a policy framework that requires forest managers to emulate natural disturbance patterns, including a specific spatial configuration of mature and old-growth forest patches that the province calls texture.

Measuring that texture is straightforward in principle. Ontario’s Forest Management Guide for Boreal Landscapes instructs planners to divide a forested landscape into hexagons of 5,000 hectares, a size chosen because it matches the average footprint of natural disturbances in the province’s boreal forests and is meaningful for characterizing wildlife habitat. Within each hexagon, planners calculate the proportion of the area occupied by mature and old-growth stands, abbreviated as MOG. Those proportions are then sorted into five bins, ranging from hexagons with less than 20 percent MOG to those with more than 80 percent, producing a histogram that summarizes the landscape’s texture at a glance. A landscape shaped only by natural disturbance has a characteristic histogram, and the provincial guidelines ask managers to keep the managed landscape’s histogram as close as possible to that theoretical target.

The difficulty has always been operational. Forest harvest planning relies on mixed-integer optimization models, classic formulations known as Model I, Model II, and Model III, that find cost-effective harvest schedules under sustainability constraints. These models have been extended over the years to control connectivity between forest patches, adjacency between harvested and unharvested sites, and maximum harvest areas. But none of them could directly control landscape texture. Texture metrics, computed with moving-window statistics using tools such as Fragstats or Ontario’s own Landscape Tool, had to be calculated after the fact, as a post-assessment of a finished plan. If the texture came out wrong, there was no way to feed that information back into the harvest schedule. The provincial guidelines, in effect, demanded an outcome the planning machinery could not deliver.

Yemshanov and his colleagues closed that gap by modifying a Model I harvest scheduling formulation so that the texture histogram itself becomes part of the optimization. The landscape is covered with 5,000-hectare hexagons, and each harvestable forest site is assigned to the hexagon containing its centroid. For every possible harvest prescription at every site, and for every ten-year planning period across a century-long horizon, the model tracks whether the site will be in mature or old-growth condition. A decision variable calculates the MOG proportion within each hexagon, and a set of binary indicator variables sorts each hexagon into one of the five histogram bins. The model then computes a penalty measuring how far the resulting histogram deviates, bin by bin, from the theoretical target values that describe a landscape under natural disturbance alone, and it minimizes that penalty alongside its traditional goal of maximizing net harvest revenue.

The theoretical targets were supplied by the Ontario Ministry of Natural Resources and Forestry, based on stochastic simulations with the Boreal Forest Landscape Dynamics Simulator, which integrates the Canadian Forest Fire Behavior Prediction System with empirical forest succession rules. For the study area, the target shares of hexagons in the five MOG bins came out at roughly 0.17, 0.22, 0.20, 0.20, and 0.21. Because achieving an exact match may be impossible given a landscape’s existing age structure, the requirement is formulated as a penalty on deviations rather than a hard constraint, keeping the problem mathematically feasible even when the starting conditions are far from the ideal.

The team tested the approach in the Wabadowgang Noopming Forest, a boreal management unit in northwestern Ontario covering roughly 438,500 hectares across more than 30,000 harvestable sites, where industrial forestry operates under a forest management plan that also implements Ontario’s Dynamic Caribou Habitat Schedule. That schedule, designed to conserve threatened woodland caribou, concentrates harvesting into compact regions that rotate across the landscape over twenty-year cycles, maintaining large tracts of undisturbed habitat through time. The researchers compared a baseline scenario following the current plan against scenarios that added MOG texture control, both with and without the caribou schedule, and even a scenario in which texture control replaced the traditional area-based limits on young, mature, and old forest entirely.

The results were revealing. The baseline plan, constrained by the caribou schedule but blind to texture, produced a lopsided mosaic: in the first three decades, hexagons with less than 20 percent MOG fell well below the target share of 0.17, while hexagons dominated by more than 80 percent mature and old-growth forest substantially exceeded their target of 0.21. Adding texture control changed the picture dramatically. Because the target histogram demands a meaningful presence of hexagons with low MOG proportions, the optimizer dispersed harvest sites more evenly across the landscape, ensuring that every 5,000-hectare window contained some recently disturbed, young forest alongside the old. Notably, the cost of this ecological refinement was modest. Because the caribou schedule already forced harvest into compact, widely distributed regions, layering texture control on top caused only a minor increase in timber supply cost and little change in sustainable harvest levels. Without the caribou schedule, however, texture control alone allowed a higher maximum sustainable harvest of 0.323 million cubic meters per year versus 0.275 million under the combined scenario, and pushed the young-forest share up to 32.1 percent, near the plan’s upper limit.

One caution emerged clearly from the fourth scenario: texture control is not a substitute for traditional age-class management. When the researchers removed the area-based constraints on young, mature, and old-growth forest and relied on texture control plus the caribou schedule, harvested volumes rose, mean forest age fell, and the old-growth area declined substantially, with young stands exceeding the provincial maximum. Texture, in other words, governs the arrangement of the mosaic but not necessarily its total composition, and both dimensions of the landscape need explicit attention.

The study also lays bare the computational price of ecological realism. The baseline problem solved in under thirty minutes, but the texture-controlled formulations ran for seventy-two hours on a workstation with 48 processor cores and still finished with optimality gaps above 15 percent. The indirect, multi-step calculation of the histogram introduces symmetries into the data, since many different harvest configurations can produce the same histogram shape, making the problem combinatorially hard, in the formal sense of NP-hardness. The authors suggest practical workarounds, including optimizing only the most critical histogram bins, dividing large landscapes into regions solved separately, or stabilizing the coarse spatial configuration before switching penalties to hard constraints. They also note that the framework is generalizable well beyond MOG forests: the same machinery could control the texture of wildlife habitat for species such as boreal caribou at ecologically relevant scales of 10,000 hectares or more, or balance working forests, protected areas, and agricultural land in multi-use regions. As climate change alters fire regimes across the boreal zone, tools that can hold a managed landscape to a nature-like pattern, even approximately and at a computable cost, may become indispensable to keeping forestry and conservation from pulling the forest apart.

Subject of Research: Optimization of forest harvest scheduling to control the spatial texture of mature and old-growth boreal forest under competing management objectives

Article Title: Forest landscape planning with textural constraints: How to control the spatial pattern under competing management objectives

Article References: Yemshanov, D., Koch, F. H., Liu, N., & Nielsen, J. (2026). Forest landscape planning with textural constraints: How to control the spatial pattern under competing management objectives. PLOS Ecosystems, 1(2), e0000033. https://doi.org/10.1371/journal.pesy.0000033

Image Credits: AI Generated

DOI: 10.1371/journal.pesy.0000033

Keywords: boreal forest, forest management, harvest scheduling, old-growth forest, landscape texture, natural disturbance emulation, mixed-integer optimization, woodland caribou, Ontario, spatial planning, biodiversity conservation, wildfire regime