Preventing declines of native and historically-abundant tree species is an important aspect of sustainable forest management, but predicting future forest composition is challenging when succession does not tend to follow a well-defined path. We evaluated the implications of site-level interactions between timber harvesting and forest succession on the regional landscape dynamics of the complex and species-rich northern Acadian Forest Region. Our expectation was that forest composition would trend away from long-lived and shade-tolerant species, because rates of landscape disturbance from timber harvesting are high relative to historic rates of natural disturbance. We used a novel modeling approach that combined Landsat-derived time series of forest disturbance to inform realistic simulations of timber harvesting across many individual commercial forest landowners using LANDIS-II, and evaluated changes in tree species’ distributions and abundance with and without harvesting. Detailed descriptions of initial forest conditions were derived from maps of relative tree species abundance, developed using Landsat satellite imagery, regional inventory data, and an innovative machine learning algorithm. If recent harvest rates persist, simulations suggest timber harvesting will generally be sustainable in our study area; however, projected rates of site-level species turnover were high, predominantly favoring species that were less abundant under the region's natural disturbance regime. As a result, broad-scale patterns of projected species co-occurrence shifted, destabilizing important regional forest types. Our results highlight both the region's capacity for forest growth and the importance of accurately capturing the local effects of land management when projecting forested regions dominated by commercial ownership.

防止本地和历史上丰富的树种减少是可持续森林管理的一个重要方面，但当演替往往不遵循明确定义的路径时，预测未来的森林构成具有挑战性。我们评估了木材采伐和森林演替之间场地层面相互作用对复杂且物种丰富的阿卡迪亚北部森林地区区域景观动态的影响。我们的预期是，森林组成将远离长寿和耐荫的物种，因为木材采伐对景观的干扰率相对于历史自然干扰率而言较高。我们使用了一种新的建模方法，该方法结合了 Landsat 衍生的森林干扰时间序列，为使用 LANDIS-II 的许多个体商业林地所有者的木材采伐提供真实模拟，并评估了在采伐和不采伐时树种分布和丰度的变化。初始森林条件的详细描述来自相对树种丰度地图，使用 Landsat 卫星图像、区域清单数据和创新的机器学习算法开发。如果最近的采伐率持续下去，模拟表明木材采伐在我们的研究区域通常是可持续的；然而，预计站点级物种周转率很高，主要有利于在该地区自然干扰机制下数量较少的物种。因此，预计物种共生的大范围模式发生了变化，破坏了重要的区域森林类型。我们的结果强调了该地区的森林生长能力以及在预测由商业所有权主导的森林地区时准确捕捉土地管理的当地影响的重要性。