Tools for managing forest productivity and biodiversity in relation to ecosystem function in boreal mixedwood forests

尽管在过去20年里，加拿大在采取更全面的可持续森林管理(SFM)方法方面取得了相当大的进展，但加拿大土地管理者需要更好的工具来有效地管理从木材生产力到生态系统功能和生物多样性等一系列价值的北方景观。新的营林方法，如可变保留采伐，正日益被接受为实现基于生态的林木管理目标的一种方法。然而，关于这种做法的影响，我们还有很多需要学习的地方，更重要的是，我们如何才能以最佳方式将它们部署在景观上，以支持自然生物多样性，同时最大限度地提高潜在的林地生产力。 我们将探索来自描述当地地点条件的各种遥感来源的数据的有用性，以解释/预测：1)森林生产力、生态系统功能(土壤水-碳库和通量)和生物多样性(特别是表层节肢动物、维管植物和非维管植物)；2)这些因素在可变保留采伐梯度后如何反应和恢复；3)这些往往相互竞争的价值之间的权衡。从这一点出发，我们将开发预测模型，这些模型可以用来在场地生产力、碳储存和自然生物多样性方面以最佳方式改进景观上的收获部署。经过改进，这些模型可以被开发成更广泛的应用于更广泛的景观干扰。 我们的合作伙伴将获得工具，利用容易从遥感获得的预测变量，在混合木材环境中实现一系列目标。我们将建立一个知识库，为森林管理做法提供信息，以最大限度地减少对森林环境的负面影响--包括维持和支持自然生物多样性--同时促进林分再生，最大限度地提高林地生产力，并了解对碳管理的影响。由此产生的工具将有助于以平衡环境成本和收益的方式在森林景观中优化采伐部署。

Despite considerable progress over the past two decades towards a more holistic approach to sustainable forest management (SFM) Canadian land managers require better tools to effectively manage boreal landscapes for a range of values - from timber productivity, to ecosystem function and biodiversity. New silvicultural approaches, such as variable retention harvesting, are increasingly being accepted as an approach to achieve the objectives of ecologically-based SFM. Still, we have much to learn about the impacts of such practices and, more importantly, how we can optimally deploy them on the landscape in order to support natural biodiversity while maximizing potential forest-site productivity. We will explore the usefulness of data derived from a variety of remote sensing sources, which describe local site conditions, to explain/predict: 1) forest productivity, ecosystem function (soil water - carbon pools and fluxes), and biodiversity (especially, epigaeic arthropods, vascular and non-vascular plants); 2) how these respond and recover following a gradient of variable retention harvesting; and 3) any trade-offs among these often competing values. From this we will develop predictive models which can be used to improve deployment of harvesting on landscapes in an optimal way with respect to site-productivity, carbon storage and natural biodiversity. With refinement, these models can be developed for expanded application to a broader range of landscape disturbances. Our partners will gain tools to achieve a range of objectives in a mixedwood setting using predictor variables easily derived from remote sensing. We will create a knowledge base to inform forest management practices that minimize negative impacts on the forest environment - including maintaining and supporting natural biodiversity - while promoting stand regeneration, maximizing potential forest-site productivity and understanding implications for carbon management. The resulting tools will be useful to optimize deployment of harvesting across forest landscapes in a way that balances environmental costs and benefits.