Scaling Forest Ecosystem Dynamics from Trees to Landscapes (Collaborative Research)

我们建议解决困扰生态学家和资源管理者的一个基本的缩放困境。当前的许多问题都与景观或区域尺度上的过程有关。但我们的传统知识基础是相对精细的。例如，人类活动引起的气候变化涉及区域（生物群落）甚至更大规模的重要性问题，但我们对生态响应机制的最佳经验理解是在单个植物的水平上。资源管理者面临着类似的困境：多用途或生态系统管理意味着流域或景观的规模，但我们的森林管理知识基础是建立在单个树木或同质林分的水平上。一般来说，缩放涉及分辨率（粒度或细节级别）和范围（研究的区域或范围）之间的权衡。大范围是以牺牲细节的细粒度分辨率为代价的，因此，专注于细节的研究在小空间域上进行，而大规模研究通常牺牲细节来拥抱粗分辨率模式。 这种缩放权衡可能会导致跨尺度的不兼容性。与精细研究相比，大规模研究通常基于不同的概念模型和不同的数据。虽然可以乐观地假设，从不同的经验基础在完全不同的尺度上得出的模型可能会在一个共同的尺度上收敛，但在实践中，这些模型不能进行严格的比较，因为它们的共同点太少了。我们建议开发一套模拟模型，解决不同尺度的问题，同时仍然保持一致的概念和经验基础。通过保持模型之间的这种共性，我们可以根据需要严格地改变尺度，并且当使用替代模型时，我们可以确信这些不同模型的预测差异是由于明确的假设或公式，而不是基础数据中不可知的不相容性。 我们将开始与林隙模型，它模拟了建立，生长和死亡率的个别树木在一个小的（0.1公顷）的模型情节，在每年的时间步长。林隙模型通常被用来推断树木水平的人口统计学林分水平（~10公顷）动态演替的时间段。然后，我们将使用此模型生成并参数化三个派生模型（元模型），这些模型再现差距模型行为的选定粗分辨率方面，但这样做的计算效率要高得多。我们建议证明这种方法与非线性阶段结构模型，半马尔可夫补丁过渡模型，和细胞自动机。每个元模型强调森林动态的不同方面，因此每个元模型都适用于特定类型的应用。 我们选择了太平洋西北部（PNW）的森林作为开发这种方法的试验平台，但由此产生的方法将适用于各种各样的森林生态系统。特别是，我们的差距模型已经在美国西北部，西南部，东北部和东南部使用。这一方法也应适用于其他类型的模型。我们将向其他用户提供我们的模型和文档。

We propose to address a fundamental scaling dilemma that plagues ecologists and resource managers. Many current issues are concerned with processes operating on scales of landscapes or regions. But our conventional knowledge base is comparatively fine-scale. For example, anthropogenic climatic change broaches issues of regional (biome) or even larger-scale importance, but our best empirical understanding of the mechanisms of ecological response is at the level of the individual plant. Resource managers face a similar dilemma: multiple-use or ecosystem management implies scales of watersheds or landscapes, yet our knowledge base for forest management is founded at the level of individual trees or homogeneous stands. In general, scaling involves a trade-off between resolution (grain, or level of detail) and extent (the area or scope of the study). Large extent comes at the expense of fine-grained resolution of detail, and so, studies that focus on details do so over a small spatial domain, while large-scale studies typically sacrifice detail to embrace coarser-resolution patterns. This scaling trade-off can enforce an incompatibility across scales. Large-scale studies typically are based on different conceptual models and different data, as compared to fine-scale studies. While it might be assumed optimistically that models derived from different empirical bases at disparate scales might nonetheless converge at a common scale, in practice such models cannot be compared rigorously because they have too little in common. We propose to develop a suite of simulation models which address questions at different scales while still maintaining a consistent conceptual and empirical basis. By preserving this commonality among models, we can change scale rigorously as needed, and when using alternative models we can be confident that discrepancies in the predictions of these various models are due to explicit assumptions or formulations rather than to unknowable incompati bilities in the underlying data. We will begin with forest gap model, which simulates the establishment, growth, and mortality of individual trees on a small (0.1 ha) model plot, at an annual timestep. Gap models typically are used to extrapolate tree-level demographics to stand-level (~10 ha) dynamics over successional time periods. We will then use this model to generate and parameterize three derived models (metamodels) that reproduce selected coarse-resolution aspects of the gap model's behavior, but do so with much greater computational efficiency. We propose to demonstrate this approach with a nonlinear stage-structured model, a semi-markovian patch transition model, and a cellular automaton. Each metamodel emphasizes a different aspect of forest dynamics, and so each is amenable to particular kinds of applications. We have selected the forests of the Pacific Northwest (PNW) as a testbed for the development of this methodology, but the resulting methods will be applicable to a diverse range of forest ecosystems. In particular, our gap model is already in use in the northwest, southwest, northeast, and the southeastern United States. The approach should also be applicable to other types of models. We will make our models and documentation available to other users.