Purpose of Review

Forest models are becoming essential tools in forest research, management, and policymaking but currently are under deep transformation. In this review of the most recent literature (2018–2022), we aim to provide an updated general view of the main topics currently attracting the efforts of forest modelers, the trends already in place, and some of the current and future challenges that the field will face.

Recent Findings

Four major topics attracting most of on current modelling efforts: data acquisition, productivity estimation, ecological pattern predictions, and forest management related to ecosystem services. Although the topics may seem different, they all are converging towards integrated modelling approaches by the pressure of climate change as the major coalescent force, pushing current research efforts into integrated mechanistic, cross-scale simulations of forest functioning and structure.

Summary

We conclude that forest modelling is experiencing an exciting but challenging time, due to the combination of new methods to easily acquire massive amounts of data, new techniques to statistically process such data, and refinements in mechanistic modelling that are incorporating higher levels of ecological complexity and breaking traditional barriers in spatial and temporal scales. However, new available data and techniques are also creating new challenges. In any case, forest modelling is increasingly acknowledged as a community and interdisciplinary effort. As such, ways to deliver simplified versions or easy entry points to models should be encouraged to integrate non-modelers stakeholders into the modelling process since its inception. This should be considered particularly as academic forest modelers may be increasing the ecological and mathematical complexity of forest models.

中文翻译：

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森林生态系统建模的最新趋势：新方法还是新方法？

审查目的

森林模型正在成为森林研究、管理和政策制定的重要工具，但目前正在经历深刻变革。在对最新文献（2018-2022）的回顾中，我们的目标是提供当前吸引森林建模者努力的主要主题的最新总体观点、已经存在的趋势以及森林建模者当前和未来的一些挑战。场将面临。

最近的发现

当前建模工作中最吸引人的四个主要主题是：数据采集、生产力估计、生态模式预测以及与生态系统服务相关的森林管理。尽管主题可能看起来不同，但在气候变化压力作为主要联合力量的情况下，它们都在向综合建模方法靠拢，推动当前的研究工作转向森林功能和结构的综合机制、跨尺度模拟。

概括

我们的结论是，由于轻松获取大量数据的新方法、统计处理此类数据的新技术以及机械建模的改进（将更高水平的生态复杂性和打破传统的空间和时间尺度障碍。然而，新的可用数据和技术也带来了新的挑战。无论如何，森林建模越来越被认为是一项社区和跨学科的工作。因此，应鼓励提供简化版本或简单的模型入口点的方法，以便自建模过程一开始就将非建模者利益相关者集成到建模过程中。应特别考虑这一点，因为学术森林建模者可能会增加森林模型的生态和数学复杂性。