Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30040 hectares) along a 3322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient and cost-effective manner.

中文翻译：

---

通过整合基于特征的模型和遥感叶面特征和冠层结构数据来改进景观规模的生产力估计

缺乏清晰、灵活和易于使用的生产力模型以及用于参数化和测试这些模型的稀缺性状和生产力数据阻碍了评估人为退化和气候变化对全球碳循环的影响。我们提供了一个简单的解决方案：将遥感叶面性状和冠层结构数据与基于性状的代谢理论相结合的机械框架 (RS-CFM)，以在大空间尺度上有效地绘制生产力图。我们通过沿 3322 米海拔梯度在高度多样化的秘鲁热带森林（30040 公顷）中以高分辨率（0.01 公顷）量化净初级生产力（NPP）来测试该框架。与使用植物功能类型的基于经验和过程的替代模型相比，我们的分析更准确、更详细地捕捉了生产力的热点和高度变化。这一结果揭示了高分辨率、特定位置的性状变异和轻度竞争如何推动生产力的变异性，为充分利用遥感数据和可靠地从性状扩大规模以更直接、更高效和成本更低地绘制全球生产力图提供了可能性。有效的方式。