High‐resolution data are improving our ability to resolve temporal patterns and controls on river productivity, but we still know little about the emergent patterns of primary production at river‐network scales. Here, we estimate daily and annual river‐network gross primary production (GPP) by applying characteristic temporal patterns of GPP (i.e., regimes) representing distinct river functional types to simulated river networks. A defined envelope of possible productivity regimes emerges at the network‐scale, but the amount and timing of network GPP can vary widely within this range depending on watershed size, productivity in larger rivers, and reach‐scale variation in light within headwater streams. Larger rivers become more influential on network‐scale GPP as watershed size increases, but small streams with relatively low productivity disproportionately influence network GPP due to their large collective surface area. Our initial predictions of network‐scale productivity provide mechanistic understanding of the factors that shape aquatic ecosystem function at broad scales.

中文翻译：

---

河网的新兴生产力制度

高分辨率数据正在改善我们解决河流生产力的时间模式和控制的能力，但我们对河流网络规模的初级生产的新兴模式仍然知之甚少。在这里，我们通过将代表不同河流功能类型的GPP的特征性时间模式（即政权）应用于模拟河流网络，来估算每日和每年的河流网络总初级生产力（GPP）。网络规模出现了可能的生产力状况的明确包络，但是网络GPP的数量和时机可以在此范围内变化很大，这取决于分水岭的大小，较大河流的生产力以及上游水流中光的到达范围变化。随着分水岭规模的扩大，较大的河流对网络规模的GPP的影响越来越大，但是生产率相对较低的小流由于其较大的集体表面积而对网络GPP的影响不成比例。我们对网络规模生产力的初步预测提供了对广泛影响水生生态系统功能的因素的机械理解。