Emergent productivity regimes of river networks
Emergent productivity regimes of river networks
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DOI：
10.1002/lol2.10115
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发表时间：
2019-10-01
影响因子：
7.8
通讯作者：
Bernhardt, Emily S.
中科院分区：
文献类型：
--
作者：
Koenig, Lauren E.;Helton, Ashley M.;Bernhardt, Emily S.
高分辨率数据正在提高我们解决时间模式和河流生产力控制的能力，但我们对河网规模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 特征时间模式（即状态）应用于模拟河流网络来估计每日和年度河流网络总初级生产（GPP）。在网络规模上出现了可能的生产力状况的明确范围，但网络 GPP 的数量和时间可能在此范围内变化很大，具体取决于流域大小、较大河流的生产力以及源头河流内光的河段规模变化。随着流域规模的增加，较大的河流对网络规模的 GPP 的影响更大，但生产力相对较低的小河流由于其集体表面积较大，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在广泛尺度上塑造水生生态系统功能的因素的机械理解。
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.