Department of Natural Resources and Environmental Science, University of Nevada Reno, Reno, NV 89557.
Division of Biological Sciences, Flathead Lake Biological Station, University of Montana, Polson, MT 59860.
Proc Natl Acad Sci U S A. 2024 Jan 30;121(5):e2307065121. doi: 10.1073/pnas.2307065121. Epub 2024 Jan 24.
River ecosystem function depends on flow regimes that are increasingly modified by changes in climate, land use, water extraction, and flow regulation. Given the wide range of variation in flow regime modifications and autotrophic communities in rivers, it has been challenging to predict which rivers will be more resilient to flow disturbances. To better understand how river productivity is disturbed by and recovers from high-flow disturbance events, we used a continental-scale dataset of daily gross primary production time series from 143 rivers to estimate growth of autotrophic biomass and ecologically relevant flow disturbance thresholds using a modified population model. We compared biomass recovery rates across hydroclimatic gradients and catchment characteristics to evaluate macroscale controls on ecosystem recovery. Estimated biomass accrual (i.e., recovery) was fastest in wider rivers with less regulated flow regimes and more frequent instances of biomass removal during high flows. Although disturbance flow thresholds routinely fell below the estimated bankfull flood (i.e., the 2-y flood), a direct comparison of disturbance flows estimated by our biomass model and a geomorphic model revealed that biomass disturbance thresholds were usually greater than bed disturbance thresholds. We suggest that primary producers in rivers vary widely in their capacity to recover following flow disturbances, and multiple, interacting macroscale factors control productivity recovery rates, although river width had the strongest overall effect. Biomass disturbance flow thresholds varied as a function of geomorphology, highlighting the need for data such as bed slope and grain size to predict how river ecosystems will respond to changing flow regimes.
河流生态系统的功能取决于水流模式,而这些水流模式正日益受到气候变化、土地利用、水资源开采和水流调节的改变的影响。鉴于水流模式改变和河流自养生物群落的广泛变化,预测哪些河流将更能抵御水流干扰一直具有挑战性。为了更好地了解河流生产力是如何受到高水流干扰事件的干扰以及从中恢复,我们使用了一个来自 143 条河流的日总初级生产力时间序列的大陆尺度数据集,使用改进的种群模型来估计自养生物量的增长和生态相关的水流干扰阈值。我们比较了横跨水文气候梯度和集水区特征的生物量恢复率,以评估对生态系统恢复的宏观控制。在较宽的河流中,水流模式调节较少,高水流期间更频繁地去除生物量,估计的生物量累积(即恢复)速度最快。尽管干扰水流阈值通常低于估计的满流洪水(即 2 年洪水),但我们的生物量模型和地貌模型估计的干扰水流的直接比较表明,生物量干扰阈值通常大于床面干扰阈值。我们认为,河流中的初级生产者在受到水流干扰后恢复的能力差异很大,多个相互作用的宏观因素控制着生产力恢复的速度,尽管河流宽度具有最强烈的总体影响。生物量干扰水流阈值随地貌形态而变化,突出表明需要像坡度和粒度等数据来预测河流生态系统将如何应对不断变化的水流模式。