Scofield Anne E, Watkins James M, Osantowski Eric, Rudstam Lars G
Cornell University Ithaca New York.
Purdue University West Lafayette Indiana.
Limnol Oceanogr. 2020 Oct;65(10):2460-2484. doi: 10.1002/lno.11464. Epub 2020 Jun 15.
Deep chlorophyll maxima (DCM) are common in stratified lakes and oceans, and phytoplankton growth within DCM often contributes significantly to total system production. Theory suggests that properties of DCM should be predictable by trophic state, with DCM becoming deeper, broader, and less productive with greater oligotrophy. However, rigorous tests of these expectations are lacking in freshwater systems. We use data generated by the U.S. EPA from 1996 to 2017, including in situ profile data for temperature, photosynthetically active radiation (PAR), chlorophyll, beam attenuation ( ), and dissolved oxygen (DO), to investigate patterns in DCM across lakes and over time. We consider trophic state, 1% PAR depth ( ), thermal structure, and degree of photoacclimation as potential drivers of DCM characteristics. DCM depth and thickness generally increased while DCM chlorophyll concentration decreased with decreasing trophic state index (greater oligotrophy). The was a stronger predictor of DCM depth than thermal structure. DCM in meso-oligotrophic waters were closely aligned with maxima in and DO saturation, suggesting they are autotrophically productive. However, the depths of these maxima diverged in ultra-oligotrophic waters, with DCM occurring deepest. This is likely a consequence of photoacclimation in high-transparency waters, where can be a better proxy for phytoplankton biomass than chlorophyll. Our results are generally consistent with expectations from DCM theory, but they also identify specific gaps in our understanding of DCM in lakes, including the causes of multiple DCM, the importance of nutriclines, and the processes forming DCM at higher light levels than expected.
深水叶绿素最大值(DCM)在分层湖泊和海洋中很常见,DCM内的浮游植物生长通常对整个系统的产量有显著贡献。理论表明,DCM的特性应由营养状态预测,随着贫营养程度增加,DCM会变得更深、更宽且生产力更低。然而,淡水系统中缺乏对这些预期的严格测试。我们使用美国环境保护局(EPA)1996年至2017年生成的数据,包括温度、光合有效辐射(PAR)、叶绿素、光束衰减( )和溶解氧(DO)的原位剖面数据,来研究湖泊中DCM随时间的变化模式。我们将营养状态、1% PAR深度( )、热结构和光适应程度视为DCM特征的潜在驱动因素。随着营养状态指数降低(贫营养程度增加),DCM深度和厚度通常增加,而DCM叶绿素浓度降低。 对DCM深度的预测比热结构更强。中贫营养水域的DCM与 和DO饱和度的最大值紧密对齐,表明它们具有自养生产力。然而,在超贫营养水域中,这些最大值的深度出现分歧,DCM出现得最深。这可能是高透明度水域光适应的结果,在这种水域中, 可能比叶绿素更能代表浮游植物生物量。我们的结果总体上与DCM理论的预期一致,但它们也指出了我们对湖泊中DCM理解的具体差距,包括多个DCM的成因、营养跃层的重要性以及在比预期更高光照水平下形成DCM的过程。