Phytoplankton are known to exhibit temporal variability in biomass and community composition. While physically driven sources of variability have been studied extensively, ecosystems often exhibit complicated intrinsic dynamics that are not as well understood. As a first step towards assessing the contribution of this intrinsic variability to the total variability in the ocean, we examine the temporal scales of intrinsic variability in a marine plankton model suitable for use in climate model projections. Our rationale is that a better understanding of the time scales over which intrinsic variability manifests could help in the attribution of observed variability. Our model includes multiple phytoplankton, dissolved inorganic nutrients, and zooplankton and supports two oscillatory mechanisms: “R-oscillations”, corresponding to patterns of species succession and associated with changes in resources, and “Z-oscillations”, corresponding to changes in total phytoplankton biomass due to predator–prey interactions.

Over a wide range of model parameters, we found that while Z-oscillations typically occurred on time scales not exceeding 60 days, R-oscillations ranged from roughly 100 to 900 days under predation-free conditions, and R-oscillations occurred on longer time scales when interacting with Z-oscillations. Thus the two kinds of oscillations can be easily distinguished. At high grazing rates, we identified aperiodic cases where the dominant period never resolved, with distinct regimes emerging over decadal (or longer) time scales. These chaotic regime shifts are likely highly dependent on the model parameters and structure. More work must be done to understand how these oscillations interact with physical forcings.

已知浮游植物在生物量和群落组成方面表现出时间变化。尽管已经广泛研究了由物理驱动的可变性来源，但生态系统通常表现出复杂的内在动力，而人们对其了解并不多。作为评估此内在变化对海洋总变化的贡献的第一步，我们研究了适用于气候模型预测的海洋浮游生物模型中内在变化的时间尺度。我们的理由是，更好地理解内在变异性表现出来的时间尺度可以帮助归因于观察到的变异性。我们的模型包含多种浮游植物，溶解的无机养分和浮游动物，并支持两种振荡机制：“ R振荡”，“

在广泛的模型参数中，我们发现，尽管Z振荡通常发生在不超过60天的时间范围内，但R振荡发生在无捕食条件下的大约100至900天，而R振荡发生在更长的时间尺度上与Z振荡互动时。因此，可以容易地区分两种振荡。在高放牧率下，我们发现了占主导地位的时期从未解决过的非周期性情况，在十年（或更长时间）的时间尺度上出现了不同的制度。这些混乱的政权转移可能高度依赖于模型参数和结构。必须做更多的工作来了解这些振荡如何与物理强迫相互作用。