Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, California, USA.
Institute of Marine Sciences, University of California, Santa Cruz, California, USA.
Ecol Lett. 2023 Mar;26(3):470-481. doi: 10.1111/ele.14168. Epub 2023 Jan 27.
Chaotic dynamics appear to be prevalent in short-lived organisms including plankton and may limit long-term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different taxonomic resolutions. Using plankton time series data from 17 lakes and 4 marine sites, we found seasonal patterns of local instability in many species, that short-term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Taxonomic aggregates were more stable and more predictable than finer groupings. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these results suggest that prediction accuracy, sensitivity to change and management efficacy may be greater at certain times of year and that prediction will be more feasible for taxonomic aggregates.
混沌动力学似乎在包括浮游生物在内的短寿命生物中普遍存在,并可能限制长期的可预测性。然而,很少有研究探索动态稳定性如何随时间、空间和不同分类分辨率而变化。使用来自 17 个湖泊和 4 个海洋站点的浮游生物时间序列数据,我们发现许多物种存在季节性局部不稳定性模式,短期可预测性与局部不稳定性有关,而局部不稳定性最常发生在春季,与高生长期有关。分类聚合体比更精细的分组更稳定和更可预测。在各个站点中,较高纬度地区的 Lyapunov 指数更高,局部不稳定性的季节性更强,但仅在较粗的分类分辨率下如此。总的来说,这些结果表明,在一年中的某些时候,预测准确性、对变化的敏感性和管理效果可能会更高,并且对于分类聚合体,预测将更加可行。