An artificial neural network approach for studying phytoplankton succession
An artificial neural network approach for studying phytoplankton succession
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研究浮游植物演替的人工神经网络方法
作者：
J. Olden
人工神经网络用于模拟浮游植物的演替，并深入了解塑造浮游植物生物量和群落组成季节性模式的自下而上和自上而下力量的相对强度。模型比较表明，叶绿素浓度模式对营养物浓度（磷 (P)、亚硝酸盐和硝酸盐 (NO2/NO3–N) 以及铵 (NH4–H) 浓度）和浮游动物生物量（水蚤枝角类和桡足类生物量）模式做出即时响应；而藻类群落组成指数的滞后反应是明显的。采用神经网络的随机化方法来揭示营养物浓度和浮游动物生物量的个体和相互作用对浮游植物生物量和群落组成的预测的贡献。结果表明，叶绿素浓度模式与 P、NO2/NO3-N 和蚤枝角生物量直接相关，并且与蚤枝角生物量、NO2/NO3-N 和 P 之间的相互作用相关。同样，浮游植物群落组成模式与 NO2/NO3-N 和蚤枝角生物量相关；然而，营养物与浮游动物以及浮游动物与浮游动物的相互作用表现出截然不同的模式。总之，这些结果为营养限制、浮游动物放牧和营养再生在塑造浮游植物群落动态方面的重要性提供了相关证据。这项研究表明，人工神经网络可以为研究浮游植物演替提供强大的工具，帮助量化和解释自然条件下营养限制和浮游动物食草对浮游植物生物量和群落组成的个体和相互作用的贡献。
Artificial neural networks are used to model phytoplankton succession and gain insight into the relative strengths of bottom-up and top-down forces shaping seasonal patterns in phytoplankton biomass and community composition. Model comparisons indicate that patterns in chlorophyll aconcentrations response instantaneously to patterns in nutrient concentrations (phosphorous (P), nitrite and nitrate (NO2/NO3–N) and ammonium (NH4–H) concentrations) and zooplankton biomass (daphnid cladocera and copepoda biomass); whereas lagged responses in an index of algal community composition are evident. A randomization approach to neural networks is employed to reveal individual and interacting contributions of nutrient concentrations and zooplankton biomass to predictions of phytoplankton biomass and community composition. The results show that patterns in chlorophyll aconcentrations are directly associated with P, NO2/NO3–N and daphnid cladocera biomass, as well as related to interactions between daphnid cladocera biomass, and NO2/NO3–N and P. Similarly, patterns in phytoplankton community composition are associated with NO2/NO3–N and daphnid cladocera biomass; however show contrasting patterns in nutrient– zooplankton and zooplankton–zooplankton interactions. Together, the results provide correlative evidence for the importance of nutrient limitation, zooplankton grazing and nutrient regeneration in shaping phytoplankton community dynamics. This study shows that artificial neural networks can provide a powerful tool for studying phytoplankton succession by aiding in the quantification and interpretation of the individual and interacting contributions of nutrient limitation and zooplankton herbivory on phytoplankton biomass and community composition under natural conditions.