Yuan Lester L, Pollard Amina I
Office of Water, U.S. Environmental Protection Agency, 1200 Pennsylvania Ave NW. Mail code 4304T, Washington, DC 20460.
Limnol Oceanogr. 2018;63(6):2493-2507. doi: 10.1002/lno.10955.
The relationship between zooplankton biomass and phytoplankton biomass can provide insight into the structure and function of lake biological communities. We used a Bayesian network model to analyze a continental-scale data dataset to estimate changes in the relationship between zooplankton (Z) and phytoplankton (P) biomasses along a eutrophication gradient. The Bayesian network model allowed us to combine two different measurements of phytoplankton biomass (chlorophyll concentration and directly observed biovolume) to improve the precision of estimates of true biomass within each sample. The model also allowed us to estimate separate relationships between P and zooplankton abundance and between P and mean individual zooplankton biomass and then to combine these two relationships into an estimate of seasonal mean zooplankton biomass. The resulting analysis indicated that seasonal mean zooplankton biomass increased proportionally with phytoplankton biomass in oligotrophic lakes, yielding a constant ratio between Z and P and suggested that bottom-up forces determined zooplankton biomass in these systems. In eutrophic lakes, seasonal mean zooplankton biomass was nearly constant with increases in phytoplankton biomass, yielding a decrease in the ratio between Z and P with increasing eutrophication. Bottom-up forces, as quantified by an increase in the proportion of cyanobacteria, accounted for approximately one fifth of the residual variance in the model as the relationship between Z and P changed from direct proportionality in oligotrophic lakes to the nearly constant value of Z observed in eutrophic lakes, suggesting that a combination of both top-down and bottom-up forces likely determined zooplankton biomass in eutrophic lakes.
浮游动物生物量与浮游植物生物量之间的关系能够为湖泊生物群落的结构和功能提供见解。我们使用贝叶斯网络模型来分析一个大陆尺度的数据集,以估计沿着富营养化梯度浮游动物(Z)和浮游植物(P)生物量之间关系的变化。贝叶斯网络模型使我们能够结合浮游植物生物量的两种不同测量方法(叶绿素浓度和直接观测的生物体积),以提高每个样本中真实生物量估计的精度。该模型还使我们能够估计P与浮游动物丰度之间以及P与浮游动物个体平均生物量之间的单独关系,然后将这两种关系结合起来以估计季节性平均浮游动物生物量。结果分析表明,在贫营养湖泊中,季节性平均浮游动物生物量与浮游植物生物量成比例增加,Z与P之间产生一个恒定比例,这表明自下而上的力量决定了这些系统中的浮游动物生物量。在富营养湖泊中,随着浮游植物生物量增加,季节性平均浮游动物生物量几乎保持不变,随着富营养化程度增加,Z与P之间的比例下降。随着Z与P之间的关系从贫营养湖泊中的直接比例关系变为富营养湖泊中观测到的Z的近乎恒定值,由蓝藻比例增加所量化的自下而上的力量占模型中剩余方差的约五分之一,这表明自上而下和自下而上的力量组合可能决定了富营养湖泊中的浮游动物生物量。