Use of ciliate and phytoplankton taxonomic composition for the estimation of eicosapentaenoic acid concentration in lakes
Use of ciliate and phytoplankton taxonomic composition for the estimation of eicosapentaenoic acid concentration in lakes
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利用纤毛虫和浮游植物分类组成估算湖泊中二十碳五烯酸浓度
DOI：
10.1111/j.1365-2427.2012.02799.x
复制
发表时间：
2012
影响因子：
2.7
通讯作者：
A. Wacker
中科院分区：
文献类型：
--
作者：
Hartwich;D. Straile;U. Gaedke;A. Wacker
1. 多不饱和脂肪酸二十碳五烯酸在水产食物网中发挥着重要作用，特别是在初级生产者-消费者界面，关键物种，如甲虫，可能会受到其饮食供应的限制。这种限制及其季节和年际变化可以通过连续测量EPA浓度来检测。然而，这种环保局的测量只是在过去20年里才变得普遍，而许多经过充分研究的湖泊都有关于浮游生物生物量的长期数据集。在这里，我们测试是否有可能根据非生物变量(光和温度)和可能为消费者提供环境保护局的被捕食生物(例如纤毛虫、硅藻和隐芽植物)的生物量来估计环境保护局的浓度。2. 我们使用多元线性回归将大小和分类解析的浮游生物生物量数据以及温度和光照强度的测量与直接测量康斯坦茨湖全年的环境保护局浓度联系起来。首先，我们从富含EPA的生物体(硅藻、隐芽植物和纤毛虫)的生物量中测试了EPA浓度的可预测性。其次，我们在模型中加入了采样深度(0-20 m)和深度(0-8和8-20 m)上的平均温度和平均光照强度作为因子，以检查大尺度季节和深度对环境保护剂浓度的影响。在第三步中，我们将光和温度与平均值的偏差包括在我们的模型中，以考虑它们对浮游生物生物化学组成的潜在影响。我们使用阿凯克信息标准来确定最佳模型。3. 所有方法都支持我们的观点，即特定浮游生物组的生物量是可用于推算Seston EPA浓度的变量。在我们的模型中，纤毛虫作为EPA来源的重要性被强调为它们在我们的模型中的高重量，尽管在大多数将脂肪酸与Seston分类组成联系起来的研究中，纤毛虫被忽略了。光强的大尺度季节变化及其与硅藻生物量的相互作用是EPA浓度的显著预测因子。温度与平均值的偏差是环境保护剂浓度的深度依赖效应，其与纤毛虫生物量的相互作用也是具有很高预测能力的变量。4. 用另一年(1997年)的环境保护剂浓度测量验证了第一种和第二种方法的最优模型。仅包括生物量的最佳模型可以解释80%的估计值，而包括平均温度和深度的第二个方法的最佳模型可以解释1997.5年环境保护剂浓度变化的87%。 我们表明，根据浮游生物生物量可靠地预测环境保护剂浓度是可能的，而非生物因素的加入只导致结果与实验室研究的预期部分一致。我们包括生物预报器的方法应该可以转移到其他系统，并允许检查对初级消费者的生化限制。
1. The polyunsaturated fatty acid eicosapentaenoic acid (EPA) plays an important role in aquatic food webs, in particular at the primary producer–consumer interface where keystone species such as daphnids may be constrained by its dietary availability. Such constraints and their seasonal and interannual changes may be detected by continuous measurements of EPA concentrations. However, such EPA measurements became common only during the last two decades, whereas long‐term data sets on plankton biomass are available for many well‐studied lakes. Here, we test whether it is possible to estimate EPA concentrations from abiotic variables (light and temperature) and the biomass of prey organisms (e.g. ciliates, diatoms and cryptophytes) that potentially provide EPA for consumers.2. We used multiple linear regression to relate size‐ and taxonomically resolved plankton biomass data and measurements of temperature and light intensity to directly measured EPA concentrations in Lake Constance during a whole year. First, we tested the predictability of EPA concentrations from the biomass of EPA‐rich organisms (diatoms, cryptophytes and ciliates). Secondly, we included the variables mean temperature and mean light intensity over the sampling depth (0–20 m) and depth (0–8 and 8–20 m) as factors in our model to check for large‐scale seasonal‐ and depth‐dependent effects on EPA concentrations. In a third step, we included the deviations of light and temperature from mean values in our model to allow for their potential influence on the biochemical composition of plankton organisms. We used the Akaike Information Criterion to determine the best models.3. All approaches supported our proposition that the biomasses of specific plankton groups are variables from which seston EPA concentrations can be derived. The importance of ciliates as an EPA source in the seston was emphasised by their high weight in our models, although ciliates are neglected in most studies that link fatty acids to seston taxonomic composition. The large‐scale seasonal variability of light intensity and its interaction with diatom biomass were significant predictors of EPA concentrations. The deviation of temperature from mean values, accounting for a depth‐dependent effect on EPA concentrations, and its interaction with ciliate biomass were also variables with high predictive power.4. The best models from the first and second approaches were validated with measurements of EPA concentrations from another year (1997). The estimation with the best model including only biomass explained 80%, and the best model from the second approach including mean temperature and depth explained 87% of the variability in EPA concentrations in 1997.5. We show that it is possible to predict EPA concentrations reliably from plankton biomass, while the inclusion of abiotic factors led to results that were only partly consistent with expectations from laboratory studies. Our approach of including biotic predictors should be transferable to other systems and allow checking for biochemical constraints on primary consumers.
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DOI：
--
发表时间：
1997
期刊：
影响因子：
--
作者：
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影响因子：
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影响因子：
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通讯作者：
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DOI：
--
发表时间：
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期刊：
影响因子：
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影响因子：
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作者：
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通讯作者：
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