Intraseasonal predictability of natural phytoplankton population dynamics.
Intraseasonal predictability of natural phytoplankton population dynamics.
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DOI：
10.1002/ece3.8234
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发表时间：
2021-11
影响因子：
2.6
通讯作者：
Barton AD
中科院分区：
文献类型：
--
作者：
Agarwal V;James CC;Widdicombe CE;Barton AD
对海洋浮游植物种群的未来动态做出巧妙的预测是很困难的。在这里，我们使用西英吉利海峡L4站（1992-2014）198个浮游植物类群的月平均丰度的22年时间序列来测试将浮游植物聚集成多物种组合是否以及如何提高其时间动态的可预测性。使用非参数框架来评估可预测性，我们证明预测技能受到物种数据如何分组、噪声的存在和物种内随机行为的显着影响。总体而言，我们发现，与单个类群的可预测性相比，当物种聚集在一起形成具有更多物种的组合时，未来一个月的可预测性会增加。然而，甲藻和较大的浮游植物（>12 μm 细胞半径）的可预测性总体较低，并且不会通过将相似物种聚集在一起而增加。由于观测误差（噪声）或种群增长率的随机性，数据的高度可变性比组合的可预测性更能降低单个物种的可预测性。这些发现表明，与浮游植物物种的个体动态相比，对物种组合或整个群落指标（例如总叶绿素或生物量）进行单变量预测的潜力更大。海洋浮游植物种群动态本质上是嘈杂且难以预测的。在这里，通过对英吉利海峡浮游植物丰度的观察，我们发现浮游植物组合的可预测性通常超过单个物种的可预测性，并且可预测性随着组合大小的增加而增加。配套的数值模型表明，跨物种的聚集减少了测量误差和浮游植物生长速率的随机性对可预测性的影响。
It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22‐year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992–2014) to test whether and how aggregating phytoplankton into multi‐species assemblages can improve predictability of their temporal dynamics. Using a non‐parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 μm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole‐community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species. Marine phytoplankton population dynamics are inherently noisy and difficult to predict. Here, using observations of phytoplankton abundance from the English Channel, we found that the predictability of phytoplankton assemblages typically exceeds predictability for individual species and that predictability increases with assemblage size. A companion numerical model reveals that aggregation across species reduces the effect of measurement error and stochasticity in phytoplankton growth rates on predictability.
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DOI：
10.1073/pnas.1519080113
发表时间：
2016-03-15
影响因子：
11.1
作者：
Barton, Andrew D.;Irwin, Andrew J.;Stock, Charles A.
通讯作者：
Stock, Charles A.
影响因子：
8.6
作者：
FARMER, JD;SIDOROWICH, JJ
通讯作者：
SIDOROWICH, JJ
影响因子：
4
作者：
CASDAGLI, M
通讯作者：
CASDAGLI, M
影响因子：
2.7
作者：
DuFour, Mark R.;May, Cassandra J.;Mayer, Christine M.
通讯作者：
Mayer, Christine M.
影响因子：
8.8
作者：
Brandenburg, Karen M.;Wohlrab, Sylke;Van de Waal, Dedmer B.
通讯作者：
Van de Waal, Dedmer B.