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首页> 外文期刊>Marine ecology progress series >Modeling long-term fluctuations in the distribution and abundance of Engraulis anchoita eggs and larvae in the southeastern Brazilian bight
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Modeling long-term fluctuations in the distribution and abundance of Engraulis anchoita eggs and larvae in the southeastern Brazilian bight

机译:模拟巴西东南部of鱼gra鱼卵和幼虫的分布和丰度的长期波动

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Most data sets of ichthyoplankton contain a high frequency of zeros, and, not considering the possible inflation of zero counts, as in most studies, may result in incorrect model predictions. Thus, we modeled abundance of Engraulis anchoita eggs and larvae sampled during 18 oceanographic cruises conducted between 1974 and 2010 using zero-inflated (ZI) models. ZI models are mixture models with 2 components: (1) containing false zeros (due to design, survey or observer errors) modeled using a binomial generalized linear model (GLM); (2) containing abundance data that may produce zeros (true zeros), and modeled with a negative binomial GLM (ZINB). Although ZINB has been used in other areas of research, we are not aware of its previous use for ichthyoplankton. Common to larvae and eggs, the probability of false zeros was lowest in the southern area of the southeastern Brazilian Bight and higher at deeper station depths. The probability of false zeros was higher in 0.505 mm mesh-size samples than in 0.333 mm mesh only for eggs. Egg and larval abundance was negatively related to temperature in the count portion of the model; only larval abundance was negatively related to salinity. Egg abundance was higher in years sampled with 0.333 mm mesh under conditions of moderate/strong El Nino. The high inter-annual variability in the abundance and distribution of eggs emphasizes the importance of long-term studies to better understand patterns of fluctuations in the occurrence of ichthyoplankton that are related to environmental conditions.
机译:鱼鳞浮游生物的大多数数据集包含零的高频率,并且像大多数研究一样,未考虑零计数的可能膨胀,可能会导致模型预测不正确。因此,我们使用零膨胀(ZI)模型对在1974年至2010年进行的18次海洋航行中采样的an鱼Engraulis凤尾鱼卵和幼虫进行了建模。 ZI模型是具有2个成分的混合模型:(1)包含使用二项式广义线性模型(GLM)建模的错误零(由于设计,调查或观察者误差而造成); (2)包含可能产生零(真实零)的丰度数据,并使用负二项式GLM(ZINB)进行建模。尽管ZINB已在其他研究领域中使用,但我们尚不知道其以前用于鱼鳞浮游生物的用途。幼虫和卵所共有的假零概率在巴西东南部东南部的南部地区最低,而在更深的站位深度较高。在0.505毫米筛目大小的样本中,错误零的可能性比仅对于鸡蛋的0.333毫米筛目高。在模型的计数部分,卵和幼虫的丰度与温度呈负相关。只有幼虫的丰度与盐度呈负相关。在中度/强厄尔尼诺现象下,以0.333毫米目采样的蛋年中的蛋丰度更高。卵的丰度和分布高度的年际变化强调了长期研究的重要性,以便更好地了解与环境条件有关的鱼鳞浮游生物发生波动的模式。

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