ENVIRONMENTAL PARAMETERS CONTROLLING THE HABITAT OF THE BRACKISHWATER CLAM CORBICULA JAPONICA IDENTIFIED BY PREDICTIVE MODELLING

ENVIRONMENTAL PARAMETERS CONTROLLING THE HABITAT OF THE BRACKISHWATER CLAM CORBICULA JAPONICA IDENTIFIED BY PREDICTIVE MODELLING
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预测模型识别的咸水蛤蚬栖息地环境参数

DOI:
10.21660/2019.59.8125
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发表时间:
2019
影响因子:
0.7
通讯作者:
Y. Sugiyama
Y. Sugiyama
中科院分区:
--
文献类型:
--
作者:
Y. Sugiyama

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梭摘要粳稻是河口和半咸水沃茨的重要生态物种,也是重要的渔业资源。然而,它的居住条件和环境因素之间的关系仍然不清楚。因此,我们建立了一个栖息地预测模型,利用GLM来定义C。日本栖息地的真寺湖,合并数据从1982年到1983年,测量人口密度,位置,淤泥/粘土含量,点火的物理环境数据,pH值,溶解O2密度,氯离子浓度,和COD的水质。我们的分析表明,标准化参数灼烧损失,测量沉积物中的有机质和碳酸盐含量,是预测模型中的主要影响(估计-2.22),并具有最大的绝对值。因此,C.在预测模型中,粳稻的分布依赖于烧失量。在影响分布的交互作用项中,溶解氧和粉粒/粘粒含量比的绝对值最大,为-2.12。此外,还揭示了限制该虫种群数量的环境因素。每个季节的粳稻都不一样。这些结果虽然来自C. japonica,也表明我们的GLM模型是有效的,以更全面地了解栖息地的不动的底栖动物一般。
The C. japonica is an ecologically important species in estuaries and brackish waters, as well as a very important fishery resource. However, the relationship between its habitation conditions and environmental factors remains unclear. We therefore made a habitat prediction model using GLM to define the environment of the C. japonica habitat of Lake Shinji, incorporating data acquired from 1982 through 1983 which measured population density, location, silt/clay content, ignition of physical environmental data, pH, dissolved O2 density, chloride ion concentration, and COD of the quality of the water. Our analysis showed that the standardization parameter ignition loss, measuring organic matter and carbonate content in the sediment, was the main effect in the predictive model (estimate -2.22) and had the greatest absolute value. Thus the C. japonica's distribution is dependent on ignition loss, in the predictive model. As for interaction terms modifying distribution, the dissolved O2 and silt/clay content ratio had the largest absolute value at -2.12. In addition, it was revealed that the environmental factors which limit population levels of the C. japonica every season are different. These results, while derived from C. japonica, also suggest that our GLM model is effective to more fully understand the habitation area of immobile benthoses in general.