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Sampling bias and the fossil record of planktonic foraminifera on land and in the deep sea

机译:陆地和深海浮游有孔虫的取样偏向和化石记录

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Large-scale trends in planktonic foraminiferal diversity have so far been based on utilization of synoptic biostratigraphic range charts. Although this approach ensures the taxonomic consistency and quality of the data being used, it takes no formal account of any sampling biases that might exist in the fossil record. We demonstrate that the occurrence data of planktonic foraminifera, as recorded in the primary literature, are strongly biased by sampling. We do this by demonstrating that raw diversity curves derived from the land-based and deep-sea records are strikingly different, but that they each correlate with the intensity of sampling in their respective environments, and thus are ultimately controlled by the structure of the geological record in each setting. Because sampling of the Mesozoic record is best in our land record whereas sampling of the Cenozoic is best in our deep-sea record, we combine the two to generate the best-supported estimates of species and genus diversity over time from these data. We correct for sampling bias using shareholder quorum subsampling and a modeling approach. The data are then transformed to generate a range-through plot of species richness that is compared with two earlier estimates of the diversity history where comparable species-in-bin data can be recovered. No robust statistical correlation is found among the three estimates. Although differences in amplitude are to be expected, differences in the actual shape of the curve are surprising. We conclude that these differences stem from the nature of the data themselves, namely the taxonomic scheme adopted and the taxonomic coverage used.
机译:迄今为止,浮游有孔虫多样性的大规模趋势是基于利用天气生物地层学范围图的。尽管此方法可确保所用数据的分类学一致性和质量,但并未正式考虑化石记录中可能存在的任何采样偏差。我们证明,浮游有孔虫的发生数据,如在原始文献中记录的那样,由于采样而有很大的偏差。我们这样做的目的是证明源自陆地和深海记录的原始多样性曲线截然不同,但它们各自与各自环境中的采样强度相关,因此最终受地质结构控制在每个设置中记录。因为中生代记录的采样在我们的陆地记录中是最好的,而新生代记录的采样在我们的深海记录中是最好的,所以我们将两者结合起来,从而根据这些数据得出物种和属多样性的最佳支持估计。我们使用股东法定二次抽样和建模方法纠正抽样偏差。然后将数据转换为生成物种丰富度的直通图,并将其与多样性历史的两个较早估计进行比较,在该估计中可以恢复可比较的箱中物种数据。在这三个估计值之间未找到可靠的统计相关性。尽管幅度的差异是可以预期的,但曲线实际形状的差异却令人惊讶。我们得出结论,这些差异源于数据本身的性质,即采用的分类方案和使用的分类范围。

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