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首页> 外文期刊>Biodiversity and Conservation >Accuracy of species richness estimators applied to fish in small and large temperate lowland rivers
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Accuracy of species richness estimators applied to fish in small and large temperate lowland rivers

机译:适用于大小温带低地河流鱼类的物种丰富度估算器的准确性

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摘要

The performance of non-parametric species richness estimators (Program SPADE) was assessed by applying them to fish from two lowland waterways in Poland: (1) a stream sampled annually at one site for 23 years (13 of which were after the stream was turned into a canal), and (2) a river sampled twice annually at two sites (one natural, the other impounded) for 16 years. On each sampling occasion consecutive electrofishing runs were made and the species richness of the total sample (obtained in all the runs) was predicted by each estimator from the sub-sample of the first run. The estimators were applied to all of the samples collected in each waterway, which were referred to as the 'rich group survey' selection, and to two smaller selections, named the 'improved survey' and 'complete survey'. The performance was evaluated using the measures PAR (percent of actual richness) and SRMSE (scaled root mean square error). Overall, the HM and Chao1-bc estimators were decisively better than others, and ACE1 and ACE were decisively worse both in terms of PAR and SRMSE. In the stream, the bed regulation little affected the performance of the estimators, but they were more correct when applied to the 'improved survey' selection rather than to the 'rich group survey' selection. In the river, the performance of most of the estimators, both in terms of PAR and SRMSE, was much improved only by selecting those samples for analysis that complied with the Chao-2 criterion (i.e., 'complete survey' selection).
机译:通过将非参数物种丰富度估算器(计划SPADE)应用于波兰两条低地水道的鱼类,来评估其性能:(1)每年在一个地点采样23年(其中13条是在转向该流之后) (2)一条河流,每年两次在两个地点(一个是自然的,另一个是被扣留的)采样了16年。在每个抽样场合,进行连续的电钓鱼,并由每个估算者从第一次抽样的子样本中预测总样本的物种丰富度(在所有样本中获得)。将估计值应用于在每个水道中收集的所有样本,这被称为“富人调查”选择,并应用于两个较小的选择,分别称为“改进调查”和“完整调查”。使用PAR(实际丰富度百分比)和SRMSE(缩放的均方根误差)对性能进行评估。总体而言,就PAR和SRMSE而言,HM和Chao1-bc估计量比其他估计量要好,而ACE1和ACE则要差得多。在流中,床的调节几乎不会影响估计器的性能,但是将其应用于“改进的调查”选择而不是“富人调查”选择时,它们更加正确。在河流中,大多数评估器的性能(无论是PAR还是SRMSE)都仅通过选择符合Chao-2标准(即``完整调查''选择)的样本进行分析才能得到很大改善。

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