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Could we obtain better estimates of plot species richness from multiple-observer plant censuses?

机译:我们能否通过多次观察植物普查获得更好的地块物种丰富度估计?

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QuestionCould we better estimate plot species richness by asking several botanists to survey the same plots and using non-parametric estimators of richness?LocationTwo French deciduous forests.MethodsUsing replicated, independent censuses made by 11 professional botanists on the same eight 100-m(2) forest plots, the relative performance of different richness estimators (Lincoln-Petersen, Jackknife 1&2, Chao 1&2, Bootstrap, Chao Mth, Darroch) and the variation in their performance with the number of botanists involved (teams with two to eight botanists) were investigated. The sensitivity of these estimators to the presence of misidentifications in the data was also assessed.ResultsWhen misidentifications are removed, Chao Mth estimators converged fastest to true richness, but none of the tested estimators correctly accounted for differences in exhaustiveness between the teams. Finally, all estimators were highly sensitive to misidentifications.ConclusionsRichness estimators are of little help in the presence of misidentifications and are ineffective at removing between-team discrepancies, thus strongly limiting their usefulness in practice. Methods are presented to show how surveys can be designed to remove misidentifications and limit between-team discrepancies. A sensible sampling design for 100-m(2) plots in temperate forests would involve triplets of botanists and correcting data with the Chao N1. Pairs of botanists would already significantly improve the richness estimates, but such estimates would still be biased low. However, further research is needed to design new richness estimators that are more robust to observer effects.
机译:问题我们是否可以通过要求几位植物学家调查同一块土地并使用非参数的富裕度估算器来更好地估算地块物种的丰富度?位置两个法国落叶林方法使用由11位专业植物学家在相同的8个100平方米上进行的重复,独立的人口普查(2)调查了森林地块,不同的丰度估算器(林肯-彼得森,1号和2号,昭1&2,Bootstrap,昭梅,达罗奇)的相对性能,以及其性能随所涉及的植物学家数量(2至8个植物学家团队)的变化。结果还评估了这些估计量对数据中存在错误标识的敏感度。结果消除了错误标识后,Cho Mth估计量收敛最快,达到了真实的富裕度,但是没有一个经过测试的估计量正确地解释了团队之间穷举性的差异。最后,所有的估计器都对错误识别高度敏感。结论丰富度估计器在存在错误识别的情况下几乎没有帮助,并且无法有效消除团队之间的差异,因此严重限制了它们在实践中的实用性。提出了一些方法来说明如何设计调查以消除错误标识并限制团队之间的差异。在温带森林中对100 m(2)地块进行明智的采样设计将涉及三重植物学家并使用Chao N1校正数据。两对植物学家已经可以大大改善其丰富度估计值,但是这样的估计值仍然偏低。但是,需要进行进一步的研究来设计对观察者效果更可靠的新的丰富度估算器。

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