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首页> 外文期刊>Animal Biodiversity and Conservation >Evaluación de estimadores no paramétricos de la riqueza de especies. Un ejemplo con aves en áreas verdes de la ciudad de Puebla, México
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Evaluación de estimadores no paramétricos de la riqueza de especies. Un ejemplo con aves en áreas verdes de la ciudad de Puebla, México

机译:物种丰富度的非参数估计量评估。墨西哥普埃布拉市绿地中鸟类的例子

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Assessing non–parametric estimators of species richness. A case study with birds in green areas of the city of Puebla, MexicoOur objective was to evaluate the performance of non–parametric estimators of spe?cies richness with real data. During the 2003 breeding season, bird communities were sampled in two green areas in the city of Puebla (Mexico), and the corresponding sample–based rarefaction curves were obtained. Mean data were adjusted to two non–asymptotic and seven asymptotic accumulation functions, and the best model was selected by means of reliability criteria in information theory. The cumulative Weibull and the beta–P functions were the best–fit models. Bias, precision and accuracy of five non–parametric estimators of species richness (ICE, Chao2, Jackknife 1, Jackknife 2, and Bootstrap) were then assessed for increasing sampling efforts (1–53 sampling units) against the asymptote of the selected accumulation functions. All the non–parametric estimators here evaluated underestimated true richness most of the time, specially in one of the sites. However, after combining data from the two assemblages, only ICE, and Jackknife 1 and 2 exhibited bias below 10% with different sampling efforts, and only Jackknife 1 was globally accurate (scaled mean squared errorx100< 5%, even with low sampling efforts, ca. 20% of the total). Therefore, we propose using the Jackknife 1 non–parametric estimator as a lower limit to measure bird species richness in urban sites similar to those in the present study.
机译:评估物种丰富度的非参数估计量。以墨西哥普埃布拉市绿地上的鸟类为例的研究我们的目标是用真实数据评估专业丰富度的非参数估计量的性能。在2003年繁殖季节期间,在墨西哥普埃布拉市的两个绿色区域中对鸟类群落进行了采样,并获得了相应的基于样本的稀疏曲线。将均值数据调整为两个非渐近累积函数和七个渐近累积函数,并根据信息论中的可靠性标准选择最佳模型。累积的Weibull和beta-P函数是最合适的模型。然后评估了针对物种丰富度的五个非参数估计量(ICE,Chao2,Jackknife 1,Jackknife 2和Bootstrap)的偏差,精度和准确性,以针对所选累积函数的渐近线增加抽样工作(1-53个抽样单位) 。在这里,所有非参数估计量大多数时候都低估了真实的丰富度,尤其是在其中一个地点。但是,将两种组合的数据相结合后,只有ICE以及Jackknife 1和2在不同的采样工作下显示出低于10%的偏差,并且只有Jackknife 1才是全球准确的(即使在较低的采样工作下,均方误差x100 <5%),约占总数的20%)。因此,我们建议使用Jackknife 1非参数估计量作为下限,以类似于本研究中的方法来测量城市地区鸟类的物种丰富度。

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