Improving precision and reducing bias in biological surveys: Estimating false-negative error rates
Improving precision and reducing bias in biological surveys: Estimating false-negative error rates
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DOI：
10.1890/02-5078
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发表时间：
2003-12-01
影响因子：
5
通讯作者：
Possingham, HP
中科院分区：
文献类型：
--
作者：
Tyre, AJ;Tenhumberg, B;Possingham, HP
在野生动物管理和生物调查中广泛使用存在/不存在数据。人们对量化与这些数据相关的误差来源越来越感兴趣。我们发现，假阴性错误（未能记录一个物种时，事实上它是存在的）可以有一个显着的影响统计估计的栖息地模型使用模拟数据。然后，我们介绍了逻辑模型的扩展，零膨胀二项（ZIB）模型，允许估计的假阴性错误率和假阴性错误的发生概率的估计，通过使用重复的校正。访问同一个网站。我们的模拟表明，即使是相对较低的假阴性率偏差的栖息地影响的统计估计。三次重复访问的方法消除了偏倚，但估计值相对不精确。六次重复访问将估计的精度提高到与不存在假阴性错误的常规统计学相当的水平。一般来说，当错误率小于或等于50%时，通过增加更多的站点可以获得更高的效率，而当错误率>50%时，最好增加重复访问的次数。我们突出了三个案例研究的方法的灵活性，清楚地展示了一系列常用的调查方法的假阴性误差的影响。
The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of error associated with these data. We show that false-negative errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of false-negative errors and the correction of estimates of the probability of occurrence for false-negative errors by using repeated. visits to the same site. Our simulations show that even relatively low rates of false negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of false-negative errors In general, when error rates are less than or equal to50% greater efficiency is gained by adding more sites, whereas when error rates are >50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of false-negative errors for a range of commonly used survey methods.