Dealing with false-positive and false-negative errors about species occurrence at multiple levels
Dealing with false-positive and false-negative errors about species occurrence at multiple levels
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DOI：
10.1111/2041-210x.12743
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发表时间：
2017-09-01
影响因子：
6.6
通讯作者：
Tingley, Reid
中科院分区：
文献类型：
--
作者：
Guillera-Arroita, Gurutzeta;Lahoz-Monfort, Jose Joaquin;Tingley, Reid
1.物种发生的准确知识是各种生态，进化和保护应用的基础。由于检测不完善，在不同的采样阶段，不同的机制可能导致假阴性和/或假阳性错误，因此评估现场是否存在物种往往变得复杂。数据中的模糊性意味着相关参数的估计可能会混淆，除非有额外的信息来解决这些不确定性。在这里，我们考虑在多个级别的假阳性和假阴性错误的物种检测数据的分析。为此，我们开发并研究了一个两阶段的占用检测模型。我们使用轮廓似然可识别性分析和估计，并研究可靠估计所需的额外数据的类型。我们用模拟数据测试模型，然后分析四种澳大利亚青蛙的环境DNA（eDNA）调查数据。在我们的案例研究中，我们认为假阳性可能是由于水样和定量PCR样品水平的污染而产生的，而假阴性可能是由于在现场样品中未捕获eDNA或由于实验室检测的敏感性而产生的。我们用来自听觉调查和实验室校准实验的数据来增强我们的eDNA调查数据。我们证明了两阶段模型的假阳性和假阴性错误是不可识别的，如果只有调查数据容易误报。可靠的估计至少需要两个额外信息来源（例如，具有明确检测的调查方法的记录和校准实验）。或者，可识别性可以通过在贝叶斯设置中将错误检测率的合理界限设置为先验信息来实现。我们的案例研究结果与我们的模拟数据要求相匹配，并显示所有物种的假阳性率均大于零。我们提供统计建模工具，以解释物种发生调查数据中的不确定性，当假阴性和假阳性可能发生在多个采样阶段。这些数据往往是支持管理和决策所必需的。处理这些不确定性与传统的调查方法有关，但也与有前途的新技术有关，如eDNA抽样。
1. Accurate knowledge of species occurrence is fundamental to a wide variety of ecological, evolutionary and conservation applications. Assessing the presence or absence of species at sites is often complicated by imperfect detection, with different mechanisms potentially contributing to false-negative and/or false-positive errors at different sampling stages. Ambiguities in the data mean that estimation of relevant parameters might be confounded unless additional information is available to resolve those uncertainties.2. Here, we consider the analysis of species detection data with false-positive and false-negative errors at multiple levels. We develop and examine a two-stage occupancy-detection model for this purpose. We use profile likelihoods for identifiability analysis and estimation, and study the types of additional data required for reliable estimation. We test the model with simulated data, and then analyse data from environmental DNA (eDNA) surveys of four Australian frog species. In our case study, we consider that false positives may arise due to contamination at the water sample and quantitative PCR-sample levels, whereas false negatives may arise due to eDNA not being captured in a field sample, or due to the sensitivity of laboratory tests. We augment our eDNA survey data with data from aural surveys and laboratory calibration experiments.3. We demonstrate that the two-stage model with false-positive and false-negative errors is not identifiable if only survey data prone to false positives are available. At least two sources of extra information are required for reliable estimation (e.g. records from a survey method with unambiguous detections, and a calibration experiment). Alternatively, identifiability can be achieved by setting plausible bounds on false detection rates as prior information in a Bayesian setting. The results of our case study matched our simulations with respect to data requirements, and revealed false-positive rates greater than zero for all species.4. We provide statistical modelling tools to account for uncertainties in species occurrence survey data when false negatives and false positives could occur at multiple sampling stages. Such data are often needed to support management and policy decisions. Dealing with these uncertainties is relevant for traditional survey methods, but also for promising new techniques, such as eDNA sampling.