Accommodating species identification errors in transect surveys
Accommodating species identification errors in transect surveys
复制标题
DOI：
10.1890/12-2124.1
复制
发表时间：
2013-11-01
期刊：
影响因子：
4.8
通讯作者：
Boveng, Peter L.
中科院分区：
文献类型：
--
作者：
Conn, Paul B.;McClintock, Brett T.;Boveng, Peter L.
生态学家经常使用样带调查来估计动物种群的密度和丰度。在这类调查中，物种分类的错误往往很明显，但很少有统计方法能正确解释这些错误。在本文中，我们研究的偏见，导致物种错误识别时被忽视，我们开发的统计模型，提供无偏估计的密度在面对这样的错误。我们的方法将真正的物种身份作为一个潜在的变量，并需要辅助信息的错误分类过程（如信息先验，使用已知物种的实验，或双观察员协议）。我们用模拟的人口普查数据和白令海冰相关海豹的双观察员调查数据来说明我们的方法。对于海豹分析，我们将误分类集成到基于模型的距离采样数据框架中。模拟数据分析表明，当有实验数据告知误分类率时，动物密度的估计是可靠的;当有未知物种的观察结果但没有彻底的误分类时，或者当误分类概率是对称的并且在估计过程中施加对称约束时，双观察员协议提供了鲁棒的推断。在我们的建模框架下，我们得到了合理的表观密度的海豹物种，即使在相当不精确的物种识别。我们得到了更可靠的推断时，模拟密度变化的样带。我们认为，生态学家应该经常使用空间明确的模型来解释物种分布的差异时，试图解释物种误认。我们的研究结果支持使用双观察员采样协议，防止物种错误分类（即，将不确定的观察记录为未知）。
Ecologists often use transect surveys to estimate the density and abundance of animal populations. Errors in species classification are often evident in such surveys, yet few statistical methods exist to properly account for them. In this paper, we examine biases that result from species misidentification when ignored, and we develop statistical models to provide unbiased estimates of density in the face of such errors. Our approach treats true species identity as a latent variable and requires auxiliary information on the misclassification process (such as informative priors, experiments using known species, or a double-observer protocol). We illustrate our approach with simulated census data and with double-observer survey data for ice-associated seals in the Bering Sea. For the seal analysis, we integrated misclassification into a model-based framework for distance-sampling data. The simulated data analysis demonstrated reliable estimation of animal density when there are experimental data to inform misclassification rates; double-observer protocols provided robust inference when there were unknown species observations but no outright misclassification, or when misclassification probabilities were symmetric and a symmetry constraint was imposed during estimation. Under our modeling framework, we obtained reasonable apparent densities of seal species even under considerable imprecision in species identification. We obtained more reliable inferences when modeling variation in density among transects. We argue that ecologists should often use spatially explicit models to account for differences in species distributions when trying to account for species misidentification. Our results support using double-observer sampling protocols that guard against species misclassification (i.e., by recording uncertain observations as unknown).