National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, Seattle, Washington, United States of America.
PLoS One. 2012;7(8):e42294. doi: 10.1371/journal.pone.0042294. Epub 2012 Aug 8.
Ecologists often use multiple observer transect surveys to census animal populations. In addition to animal counts, these surveys produce sequences of detections and non-detections for each observer. When combined with additional data (i.e. covariates such as distance from the transect line), these sequences provide the additional information to estimate absolute abundance when detectability on the transect line is less than one. Although existing analysis approaches for such data have proven extremely useful, they have some limitations. For instance, it is difficult to extrapolate from observed areas to unobserved areas unless a rigorous sampling design is adhered to; it is also difficult to share information across spatial and temporal domains or to accommodate habitat-abundance relationships. In this paper, we introduce a hierarchical modeling framework for multiple observer line transects that removes these limitations. In particular, abundance intensities can be modeled as a function of habitat covariates, making it easier to extrapolate to unsampled areas. Our approach relies on a complete data representation of the state space, where unobserved animals and their covariates are modeled using a reversible jump Markov chain Monte Carlo algorithm. Observer detections are modeled via a bivariate normal distribution on the probit scale, with dependence induced by a distance-dependent correlation parameter. We illustrate performance of our approach with simulated data and on a known population of golf tees. In both cases, we show that our hierarchical modeling approach yields accurate inference about abundance and related parameters. In addition, we obtain accurate inference about population-level covariates (e.g. group size). We recommend that ecologists consider using hierarchical models when analyzing multiple-observer transect data, especially when it is difficult to rigorously follow pre-specified sampling designs. We provide a new R package, hierarchicalDS, to facilitate the building and fitting of these models.
生态学家通常使用多观察员样线调查来对动物种群进行计数。除了动物数量的统计，这些调查还会为每个观察员生成一系列的检测和未检测记录。当与其他数据（如距样线的距离等协变量）相结合时，这些序列提供了额外的信息，以便在样线上的可检测性小于 1 时估计绝对丰度。虽然现有的此类数据分析方法已经被证明非常有用，但它们也存在一些局限性。例如，除非遵循严格的抽样设计，否则很难从观测区域推断到未观测区域；也很难在空间和时间域内共享信息，或者适应栖息地-丰度关系。在本文中，我们介绍了一种用于多观察员样线的层次建模框架，该框架消除了这些限制。特别是，丰度强度可以建模为栖息地协变量的函数，从而更容易推断到未采样的区域。我们的方法依赖于状态空间的完整数据表示，其中未观测的动物及其协变量使用可逆跳跃马尔可夫链蒙特卡罗算法进行建模。观察员的检测通过概率尺度上的双变量正态分布进行建模，依赖于距离相关的相关参数。我们使用模拟数据和已知的高尔夫球座种群来演示我们方法的性能。在这两种情况下，我们都表明，我们的层次建模方法可以对丰度和相关参数进行准确的推断。此外，我们还可以对群体水平的协变量（如群体大小）进行准确推断。我们建议生态学家在分析多观察员样线数据时考虑使用层次模型，特别是在难以严格遵循预定抽样设计的情况下。我们提供了一个新的 R 包 hierarchicalDS，以方便这些模型的构建和拟合。
