Okamura Hiroshi, Minamikawa Shingo, Skaug Hans J, Kishiro Toshiya
National Research Institute of Far Seas Fisheries, Fisheries Research Agency, 2-12-4 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-8648, Japan.
Biometrics. 2012 Jun;68(2):504-13. doi: 10.1111/j.1541-0420.2011.01689.x. Epub 2011 Oct 12.
Line transect sampling is one of the most widely used methods for estimating the size of wild animal populations. An assumption in standard line transect sampling is that all the animals on the trackline are detected without fail. This assumption tends to be violated for marine mammals with surfacing/diving behaviors. The detection probability on the trackline is estimated using duplicate sightings from double-platform line transect methods. The double-platform methods, however, are insufficient to estimate the abundance of long-diving animals because these animals can be completely missed while the observers pass. We developed a more flexible hazard probability model that incorporates information on surfacing/diving patterns obtained from telemetry data. The model is based on a stochastic point process and is statistically tractable. A simulation study showed that the new model provides near-unbiased abundance estimates, whereas the traditional hazard rate and hazard probability models produce considerably biased estimates. As an illustration, we applied the model to data on the Baird's beaked whale (Berardius bairdii) in the western North Pacific.
样线抽样是估计野生动物种群数量最常用的方法之一。标准样线抽样的一个假设是,样线上的所有动物都能被无一遗漏地检测到。对于具有浮出水面/潜水行为的海洋哺乳动物来说,这一假设往往会被违反。样线上的检测概率是使用双平台样线法的重复观测来估计的。然而,双平台方法不足以估计长时间潜水动物的数量,因为当观测者经过时,这些动物可能会完全错过。我们开发了一种更灵活的风险概率模型,该模型纳入了从遥测数据中获得的浮出水面/潜水模式信息。该模型基于一个随机点过程,在统计上易于处理。一项模拟研究表明,新模型提供了近乎无偏的数量估计,而传统的风险率和风险概率模型产生了相当大的偏差估计。作为一个例证,我们将该模型应用于北太平洋西部的贝氏喙鲸(Berardius bairdii)的数据。