Spatial Variation on Multiple Scales in Line Transect Data; the Case of Antarctic Fin Whales
提出一种新模型，在线样线数据中同时处理动物密度的长期趋势和短期突变，用于估计南极长须鲸的丰度，并通过模拟验证模型效果。
Line transect sampling is a widely used survey method for estimating animal density or abundance. We present a novel model for such data that allows for spatial variation in animal density at two scales: a long scale, representing trends caused by for instance climatic or terrain gradients, and a short scale, representing abrupt shifts due to local effects such as prey patchiness. The long-range variation is modeled as a latent Gaussian random field, while the abrupt changes are modeled as a two-state continuous-time Markov process along the transect line. For species that form dense groups, a separate spatial model for group size is proposed. The model is implemented in the R package TMB, allowing for efficient and computationally feasible inference. We apply our approach to estimate the abundance of Antarctic fin whales in the Scotia Sea. The effect of the different spatial scales is discussed, and a comparison is made to another spatially explicit method for line transect data. We validate our approach with a simulation study using the fitted model, wherein we re-fit models on samples generated from our initial model. These re-fitted models effectively capture the true simulated abundance.