论文标题
基于稀缺动物痕迹数据估算丰度的贝叶斯模型
A Bayesian Model to Estimate Abundance Based on Scarce Animal Vestige Data
论文作者
论文摘要
我们提出了一个建模框架,该框架允许估计痕量计数的丰度。这种间接估计丰度的间接方法由于可以执行的相对负担能力而具有吸引力,并且与估计动物丰度的直接方法相比,动物和人类可能造成的风险降低。我们通过进行模拟来评估这些方法,使我们能够检查模型估计的准确性。然后将这些模型拟合到几个案例研究中,以获得巴西领取的奇特,亚利桑那州的基特狐狸,意大利的红狐狸和苏格兰的西卡鹿的丰度估计。模拟结果表明,这些模型在一系列样本量范围内产生了丰度的准确估计。特别是,当数据非常稀缺时,该建模框架会产生准确的估计。在估计丰度估计痕迹的情况下,使用痕迹计数可以监测物种,否则这些物种可能由于其隐居性而未被发现。此外,与较大规模的数据收集相比,当很少有样本收集数据时,这些模型的功效将允许使用小规模的数据收集程序。
We propose a modelling framework which allows for the estimation of abundances from trace counts. This indirect method of estimating abundance is attractive due to the relative affordability with which it may be carried out, and the reduction in possible risk posed to animals and humans when compared to direct methods for estimating animal abundance. We assess these methods by performing simulations which allow us to examine the accuracy of model estimates. The models are then fitted to several case studies to obtain abundance estimates for collared peccaries in Brazil, kit foxes in Arizona, red foxes in Italy and sika deer in Scotland. Simulation results reveal that these models produce accurate estimates of abundance at a range of sample sizes. In particular, this modelling framework produces accurate estimates when data is very scarce. The use of vestige counts in estimating abundance allows for the monitoring of species which may otherwise go undetected due to their reclusive nature. Additionally, the efficacy of these models when data is collected at very few transects will allow for the use of small-scale data collection programmes which may be carried out at reduced cost, when compared to larger-scale data collection.