Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R
Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R
复制标题
使用 R-INLA 包通过 N 混合模型估计动物丰度
DOI：
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发表时间：
2017
影响因子：
5.8
通讯作者：
H. Rue
中科院分区：
文献类型：
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作者：
T. Meehan;N. Michel;H. Rue
成功的野生动物种群管理需要准确的丰度估计。在野生动物调查中，不完善的检测可能会混淆粗略的估计。N-混合物模型能够量化检测概率，并在适当的条件下，产生偏差较小的丰度估计值。在这里，我们演示了使用R的R-INLA包来分析N-混合模型，并将R-INLA的性能与其他两种常见方法进行比较：JAGS（通过R的runjags包），它使用马尔可夫链蒙特卡罗并允许贝叶斯推理，以及R的未标记包，它使用最大似然并允许频率论推理。我们表明，R-INLA是分析N-混合模型的一个有吸引力的选择，当（i）快速计算时间是必要的（R-INLA比未标记的快10倍，比JAGS快500倍），（ii）熟悉的模型语法和数据格式（相对于其他R包）是必要的，（iii）调查水平的协变量检测不是必不可少的，（iv）贝叶斯推理是首选。
Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixture models enable quantification of detection probability and, under appropriate conditions, produce abundance estimates that are less biased. Here, we demonstrate use of the R-INLA package for R to analyze N-mixture models and compare performance of R-INLA to two other common approaches: JAGS (via the runjags package for R), which uses Markov chain Monte Carlo and allows Bayesian inference, and the unmarked package for R, which uses maximum likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (i) fast computing times are necessary (R-INLA is 10 times faster than unmarked and 500 times faster than JAGS), (ii) familiar model syntax and data format (relative to other R packages) is desired, (iii) survey-level covariates of detection are not essential, and (iv) Bayesian inference is preferred.
影响因子：
5.8
作者：
Carpenter, Bob;Gelman, Andrew;Riddell, Allen
通讯作者：
Riddell, Allen