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A Hierarchical Distance Sampling Approach to Estimating Mortality Rates from Opportunistic Carcass Surveillance Data.
Methods in Ecology and Evolution ( IF 6.6 ) Pub Date : 2013-01-10 , DOI: 10.1111/2041-210x.12021
Steve E Bellan 1 , Olivier Gimenez , Rémi Choquet , Wayne M Getz
Affiliation  

  1. Distance sampling is widely used to estimate the abundance or density of wildlife populations. Methods to estimate wildlife mortality rates have developed largely independently from distance sampling, despite the conceptual similarities between estimation of cumulative mortality and the population density of living animals. Conventional distance sampling analyses rely on the assumption that animals are distributed uniformly with respect to transects and thus require randomised placement of transects during survey design. Because mortality events are rare, however, it is often not possible to obtain precise estimates in this way without infeasible levels of effort. A great deal of wildlife data, including mortality data, are available via road‐based surveys. Interpreting these data in a distance sampling framework requires accounting for the non‐uniformity of sampling. In addition, analyses of opportunistic mortality data must account for the decline in carcass detectability through time. We develop several extensions to distance sampling theory to address these problems.
  2. We build mortality estimators in a hierarchical framework that integrates animal movement data, surveillance effort data and motion‐sensor camera trap data, respectively, to relax the uniformity assumption, account for spatiotemporal variation in surveillance effort and explicitly model carcass detection and disappearance as competing ongoing processes.
  3. Analysis of simulated data showed that our estimators were unbiased and that their confidence intervals had good coverage.
  4. We also illustrate our approach on opportunistic carcass surveillance data acquired in 2010 during an anthrax outbreak in the plains zebra of Etosha National Park, Namibia.
  5. The methods developed here will allow researchers and managers to infer mortality rates from opportunistic surveillance data.


中文翻译:

从机会主义尸体监测数据估计死亡率的分层距离抽样方法。

  1. 距离抽样广泛用于估计野生动物种群的丰度或密度。尽管累积死亡率的估计与活体动物的种群密度之间存在概念相似性,但估计野生动物死亡率的方法在很大程度上独立于距离抽样。传统的距离抽样分析依赖于动物相对于横断面均匀分布的假设,因此需要在调查设计期间随机放置横断面。然而,由于死亡率事件很少见,如果没有不可行的努力,通常不可能以这种方式获得精确的估计值。通过基于道路的调查可以获得大量野生动物数据,包括死亡率数据。在距离抽样框架中解释这些数据需要考虑抽样的非均匀性。此外,对机会性死亡率数据的分析必须说明随着时间的推移屠体可检测性的下降。我们开发了几个距离采样理论的扩展来解决这些问题。
  2. 我们在一个分层框架中构建死亡率估计器,该框架分别集成了动物运动数据、监视工作数据和运动传感器相机陷阱数据,以放宽均匀性假设,解释监视工作的时空变化,并明确地将尸体检测和消失建模为正在进行的竞争过程。
  3. 对模拟数据的分析表明,我们的估计量是无偏的,并且它们的置信区间具有良好的覆盖范围。
  4. 我们还说明了我们对 2010 年纳米比亚埃托沙国家公园平原斑马炭疽爆发期间获得的机会性尸体监测数据的方法。
  5. 这里开发的方法将使研究人员和管理人员能够从机会监测数据中推断死亡率。
更新日期:2013-01-10
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