Industrial Economics Incorporated, 2067 Massachusetts Avenue, Cambridge, MA, 02140, USA.
Natural Resource Damage Assessment and Restoration Program, United States Fish and Wildlife Service, Alaska Regional Office, 1011 East Tudor Road, Mail Stop #361, Anchorage, AK, 99503, USA.
Environ Monit Assess. 2020 Mar 17;191(Suppl 4):812. doi: 10.1007/s10661-019-7922-1.
Deposition models, such as the Shoreline Deposition Model (SDM) used to quantify nearshore avian injuries resulting from the 2010 Deepwater Horizon (DWH) oil spill, were developed to improve the estimates of nearshore avian mortality resulting from the release of oil into coastal and marine environments. Unlike earlier approaches to injury quantification, such as simple counts of carcasses on the shoreline, a modeling approach allows trustees to evaluate the precision of their estimate (i.e., to develop a confidence interval) and can inform decision-making and the likely utility of additional primary data collection activities through sensitivity analyses. In this paper, we rely on published literature, actual DWH data, and a deposition model simulation to evaluate how different model inputs and assumptions can affect the accuracy and precision of model results. We find that the precision of deposition models is strongly affected by the length of time between subsequent shoreline searches, the underlying magnitude of carcass deposition, carcass persistence probabilities, and carcass detection probabilities. In addition, the accuracy of deposition model results may be affected by natural fluctuations in deposition rates. Given these findings, we recommend that natural resource trustees include an evaluation of future model uncertainty as part of their initial data collection efforts. This will allow them to deploy resources in a way that maximizes the utility of future deposition model results. We also identify several factors that do not need to be assessed immediately following a spill event, thereby potentially freeing resources for more time critical data collection efforts.
沉积模型，如用于量化因 2010 年深水地平线（DWH）石油泄漏而导致近岸鸟类受伤的海岸线沉积模型（SDM），是为了提高因石油释放到沿海和海洋环境中而导致的近岸鸟类死亡率的估计值而开发的。与早期评估海岸线上鸟类尸体数量等简单方法不同，建模方法可以让受托人评估其估计值的精度（即，开发置信区间），并通过敏感性分析为决策提供信息，以及告知额外的主要数据收集活动的可能效用。在本文中，我们依赖于已发表的文献、实际的 DWH 数据和沉积模型模拟，来评估不同的模型输入和假设如何影响模型结果的准确性和精度。我们发现，沉积模型的精度受到后续海岸线搜索之间的时间长度、尸体沉积的基本幅度、尸体持久性概率和尸体检测概率的强烈影响。此外，沉积模型结果的准确性可能受到沉积率的自然波动的影响。鉴于这些发现，我们建议自然资源受托人将未来模型不确定性的评估作为其初始数据收集工作的一部分。这将使他们能够以最大程度地利用未来沉积模型结果的方式部署资源。我们还确定了几个不需要在溢油事件后立即评估的因素，从而为更紧迫的数据收集工作释放资源。
