O'Brien Susan H, Cook Aonghais S C P, Robinson Robert A
Joint Nature Conservation Committee, Inverdee House, Baxter Street, Aberdeen, AB11 9QA, UK.
British Trust for Ornithology, The Nunnery, Thetford, Norfolk, IP24 2PU, UK.
J Environ Manage. 2017 Oct 1;201:163-171. doi: 10.1016/j.jenvman.2017.06.037. Epub 2017 Jun 26.
Assessing the potential impact of additional mortality from anthropogenic causes on animal populations requires detailed demographic information. However, these data are frequently lacking, making simple algorithms, which require little data, appealing. Because of their simplicity, these algorithms often rely on implicit assumptions, some of which may be quite restrictive. Potential Biological Removal (PBR) is a simple harvest model that estimates the number of additional mortalities that a population can theoretically sustain without causing population extinction. However, PBR relies on a number of implicit assumptions, particularly around density dependence and population trajectory that limit its applicability in many situations. Among several uses, it has been widely employed in Europe in Environmental Impact Assessments (EIA), to examine the acceptability of potential effects of offshore wind farms on marine bird populations. As a case study, we use PBR to estimate the number of additional mortalities that a population with characteristics typical of a seabird population can theoretically sustain. We incorporated this level of additional mortality within Leslie matrix models to test assumptions within the PBR algorithm about density dependence and current population trajectory. Our analyses suggest that the PBR algorithm identifies levels of mortality which cause population declines for most population trajectories and forms of population regulation. Consequently, we recommend that practitioners do not use PBR in an EIA context for offshore wind energy developments. Rather than using simple algorithms that rely on potentially invalid implicit assumptions, we recommend use of Leslie matrix models for assessing the impact of additional mortality on a population, enabling the user to explicitly define assumptions and test their importance.
评估人为因素导致的额外死亡率对动物种群的潜在影响需要详细的种群统计学信息。然而，这些数据常常缺失，这使得那些对数据要求不高的简单算法颇具吸引力。由于其简单性，这些算法往往依赖于一些隐含假设，其中一些假设可能具有相当大的局限性。潜在生物移除量（PBR）是一种简单的捕捞模型，它估计一个种群在理论上能够承受而不导致种群灭绝的额外死亡数量。然而，PBR依赖于许多隐含假设，特别是围绕密度依赖性和种群轨迹的假设，这限制了它在许多情况下的适用性。在多种用途中，它已在欧洲被广泛应用于环境影响评估（EIA），以检验海上风电场对海鸟种群潜在影响的可接受性。作为一个案例研究，我们使用PBR来估计具有典型海鸟种群特征的种群在理论上能够承受的额外死亡数量。我们将这种额外死亡率水平纳入莱斯利矩阵模型，以检验PBR算法中关于密度依赖性和当前种群轨迹的假设。我们的分析表明，对于大多数种群轨迹和种群调节形式，PBR算法所确定的死亡率水平会导致种群数量下降。因此，我们建议从业者在海上风能开发的环境影响评估中不要使用PBR。与其使用依赖于可能无效的隐含假设的简单算法，我们建议使用莱斯利矩阵模型来评估额外死亡率对种群的影响，这样使用者能够明确界定假设并检验其重要性。
