Institute of Zoology, Zoological Society of London Regent's Park, London, NW1 4RY, UK ; Division of Ecology and Evolution, Imperial College London Silwood Park, Ascot, SL5 7PY, UK ; ARC Centre of Excellence for Environmental Decisions, the NERP Environmental Decisions Hub, Centre for Biodiversity and Conservation Science, University of Queensland Brisbane, Queensland, 4072, Australia.
Ecol Evol. 2013 Jul;3(7):2378-91. doi: 10.1002/ece3.596. Epub 2013 Jun 14.
Tracking trends in the abundance of wildlife populations is a sensitive method for assessing biodiversity change due to the short time-lag between human pressures and corresponding shifts in population trends. This study tests for proposed associations between different types of human pressures and wildlife population abundance decline-curves and introduces a method to distinguish decline trajectories from natural fluctuations in population time-series. First, we simulated typical mammalian population time-series under different human pressure types and intensities and identified significant distinctions in population dynamics. Based on the concavity of the smoothed population trend and the algebraic function which was the closest fit to the data, we determined those differences in decline dynamics that were consistently attributable to each pressure type. We examined the robustness of the attribution of pressure type to population decline dynamics under more realistic conditions by simulating populations under different levels of environmental stochasticity and time-series data quality. Finally, we applied our newly developed method to 124 wildlife population time-series and investigated how those threat types diagnosed by our method compare to the specific threatening processes reported for those populations. We show how wildlife population decline curves can be used to discern between broad categories of pressure or threat types, but do not work for detailed threat attributions. More usefully, we find that differences in population decline curves can reliably identify populations where pressure is increasing over time, even when data quality is poor, and propose this method as a cost-effective technique for prioritizing conservation actions between populations.
追踪野生动物种群数量的变化趋势是评估生物多样性变化的一种敏感方法,因为人类压力与种群趋势变化之间存在短暂的时间滞后。本研究检验了不同类型的人类压力与野生动物种群数量减少曲线之间的假设关联,并引入了一种方法来区分种群时间序列中自然波动与减少轨迹。首先,我们模拟了不同人类压力类型和强度下典型哺乳动物种群的时间序列,并确定了种群动态的显著差异。基于平滑种群趋势的凹度以及与数据最接近的代数函数,我们确定了那些可归因于每种压力类型的减少动态差异。通过模拟不同环境随机性水平和时间序列数据质量下的种群,我们检验了在更现实的条件下,将压力类型归因于种群减少动态的稳健性。最后,我们将我们新开发的方法应用于 124 个野生动物种群时间序列,并研究了我们的方法诊断的威胁类型如何与这些种群报告的特定威胁过程进行比较。我们展示了如何使用野生动物种群减少曲线来区分压力或威胁类型的广泛类别,但不适用于详细的威胁归因。更有用的是,我们发现种群减少曲线的差异可以可靠地识别出随着时间推移压力不断增加的种群,即使数据质量较差,并且提出了这种方法作为在种群之间进行保护行动优先级排序的一种具有成本效益的技术。