Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA.
Glob Chang Biol. 2013 Jul;19(7):2036-57. doi: 10.1111/gcb.12195. Epub 2013 Apr 15.
This review focuses on the impacts of climate change on population dynamics. I introduce the MUP (Measuring, Understanding, and Predicting) approach, which provides a general framework where an enhanced understanding of climate-population processes, along with improved long-term data, are merged into coherent projections of future population responses to climate change. This approach can be applied to any species, but this review illustrates its benefit using birds as examples. Birds are one of the best-studied groups and a large number of studies have detected climate impacts on vital rates (i.e., life history traits, such as survival, maturation, or breeding, affecting changes in population size and composition) and population abundance. These studies reveal multifaceted effects of climate with direct, indirect, time-lagged, and nonlinear effects. However, few studies integrate these effects into a climate-dependent population model to understand the respective role of climate variables and their components (mean state, variability, extreme) on population dynamics. To quantify how populations cope with climate change impacts, I introduce a new universal variable: the 'population robustness to climate change.' The comparison of such robustness, along with prospective and retrospective analysis may help to identify the major climate threats and characteristics of threatened avian species. Finally, studies projecting avian population responses to future climate change predicted by IPCC-class climate models are rare. Population projections hinge on selecting a multiclimate model ensemble at the appropriate temporal and spatial scales and integrating both radiative forcing and internal variability in climate with fully specified uncertainties in both demographic and climate processes.
这篇综述聚焦于气候变化对人口动态的影响。我介绍了 MUP（测量、理解和预测）方法，该方法提供了一个通用框架，其中包括增强对气候-人口过程的理解，以及改进的长期数据，这些数据被整合到对未来人口对气候变化的响应的一致预测中。这种方法可以应用于任何物种，但本综述以鸟类为例说明了它的好处。鸟类是研究最多的群体之一，大量研究已经检测到气候对关键率（即影响种群规模和组成变化的生命史特征，如生存、成熟或繁殖）和种群丰度的影响。这些研究揭示了气候的多方面影响，包括直接、间接、时滞和非线性影响。然而，很少有研究将这些影响整合到一个依赖气候的种群模型中，以了解气候变量及其组成部分（均值、变异性、极值）对种群动态的各自作用。为了量化种群如何应对气候变化的影响，我引入了一个新的通用变量：“种群对气候变化的稳健性”。比较这种稳健性，以及前瞻性和回顾性分析，可以帮助识别主要的气候威胁和受威胁鸟类物种的特征。最后，很少有研究预测未来气候变化对鸟类种群的影响，这些预测是基于 IPCC 气候模型的。种群预测取决于在适当的时间和空间尺度上选择多气候模型集合，并将气候的辐射强迫和内部变率与人口和气候过程的完全指定不确定性结合起来。
