Uncertainty in predicting range dynamics of endemic alpine plants under climate warming
Uncertainty in predicting range dynamics of endemic alpine plants under climate warming
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DOI：
10.1111/gcb.13232
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发表时间：
2016-07-01
影响因子：
11.6
通讯作者：
Dullinger, Stefan
中科院分区：
文献类型：
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作者：
Huelber, Karl;Wessely, Johannes;Dullinger, Stefan
长期以来，相关物种分布模型一直是预测物种对气候变化的范围响应的主要方法。最近，越来越多的人提倡使用动态模型，因为这些模型更好地描述了距离漂移所涉及的主要过程，并且还模拟了瞬时动态。应用这些模型的一个众所周知的问题是，缺乏估计人口和扩散过程的必要参数的数据。然而，到目前为止，几乎没有人考虑到这样一个事实，即模拟瞬变动力学可能意味着由于我们对未来气候趋势中的短期气候变异性的忽视而产生的额外不确定性。在这里，我们使用奥地利特有的山地植物作为案例研究，以评估未来气候中年代际变化的整合如何影响动态范围模型的结果，并与预测的长期趋势以及人口和扩散参数的不确定性进行比较。为此，我们对比了在波动气候条件下运行的所谓混合模型的模拟，以及基于当前气候条件和21世纪末预测气候条件之间的线性内插的模拟。我们发现，考虑到短期气候变异性对模型结果的修正几乎与预测的长期趋势的差异一样，而且远远超过人口/扩散参数的不确定性。特别是，当模拟运行在波动条件下时，距离损失和消光率要高得多。这些结果强调了在参数化和应用靶场动态模型，特别是混合模型时，考虑适当的时间分辨率的重要性。就我们特有的山地植物而言，我们假设平滑的线性时间序列提供了更可靠的结果，因为这些长期生存的物种主要对长期气候平均做出反应。
Correlative species distribution models have long been the predominant approach to predict species' range responses to climate change. Recently, the use of dynamic models is increasingly advocated for because these models better represent the main processes involved in range shifts and also simulate transient dynamics. A well-known problem with the application of these models is the lack of data for estimating necessary parameters of demographic and dispersal processes. However, what has been hardly considered so far is the fact that simulating transient dynamics potentially implies additional uncertainty arising from our ignorance of short-term climate variability in future climatic trends. Here, we use endemic mountain plants of Austria as a case study to assess how the integration of decadal variability in future climate affects outcomes of dynamic range models as compared to projected long-term trends and uncertainty in demographic and dispersal parameters. We do so by contrasting simulations of a so-called hybrid model run under fluctuating climatic conditions with those based on a linear interpolation of climatic conditions between current values and those predicted for the end of the 21st century. We find that accounting for short-term climate variability modifies model results nearly as differences in projected long-term trends and much more than uncertainty in demographic/dispersal parameters. In particular, range loss and extinction rates are much higher when simulations are run under fluctuating conditions. These results highlight the importance of considering the appropriate temporal resolution when parameterizing and applying range-dynamic models, and hybrid models in particular. In case of our endemic mountain plants, we hypothesize that smoothed linear time series deliver more reliable results because these long-lived species are primarily responsive to long-term climate averages.