Department of Ecology, Montana State University, Bozeman, MT, USA.
Graduate Program in Organismic and Evolutionary Biology, University of Massachusetts, Amherst, MA, USA.
Glob Chang Biol. 2018 Jan;24(1):424-438. doi: 10.1111/gcb.13900. Epub 2017 Oct 11.
A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.
已经开发出许多建模方法来预测气候变化对物种分布、性能和丰度的影响。代表不同过程的模型之间的一致性越强，并且基于不同和独立的信息来源，我们对其预测的信心就越大。当预测被用于指导保护或恢复决策时，评估置信度尤为重要。我们使用多模型方法来预测气候变化对大 sagebrush（Artemisia tridentata）的影响，大 sagebrush 是美国西部约 4300 万公顷土地上的主要植物物种，也是许多特有野生动物物种的关键资源。为了评估 A. tridentata 的气候敏感性，我们开发了四个预测模型，其中两个基于经验得出的时空关系，另外两个则应用机械方法来模拟 sagebrush 的繁殖和生长。这种方法使我们能够根据模型之间的一致性水平生成气候变化脆弱性和不确定性的综合指数。尽管模型结构存在很大差异，但 sagebrush 对气候变化的反应预测基本一致。作为覆盖、生长或繁殖变化的性能预计在最温暖的地点下降，但在 sagebrush 分布范围的较凉爽部分增加。敏感性分析表明，sagebrush 的性能对温度变化的反应比对降水变化更强烈。模型预测中的大部分不确定性反映了生态模型之间的差异，这引发了对基于单一建模方法的预测的可靠性的质疑。我们的研究结果强调了多模型方法在预测气候变化影响和不确定性方面的价值，并应有助于土地管理者最大限度地提高保护投资的价值。
