Franklin Janet, Serra-Diaz Josep M, Syphard Alexandra D, Regan Helen M
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287;
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287; Harvard Forest, Harvard University, Petersham, MA 01633;
Proc Natl Acad Sci U S A. 2016 Apr 5;113(14):3725-34. doi: 10.1073/pnas.1519911113. Epub 2016 Feb 29.
Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.
全球变化的人为驱动因素包括大气中二氧化碳和其他温室气体浓度上升及其导致的气候变化,以及氮沉降、生物入侵、干扰格局改变和土地利用变化。预测全球变化对陆地植物群落的影响至关重要,因为植被提供了从气候调节到林产品等多种生态系统服务。在本文中,我们提出了一个检测植被变化并将其归因于全球变化驱动因素的框架,该框架整合了来自空间广泛的监测网络、分布式实验、遥感数据和历史记录的多条证据。基于文献综述,我们总结了观察到的变化,然后描述了在快速变化的时代可以预测多种驱动因素对植物群落影响的建模工具。观察到的对温度、水、养分、土地利用和干扰变化的响应表明,生态系统生产力和植物种群动态对水平衡具有很强的敏感性,干扰对植物群落动态具有长期影响。土地利用变化和人为改变的火灾格局对植被的持续影响可能会掩盖气候变化的影响或与之相互作用。预测植物群落对全球变化响应的模型纳入了不断变化的生态位、种群动态、物种相互作用、空间明确的干扰、生态系统过程和植物功能响应。需要通过监测、实验和评估多种变化驱动因素的模型来检测和预测21世纪全球变化导致的植被变化。