MSB-ECA: Climate change and plants on unusual soils: Detecting and modeling ecosystem response of Caribbean serpentine floras

异常恶劣的土壤(如石灰岩、蛇纹石和石膏)往往支持独特的植物群，这些植物群含有丰富的特有植物物种，是世界上许多生物多样性的热点之一？S。与正常土壤生态系统相比，异常土壤生态系统对气候变化的反应是否不同？土壤类型未知。一方面，异常土壤植物群的典型耐压力植物特征可能会导致对气候变化的更大适应能力。另一方面，空间孤立和人为干扰，如栖息地碎片化，可能会使不寻常的土壤生态系统更容易受到气候变化的影响。该项目将结合实地测量、遥感和气候建模来确定加勒比地区的蛇纹岩土壤如何对正在进行的和预测的气候变化作出反应。这项研究是了解热带蛇纹土壤如何响应气候变化的第一次全面努力，因此将极大地提高我们对这些独特生态系统的理解。这项研究产生的信息将有助于保护生物多样性，并改进对世界各地其他不寻常的土壤生态系统的预测。在该项目期间收集的数据将加强国家和全球数据库，促进进一步研究。该项目将通过为学生和教职员工提供数据管理和分析技能方面的培训工作坊，将本研究收集的数据整合到生物学入门教学模块中，并在从数据收集到出版的各个方面培训4名本科生和4名研究生，从而加强劳动力和增加科学多样性。拟议的研究将确定蛇形和非蛇形生态系统功能特征多样性和环境属性之间的尺度关系，利用卫星图像量化蛇形和非蛇形生态系统土地利用变化的历史模式和速率，并通过将土壤和功能特征整合到气候和物种分布模型中来模拟预测的生态系统功能。该项目将测量与整个加勒比海盆地的蛇形和非蛇形生态系统的气候耐受性和扩散能力有关的关键植物功能特征。土壤化学也将被量化，允许在广泛的空间尺度上对土壤特性进行标准化比较。气候和物种分布模型将确定对气候变化特别脆弱或特别有弹性的区域和物种。将利用陆地卫星图像数据分析植被绿度和初级生产的年际趋势。遥感分析将确定蛇形和非蛇形生态系统栖息地碎片化的程度和速度，以及对降水年际变化的反应。该项目将全球生物多样性热点地区的精细多样性模式与大规模生态系统变化模式结合在一起，并将确定不寻常的土壤生态系统对气候变化具有独特的恢复力还是独特的脆弱性。

Soils that are unusually harsh (like limestone, serpentine, and gypsum) tend to support distinct floras that are rich in endemic plant species and are among many of the world?s biodiversity hotspots. Whether unusual soil ecosystems respond differently to climate change compared to ?normal? soil types is unknown. On the one hand, stress-tolerant plant traits typical of unusual soil flora may result in greater resilience to climate change. On the other hand, spatial isolation and anthropogenic disturbances, like habitat fragmentation, may make unusual soil ecosystems more vulnerable to climate change. This project will use a combination of field measurements, remote sensing, and climate modeling to determine how serpentine soils in the Caribbean region respond to ongoing and predicted climate change. This research represents the first comprehensive effort to understand how tropical serpentine soils respond to climate change, and consequently will vastly improve our understanding of these unique ecosystems. The information generated by this research will be useful for conserving biodiversity and for improving predictions for other unusual soil ecosystems around the world. The data collected during this project will enhance national and global databases, facilitating further research. This project will strengthen the workforce and increase diversity in science by providing training workshops for students and faculty in data management and analytical skills, integrating the data collected in this research into introductory biology teaching modules, and training four undergraduate and four graduate students in all aspects of the study from data collection to publication.The proposed research will determine scaling relationships between functional trait diversity and environmental attributes of serpentine and non-serpentine ecosystems, quantify historical patterns and rates of land-use change of serpentine and non-serpentine ecosystems using satellite imagery, and model predicted ecosystem function by integrating soil and functional traits into climate and species distribution models. This project will measure key plant functional traits related to climatic tolerances and dispersal ability in serpentine and non-serpentine ecosystems throughout the Caribbean Basin. Soil chemistry will also be quantified, allowing a standardized comparison of soil characteristics across broad spatial scales. Climate and species distribution models will identify regions and species that are particularly vulnerable or resilient to climate change. Inter-annual trends in vegetation greenness and primary production will be analyzed using Landsat imagery data. Remote sensing analyses will determine the magnitude and rate of habitat fragmentation in serpentine and non-serpentine ecosystems and responses to inter-annual variation in precipitation. This project integrates fine-scale patterns of diversity with large-scale patterns of ecosystem change in a global biodiversity hotspot and will determine whether unusual soil ecosystems are uniquely resilient or uniquely vulnerable to climate change.