LTER: Long –Term Research at the Jornada Basin (LTER VII)

旱地生态系统占地球陆地面积的近一半，为10亿多人提供货物和服务。Jornada盆地长期生态研究方案的目标是了解哪些因素对旱地生态最重要-旱地的外观和功能。Jornada盆地位于奇瓦瓦沙漠，这是世界上许多其他干旱地区的典型。该项目建立在美国农业部1915年开始的研究基础上。早期的研究揭示了“荒漠化”--栖息地从草原变成了分散的灌木和裸露的地面。最近，研究人员探讨了风和水的差异如何导致旱地生境发生其他植被变化。培训机会将提供给研究生和本科生在四个拉美裔服务机构在新墨西哥州，亚利桑那州和得克萨斯州。研究结果将通过一个多方面的计划分发给K-12学生和教师，科学家，土地管理者和公众，该计划包括基于调查的课程，研讨会，基于互联网和移动电话的系统。研究人员将量化干旱地区产生替代状态的关键过程，从而能够预测未来的状态及其对生态系统服务的影响。基于长期数据库，新的研究将包括四种类型的动态：（1）从多年生草地到荒漠化灌木地的转变，（2）从灌木地到稀树草原状态的逆转，（3）由不同灌木物种主导的状态之间的转换，以及（4）非本地草的入侵。这些转变对反映在5个核心LTER研究主题（初级生产，营养相互作用，碳和营养循环，以及对干扰的敏感性）的过程有着深远的影响。在今后六年中，主要调查人员将采用“生物-反馈-异质性”框架来理解和预测旱地景观状态变化的动态。这项工作将有助于新兴的生态理论：（a）替代状态和复原力，（B）生态系统对全球变化的敏感性，（c）跨尺度的相互作用。该框架将整合以下方面的知识和长期数据：（1）生物过程，（2）土壤地貌模板的空间异质性，（3）从多条证据中得出的环境驱动因素的变异性（即，观察，实验操作，分析和数值模型，图像产品，概念模型推理和理论）。研究人员将开发一个数据科学集成系统，使Jornada的结果能够被翻译到奇瓦瓦沙漠的其他地方和全球干旱地区。五个主要产品被设想：（1）一个新的理解状态变化，将导致理论发展，可检验的假设，和新的实验;（2）可访问的数据，衍生的数据产品，和可视化工具适用于多个尺度;（3）解释和预测之间的关系驱动程序，模式，和过程，可用于（4）预测未来的替代状态;和（5）培训，推广和信息传递到广泛的观众在当地，国家和国际。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

Dryland ecosystems occupy nearly half of the Earth’s land surface and provide goods and services for more than 1 billion people. The goal of the Jornada Basin Long Term Ecological Research (LTER) program is to understand what factors are most important to the ecology of drylands – what they look like and how they function. Jornada Basin is in the Chihuahuan Desert, which is typical of many other drylands around the world. The project builds on studies started in 1915 by the U.S. Department of Agriculture. That early research has revealed “desertification” -- a change in habitat from grasslands to scattered shrubs and bare ground. More recently, researchers have explored how differences in wind and water cause dryland habitats to undergo other changes in vegetation A new way of looking at data will enable results of this ongoing study to be applied to other drylands. Training opportunities will be provided for graduate and undergraduate students at four Hispanic-serving institutions in New Mexico, Arizona, and Texas. Findings will be distributed to K-12 students and teachers, scientists, land managers, and the public through a multi-faceted program that includes inquiry-based curricula, workshops, internet- and mobile-phone-based systems.Researchers will quantify the key processes that generate alternative states in drylands, enabling prediction of future states and their consequences for the provisioning of ecosystem services. Based on long-term databases, new studies will include four types of dynamics: (1) a shift from perennial grasslands to desertified shrublands, (2) a reversal from shrubland to savanna states, (3) transitions among states dominated by different shrub species, and (4) invasion by non-native grasses. These transitions have profound implications for the processes reflected in the 5 core LTER research themes (primary production, trophic interactions, carbon and nutrient cycles, and susceptibility to disturbance). In the next six years, Principal Investigators will employ a "trigger-feedback-heterogeneity" framework for understanding and predicting the dynamics of state changes in dryland landscapes. The work will contribute to emerging ecological theory on: (a) alternative states and resilience, (b) ecosystem sensitivity to global change, and (c) cross-scale interactions. The framework will integrate knowledge and long-term data on: (1) biological processes, (2) spatial heterogeneity in the soil-geomorphic template, and (3) variability in environmental drivers drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, and theory). Researchers will develop a Data Science Integrated System that will allow Jornada results to be translated to other locations in the Chihuahuan Desert and to drylands globally. Five major products are envisioned: (1) a new understanding of state changes that will lead to theory development, testable hypotheses, and new experiments; (2) accessible data, derived data products, and visualization tools applicable at multiple scales; (3) explanatory and predictive relationships among drivers, patterns, and processes that can be used to (4) predict future alternative states; and (5) training, outreach and information transfer to a broad audience locally, nationally, and internationally.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.