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10亿多人提供商品和服务。Jornada盆地长期生态研究（LTER）项目的目标是了解对旱地生态最重要的因素是什么——它们是什么样子的以及它们是如何起作用的。Jornada盆地位于奇瓦瓦沙漠，这是世界上许多其他旱地的典型特征。该项目建立在1915年由美国农业部开始的研究基础上。早期的研究揭示了“沙漠化”——栖息地从草原到分散的灌木和裸地的变化。最近，研究人员探索了风和水的差异是如何导致旱地栖息地发生其他植被变化的。一种观察数据的新方法将使这项正在进行的研究的结果能够应用于其他旱地。将在新墨西哥州、亚利桑那州和德克萨斯州的四所拉美裔服务机构为研究生和本科生提供培训机会。调查结果将通过一个多方面的项目分发给K-12学生和教师、科学家、土地管理者和公众，该项目包括以探究为基础的课程、研讨会、基于互联网和移动电话的系统。研究人员将量化旱地产生替代状态的关键过程，从而能够预测未来状态及其对生态系统服务提供的影响。基于长期数据库，新的研究将包括四种类型的动态：(1)多年生草地向沙漠化灌丛地的转变，(2)灌丛地向稀树草原状态的逆转，(3)不同灌木物种主导状态的转变，以及(4)非本地草的入侵。这些转变对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易感性）所反映的过程具有深远的影响。在接下来的六年里，首席研究员将采用“触发-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学理论：(a)替代状态和恢复力，(b)生态系统对全球变化的敏感性，以及(c)跨尺度相互作用。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)从多种证据（即观测、实验操作、分析和数值模型、图像产品、概念模型推理和理论）中得出的环境驱动因素的可变性。研究人员将开发一个数据科学集成系统，该系统将允许将加拿大的结果翻译到奇瓦瓦沙漠的其他地方和全球干旱地区。设想了五个主要产品：(1)对状态变化的新理解，这将导致理论发展，可测试的假设和新的实验；(2)适用于多尺度的可访问数据、衍生数据产品和可视化工具；(3)驱动因素、模式和过程之间的解释和预测关系，可用于(4)预测未来的替代状态；(5)向当地、全国和国际上的广大受众进行培训、推广和信息传递。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.