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

旱地生态系统占据了地球近一半的陆地表面，为10多亿人提供了商品和服务。Jornada盆地长期生态研究(LTER)计划的目标是了解哪些因素对旱地生态最重要--它们看起来像什么，它们是如何发挥作用的。乔尔纳达盆地位于奇瓦瓦沙漠，这是世界上许多其他旱地的典型。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”--栖息地从草原变成零星的灌木和裸露的土地。最近，研究人员探索了风和水的差异如何导致旱地栖息地经历其他植被变化。一种看待数据的新方式将使这项正在进行的研究结果适用于其他旱地。将为新墨西哥州、亚利桑那州和德克萨斯州的四所拉美裔服务机构的研究生和本科生提供培训机会。研究结果将通过一个多方面的项目分发给K-12年级的学生和教师、科学家、土地管理人员和公众，其中包括基于探究的课程、研讨会、基于互联网和手机的系统。研究人员将量化在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)从多年生草原到沙化灌丛的转变，(2)从灌丛到稀树草原的逆转，(3)由不同灌木物种主导的州之间的转变，以及(4)非本地草的入侵。这些转变对LTER研究的5个核心主题(初级生产、营养相互作用、碳和养分循环以及对干扰的敏感性)所反映的过程具有深远的影响。在未来六年，首席调查员将采用“触发-反馈-异质性”框架来了解和预测旱地景观状态变化的动态。这项工作将有助于关于以下问题的新兴生态学理论：(A)替代状态和复原力，(B)生态系统对全球变化的敏感性，以及(C)跨尺度相互作用。该框架将整合关于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，以及(3)从多条证据线(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)提取的环境驱动因素的可变性。研究人员将开发一种数据科学综合系统，允许Jornada结果被转换到奇瓦瓦沙漠的其他地点和全球旱地。预计将有五个主要产品：(1)对状态变化的新理解，这将导致理论发展、可验证的假设和新的实验；(2)可访问的数据、派生的数据产品和适用于多个尺度的可视化工具；(3)可用于(4)预测未来替代状态的驱动因素、模式和过程之间的解释性和预测性关系；以及(5)向当地、国家和国际的广泛受众提供培训、推广和信息传输。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.