MCA: A Meta-analytic approach to resolving mechanisms of plant-soil-herbivore interactions

MCA：解决植物-土壤-草食动物相互作用机制的荟萃分析方法

• 批准号: 2120677
• 负责人: Jennifer Krumins
• 金额: $ 24.14万
• 依托单位: Montclair State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120677&HistoricalAwards=false
• 关键词: MCA; Meta; analytic; approach; resolving

Plants and their associated soils are in a close bidirectional relationship, and this relationship is affected by organisms such as herbivores that consume plants. Feeding by herbivores may influence the numbers and species of plants present in the ecosystem. However, consistent relationships between feeding by herbivores does and the composition or functioning of the soil community have not yet been described. The resolution of this paradox is a fascinating ecological question because indirect effects among organisms are tricky to resolve, but it is also important from a human perspective due to the potential implications to soil properties. For example, changes in soil functioning can influence agricultural success or carbon storage for climate mitigation. In addition, human demographic patterns and shifts in land use have markedly changed the distribution of wild herbivores and livestock, making this a more urgent problem. Therefore, the need to define the role of herbivory on soils and their functioning is profound. This research will tease apart and define the effect of herbivory on soil community composition and functioning as it occurs across varied environments. Interactions among herbivores, plants, and soils are driven by gradients of multi-variate factors, both biotic and abiotic, and establishing controlled and repeatable experiments to capture these factors is expensive and challenging. Modern and diverse data sets from prior manipulative and observational herbivore experiments are abundant. However, when considered in isolation, individual experiments typically reveal inconsistent and weak responses in the soil. This research will compile and analyze a large number of experimental and observational data sets, thus revealing the full continuum of biotic and abiotic influences of herbivores on the soil. Specifically, the methods will include a regression based and multi-variate meta-analytic approach. In so doing, the following research goals will be addressed: First, meta-analytic regression will capture non-linear or indirect effects of herbivory on soil functioning. Second, multivariate herbivore effects will be explored with respect to both abiotic environmental variables and community level interaction variables, i.e. nutrient flux via dung, trampling or seed dispersal. In partnership with a world expert in meta-analysis, this research will gather diverse data sets to resolve the most elusive mechanisms explaining the link between herbivory and soil ecosystem functioning. The outcome of this work will be a substantial and publicly available database in addition to student training and numerous scientific publications and presentations.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.

植物与土壤之间存在着密切的双向关系，这种关系受到食草动物等生物的影响。食草动物的取食可能会影响生态系统中植物的数量和种类。然而，食草动物的喂养和土壤群落的组成或功能之间的一致关系尚未得到描述。这个悖论的解决是一个迷人的生态问题，因为生物体之间的间接影响很难解决，但从人类的角度来看，由于对土壤性质的潜在影响，它也很重要。例如，土壤功能的变化可能影响农业的成功或减缓气候变化的碳储存。此外，人类的人口模式和土地使用的变化显著改变了野生食草动物和牲畜的分布，使这一问题更加紧迫。因此，需要确定草食动物对土壤的作用及其功能是深刻的。这项研究将梳理和定义草食动物对土壤群落组成和功能的影响，因为它发生在不同的环境。 食草动物，植物和土壤之间的相互作用是由生物和非生物的多变量因素的梯度驱动的，建立受控和可重复的实验来捕获这些因素是昂贵和具有挑战性的。从先前的操纵和观察草食动物实验的现代和不同的数据集是丰富的。然而，当孤立地考虑时，单个实验通常揭示土壤中的不一致和弱响应。这项研究将汇编和分析大量的实验和观测数据集，从而揭示食草动物对土壤的生物和非生物影响的完整连续性。具体而言，这些方法将包括基于回归的多变量荟萃分析方法。在这样做，以下研究目标将得到解决：首先，元分析回归将捕捉非线性或间接影响的草食动物对土壤功能。第二，多变量草食动物的影响将探讨非生物环境变量和社区水平的相互作用变量，即养分通量通过粪便，践踏或种子传播。这项研究将与荟萃分析领域的世界专家合作，收集各种数据集，以解决解释食草动物与土壤生态系统功能之间联系的最难以捉摸的机制。这项工作的成果将是一个实质性的和公开的数据库，除了学生培训和众多的科学出版物和presentation.This奖项反映了NSF的法定使命，并已被认为是值得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的支持。