CAREER: Top-down and bottom-up controls on species coexistence in a variable world

在特定的生态系统中，植物物种的多样性可以由资源的可获得性(称为“自下而上的过程”)和食草性动物(称为“自上而下的过程”)决定。生态学家经常问，一个群落中怎么会有这么多物种共存？多种限制资源可以增加物种多样性；例如，如果一种植物对氮的竞争更强，另一种对水的竞争更强，对这两种资源的限制可以帮助这两个物种在系统中持续存在，而不会有一个物种胜过另一个物种。资源的时间变异性也可以增加多样性；例如，如果一些植物物种在潮湿条件下生长迅速，但其他植物物种更耐旱，随着时间的推移，降雨量的变异性可以帮助在一个地点保持更多的植物物种。目前，限制植物生长的资源的时间动态正在发生变化--由于人类相关来源的大气输入，可用氮量翻了一番，气候正在变化--这可能会对植物生物多样性产生深远的影响。然而，自上而下的过程，如放牧动物或昆虫食草动物，可能会缓和这些资源变化的影响；例如，有利于增氮的物种的增长可能被有利于那些喜欢N的植物的动物所抵消。这个职业项目首先在实验中解开了在高度多样化的加州草原上自下而上和自上而下转变过程的影响。其次，它利用一项名为NutNet的全球分布实验的数据，评估了这些模式如何随着气候梯度的变化而变化。NutNet已经在世界各地的草原上操纵养分和放牧动物超过10年。该项目的总体目标是促进我们对维持植物生物多样性的过程的理解，并进一步提高预测生物多样性将如何应对全球变化的能力。该项目通过让本科生和研究生参与数据收集、管理和综合，将研究和教育目标贯穿始终。第二部分：现代共存理论已经成为划分物种之间共存机制的工具，为预测多样性将如何应对正在进行的全球环境变化提供了一个潜在的框架。然而，众所周知，现代共存理论很难在现场进行经验测试，这降低了它对自然系统的适用性。这个职业项目扩展了现代共存理论的经验测试，以量化自下而上的营养添加和自上而下的消费者压力如何改变共存环境中不同年份和不同空间的降雨量。首先，在以高气候变异性为特征的年度草原系统中，该项目以关键资源-消费者-竞争者相互作用为试验性目标，并使用定量方法对其进行解释，这些方法以经验上易于处理的方式划分共存机制。此外，它对支持多物种共存的条件范围进行了参数化，比共存模型中经常使用的成对物种比较更好地反映了群落动态。其次，该项目通过在研究生研讨会中合成营养网络数据来评估这些模式的普适性，该研讨会培训学生团队科学、数据分析和数据可视化。此外，当地的营养网络数据是与本科课程序列一起收集的，该课程序列将夏季田野方法课程与使用实地收集的数据的数据科学课程配对。最后，该项目纳入了一个独立的本科生研究计划，以支持论文学生在当地的营养网络站点测试额外的自上而下和自下而上的动态。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。

Part 1. The diversity of plants species in a given ecosystem can be shaped by resource availability (called a “bottom up process”), and herbivorous animals (called a “top down process”). Ecologists frequently ask how so many species can coexist in a community? Multiple limiting resources can increase species diversity; for example, if one plant species is more competitive for nitrogen and another is more competitive for water, limitation in both resources can help both species persist in the system without one outcompeting the other. Temporal variability in resources can also increase diversity; for example, if some plant species grow fast under wet conditions, but others better tolerate dry conditions, rainfall variability over time can help to maintain more plant species in a site. At present, the temporal dynamics of resources that limit plant growth are changing – the amount of available nitrogen has doubled due to atmospheric inputs from human-related sources, and the climate is shifting – with potentially profound consequences for plant biodiversity. Top-down processes like grazing animals or insect herbivores, may moderate the effect of these resource changes, however; for example, an increase in the growth of species favored by nitrogen enrichment may be offset by animals favoring those N loving plants. This CAREER project first experimentally disentangles the effect of shifting bottom-up and top-down processes in a highly diverse California grassland. Second, it assesses how these patterns shift along climate gradients by capitalizing on data from the a globally distributed experiment called NutNet, that has been manipulating nutrients and grazing animals in grasslands around the world for 10+ years. The overall goal of this project is to advance our understanding of the processes that maintain plant biodiversity and to further the capacity to predict how biodiversity will respond to global change. The project integrates research and educational aims throughout by involving undergraduate and graduate students in data collection, management, and synthesis.Part 2. Modern coexistence theory has emerged as a tool to partition the mechanisms of coexistence among species, providing a potential framework to predict how diversity will respond to on-going global environmental change. However, modern coexistence theory has been notoriously difficult to test empirically in field settings, reducing its applicability to natural systems. This CAREER project extends empirical tests of modern coexistence theory to quantify how bottom-up nutrient addition and top-down consumer pressure alter coexistence in environments varying in rainfall both across years, and over space. First, in an annual grassland system characterized by high climate variability, the project experimentally targets key resource-consumer-competitor interactions and interprets them using quantitative methods that partition coexistence mechanisms in an empirically tractable way. In addition, it parameterizes the range of conditions that support multi-species coexistence, better reflecting community dynamics than the pairwise species comparisons frequently employed in coexistence models. Second, the project assesses the generalizability of these patterns by synthesizing Nutrient Network data within a graduate seminar that trains students in team science, data analysis and data visualization. In addition, local Nutrient Network data is collected in conjunction with an undergraduate course sequence that pairs a summer field methods class with a data science class that uses the field-collected data. Finally, the project incorporates an independent undergraduate research program to support thesis students to test additional top-down and bottom-up dynamics at a local Nutrient Network site.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.